Latest Articles Include:
- Crafting a revision
- Nat Neurosci 14(8):941 (2011)
Nature Neuroscience | Editorial Crafting a revision Journal name:Nature NeuroscienceVolume: 14,Page:941Year published:(2011)DOI:doi:10.1038/nn0811-941Published online26 July 2011 Responding to referee comments constructively improves the quality of published papers. Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The scenario is familiar to many of us at some point or another in our careers; after spending years working on a project and building what we think is a complete story, the long-awaited reviews finally arrive with a long list of criticisms. Disappointment, however, turns to outrage as you start scrolling through the referee reports, which seem to go on and on. You stare at the comments in disbelief, noting that one of the referees has made critical mistakes, whereas another expects you to spend several years following up the story, which would involve both gobs of money and the careers of several postdocs. Even when editors allow the option of a resubmission, the process of revising a paper can sometimes be a frustrating journey for the authors, editors and referees. Some authors may even begin to suspect that referees are out to deliberately thwart the publication of individual papers and that one must fight with editors and referees to get the paper published. Although some may argue that low funding rates and an ever increasing number of competitors have contributed to a cut-throat atmosphere in publishing, we continue to view the review process as an opportunity. This is not to say that endless rounds of peer-review are productive; on the contrary, we have always tried to prevent multiple rounds of peer-review on the grounds that this just tends to frustrate authors, referees and editors. Undoubtedly, there are also cases in which both referees and editors make mistakes, and we routinely overrule referees on requests for additional extensions. Nonetheless, we strongly feel that thoughtful revisions based on editorial and referee feedback do improve papers. It has always been extremely rare for a paper to be published in Nature Neuroscience without any revision; for example, of the original research papers that we published during 2005, only 2.3% were accepted after a single round of peer-review, and this number was 3.2% in 2010.! Practically, this means that authors must be open to referee criticisms and that they must remain committed to revising their paper and working with both referees and editors. We outline the basics of such a constructive revision below. When responding to referee reports, although it may be natural for frustrated authors to lash out at referees, this is rarely productive. We ask that authors go through the referee comments point by point and respond constructively and diplomatically to each point in turn, keeping in mind that referees are busy, and with the assumption that they are not out to stall publication. Try not to accuse the referee of bias, and keep the tone of the response polite and professional. Make it easy for the referees and the editors to evaluate the revision by including the new data in your response to the referees and by sending us a revised version of the paper with the changes highlighted. Even if you find that the referee has overlooked some data stashed away in the Supplementary Information or made some factual errors, this does not negate the rest of his/her points. Disregarding a concern made by the referee is not helpful; it simply makes both editors and referees feel that the co! ncern was just dismissed out of hand and raises a red flag in revision. Similarly, try not to play one referee against another; pointing out, for instance, that the experiment that they found questionable was suggested earlier by one of the other referees. It also helps to keep in mind that, although referees do have an obligation to fairly evaluate the paper, the onus is not on the referees to suggest the exact experiments that the authors need to do to make a better case for high-profile publication. Referees may feel, for instance, that further mechanism and/or better evidence for in vivo relevance are essential for publication, but are not required to suggest specific experiments for the authors to do next. In cases such as this, it is up to the authors to take the initiative and design and conduct suitable experiments that they think will address the main issue. Similarly, when such extensions are included in the revision, it is up to the authors to make a well-reasoned case for why the new data they included substantially increases the conceptual advance of the paper. Critically, during the revision, make an earnest effort to improve the paper, paying attention to both requests to improve the clarity of the presentation as well as its scientific foundations. Keep in mind that your referees and editors are fellow scientists and are likely to respond better to well-reasoned, logical arguments as to why you feel certain experiments are unnecessary. For example, if you feel that some of the referee requests are unreasonable, spell out your arguments why you feel a certain experiment is unlikely to yield the desired answer. Simply listing a slew of prior publications with the same level of analysis with the argument that "they got away with this, so we should too" is less likely to help your cause. Finally, keep in mind that editors are active members of the process. When revising a paper, authors should feel free to discuss their plans for a revision with the editors, particularly if they feel that the requested experiments are unreasonable or are not likely to be productive. Editors can help counsel authors on what they view as essential revisions and can help explain both the referees' and journal's points of view. We recognize that peer-review is not always perfect and are sensitive to our authors' needs for timely publication. We are also aware that referees (consciously or unconsciously) sometimes request extensions that are unnecessary. Nonetheless, authors, referees and editors all benefit from a collaborative and collegial peer-review process. Although communication among these parties can sometimes be difficult, working with editors and referees will help authors showcase their best science. Additional data - Prior and prejudice
- Nat Neurosci 14(8):943-945 (2011)
Article preview View full access options Nature Neuroscience | News and Views Prior and prejudice * Emilio Salinas1Journal name:Nature NeuroscienceVolume: 14,Pages:943–945Year published:(2011)DOI:doi:10.1038/nn.2883Published online26 July 2011 To best interpret new sensory information, populations of sensory neurons must represent the lessons of past experience. How do they do this? The same solution to this problem is now reported in two very different sensory systems, providing a classic example of computational convergence. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Emilio Salinas is in the Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Emilio Salinas Author Details * Emilio Salinas Contact Emilio Salinas Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Anti-TANKyrase weapons promote myelination
- Nat Neurosci 14(8):945-947 (2011)
Article preview View full access options Nature Neuroscience | News and Views Anti-TANKyrase weapons promote myelination * Patrizia Casaccia1Journal name:Nature NeuroscienceVolume: 14,Pages:945–947Year published:(2011)DOI:doi:10.1038/nn.2885Published online26 July 2011 A study identifies mechanisms responsible for the inability to form new myelin after neonatal hypoxia. It identifies Axin2 as a potential therapeutic target for reversing the 'differentiation block' of oligodendrocyte-lineage cells. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Patrizia Casaccia is in the Department of Neuroscience and Friedman Brain Institute, Mount Sinai School of Medicine, New York, New York, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Patrizia Casaccia Author Details * Patrizia Casaccia Contact Patrizia Casaccia Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - What birds have to say about language
- Nat Neurosci 14(8):947-948 (2011)
Article preview View full access options Nature Neuroscience | News and Views What birds have to say about language * Tiffany C Bloomfield1 * Timothy Q Gentner3 * Daniel Margoliash1, 2 * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:947–948Year published:(2011)DOI:doi:10.1038/nn.2884Published online26 July 2011 Controversy surrounds the suggestion that recursion is a uniquely human computational ability that enables language. A study now finds this ability in a songbird and takes steps toward a model system for syntactic competence. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Tiffany C. Bloomfield and Daniel Margoliash are in the Department of Psychology, University of Chicago, Chicago, Illinois, USA. * Daniel Margoliash is also in the Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, USA. * Timothy Q. Gentner is in the Department of Psychology, and The Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Daniel Margoliash Author Details * Tiffany C Bloomfield Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy Q Gentner Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Margoliash Contact Daniel Margoliash Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - An overlooked neurotoxic species in Alzheimer's disease
- Nat Neurosci 14(8):949-950 (2011)
Article preview View full access options Nature Neuroscience | News and Views An overlooked neurotoxic species in Alzheimer's disease * Iryna Benilova1 * Bart De Strooper1 * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:949–950Year published:(2011)DOI:doi:10.1038/nn.2871Published online26 July 2011 A study now finds early memory impairment in a mouse model of amyloid β43 (Aβ43)-overproducing familial Alzheimer's disease and suggests that this overlooked amyloidogenic Aβ species contributes to pathology. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Iryna Benilova and Bart De Strooper are in the Center for Human Genetics, University of Leuven, and the Department for Molecular and Developmental Genetics, Flanders Institute for Biotechnology, Leuven, Belgium. Competing financial interests Bart De Strooper is a consultant for Janssen Pharmaceutica, Envivo Pharmaceuticals and Remynd NV. He also receives research funding from Janssen Pharmaceutica. Corresponding author Correspondence to: * Bart De Strooper Author Details * Iryna Benilova Search for this author in: * NPG journals * PubMed * Google Scholar * Bart De Strooper Contact Bart De Strooper Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Reward and autoreceptors
- Nat Neurosci 14(8):950 (2011)
Article preview View full access options Nature Neuroscience | News and Views Reward and autoreceptors * Brigitta GundersenJournal name:Nature NeuroscienceVolume: 14,Page:950Year published:(2011)DOI:doi:10.1038/nn0811-950Published online26 July 2011 Both natural rewards and addictive drugs increase extracellular dopamine (DA) in the striatum. Although studies have found that DA receptors are involved in addiction, the results are conflicting. Susceptibility to drug addiction is correlated with reduced availability of striatal D2 receptors, yet D2 receptor knockout mice show reduced responses to drugs of abuse. These contradictory results may arise because there are two populations of D2 receptors. Most D2 receptors are postsynaptic, responding to DA release from striatal dopaminergic neurons. However, D2 receptors are also expressed presynaptically on DA-releasing neurons (autoreceptors), which exert negative feedback. Previous genetic and pharmacological studies have not been able to differentiate between these two populations of D2 receptors. On page 1033, Bello and colleagues dissect the selective role of D2 autoreceptors and find that deleting D2 autoreceptors increases DA synthesis and release, resulting in increas! ed sensitivity to cocaine. The authors created mice lacking D2 receptors only in DA-releasing neurons (autoDrd2KO mice). Striatal dopaminergic neurons in autoDrd2KO mice did not show inhibitory currents in response to D2 agonists. This lack of negative feedback was accompanied by increased DA synthesis and release. AutoDrd2KO mice were hyperactive, and hypersensitive to cocaine. They exhibited increased cocaine-seeking in a conditioned place preference procedure and worked harder for a food reward in an operant conditioning procedure, suggesting that the role of D2 autoreceptors extends to natural rewards. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - In vivo evidence that retinal bipolar cells generate spikes modulated by light
- Nat Neurosci 14(8):951-952 (2011)
Nature Neuroscience | Brief Communication In vivo evidence that retinal bipolar cells generate spikes modulated by light * Elena Dreosti1, 2 * Federico Esposti1, 2 * Tom Baden1 * Leon Lagnado1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:951–952Year published:(2011)DOI:doi:10.1038/nn.2841Received10 February 2011Accepted14 April 2011Published online26 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Retinal bipolar cells have been assumed to generate purely graded responses to light. To test this idea we imaged the presynaptic calcium transient in live zebrafish. We found that ON, OFF, transient and sustained bipolar cells are all capable of generating fast 'all-or-none' calcium transients modulated by visual stimulation. View full text Figures at a glance * Figure 1: Imaging fast presynaptic calcium transients in bipolar cells in vivo. () Synaptic terminals of bipolar cells expressing SyGCaMP2 in a zebrafish (10 d post-fertilization). Regions of interest corresponding to terminals shown below. Scale bar represents 20 μm. () Raster plot showing spontaneous SyGCaMP2 signals in darkness. The sampling interval was 128 ms. () Spontaneous calcium transients in one terminal. Note the relatively fixed amplitude and time course. () Two calcium transients from on an expanded timescale. An exponential fit to the first is shown in red (τ = 1.18 s) and superimposed on both spikes. From a sample of 1,008 terminals, 65% generate one or more calcium transients over a 60-s period. () A single presynaptic calcium transient sampled at 200 Hz (black) and smoothed by interpolation (red). a.u., arbitrary units. The lower trace is the derivative: the signal describing the rate of calcium influx had a width of 65 ms at half-maximum. All procedures were carried out according to the UK Animals (Scientific Procedures) Act 1986 and! approved by the UK Home Office. * Figure 2: Bipolar cells responded to light with both graded signals and spikes. Presynaptic calcium spikes were observed in a variety of functionally distinct bipolar cells. A full-field stimulus (amber; I = 1.7 nW mm−2) was applied at 15 s and modulated around this mean from 25–35 s (square wave, 100% contrast, 2.5 Hz). (–) All traces are examples from individual terminals and have been grouped to show sustained and graded ON terminals (), sustained and graded OFF (), transient ON (), sustained OFF encoding light with spikes (), transient ON terminals generating calcium spikes in response to temporal contrast (), terminals generating calcium spikes at low rates, but without clear modulation by the stimulus (). Scale bars represent ΔF/F = 2. () Sustained ON and OFF terminals also generating spikes (upper and lower traces, respectively). () Upper trace, sustained ON terminal generating a slow response to contrast and then spikes. Lower trace, transient ON cell that spikes at light onset, but then generates a slow sustained response to contrast (ma! ximum intensity = 1.7 nW mm−2). Scale bars in and represent ΔF/F = 1. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Elena Dreosti & * Federico Esposti Affiliations * MRC Laboratory of Molecular Biology, Hills Road, Cambridge, UK. * Elena Dreosti, * Federico Esposti, * Tom Baden & * Leon Lagnado Contributions Experiments were designed by E.D., F.E. and L.L. and performed by E.D., F.E. and L.L. Analysis was carried out by E.D., F.E. and L.L. The manuscript was written by F.E., T.B. and L.L. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Leon Lagnado Author Details * Elena Dreosti Search for this author in: * NPG journals * PubMed * Google Scholar * Federico Esposti Search for this author in: * NPG journals * PubMed * Google Scholar * Tom Baden Search for this author in: * NPG journals * PubMed * Google Scholar * Leon Lagnado Contact Leon Lagnado Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (242K) Supplementary Figure 1, Supplementary Methods and Algorithm Additional data - Preventing interference between different memory tasks
- Nat Neurosci 14(8):953-955 (2011)
Nature Neuroscience | Brief Communication Preventing interference between different memory tasks * Daniel A Cohen1 * Edwin M Robertson1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:953–955Year published:(2011)DOI:doi:10.1038/nn.2840Received22 February 2011Accepted04 April 2011Published online26 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg When learned in quick succession, declarative and motor skill tasks interfere with one another and subsequent recall is impaired. Depending on the order of the tasks, we were able to prevent memory interference in humans by applying transcranial magnetic stimulation to either the dorsolateral prefrontal or the primary motor cortex, and neither memory was impaired. Our observations suggest that distinct mechanisms support the communication between different types of memory processing. View full text Figures at a glance * Figure 1: Experiment 1, interference between word list and motor skill learning. () Participants learned a word list and a motor skill in quick succession, TMS (to DLPFC or M1) or sham stimulation was applied, and participants word recall and motor skill was retested 12 h later. () Word recall was impaired by the motor skill learning task despite sham or real stimulation to M1 (mean ± s.e.m.). In contrast, applying TMS to the DLPFC prevented the impairment of word recall by the motor skill learning task. Preventing the interference between the tasks was not dependent on disrupting the interfering memory, as motor skill changes were not significantly different across the groups (mean ± s.e.m.). () The relationship between the tasks was affected by stimulation. There was a significant correlation between the decrease in word recall and initial motor skill following M1 stimulation, whereas there was no significant correlation following DLPFC stimulation. The correlation following M1 stimulation was significantly greater than the correlation following DLPF! C stimulation (see above R2 values). * Figure 2: Experiment 2, interference between motor skill and word list learning. () Participants learned a motor skill and then a word list in quick succession, TMS (to DLPFC or M1) or sham stimulation was applied, and participants motor skill and word recall was retested 12 h later. () Motor skill was impaired by the word list learning task after sham or real stimulation to DLPFC (mean ± s.e.m.). In contrast, applying TMS to M1 prevented the impairment of motor skill by the word list learning task. Preventing interference between the tasks was not dependent on disrupting the interfering memory because word recall changes were not significantly different across the groups (mean ± s.e.m.). () The relationship between the tasks was affected by stimulation. There was a significant correlation between the decrease in motor skill and initial word recall following DLPFC stimulation, whereas, there was no significant correlation following M1 stimulation. The correlation following DLPFC stimulation was significantly greater than the correlation following M1 st! imulation (see above R2 values). Author information * Author information * Supplementary information Affiliations * Berenson-Allen Center for Non-Invasive Brain Stimulation, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. * Daniel A Cohen & * Edwin M Robertson Contributions D.A.C. conducted the experiments and helped write the manuscript. E.M.R. designed the study, conducted the experiments, analyzed the data and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Edwin M Robertson Author Details * Daniel A Cohen Search for this author in: * NPG journals * PubMed * Google Scholar * Edwin M Robertson Contact Edwin M Robertson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (266K) Supplementary Figures 1–4, Supplementary Introduction, Supplementary Results, Supplementary Discussion and Supplementary Methods Additional data - Mammalian Gcm genes induce Hes5 expression by active DNA demethylation and induce neural stem cells
- Nat Neurosci 14(8):957-964 (2011)
Nature Neuroscience | Article Mammalian Gcm genes induce Hes5 expression by active DNA demethylation and induce neural stem cells * Seiji Hitoshi1, 2 * Yugo Ishino1, 2 * Akhilesh Kumar1, 2 * Salma Jasmine1, 2 * Kenji F Tanaka1, 2 * Takeshi Kondo3 * Shigeaki Kato3 * Toshihiko Hosoya4 * Yoshiki Hotta5 * Kazuhiro Ikenaka1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:957–964Year published:(2011)DOI:doi:10.1038/nn.2875Received20 January 2011Accepted13 June 2011Published online17 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Signaling mediated by Notch receptors is crucial for the development of many organs and the maintenance of various stem cell populations. The activation of Notch signaling is first detectable by the expression of an effector gene, Hes5, in the neuroepithelium of mouse embryos at embryonic day (E) 8.0–8.5, and this activation is indispensable for the generation of neural stem cells. However, the molecular mechanism by which Hes5 expression is initiated in stem-producing cells remains unknown. We found that mammalian Gcm1 and Gcm2 (glial cells missing 1 and 2) are involved in the epigenetic regulation of Hes5 transcription by DNA demethylation independently of DNA replication. Loss of both Gcm genes and subsequent lack of Hes5 upregulation in the neuroepithelium of E7.5–8.5 Gcm1−/−; Gcm2−/− mice resulted in the impaired induction of neural stem cells. Our data suggest that Hes5 expression is serially activated first by Gcms and later by the canonical Notch pathway. View full text Figures at a glance * Figure 1: DNA methylation in the Hes5 promoter. (,) Top, promoter region (bases −120 to +1) of Hes5 containing the second RBP-J binding site (BS; large white oval) and CpG sites (small ovals). Eight CpG sites that are variously methylated are shown in red and other nonmethylated sites in yellow. Bottom left, methylation status of the Hes5 promoter analyzed by bisulfite sequencing. Closed and open circles indicate methylated and non-methylated CpG sites, respectively. Bottom right, methylation frequency of the CpG sites in the Hes5 proximal promoter region. () Developmental course of methylation status of the Hes5 promoter region in the head primordium. Tissues from several embryos were pooled and two or more independent experiments were carried out. () Methylation status of the Hes5 promoter in Neuro2a and STO cells. (,) ChIP analysis of Neuro2a and STO cells using an antibody to RBP-J, followed by PCR for the promoter region containing RBP-J binding sites (−378 to +78) or downstream region (+492 to +768) () and qPCR ! analysis (). () RT-PCR for Hes5 and Rbpj expression in Neuro2a and STO cells after transfection of NICD cDNA. Error bars indicate s.e.m. and n values are shown in or above columns. *P < 0.05 by Student's t test. * Figure 2: Gcms are responsible for the demethylation of the Hes5 promoter. () RT-PCR for Hes5, Gcm1 and Gcm2 expression in the E6.5 whole embryo, E7.5 head primordium and E8.5 forebrain. () RT-PCR for Hes5 expression in STO cells after transfection of Gcm1, Gcm2 or both. (,) Methylation status analyzed by bisulfite sequencing using genomic DNA extracted from STO cells transfected with pCX expression vector, or vectors encoding Gcm1 or Gcm2 or both (), or using genomic DNA from the forebrain and midbrain of E8.33 Gcm1 and Gcm2 mutants (). Top, promoter region (bases −120 to +1) of Hes5 containing the second RBP-J binding site (BS; large white oval), fourth GCM binding site (black diamond) and CpG sites (small ovals). Eight CpG sites that are variously methylated are shown in red and other nonmethylated sites in yellow. Bottom left, methylation status of the Hes5 promoter analyzed by bisulfite sequencing. Closed and open circles indicate methylated and nonmethylated CpG sites, respectively. Right, methylation frequency of the CpG sites in the Hes5 ! proximal promoter region. Control: Gcm1+/+; Gcm2+/+, Gcm1+/−; Gcm2+/+, Gcm1+/+; Gcm2+/− and Gcm1+/−; Gcm2+/−. Gcm1 KO: Gcm1−/−; Gcm2+/+ and Gcm1−/−; Gcm2+/−. Gcm2 KO: Gcm1+/+; Gcm2−/− and Gcm1+/−; Gcm2−/−. Gcm1/2 KO: Gcm1−/−; Gcm2−/−. Error bars, s.e.m.; n values are shown in columns. *P < 0.05 by one-way ANOVA followed by Dunnett's post hoc comparison () or by Kruskal-Wallis test followed by Dunn's multiple comparison test (). * Figure 3: Gcms are indispensable for Hes5 induction and neural stem cell generation. () Analysis of Hes5 (top) and Hes3 (bottom) expression in E8.33 mutant embryos by whole mount in situ hybridization. Hes5 mRNA is present in the portion of midbrain (arrow) and hindbrain (arrowhead) and neural tube (double arrowhead) in E8.33 control embryos. The reduction of Hes5 expression in Gcm2−/− mutants varied embryo to embryo, and the severest one is shown. n ≥ 3 for Hes5 and n ≥ 2 for Hes3. () Top, dotted line in E8.33 embryo indicates the level of coronal sections. Bottom, in situ hybridization for Hes5 and Hes3 and immunohistochemistry for BrdU. Insets, higher magnification pictures for BrdU and Hoechst. NE, neuroectoderm; Me, mesenchymal cells. () Left, scheme of in vitro induction of neural stem cells from E7.5 embryos. Right, bar graphs of the number of tertiary (3°) FGF2/EGF-responsive spheres from E7.5 Gcm1 and Gcm2 mutants. Scale bars: 0.5 mm (), 100 μm (). Error bars represent s.e.m. and n values are shown in or above columns. *P < 0.05 by Kruskal! -Wallis test followed by Dunn's multiple comparison test. * Figure 4: Abnormal neural development in Gcm2 null mutants. (,) Gross morphology of E10.5 Gcm2 mutant embryos. The lateral (, left) and frontal (, right) views of Gcm2−/− mutants show the opening of the anterior neuropore. () Coronal sections of the forebrain through the eye primordium (dotted line in and ) analyzed by in situ hybridization for Hes5 (top) or by immunostaining for nestin and βIII tubulin (bottom). Scale bars: 1.0 mm (,), 200 μm (). * Figure 5: Gcms induce Hes5 expression in embryonic brains. (–) In utero electroporation of pCX (control) or vectors encoding Gcm1 and Gcm2, together with GFP expression plasmid, into the E14.5 mouse cortex and analysis after 24 h. () Embryos were either immunostained for GFP or in situ hybridized with probes for Hes5, Gcm1 or Gcm2. Arrowheads, Hes5+ cells; boxed area is magnified in inset. () Distribution of GFP+ cells in VZ/SVZ, IMZ or cortical plate. () Double immunostaining for GFP and Pax6 (left) or GFP and Tbr2 (right). Arrowheads, double-positive cells. () In utero electroporation of pCX (control) or vectors encoding Gcm1 and Gcm2, or Hes5, together with pCX-NLS-Cre expression plasmid into the E14.5 Z/EG reporter mouse cortex and analysis after 72 h. Embryos were immunostained for GFP (top) or GFP and BrdU (bottom). Arrowheads, double-positive cells. () Bar graphs showing the percentage of GFP+ cells in the cortical plate (top) or BrdU+ GFP+ cells relative to total GFP+ cells (bottom). Scale bars: 100 μm (,, top), 50 μm (,! , bottom). Error bars, s.e.m.; n values shown in columns. *P < 0.05 by Student's t test () or by one-way ANOVA followed by Dunnett's post hoc comparison (). * Figure 6: Gcms demethylate mitotically inactive DNA and methylated plasmids. () Methylation status of the Hes5 promoter in mitomycin C (MMC)-treated STO cells transfected with pCX or vectors encoding Gcm1 and Gcm2, together with GFP expression plasmid. After 72 h, transfected cells were collected by FACS against GFP and analyzed. Left, methylation status of the Hes5 promoter analyzed by bisulfite sequencing. Closed and open circles indicate methylated and non-methylated CpG sites, respectively. Right, methylation frequency of the CpG sites in the Hes5 proximal promoter region. (,) Ratio of relative promoter activities from methylated to those from naive luciferase reporter plasmids by NICD in the presence of either pCX alone, or both Gcm1-pCX and Gcm2-pCX plasmids. The reporter plasmids that contain intact Hes5 promoter () or the promoter with mutations at the fourth GCM binding site () were transected into Neuro2a cells. () Methylation status of the HhaI-methylated reporter plasmid. Among eight CpG sites that are variously methylated (red ovals) in ! the promoter region (bases −120 to +1) of Hes5 gene, three (blue ovals shown below) are methylated by HhaI. Bottom left, methylation status of the three HhaI-methylated CpGs in COS1 cells transfected with either pCX alone or both Gcm1-pCX and Gcm2-pCX. Closed and open circles indicate methylated and nonmethylated CpG sites, respectively. Right, methylation frequency of the HhaI-methylated CpG sites. Error bars, s.e.m.; n values shown in columns. *P < 0.05 by Student's t test. * Figure 7: Active demethylation by Gcms in vivo. () A scheme of explant culture from E7.0 embryos. Head primordium was incubated with mitomycin C (MMC) for 2 h and then cultured in the presence of LIF and FGF2. The explants were immunostained for BrdU that had been added to the culture 2 h before fixation. Scale bar, 20 μm. (,,) Methylation status of the Hes5 promoter in explants from E7.0 or E6.5 embryos. Left, methylation status of the Hes5 promoter analyzed by bisulfite sequencing. Closed and open circles indicate methylated and nonmethylated CpG sites, respectively. Right, methylation frequency of CpG sites in the Hes5 proximal promoter region. () Methylation analysis of explants from E7.0 embryos and MMC-treated explants cultured for 36 h. (,) Head primordium of E7.0 Gcm2 mutant embryos was incubated with MMC for 2 h and then cultured in the presence of LIF and FGF2. After 36 h in culture, the explants were subjected to bisulfite sequencing () and RT-PCR (). () Scheme of explant culture from E6.5 embryos. The distal ! portion of E6.5 embryos was excised and cultured in serum-free medium containing LIF, in the presence or absence of 1 μM 5-AzadC. (,) After 24 h in culture, the explants were subjected to bisulfite sequencing () and RT-PCR (). Error bars, s.e.m.; n values shown in columns. *P < 0.05 by one-way ANOVA followed by Dunnett's post hoc comparison () or by Student's t test (,). Author information * Abstract * Author information * Supplementary information Affiliations * Division of Neurobiology and Bioinformatics, National Institute for Physiological Sciences, Okazaki, Japan. * Seiji Hitoshi, * Yugo Ishino, * Akhilesh Kumar, * Salma Jasmine, * Kenji F Tanaka & * Kazuhiro Ikenaka * Department of Physiological Sciences, School of Life Sciences, Graduate University for Advanced Studies, Okazaki, Japan. * Seiji Hitoshi, * Yugo Ishino, * Akhilesh Kumar, * Salma Jasmine, * Kenji F Tanaka & * Kazuhiro Ikenaka * Institute of Molecular and Cellular Biosciences, University of Tokyo, Tokyo, Japan. * Takeshi Kondo & * Shigeaki Kato * Hosoya Research Unit, Brain Science Institute, RIKEN, Wako, Saitama, Japan. . * Toshihiko Hosoya * Research Organization of Information and Systems, Tokyo, Japan. * Yoshiki Hotta Contributions S.H. designed and carried out the experiments, analyzed the data and wrote the paper. Y.I., A.K., S.J., K.F.T. and T.H. generated Gcm mutant mice and analyzed the phenotypes. T.K. and S.K. carried out the experiments related to MBD4 knockout mice. Y.H. and K.I. supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Seiji Hitoshi Author Details * Seiji Hitoshi Contact Seiji Hitoshi Search for this author in: * NPG journals * PubMed * Google Scholar * Yugo Ishino Search for this author in: * NPG journals * PubMed * Google Scholar * Akhilesh Kumar Search for this author in: * NPG journals * PubMed * Google Scholar * Salma Jasmine Search for this author in: * NPG journals * PubMed * Google Scholar * Kenji F Tanaka Search for this author in: * NPG journals * PubMed * Google Scholar * Takeshi Kondo Search for this author in: * NPG journals * PubMed * Google Scholar * Shigeaki Kato Search for this author in: * NPG journals * PubMed * Google Scholar * Toshihiko Hosoya Search for this author in: * NPG journals * PubMed * Google Scholar * Yoshiki Hotta Search for this author in: * NPG journals * PubMed * Google Scholar * Kazuhiro Ikenaka Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figure 1–9 and Supplementary Table 1 Additional data - Neurod6 expression defines new retinal amacrine cell subtypes and regulates their fate
- Nat Neurosci 14(8):965-972 (2011)
Nature Neuroscience | Article Neurod6 expression defines new retinal amacrine cell subtypes and regulates their fate * Jeremy N Kay1, 2, 3 * P Emanuela Voinescu1, 2, 3 * Monica W Chu1, 2 * Joshua R Sanes1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:965–972Year published:(2011)DOI:doi:10.1038/nn.2859Received25 February 2011Accepted05 May 2011Published online10 July 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Most regions of the CNS contain many subtypes of inhibitory interneurons with specialized roles in circuit function. In the mammalian retina, the ~30 subtypes of inhibitory interneurons called amacrine cells (ACs) are generally divided into two groups: wide/medium-field GABAergic ACs and narrow-field glycinergic ACs, which mediate lateral and vertical interactions, respectively, within the inner plexiform layer. We used expression profiling and mouse transgenic lines to identify and characterize two closely related narrow-field AC subtypes. Both arise postnatally and one is neither glycinergic nor GABAergic (nGnG). Two transcription factors selectively expressed by these subtypes, Neurod6 and special AT-rich-sequence-binding protein 2 (Satb2), regulate a postmitotic cell fate choice between these subtypes. Satb2 induces Neurod6, which persists in nGnG ACs and promotes their fate but is downregulated in the related glycinergic AC subtype. Our results support the view that cel! l fate decisions made in progenitors and their progeny act together to diversify ACs. View full text Figures at a glance * Figure 1: nGnG ACs. () Mouse (top) and macaque (bottom) retinal sections triple-stained for a pan-AC marker, Stx1 (white); a GABAergic-AC marker, GAD (green); and a glycinergic-AC marker, Glyt1 (red). Glycinergic and GABAergic ACs were mutually exclusive. Stars mark ACs that did not show GAD or Glyt1 immunoreactivity (nGnG ACs). () The percentage of Stx1+ ACs that were GABAergic, glycinergic or nGnG in the adult mouse retina (≥200 cells were counted for each cell type). (,) Retinal sections from the MP transgenic mouse line stained with anti-GFP to reveal CFP+ cells (blue). A subset of Stx1+ ACs (; red) and Chx10+ bipolar cells (; red) were CFP+. The CFP+ AC and bipolar cell populations could be distinguished based on their laminar position within the INL. (–) CFP+ ACs from the MP line (blue) were not immunoreactive for GAD (; red), Glyt1 (; red), GABA (; red) or glycine (; red). Right panels show marker alone, with asterisks to mark the location of CFP+ ACs. Mouse tissue was from P15 (,–! ) or adult (>P40) (,) animals. Scale bars, 10 μm (,–). * Figure 2: Transcriptional profiling of MP-line CFP+ ACs and bipolar cells. () Method used to purify CFP+ ACs and bipolar cells (BCs) from MP transgenic mice. Dissociated retina contains CFP− cells (white), CFP+ ACs (dark blue) and CFP+ BCs (light blue). Monoclonal antibody VC1.1, recognizing a cell surface carbohydrate epitope specific to ACs, was applied to the suspension, allowing a second fluorophore (red) to be introduced onto ACs. Two-color flow cytometry (fluorescence-activated cell sorting (FACS)) was then used to collect CFP+VC1.1+ ACs or CFP+VC1.1− BCs. () Purity of the sorted populations. CFP+ cells were plated with or without VC1.1-based sorting and stained for pan-AC (Stx1) or pan-BC (Chx10) markers. Expected cell types were strongly enriched. () Top: heat map showing genes clustered according to their expression level across the seven cell types in the microarray data set. Color scale (red, high; blue, low) indicates gene expression level in each cell type relative to the mean for that gene. Genes for which expression was enriched ! in ACs of the MP line (MP ACs) are marked. Bottom: the portion of the heat map containing genes that were strongly expressed by MP ACs but not by other cell types (labeled at right) in the data set. Cell types included glycinergic ACs (AII, JamAC), GABAergic ACs (Starburst, PaxCre) and BC subsets. Genes indicated by arrows: 1, Ebf3; 2, Neurod6; 3, 6430573F11Rik; 4, Satb2; 5, Pde5a; 6, Galr2; 7, Frem1; 8, Pkdcc. () In situ hybridization for Neurod6 in the MP-line retina at P7. Cells expressing Neurod6 (red) were CFP+ (blue). () Neurod6+/cre mice carrying the MP transgene immunostained for CFP and Cre at P15. All Cre+ cells (red) were CFP+ (blue), as indicated by arrowheads. Secondary antibodies nonspecifically stain blood vessels (bv). Scale bar, 10 μm (applies to ,). * Figure 3: Morphology and development of nGnG ACs. () Morphology of P25 ACs labeled in Nd6CY mice (using anti-GFP to label YFP; left panel, green). Anti-calbindin (right panel, red) marked IPL sublaminae S2, S3 and S4. Arbors of YFP+ ACs stratified predominantly in S1–S3. Nissl counterstain (; blue) revealed the retinal layers. The vertical bar in the left panel indicated the IPL. Scale bar, 25 μm. () Wholemount adult Nd6CY retina stained with anti-GFP, viewed en face. Narrow-field YFP+ cells were evident in this retinal region with sparse YFP labeling. Scale bar, 25 μm. () A single nGnG cell from a P20 retina (green), labeled with YFP-encoding retrovirus, identified as nGnG using markers described in Online Methods. Nissl counterstain, blue. nGnG ACs were multistratified in S1–S3 (vertical bar, IPL). Scale bar, 10 μm. (–) The morphology of nGnG ACs in MP (,) and Nd6CY () mice at P7. Note the bistratified projection to IPL strata S1 and S3 (arrowheads; vertical bar, IPL). Scale bar, 10 μm. () Morphology of nGnG ACs! at P14 in Nd6CY mice (stained with anti-GFP to label YFP; green). Co-labeling with anti-calbindin (red) or Nissl (blue) is shown. Projections to S1 and S3 were still prominent (arrowheads), but S2 was also labeled. Scale bar, 25 μm. () BrdU immunolabeling (red) at P15 in MP mice injected with BrdU at P0. Many nGnG ACs (gray, left panel; blue, right) were BrdU+ (arrowhead), indicating that they underwent their final cell cycle at P0. Scale bar, 10 μm. () Birthdate curve for nGnG ACs, constructed by injecting BrdU at nine time points (P5 not shown) and counting cells that were CFP+BrdU+ at P15 (as in ). Plot shows cumulative percentage of MP mouse AC population that was born by a given age. For comparison, birthdate curves of GABAergic and glycinergic ACs are replotted from reference 12. Error bars, s.e.m. * Figure 4: Neurod6 regulated the nGnG versus glycinergic fate decision. (,) Many MP-line CFP+ ACs adopted a glycinergic fate in Neurod6cre/cre mutants. () Double labeling of Neurod6cre/cre sections for CFP (blue) and Glyt1 (red) revealed double-positive ACs (arrowhead). Scale bar, 10 μm. () Quantification of CFP+Glyt1+ cells showed a large increase in Neurod6cre/cre (Nd6cre/cre) mice relative to Neurod6+/+ and Neurod6+/cre littermates, in which double-positive cells were almost never observed (P < 0.001 for Neurod6cre/cre versus Neurod6+/cre; ≥80 MP-line ACs per genotype). () More ACs (Pax6+) were glycinergic (Glyt1+) in Neurod6cre/cre mutants than in littermate controls (*P < 0.01). (,) Sections from retinas electroporated (EP) with an RFP-encoding plasmid () or a mix of Neurod6-encoding and RFP-encoding plasmids (). The vertical bar marks the IPL. In control experiments (), the cell bodies of transfected neurons were scattered throughout the INL and neuronal processes projected throughout the IPL. After electroporation with the Neurod6-enco! ding plasmid (), the cell bodies of transfected ACs clustered in a single INL stratum and projected preferentially to IPL sublaminae S1 and S3. Scale bar, 25 μm. () Neurod6 promoted AC fate over other INL fates. The graph shows the fraction of RFP+ INL cells that became ACs (Stx1+; *P < 1 × 10−6; n ≥ 500 cells per experiment). In both experimental and control retinas, remaining non-AC RFP+ INL cells were bipolar cells (Chx10+) or Müller glia (Sox9+; not shown). () Neurod6 promoted a subtype fate shift among ACs toward the nGnG fate. Graph shows fraction of RFP+ ACs that were CFP+ after electroporation in MP transgenic mice (*P < 0.001; n ≥ 50 CFP+ cells per experiment). Error bars, s.e.m. in all graphs. All mice were at P15–P17. GCL, ganglion cell layer; OPL, outer plexiform layer. * Figure 5: Satb2 and Ebf3 marked nGnG ACs and a related glycinergic subtype. (,) Retinal sections stained with antisera to Ebf3 () or Satb2 (). Immunoreactivity (red) was observed in nuclei of a subset of ACs in the INL and a subset of RGCs in the ganglion cell layer (GCL). The Nissl counterstain is in blue. Scale bar, 25 μm. () Staining for Ebf3 (blue), Satb2 (red) and Neurod6 (YFP+ cells; green in the bottom panel) in the INL of Nd6CY retinas. The top panel shows that Ebf3 and Satb2 were expressed by the same AC subset (pink-purple nuclei). The bottom panel shows that almost all Neurod6+ (YFP+) ACs expressed Satb2 and Ebf3 (arrows), although Neurod6 was expressed by only a subset of the Satb2+Ebf3+ population. Scale bar, 10 μm. () Triple staining for MP-line ACs (CFP+; blue) Ebf3 (red) and GlytT1 (green) revealed that all Ebf3+ ACs were either nGnG or glycinergic: CFP+Ebf3+ cells (arrowheads) did not express Glyt1, and Ebf3+Glyt1+ cells did not express the MP transgene. Scale bar, 10 μm. () AC classes defined by neurotransmitter or by expression! of the transcription factors Satb2, Ebf3 and Neurod6. Lines are drawn approximately to scale. The line labeled SEG denotes the population of ACs that are Satb2+Ebf3+Glyt1+. The size of the AII population was estimated from refs. 6 and 10. () The morphology of a SEG AC (green) in the P20 retina, projecting to sublaminae S1 (OFF) and S4 (ON), was revealed by a YFP-encoding retrovirus. Cells were identified by triple staining for YFP, Ebf3 (not shown) and Glyt1 (blue). The vertical line marks the IPL. Scale bar, 10 μm. See also Supplementary Figure 5. * Figure 6: Neurod6 controlled a postmitotic choice between nGnG and SEG fates. () A section from a MP-line Neurod6cre/cre retina that was triple stained for CFP+ ACs (anti-GFP; green), Glyt1 (red) and Ebf3 (blue). CFP+ ACs that adopted a glycinergic fate also expressed Ebf3 (arrowheads), consistent with a fate switch from nGnG to SEG ACs. Mice were at P15. Scale bar, 10 μm. (,) The total size of the Ebf3+ AC population () was not changed in Neurod6cre/cre (Nd6cre/cre) mutants relative to littermate controls, but the composition of the Ebf3+ population () was altered. Loss of Neurod6 reduced the fraction of Ebf3+ cells that were nGnGs, and increased the fraction that were Glyt1+ SEGs (*P < 0.002; n > 450 cells). Mice were at P15. Error bars, s.e.m. () Glycinergic AII ACs (immunoreactive for Disabled-1) were not affected in Neurod6cre/cre mice. The density was calculated from 20 μm sections (n = 4 samples per genotype). Mice were at P15. (–) Ebf3 was expressed exclusively by postmitotic neurons in the developing retina. At P0 (), Ebf3 (purple) was co! nfined to Brn3a+ RGCs (green) in the ganglion cell layer (GCL). No expression was seen in the outer neuroblast layer (ONBL). At P4 (,), Ebf3 (blue) was expressed by migratory newborn Pax6+ ACs (green) in the ONBL (arrows) and by more mature Pax6+ ACs in the INL (asterisks). The Ebf3+ migratory cells did not express the bipolar cell and Müller glia marker Chx10 (red). Scale bar, 25 μm (,); 10 μm (). () Satb2 (green) and Ebf3 (red) were coexpressed in the same AC population at P4, including migrating newborn ACs (arrows). Scale bar, 10 μm. n.s., not significant. * Figure 7: Satb2 promoted the nGnG fate by inducing Neurod6. () Satb2 induced Ebf3. Electroporation (EP) with RFP plasmid alone (top panels) or with plasmids encoding Satb2 and RFP (bottom panels). Overproduction of Ebf3+ cells in Satb2-transfected retinal patches (bottom panels; arrow, yellow cells) was evident relative both to adjacent untransfected territory and to controls (top panels). Satb2+RFP+ cells were also shifted to the AC zone of the INL. Scale bar, 25 μm. (–) The AC zone of the INL (Stx1; green) contained more RFP+ cells in the Satb2-overexpressing retina () than in controls (). Clusters of Stx1+RFP+ACs formed in the Satb2-electroporated retina (,) but not in the control (). Scale bars for , and for , 10 μm. () Electroporation of plasmid encoding Satb2-GFP fusion protein, but not a YFP control plasmid, induced Neurod6 expression in transfected cells (arrows). Compared with baseline endogenous expression (top panels), the Neurod6 in situ hybridization signal was much higher in Satb2-GFP+ retinal patches (bottom panels! ). Scale bar, 25 μm. (,) Electroporation of Satb2, but not YFP, induced expression of nGnG markers 6430573F11Rik (6xRik; ) and Frem1 (). The in situ hybridization signal is in red, and transfected cells (anti-GFP) are in green. Arrows indicate Satb2GFP+ marker+ cells. Scale bar, 25 μm. () Excess ACs induced by Satb2 were Glyt1−. Clusters of Satb2+RFP+ cells (bottom panels) produced marked discontinuities in the normal pattern of Glyt1 immunoreactivity because they were Glyt1− (arrow). Scale bar, 25 μm. () The phenotype (nGnG or SEG) of supernumerary Ebf3+ ACs induced by Satb2 (as in , bottom panels) was scored in wild-type (WT) and Neurod6 (Nd6) mutant mice. Loss of Neurod6 function substantially blocked the ability of Satb2 to induce nGnG ACs. Instead, many of the excess Ebf3+ ACs were SEGs. n > 150 cells for each genotype. Mice were at P7–P9 (–) or P14 (). Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE30324 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Jeremy N Kay & * P Emanuela Voinescu Affiliations * Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA. * Jeremy N Kay, * P Emanuela Voinescu, * Monica W Chu & * Joshua R Sanes * Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA. * Jeremy N Kay, * P Emanuela Voinescu, * Monica W Chu & * Joshua R Sanes Contributions J.N.K., P.E.V. and J.R.S. conceived experiments and wrote the manuscript. J.N.K., P.E.V. and M.W.C. performed experiments. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Joshua R Sanes Author Details * Jeremy N Kay Search for this author in: * NPG journals * PubMed * Google Scholar * P Emanuela Voinescu Search for this author in: * NPG journals * PubMed * Google Scholar * Monica W Chu Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua R Sanes Contact Joshua R Sanes Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (983K) Supplementary Figures 1–7 and Supplementary Tables 1 and 2 Additional data - A CaMKIIβ signaling pathway at the centrosome regulates dendrite patterning in the brain
- Nat Neurosci 14(8):973-983 (2011)
Nature Neuroscience | Article A CaMKIIβ signaling pathway at the centrosome regulates dendrite patterning in the brain * Sidharth V Puram1, 3, 3 * Albert H Kim1, 4 * Yoshiho Ikeuchi1 * Joshua T Wilson-Grady2, 5 * Andreas Merdes6 * Steven P Gygi5 * Azad Bonni1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:973–983Year published:(2011)DOI:doi:10.1038/nn.2857Received11 March 2011Accepted28 April 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The protein kinase calcium/calmodulin-dependent kinase II (CaMKII) predominantly consists of the α and β isoforms in the brain. Although CaMKIIα functions have been elucidated, the isoform-specific catalytic functions of CaMKIIβ have remained unknown. Using knockdown analyses in primary rat neurons and in the rat cerebellar cortex in vivo, we report that CaMKIIβ operates at the centrosome in a CaMKIIα-independent manner to drive dendrite retraction and pruning. We also find that the targeting protein PCM1 (pericentriolar material 1) localizes CaMKIIβ to the centrosome. Finally, we uncover the E3 ubiquitin ligase Cdc20-APC (cell division cycle 20–anaphase promoting complex) as a centrosomal substrate of CaMKIIβ. CaMKIIβ phosphorylates Cdc20 at Ser51, which induces Cdc20 dispersion from the centrosome, thereby inhibiting centrosomal Cdc20-APC activity and triggering the transition from growth to retraction of dendrites. Our findings define a new, isoform-specific fu! nction for CaMKIIβ that regulates ubiquitin signaling at the centrosome and thereby orchestrates dendrite patterning, with important implications for neuronal connectivity in the brain. View full text Figures at a glance * Figure 1: CaMKIIβ restricts the elaboration of dendrites. () Immunoblot of granule neuron lysates. Molecular weight in kDa is indicated in all blots. Full-length blots for all immunoblot analyses are presented in Supplementary Figure 11. () Immunoblot of lysates from COS cells transfected with GFP-CaMKIIβ or GFP-CaMKIIα together with the CaMKIIβ RNAi or control U6 plasmid. ERK1/2, extracellular signal-regulated kinases 1 and 2. () Immunoblot of lysates from granule neurons electroporated with the CaMKIIβ RNAi or control U6 plasmid. () Granule neurons transfected with a CaMKIIβ RNAi or control U6 plasmid together with GFP were subjected to immunocytochemistry using the GFP antibody. In all images of neuronal morphology, arrows and arrowheads indicate dendrites and axons, respectively. Scale bar, 10 μm. () Total dendrite length for granule neurons treated as in . Total dendrite length was significantly greater in CaMKIIβ knockdown neurons than in control U6-transfected neurons (analysis of variance (ANOVA), P < 0.005). In tota! l, 270 neurons were measured. () Granule neurons were analyzed as in . Total axon length was not significantly different between CaMKIIβ knockdown neurons and control U6-transfected neurons. () Granule neurons were transfected with a CaMKIIβ RNAi or control U6 plasmid and analyzed for cell survival. Cell survival was not significantly different between CaMKIIβ knockdown neurons and control U6-transfected neurons. () Immunoblot of lysates from COS cells transfected with GFP-CaMKIIβ-WT or GFP-CaMKIIβ-RES together with the CaMKIIβ RNAi or control U6 plasmid. () Granule neurons transfected with the CaMKIIβ RNAi or control U6 plasmid together with the expression plasmid encoding CaMKIIβ-WT, CaMKIIβ-RES or control vector and GFP, analyzed as in . Scale bar, 10 μm. () Total dendrite length for granule neurons treated as in was quantified. CaMKIIβ-RES, but not CaMKIIβ-WT, significantly reduced total dendrite length compared to control vector in the background of CaMKII�! � RNAi (ANOVA, P < 0.005). In total, 360 neurons were measured! . () Granule neurons transfected with constitutively active T287D CaMKIIβ or control vector were analyzed as in . Total dendrite length was significantly lower in T287D CaMKIIβ-expressing neurons than in control vector-transfected neurons (t-test, P < 0.0005). In total, 180 neurons were measured. Error bars, s.e.m. * Figure 2: CaMKIIβ regulates dendrite patterning in vivo. () P6 rat cerebellar slices transfected by a biolistics method with the CaMKIIβ RNAi or control U6 plasmid together with GFP were subjected to immunohistochemistry using the GFP antibody. Scale bar, 10 μm. () CaMKIIβ knockdown neurons had significantly longer dendrites than control U6-transfected neurons (ANOVA, P < 0.0001). In total, 199 neurons were measured. () Schematic of in vivo electroporation approach. Cb, cerebellum; EGL, external granule layer; ML, molecular layer; PL, Purkinje cell layer; IGL, internal granule layer; GN, granule neurons; MF, mossy fiber. () Rat pups electroporated in vivo with a U6-CaMKIIβi/CMV-GFP RNAi or control U6/CMV-GFP plasmid were killed 5 d after electroporation and cerebella were subjected to immunohistochemistry using the GFP and calbindin antibodies. Representative control-transfected granule neurons are shown. Asterisks indicate cell somas. Scale bar, 10 μm. () Rat pups were electroporated as in , and representative neurons for ea! ch condition are shown. Scale bar, 10 μm. () IGL granule neurons analyzed as in were subjected to morphometric analysis. Total dendrite length was significantly greater in IGL granule neurons in CaMKIIβ knockdown rats than in control U6 rats (ANOVA, P < 0.0001). In total, 266 neurons were measured. () Rat pups electroporated in vivo with the U6-CaMKIIβi/CMV-GFP RNAi or control U6/CMV-GFP plasmid were killed 9 d after electroporation and cerebella were analyzed as in . Top two panels, representative cerebellar sections from each condition. Scale bar, 10 μm. Middle two panels, representative IGL granule neurons for each condition. Scale bar, 10 μm. Bottom two panels, zoomed views of dendritic tips of individual neurons. Scale bar, 2.5 μm. Brackets identify dendritic claws. Error bars, s.e.m. * Figure 3: CaMKIIβ operates specifically at the centrosome to control dendrite patterning. () Granule neurons transfected with the CaMKIIβ RNAi or control U6 plasmid together with CaMKIIβ-RES, K43R CaMKIIβ-RES or control vector and GFP were analyzed as in Figure 1d. CaMKIIβ-RES, but not K43R CaMKIIβ-RES, significantly reduced total dendrite length compared to control vector in the background of CaMKIIβ RNAi (ANOVA, P < 0.0001). In total, 390 neurons were measured. () Schematic of main structural domains of CaMKIIβ. Autoreg, autoregulatory; var, variable (varying across isoforms). () Granule neurons transfected with the CaMKIIβ RNAi or control U6 plasmid together with CaMKIIβ-RES, CaMKIIβ-RESΔFABD, CaMKIIβ-RESΔCTRv, PACT-CaMKIIβ-RESΔCTS or control vector and GFP were analyzed as in . CaMKIIβ-RES, CaMKIIβ-RESΔFABD and PACT-CaMKIIβ-RESΔCTS, but not CaMKIIβ-RESΔCTRv, significantly reduced total dendrite length compared to control vector in the background of CaMKIIβ RNAi (ANOVA, P < 0.0001). In total, 660 neurons were measured. () Granule neurons! transfected with GFP fused to the C-terminal variable region (GFP-CTRv) were subjected to immunocytochemistry using the GFP and pericentrin antibody. Arrows indicate colocalization of GFP-CTRv with pericentrin. Scale bar, 5 μm. () Immunoblot of fractions isolated from a granule neuron centrosome preparation. Asterisk indicates a nonspecific band. WCL, whole cell lysate. SnoN and BAD mark nuclear and cytosolic fractions, respectively. () CaMKIIβ-RES, CaMKIIα-CTS and PACT-CaMKIIα, but not CaMKIIα, significantly reduced total dendrite length compared to control vector in the background of CaMKIIβ RNAi in granule neurons (ANOVA, P < 0.0001). In total, 600 neurons were measured. () CaMKIIβ-RES and CaMKIIβ-RESΔAssoc, but not CaMKIIβ-WT or CaMKIIβ-RESΔAssocΔCTS, significantly reduced granule neuron total dendrite length compared to control vector in the background of CaMKIIβ RNAi in cerebellar slices (ANOVA, P < 0.0001). In total, 479 neurons were measured. Error ba! rs, s.e.m. * Figure 4: The centrosomal targeting protein PCM1 localizes CaMKIIβ to the centrosome. () Immunoblot (W) of lysates of 293T cells transfected with Myc-CaMKIIβ or Myc-CaMKIIα together with PCM1-GFP or control vector and immunoprecipitated (IP) using the GFP antibody. () Immunoblot of lysates of 293T cells transfected with Flag-CaMKIIβΔAssoc or Flag-CaMKIIβΔAssocΔCTS together with PCM1-GFP and immunoprecipitated using the GFP antibody. () Top panels, immunoblot of fractions isolated from a granule neuron centrosome preparation. Bottom panels, immunoblot of pooled centrosomal fractions immunoprecipitated with the PCM1 or control immunoglobulin G (IgG) antibody. () Immunoblot of lysates of granule neurons electroporated with the PCM1 RNAi or control U6 plasmid. () Granule neurons transfected with a PCM1 RNAi or control U6 plasmid together with GFP-CaMKIIβΔAssoc were analyzed as in Figure 3d. The percentage of neurons lacking centrosomal enrichment of GFP-CaMKIIβΔAssoc was significantly greater in PCM1 knockdown neurons than in control U6-transfected neu! rons (ANOVA, P < 0.005). In total, 394 neurons were analyzed. () Total dendrite length was significantly greater in PCM1 knockdown neurons than in control U6-transfected neurons (ANOVA, P < 0.0001). In total, 180 neurons were measured. () Granule neurons transfected with the PCM1 RNAi or control U6 plasmid together with PCM1-WT, PCM1-RES or control vector and GFP were analyzed as in . Scale bar, 10 μm. () PCM1-RES, but not PCM1-WT, significantly reduced total dendrite length compared to control vector in the background of PCM1 RNAi (ANOVA, P < 0.0001). In total, 240 neurons were measured. () Rat pups electroporated in vivo with a U6-PCM1i/CMV-GFP RNAi or control U6/CMV-GFP plasmid were killed at P8 and analyzed as in Figure 2d. Scale bar, 10 μm. () Total dendrite length was significantly greater in IGL granule neurons in PCM1 knockdown rats than in control U6 rats (ANOVA, P < 0.0001). In total, 270 neurons were measured. () Rat pups electroporated in vivo with the U6-PCM1! i–CMV-GFP RNAi or control U6–CMV-GFP plasmid were killed a! t P12 and analyzed as in Figure 2g. Scale bar, 10 μm. Inset: scale bar, 2.5 μm. Error bars, s.e.m. * Figure 5: CaMKIIβ phosphorylates Cdc20 at Ser51 and thereby inhibits centrosomal Cdc20-APC activity in neurons. () Immunoblot (W) of lysates of 293T cells transfected with GFP-CaMKIIβ together with Flag-Cdc20 or control vector and immunoprecipitated (IP) using the Flag antibody. Asterisk, IgG heavy chain. () Immunoblot of lysates of granule neurons immunoprecipitated with the Cdc20 or control (IgG) antibody. Asterisk, IgG heavy chain. () Top, schematic of CaMKII consensus sequence. Asterisk, the phosphorylated serine or threonine; Φ, hydrophobic amino acid; NB, non-basic amino acid. Bottom, phosphorylation of recombinant wild-type (WT) and S51A, S84A, S86A and S51A S86A mutants of an N-terminal region of Cdc20 (1–101) fused to glutathione S-transferase (GST) upon incubation in vitro with purified CaMKII and 32P-ATP. () Immunoblot of lysates of 293T cells transfected with Flag-WT Cdc20 or Flag-S51A Cdc20 together with GFP-T287D CaMKIIβ or control vector. () Granule neurons were transfected with the Cdc20 RNAi, CaMKIIβ RNAi or control U6 plasmid together with Myc-PACT-securin-luci! ferase or Myc-PACT-securin-DBM-luciferase and analyzed by luminometry. The relative amount of the centrosomal securin-luciferase reporter was significantly greater in Cdc20 knockdown neurons and significantly lower in CaMKIIβ knockdown neurons than in control U6-transfected neurons (Kruskal–Wallis test, P < 0.01, n = 4). () Immunoblot of lysates of granule neurons electroporated with the CaMKIIβ RNAi or control U6 plasmid. () CaMKIIβ knockdown significantly increased total dendrite length compared to control, and Cdc20 RNAi significantly reduced total dendrite length in granule neurons in the presence or absence of CaMKIIβ RNAi (ANOVA, P < 0.0001). In total, 420 neurons were measured. () Expression of S51A Cdc20-RES, but not S51D Cdc20-RES, significantly increased total dendrite length compared to control vector or WT Cdc20-RES in the background of Cdc20 RNAi in granule neurons (ANOVA, P < 0.0001). In total, 428 neurons were measured. () Expression of T287D CaMKIIβ s! ignificantly reduced total dendrite length compared to control! in the background of control vector or WT Cdc20, but not in the background of S51A Cdc20 (ANOVA, P < 0.0001). In total, 540 neurons were measured. Error bars, s.e.m. * Figure 6: CaMKIIβ-induced phosphorylation of Cdc20 promotes Cdc20 dispersion from the centrosome, leading to the restriction of dendrite elaboration. () Granule neurons transfected with CaMKIIβ or control vector together with farnesylated GFP (fGFP) were subjected to immunocytochemistry using the GFP or Cdc20 antibody. Arrow, Cdc20 localized to the centrosome; arrowheads, dispersed Cdc20. Scale bar, 5 μm. () The percentage of neurons showing dispersed endogenous Cdc20 was significantly greater in T287D CaMKIIβ- and WT CaMKIIβ-expressing neurons, but not in K43R CaMKIIβ- or CaMKIIα-expressing neurons, than in control vector-transfected neurons (ANOVA, P < 0.0001). In total, 622 neurons were analyzed. () The percentage of neurons showing dispersed endogenous Cdc20 was significantly greater in CaMKIIβ- and CaMKIIβΔAssoc-expressing neurons, but not in CaMKIIβΔAssocΔCTS-expressing neurons, than in control vector–transfected neurons (ANOVA, P < 0.0001). In total, 360 neurons were analyzed. () Granule neurons transfected with T287D CaMKIIβ together with fGFP were analyzed as in . Arrows indicate dendrites, whereas! the dashed boxes indicate the magnified regions in inset images. Scale bar, 5 μm. () Granule neurons transfected with GFP-Cdc20 or GFP-PACT-Cdc20 together with T287D CaMKIIβ or control vector were analyzed as in Figure 3d. Arrows indicate the location of the centrosome, which is labeled with pericentrin. Scale bar, 5 μm. () Total dendrite length was significantly lower in T287D CaMKIIβ-expressing neurons than in control vector–transfected neurons in the background of Cdc20 (ANOVA, P < 0.0005), but not in the background of PACT-Cdc20. In total, 360 neurons were measured. () Granule neurons transfected with GFP-centrin were subjected to immunocytochemistry using the GFP or phosphoSer51-Cdc20 antibody. Arrows, centrosome (labeled with GFP-centrin). Scale bar, 5 μm. () Granule neurons transfected with GFP-Cdc20, GFP-S51A Cdc20 or GFP-S51D Cdc20 were analyzed as in . Arrows, centrosome (labeled with pericentrin). Scale bar, 5 μm. () Line scan analysis of granule neurons! treated as in . Centrosome location was identified using peri! centrin immunoreactivity. S51A Cdc20 had enhanced signal at the centrosome and reduced cytosolic signal compared to WT Cdc20. By contrast, S51D Cdc20 had reduced centrosomal signal and broader non-centrosomal signal compared to WT Cdc20 (based on peak centrosomal signal intensity, ANOVA, P < 0.0001). In total, 270 neurons were analyzed. () Expression of T287D CaMKIIβ significantly reduced the percentage of neurons with centrosomally enriched WT Cdc20 compared to control vector, but had little or no effect on the centrosomal localization of S51A Cdc20 (ANOVA, P < 0.01). In total, 361 neurons were analyzed. Error bars, s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA. * Sidharth V Puram, * Albert H Kim, * Yoshiho Ikeuchi & * Azad Bonni * Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA. * Joshua T Wilson-Grady & * Azad Bonni * MD-PhD Program, Harvard Medical School, Boston, Massachusetts, USA. * Sidharth V Puram * Department of Neurosurgery, Brigham and Women's Hospital, Children's Hospital, Boston, Massachusetts, USA. * Albert H Kim * Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA. * Joshua T Wilson-Grady & * Steven P Gygi * Centre de Recherche en Pharmacologie-Santé, Unité Mixte de Recherche 2587, Centre National de la Recherche Scientifique–Pierre Fabre, Toulouse, France. * Andreas Merdes Contributions A.B. directed and coordinated the project. S.V.P., A.H.K. and Y.I. designed and performed in vivo experiments, biochemical assays and morphological analyses. J.T.W.-G. prepared mass spectrometry samples and completed analyses of Cdc20 phosphorylation in S.P.G.'s laboratory. A.M. contributed molecular reagents. The manuscript was written by S.V.P. and A.B. and critically reviewed and commented on by all authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Azad Bonni Author Details * Sidharth V Puram Search for this author in: * NPG journals * PubMed * Google Scholar * Albert H Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Yoshiho Ikeuchi Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua T Wilson-Grady Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Merdes Search for this author in: * NPG journals * PubMed * Google Scholar * Steven P Gygi Search for this author in: * NPG journals * PubMed * Google Scholar * Azad Bonni Contact Azad Bonni Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (823K) Live Imaging Analyses of Control Vector and T287D CaMKIIβ-transfected Neurons. Granule neurons were transfected on DIV1 with the T287D CaMKIIβ expression plasmid or control vector together with the GFP expression plasmid. Starting at DIV2, granule neuron dendrites were assessed by live spinning disk confocal imaging every eight hours over a 48 hr interval in an environment-controlled chamber. At each time point, neurons were imaged every ten minutes for a one hour period. Representative neurons at DIV 2 + 24 hrs are shown. Expression of T287D CaMKIIβ reduced dendrite extension events and promoted dendrite retraction events. * Supplementary Video 2 (1M) Live Imaging Analyses of Control U6 and CaMKIIβ RNAi-transfected Neurons. Granule neurons were transfected on DIV0 with the CaMKIIβ RNAi or control U6 plasmid together with the GFP expression plasmid. Starting at DIV2, granule neuron dendrites were assessed by live spinning disk confocal imaging every eight hours over a 48 hr interval in an environment-controlled chamber. At each time point, neurons were imaged every ten minutes for a one hour period. Representative neurons at DIV 2 + 24 hrs are shown. CaMKIIβ knockdown promoted dendrite extension events and reduced dendrite retraction events. PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–11 Additional data - Regulation of behavioral plasticity by systemic temperature signaling in Caenorhabditis elegans
- Nat Neurosci 14(8):984-992 (2011)
Nature Neuroscience | Article Regulation of behavioral plasticity by systemic temperature signaling in Caenorhabditis elegans * Takuma Sugi1, 4 * Yukuo Nishida1 * Ikue Mori1, 2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:984–992Year published:(2011)DOI:doi:10.1038/nn.2854Received01 April 2011Accepted03 May 2011Published online26 June 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Animals cope with environmental changes by altering behavioral strategy. Environmental information is generally received by sensory neurons in the neural circuit that generates behavior. However, although environmental temperature inevitably influences an animal's entire body, the mechanism of systemic temperature perception remains largely unknown. We show here that systemic temperature signaling induces a change in a memory-based behavior in C. elegans. During behavioral conditioning, non-neuronal cells as well as neuronal cells respond to cultivation temperature through a heat-shock transcription factor that drives newly identified gene expression dynamics. This systemic temperature signaling regulates thermosensory neurons non-cell-autonomously through the estrogen signaling pathway, producing thermotactic behavior. We provide a link between systemic environmental recognition and behavioral plasticity in the nervous system. View full text Figures at a glance * Figure 1: The essential role of HSF-1 in a memory-based behavior revealed by genome-wide microarray analyses. () Procedure for microarray analysis of the thermotactic (TTX) behavior conditioning process. C. elegans were cultivated at 23 °C for 2.5 d and were then shifted to a new temperature of 17 °C. Immediately after start of the assay, we isolated mRNA from worms that were not used for the behavioral assays. Error bars represent s.e.m. (–) Overall transition of gene expression change during behavioral conditioning. Scatter plot of intensity values (log2) for a representative hybridization of RNA isolated from worms at 1 h (), 2 h () or 4 h () after shifting the cultivation temperature from 23 °C to 17 °C compared with that isolated from worms just before shifting the cultivation temperature. A.u., arbitrary units. (–) The HSF-1 activity within a range of cultivation temperatures in vivo. The hsp-16.2p::gfp reporter gene, including the two HSF-1 binding elements (HSEs), was expressed in a wild-type background (). Each worm was cultivated with food at 15 °C for 7 d. The GF! P observations were performed before shifting the temperature () and at 2 h after shifting to the new temperatures (which were within the range of normal cultivation temperatures); 23 °C () or 25 °C (). For the transient heat-shock treatment (), each worm was incubated at 35 °C for 15 min and then allowed to recover at 15 °C for 2 h before the GFP observation. The GFP observations were conducted at least four times in each experimental condition. * Figure 2: Characterization of thermotaxis behavior of hsf-1 mutants. (,) Population thermotaxis (TTX) temperature shift assay of wild-type worms, hsf-1(sy441) mutants19 and hsf-1-overexpressing worms10. Cultivation temperature was shifted from 17 °C to 23 °C () and vice versa (). The population TTX assay was conducted just before shifting the cultivation temperature (0 h) and at every 1 h after shifting the temperature. () Thermotaxis of hsf-1(ok600) null mutants. Population TTX assays were conducted at 2 h after shifting the temperature from 17 °C to 23 °C. **P < 0.01 (t-tests, n = 3). () Evaluation of the effect of long-term cultivation at 23 °C after shifting temperature, on the thermotaxis of hsf-1 mutants and hsf-1-overexpressing worms. Population TTX assays were conducted at 7 h and 10 h after shifting temperature from 17 °C to 23 °C. *P < 0.05, **P < 0.01 (t-tests, n = 3). () Assessment of the dominant negative effect of HSF-1 on thermotaxis in the adult stage of wild-type worms. HSF-1DN was expressed in wild-type worms under hs! p-16.2 promoter (hsp-16.2p::hsf-1dn). Population TTX assays were performed at 3 h after heat-shock treatment followed by the temperature shift from 17 °C to 23 °C. **P < 0.01 (t-tests, n = 3). NS, not significant. Error bars represent s.e.m. * Figure 3: Thermotaxis controlled by HSF-1 downstream signaling. () Behavioral effects of cultivation temperature shift and heat-shock treatment, both of which activate HSF-1. The thermotaxis (TTX) assays were also performed at 2 h after shifting the temperature from 17 °C to 23 °C or at 2 h during recovery after the transient heat-shock treatment at 34 °C for 15 min (n = 3). (–) Identification of HSF-1 downstream genes. Expressions of genes possessing the catalytic activity (), genes encoding ion and peptide transporter () and other functional genes () were analyzed using quantitative PCR in wild-type worms and hsf-1 mutants. mRNAs were isolated from each worm before and at 5 h after shifting the cultivation temperature from 15 °C to 25 °C or from 25 °C to 15 °C. Genes that possess HSEs within their 6.0-kb promoter regions are underlined. *P < 0.05, **P < 0.01 (t-tests, n ≥ 3) in a comparison between wild-type worms and hsf-1 mutants. () Thermotaxis of worms carrying mutations in putative downstream genes of HSF-1. The importa! nce of each gene in thermotaxis was investigated using available mutants. Worms were cultivated at 23 °C for 2.5 d or 17 °C for 4.5 d. *P < 0.05, **P < 0.01 (t-tests, n = 3), in a comparison of each mutant with wild-type worms. Error bars represent s.e.m. * Figure 4: Cell-specific rescue of defective thermotactic behavior in hsf-1 mutants. (–) Expression pattern of HSF-1 in wild-type worms. To drive the GFP fluorescence under the hsf-1 promoter, hsf-1 promoter::gfp reporter gene was injected into the wild-type worms. The GFP fluorescence was detected in nearly all cells (): for example, in the nerve ring (), body wall muscles () and intestine (). (–) Cell-specific rescue experiments. Thermotactic behavior was examined at 2 h after shifting temperature from 17 °C to 23 °C by population thermotaxis (TTX) assays. hsf-1 cDNA was expressed as a transgene (1 ng μl−1 or 10 ng μl−1) in almost all neurons (pan-neuronal; or in the body wall muscles or intestine () of hsf-1 mutants using individual cell-specific promoters with ges-1p::gfp (50 ng μl−1) as an injection marker. The transgene was also coordinately expressed in AFD neurons at 1 ng μl−1, AWC neurons at 10 ng μl−1 and AIZ and RIA neurons at 1 ng μl−1 () and individually expressed in the sensory neurons AFD, AWC, ASH or ASE () or in the i! nterneurons AIY, AIZ or RIA (). At least two lines were assayed for each transgenic strain. Experiments for each transgenic strain were carried out at least three times. The significant differences of thermotactic plasticity between each transgenic strain and hsf-1(sy441) mutants were determined by Fisher's protected least significant difference multiple-comparison test. *P < 0.05, **P < 0.01. Error bars represent s.e.m. * Figure 5: Genetic interactions between HSF-1 signaling and the genes that act in the thermotactic neural circuit. (–) Genetic epistasis analysis. Thermotactic plasticity of wild-type worms and each mutant was examined using population thermotaxis (TTX) assays at 2 h after shifting the temperature from 17 °C to 23 °C. The TTX plate was divided into eight regions, −4 (coldest) to +4 (warmest) (Supplementary Fig. 1a). Epistatic relations between hsf-1 mutants and gcy-23 gcy-8 gcy-18 mutants (), odr-3 mutants () or ttx-3 mutants () are shown. The ttx-7;hsf-1 double mutants were also constructed, but it was difficult to analyze their thermotactic behaviors owing to their severe locomotory defects. () Summary of genetic epistasis analysis for the relationship between systemic temperature perception mechanism and thermotactic neural circuit. Thermotactic plasticity was examined as above. The significance of differences of thermotactic plasticity was determined using the Fisher's protected least significant difference multiple-comparison test. **P < 0.01; NS, not significant. Error bars r! epresent s.e.m. * Figure 6: Regulation of the thermosensory neurons by the HSF-1 signaling. (,) In vivo calcium imaging of the thermosensory neurons in 23 °C-conditioned wild-type worms, hsf-1 mutants and hsf-1 mutants expressing hsf-1 in body wall muscles during transient up-and-down warming. This method of warming was used to examine calcium transients in AFD () and AWC (). The average ratio changes of AFD and AWC were measured for wild-type worms (blue), hsf-1 mutants (red) and hsf-1 mutants expressing hsf-1 in body wall muscles (green). Stimulus temperatures are shown at the bottom. n = 11 to 17 worms. (–) In vivo calcium imaging of the thermosensory neurons in 23 °C-conditioned wild-type worms, hsf-1 mutants and hsf-1 rescue lines during step-wise warming. Temperature was increased in a step-wise manner to examine the temperature memory of the AFD () and AWC () thermosensory neurons, which was set by the previous cultivation temperature. The average ratio changes in AFD () and AWC () were measured each for wild-type worms (blue), hsf-1 mutants (red) and hs! f-1 mutants expressing hsf-1 in body wall muscles (green). Arrows indicate the first marked ratio changes during step-wise warming. The average ratio change of the AFD neurons in response to the temperature change from 19 °C to 21 °C during step-wise warming is shown in a bar graph (). **P < 0.01; NS, not significant (t-tests, n = 12 to 20 worms). Error bars represent s.e.m. * Figure 7: Effect of estradiol application on thermotaxis. () The dose-dependent effect of estradiol application. Wild-type worms were cultivated at 23 °C with indicated concentration of estradiol. To clearly observe the thermophilic movement of worms cultivated at 23 °C, the center of the thermotaxis (TTX) plate was adjusted to 23 °C to establish a linear thermal gradient ranging from approximately 20 °C to 26 °C. However, other behavioral assays were conducted by adjusting the center of the TTX plate to 20 °C to establish a thermal gradient ranging from approximately 17 °C to 23 °C (see Supplementary Fig. 1). (,) Effect of exogenously applied estrogen on thermotactic behaviors of wild-type worms and several mutants. Each worm was cultivated at 23 °C () or 17 °C (), either with (on) or without (off) 10 μM estradiol. () Effect of estradiol on the behavioral conditioning process. Assay was conducted as described in Supplementary Figure 1c. *P < 0.05, **P < 0.01 (t-tests). Error bars represent s.e.m. * Figure 8: HSF-1 signaling acts through estrogen signaling to regulate the AFD thermosensory neurons. () The thermotactic (TTX) behavior of the mutants impaired in the putative estrogen receptors. nhr-14(tm1473) mutants, nhr-69(tm2365) mutants, nhr-121(tm1797) mutants and their double or triple mutants were cultivated at 23 °C or 17 °C and then assayed. () Genetic interactions between hsf-1 and nhr-69. Thermotaxis of worms was examined 2 h after shifting temperature from 17 °C to 23 °C. Concentrations of nhr-69 cDNA expressed in nhr-69 hsf-1 mutants under cell-specific promoter were indicated. (,) Effect of estradiol on thermotaxis of nhr-69 mutants. Worms were grown at 23 °C () or 17 °C () with or without 100 μM of estradiol. () Calcium imaging of AFD neurons in 23 °C-conditioned wild-type worms (n = 9), nhr-69 hsf-1 mutants (n = 12) and nhr-69 hsf-1 mutants expressing nhr-69 in AFD neurons at the concentration of 0.3 ng μl−1 (n = 15) or 3 ng μl−1 (n = 13). **P < 0.01; NS, not significant (t-tests). Error bars represent s.e.m. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE28856 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Group of Molecular Neurobiology, Graduate School of Science, Nagoya University, Nagoya, Japan. * Takuma Sugi, * Yukuo Nishida & * Ikue Mori * CREST, Japan Science and Technology Agency, Tokyo, Japan. * Ikue Mori * Institute for Advanced Research, Nagoya University, Nagoya, Japan. * Ikue Mori * Present address: Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto, Japan. * Takuma Sugi Contributions T.S. designed the research, performed most experiments, analyzed data and wrote the manuscript; Y.N. performed the quantitative PCR experiments and conducted germline transformation to construct the C. elegans transgenic line; I.M. supervised the project, conducted initial identification of cells expressing the hsf-1 promoter::gfp reporter gene and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ikue Mori Author Details * Takuma Sugi Search for this author in: * NPG journals * PubMed * Google Scholar * Yukuo Nishida Search for this author in: * NPG journals * PubMed * Google Scholar * Ikue Mori Contact Ikue Mori Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (15M) Supplementary Figures 1–9, Supplementary Strains and Plasmids Additional data - Laminin-332 coordinates mechanotransduction and growth cone bifurcation in sensory neurons
- Nat Neurosci 14(8):993-1000 (2011)
Nature Neuroscience | Article Laminin-332 coordinates mechanotransduction and growth cone bifurcation in sensory neurons * Li-Yang Chiang1, 5 * Kate Poole1, 5 * Beatriz E Oliveira2 * Neuza Duarte1 * Yinth Andrea Bernal Sierra1 * Leena Bruckner-Tuderman3 * Manuel Koch2 * Jing Hu1, 4 * Gary R Lewin1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:993–1000Year published:(2011)DOI:doi:10.1038/nn.2873Received09 February 2011Accepted10 May 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Laminin-332 is a major component of the dermo-epidermal skin basement membrane and maintains skin integrity. The transduction of mechanical force into electrical signals by sensory endings in the skin requires mechanosensitive channels. We found that mouse epidermal keratinocytes produce a matrix that is inhibitory for sensory mechanotransduction and that the active molecular component is laminin-332. Substrate-bound laminin-332 specifically suppressed one type of mechanosensitive current (rapidly adapting) independently of integrin-receptor activation. This mechanotransduction suppression could be exerted locally and was mediated by preventing the formation of protein tethers necessary for current activation. We also found that laminin-332 could locally control sensory axon branching behavior. Loss of laminin-332 in humans led to increased sensory terminal branching and may lead to a de-repression of mechanosensitive currents. These previously unknown functions for this mat! rix molecule may explain some of the extreme pain experienced by individuals with epidermolysis bullosa who are deficient in laminin-332. View full text Figures at a glance * Figure 1: Keratinocyte-derived matrix suppresses mechanotransduction currents. () Example traces of rapidly adapting (RA), intermediately adapting (IA) and slowly adapting (SA) mechanosensitive currents evoked by mechanical stimulation of neurites. Stacked histograms show the proportion of the three types of mechanosensitive current observed in neurons recorded on different substrates. Note the marked loss of rapidly adapting mechanosensitive current in cells cultured on keratinocytes and keratinocyte-derived matrix (number of recorded neurons is indicated above each histogram, **P < 0.01, χ2 test). () Bright-field images of sensory neuron cultured on keratinocytes (top). Immunostaining (NF-200) of a neuron cultured on keratinocytes shows the neurite outgrowth (bottom). RE denotes recording pipette and MS denotes mechanical stimuli. () Example current traces of slowly adapting mechanosensitive currents for each culture condition. Note the very long latency and relatively slow activation (mono-exponential fit shown with red line) for inward currents ev! oked on keratinocytes. () Quantitative comparison of the latency and activation time constant of neurons cultured on laminin, keratinocytes and keratinocyte-derived matrix. The latency of slowly adapting mechanosensitive current in keratinocytes co-culture was significantly longer than on laminin. The time constant for slowly adapting mechanosensitive current activation (τ1) on a keratinocyte monolayer was also significantly longer than on the laminin substrate (*P < 0.05, **P < 0.01, Mann Whitney U test). Error bars represent ±s.e.m. * Figure 2: Laminin-332 reproduces the suppression of rapidly adapting mechanosensitive currents. () Western blot analysis showing that laminin-332 is present in the keratinocyte-derived matrix but not in the EHS-derived laminin nor in 3T3 fibroblast–derived extracellular matrix (ECM). () Laminin-332 selectively suppresses rapidly adapting mechanosensitive currents. In mechanoreceptors (neurons with narrow, non-humped spikes), the rapidly adapting mechanosensitive current was significantly suppressed by a laminin-332 substrate compared with the laminin control (left, P < 0.01, Fisher's exact test). In the nociceptor group (neurons with wide, humped action potential spikes), the rapidly adapting mechanosensitive current was also significantly suppressed by a laminin-332 substrate (right, P < 0.01, Fisher's exact test). () Sample slowly adapting mechanosensitive current trace on a laminin-332 substrate (top). Latency and activation time constant for slowly adapting mechanosensitive current are shown for neurons on a laminin-332– or SCC25-derived matrix. () Stacked hist! ograms reveal a marked inhibition of rapidly adapting mechanosensitive currents when different dilutions of purified laminin-332 (L-332) were mixed with a constant concentration of laminin. Prior denaturation (boiling) of laminin-332 rendered it ineffective in suppressing rapidly adapting mechanosensitive current expression (the number on top of each histogram denotes the number of recorded neurons; *P < 0.05, **P < 0.01, χ2 test). Error bars represent ±s.e.m. * Figure 3: Laminin-332–containing matrix does not support tether formation and exerts its effect independent of integrin receptors. () Left, example TEM micrographs from cultured sensory neurons plated on one of four types of matrix: laminin (top), laminin-111 (second row), laminin-332 (third row) and laminin:laminin-332 mixture (bottom). We observed long tether proteins (black arrows) at the interface between sensory neuron membranes in cultures plated on laminin and laminin-111, but these were rare in neurons plated on laminin-332. Left panels show tight attachments found in all conditions. Right, quantification of the electron-dense attachments from neurons cultured on laminin and on laminin-332. The length of each measured attachment is plotted a random location in the vertical dimension space to illustrate the range of attachment lengths observed. Each dot represents the measured length of each linking object. Long, tether-like proteins greater than 75 nm on laminin-332 are largely missing. Scale bar, 100 nm. () Monoclonal antibody CM6 blocked the interaction of the integrin-binding G domain of lami! nin-332 (L-332) and completely prevented attachment and growth of sensory neurons on a purified laminin-332 substrate (top). Mixture of laminin-332 with laminin rescued attachment and neuritic growth (bottom). Scale bars, 25 μm. () The presence of CM6 did not rescue the suppression of the rapidly adapting mechanosensitive current on a mixed laminin/laminin-332 substrate. The number above each histogram denotes the number of recorded neurons. * Figure 4: Laminin-332 suppression of the rapidly adapting mechanosensitive current is local, not global. () Light micrograph showing that neurite outgrowth followed the cross-hatched grid patterns (RE, recording pipette; MS, mechanical stimulation). () Each neuron was filled with the fluorescent dye lucifer yellow to confirm this growth pattern and the neurites of such cells were then subjected to mechanical stimulation. Laminin stripes in one direction (green) were crossed with stripes of either laminin alone or laminin mixed with an inhibitory concentration of laminin-332 (purple) at 90° to the laminin stripe. () Neurons cultured on stripes consisting of laminin:laminin-332 (15:1) in one direction and laminin at 90° (laminin/laminin-332 cross-hatch) in the other direction. () The rapidly adapting mechanosensitive current was recorded when stimulating the neurite on laminin (green), but was rarely observed when stimulating the same neuritic tree on a laminin-332–containing substrate (purple). () Mechanosensitive currents could be evoked from neurites on the same cell on pr! otein stripes consisting of laminin in both directions, regardless of stripe orientation. () Rapidly adapting mechanosensitive currents evoked from neurites on the same cell were significantly reduced on laminin-332 stripes compared with laminin (numbers above bars indicate the numbers of neurons recorded, P < 0.05, χ2 test). * Figure 5: Differential growth behavior on laminin and laminin-332. (,) Neurites showed no directional preference when grown on laminin/laminin cross-hatched patterns (), but preferentially grew along laminin stripes when laminin was crossed with laminin:laminin-332 (15:1) (). () Surfaces patterned with stripes containing laminin-332 in both directions supported symmetrical growth. In –, the colored bars indicate the direction of each substrate (laminin, green; laminin-332, purple); each colored bar is 25 μm. () Quantification of the ratio of neurite outgrowth in each direction. The neurite length in each direction was summed and a ratio between the two directions calculated. For neurons grown on laminin/laminin-332 (L-332) stripes, a clear bias was observed for neurite outgrowth along the laminin substrate. () Schemes of branching behaviors observed at branch nodes (data obtained from time-lapse movies). At the node, the growth cone can bifurcate or trifurcate (top). Alternatively, a collateral branch may form after the growth cone has e! xtended past the junction (bottom). () Quantification of branching events during neurite outgrowth. Most branching events on the laminin/laminin were bi/trifurcations (40/50); however, on the laminin/laminin-332 nodes, nearly all events were collateral formation (27 of 29) (***P < 0.001, χ2 test). () Quantification of neurite width. Individual frames from bright-field, time-lapse experiments were analyzed by taking an intensity line scan and determining the width of each neurite at the half maximal intensity. Neurites were binned depending on matrix composition and for cells grown on laminin/laminin matrices, on the nature of the preceding branching event (that is, bifurcation versus collateralization). The collaterals formed on laminin were not significantly smaller than bi/trifurcation branches on the laminin/laminin pattern but the collateral branch on the laminin-332 stripe was significantly thinner (right). *P < 0.05, Student's t test. However, neurites that formed on! laminin-332/laminin-332 control matrices were the same width ! as those on laminin/laminin substrates. Error bars represent ±s.e.m; the number above each histogram denotes the number of measured neurites. * Figure 6: Laminin-332 markedly alters the network structure of the matrix. (–) Topographic images of the matrix at low (left) and high (right) resolution are shown for control laminin () and laminin doped with laminin-332 30:1 () and at a higher molar ratio 15:1 (). Note the irregularity of the surface structure with increasing concentrations of added laminin-332 and that laminin-doped with laminin-332 at 15:1 often showed very little protein coverage in the printed stripe (bottom half of , left). () Quantification of the percentage coverage in the three situations revealed that the surface coverage decreased significantly with increasing ratios of laminin-332. () The mean height of the matrix also tended to increase with increasing laminin-332. *P < 0.05, **P < 0.01 and ***P < 0.001, Student's t test. Error bars represent ±s.e.m; the number above each histogram denotes the number of imaged surfaces. * Figure 7: Human laminin-332 deficiency sensitizes mechanotransduction. () Example traces of rapidly adapting, intermediately adapting and slowly adapting mechanosensitive currents evoked by stimulating sensory neurons cultured on human keratinocyte–derived matrix. () Stacked histograms of the proportion of the three types of mechanosensitive current observed in neurons recorded on different substrates. On a human laminin-111 substrate, mechanotransduction is robust as compared to neurons on EHS laminin with only a few non-responding cells (2 of 18) showing that purified laminin-111 is a positive control. Note suppression of rapidly adapting mechanosensitive currents in cells cultured on normal human keratinocyte–derived matrix (control matrix; number of recorded neurons is noted on top of each histogram, *P < 0.05, **P < 0.01, χ2 test). On laminin-332–deficient JEB keratinocyte-derived matrix (JEB matrix), neuronal mechanotransduction is sensitized to a normal level as compared to neurons on laminin or on a laminin-111 substrate (data re! produced for comparison from ref. 12). () Example traces show typical measurements of current latency for each culture condition. Note the very long latency and relatively slow activation for inward currents evoked on normal human keratinocyte–derived matrix (control matrix) as well as on JEB keratinocyte matrix (JEB matrix). () Quantitative comparison of the latency and activation time constant of neurons cultured on laminin, control human matrix and JEB matrix. The latency of slowly adapting mechanosensitive current on control matrix was significantly longer than on laminin. A lack of laminin-332 (JEB matrix) in the matrix did not rescue the alteration of slowly adapting mechanosensitive current gating latency (top, P < 0.01, Mann Whitney U test). The time constant for slowly adapting mechanosensitive current activation (τ1) on both control matrix and JEB matrix was on average longer than on the laminin substrate, but this did not reach statistical significance (bottom! , P > 0.2, Mann Whitney U test). Error bars represent ±s.e.m. * Figure 8: Altered sensory afferent branching in the skin of laminin-332–deficient individuals. Tissue sections of biopsies from normal skin (control) and JEB skin were labeled using antibody to PGP9.5 and imaged with epifluorescence. The number of nerves crossing the dermo-epidermal boundary (marked with yellow, dotted line in all three images), branch points per nerve in the epidermis and the percent of the dermo-epidermal interface innervated were quantified from the images (see text). A typical image from control skin with a white arrow indicating a nonbranched fiber is shown on the left. Also shown are images from JEB skin (middle, nonblistered region; right, blistered region) with a yellow arrow indicating branched fiber. Many fibers course along the dermo-epidermal boundary in JEB skin, and these were termed interface fibers; one example is marked with a red arrow. e, lighter colored epidermal layer. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Li-Yang Chiang & * Kate Poole Affiliations * Department of Neuroscience, Max-Delbrück Center for Molecular Medicine and Charité Universitätsmedizin Berlin, Berlin, Germany. * Li-Yang Chiang, * Kate Poole, * Neuza Duarte, * Yinth Andrea Bernal Sierra, * Jing Hu & * Gary R Lewin * Institute for Oral and Musculoskeletal Biology, Center for Biochemistry, Department of Dermatology and Center for Molecular Medicine Cologne, Medical Faculty, University of Cologne, Cologne, Germany. * Beatriz E Oliveira & * Manuel Koch * Department of Dermatology and Freiburg Institute for Advanced Studies, School of Life Sciences LifeNet, University of Freiburg, Freiburg, Germany. * Leena Bruckner-Tuderman * Center for Integrative Neuroscience, Tübingen, Germany. * Jing Hu Contributions J.H., L.-Y.C. and K.P. performed the electrophysiology experiments and L.-Y.C. carried out the TEM analysis. B.E.O. and M.K. provided laminin-111/Nidogen complexes and recombinant β3 and γ2. K.P. carried out micro-contact printing with L.-Y.C. and performed time-lapse experiments. K.P. carried out AFM experiments. Neurite outgrowth assays and human skin immunocytochemistry were performed by K.P. with help from N.D. and Y.A.B.S. L.B.-T. provided and characterized the human keratinoctyes. L.-Y.C., J.H., K.P. and G.R.L. planned the experiments and analyzed data. G.R.L. and J.H. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Gary R Lewin or * Jing Hu Author Details * Li-Yang Chiang Search for this author in: * NPG journals * PubMed * Google Scholar * Kate Poole Search for this author in: * NPG journals * PubMed * Google Scholar * Beatriz E Oliveira Search for this author in: * NPG journals * PubMed * Google Scholar * Neuza Duarte Search for this author in: * NPG journals * PubMed * Google Scholar * Yinth Andrea Bernal Sierra Search for this author in: * NPG journals * PubMed * Google Scholar * Leena Bruckner-Tuderman Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel Koch Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Hu Contact Jing Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Gary R Lewin Contact Gary R Lewin Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (39M) Neurite outgrowth over laminin/laminin cross-hatched pattern. * Supplementary Video 2 (25M) Neurite outgrowth over laminin/laminin-332 cross-hatched pattern. PDF files * Supplementary Text and Figures (4M) Supplementary Results Additional data - Loss of activity-induced phosphorylation of MeCP2 enhances synaptogenesis, LTP and spatial memory
- Nat Neurosci 14(8):1001-1008 (2011)
Nature Neuroscience | Article Loss of activity-induced phosphorylation of MeCP2 enhances synaptogenesis, LTP and spatial memory * Hongda Li1, 2 * Xiaofen Zhong2 * Kevin Fongching Chau2 * Emily Cunningham Williams1, 2 * Qiang Chang1, 2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1001–1008Year published:(2011)DOI:doi:10.1038/nn.2866Received29 April 2011Accepted31 May 2011Published online17 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg DNA methylation–dependent epigenetic mechanisms underlie the development and function of the mammalian brain. MeCP2 is highly expressed in neurons and functions as a molecular linker between DNA methylation, chromatin remodeling and transcription regulation. Previous in vitro studies have shown that neuronal activity–induced phosphorylation (NAIP) of methyl CpG–binding protein 2 (MeCP2) precedes its release from the Bdnf promoter and the ensuing Bdnf transcription. However, the in vivo function of this phosphorylation event remains elusive. We generated knock-in mice that lack NAIP of MeCP2 and found that they performed better in hippocampus-dependent memory tests, presented enhanced long-term potentiation at two synapses in the hippocampus and showed increased excitatory synaptogenesis. At the molecular level, the phospho-mutant MeCP2 protein bound more tightly to several MeCP2 target gene promoters and altered the expression of these genes. Our results suggest that N! AIP of MeCP2 is required for modulating dynamic functions of the adult mouse brain. View full text Figures at a glance * Figure 1: Phosphorylation at serine 421 induced by depolarization, HFS and behavioral training. (–) Representative confocal microscope images of co-staining of DAPI (labeling all cell nuclei), NeuN (labeling all neuronal nuclei) and p-S421 in the CA1 region of hippocampal slices treated with 1 μM TTX or 50 mM KCl for 1 h (), receiving baseline stimulation or HFS (100 Hz, 1 s, twice with a 20-s interval; ), from mice that received shock immediately after being put into the box (immediate shock control) and mice that received shock 2 min after being put into the box (context trained; ), or from mice that received the hidden platform version of Morris water-maze training and mice that swam the same amount of time in the same tank with no platform (yoked/swimming control; ). Scale bars represent 20 μm. () Quantification of relative p-S421 intensity in the depolarization experiment (n = 4 for each group), HFS experiment (n = 4 for each group), the contextual fear-training experiment (n = 5 for each group) and the Morris water-maze training experiment (n = 3 for each gro! up). Data are presented as mean ± s.e.m. *P < 0.05, **P < 0.01. * Figure 2: Loss of NAIP at S421 in the Mecp2S421A;S424A/y mice. (–) Representative images of the CA1 region of the hippocampus from the Mecp2S421A;S424A/y mice (,,,) and their wild-type littermates (,,,) stained with MeCP2 (,) and NeuN (,) or p-S421 (,) and NeuN (,). Images on the left correspond with the adjacent image on the right (red and green channels, respectively). Arrowheads indicate the pyramidal cell layer in CA1. Scale bar represents 100 μm. () Western blot analysis was performed to compare the expression levels of p-S421 (top), MeCP2 (middle) and actin (bottom) in the brains of the wild-type (left two lanes) and Mecp2S421A;S424A/y mice (right two lanes) under normal condition and after seizure. Full-length blots are shown in Supplementary Figure 8. * Figure 3: Enhanced contextual fear memory in the Mecp2S421A;S424A/y mice. () The design of training procedure 1 (tone paired with shock) and the ensuing context test and cue test. CS, conditioned stimulus. () The contextual memory and the cue memory were measured by the percentage of time the mice spent freezing 22 and 24 h, respectively, after training. Because none of the wild-type and mutant mice froze pre-shock in training sessions in training procedure 1, those bars are 0 s (n = 10 in each genotype). () The design of training procedure 2 (shock only) and the ensuing context test. () The contextual memory was measure by the percentage of time the mice spent freezing 22 h after training. Because none of the wild-type mice froze pre-shock in training sessions in training procedure 2, that bar is 0 (n = 5 in each genotype). Bar graphs show mean ± s.e.m. *P < 0.05. * Figure 4: Enhanced spatial memory in the Mecp2S421A;S424A/y mice. () Schematic drawing of the Morris water-maze test design. () Swimming speeds of the wild-type and Mecp2S421A;S424A/y mice measured during the training sessions and probe trials. () Escape latency (time to find the hidden platform) plotted against the eight training sessions over 4 d. () Percentage of time spent in the target quadrant (TQ), the quadrant left of the TQ (LQ), the quadrant opposite the TQ (OQ) and the quadrant right of the TQ (RQ) during the probe trial (n = 20 in each genotype). The bar graph shows the mean ± s.e.m. Statistical analysis was performed as follows. First, a two-way repeated-measures ANOVA (one factor repetition) was performed, which revealed a statistically significant interaction between genotype and quadrant (F3,114 = 3.77, P = 0.01). Subsequently, a Tukey-HSD post hoc test was performed on that interaction to complete the pair-wise comparison of the percentage of time spent by each genotype in each quadrant, which revealed that both the wild-! type and the Mecp2S421A;S424A/y mice spent significantly more time in the target quadrant (in which a hidden platform was placed during the training sessions) than the other quadrants during the probe trial, and that the Mecp2S421A;S424A/y mice spent significantly more time in the target quadrant than their wild-type littermates did (49% versus 37%, P = 0.01). **P < 0.01. * Figure 5: Enhanced Schaffer collateral–CA1 LTP in Mecp2S421A;S424A/y mice. (,) Representative recording of field excitatory postsynaptic potential (fEPSP) rise slope in wild-type () and Mecp2S421A;S424A/y () slices. Sample traces in each genotype represent before (1) and 160 min after (2) the tetanic stimulation (100 Hz, 1 s, twice with a 20-s interval). () Average normalized (against baseline) fEPSP rise slope in the wild-type (closed circle) and the Mecp2S421A;S424A/y (open circle) slices. Error bars represent s.e.m. Statistical analysis was carried out using two-way ANOVA with repeated measure (F1,30 = 4.67, P = 0.04). () Average normalized (against baseline) fEPSP rise slope 160–180 min after the LTP induction in wild-type and Mecp2S421A;S424A/y slices. The bar graph shows the mean ± s.e.m. 16 slices from 14 Mecp2S421A;S424A/y mice and 16 slices from 15 wild-type littermates were included in and . *P < 0.05. * Figure 6: Enhanced mossy fiber–CA3 LTP in Mecp2S421A;S424A/y mice. (,) Representative recording of fEPSP amplitudes in wild-type () and Mecp2S421A;S424A/y () slices. Sample traces in each genotype represent before (1) and 40 min after (2) the tetanic stimulation (100 Hz, 1 s, twice with a 20-s interval), and after the application of DCG-IV (3). () Average normalized (against baseline) amplitudes in wild-type (closed circle) and Mecp2S421A;S424A/y (open circle) slices. Error bars represent s.e.m. Statistical analysis was carried out using two-way ANOVA with repeated measures (F1,32 = 11.203, P = 0.002). () Average normalized (against baseline) amplitudes 40–60 min after the LTP induction in wild-type and Mecp2S421A;S424A/y slices. The bar graph shows the mean ± s.e.m. 18 slices from 12 Mecp2S421A;S424A/y mice and 16 slices from 11 wild-type littermates were included in and . *P < 0.05, **P < 0.01. * Figure 7: Increased excitatory synaptogenesis in Mecp2S421A;S424A/y mice. () Representative confocal microscope images of cultured hippocampal neurons (21 DIV) from either the Mecp2S421A;S424A/y mice or their wild-type littermates stained with antibodies to VGLUT1, PSD95 and MAP2. (,) Representative images of neurites from both genotypes that were analyzed in the hippocampal culture () and the cortical culture (). Scale bars represent 10 μm. (,) Quantification of the densities (number of puncta per μm) of VGLUT1 puncta, PSD95 puncta, and colocalized VGLUT1 and PSD95 puncta in the hippocampal (n = 10 in each genotype; ) and the cortical cultures (n = 12 in each genotype; ). Numbers from the Mecp2S421A;S424A/y neurons were normalized against those from the wild-type neurons. *P < 0.05, **P < 0.01, ***P < 0.001. * Figure 8: Altered expression of MeCP2 target genes in the hippocampus of the Mecp2S421A;S424A/y mice and increased promoter binding by the Flag-tagged mutant MeCP2. () Relative expression levels of Bdnf CDS, Bdnf E4, Bmp4, Mef2c and Grm1 in the adult hippocampus of wild-type and Mecp2S421A;S424A/y mice (n = 14 in each genotype for Bdnf CDS, Bdnf E4; n = 8 in each genotype for Bmp4, Mef2c and Grm1; P = 0.02 for Bdnf CDS, P = 0.04 for Bdnf E4, P = 0.01 for Bmp4, P = 0.05 for Mef2c, P = 0.02 for Grm1). () Relative binding of Flag-tagged wild-type and mutant MeCP2 to the promoters of Bdnf (exon IV), Bmp4, Mef2c and Grm1 in the adult hippocampus of the Flag-Mecp2+/y and Flag-Mecp2S421A;S424A/y mice, respectively (n = 6 in each genotype, P = 0.03 for Bdnf, P = 0.01 for Bmp4, P = 0.05 for Mef2c, P = 0.04 for Grm1). *P < 0.05, **P < 0.01. Error bars represent s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Genetics Training Program, University of Wisconsin-Madison, Madison, Wisconsin, USA. * Hongda Li, * Emily Cunningham Williams & * Qiang Chang * Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA. * Hongda Li, * Xiaofen Zhong, * Kevin Fongching Chau, * Emily Cunningham Williams & * Qiang Chang * Department of Genetics and Neurology, University of Wisconsin-Madison, Madison, Wisconsin, USA. * Qiang Chang Contributions Q.C. directed the studies. H.L., X.Z. and Q.C. conceived and designed the experiments. H.L. performed the western blot, mouse behavioral tests, ChIP experiments, gene expression studies, imaging and image analysis. K.F.C. performed mouse behavioral tests. X.Z. performed all of the electrophysiology experiments, neuronal culture and hippocampal slice experiments, and immunostaining. E.C.W. performed immunostaining. The paper was written by Q.C., H.L. and X.Z. and commented on by all authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Qiang Chang Author Details * Hongda Li Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaofen Zhong Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin Fongching Chau Search for this author in: * NPG journals * PubMed * Google Scholar * Emily Cunningham Williams Search for this author in: * NPG journals * PubMed * Google Scholar * Qiang Chang Contact Qiang Chang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–8 Additional data - Axin2 as regulatory and therapeutic target in newborn brain injury and remyelination
- Nat Neurosci 14(8):1009-1016 (2011)
Nature Neuroscience | Article Axin2 as regulatory and therapeutic target in newborn brain injury and remyelination * Stephen P J Fancy1 * Emily P Harrington1, 2 * Tracy J Yuen1 * John C Silbereis1 * Chao Zhao3 * Sergio E Baranzini4 * Charlotte C Bruce3 * Jose J Otero1, 5 * Eric J Huang5 * Roel Nusse6 * Robin J M Franklin3 * David H Rowitch1, 7 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1009–1016Year published:(2011)DOI:doi:10.1038/nn.2855Received16 March 2011Accepted03 May 2011Published online26 June 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Permanent damage to white matter tracts, comprising axons and myelinating oligodendrocytes, is an important component of brain injuries of the newborn that cause cerebral palsy and cognitive disabilities, as well as multiple sclerosis in adults. However, regulatory factors relevant in human developmental myelin disorders and in myelin regeneration are unclear. We found that AXIN2 was expressed in immature oligodendrocyte progenitor cells (OLPs) in white matter lesions of human newborns with neonatal hypoxic-ischemic and gliotic brain damage, as well as in active multiple sclerosis lesions in adults. Axin2 is a target of Wnt transcriptional activation that negatively feeds back on the pathway, promoting β-catenin degradation. We found that Axin2 function was essential for normal kinetics of remyelination. The small molecule inhibitor XAV939, which targets the enzymatic activity of tankyrase, acted to stabilize Axin2 levels in OLPs from brain and spinal cord and accelerated t! heir differentiation and myelination after hypoxic and demyelinating injury. Together, these findings indicate that Axin2 is an essential regulator of remyelination and that it might serve as a pharmacological checkpoint in this process. View full text Figures at a glance * Figure 1: AXIN2 mRNA expression identifies Wnt pathway activation in immature oligodendrocytes in neonatal human WMI. () AXIN2 mRNA was expressed in areas of affected subcortical white matter in human pediatric cases of HIE and also adjacent to the cystic core of a periventricular lesion of PVL but was not seen in age-matched controls. () AXIN2 mRNA was expressed solely in Olig2-positive cells in affected white matter in neonatal HIE. Filled arrowheads indicate colocalization. Unfilled arrowheads indicate lack of colocalization. () Quantification of the number of AXIN2 mRNA–expressing cells in areas of white matter from subjects with HIE and control subjects. Error bars represent s.d. (,) In the subjects with HIE, AXIN2 mRNA was expressed in a subset of the Tcf4-positive cells (), which segregated from the mature oligodendrocyte marker PLP in situ (). () AXIN2 mRNA expression in HIE was completely separate from GFAP expression. (,) The independent Wnt-activated target Naked1 (Nkd1) was upregulated in neonatal HIE; proteins were expressed cytoplasmically in OLPs expressing PDGFRα () but w! as not expressed in cells expressing GFAP proteins (). Scale bars represent 10 μm. * Figure 2: Axin2 functions as a negative regulator of Wnt signaling in OLPs and promotes differentiation. () Schematic for Axin2 mRNA expression in wild-type (WT) mice and β-catenin (β-cat)/Tcf4-driven reporter expression in heterozygous Axin2-lacZ mice. () During developmental myelination of corpus callosum (CC), Axin2 mRNA was confined to earlier stage immature OLPs that expressed Nkx2.2 protein and was not seen in mature oligodendrocytes expressing APC (CC1) protein. Filled arrowheads indicate colocalization. Unfilled arrowheads indicate lack of colocalization. (,) In Axin2-lacZ heterozygous mice during developmental myelination, β-galactosidase (β-gal) proteins were first detected at a stage of the oligodendrocyte lineage in P9 corpus callosum that expressed the mature marker APC (CC1) but were not detected in cells expressing the OLP markers PDGFRα () and Nkx2.2 (), suggesting that the kinetics of reporter expression lagged behind that of Axin2 mRNA during developmental myelination. Tcf4 expression was observed in only a subset of β-galactosidase–positive cells (). ! Scale bars represent 10 μm (–). () Axin2-lacZ homozygous null animals demonstrated a significant reduction in mature oligodendrocytes expressing PLP mRNA (and MBP protein, inset) at P9 during developmental myelination of the corpus callosum, despite having normal numbers of OLPs expressing Nkx2.2 protein. Scale bars represent 600 μm, 100 μm (MBP inset) and 15 μm (Nkx2.2 inset). () Quantification during developmental myelination of the reduced number of (t test, P = 0.007) mature PLP-expressing oligodendrocytes in P9 Axin2−/− corpus callosum (black bars) compared with heterozygous littermates (gray bars), despite normal numbers of Nkx2.2-expressing OLPs. () Although β-galactosidase was not coexpressed with OLP markers in Axin2-lacZ heterozygotes (,), it was coexpressed with PDGFRα in Axin2−/− mice as OLPs were delayed in their differentiation. (,) There was a marked impairment of Axin2−/− oligodendrocyte differentiation in vitro, as evidenced by a signifi! cant (t test, P < 0.005) reduction in the percentage of Olig2-! positive cells expressing MBP at 60 h post differentiation in culture. Scale bar represents 25 μm. () There was a strong activation of Notum mRNA, an independent Wnt target, in Axin2−/− OLP cultures as compared with wild type, indicating that loss of Axin2 leads to an increase in Wnt pathway activity in these cultures. * Figure 3: AXIN2 is expressed in OLPs in active multiple sclerosis lesions. () Multiple sclerosis (MS) lesions were characterized as described previously33, using Luxol Fast Blue (LFB) to assess demyelination and LN3 immunohistochemistry to assess inflammatory cell activity. AXIN2 mRNA was expressed in cells in active multiple sclerosis lesions but not in normal-appearing white matter (NAWM) or chronic plaques. Scale bar represents 100 μm. () AXIN2 mRNA expression in active multiple sclerosis lesions was specific to the oligodendrocyte lineage, where it colocalized with Olig2 proteins. Scale bar represents 10 μm. () The independent Wnt-activated target Nkd1 was also upregulated in active multiple sclerosis lesions (Supplementary Fig. 5). Nkd1 proteins were expressed in the cytoplasm of OLPs with characteristic simple bipolar morphology. The density of cells expressing Nkd1 proteins in active multiple sclerosis lesions was similar to the density of cells expressing AXIN2 mRNA. Scale bars represent 10 μm. Error bars represent s.d. * Figure 4: Axin2 function is essential for timely myelin repair. () Schematic showing use of adult murine lysolecithin injury for investigating remyelination kinetics. Such lesions in spinal cord (SC) showed OLP recruitment (5 dpl), differentiation (10 dpl) and myelination (14 dpl) with stereotyped timing in young adult animals, allowing precise assessment of remyelination kinetics. Filled arrowheads indicate colocalization. Unfilled arrowheads indicate lack of colocalization. () Following demyelination of adult Axin2-lacZ heterozygote animals with lysolecithin in the spinal cord, β-galactosidase proteins were observed in the lesion in mature oligodendrocytes (coexpressing APC) at 10 dpl but not in cells expressing the OLP marker Nkx2.2. Scale bars represent 80 μm (left) and 10 μm (right). () Axin2−/− mice showed delayed repair compared with wild-type littermates. This was a result of reduced (t test, P = 0.03 at 10 dpl, P = 0.02 at 14 dpl) OLP differentiation into mature oligodendrocytes expressing PLP mRNA in lesions at 10 dpl, d! espite a normal recruitment of Nkx2.2-expressing OLPs into lesions. Scale bar represents 80 μm. () OLPs with dysregulated Wnt signaling in Axin2−/− (Axin2-lacZ homozygote) mice at 10 dpl following demyelination showed abnormal kinetics of mature marker acquisition and β-galactosidase proteins colocalized with the OLP marker Nkx2.2, in contrast with the Axin2-lacZ heterozygote mice (). Scale bar represents 10 μm. (,) Quantification of mature oligodendrocytes (expressing PLP mRNA, ) and OLPs (expressing Nkx2.2 protein, ) in unlesioned and 5 dpl, 10 dpl and 14 dpl demyelinated spinal cord of Axin2−/− animals (black) and wild-type littermates (gray). *P = 0.03, **P = 0.02. Error bars represent s.d. * Figure 5: Axin protein stabilization through small molecule tankyrase inhibition promotes OLP differentiation in vitro. () Tankyrase proteins were detected in the cytoplasm of mouse Olig2-positive cells but not at the PDGFRα-positive (OLP) stage, at P9 during developmental myelination in the spinal cord. Filled arrowheads indicate colocalization. Unfilled arrowheads indicate lack of colocalization. () The onset of tankyrase expression coincided with expression of β-galactosidase in Axin2-lacZ heterozygous reporter mice, at approximately the CC1-positive stage of oligodendrocyte development. () Tankyrase was also expressed in the oligodendrocyte lineage at 10 dpl following demyelination with lysolecithin in the adult spinal cord white matter of the mouse, colocalizing with mature oligodendrocyte marker cytoplasmic Olig1 (inset). Scale bars represent 10 μm (–). () XAV939 treatment of mouse OLPs in vitro with 0.01 or 0.1 μM for 24 h produced marked increases in the protein levels of both Axin2 and Axin1 versus vehicle controls, leading to an increase in the activity of the β-catenin degra! dation complex, as evidenced by an increase in degraded phospho–β-catenin protein levels. () At 0.01 μM, XAV939 treatment effectively inhibited the Wnt pathway in OLP in vitro after 96 h, as evidenced by a reduction in Axin2 mRNA levels. (,) In vitro OLP differentiation assays revealed a significant increase in the proportion of Olig2-positive cells expressing the mature oligodendrocyte marker MBP in the presence of either 0.1 or 0.01 μM XAV939 at both 48 and 60 h post-differentiation compared with vehicle control treatment. Scale bar represents 40 μm. **P < 0.001. Error bars represent s.d. () At 60 h post differentiation of OLP in vitro in the presence of 0.01 μM XAV939, there was a significant increase in the quantity of MBP protein harvested from the culture compared with vehicle control. () Tankyrase protein was expressed in oligodendrocyte lineage in human pediatric HIE WMI, where it was expressed in Olig2-positive, NOGO-A–positive and cytoplasmic Olig1-positi! ve cells but not in Iba1-positive macrophages/microglia or GFA! P-positive astrocytes. Scale bar represents 10 μm. * Figure 6: XAV939 treatment increases myelination, myelination following hypoxia and remyelination in ex vivo mouse cerebellar slice cultures. () XAV939 promoted developmental myelination. Axons (NFH) are shown in red, myelin (MBP) is shown in green and paranodes with compacted myelin sheaths (Caspr) are shown in white. Quantification of myelination using the ratio of the area stained for Caspr to the area stained for NFH (%) is shown below. Values shown are mean ± s.d. and the data were analyzed by one-way ANOVA with Dunnett's multiple comparison test (**P < 0.01 and ***P < 0.001). () XAV939 promoted myelination and recovery following acute hypoxic insult. Data are presented as in . Acute hypoxia impeded differentiation of OLPs in cerebellar slice cultures. Data were analyzed by unpaired t test. () XAV939 promoted remyelination following demyelination by lysolecithin. Data are presented as in . Data were analyzed by one-way ANOVA with Dunnett's multiple comparison test. Scale bars represent 50 μm (MBP/NFH panels) and 25 μm (Caspr panels). Three independent experiments were conducted per condition tested and 5�! �10 separate slices were counted per experiment. LPC; lysophosphatidylcholine/lysolecithin. * Figure 7: XAV939 treatment markedly accelerates OLP differentiation and myelin regeneration during remyelination in vivo. () Injection of 0.1 μM XAV939 into demyelinated lesions in young adult mouse spinal cord at the time of lysolecithin injection produced significant increases in the appearance of PLP mRNA–expressing mature oligodendrocytes at 6 dpl in dorsal or ventral funicular lesions compared with vehicle-treated controls. () XAV939 effects were Axin2 dependent, as Axin2−/− mice showed significantly less OLP differentiation at 6 dpl following XAV and lysolecithin treatment versus controls. () Quantification of and at 6 dpl during remyelination showing number of PLP mRNA–expressing cells per mm2 after respective treatments. *P < 0.05. Error bars represent s.d. (,) Although total Olig2-positive oligodendrocyte lineage numbers were similar at 6 dpl in XAV939-treated lesions and controls, XAV939 produced a shift in the proportion of mature PLP-positive oligodendrocytes versus immature Nkx2.2-positive OLP, with a significant increase (t test P = 0.002, n = 4) in mature oligodendrocyte! s. Error bars represent s.d. () XAV939 treatment did not affect the astrocyte (GFAP) or macrophage (Iba1) infiltration into lesions at 6 dpl. () XAV939 treatment in lesions was not cytoprotective to existing mature oligodendrocytes, as there were no PLP-expressing cells at 3 dpl in lesions of either group. (,) The OLP recruitment into lesions and proliferation phase at 3 dpl were unaffected by XAV939 treatment, evidenced by similar numbers of Olig2-positive cells in the lesion, a similar proportion of which were Ki67 positive. Error bars represent s.d. Scale bars represent 50 μm (,,,–). () The accelerated OLP differentiation produced by XAV939 treatment in lesions led to accelerated myelin regeneration at 10 dpl, as evidenced by significant increases in the thickness of restored myelin sheaths. G ratios were significantly different (t test, P < 0.0001) between the control group (G ratio mean = 0.94, s.e.m. = 0.003) and the XAV939-treated group (G ratio mean = 0.90, s.e.m! . = 0.004). Scale bar represents 2 μm. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE19403 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Departments of Pediatrics and Neurosurgery, Eli and Edythe Broad Institute for Stem Cell Research and Regeneration Medicine and Howard Hughes Medical Institute, University of California, San Francisco, California, USA. * Stephen P J Fancy, * Emily P Harrington, * Tracy J Yuen, * John C Silbereis, * Jose J Otero & * David H Rowitch * Medical Scientist Training Program, University of California, San Francisco, California, USA. * Emily P Harrington * MRC Centre for Stem Cell Biology and Regenerative Medicine and Department of Veterinary Medicine, University of Cambridge, Cambridge, UK. * Chao Zhao, * Charlotte C Bruce & * Robin J M Franklin * Department of Neurology, University of California, San Francisco, California, USA. * Sergio E Baranzini * Department of Pathology, University of California, San Francisco, California, USA. * Jose J Otero & * Eric J Huang * Department of Developmental Biology and Howard Hughes Medical Institute, Stanford University, Stanford, California, USA. * Roel Nusse * Division of Neonatology, University of California, San Francisco, California, USA. * David H Rowitch Contributions S.P.J.F. helped conceive the project and performed all experiments and analysis, with the exception of the following. E.P.H. performed and analyzed all experiments related to in vitro OLP cultures. T.J.Y. performed and analyzed the ex vivo cerebellar slice cultures. J.C.S. helped analyze Wnt pathway activation in murine hypoxic injury. C.Z. performed the electron microscopy and C.C.B. performed the G ratio analysis. S.E.B. performed bioinformatics. J.J.O. and E.J.H. procured human brain developmental tissue. R.J.M.F. and D.H.R. conceived the experiments and oversaw all aspects of the analysis. The paper was written by S.P.J.F., R.N., R.J.M.F. and D.H.R. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David H Rowitch Author Details * Stephen P J Fancy Search for this author in: * NPG journals * PubMed * Google Scholar * Emily P Harrington Search for this author in: * NPG journals * PubMed * Google Scholar * Tracy J Yuen Search for this author in: * NPG journals * PubMed * Google Scholar * John C Silbereis Search for this author in: * NPG journals * PubMed * Google Scholar * Chao Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Sergio E Baranzini Search for this author in: * NPG journals * PubMed * Google Scholar * Charlotte C Bruce Search for this author in: * NPG journals * PubMed * Google Scholar * Jose J Otero Search for this author in: * NPG journals * PubMed * Google Scholar * Eric J Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Roel Nusse Search for this author in: * NPG journals * PubMed * Google Scholar * Robin J M Franklin Search for this author in: * NPG journals * PubMed * Google Scholar * David H Rowitch Contact David H Rowitch Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (8M) Supplementary Figures 1–13 and Supplementary Table 1 Additional data - Zinc alleviates pain through high-affinity binding to the NMDA receptor NR2A subunit
- Nat Neurosci 14(8):1017-1022 (2011)
Nature Neuroscience | Article Zinc alleviates pain through high-affinity binding to the NMDA receptor NR2A subunit * Chihiro Nozaki1, 2, 3, 4, 8 * Angela Maria Vergnano5, 6, 7, 8 * Dominique Filliol1, 2, 3, 4 * Abdel-Mouttalib Ouagazzal1, 2, 3, 4 * Anne Le Goff5, 6, 7 * Stéphanie Carvalho5, 6, 7 * David Reiss1, 2, 3, 4 * Claire Gaveriaux-Ruff1, 2, 3, 4 * Jacques Neyton5, 6, 7 * Pierre Paoletti5, 6, 7, 9 * Brigitte L Kieffer1, 2, 3, 4, 9 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1017–1022Year published:(2011)DOI:doi:10.1038/nn.2844Received15 November 2010Accepted19 April 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Zinc is abundant in the central nervous system and regulates pain, but the underlying mechanisms are unknown. In vitro studies have shown that extracellular zinc modulates a plethora of signaling membrane proteins, including NMDA receptors containing the NR2A subunit, which display exquisite zinc sensitivity. We created NR2A-H128S knock-in mice to investigate whether Zn2+–NR2A interaction influences pain control. In these mice, high-affinity (nanomolar) zinc inhibition of NMDA currents was lost in the hippocampus and spinal cord. Knock-in mice showed hypersensitivity to radiant heat and capsaicin, and developed enhanced allodynia in inflammatory and neuropathic pain models. Furthermore, zinc-induced analgesia was completely abolished under both acute and chronic pain conditions. Our data establish that zinc is an endogenous modulator of excitatory neurotransmission in vivo and identify a new mechanism in pain processing that relies on NR2A NMDA receptors. The study also po! tentially provides a molecular basis for the pain-relieving effects of dietary zinc supplementation. View full text Figures at a glance * Figure 1: Targeting the NMDA receptor NR2A subunit gene in mice. () The NR2A-H128S mutant allele was created by homologous recombination. The scheme shows the WT NR2A allele, the targeting vector, the targeted allele and the H128S knock-in allele. The CAT codon encoding histidine 128 (H128) in exon 1 was replaced by the TCC codon encoding serine (S), and a loxP-flanked ('floxed') neo cassette was introduced 3′ from exon 1 to select for embryonic stem cells harboring the knock-in allele. The final mutant allele was obtained after excision of the neo cassette by a Cre recombinase treatment of embryonic stem cells. White box, exon 1; star in white box shows the H128S mutation; dark line, intronic sequences; Ba, BamH I; Bs, Bsu361; Sp, SpeI (restriction sites); triangles, loxP sites; neo box, neomycin-resistance cassette; gray bars, probes for Southern blot analysis; lines above gene indicate expected labeled DNA fragments in Southern blot analysis. () Southern blot analysis of WT and targeted alleles in the selected embryonic stem cell clo! ne. Genomic DNA was digested with Bsu361 and BamHI, and hybridized with 5′ and 3′ external probes, respectively or the neo probe. Expected bands at each size as indicated in are obtained. () Genomic DNA sequence analysis using tail biopsies from WT (left panel) and knock-in homozygous mutant (right panel) mice, showing the replacement of the CAT codon by the mutated TCC codon. () Genotyping of the NR2A-H128S knock-in line. PCR analysis using mutation-specific primers (strategy on left) reveals WT (280 bp) and mutant (315 bp) alleles (right). White arrow, forward primer for WT DNA, black arrow, forward primer for mutant DNA, gray arrows, reverse primer. Analysis of genomic DNA from WT NR2A, homozygous NR2A-H128S and heterozygous NR2A WT/NR2A-H128S mice is shown. The 35-bp differences between mutant and WT alleles results from the remaining loxP site in the mutant allele. * Figure 2: High-affinity zinc inhibition of NMDA currents is lost in NR2A-H128S mice. () Sensitivity to subunit-specific modulators of brain NMDARs from WT and knock-in NR2A-H128S (KI) mice transplanted into Xenopus oocytes. Inhibition by 20 nM zinc was abolished in KI mice (upper traces; 2.3 ± 1.3%, n = 6 versus 30.3 ± 4.2% n = 10 for WT; mean ± s.d., *P < 0.001, Student's t-test), whereas inhibition by the NR2B-selective antagonist ifenprodil was unchanged (lower traces; 25.2 ± 3.2%, n = 6 versus 24.8 ± 2.4% n = 13 for WT; P = 0.7). NMDA currents were induced by co-application of 300 μM NMDA, 100 μM glycine and 10 μM strychnine (N + G + S). () Hippocampal Schaeffer collateral to CA1 NMDA EPSCs from KI mice were insensitive to 300 nM zinc, contrasting with the marked inhibition seen in WT mice (peak current ratio: 0.48 ± 0.03 n = 4 versus 0.93 ± 0.06 n = 5 for KI; *P < 0.001, Student's t-test). Same for NMDA EPSCs recorded in the dorsal horn of the spinal cord (0.59 ± 0.05, n = 6 for WT versus 1.03 ± 0.07, n = 6 for KI; mean ± s.d., *P < 0.001).! () Unaltered protein expression levels in KI mice compared with those of WT mice in forebrain (top) and spinal cord (bottom). Lower band, α-tubulin control (α-tub). Full-length blots are presented in Supplementary Figure 1. For each protein, quantification was performed on two or three different WT–KI couples; no significant changes in expression was detected (P > 0.05, one-sample Student's t-test; see Supplementary Fig. 2). * Figure 3: NR2A-H128S mice show enhanced basal pain sensitivity in response to radiant heat and capsaicin. (–) Tail immersion (TI), hot plate (HP), tail flick (TF), Hargreaves (HT), von Frey filaments (VF), tail pressure (TP) and chemical tests with capsaicin (CAP) and TIP39 (TIP) were used to evaluate basal pain sensitivity in response to thermal (–, ), mechanical (,) and chemical (–) noxious stimuli (see Online Methods). No genotype effect was observed in TI withdrawal at three temperatures (), in the HP response (), in the mechanical responses (,) or in the TIP response (). By contrast, significant hypersensitivity of NR2A-H128 mice was detected in responses to radiant heat stimuli (,) and capsaicin (,). Furthermore, TF test with three different heat rates showed thermal hypersensitivity at 0.9 and 2.2 °C s−1 (). Data are expressed as means ± s.e.m. of eight mice per group. *P < 0.05 and **P < 0.001, NR2A-H128S mutants versus controls, Student's t-test. * Figure 4: NR2A-H128S mice show increased mechanical allodynia under chronic pain. Thermal and mechanical sensitivity of mutant knock-in (KI) mice and their WT controls was examined under CFA inflammatory or SNL neuropathic pain. Raw values (left) and percentage pain (right: (valuecontralateral – valueipsilateral)/valuecontralateral) are shown. Dotted line, baseline value. () Thermal sensitivity (Hargreaves test). Areas under the curve (AUC) of percentage pain showed no genotype difference in intensity and duration of thermal hyperalgesia under either CFA () or SNL () conditions. () Mechanical sensitivity (von Frey test). Upon CFA injection (), as well as after partial sciatic nerve ligation (), mechanical allodynia developed with higher intensity and duration in mutant mice (AUC of percentage pain: CFA, 316 ± 15 for WT mice versus 510 ± 7.3 for KI mice; SNL, 565 ± 12 for WT mice versus 778 ± 14 for KI mice). Data are expressed as means ± s.e.m. of eight mice per group. *P < 0.05, **P < 0.01 and ***P < 0.001, NR2A-H128S mutants versus controls for i! ndividual time points, Student's t-test. * Figure 5: Zinc analgesia is abolished in NR2A-H128S mice. Zinc analgesia was examined in mutant knock-in (KI) mice and control WT mice after intrathecal (i.t.; 0.2 nM, left panels) or subcutaneous (s.c.; 0.1–1 mg per kilogram body weight, right panels) ZnCl2 administration. Baseline thresholds (BL) were measured in naive mice before the drug injection or induction of chronic pain. () Acute thermal pain (TF; raw values (left panels) and percentage of maximum possible effect (%MPE, right panels; see Online Methods)) shows zinc analgesia in WT but not KI mice. Data are expressed as means ± s.e.m. of eight mice per group. *P < 0.05, **P < 0.01 and ***P < 0.001, zinc-treated group versus saline-treated group for individual time points, Student's t-test. () CFA inflammatory pain. Both i.t. or s.c. zinc administrations inhibited CFA-induced thermal hyperalgesia (HT, left panels) and mechanical allodynia (VF, right panels). These antihyperalgesic and antiallodynic effects were absent in mutant mice. () SNL neuropathic pain. As for infla! mmatory pain, both i.t. or s.c. zinc inhibited thermal hyperalgesia (HT, left panels) and mechanical allodynia (VF, right panels) induced by sciatic nerve ligation. These antihyperalgesic and antiallodynic effects were absent in KI mice. For both CFA and SNL experimental series, data are expressed as means ± s.e.m. of eight mice per group. ***P < 0.001 for zinc-treated versus saline-treated groups, §§§P < 0.001 for mutants versus controls, two-way repeated measures analysis of variance (ANOVA) followed by Bonferroni–Dunn test. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Chihiro Nozaki & * Angela Maria Vergnano Affiliations * Institut de Génétique et Biologie Moléculaire et Cellulaire, Illkirch, France. * Chihiro Nozaki, * Dominique Filliol, * Abdel-Mouttalib Ouagazzal, * David Reiss, * Claire Gaveriaux-Ruff & * Brigitte L Kieffer * Centre National de la Recherche Scientifique (CNRS) UMR7104, Illkirch, France. * Chihiro Nozaki, * Dominique Filliol, * Abdel-Mouttalib Ouagazzal, * David Reiss, * Claire Gaveriaux-Ruff & * Brigitte L Kieffer * Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Illkirch, France. * Chihiro Nozaki, * Dominique Filliol, * Abdel-Mouttalib Ouagazzal, * David Reiss, * Claire Gaveriaux-Ruff & * Brigitte L Kieffer * Université de Strasbourg, Illkirch, France. * Chihiro Nozaki, * Dominique Filliol, * Abdel-Mouttalib Ouagazzal, * David Reiss, * Claire Gaveriaux-Ruff & * Brigitte L Kieffer * Ecole Normale Supérieure, Institut de Biologie de l'Ecole Normale Supérieure, Paris, France. * Angela Maria Vergnano, * Anne Le Goff, * Stéphanie Carvalho, * Jacques Neyton & * Pierre Paoletti * CNRS UMR8197, Paris, France. * Angela Maria Vergnano, * Anne Le Goff, * Stéphanie Carvalho, * Jacques Neyton & * Pierre Paoletti * INSERM U1024, Paris, France. * Angela Maria Vergnano, * Anne Le Goff, * Stéphanie Carvalho, * Jacques Neyton & * Pierre Paoletti * These authors jointly directed this work. * Pierre Paoletti & * Brigitte L Kieffer Contributions B.L.K. and P.P. designed and supervised the study. D.F., J.N. and A.L.G. contributed to the creation NR2A-H128S mutant mouse line. J.N. and P.P. performed the electrophysiologial experiments on Xenopus oocytes. A.M.V. ran electrophysiological characterization of mutant mice and performed the Timm's staining. C.N. performed all of the pain experiments. D.R. and A.-M.O. conducted neurological examination of mutant mice. S.C. and A.M.V. performed immunochemistry. C.G.-R. and J.N. contributed to conceptual aspects of the study. C.N., A.M.V., C.G.-R., A.-M.O., P.P. and B.L.K. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Pierre Paoletti Author Details * Chihiro Nozaki Search for this author in: * NPG journals * PubMed * Google Scholar * Angela Maria Vergnano Search for this author in: * NPG journals * PubMed * Google Scholar * Dominique Filliol Search for this author in: * NPG journals * PubMed * Google Scholar * Abdel-Mouttalib Ouagazzal Search for this author in: * NPG journals * PubMed * Google Scholar * Anne Le Goff Search for this author in: * NPG journals * PubMed * Google Scholar * Stéphanie Carvalho Search for this author in: * NPG journals * PubMed * Google Scholar * David Reiss Search for this author in: * NPG journals * PubMed * Google Scholar * Claire Gaveriaux-Ruff Search for this author in: * NPG journals * PubMed * Google Scholar * Jacques Neyton Search for this author in: * NPG journals * PubMed * Google Scholar * Pierre Paoletti Contact Pierre Paoletti Search for this author in: * NPG journals * PubMed * Google Scholar * Brigitte L Kieffer Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–8 and Supplementary Table 1 Additional data - Potent amyloidogenicity and pathogenicity of Aβ43
- Nat Neurosci 14(8):1023-1032 (2011)
Nature Neuroscience | Article Potent amyloidogenicity and pathogenicity of Aβ43 * Takashi Saito1, 8 * Takahiro Suemoto1, 8 * Nathalie Brouwers2, 3 * Kristel Sleegers2, 3 * Satoru Funamoto4 * Naomi Mihira1 * Yukio Matsuba1 * Kazuyuki Yamada5 * Per Nilsson1 * Jiro Takano1 * Masaki Nishimura6 * Nobuhisa Iwata1, 7 * Christine Van Broeckhoven2, 3 * Yasuo Ihara4 * Takaomi C Saido1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1023–1032Year published:(2011)DOI:doi:10.1038/nn.2858Received17 March 2011Accepted13 May 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The amyloid-β peptide Aβ42 is known to be a primary amyloidogenic and pathogenic agent in Alzheimer's disease. However, the role of Aβ43, which is found just as frequently in the brains of affected individuals, remains unresolved. We generated knock-in mice containing a pathogenic presenilin-1 R278I mutation that causes overproduction of Aβ43. Homozygosity was embryonic lethal, indicating that the mutation involves a loss of function. Crossing amyloid precursor protein transgenic mice with heterozygous mutant mice resulted in elevated Aβ43, impairment of short-term memory and acceleration of amyloid-β pathology, which accompanied pronounced accumulation of Aβ43 in plaque cores similar in biochemical composition to those observed in the brains of affected individuals. Consistently, Aβ43 showed a higher propensity to aggregate and was more neurotoxic than Aβ42. Other pathogenic presenilin mutations also caused overproduction of Aβ43 in a manner correlating with Aβ42! and with the age of disease onset. These findings indicate that Aβ43, an overlooked species, is potently amyloidogenic, neurotoxic and abundant in vivo. View full text Figures at a glance * Figure 1: Phenotypic and biochemical characterization of PS1-R278I knock-in mice. () Embryonic lethality in homozygous PS1-R278I knock-in mice. An overall size reduction, stubby tail (arrowhead), limb ateliosis (yellow arrows) and hemorrhage in the CNS (white arrow) were observed. Scale bars represent 2 mm. (–) Embryonic brains (–) and MEFs () were analyzed by western blot (see Supplementary Fig. 4). Antibodies are listed to the left of each panel. () Expression of γ-secretase components. FL, full-length PS1. () BN-PAGE analysis of native γ-secretase complexes. −/− indicates homozygous PS1 knockout mice. Arrows indicate the position of the native wild-type, 360-kDa PS1 and PS2 γ-secretase complexes, whereas arrowhead points to the atypical high molecular weight (750 kDa) γ-secretase complex. () Immunoprecipitation by antibodies to PS1-NTF. IgG(H) and IgG(L) indicate immunoglobulin heavy and light chains, respectively. () γ-secretase activity in PS1-R278I knock-in brains. Brain extracts were analyzed by western blotting to detect endogenous AP! P CTF-β, APP CTF-α, APP intracellular domain (AICD), full-length N-cadherin, N-cadherin CTF and NICD products. * indicates an additional signal, smaller than that of CTF-α, which appeared in the knock-in mice. () Notch1 processing in PS1-R278I knock-in MEFs. Myc-tagged ΔNotch was transiently expressed in the MEFs, and cell lysates were subjected to western blot analysis using antibody to Myc. β-actin levels are shown as internal controls. * Figure 2: Aβ levels in adult PS1-R278I knock-in mouse brains and MEFs. (,) Establishment of ELISA system to specifically quantify Aβ42 and Aβ43 (see Supplementary Figs. 6a,b and 7). (–) Specificity of the antibodies to Aβ40, Aβ42 and Aβ43 used in this ELISA system. Synthetic Aβ1–40, Aβ1–42 and Aβ1–43 were separated by tris/tricine PAGE (15% polyacrylamide gel) and subjected to western blotting. Antibodies to Aβ C40, C42 and C43 specifically recognized Aβ1–40, Aβ1–42 and Aβ1–43, respectively (see Supplementary Fig. 6c–e). (–) Quantification of Aβ40, Aβ42 and Aβ43 by ELISA in adult mouse brains (–) and MEFs (–). (–) Cortical hemispheres from 24-month-old wild-type and heterozygous knock-in mice were homogenized and fractionated into Tris-HCl–buffered saline–soluble (TS) and GuHCl-soluble fractions. Data represent mean ± s.e.m. (n = 9). *P < 0.05 and **P < 0.01 between wild-type and heterozygous knock-in mice, Student's t test. (–) Aβ concentrations in conditioned medium from MEFs. We inoculated 8 × ! 105 cells in a 1-ml culture. The conditioned medium was collected after 24 h and subjected to ELISA. R278I/– indicates a double heterozygote PS1-R278I knock-in × PS1 knockout mice. Data represent mean ± s.d. from two independent experiments (n = 16). **P < 0.01 compared with wild type, one-way ANOVA with Scheffe's F test. ND, not detected. * Figure 3: Acceleration of Aβ pathology and short-term memory impairment by the R278I knock-in mutation in APP mice. (–) Brain sections from APP × PS1-R278I mice (3 (), 6 () and 9 months (,) old) and 9-month-old single APP mice (,) were immunostained with the 4G8 antibody to Aβ (–) and antibody to GFAP (green) with 4G8 counterstaining (red) (,). Aβ-immunostained brain sections from cortex () and hippocampus () of 3-, 6-, 9- and 12-month-old wild-type, APP and heterozygous PS1-R278I knock-in mice, as well as APP × PS1-R278I mice were analyzed (n = 5–6 each genotype). *P < 0.05 and **P < 0.01 compared with APP mice, two-way ANOVA with Scheffe's F test. Scale bars represent 500 μm (–) and 50 μm (,). (,) Y-maze test was performed before plaque formation using 3–4-month-old male wild-type, PS1-R278I knock-in, APP and APP × PS1-R278I mice. Data represent mean ± s.e.m. (n = 10 each genotype). *P < 0.05 compared with wild-type or PS1-R278I knock-in mice, one-way ANOVA with Scheffe's F test. * Figure 4: Aβ40, Aβ42 and Aβ43 in APP × PS1-R278I mice. (–) The levels of Aβ40 (,), Aβ42 (,) and Aβ43 (,) were quantified by ELISA and the ratios of the Aβ species (–) were subsequently determined. Cortical hemispheres from single APP and APP × PS1-R278I mouse brain (3 and 9 months old) were homogenized and fractionated into Tris-HCl–buffered saline–soluble fractions (–) and GuHCl-extractable fractions (–). Data represent mean ± s.e.m. (n = 7, 3 months old; n = 5, 9 months old). *P < 0.05 and **P < 0.01 between APP mice and APP × PS1-R278I mice, Student's t test. * Figure 5: Effect of various pathogenic PS1 mutations on Aβ43 production. The constructs containing PSEN1 with FAD-associated mutations were transfected into HEK293 cells stably expressing APP with the Swedish mutation. () Expression levels of PS1-FAD mutants. (–) Quantification of the steady-state levels of Aβ40, Aβ42 and Aβ43 and the correlation between Aβ levels and the age of disease onset. Age of onset is shown as follows: wild type (WT, 75 years old), A79V (59.3), V94M (53), I143T (32.5), A231V (58), L262F (50.3), L282V (44) and G384A (34.9)24, 44. Data represent mean ± s.d. from five independent series each consisting of duplicate measurements. *P < 0.05 and **P < 0.01 compared with wild type, one-way ANOVA with Dunnett test. * Figure 6: Localization of Aβ species in amyloid plaques of APP × PS1-R278I mice. (,) A set of serial brain sections from 9-month-old APP × PS1-M146V mice () and APP × PS1-R278I mice () were immunostained with the following antibodies to Aβ: 4G8 (total Aβ), C40 (Aβ1–40), C42 (Aβ1–42) and C43 (Aβ1–43). (–) The immunoreactive areas in single APP (left), APP × PS1-M146V (middle) and APP × PS1-R278I (right) mice were quantified as indicated (n = 6). **P < 0.01 between APP × PS1-M146V mice and APP × PS1-R278I mice, one-way ANOVA with Scheffe's F test. ND, not detected. (–) Double-staining with 4G8 (green) and Aβ40 (), Aβ42 () or Aβ43 () (red). The images in the left (green) and middle (red) are merged (yellow) on the right. Scale bars represent 500 μm (,) and 50 μm (–). * Figure 7: Mature amyloid plaques in APP × PS1-R278I mice and in vitro aggregation property and neural cell toxicity of Aβ43. (–) A set of serial brain sections from 9-month-old APP × PS1-M146V mice (,) and APP × PS1-R278I mice (,) were stained with thioflavin S (,) and immunostained with 4G8 (,). Thioflavin S–positive plaque are marked with arrows (,) and the corresponding plaques in the serial brain sections are also marked (,). Scale bars represent 500 μm. (,) The intensity of cortical and hippocampal Aβ immunoreactivity and thioflavin S signals were quantified (), and the ratio of thioflavin S/total Aβ signal of amyloid plaques was determined () (n = 12). Data represent mean ± s.e.m. **P < 0.01 between APP × PS1-M146V mice and APP × PS1-R278I mice, Student's t test. (,) In vitro Aβ aggregation experiments. Incorporation of thioflavin T into Aβ aggregates was measured by fluorescence spectroscopy. The aggregation properties of 20 μM Aβ40, Aβ42 and Aβ43 at 20 μM were measured individually in . The effect of Aβ40, Aβ42 and Aβ43 at a concentration of 0.2 μM on the mixture of ! 20 μM Aβ40 and 2 μM Aβ42 was then assessed in . Data represent mean ± s.d. from three independent series each consisting of 6–8 individual measurements. **P < 0.01 between Aβ40 and Aβ42 or between Aβ42 and Aβ43, *P < 0.05 between Aβ40 and Aβ43, one-way ANOVA with Scheffe's F test. (,) Neural cell toxicity of Aβ43. Cell viability () and lactate dehydrogenase (LDH) release as a measure of cell toxicity () were assayed. Aβs were administrated at 1, 3 and 10 μM, respectively. The results obtained after treatment with Aβ40 (white), Aβ42 (gray) and Aβ43 (black) are indicated, and vehicle (veh) treatment was also indicated by open column in (). Data represent mean ± s.d. from three independent series each consisting of six individual measurements. **P < 0.01 between Aβ40 and Aβ42 or between Aβ42 and Aβ43, and *P < 0.05 between Aβ40 and Aβ43, two-way ANOVA with Scheffe's F test or Dunnett test. * Figure 8: Aβ43 in amyloid plaques in Alzheimer's disease brains. (–) Serial sections of the hippocampal region (–,,,–) and the frontal cortical region of brains from individuals with Alzheimer's disease (,) were stained with 4G8 (total Aβ), C40 (Aβ1–40), C42 (Aβ1–42) and C43 (Aβ1–43), as well as thioflavin S, as indicated. The single staining (–,–) was developed using 3,3′-diaminobenzidine, whereas the double staining (–) used the fluorescent dyes fluorescein (green, Aβ) and rhodamine (red, Aβ43). The images in and are merged (yellow) in . Scale bars represent 250 μm (–) and 25 μm (–). The ratio of Aβ40, Aβ42 and Aβ43 of the plaque areas in the hippocampal region of brain sections from four individuals with Alzheimer's disease were quantified in . **P < 0.01 between Aβ40 and Aβ43, one-way ANOVA with Scheffe's F test (see Supplementary Fig. 15). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Takashi Saito & * Takahiro Suemoto Affiliations * Laboratory for Proteolytic Neuroscience, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan. * Takashi Saito, * Takahiro Suemoto, * Naomi Mihira, * Yukio Matsuba, * Per Nilsson, * Jiro Takano, * Nobuhisa Iwata & * Takaomi C Saido * Neurodegenerative Brain Diseases Group, Department of Molecular Genetics, Flanders Interuniversity Institute for Biotechnology, Antwerpen, Belgium. * Nathalie Brouwers, * Kristel Sleegers & * Christine Van Broeckhoven * Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerpen, Belgium. * Nathalie Brouwers, * Kristel Sleegers & * Christine Van Broeckhoven * Faculty of Life Sciences, Doshisha University, Kyoto, Japan. * Satoru Funamoto & * Yasuo Ihara * Research Resource Center, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan. * Kazuyuki Yamada * Molecular Neuroscience Research Center, Shiga University of Medical Science, Shiga, Japan. * Masaki Nishimura * Department of Molecular Medicinal Sciences, Division of Biotechnology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan. * Nobuhisa Iwata Contributions This study was jointly designed by T. Saito, T. Suemoto and T.C.S. Experiments were performed by T. Saito, T. Suemoto, N.M., Y.M., K.Y. and S.F. T. Saito, T. Suemoto, S.F., K.Y., P.N., J.T., M.N., N.I., C.V.B., Y.I. and T.C.S. jointly analyzed and interpreted data. N.B., K.S. and C.V.B. identified pathogenic PS1 mutations in patients and families and generated PSEN1 vector constructs for expression studies. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Takaomi C Saido Author Details * Takashi Saito Search for this author in: * NPG journals * PubMed * Google Scholar * Takahiro Suemoto Search for this author in: * NPG journals * PubMed * Google Scholar * Nathalie Brouwers Search for this author in: * NPG journals * PubMed * Google Scholar * Kristel Sleegers Search for this author in: * NPG journals * PubMed * Google Scholar * Satoru Funamoto Search for this author in: * NPG journals * PubMed * Google Scholar * Naomi Mihira Search for this author in: * NPG journals * PubMed * Google Scholar * Yukio Matsuba Search for this author in: * NPG journals * PubMed * Google Scholar * Kazuyuki Yamada Search for this author in: * NPG journals * PubMed * Google Scholar * Per Nilsson Search for this author in: * NPG journals * PubMed * Google Scholar * Jiro Takano Search for this author in: * NPG journals * PubMed * Google Scholar * Masaki Nishimura Search for this author in: * NPG journals * PubMed * Google Scholar * Nobuhisa Iwata Search for this author in: * NPG journals * PubMed * Google Scholar * Christine Van Broeckhoven Search for this author in: * NPG journals * PubMed * Google Scholar * Yasuo Ihara Search for this author in: * NPG journals * PubMed * Google Scholar * Takaomi C Saido Contact Takaomi C Saido Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–16 Additional data - Cocaine supersensitivity and enhanced motivation for reward in mice lacking dopamine D2 autoreceptors
- Nat Neurosci 14(8):1033-1038 (2011)
Nature Neuroscience | Article Cocaine supersensitivity and enhanced motivation for reward in mice lacking dopamine D2 autoreceptors * Estefanía P Bello1, 6 * Yolanda Mateo2, 6 * Diego M Gelman1, 6 * Daniela Noaín1, 6 * Jung H Shin3 * Malcolm J Low4 * Verónica A Alvarez3 * David M Lovinger2 * Marcelo Rubinstein1, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1033–1038Year published:(2011)DOI:doi:10.1038/nn.2862Received11 January 2011Accepted11 May 2011Published online10 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Dopamine (DA) D2 receptors expressed in DA neurons (D2 autoreceptors) exert a negative feedback regulation that reduces DA neuron firing, DA synthesis and DA release. As D2 receptors are mostly expressed in postsynaptic neurons, pharmacological and genetic approaches have been unable to definitively address the in vivo contribution of D2 autoreceptors to DA-mediated behaviors. We found that midbrain DA neurons from mice deficient in D2 autoreceptors (Drd2loxP/loxP; Dat+/IRES−cre, referred to as autoDrd2KO mice) lacked DA-mediated somatodendritic synaptic responses and inhibition of DA release. AutoDrd2KO mice displayed elevated DA synthesis and release, hyperlocomotion and supersensitivity to the psychomotor effects of cocaine. The mice also exhibited increased place preference for cocaine and enhanced motivation for food reward. Our results highlight the importance of D2 autoreceptors in the regulation of DA neurotransmission and demonstrate that D2 autoreceptors are impo! rtant for normal motor function, food-seeking behavior, and sensitivity to the locomotor and rewarding properties of cocaine. View full text Figures at a glance * Figure 1: Selective ablation of DA D2 autoreceptors prevents somatodendritic D2 like–mediated inhibition of midbrain DA neurons. () Schematic of conditional mutagenesis in the mouse D2 receptor gene Drd2. Exon 2 (black) is flanked by loxP sites (black triangles). Drd2 exon 2 is excised by Cre in dopaminergic neurons in Drd2loxP/loxP; Dat+/IRES−cre, mice. () [3H]Nemonapride-binding autoradiography. Scale bars represent 1 mm for brain sections and 100 μm for pituitary sections. () Schematic comparing midbrain DA neurons of Drd2loxP/loxP and autoDrd2KO mice. The absence of D2 autoreceptors predicts enhanced DA synthesis and release. () Whole-cell voltage-clamp recordings (Vh = −55 mV) from midbrain DA neurons. Baclofen (BACL, 5 μM) and quinpirole (QUINP, 200 nM) were applied as indicated by horizontal black bars. () The current density induced by each agonist was plotted for neurons obtained from Drd2loxP/loxP and autoDrd2KO mice (n = 6–7). ND, not detected. () The averages of five traces showing IPSCs evoked by electrical stimulation before (black) and after (blue) sulpiride application, as well! as the sulpiride-sensitive component (gray), are plotted. The dashed vertical lines indicate the average time to peak of the sulpiride-sensitive component of the IPSC (0.43 ± 0.10 s, n = 7) in Drd2loxP/loxP neurons. () IPSC densities measured at the average time to peak before and after sulpiride are shown for Drd2loxP/loxP and autoDrd2KO mice (n = 6–8). *P < 0.005. Error bars represent s.e.m. * Figure 2: Increased DA release and DA synthesis in autoDrd2KO mice. () DA release in the dorsal striatum evoked by a single stimulus pulse (300–600 μA, 0.6 ms per phase, biphasic; arrows). Top, time course of DA concentration changes. Insets represent the background-subtracted cyclic voltammograms indicative of DA. Bottom, two-dimensional representations of the voltammetric data. The voltammetric current is plotted against the applied potential (Eapp) and the acquisition time. () Input-output relationship of DA release elicited by single-pulse stimulation across a range of stimulus intensities in the dorsal striatum of Drd2loxP/loxP (n = 4) and autoDrd2KO mice (n = 5) (F1,29 = 10.27, P < 0.001). () Stimulated DA release in autoDrd2KO (n = 16) and control mice (n = 11) does not change in the presence of 2 μM sulpiride (Drd2loxP/loxP mice, n = 8; autoDrd2KO mice, n = 7). () Effect of quinpirole on electrically stimulated DA release (F5,34 = 17.94, P < 0.001). () Tyrosine hydroxylase activity assessed by L-DOPA accumulation in striata of Dr! d2loxP/loxP and autoDrd2KO mice receiving saline or 100 mg per kg, intraperitoneal, of NSD1015. Quinpirole (0.5 mg per kg, intraperitoneal) was given 30 min before NSD1015 (two-way ANOVA genotype × treatment interaction: F2,17 = 8.58, P < 0.005; treatment: F2,17 = 48.15, *P < 0.05 between NSD1015 treated mice receiving or not receiving quinpirole; genotype: F1,17 = 34.84, **P < 0.001, post hoc Fisher analysis). Error bars represent s.e.m. * Figure 3: Spontaneous locomotor hyperactivity in autoDrd2KO mice. () Locomotor activity in a novel open field for 60 min (repeated-measures ANOVA genotype: F1,21 = 6.32, P < 0.05). () AutoDrd2KO mice avoided the center of the open field, similar to control mice (one-way ANOVA: F1,27 = 0.17, P = 0.68). () Locomotor activity along three consecutive days (repeated-measures ANOVA time: F2,50 = 28.22, *P < 0.01 compared to day 1; repeated-measures ANOVA genotype: F1,25 = 15.60, **P < 0.001 compared to Drd2loxP/loxP mice). Both genotypes habituate similarly (time × genotype interaction: F2,50 = 2.97, P = 0.06). () Locomotor activity during 30 min after quinpirole (two-way ANOVA treatment: F1,45 = 15.18, #P < 0.001; genotype: F1,67 = 17.00, ##P < 0.001). Error bars represent s.e.m. * Figure 4: Normal DA reuptake and supersensitivity for cocaine in autoDrd2KO mice. () Representative electrically evoked (one pulse, arrows) DA signals before and after cocaine application. () Decay time constants (τ) of DA signal in the absence or presence of DAT blockers cocaine (COC) or methylphenidate (MPH) measured in autoDrd2KO (n = 14) and Drd2loxP/loxP mice (n = 11) (*P < 0.01). Error bars represent s.e.m. () Differential locomotor response to cocaine over 30 min (two-way ANOVA treatment: F2,39 = 88.91, #P < 0.001; genotype: F1,39 = 34.23, **P < 0.001; genotype × treatment interaction: F2,39 = 7.22, P < 0.05, post hoc Fisher analysis). () Mean s min−1 + s.e.m. spent on the drug-paired floor before and after 4 d of place preference conditioning using 5 mg per kg cocaine in Drd2loxP/loxP (n = 4) and autoDrd2KO mice (n = 4) (repeated-measures ANOVA conditioning: F1,6 = 93.05, ##P < 0.001; repeated-measures ANOVA genotype: F1,6 = 0.76, P = 0.42). () A tenfold lower dose of cocaine (0.5 mg per kg) induced place preference in autoDrd2KO mice (n = 6),! but not in Drd2loxP/loxP mice (n = 6) (repeated-measures ANOVA genotype: F1,10 = 13.18, ***P < 0.05). Dashed lines indicate 50% of the test time (30 s). Error bars represent s.e.m. * Figure 5: AutoDrd2KO mice displayed supramaximal DA release during train stimulation. () DA release in dorsal striatum evoked by trains of 30 pulses delivered at 10 Hz and 10-min intervals (pulse duration of 0.6 ms, biphasic, amplitude of 600 μA). Top, time course of DA concentration changes with insets and color plots as described in Figure 2a. () Effect of sulpiride on train-evoked DA release. Each figure represents average concentration-time plots for eight Drd2loxP/loxP and nine autoDrd2KO mice. () Horizontal locomotor activity recorded over 30 min in mice receiving saline, 0.1 or 0.6 mg per kg (intraperitoneal) of haloperidol (two-way ANOVA drug: F2,17 = 21.71, *P < 0.001 compared with saline). Error bars represent s.e.m. * Figure 6: AutoDrd2KO mice displayed increased motivation to work for a natural reward. () Mice (n = 7 per genotype) were subjected to an escalating fixed ratio schedule (pressing 3, 10, 30 and 100 times) (repeated-measures ANOVA genotype: F1,11 = 4.92, *P < 0.05; interaction: F3,33 = 4.35, P < 0.05). () Progressive ratio (2n) schedule. Left, number of presses (one-way ANOVA presses: F1,14 = 6.37, **P < 0.01). Right, maximum number of pellets obtained (break point; one-way ANOVA: F1,14 = 8.94, *P < 0.05). () Two day extinction protocol for 60 min (no food delivered) (repeated-measures ANOVA, post hoc Fisher analysis genotype: F1,14 = 11.58, *P < 0.05). Error bars represent s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Estefanía P Bello, * Yolanda Mateo, * Diego M Gelman & * Daniela Noaín Affiliations * Instituto de Investigaciones en Ingeniería Genética y Biología Molecular, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina. * Estefanía P Bello, * Diego M Gelman, * Daniela Noaín & * Marcelo Rubinstein * Section on Synaptic Pharmacology, Laboratory for Integrative Neuroscience, National Institute on Alcohol Abuse and Alcoholism, US National Institutes of Health, Bethesda, Maryland, USA. * Yolanda Mateo & * David M Lovinger * Section on Neuronal Structure, Laboratory for Integrative Neuroscience, National Institute on Alcohol Abuse and Alcoholism, US National Institutes of Health, Bethesda, Maryland, USA. * Jung H Shin & * Verónica A Alvarez * Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, USA. * Malcolm J Low * Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina. * Marcelo Rubinstein Contributions D.M.G. and M.R. generated the conditional mutant mice. D.N. and E.P.B. characterized, raised and maintained mouse colonies and performed backcrossing. E.P.B. and D.N. conducted neurochemical, histological and behavioral experiments. Y.M. and J.H.S. conducted electrochemical and electrophysiological experiments. E.P.B., Y.M., J.H.S., D.N. and M.R. prepared the figures. E.P.B., Y.M. and M.R. wrote the manuscript. All of the authors designed experiments, analyzed data and edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Marcelo Rubinstein Author Details * Estefanía P Bello Search for this author in: * NPG journals * PubMed * Google Scholar * Yolanda Mateo Search for this author in: * NPG journals * PubMed * Google Scholar * Diego M Gelman Search for this author in: * NPG journals * PubMed * Google Scholar * Daniela Noaín Search for this author in: * NPG journals * PubMed * Google Scholar * Jung H Shin Search for this author in: * NPG journals * PubMed * Google Scholar * Malcolm J Low Search for this author in: * NPG journals * PubMed * Google Scholar * Verónica A Alvarez Search for this author in: * NPG journals * PubMed * Google Scholar * David M Lovinger Search for this author in: * NPG journals * PubMed * Google Scholar * Marcelo Rubinstein Contact Marcelo Rubinstein Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–4 Additional data - Precise olfactory responses tile the sniff cycle
- Nat Neurosci 14(8):1039-1044 (2011)
Nature Neuroscience | Article Precise olfactory responses tile the sniff cycle * Roman Shusterman1 * Matthew C Smear1, 2 * Alexei A Koulakov3 * Dmitry Rinberg1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1039–1044Year published:(2011)DOI:doi:10.1038/nn.2877Received24 February 2011Accepted02 June 2011Published online17 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In terrestrial vertebrates, sniffing controls odorant access to receptors, and therefore sets the timescale of olfactory stimuli. We found that odorants evoked precisely sniff-locked activity in mitral/tufted cells in the olfactory bulb of awake mouse. The trial-to-trial response jitter averaged 12 ms, a precision comparable to other sensory systems. Individual cells expressed odor-specific temporal patterns of activity and, across the population, onset times tiled the duration of the sniff cycle. Responses were more tightly time-locked to the sniff phase than to the time after inhalation onset. The spikes of single neurons carried sufficient information to discriminate odors. In addition, precise locking to sniff phase may facilitate ensemble coding by making synchrony relationships across neurons robust to variation in sniff rate. The temporal specificity of mitral/tufted cell output provides a potentially rich source of information for downstream olfactory areas. View full text Figures at a glance * Figure 1: Odor response analysis: sniff-alignment and warping. () Intranasal pressure signal for three trials: aligned by odor onset (black), by the first inhalation after odor onset (blue) and temporally warped, so that each inhalation interval and the reminder of the sniff duration are equal to the average values (red). Yellow bars indicate the stimulus duration. Dashed lines indicate the beginning and the end of the inhalation intervals. () Schematic of the experiment. A head-fixed mouse was positioned in front of the odor delivery port. It was implanted with intranasal cannula and a multi-electrode chamber. Top, pressure waveform of a typical breathing cycle. Red dots indicate the inhalation onsets and offsets. The blue line is the parabolic fit to the first minimum after the inhalation onset. The sniff offset was defined as the second zero crossing of the parabolic fit. The gray shaded area marks the inhalation interval. (,) Raster and PSTH plots for two mitral/tufted cells (left and right columns) in response to an odor stimulus: ! synchronized by odor onset (black), inhalation onset (blue) and temporally warped (red). The light blue lines underlying the raster plots indicate the duration of the first sniff after odor onset. Vertical dashed lines indicate the beginning and end of inhalation intervals. Yellow bars indicate the odor stimulus duration. * Figure 2: Diversity of odor responses. () Sniff-warped raster plots and corresponding PSTHs of one cell's response to four odors: amyl acetate (red), benzaldehyde (black), hexanoic acid (brown) and methyl salicylate (blue). Response to blank is shown only on PSTH plot (gray). () Left, sniff-warped raster plots showing responses of six cells to the same odor (benzaldehyde). The stimulus interval is shown as a yellow bar above the plots. The sniff waveforms are shown above the plots and gray areas indicate inhalation intervals. Right, corresponding PSTH during the first sniff after odor onset (thin black line) and PSTH for unodorized sniffs (thick gray line). Vertical dashed lines indicate the beginning and end of inhalation. Red arrows indicate sharp event of firing rate increase. * Figure 3: Excitatory and inhibitory odor responses tile the sniff cycle. (–) Examples of common types of odor response PSTHs: initially excitatory (), initially inhibitory () and initially inhibitory switching to excitatory later in the sniffing cycle (). The gray lines are PSTHs from unodorized sniffs. The black lines indicate response latency (τlat), defined as the first moment when cumulative distributions with and without stimulus become statistically different. (,) Color plot of all excitatory () and all inhibitory () responses. Each horizontal line represents the difference in spike histograms between odorized and unodorized sniffs for one cell-odor pair. The cell-odor pairs are ordered by response latency marked by black dots. The black crosses on the right of the color plot mark cell-odor pairs for which full sniff spike counts did not significantly differ between odorized and unodorized sniffs. * Figure 4: Sharp events of firing rate increase. () PSTHs of odor response (blue line) and spontaneous activity (gray line) in warped time for one cell-odor pair, 10-ms bin. fm is the amplitude of the firing rate peak. () Zoomed raster plot of a sharp firing rate increase event. The gray area shows the interval around the peak of PSTH where sharp event is defined: from −2/fm to +4/fm. The red dots are spikes with inter-spike interval less than 1.5/fm. The first of these spikes in a trial is colored black. The mean and s.d. of these first spikes define the sharp event's latency (indicated by black arrows) and precision (horizontal bars above arrows), respectively. () Color plot for cell-odor pairs with sharp events of differences between odor response and spontaneous activity firing rates for all cell odor pairs for which sharp events were detected, ordered by latency (black dots) in warped time coordinates. Gray bars around black dots show the s.d. (precision) of each sharp event. () Distribution of latencies for all sha! rp events in the same coordinates as . Waveforms above and show the typical sniffing signal. * Figure 5: Mitral cell response precision in warped-time and real-time coordinates. () Scatter plot comparing precision of sharp events estimated in real time and warped time. Adjacent panels are distributions of precisions in warped and real time. () Scatter plot comparing firing rate peak amplitudes in warped and real time. Adjacent panel is the distribution of differences of the amplitudes in real and warped time. () Scatter plot of reliability of events versus precision of these events in warped time. Right, the distribution of reliabilities of the events. () Comparison of sharp event precision in real time domain for slow and fast inhalations. All sessions, which have more than 30 trials with detected sharp events, were split into two halves by the duration of the inhalation intervals. Pairs of connected dots are average jitters and inhalation durations for fast and slow inhalation intervals for individual sessions. Inset, distribution of slopes of the connecting lines. * Figure 6: Discrimination performance of individual neurons. () Top, sniff-warped PSTHs for two neurons' responses to two odors each. Bottom, corresponding two-odor discrimination success as a function of sniff-warped time. Bin size is 5 ms in warped time. Vertical dashed lines indicate the beginning and the end of the inhalation interval and whole sniff cycle. The horizontal dashed line is chance level performance. The thin black line is a sigmoidal fit, which yields the latency of the transition, τ, the rise time of the transition, δ, and the maximal discrimination success, D (left: τ = 59 ms, δ = 8 ms, D = 0.88; right, τ = 133 ms, δ = 60 ms, D = 0.77). Blue dots and bars show the latencies and the widths of the transitions. () Scatter plot of rise time, δ, versus amplitude, D, for the 835 cell-odor-odor combinations. Distributions of δ and D are in the top and right panels, respectively. () Color plot of the time course of discrimination performance for 835 cell-odor-odor combinations, ordered by the latency of the transiti! on, τ (black dots). * Figure 7: Discrimination among five odors by mitral/tufted cell populations. () Discrimination success as a function of number of neurons for 30-ms bin size. Dots are mean success over repeated permutations for random subsets (black dots) and the best subsets (blue dots). Error bars are s.d. () Discrimination performance of five-neuron ensembles as a function of sniff-warped time (30-ms bin). The black lines show individual permutations with random sets of five neurons and the blue lines show permutations with the best five neurons. The horizontal black dashed line marks chance level performance. Vertical dashed lines demarcate one sniff cycle. Author information * Abstract * Author information * Supplementary information Affiliations * Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. * Roman Shusterman, * Matthew C Smear & * Dmitry Rinberg * Department of Neurobiology and Physiology, Northwestern University, Evanston, Illinois, USA. * Matthew C Smear * Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA. * Alexei A Koulakov Contributions R.S. and D.R. conceived and designed the experiments. R.S., M.C.S. and D.R. performed experiments. R.S., A.A.K. and D.R. analyzed the data. R.S., M.C.S., A.A.K. and D.R. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Dmitry Rinberg Author Details * Roman Shusterman Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew C Smear Search for this author in: * NPG journals * PubMed * Google Scholar * Alexei A Koulakov Search for this author in: * NPG journals * PubMed * Google Scholar * Dmitry Rinberg Contact Dmitry Rinberg Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–3, Supplementary Tables 1 and 2, and Supplementary Note Additional data - Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex
- Nat Neurosci 14(8):1045-1052 (2011)
Nature Neuroscience | Article Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex * Sonja B Hofer1, 6 * Ho Ko1, 6 * Bruno Pichler1, 5 * Joshua Vogelstein2 * Hana Ros1 * Hongkui Zeng3 * Ed Lein3 * Nicholas A Lesica4 * Thomas D Mrsic-Flogel1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1045–1052Year published:(2011)DOI:doi:10.1038/nn.2876Received08 March 2011Accepted14 June 2011Published online17 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Neuronal responses during sensory processing are influenced by both the organization of intracortical connections and the statistical features of sensory stimuli. How these intrinsic and extrinsic factors govern the activity of excitatory and inhibitory populations is unclear. Using two-photon calcium imaging in vivo and intracellular recordings in vitro, we investigated the dependencies between synaptic connectivity, feature selectivity and network activity in pyramidal cells and fast-spiking parvalbumin-expressing (PV) interneurons in mouse visual cortex. In pyramidal cell populations, patterns of neuronal correlations were largely stimulus-dependent, indicating that their responses were not strongly dominated by functionally biased recurrent connectivity. By contrast, visual stimulation only weakly modified co-activation patterns of fast-spiking PV cells, consistent with the observation that these broadly tuned interneurons received very dense and strong synaptic input fr! om nearby pyramidal cells with diverse feature selectivities. Therefore, feedforward and recurrent network influences determine the activity of excitatory and inhibitory ensembles in fundamentally different ways. View full text Figures at a glance * Figure 1: Calcium imaging and electrophysiological recordings of visually evoked responses in PV neurons. () An OGB-1–labeled PV neuron in a Cre-PV-lsl-tdTomato mouse from which a cell-attached recording was made. Scale bar, 20 μm. () Average calcium signal (ΔF/F, top) and action potential (AP) rate per imaging frame (bottom) from simultaneous calcium imaging and electrophysiological recording from a PV neuron during stimulation with drifting gratings. Scale bars, 5% ΔF/F, 4 spikes per bin; bin size 131 ms. Directions of drifting gratings are shown; dashed lines show drift onset. () Polar plot of normalized responses to grating directions from neuron in , calculated from action potentials (black solid line) and calcium signal (red dotted line). (,) Average peak calcium signal plotted against absolute number of action potentials () or number of action potentials normalized to maximum number of action potentials () for each of 12 PV neurons from 6 mice, calculated for bins of 393 ms. Error bars are omitted for clarity. (,) Correspondence of preferred grating orientation () an! d orientation selectivity index (OSI, see Online Methods; ) calculated from calcium signal and from action potentials for nine PV neurons (red circles) and seven pyramidal cells (green diamonds) that were visually stimulated and responsive to moving gratings. () OGB-1–labeled tissue, including four PV neurons (red) in layer 2/3. Scale bar, 20 μm. () Average calcium traces (ΔF/F) from three PV neurons (red, left) and two parvalbumin-negative neurons (putative pyramidal cells, green, right) during stimulation with episodically presented drifting gratings (eight directions, six repetitions). The directions of drifting gratings are shown; dashed lines show drift onset. () OSI for pyramidal cells (green) and PV interneurons (red) that responded significantly to grating stimuli (ANOVA, P < 0.0001). OSI for highly selective, sharply tuned neurons approaches 1, whereas OSI for broadly tuned, nonselective neurons approaches zero. Black lines, median OSI. Pyramidal cell: median O! SI, 0.60; PV cells: median OSI, 0.26; 15 regions, 7 animals, P! < 10−6. * Figure 2: Assessing synaptic connectivity in vitro between neurons functionally characterized in vivo. () OGB-1–labeled V1 tissue in a slice (top left) and of the same cells in vivo before slicing (bottom left, after registration of the image stacks; see Online Methods). White circles, matched neurons in vivo and in vitro, which were targeted for whole-cell recording and filled with Alexa 594 (top right). Three pyramidal cells (PC1–3) and one fast-spiking interneuron (FS) were patched. Bottom right, polar plots of normalized responses to gratings drifting in eight directions, illustrating orientation/direction preference and tuning. Scale bar represents 30 μm. () Action potential firing pattern in response to depolarizing current injection for cells from . Scale bars: 20 mV, 50 ms. () Average traces of postsynaptic potentials in the fast-spiking interneuron in response to spike-evoking current injections in each of the three pyramidal cells (PCs) from , showing that all three provided synaptic input onto the fast-spiking neuron. Scale bars: left, 40 mV, 50 ms; right, 2 m! V (upper two), 0.2 mV (lower), 50 ms. () Probability of finding synaptic connections between pairs of pyramidal cells and from pyramidal cell to fast-spiking PV neurons. () Amplitudes of EPSPs between pyramidal cells and from pyramidal cells to fast-spiking PV cells. Black lines depict median amplitudes. () Another example of connectivity between six pyramidal cells and one fast-spiking PV interneuron and their orientation preferences. Five out of the six pyramidal cells provided input onto the fast-spiking PV neuron, which was held in whole-cell mode continuously while two sets of three pyramidal cells were patched and their connectivity assayed sequentially. () Polar plots with normalized responses to drifting grating stimuli (8 directions) of 15 more visually responsive, fast-spiking PV interneurons (red lines) overplotted with normalized responses of the pyramidal cells that provided synaptic input onto them (green lines). Pyramidal cells that provided stronger connecti! ons (>2 mV EPSP amplitude) are indicated by darker and thicker! green lines. () Relationship between connection probability and difference in preferred orientation (ΔOri) for pairs of orientation-tuned (OSI > 0.4) pyramidal cells (green), from pyramidal cells to fast-spiking PV interneurons (black) and from pyramidal cells to fast-spiking PV interneurons with OSI > 0.25 (open bars). Two pyramidal cells were more likely to be connected if they preferred similar grating orientations. Connection probability from pyramidal cell onto fast-spiking PV cells did not depend on response similarity, irrespective of response selectivity. () Connection strength (EPSP amplitude) from pyramidal cells to fast-spiking PV cells plotted against ΔOri. Closed circles, pairs with OSI ≤ 0.25; open circles, pairs with OSI > 0.25. Strength of input did not depend on orientation preference similarity: all cell pairs, P = 0.59, only cell pairs with OSI > 0.25, P = 0.94, Kruskal-Wallis test. () Relationship between paired-pulse ratio (PPR) of synaptic connect! ions from pyramidal cells to fast-spiking PV cells and ΔOri. Degree of facilitation (PPR > 1) or depression (PPR < 1) of synapses was not related to response similarity to gratings: all cell pairs, P = 0.54, only cell pairs with OSI > 0.25, P = 0.11, Kruskal-Wallis test. Black lines, median amplitudes; dotted lines, median amplitudes for pairs with OSI > 0.25. Bins include difference in preferred orientation values of 0–22.5° (0° bin), 22.5–67.5° (45° bin) and 67.5–90° (90° bin). * Figure 3: Relationship between response similarity and pair-wise correlations during spontaneous activity. (,) Spontaneous pair-wise correlation coefficients plotted against pair-wise signal correlation coefficients (from averaged responses to gratings drifting in eight directions) from two imaging regions, for pairs of parvalbumin-negative neurons (pyramidal cells, PCs green), mixed pairs of one pyramidal cell and one PV neuron (black) and pairs of PV neurons (red). () Boxplots of the correlation coefficients (R) and slopes of the relationship between spontaneous correlations and signal correlations from all imaged regions. Insert, horizontal lines are group medians. () Pooled data from all pyramidal cell pairs (green), mixed pairs (black) and PV cell pairs (red) normalized for comparison across animals and imaged regions by computing z-scores (see Online Methods). Pyramidal cell pairs: R = 0.10, slope = 0.11; pyramidal cell–PV cell pairs: R = 0.22, slope = 0.37; PV cell pairs: R = 0.61, slope = 1.08 (15 regions, 7 animals, 7,285 pyramidal cell pairs, 2,562 pyramidal cell–PV! cell pairs, 187 PV cell pairs). * Figure 4: Comparison of population activity patterns with and without visual stimulation. (,,) Calcium signals of 30 pyramidal cells (PCs, top) and 6 PV neurons (bottom) simultaneously imaged in darkness with the monitor switched off (), during stimulation with episodically drifting gratings () or with natural movie sequences (). Schematic of stimulus sequence is shown above each plot. (,,) Strength of pair-wise time-varying (total) correlations from calcium signals for pyramidal cell pairs (left), PV cell pairs (right) and mixed pyramidal cell–PV cell pairs (middle) during spontaneous activity (), visual stimulation with gratings () or natural movies (). Circles, median values of each imaged region; colored lines, group medians. Gray lines connect values from the same imaged region. () Matrices of pair-wise response rate correlation coefficients between significantly responsive PV and pyramidal cells of one imaged region. Cells were ordered such that the strongest correlations during spontaneous activity were close to the diagonal in the spontaneous condition,! and the same order was applied to correlation matrices of the other conditions. Positions on the diagonal were set to the lowest value. () The similarity between two matrices is the correlation coefficient of their off-diagonal elements (pattern correlation). Comparisons were made between correlation matrices of spontaneous and each of the evoked conditions and between different visually evoked conditions for pyramidal cells (green), PV cells (red) and for matrices from mixed pyramidal cell–PV cell pairs (black). Boxplots of pattern correlation values of all imaged regions that included three or more responsive PV cells (horizontal lines are group medians, 6 animals, 13 regions). * Figure 5: Comparison between spontaneous and noise correlation patterns during visual stimulation. (,) Noise correlation coefficients from calcium signals during stimulation with drifting gratings () or with natural movie sequences () for pyramidal cell pairs (left, green) and PV cell pairs (right, red). Circles, median values for each region; colored lines, group medians. () Boxplots of similarity of matrices of noise correlations during visually evoked conditions (see Online Methods) and correlations during spontaneous activity (left and middle), and similarity of noise correlation matrices during grating and natural movie stimulation (right) for pyramidal cell (green) and PV cell (red) populations. Pattern correlation values are correlation coefficients of off-diagonal matrix elements for each imaged region with three or more responsive PV cells; 6 animals, 13 regions. Insert, horizontal lines are median values. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Sonja B Hofer & * Ho Ko Affiliations * Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK. * Sonja B Hofer, * Ho Ko, * Bruno Pichler, * Hana Ros & * Thomas D Mrsic-Flogel * Johns Hopkins University, Baltimore, Maryland, USA. * Joshua Vogelstein * Allen Institute for Brain Science, Seattle, Washington, USA. * Hongkui Zeng & * Ed Lein * Ear Institute, University College London, London, UK. * Nicholas A Lesica * Present address: National Institute for Medical Research, Mill Hill, London, UK. * Bruno Pichler Contributions S.B.H. and H.K. performed all in vivo and slice experiments. S.B.H., H.K., N.A.L. and T.D.M.-F. analyzed the data. H.R. carried out antibody labeling. B.P. developed software for visual stimulation, image acquisition and image analysis. J.V. developed spike inference algorithms. E.L. and H.Z. generated and supplied the mice. S.B.H., H.K., N.A.L. and T.D.M.-F. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Thomas D Mrsic-Flogel Author Details * Sonja B Hofer Search for this author in: * NPG journals * PubMed * Google Scholar * Ho Ko Search for this author in: * NPG journals * PubMed * Google Scholar * Bruno Pichler Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua Vogelstein Search for this author in: * NPG journals * PubMed * Google Scholar * Hana Ros Search for this author in: * NPG journals * PubMed * Google Scholar * Hongkui Zeng Search for this author in: * NPG journals * PubMed * Google Scholar * Ed Lein Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas A Lesica Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas D Mrsic-Flogel Contact Thomas D Mrsic-Flogel Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–9 Additional data - Adaptation of the simple or complex nature of V1 receptive fields to visual statistics
- Nat Neurosci 14(8):1053-1060 (2011)
Nature Neuroscience | Article Adaptation of the simple or complex nature of V1 receptive fields to visual statistics * Julien Fournier1 * Cyril Monier1 * Marc Pananceau1, 2 * Yves Frégnac1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:1053–1060Year published:(2011)DOI:doi:10.1038/nn.2861Received21 March 2011Accepted10 May 2011Published online17 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Receptive fields in primary visual cortex (V1) are categorized as simple or complex, depending on their spatial selectivity to stimulus contrast polarity. We studied the dependence of this classification on visual context by comparing, in the same cell, the synaptic responses to three classical receptive field mapping protocols: sparse noise, ternary dense noise and flashed Gabor noise. Intracellular recordings revealed that the relative weights of simple-like and complex-like receptive field components were scaled so as to make the same receptive field more simple-like with dense noise stimulation and more complex-like with sparse or Gabor noise stimulations. However, once these context-dependent receptive fields were convolved with the corresponding stimulus, the balance between simple-like and complex-like contributions to the synaptic responses appeared to be invariant across input statistics. This normalization of the linear/nonlinear input ratio suggests a previously u! nknown form of homeostatic control of V1 functional properties, optimizing the network nonlinearities to the statistical structure of the visual input. View full text Figures at a glance * Figure 1: White-noise stimuli and second-order Volterra receptive field decomposition. () Example of single-trial intracellular responses evoked in the same cell (cell 1) by sparse (SN, black), dense (DN, red) and Gabor noise (GBN, gray) stimuli. The visual stimulation period is indicated by the horizontal black line. Spike amplitudes have been cut off at −30 mV to facilitate the comparison between Vm fluctuation dynamics. () First- and second-order Volterra kernels were estimated using a least-squares method. In this decomposition, the h1st kernel linearly filters the stimulus contrast variations and can be considered to be the simple-like part of the receptive field in the strict sense. In contrast, the second-order diagonal h2Diag corresponds to the projection of the second-order receptive field nonlinearities in the first-order stimulus space, pooling receptive field components independent of the contrast sign, and can be considered as the complex-like part of the receptive field. The feature selectivity underlying this h2Diag complex-like component is p! rovided by the off-diagonal terms of the second-order kernel h2nd. () Probability of stimulation (P(stim)) of the second-order kernel by sparse noise (left, 10 × 10 stimulation grid) and dense noise (right). In contrast with dense noise stimuli, pixels are activated one at a time in sparse noise condition. Consequently, off-diagonal components of the second-order kernel are barely stimulated by sparse noise compared to the diagonal elements, making their estimation irrelevant in sparse stimulation contexts. * Figure 2: Stimulus dependence of simple-like and complex-like receptive field components. () First-order kernel (left column, simple-like, h1st) and second-order diagonal kernel (right column, complex-like, h2Diag) of subthreshold (Vm) and spiking (spikes) receptive field estimates for a typical V1 cell (cell 1). Kernels are depicted as spatial (X, Y) and two-dimensional spatiotemporal (Y, time (t)) z-scored maps. The X,Y spatial maps are shown for the lag time corresponding to their maximal spatial extent (indicated by the vertical black line in Y,t spatiotemporal profiles). The thin gray lines show the pixel size used for sparse and dense noise. () Examples of elementary responses corresponding to positions indicated in the inset, overlaid over the shaded responsive area. Note the differences of scale between sparse (black) and dense noise (red) waveforms, reflecting a divisive gain control of both simple-like and complex-like receptive field components when switching from sparse to dense visual stimulation. (,) Data are presented as in , for another example ce! ll (cell 2). * Figure 3: Receptive field Simpleness and gain control of simple-like and complex-like receptive field components. (,) Comparison over the population of recorded cells of the SI measured from synaptic () or spiking () receptive field estimates between sparse and dense noise conditions. All points lie above the identity line, indicating that all receptive fields underwent a systematic change in the balance between simple-like and complex-like receptive field components such that they appeared to be more simple in dense than in sparse noise conditions. The data points corresponding to the example cells (shown in Fig. 2) have been circled. (,) Comparison between the gainSN/DN measured for complex-like (h2Diag gainSN/DN) and simple-like (h1st gainSN/DN) receptive field components, at the subthreshold () and spiking () levels. The gain factors affecting the complex-like components were systematically higher and appeared to be linearly related to the amplitude of the gain controls measured from the first-order component h1st, except for two outliers (gray symbols) (blue regression lines; Vm: s! lope = +3.53, r2 = 0.98, P << 0.01, n = 30; spikes: slope = +1.81, r2 = 0.90, P << 0.01, n = 11). The vertical dotted line indicates the value that we would expect from perfectly adapting linear receptive field components; h1st gainSN/DN would correspond to the ratio of sparse and dense noise s.d. of luminance values (~8.16, see Online Methods). * Figure 4: Spatiotemporal reconfiguration of simple-like and complex-like receptive field components. () Comparison between dense noise and sparse noise conditions of the maximal spatial extents of significant responses measured in simple-like (h1st, top) and complex-like (h2Diag, bottom) receptive field components (units, visual degree of apparent diameter). Although simple-like receptive field components appeared to be significantly larger in the dense noise than in the sparse noise condition (paired Student's t test, P < 0.01), the complex-like receptive field components were significantly smaller (paired Student's t test, P << 0.01). () Comparison of onset latencies of simple-like (h1st, top) or complex-like (h2Diag, bottom) receptive field components between dense noise and sparse noise conditions. () Comparison of peak latencies of simple-like (h1st, top) or complex-like (h2Diag, bottom) receptive field components between dense noise and sparse noise conditions. * Figure 5: V1 receptive field simpleness adapts to visual statistics. () In each stimulus condition, the simpleness was measured in two ways: by the SI, which compares the relative power of the simple-like (h1st) and complex-like (h2Diag) components of receptive field estimates (RF, middle), and by the SI*, which measures the balance between simple-like and complex-like synaptic contributions once the stimulus-dependent receptive fields have been convolved with the corresponding stimulus sequences (RF * Stim, right). In sparse stimulation conditions, as the pixels are activated one at a time, the nonlinear contributions conveyed by the off-diagonal terms of the h2nd kernel have barely any weight in the response, and the output of the h2Diag filter provides an almost complete estimate of the complex-like synaptic contributions. In contrast, in the dense noise condition, as multiple pixels are activated at the same time, the dynamics of the evoked complex-like response also relies on the selectivity of the h2Diag receptive field components to th! e spatiotemporal patterns that are presented. We thus computed the convolution of the stimulus with the full second-order kernel estimate h2nd to reconstruct the complex-like synaptic contributions evoked by dense noise stimuli. () Comparison of SI values between sparse and dense noise conditions. Left, graph shown in Figure 3a. Right, comparison of SI* values. Note that over the population the SI* values are much more aligned along the identity line than the SI values. () Data are presented as in for the comparison of the Gabor noise and dense noise conditions. * Figure 6: Simpleness in non-adaptive receptive field models. () Parallel LN cascade receptive field architecture in which linear filter outputs corresponding to different stimulus feature selectivities are passed through a second-order polynomial nonlinearity (one linear branch and several quadratic branches). In this model architecture, the linear component provides simple-like contributions, whereas the quadratic components contribute in a complex-like manner to the cell response. By keeping the same receptive field structure while imposing the relative weights of these two types of afferent components, we simulated a set of receptive fields, each expressing a fixed degree of simpleness, and simulated their responses to sparse noise, Gabor noise and dense noise stimulus sequences. () Left, comparison of the SI between sparse and dense noise conditions when considering the receptive fields estimated from the responses of the non-adaptive model depicted in . Right, comparison of the SI* (measured directly from the receptive field mode! l outputs) between sparse and dense noise conditions. () Data are presented as in for the comparison of the Gabor noise and dense noise conditions. * Figure 7: Simpleness in gain control receptive field models. () Differential gain control model. The adaptation of V1 receptive field simpleness can be explained by adding two separate gain control processes (α and β) to the receptive field models depicted in Figure 6a. These processes respectively divide simple-like and complex-like receptive field components when switching from sparse to dense noise. () Shown is the graph depicted in Figure 5b. () Shown is the graph depicted in Figure 3c. The colors of the symbols correspond to three different ranges of value for the SI values measured in the sparse noise condition. () Post-NL gain control model (GC1). A gain control process γ acts post-NL and normalizes the variance of the evoked response across stimulus conditions. () SI (left) and SI* (right) measured from GC1 receptive field model responses in sparse and dense noise conditions. () GainSN/DN measured from the h1st and h2Diag kernels estimated from the GC1 receptive field responses. Dark and light colors of the symbols indicate! low and high values of γ, respectively. () Pre-NL gain control model (GC2). A gain control process g acts pre-NL and results in a division of linear filter outputs by g when switching from sparse noise to dense noise, independently of the receptive field simpleness. () SI (left) and SI* (right) measured from GC2 receptive field model responses in sparse and dense noise conditions. () GainSN/DN measured from the h1st and h2Diag kernels estimated from the GC2 receptive field responses. The purple curve indicates the quadratic relationship. Dark and light colors of the symbols indicate low and high values of g, respectively. g* corresponds to the value for which we observed a complete adaptation of the receptive field simpleness between sparse noise and dense noise conditions (SI*DN = SI*SN). () Hybrid gain control model (GC3), a combination of the GC1 and GC2 models (with g = g*). () SI (left) and SI* (right) measured from GC3 model responses in sparse and dense noise condi! tions. () GainSN/DN measured from the h1st and h2Diag kernels ! estimated from the GC3 responses. The slope of the regression line (blue) corresponds to g*. Note that this model is mathematically equivalent to the differential gain control model () considering α = g* × γ and β = g*2 × γ. Dark and light colors of the symbols indicate low and high values of γ, respectively. Author information * Abstract * Author information * Supplementary information Affiliations * Unité de Neuroscience, Information et Complexité (CNRS-UNIC), UPR CNRS 3293, Gif-sur-Yvette, France. * Julien Fournier, * Cyril Monier, * Marc Pananceau & * Yves Frégnac * Université Paris-Sud, Orsay, France. * Marc Pananceau Contributions The study was conceived by J.F., C.M. and Y.F. The experiments were performed by J.F., C.M. and M.P. J.F. performed the data analysis and model simulations. J.F., C.M. and Y.F. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Julien Fournier or * Yves Frégnac Author Details * Julien Fournier Contact Julien Fournier Search for this author in: * NPG journals * PubMed * Google Scholar * Cyril Monier Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Pananceau Search for this author in: * NPG journals * PubMed * Google Scholar * Yves Frégnac Contact Yves Frégnac Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–11 and Notes 1–3 Additional data - Owl's behavior and neural representation predicted by Bayesian inference
- Nat Neurosci 14(8):1061-1066 (2011)
Nature Neuroscience | Article Owl's behavior and neural representation predicted by Bayesian inference * Brian J Fischer1, 2 * José Luis Peña3 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1061–1066Year published:(2011)DOI:doi:10.1038/nn.2872Received21 December 2010Accepted29 April 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The owl captures prey using sound localization. In the classical model, the owl infers sound direction from the position of greatest activity in a brain map of auditory space. However, this model fails to describe the actual behavior. Although owls accurately localize sources near the center of gaze, they systematically underestimate peripheral source directions. We found that this behavior is predicted by statistical inference, formulated as a Bayesian model that emphasizes central directions. We propose that there is a bias in the neural coding of auditory space, which, at the expense of inducing errors in the periphery, achieves high behavioral accuracy at the ethologically relevant range. We found that the owl's map of auditory space decoded by a population vector is consistent with the behavioral model. Thus, a probabilistic model describes both how the map of auditory space supports behavior and why this representation is optimal. View full text Figures at a glance * Figure 1: Models of the owl's behavior. () Owl's behavior, modified from ref. 2. The solid gray line is the identity. () The Bayesian estimate is the direction of the vector found by averaging unit vectors in each direction weighted by the posterior density (medium gray). The posterior is proportional to the product of the likelihood (light gray) and the prior (black). All probability densities were normalized by their peak for display. The source direction is 70 deg, at one of the peaks of the likelihood. () The population vector (gray) is the average of the preferred direction vectors of the neurons, weighted by the firing rates (black). () Measured relationship between direction and ITD (black) under normal conditions2, along with the sinusoidal approximation (gray). () Owl's behavior2 (medium gray circle, dotted line), Bayesian estimator (black square, solid line) and population vector (light gray diamond, dashed line) under the normal condition. () Measured relationship between direction and ITD (black) under! ruff-removed conditions2, along with the sinusoidal approximation (gray). () Owl's behavior2, Bayesian estimator and population vector under the ruff-removed condition. Error bars in , and represent the s.d. over trials. * Figure 2: Predicted behavior under varying levels of interaural correlation. () Variability of ITD with interaural correlation. ITD was estimated from the peak of the cross-correlation of the left and right input signals. () Direction estimates from the Bayesian model using levels of the s.d. of the noise corrupting ITD that follow the exponential relationship shown in with a minimum value of 41.2 μs, estimated from the behavioral data (s.d. = 219.34e−11.31×IC + 41.2, where IC is the interaural correlation). Symbols correspond to four different source directions (±55, ±75 deg). Error bars represent the s.d. over trials. () The predicted trend is similar to observations in behaving owls (modified from Fig. 1 in ref. 20). Error bars represent the s.d. over trials. * Figure 3: Measured prior distribution of target direction. The relative frequency of different oppositions between an owl and two types of prey (vole on the left and spiny mouse on the right) during prey capture (modified from Fig. 3 in ref. 22). Front is the prey positioned at 0 deg relative to the owl's center of gaze, front-side corresponds to the regions centered at ±45 deg, side corresponds to the regions centered at ±90 deg, side-back corresponds to the regions centered at ±135 deg and back corresponds to the region centered at 180 deg. * Figure 4: Performance of alternative estimators. () Owl's behavior2 (black) and maximum likelihood estimate (gray). The thin black line is the identity. () Owl's behavior2 (black) and Bayesian estimate using the mean of the posterior distribution when using a Gaussian-shaped prior that is wider than the optimal value (gray). () Owl's behavior2 (black) and Bayesian estimate using the mean of the posterior distribution when using a uniform prior (gray). Error bars represent the s.d. over trials. * Figure 5: Population vector approximation to Bayesian estimator. The r.m.s. differences in direction estimates between the population vector and the Bayesian estimator for different correlation coefficients in the noise between neurons are shown (black circles = 0.25, white circles = 0.5, black squares = 0.75). * Figure 6: Predicted midbrain representation of auditory space. () Example tuning curves in the model optic tectum population. () Plot of model tuning curve half-widths (black circles) along with experimental data measured in the optic tectum28 (solid lines, showing ±1 s.d., as reported in ref. 28). Gray and white circles correspond to the tuning curves highlighted in . The two outlier points correspond to receptive fields in the periphery that wrap around the owl's head and which are indeed observed in the owl's optic tectum data as well28. () Measured values of space map positions of optic tectum neurons (modified from ref. 28) together with the fit by a scaled cumulative Gaussian distribution function (solid line). () The model prior density of preferred direction (dashed gray) and the measured bilateral density (solid black) found by combining the unilateral densities derived from the cumulative Gaussian in . Author information * Abstract * Author information * Supplementary information Affiliations * Group for Neural Theory, Département d'Etudes Cognitives, Ecole Normale Supérieure, Paris, France. * Brian J Fischer * Laboratoire de Neurosciences Cognitives, INSERM U960, Paris, France. * Brian J Fischer * Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, USA. * José Luis Peña Contributions B.J.F. designed the model and performed the model simulations. J.L.P. supervised the project. B.J.F. and J.L.P. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Brian J Fischer Author Details * Brian J Fischer Contact Brian J Fischer Search for this author in: * NPG journals * PubMed * Google Scholar * José Luis Peña Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (946K) Supplementary Discussion and Supplementary Figures 1 and 2 Additional data - Songbirds possess the spontaneous ability to discriminate syntactic rules
- Nat Neurosci 14(8):1067-1074 (2011)
Nature Neuroscience | Article Songbirds possess the spontaneous ability to discriminate syntactic rules * Kentaro Abe1, 2 * Dai Watanabe1, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1067–1074Year published:(2011)DOI:doi:10.1038/nn.2869Received20 December 2010Accepted27 April 2011Published online26 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Whether the computational systems in language perception involve specific abilities in humans is debated. The vocalizations of songbirds share many features with human speech, but whether songbirds possess a similar computational ability to process auditory information as humans is unknown. We analyzed their spontaneous discrimination of auditory stimuli and found that the Bengalese finch (Lonchura striata var. domestica) can use the syntactical information processing of syllables to discriminate songs). These finches were also able to acquire artificial grammatical rules from synthesized syllable strings and to discriminate novel auditory information according to them. We found that a specific brain region was involved in such discrimination and that this ability was acquired postnatally through the encounter with various conspecific songs. Our results indicate that passerine songbirds spontaneously acquire the ability to process hierarchical structures, an ability that was! previously supposed to be specific to humans. View full text Figures at a glance * Figure 1: Discrimination of SM songs. () Habituation test for adult Bengalese finches (n = 19, 10 birds). Left, schematic drawing of the habituation test. Right, a significant increase in the call response number was observed when the birds were exposed to the song for the first time (before), but not after the habituation (after). () Left, schematic drawing of the song discrimination test and SMSD test. Right, summary of the responses to the exposure to original songs or novel conspecific songs after habituation (n = 20, 10 birds). () Part of the sonograph of songs used in the SMSD test. The same sequence modifications were performed to each of the eight original songs. Each syllable is indicated as a letter above. (,) Normalized shift in call responses in the SMSD test on adult male (, n = 52, 17 birds) and adult female (, n = 52, 17 birds) finches. () SMSD test on adult male finches, using a further modified SEQ2 (n = 39, 17 birds). () Syllable sequence of songs used. Bold, replaced syllables; underlined pair! s, novel syllable transitions not appearing in the original song. Mean shift (after – before) in call counts over the 5-min periods during the stimuli change are shown. Data are normalized to the total call counts (after + before) for each stimuli change. Data are shown as mean ± s.e.m. N.s. indicates P > 0.05, paired t test of raw call counts before and after the stimuli change. * Figure 2: Acquisition and discrimination of artificial syntactic rules. () Rewrite rules of predictive and nonpredictive languages. Predictive language has predictive dependencies among the elements; the presence of an A syllable indicates that a C syllable should follow, with an F syllable after that. Nonpredictive language lacks these strict dependencies between the elements. For example, the F syllables may appear without the occurrence of A or C syllables in some strings. () The syllables of Bengalese finch song used to synthesize the strings. () Schematic drawing of the ASRD test. () Normalized shift in call responses after exposure to the test strings of predictive language and nonpredictive language. The responses of subjects familiarized with predictive language (n = 36, 16 birds) and nonpredictive language (n = 32, 16 birds) are shown. A significant difference was found only with the nonpredictive strings in predictive language–habituated birds. () The ASRD test was carried out on independent subjects using the shuffled syllables (pre! dictive language, n = 23, 10 birds; nonpredictive language, n = 25, 10 birds). A significant difference was found only with the shuffled nonpredictive strings in shuffled predictive language–habituated birds. The mean shift (after – before) in call counts over the 5-min periods during the stimuli change are shown. Data are normalized to the total call counts (after + before) for each stimuli change. Data are presented as mean ± s.e.m. N.s. indicates P > 0.05, paired t test of raw call counts before and after the stimuli change. * Figure 3: Acquisition and discrimination of artificial syntactic rules with embeddings. () Rewrite rules of center-embedding language and the structure of the familiarization strings and the test strings. For familiarization, 16 strings of non-embedded structure (AX-CZ-FX) and 36 strings of single embedded structure (AX-AY-CZ-FY-FX) were used. Each AX and FX has a pair-wise relation and always co-occur in a string; appearance of F1 (white star) is expected by the appearance of A1 (black star). AES, abnormally embedded structure; AES2, abnormally embedded and A-C-F sequence breaking; COR, novel test string obeying the center-embedding language. () Syllables used in this experiment. () Schematic drawing of the ASRD test of center-embedding language. (,) Normalized shift in call response to the test strings (, n = 44, 22 birds; , n = 46, 23 birds). FAM, string used for familiarization. The mean shifts (after – before) in call counts over the 5-min periods during the stimuli change are shown. Data are normalized to the total call counts (after + before) for each ! stimuli change. Data are presented as mean ± s.e.m. N.s. indicates P > 0.05, paired t test of raw call counts before and after the stimuli change. * Figure 4: Development of the ability for syntactical analysis of syllables. () Schematic drawing of the life events of Bengalese finches in our aviary. () Normalized shift in call responses in the habituation test, the SMSD test and the ASRD test. Bengalese finches at PHD40 (HAB, n = 20; SMSD, n = 33; ASRD, n = 21), PHD70 (HAB, n = 12; SMSD, n = 27; ASRD, n = 20), PHD100 (HAB; n = 13, SMSD; n = 28, ASRD; n = 20 for each) and PHD130 (HAB, n = 13; SMSD, n = 28; ASRD, n = 23) were analyzed (nine birds for each stage). In the ASRD test, birds were familiarized with predictive language strings. () Schematic drawing of the experimental schedule of isolation rearing. (,) Normalized shift call response in the behavioral tests of PHD130 isolated birds (, HAB, n = 13, 9 birds; SMSD, n = 54, 12 birds; ASRD, n = 23, 10 birds) and the birds kept in aviary for 2 weeks after the isolation (, HAB, n = 12, 9 birds; SMSD, n = 46, 13 birds). In the ASRD test, the birds were familiarized with predictive language strings. The mean shifts (after – before) in call count! s over the 5-min periods during the stimuli change are shown. Data are normalized to the total call counts (after + before) for each stimuli change. Data are presented as mean ± s.e.m. N.s. indicates P > 0.05, paired t test of raw call counts before and after the stimuli change. * Figure 5: Involvement of the anterior nidopallium in the syntactical analysis of syllables. (,) Adult male Bengalese finches, isolated in a dark, sound-proof chamber, were familiarized with predictive language strings followed by the exposure to the grammatical test strings (predictive strings, P) or the ungrammatical test strings (nonpredictive strings, NP) for 90 min. () Parasagittal section of anterior nidopallium, immunostained for NeuN (neuron marker, green) and Egr-1 (magenta). Scale bar represents 200 μm. () Percentage of Egr-1–positive neurons in the nucleus involved in the song processing. Data are shown as mean ± s.d. (n = 8, 8 birds). n.s. indicates P > 0.05, Mann-Whitney U test. CMM, caudomedial mesopallium; DLM, dorsal lateral nucleus of medial thalamus. (,) Normalized call response in the behavioral tests of anterior nidopallium–lesioned birds (, HAB, n = 10, 9 birds; SMSD, n = 32, 10 birds; ASRD, n = 23, 9 birds) and robust nucleus of the arcopallium (RA)-lesioned birds (, HAB, n = 12, 8 birds; SMSD, n = 34, 9 birds; ASRD, n = 25, 8 birds). In ! the ASRD test, the birds were familiarized with predictive language strings. Data are presented as mean ± s.e.m. N.s. indicates P > 0.05, paired t test of raw call counts before and after the stimuli change. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Molecular and Systems Biology, Graduate School of Biostudies, Kyoto University, Kyoto, Japan. * Kentaro Abe & * Dai Watanabe * Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, Saitama, Japan. * Kentaro Abe * Department of Biological Sciences, Faculty of Medicine, Kyoto University, Kyoto, Japan. * Dai Watanabe Contributions K.A. conceived the project, performed all the experiments and wrote the paper. D.W. supervised the project and provided feedback on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Kentaro Abe Author Details * Kentaro Abe Contact Kentaro Abe Search for this author in: * NPG journals * PubMed * Google Scholar * Dai Watanabe Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (864K) Supplementary Figures 1–4 Additional data - Generalized associative representations in parietal cortex
- Nat Neurosci 14(8):1075-1079 (2011)
Nature Neuroscience | Article Generalized associative representations in parietal cortex * Jamie K Fitzgerald1 * David J Freedman1, 2 * John A Assad1, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1075–1079Year published:(2011)DOI:doi:10.1038/nn.2878Received14 April 2011Accepted20 May 2011Published online17 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Making associations between sensory stimuli is a critical aspect of behavior. We previously found that neurons in the lateral intraparietal area (LIP) of Macaca mulatta reflect learned associations between directions of moving visual stimuli. Individual LIP neurons might encode associations only for specific stimuli, such as motion directions; alternatively, they may encode more general associations whenever animals must decide between discrete alternatives. To test this, we asked whether LIP neurons encode learned associations between pairs of arbitrarily chosen static shapes and, in a separate task, whether the same neurons also encode associations between motion directions. Our experimental design dissociated the visual associations from the movements used to report those associations. We found robust encoding of the learned pair associations between shapes, and shape-pair–selective neurons tended to be selective for direction associations. These findings suggest that r! epresenting generic categorical outcomes may be a fundamental role of parietal neurons. View full text Figures at a glance * Figure 1: Behavioral task. () Monkeys associated six shapes into three pairs. Different pairings were used for each monkey. () Delayed shape-pair association task. After monkeys fixated their gaze and gripped a touch-sensitive bar, a sample shape appeared in the receptive field (RF). After a subsequent delay period, a test shape appeared in the receptive field. If the sample shape and the test shape belonged to the same associated pair, the monkey released the touch bar to receive a juice reward. If the sample shape and the test shape did not belong to the same pair, the monkey maintained his hold on the touch bar throughout the test period and a second delay period until the associated shape appeared (test 2), when he released the touch bar to receive a juice reward. () Monkeys' mean session performance (chance = 50%, dashed line). Error bars indicate ±s.e.m. * Figure 2: Example responses of LIP neurons. (–) Average activity evoked by the six sample stimuli for three LIP neurons. Neuronal responses are sorted by the identity of the sample shape. Same-color traces correspond to associated pairs of shapes (see Fig. 1c). * Figure 3: Shape-pair selectivity across the population of LIP neurons. () Shape-pair selectivity for all neurons, as quantified by the proportion of variance explained by pairs in the nested ANOVA (η2). Black points indicate neurons with significant shape-pair selectivity (nested ANOVA, P < 0.01); non-selective neurons are shown in gray. Points are arbitrarily shifted along the horizontal access for clarity. () Best pairing scheme. The learned pairing scheme is shown in black, pairing schemes that include one learned pairing are shown in gray and pairing schemes that include no learned pairings are shown in white. * Figure 4: Delayed direction-categorization task. () Monkeys grouped six motion directions into two categories in a modified version of the delayed match-to-category task3. () Delayed match-to-category task. After monkeys fixated their gaze and gripped a touch-sensitive bar, one sample motion patch appeared in the receptive field. After a subsequent delay period, a test motion patch appeared in the receptive field. If the sample and test directions belonged to the same category, the monkey released the touch bar to receive a juice reward. Otherwise, the monkey maintained his hold on the touch bar throughout the test period and a second delay period, until a second test stimulus belonging to the same category appeared (test 2), when he released the touch bar to receive reward. () Monkeys' mean performance across all sessions (chance = 50%, dashed line). Error bars indicate ±s.e.m. * Figure 5: Selectivity for shape pairs and direction categories. (,) Responses of two example LIP neurons tested with both the shape-pair task (above) and the direction-category task (below). Same-color traces correspond to associated shapes and directions. () Explained variance (η2) for shape pairs versus direction categories for all 78 neurons tested with both tasks. The solid line is regression fit; the dashed line has a slope of 1. () Time course of explained variance for shape pairs (magenta) and direction categories (black), averaged across all 78 neurons tested with both tasks. Error bars are ±s.e.m. Note that explained variance during the fixation period is slightly higher for the shape task than the direction task because there are three pair predictors for shape and only two category predictors for direction. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA. * Jamie K Fitzgerald, * David J Freedman & * John A Assad * Department of Neurobiology, The University of Chicago, Chicago, Illinois, USA. * David J Freedman * Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy. * John A Assad Contributions J.K.F., D.J.F. and J.A.A. designed the experiments. J.K.F. collected and analyzed the data and wrote the manuscript. D.J.F. and J.A.A. assisted in data analysis and manuscript preparation. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * John A Assad Author Details * Jamie K Fitzgerald Search for this author in: * NPG journals * PubMed * Google Scholar * David J Freedman Search for this author in: * NPG journals * PubMed * Google Scholar * John A Assad Contact John A Assad Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (299) Supplementary Results, Supplementary Tables 1–5 and Supplementary Figure 1 Additional data - High-accuracy neurite reconstruction for high-throughput neuroanatomy
- Nat Neurosci 14(8):1081-1088 (2011)
Nature Neuroscience | Technical Report High-accuracy neurite reconstruction for high-throughput neuroanatomy * Moritz Helmstaedter1 * Kevin L Briggman1 * Winfried Denk1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1081–1088Year published:(2011)DOI:doi:10.1038/nn.2868Received28 February 2011Accepted23 May 2011Published online10 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Neuroanatomic analysis depends on the reconstruction of complete cell shapes. High-throughput reconstruction of neural circuits, or connectomics, using volume electron microscopy requires dense staining of all cells, which leads even experts to make annotation errors. Currently, reconstruction speed rather than acquisition speed limits the determination of neural wiring diagrams. We developed a method for fast and reliable reconstruction of densely labeled data sets. Our approach, based on manually skeletonizing each neurite redundantly (multiple times) with a visualization-annotation software tool called KNOSSOS, is ~50-fold faster than volume labeling. Errors are detected and eliminated by a redundant-skeleton consensus procedure (RESCOP), which uses a statistical model of how true neurite connectivity is transformed into annotation decisions. RESCOP also estimates the reliability of consensus skeletons. Focused reannotation of difficult locations promises a rather steep i! ncrease of reliability as a function of the average skeleton redundancy and thus the nearly error-free analysis of large neuroanatomical datasets. View full text Figures at a glance * Figure 1: Comparison of volume and skeleton annotation. (,) Examples of volume labeling () and skeletonization () for the same two neurite fragments; cell-surface labeled data (data set E1088; Online Methods). Scale bars represent 250 nm. () Sketch of a neurite skeleton. () Rate of time consumption for volume labeling10 and for skeleton annotation (data from this study; annotated using KNOSSOS, see Supplementary Movie 1), for both cell surface–labeled data (black) and conventionally stained data set (K0563, gray; see Fig. 5d). Error bars represent range for volume labeling and s.d. for skeletonization. () Outline of RESCOP. * Figure 2: Skeletonization by expert annotators. () Two complete skeletons of the same amacrine cell annotated independently by M.H. and K.L.B., starting at the soma. () Same skeletons shown looking onto the plane of the retina. Green, agreement among annotators; black, disagreement; numbers, disagreement locations. Stacks of original data surrounding disagreement locations are shown in Supplementary Image Stacks 1,2,3,4,5,6,7,8,9,10,11 and 12. INL, inner nuclear layer; IPL, inner plexiform layer; GCL, ganglion cell layer. Scale bars, 5 μm. * Figure 3: RESCOP step 1, skeleton-to-skeleton agreement measurement. () Overlay of seven independent skeletons of the same neurite (bipolar cell axon) annotated by slightly trained nonexperts, all starting at the soma (red cross). (–) Schematic of procedure for measuring agreement among multiple annotators for one skeleton edge (dashed line) in skeleton A. () Histograms of edge votes for 50-fold annotation of one cell (left) and dense skeletonization of 98 neurites (right). Bottom, vote count versus total votes (log scale). Histograms were corrected for multiple counting of the same location; see Online Methods. (,) Predicted vote histograms for single cell (left) and for dense skeletons (right) (), using the distribution of edge detectabilities pfit(pe) () that best predicted the respective histograms in . (,) Schematic of how the truth (top) is converted to detection probability (middle). Bottom, probabilities for different T (number of agreeing votes) for one edge (, binomial distribution for pe = 0.7 and N = 10 annotators) and for all e! dges combined (, schematic). * Figure 4: RESCOP steps 2 and 3, edge elimination and skeleton recombination. () Probability that edge detectability pe has a certain value, given different edge votes, without prior knowledge (blue) and for the fitted distribution of edge detectabilities pfit(pe) (red). Whether an edge is kept or eliminated depends on whether the integral of p(pe|T,N) for pe > 0.5 (green shading) is larger or smaller than that for pe < 0.5 (red shading). In this example, edges with T = 1 and N = 4 would be eliminated and those with T = 2 to 4 would be kept. () Decision error, perr(T,N), with optimal (stepped line) and majority vote (dashed straight line) decision boundaries for the single-cell (top) and dense skeletonization data (bottom). () Elimination procedure illustrated at a branch point. Red, eliminated edges. Green, discarded skeleton pieces. () Variation of annotator performance reflected in average total number of votes per edge and average ratio of agreeing to total votes for each annotator. Circle, worst-performing annotator who skeletonized black skeleto! n in . () Fifty skeletons of one amacrine cell before (left) and after (right) edge validation and consensus computation. Scale bar, 5 μm. * Figure 5: RESCOP step 4, estimating error rate of RESCOPed skeletons. () Stereo view of two superimposed sets (red and blue) of five-fold consensus skeletons. Black asterisks, disagreements. Total neurite path length, 600 μm. () Estimated detectability distribution for one edge for a fixed ratio of agreeing to total votes (T/N) of 0.33, but different numbers of total votes (N). Probabilities are given that the edge was erroneously kept. () Top, mean path length between errors as a function of number of annotators. Solid lines, estimates using equation (11) for dense neurites (red) and single cell (green); crosses, errors detected by visual comparison with the 50-fold consensus skeleton for the consensus of 1, 5 (includes ), 10 and 25 skeletons (error bars, s.e.m.). Dashed lines, average redundancy as a function of the target error rate for focused reannotation (Monte Carlo simulations). Bottom, same analysis for a conventionally stained data set annotated using the original data (blue, K0563, mag1, s.d.), data with added noise (magenta, K0563! , mag1, noise) and data at half the resolution (cyan, K0563, mag2). () Examples from the original and degraded data sets. Scale bar, 250 nm. * Figure 6: Doubly annotated skeletons of 114 putative rod bipolar cells in a block of mouse retina. () View onto the block face. INL, inner nuclear layer; IPL, inner plexiform layer; GCL, ganglion cell layer. Dashed lines indicate bounding boxes for –. () Two skeletons of a single rod bipolar cell. (,) View onto the plane of the retina confined to the dendrites () and axons () of bipolar cells, respectively. Cells are colored randomly in ,. Scale bars, 10 μm. Author information * Abstract * Author information * Supplementary information Affiliations * Max Planck Institute for Medical Research, Heidelberg, Germany. * Moritz Helmstaedter, * Kevin L Briggman & * Winfried Denk Contributions M.H. and W.D. designed the study and devised the analysis algorithms; K.L.B. carried out the SBEM experiments; M.H., K.L.B. and W.D. specified the KNOSSOS software; M.H. analyzed the data; M.H., K.L.B. and W.D. wrote the paper. Competing financial interests Moritz Helmstaedter and Winfried Denk have applied for a patent (Published Patent Application US 20100183217). Winfried Denk receives IP license income from Gatan Inc. for serial blockface imaging. Corresponding author Correspondence to: * Moritz Helmstaedter Author Details * Moritz Helmstaedter Contact Moritz Helmstaedter Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin L Briggman Search for this author in: * NPG journals * PubMed * Google Scholar * Winfried Denk Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Zip files * Supplementary Movie 1 (68M) Skeleton tracing with KNOSSOS. 3 orthogonal views are displayed (xy, top left, yz, top right, xz, bottom left) and a 3-dimensional view of the dataset bounding box and the skeleton being created (bottom right). The 19MB version requires a video player compatible with DivX-encoded files. The 68MB version can be viewed with a generic player but is shorter. * Supplementary Image Stack 1 (3M) * Supplementary Image Stack 2 (3M) * Supplementary Image Stack 3 (3M) * Supplementary Image Stack 4 (3M) * Supplementary Image Stack 5 (3M) * Supplementary Image Stack 6 (3M) * Supplementary Image Stack 7 (3M) * Supplementary Image Stack 8 (3M) * Supplementary Image Stack 9 (3M) * Supplementary Image Stack 10 (3M) * Supplementary Image Stack 11 (3M) * Supplementary Image Stack 12 (3M) * Supplementary Image Stack 13 (54M) * Supplementary Image Stack 14 (56M) * Supplementary Image Stack 15 (6M) PDF files * Supplementary Text and Figures (594K) Supplementary Figures 1–5 Additional data - Two-photon calcium imaging of evoked activity from L5 somatosensory neurons in vivo
- Nat Neurosci 14(8):1089-1093 (2011)
Nature Neuroscience | Technical Report Two-photon calcium imaging of evoked activity from L5 somatosensory neurons in vivo * Wolfgang Mittmann1, 2 * Damian J Wallace1 * Uwe Czubayko1 * Jan T Herb3 * Andreas T Schaefer3 * Loren L Looger4 * Winfried Denk2 * Jason N D Kerr1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1089–1093Year published:(2011)DOI:doi:10.1038/nn.2879Received02 December 2010Accepted16 May 2011Published online10 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Multiphoton imaging (MPI) is widely used for recording activity simultaneously from many neurons in superficial cortical layers in vivo. We combined regenerative amplification multiphoton microscopy (RAMM) with genetically encoded calcium indicators to extend MPI of neuronal population activity into layer 5 (L5) of adult mouse somatosensory cortex. We found that this approach could be used to record and quantify spontaneous and sensory-evoked activity in populations of L5 neuronal somata located as much as 800 μm below the pia. In addition, we found that RAMM could be used to simultaneously image activity from large (~80) populations of apical dendrites and follow these dendrites down to their somata of origin. View full text Figures at a glance * Figure 1: Imaging L5 Somatosensory cortex neurons labeled with GCaMP3. () Basic optical path of the microscope and the regenerative amplifier (RegA) seeded by a pulsed Ti:Al2O3 laser source (Mira). Pulses from the source laser were subsampled and amplified in the RegA (not to scale) and dispersion compensated (data not shown). The pulses entered the microscope at a pixel frequency of <200 kHz, whereas the pulses entering the regenerative amplifier had a frequency of ~76 MHz. Fluorescence emitted as a result of each pulse amplified by a photon multiplier tube (PMT). () Two-photon microscope image (side projection) from a z stack (2-μm steps). L5 neurons were labeled with GCaMP3 (green) in an anesthetized mouse. (,) Post mortem fixed tissue slice imaged using widefield fluorescence. Note the staining of axons spreading to thalamus and to contralateral cortex. A higher resolution image and additional staining with DAPI (Blue) showing cortical layers and corpus collosum (cc) are depicted in . L1, layer 1; L2/3, layer 2/3; L4, layer 4; L5, layer 5;! L6, layer 6. * Figure 2: Sensory stimulation–evoked calcium transients from populations of L5 somata and dendrites in vivo. (,) Somata and dendrites imaged at two different depths in L5 (distances below pia are as indicated). Scale bars represent 20 μm. () Ca2+ transients recorded from a soma (yellow circle) and two dendrites (red, blue circles) at 680 μm below pia during whisker stimulation (blue stripes, yellow circle denote the trace from neuron in , blue and red circles indicate corresponding dendrites). () Ca2+ transients recorded from three somata and neuropil (light blue bars) at 740 μm below the pia during whisker stimulation. The neuropil trace (np) was averaged across the space between neurons in the entire frame (numbers refer to neurons in ). () Single transients (gray) and their average (black) from L5 neuronal somata (left) dendrites (right) in response to whisker stimulation (arrows). Upper left, 11 individual traces recorded from a neuronal soma at 630 μm below the pia and meeting the criteria for stimulus-evoked Ca2+ transients (see Online Methods). Lower left, 12 individual ! traces determined by the same criteria as response failures. Right, 9 responses (upper) and 15 failures (lower) recorded from a dendrite 537 μm below the pia. () Single transients (seven traces shown, gray) and their average (black) recorded from L5 somata in response to whisker stimulation (arrow) at 800 μm below the pia. () Distribution of response rates. * Figure 3: L5 activity correlation structure. () Distribution of evoked pair-wise correlations. () Pair-wise correlation matrix for all neuronal pairs ranked by response rate (lowest (top left) to highest (bottom right)) for a single population. () Correlation of stimulus-evoked firing in neuronal pairs compared to their response rate (geometric mean, bars depict s.e., n = 6 populations). () Mean distributions of the fraction of neurons in the population that were active during each trial (green) and the distributions expected for independent firing (black). * Figure 4: Measuring activity in populations of dendrites. () Overview taken at the border of layers 1 and 2, 200 μm below the pial surface, showing GCaMP3-labeled dendrites and several superficial neuronal somata from layer 2 neurons (arrows). () Activity simultaneously recorded from multiple apical L5 neuronal dendritic branches passing through the image plane. () Overlaid transients from different dendrites (1 and 2, same traces as in ). * Figure 5: Spontaneous activity in dendrites and its identification with the originating somata. () yz side projection from a z stack taken at 2-μm steps showing multiple L5 neurons and ascending apical dendrites containing GCaMP3 (left) with individual xy frames from three different depths, indicated by white dashed lines on the yz projection. () Ca2+ transients from three separate dendrites indicated in . Note the similarity between transients (gray box). () Ca2+ transient recorded from the apical dendrite closer to the soma (soma location, indicated by yellow circle in middle panel of ). () Reconstructed apical dendrite showing the common source of dendritic activity shown in (branch colors correspond to colors in ) and . The locations of the recordings are indicated by dashed lines. () Image taken at 800 μm below pia showing basal dendrites (arrowheads). Author information * Abstract * Author information * Supplementary information Affiliations * Network Imaging Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany. * Wolfgang Mittmann, * Damian J Wallace, * Uwe Czubayko & * Jason N D Kerr * Department of Biomedical Optics, Max Planck Institute for Medical Research, Heidelberg, Germany. * Wolfgang Mittmann & * Winfried Denk * Behavioural Neurophysiology, Max Planck Institute for Medical Research, Heidelberg, Germany. * Jan T Herb & * Andreas T Schaefer * Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA. * Loren L Looger Contributions W.M., D.J.W., W.D. and J.N.D.K. designed the research. W.M., D.J.W. and J.N.D.K. performed the research. D.J.W. and J.N.D.K. analyzed the data. U.C. performed the histological reconstructions. J.T.H., A.T.S. and L.L.L. produced the virus. W.M., D.J.W., J.N.D.K. and W.D. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jason N D Kerr Author Details * Wolfgang Mittmann Search for this author in: * NPG journals * PubMed * Google Scholar * Damian J Wallace Search for this author in: * NPG journals * PubMed * Google Scholar * Uwe Czubayko Search for this author in: * NPG journals * PubMed * Google Scholar * Jan T Herb Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas T Schaefer Search for this author in: * NPG journals * PubMed * Google Scholar * Loren L Looger Search for this author in: * NPG journals * PubMed * Google Scholar * Winfried Denk Search for this author in: * NPG journals * PubMed * Google Scholar * Jason N D Kerr Contact Jason N D Kerr Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Movie 1 (32M) Z-stack from pia to layer 5b PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–5 Additional data
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