Latest Articles Include:
- Wanted: women in research
- Nature neuroscience 13(3):267 (2010)
Women are still underrepresented in senior academic positions in science. A fundamental restructuring of the way scientists are evaluated is essential to remedy this disparity. - What to expect when you're expecting (a PhD)
- Nature neuroscience 13(3):269 (2010)
The decision to enter into a career in research is often made rather haphazardly. Curious young undergraduate minds can become intoxicated by the possibility of being a part of the enterprise that unravels great mysteries of the natural world, and the lure of escaping some of the craziness of the 'real world' adds to the appeal. - Changing tune in auditory cortex
- Nature neuroscience 13(3):271-273 (2010)
Investigating the organization of tone representation in the rodent auditory cortex at high resolution, two new studies in this issue find that the arrangement of relative frequency responsiveness is not preserved at a fine-scale cortical level. - The longer U(T)R, the further you go
- Nature neuroscience 13(3):273-275 (2010)
A new localization element in the 3′ untranslated region of the IMPA1 mRNA enables its NGF-dependent targeting to sympathetic axons, suggesting that high local inositol levels are required for axon growth and maintenance. - Myelin maintenance: axonal support required
- Nature neuroscience 13(3):275-277 (2010)
Axonal integrity depends on an intact myelin sheath, but the role of the axon in myelin maintenance is more mysterious. A new study reports that preservation of the myelin sheath requires neuronal expression of the enigmatic prion protein. - Timing isn't everything
- Nature neuroscience 13(3):277-279 (2010)
Synaptic long-term potentiation and depression are determined by the frequency and timing of coactivated synapses. A new model explains many experimental plasticity observations and allows new predictions about neural circuit function. - Quick thinking: perceiving in a tenth of a blink of an eye
- Nature neuroscience 13(3):279-280 (2010)
What is the minimal sensory processing time before we can make a decision about a stimulus? A study now reports that, for simple perceptual decisions, this can take as little as 30 ms. - Bimodal control of stimulated food intake by the endocannabinoid system
Bellocchio L Lafenêtre P Cannich A Cota D Puente N Grandes P Chaouloff F Piazza PV Marsicano G - Nature neuroscience 13(3):281-283 (2010)
Nature Neuroscience | Brief Communication Bimodal control of stimulated food intake by the endocannabinoid system * Luigi Bellocchio1, 2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Pauline Lafenêtre1, 2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Astrid Cannich1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniela Cota2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Nagore Puente4 Search for this author in: * NPG journals * PubMed * Google Scholar * Pedro Grandes4 Search for this author in: * NPG journals * PubMed * Google Scholar * Francis Chaouloff1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Pier Vincenzo Piazza2, 5, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Giovanni Marsicano1, 2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:281–283Year published:(2010)DOI:doi:10.1038/nn.2494 Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Activation of cannabinoid type-1 receptors (CB1) is universally recognized as a powerful endogenous orexigenic signal, but the detailed underlying neuronal mechanisms are not fully understood. Using combined genetic and pharmacological approaches in mice, we found that ventral striatal CB1 receptors exerted a hypophagic action through inhibition of GABAergic transmission. Conversely, brain CB1 receptors modulating excitatory transmission mediated the well-known orexigenic effects of cannabinoids. View full text Figures at a glance * Figure 1: Deletion of CB1 from cortical glutamatergic or GABAergic neurons has opposing effects on fasting-induced food intake. () Cumulative food intakes of CB1−/− mice (open squares, n = 12) and wild-type littermates (black circles, n = 11). () Cumulative food intakes of Glu-CB1+/+ (black circles, n = 15) and Glu-CB1−/− littermates (open triangles, n = 17). () Cumulative food intakes of GABA-CB1+/+ (black circles, n = 19) and GABA-CB1−/− littermates (open circles, n = 20). () Food intakes of double-mutant Glu/GABA-CB1−/− (light gray, n = 15), wild-type (black, n = 15), Glu-CB1−/− (white, n = 13) and GABA-CB1−/− littermates (dark gray, n = 14). () Effects of the NMDA receptor antagonist MK-801 (MK, 0.03 mg per kg, intraperitoneal) on wild-type (black, n = 23; dark gray, n = 16) and Glu-CB1−/− littermates (KO; white, n = 9; light gray, n = 6). () Effects of the GABAA receptor antagonist picrotoxin (PTX, 0.3 mg per kg, intraperitoneal) on wild-type (black, n = 23; dark gray, n = 8) and GABA-CB1−/− littermates (KO; white, n = 22; light gray, n = 11). All data are express! ed as mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001 as compared with wild-type littermate controls. ###P < 0.001. All experimental procedures were approved by the Committee on Animal Health and Care of INSERM and the French Ministry of Agriculture and Forestry (authorization number, 3306369). * Figure 2: The hyperphagic and hypophagic effects of THC depend on CB1-mediated modulation of glutamatergic and ventrostriatal GABAergic transmission, respectively. () Effects of intraperitoneal injections of 1 mg per kg THC (circles; CB1+/+, n = 4; CB1−/−, n = 6) and 2.5 mg per kg THC (triangles; CB1+/+, n = 5; CB1−/−, n = 3) in CB1+/+ mice (top) and CB1−/− littermates (bottom). Squares indicate vehicle groups (CB1+/+, n = 5; CB1−/−, n = 9). () Effects of intraperitoneal injections of 1 mg per kg THC (circles; Glu-CB1+/+, n = 9; Glu-CB1−/−, n = 8) and 2.5 mg per kg THC (triangles; Glu-CB1+/+, n = 8; Glu-CB1−/−, n = 5) in Glu-CB1+/+ mice (top) and Glu-CB1−/− littermates (bottom). Squares indicate vehicle groups (Glu-CB1+/+, n = 8; Glu-CB1−/−, n = 13). () Effects of intraperitoneal injections of 1 mg per kg THC (circles; GABA-CB1+/+, n = 11; GABA-CB1−/−, n = 5) and 2.5 mg per kg THC (triangles; GABA-CB1+/+, n = 6; GABA-CB1−/−, n = 7) in GABA-CB1+/+ mice (top) and GABA-CB1−/− littermates (bottom). Squares indicate vehicle groups (GABA-CB1+/+, n = 8; GABA-CB1−/−, n = 13). () Effects of intrap! eritoneal injections of D-cyclo-serine (DCS, 3 mg per kg) in combination with 1 mg per kg and 2.5 mg per kg THC (numbers of C57BL/6NCrl mice per group are shown in parentheses). () Effects of intraperitoneal injections of DZP (0.3 mg per kg) in combination with 1 mg per kg and 2.5 mg per kg THC (numbers of C57BL/6NCrl mice per group are shown in parentheses). () Intra-ventrostriatal injections of vehicle, AM251 (1 μg per side) or diazepam (5 μg per side) in combination with intraperitoneal administration of 2.5 mg per kg THC or vehicle (n = 4–5 mice per group). All data are expressed as mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001 as compared with vehicle treatments. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Luigi Bellocchio, * Pauline Lafenêtre, * Pier Vincenzo Piazza & * Giovanni Marsicano Affiliations * INSERM U862, NeuroCentre Magendie, Endocannabinoids and Neuroadaptation, Bordeaux, France. * Luigi Bellocchio, * Pauline Lafenêtre, * Astrid Cannich, * Francis Chaouloff & * Giovanni Marsicano * University of Bordeaux, Bordeaux, France. * Luigi Bellocchio, * Pauline Lafenêtre, * Astrid Cannich, * Daniela Cota, * Francis Chaouloff, * Pier Vincenzo Piazza & * Giovanni Marsicano * INSERM U862, NeuroCentre Magendie, Energy Balance and Obesity, Bordeaux, France. * Daniela Cota * Department of Neurosciences, Faculty of Medicine and Dentistry, Basque Country University, Leioia, Spain. * Nagore Puente & * Pedro Grandes * INSERM U862, NeuroCentre Magendie, Physiopathology of Addiction, Bordeaux, France. * Pier Vincenzo Piazza Contributions L.B. designed and performed experiments. P.L. carried out part of the food-intake experiments. A.C., N.P. and P.G. performed the anatomical experiments. D.C., F.C. and P.V.P. contributed to experimental design. P.V.P. and G.M. wrote the manuscript. All the authors edited the manuscript. G.M. conceived and supervised the project. Competing financial interests The authors have filed a patent application (European Patent Office, "Compositions targeting CB1 receptor for controlling food intake", EP09306163.8) that is based, in part, on the results of this study. Corresponding author Correspondence to: * Giovanni Marsicano (giovanni.marsicano@inserm.fr) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–14 and Supplementary Methods Additional data - Opioids activate brain analgesic circuits through cytochrome P450/epoxygenase signaling
Conroy JL Fang C Gu J Zeitlin SO Yang W Yang J Vanalstine MA Nalwalk JW Albrecht PJ Mazurkiewicz JE Snyder-Keller A Shan Z Zhang SZ Wentland MP Behr M Knapp BI Bidlack JM Zuiderveld OP Leurs R Ding X Hough LB - Nature neuroscience 13(3):284-286 (2010)
Nature Neuroscience | Brief Communication Opioids activate brain analgesic circuits through cytochrome P450/epoxygenase signaling * Jennie L Conroy1, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Cheng Fang2, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Gu2 Search for this author in: * NPG journals * PubMed * Google Scholar * Scott O Zeitlin3 Search for this author in: * NPG journals * PubMed * Google Scholar * Weizhu Yang2 Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Yang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Melissa A VanAlstine1 Search for this author in: * NPG journals * PubMed * Google Scholar * Julia W Nalwalk1 Search for this author in: * NPG journals * PubMed * Google Scholar * Phillip J Albrecht1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph E Mazurkiewicz1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Abigail Snyder-Keller2 Search for this author in: * NPG journals * PubMed * Google Scholar * Zhixing Shan5 Search for this author in: * NPG journals * PubMed * Google Scholar * Shao-Zhong Zhang5 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark P Wentland5 Search for this author in: * NPG journals * PubMed * Google Scholar * Melissa Behr2 Search for this author in: * NPG journals * PubMed * Google Scholar * Brian I Knapp6 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean M Bidlack6 Search for this author in: * NPG journals * PubMed * Google Scholar * Obbe P Zuiderveld7 Search for this author in: * NPG journals * PubMed * Google Scholar * Rob Leurs7 Search for this author in: * NPG journals * PubMed * Google Scholar * Xinxin Ding2, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * Lindsay B Hough1, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:284–286Year published:(2010)DOI:doi:10.1038/nn.2497 Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To assess the importance of brain cytochrome P450 (P450) activity in μ opioid analgesic action, we generated a mutant mouse with brain neuron–specific reductions in P450 activity; these mice showed highly attenuated morphine antinociception compared with controls. Pharmacological inhibition of brain P450 arachidonate epoxygenases also blocked morphine antinociception in mice and rats. Our findings indicate that a neuronal P450 epoxygenase mediates the pain-relieving properties of morphine. View full text Figures at a glance * Figure 1: Double immunolabeling showing expression of neuronal CPR in cerebral cortex (Ctx, top) and PAG (bottom) in control (WT, left) and null (right) mice. Coronal, free-floating sections were co-incubated with a polyclonal antibody to CPR (green) and a monoclonal antibody to the neuronal marker NeuN (red). Top, merged images show expression of CPR in control, but not in null, cortical neurons (corpus callosum and cerebral cortex layer VI shown near bottom). Scale bar represents 200 μm. Bottom, confocal merged images of ventrolateral PAG (−4.2 to −4.6 mm from bregma) showing large neurons with cytoplasmic CPR expression in control mice (left). A field of similar-sized neurons from null mice (right) shows the absence of CPR expression. Typical results, representative of at least four mice in each group, are shown. * indicates the cerebral aqueduct. Scale bar represents 50 μm. * Figure 2: Morphine antinociception in null and control mice. () Females were tested for baseline nociceptive responses (Pre), received saline or morphine (MOR, 3.2 mg per kg of body weight, subcutaneous), and were re-tested. Ordinate shows latencies (s, mean ± s.e.m.) for the n values in parentheses. *P < 0.01 versus control/saline and +P < 0.01 versus control/MOR. () Dose-response curves were constructed by testing males and females as in following saline or morphine treatment (5.6 mg per kg, subcutaneous). Latencies (ordinate, s, mean ± s.e.m.; 40 min after drug) did not differ between sexes by ANOVA and were pooled. Data from the 3.2 mg per kg morphine treatment groups (40 min) are re-drawn from . **P < 0.01 versus saline within genotype, ++P < 0.05 and +++P < 0.01 versus control at the same dose. () Tail-pinch latencies (ordinate, s, mean ± s.e.m.) were measured before (Pre) and 41 min after (Post) morphine (5.6 mg per kg, subcutaneous) in males. *P < 0.01 versus control/saline and +P < 0.01 versus control/MOR. * Figure 3: Effects of P450 and epoxygenase inhibitors on morphine antinociception. () Control mice were baseline (Pre) tested for nociceptive latencies (ordinate, s, mean ± s.e.m.), re-tested 15 min after intracerebroventricular (icv) infusion (Post) of inhibitors or vehicle (DMSO) and at the time points (abscissa) following morphine administration (MOR, 5.6 mg per kg, subcutaneous). For each group, n values are in parentheses; doses of inhibitors (nmol, icv) are in brackets. **P < 0.01 versus DMSO/Sal, and +P < 0.05 and ++P < 0.01 versus DMSO/MOR at the same time. () Rats were tested for tail flick nociceptive responses exactly as in , except that icv morphine (MOR, 20 μg) was administered immediately following post-icv testing. When tested in the absence of morphine, none of these inhibitor treatments modified nociceptive latencies (data not shown). Significant differences are labeled as in . Results were nearly identical on the hot plate test (data not shown). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Jennie L Conroy & * Cheng Fang Affiliations * Center for Neuropharmacology and Neuroscience, Albany Medical College, Albany, New York, USA. * Jennie L Conroy, * Jun Yang, * Melissa A VanAlstine, * Julia W Nalwalk, * Phillip J Albrecht, * Joseph E Mazurkiewicz & * Lindsay B Hough * Wadsworth Center, New York State Department of Health, and School of Public Health, State University of New York at Albany, Albany, New York, USA. * Cheng Fang, * Jun Gu, * Weizhu Yang, * Abigail Snyder-Keller, * Melissa Behr & * Xinxin Ding * Department of Neuroscience, University of Virginia School of Medicine, Charlottesville, Virginia, USA. * Scott O Zeitlin * Integrated Tissue Dynamics, Rensselaer, New York, USA. * Phillip J Albrecht & * Joseph E Mazurkiewicz * Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, New York, USA. * Zhixing Shan, * Shao-Zhong Zhang & * Mark P Wentland * Department of Pharmacology and Physiology, School of Medicine and Dentistry, University of Rochester, Rochester, New York, USA. * Brian I Knapp & * Jean M Bidlack * Leiden/Amsterdam Center for Drug Research, VU University Amsterdam, Amsterdam, The Netherlands. * Obbe P Zuiderveld & * Rob Leurs * These authors jointly directed the study. * Xinxin Ding & * Lindsay B Hough Contributions J.L.C., J.W.N. and J.Y. performed all in vivo studies. C.F., J.G. and W.Y. contributed to the generation of the null mouse. C.F. performed most of the null mouse characterization, with additional help from J.G., M.B. and J.L.C. P.J.A., J.E.M. and A.S.-K. assisted with histochemistry and microscopy. M.A.V., J.L.C., B.I.K., J.M.B., O.P.Z. and R.L. performed drug, receptor and enzyme assays. Z.S., S.-Z.Z. and M.P.W. synthesized MW06-25. S.O.Z. provided the Camk2a-cre mouse. J.L.C., C.F., X.D. and L.B.H. wrote the manuscript. X.D. and L.B.H. supervised the overall design and performance of the project. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Lindsay B Hough (houghl@mail.amc.edu) or * Xinxin Ding (xxd01@health.state.ny.us) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–8, Supplementary Tables 1 and 2, and Supplementary Methods Additional data - Adult-born SVZ progenitors receive transient synapses during remyelination in corpus callosum
- Nature neuroscience 13(3):287-289 (2010)
Nature Neuroscience | Brief Communication Adult-born SVZ progenitors receive transient synapses during remyelination in corpus callosum * Ainhoa Etxeberria1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Marie Mangin1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Adan Aguirre1 Search for this author in: * NPG journals * PubMed * Google Scholar * Vittorio Gallo1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:287–289Year published:(2010)DOI:doi:10.1038/nn.2500 Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We found that demyelinated axons formed functional glutamatergic synapses onto adult-born NG2+ oligodendrocyte progenitor cells (OPCs) migrating from the subventricular zone after focal demyelination of adult mice corpus callosum. This glutamatergic input was substantially reduced compared with endogenous callosal OPCs 1 week after lesion and was lost on differentiation into oligodendrocytes. Therefore, axon–oligodendrocyte progenitor synapse formation is a transient and regulated step that occurs during remyelination of callosal axons. View full text Figures at a glance * Figure 1: Adult-born SVZ NG2-positive cells display synaptic currents after migrating into a demyelinated lesion of the corpus callosum. () Confocal images showing an example of a GFP-positive cell recorded inside the lesion filled with biocytin (red) during patch-clamp recording and subsequently stained for NG2 (blue). Scale bar represents 200 μm. The insert shows the location of GFP retrovirus injection in the SVZ (green arrow; 0.5, 1.25 and 2.5 mm, anteroposterior relative to bregma, mediolateral and dorsoventral from surface of the brain) and lysolecithin injection in the corpus callosum (red arrow; 1.3, 1.0 and 2.0 mm, anteroposterior relative to bregma, mediolateral and dorsoventral from surface of the brain). () Example of a current evoked by callosal axon stimulation (arrowhead) in the GFP- and NG2-positive cell shown in (Vh = −80 mV) under control conditions, in the presence of 100 μM CTZ and after application of 10 μM CNQX. () Spontaneous synaptic glutamatergic activity recorded from the same cell under control conditions, in the presence of 100 μM CTZ and after blockade by 10 μM CNQX. () Spo! ntaneous excitatory postsynaptic currents recorded from a GFP- and NG2-positive cell in the corpus callosum under control conditions and in the presence of the secretagogue ruthenium red (100 μM). () Graph showing the amplitude distribution of the spontaneous events of the cell shown in . The insert shows the events in the histogram. All procedures were approved by the Institutional Animal Care and Use Committee of Children's National Medical Center. * Figure 2: Synaptically connected SVZ-derived OPCs give rise to oligodendrocytes. () Histogram showing that the percentage of connected GFP+NG2+ cells in corpus callosum significantly increased from 48% (12 of 25) at 2–3 DPL to 91% (11 of 12) (*P < 0.05, Fisher exact test) at 6–7 DPL. () Histograms showing the percentage of GFP- and NG2-positive oligodendrocyte progenitors and mature GFP- and CC1-positive oligodendrocytes in the total GFP-positive population at 3, 6 and 10 DPL. The remaining GFP-positive, NG2-negative cells and GFP-positive, CC1-negative cells were either GFAP or Dcx positive (see also ref. 4). Data are shown as mean ± s.e.m. (n ≥ 3). The percentage of GFP- and NG2-positive cells significantly decreased in favor of mature GFP- and CC1-positive cells between 3 and 10 DPL. (,) Immunostaining of GFP-positive cells in corpus callosum with antibodies to NG2 and CC1 at 3 () and 10 DPL (). Empty arrows point to GFP- and NG2-positive cells, and white arrows point to GFP- and CC1-positive cells. Scale bars represent 40 μm. () Immunostainin! g of a biocytin-filled CC1-positive, NG2-negative oligodendrocyte with the glutamatergic synaptic currents shown in . Insert represents a current-voltage profile of the cell (voltage steps from −100 to 30 mV in 10-mV increments, vertical bar represents 500 pA and horizontal bar represents 200 ms). Scale bar represents 20 μm. () Voltage-clamp recording showing spontaneous glutamatergic currents in the presence of 100 μM CTZ and after blockade by 10 μM CNQX. * Figure 3: Glutamatergic synaptic transmission between axons and NG2-positive cells is reduced 1 week after demyelination. () Representative western blots of VGlut-1 and GluR2/3 in control and lysolecithin-injected white-matter tissue at 4, 7 and 21 DPL. () Histograms showing the ratio of VGlut-1 (top) and GluR2/3 (bottom) protein expression normalized to actin levels at 4, 7 and 21 DPL (mean ± s.e.m., n ≥ 3). Expression of both VGlut-1 and GluR2/3 was significantly decreased at 7 DPL (*P < 0.05). () Histograms comparing the average eEPSC amplitude in GFP- and NG2-DsRed–positive cells at 7 DPL (mean ± s.d., n = 16) and in NG2-DsRed–positive cells in age-matched, uninjected control littermates (n = 12). Insert shows a representative average eEPSC in lysolecithin-injected corpus callosum and uninjected controls. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Ainhoa Etxeberria & * Jean-Marie Mangin Affiliations * Center for Neuroscience Research, Children's National Medical Center, Washington, DC, USA. * Ainhoa Etxeberria, * Jean-Marie Mangin, * Adan Aguirre & * Vittorio Gallo Contributions A.E. and J.-M.M. designed, performed and analyzed all of the experiments. A.A. helped with the LPC injection experiments and with the preparation of the retrovirus. V.G. designed the experiments with A.E. and J.-M.M. supervised the project and wrote the manuscript with A.E. and J.-M.M. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Vittorio Gallo (vgallo@cnmcresearch.org) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–6 and Supplementary Methods Additional data - An NGF-responsive element targets myo-inositol monophosphatase-1 mRNA to sympathetic neuron axons
- Nature neuroscience 13(3):291-301 (2010)
Nature Neuroscience | Article An NGF-responsive element targets myo-inositol monophosphatase-1 mRNA to sympathetic neuron axons * Catia Andreassi1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Carola Zimmermann1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Richard Mitter3 Search for this author in: * NPG journals * PubMed * Google Scholar * Salvatore Fusco1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Serena Devita1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Adolfo Saiardi1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Antonella Riccio1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:291–301Year published:(2010)DOI:doi:10.1038/nn.2486 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg mRNA localization is an evolutionary conserved mechanism that underlies the establishment of cellular polarity and specialized cell functions. To identify mRNAs localized in subcellular compartments of developing neurons, we took an original approach that combines compartmentalized cultures of rat sympathetic neurons and sequential analysis of gene expression (SAGE). Unexpectedly, the most abundant transcript in axons was mRNA for myo-inositol monophosphatase-1 (Impa1), a key enzyme that regulates the inositol cycle and the main target of lithium in neurons. A novel localization element within the 3′ untranslated region of Impa1 mRNA specifically targeted Impa1 transcript to sympathetic neuron axons and regulated local IMPA1 translation in response to nerve growth factor (NGF). Selective silencing of IMPA1 synthesis in axons decreased nuclear CREB activation and induced axonal degeneration. These results provide insights into mRNA transport in axons and reveal a new NGF-re! sponsive localization element that directs the targeting and local translation of an axonal transcript. View full text Figures at a glance * Figure 1: SAGE screen of transcripts isolated from cell bodies or axons of sympathetic neurons. () Schematic representation of compartmentalized cultures of sympathetic neurons. Neurons are plated in the central compartment and after a few days the axons project to the lateral compartments, growing extensively. () Compartmentalized cultures of sympathetic neurons grown with NGF in the distal compartments for 8 d and immunostained for 150-kDa neurofilament and α-tubulin (top). Nuclear staining with Hoechst 33258 (top and bottom) shows absence of cell bodies in the lateral compartments. Scale bars 200 μm. () RT-PCR assay of mRNA isolated from the lateral compartments of two independent cultures. Actb (β-actin), Hist1h4d, Map2 and Gfap transcripts were assayed. Actb primers also amplify the genomic fragment, which is 87 bp bigger than the cDNA fragment. –RT, axonal mRNA sample not reverse transcribed (n = 10). () RT-PCR assays of transcripts identified in axons and classified according to their main function: ferritin (Fth1), Gnas, tumor protein translationally-contr! olled 1 (Tpt1), survival motor neuron 1 (Smn1), connective tissue growth factor (Ctgf), Wnt5a, S100 calcium binding protein A10 (S100a10), Cdk105, Pip4k2a, Gapdh, trafficking protein kinesin binding-2 (Trak2), cofilin 1 (Cfl1), X-linked thymosin beta 4 (Tmsb4x), neurofilament (Nefm), tumor protein p53 inducible nuclear protein 2 (Trp53inp2), Eef1a1 and Creb1. Cortex mRNAwas used as positive control (n = 3). () Axonal and cell body contents of the indicated mRNAs were analyzed by RT-qPCR and normalized by β-actin content. Averages and s.e.m. of three independent experiments, each performed in triplicate. *P < 0.05. * Figure 2: Impa1 mRNA and protein are localized in axons. () FISH assay of Impa1 mRNA with antisense (left, top and bottom) or sense (right, top and bottom) Impa1 locked nucleic acid (LNA) oligoprobe. Confocal pictures of fluorescence alone (top) and merged with differential interference contrast (DIC) (bottom) of cell bodies and distal axons are shown. Scale bars 25 μm; n = 3. () Impa1 in situ RT-PCR (top) and DIC pictures (bottom) of neurons maintained with NGF for 8–10 d in vitro. Scale bars 5 μm; n = 3. () RT-PCR of Impa1 and Hist1h4d (histone H4) transcripts isolated either from axons or from cell bodies of sympathetic neurons grown in compartmentalized chambers; n = 3. () Immunostaining of IMPA1 and 150-kDa neurofilament subunit in axons and cell bodies. Scale bars, 10 μm; n = 4. () Western blot analysis of IMPA1, PI3K p85 subunit and mitochondrial protein ATPase-β in cell body and distal axons of sympathetic neurons grown in compartmentalized chambers (n = 3). Full-length blots are shown in Supplementary Figure 8. () H! PLC analysis of radiolabeled inositide content in acidic extracts of cell bodies or axons. C.p.m., counts per minute. InsP5 and InsP6 are nearly absent in axons (n = 4). (,) Cell bodies or axons of sympathetic neurons were exposed to lithium (10 mM) for 24 h and a HPLC assay was run from acidic extracts of either cell bodies or axons (n = 3). Results (averages ± s.e.m.) are presented as IP1/inositol ratio; n = 3. *P < 0.01 versus corresponding untreated compartment. * Figure 3: Cell bodies and axons of sympathetic neurons express different Impa1 isoforms. () 5′ (left) and 3′ (right) Impa1 RACE of cDNA reverse transcribed from mRNA isolated either from cell bodies or from axons of sympathetic neurons. bp, base pairs. () Northern blot analysis of Impa1 transcripts isolated from sympathetic neurons (SCG), PC12 cells (PC12) and cortical neurons (CTX). A probe directed against the coding region (left) identified two transcripts of 2,025 and 2,145 nt (Impa1 total), whereas a probe directed against Impa1-L (right) hybridized only with the 2,145-nt transcript (Impa1-L); n = 2. () Schematic representation of Impa1 gene. Cds, coding region. () FISH analysis of sympathetic neurons electroporated with GFP vectors (right) and probed with Impa1-L antisense LNA oligoprobe (left). Confocal pictures of cell bodies (top), proximal axons (0–300 μm from soma; middle) and distal axons and growth cones (800–2,000 μm from soma; bottom). Scale bar, 25 μm; n = 3. () Quantification of data in (n = 3). AU, arbitrary units. *P < 0.001. () RT-! qPCR of β-actin and Impa1-L in axons and cell bodies. Data were normalized to cell body content. Actb: cell bodies, 31.88 ± 8.2 pg; axons, 1.42 ± 0.2 pg. Impa1-L: cell bodies, 0.055 ± 0.002 pg; axons, 1.407 ± 0.19 pg. Averages and s.e.m. of three independent experiments, each performed in triplicate. () Predicted folding of axonal and cell body Impa1 isoforms. The axonal 3′ UTR induces a steric rearrangement that may result in a change of affinity for mRNA-binding proteins. * Figure 4: Axonal localization of Impa1-L depends on a previously undescribed NGF-responsive localization element. (–) FISH assay of neurons transfected with MyrdEGFP-Impa1-L or MyrdEGFP-Impa1-S and probed either with antisense (left, top and bottom) or with sense (right, top and bottom) GFP LNA oligoprobe. Confocal fluorescence alone (top) and merged with differential interference contrast (bottom) pictures of cell bodies and distal axons (n = 3). Scale bar, 25 μm. () GFP protein expression in sympathetic neurons transfected with MyrdEGFP-Impa1-L or MyrdEGFP-Impa1-S. ICC, immunocytochemistry. Scale bar, 100 μm; arrowheads, GFP immunoreactivity along the axons of electroporated neurons. () Quantification of GFP protein in axons of sympathetic neurons expressing either MyrdEGFP-Impa1-L or MyrdEGFP-Impa1-S. (,) Quantification of GFP protein in neurons expressing either MyrdEGFP-Impa1-S or MyrdEGFP-Impa1-L and maintained in NGF-free medium supplemented with boc-aspartyl(O-methyl)-fluoromethylketone (BAF) for 36 h (NGF-deprived). Re-stimulation of neurons with NGF induced axonal transpor! t and local translation of MyrdEGFP-Impa1-L, but not of MyrdEGFP-Impa1-S. Experiments in – were performed simultaneously using the same controls. Averages and s.e.m. of six independent experiments. Insets: statistical analyses of data in –. *P < 0.005, IMPA1-L versus IMPA1-S (). *P < 0.005, IMPA1-L control, or re-stimulated, versus IMPA1-L NGF-deprived (). No statistically significant differences between control and NGF-deprived or restimulated conditions were observed in neurons electroporated with Impa1-S construct (). One-way analysis of variance, Tukey's post hoc test. * Figure 5: NGF-dependent IMPA1-L transport and local translation in axons of SCG explants. () Top: neurofilament (200-kDa) immunostaining of a representative SCG explant maintained for 3 d in culture. Bottom: experimental outline for the data shown in ,. Ganglia were electroporated (EP) with both MyrdEGFP-Impa1-L and mCherry-expressing vector. After 24 h in medium containing NGF, axons were severed on one side and explants were incubated for 8 h with or without cycloheximide (CHX). In some experiments, NGF was removed from the medium and replaced with BAF for 18 h. Axons were then severed from the cell bodies and explants were exposed to medium containing NGF with or without CHX for 8 h, as indicated. Scale bar, 500 μm. () Quantification of experiments in ; 6–8 axons from three independent experiments were analyzed and GFP fluorescence normalized by mCherry fluorescence was expressed in arbitrary units (AU) per square micrometer. Most neurons expressing MyrdGFP-Impa1-L showed GFP immunoreactivity in the distal portion and the tip of the axon. GFP+ axons located! at least 800 μm from the soma were analyzed. *P < 0.001, control versus +CHX. **P < 0.01, control versus NGF-deprived−NGF. ***P < 0.01, NGF-deprived versus NGF-deprived + NGF. ****P < 0.01, NGF-deprived + NGF versus NGF-deprived + NGF + CHX (intact axons, white bars). () Confocal images of axons from ganglia electroporated with both MyrdEGFP-Impa1-L (right) and mCherry-expressing (left) vectors and treated as indicated in . Scale bar, 25 μm. * Figure 6: Impa1-L directs localization of reporter vectors containing heterologous 3′ UTRs. () Jun and Mycn transcripts were amplified by RT-PCR using mRNA isolated either from axons or from cell bodies. No amplification of Jun and Mycn was detected in axons. –RT, axonal mRNA sample not reverse transcribed (n = 3). () FISH analysis of neurons transfected either with MyrdEGFP-Jun+Impa1-L or with MyrdEGFP-Jun and probed with antisense GFP LNA oligoprobe. Confocal fluorescence alone (top) and merged with differential interference contrast (bottom) pictures of cell bodies and distal axons (n = 3). Scale bar, 25 μm. (–) Quantification of GFP protein in sympathetic neurons transfected with MyrdEGFP-Jun+Impa1-L and MyrdEGFP-Jun (,) or MyrdEGFP-Mycn+Impa1-L and MyrdEGFP-Mycn (,). Averages and s.e.m. of data from four independent cultures. Insets: statistical analysis of data shown in –. *P < 0.005 of Jun+Impa1-L control versus Jun+Impa1-L NGF-deprived or restimulated (), Jun restimulated versus NGF-deprived (), Mycn+Impa1-L control versus Mycn+Impa1-L NGF-deprived o! r restimulated () and of Mycn restimulated versus Mycn NGF-deprived (). One-way analysis of variance, Tukey's post hoc test. * Figure 7: Selective silencing of Impa1-L induces axonal degeneration. () Immunoblotting of IMPA1, phosphorylated and total TrkA,and CREB, and PI3K p85 subunit in cell bodies and axons of neurons deprived of NGF for 36 h in the presence of BAF; n = 3. Ctr, untreated control. () Axons of sympathetic neurons were exposed to CHX for 8 h and analyzed by western blot; n = 3. () IMPA1 and α-tubulin western blot of PC12 cells electroporated with either control (sirRNA), total IMPA1 (sirImpa1) or IMPA1-L (sirImpa1-L) siRNA (150nM); n = 3. Full-length blots are shown in Supplementary Figure 8. () sirImpa1-L decreases IMPA1 expression in axons (top row) and nuclear phosphorylated CREB (middle row). Scale bar, 10 μm. Bottom row: axonal degeneration of SCG explants electroporated with either sirImpa1 or sirImpa1-L siRNAs. sirRNA did not cause axonal degeneration. Scale bar, 25 μm. Insets: 150-kDa neurofilament immunostaining; scale bar, 100 μm. () Cleaved caspase-6 immunostaining of sympathetic neurons transfected with GFP-expressing vector and either ! sirRNA, sirImpa1 or sirImpa1-L. Axonal degeneration was inhibited by the caspase-6 inhibitor zVEID-FMK; n = 3. Scale bar, 25 μm. (,) Quantification of data in ; averages ± s.e.m. of three independent experiments. AU, arbitary units. *P < 0.0001 and **P < 0.0005 of control sirRNA versus sirImpa1 or sirImpa1-L. One-way analysis of variance, Tukey's post hoc test. () Quantitative analysis of nuclear viability of neurons electroporated with indicated siRNAs and a GFP vector, and stained with Hoechst 33258. Averages and s.e.m. of three independent experiments. * Figure 8: Expression of mouse Impa1-L, but not Impa1-S, rescues the axonal degeneration induced by rat Impa1 silencing. () IMPA1, hemagglutinin (HA) and α-tubulin western blot analysis of PC12 cells transfected with the indicated sirRNAs and vectors encoding HA-tagged mouse IMPA1 with either the short or the long rat Impa1 3′ UTR (HAmImpa1-rS and HAmImpa1-rL, respectively). Higher band in top panels is HA-tagged mouse IMPA1; lower band is endogenous rat IMPA1 (n = 3). Full-length blots are presented in Supplementary Figure 8. (–) Neurons or (–) SCG explants transfected with control sirRNA (,,,), sirImpa1 (,,,) or sirImpa1-L (,,,) in the presence of HAmImpa1-rS (–,–) or HAmImpa1-rL (–,–), and also with GFP (–). Expression of HAmIMPA1-rL (,), but not HAmIMPA1-rS (,), rescued the axonal degeneration (arrows) induced by Impa1 silencing. The specificity of sirImpa1-L silencing is demonstrated by the lack of HA-IMPA1 staining in neurons cotransfected with sirImpa1-L and HAmImpa1-rL (). Scale bar, 20 μm. Insets: 150-kDa neurofilament immunostaining; scale bar, 100 μm. () Quantific! ation of data in –; averages ± s.e.m. of four independent experiments. *P < 0.05 of sirRNA versus sirImpa1 (or sirImpa1-L)+HAmIMPA1-rS, and of sirRNA versus sirImpa1-L+HAmIMPA1-rL, and of sirImpa1+HAmIMPA1-rL versus sirImpa1 (or sirImpa1-L)+HAmIMPA1-rS and versus sirImpa1-L+HAmIMPA1-rL. One-way analysis of variance, Tukey's post hoc test. () Progressive cell death was observed when axons were treated with lithium. Length measurements were performed on surviving axons that did not show signs of degeneration. Averages and s.e.m. of four independent experiments. *P < 0.01. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * GU441530 Gene Expression Omnibus * GSE13788 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Medical Research Council Laboratory for Molecular and Cell Biology, University College London, London, UK. * Catia Andreassi, * Carola Zimmermann, * Salvatore Fusco, * Serena Devita, * Adolfo Saiardi & * Antonella Riccio * Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK. * Catia Andreassi, * Carola Zimmermann, * Salvatore Fusco, * Serena Devita & * Antonella Riccio * London Research Institute, Cancer Research UK, London, UK. * Richard Mitter * Medical Research Council Cell Biology Unit, University College London, London, UK. * Adolfo Saiardi Contributions C.A. helped to design the project, performed most of the experiments, analyzed the data and helped to write the manuscript. C.Z. performed the mRNA targeting assay and contributed to many other experiments throughout the study. R.M. was responsible for the bioinformatics analysis. S.D. and S.F. contributed to the SAGE assay. A.S. analyzed inositol polyphosphate content and helped write the manuscript. A.R., the senior author, designed the project, performed some of the RT-qPCR and axon degeneration experiments, analyzed the data and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Antonella Riccio (a.riccio@ucl.ac.uk) Supplementary information * Abstract * Accession codes * Author information * Supplementary information Movies * Supplementary Movie 1 (956K) Time-lapse movie of GFP signal in sympathetic neurons deprived of NGF for 36 hours and exposed to NGF for 12 hours. PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–8, Supplementary Table 1, Supplementary Methods and Supplementary Text Additional data - Acute and gradual increases in BDNF concentration elicit distinct signaling and functions in neurons
- Nature neuroscience 13(3):302-309 (2010)
Nature Neuroscience | Article Acute and gradual increases in BDNF concentration elicit distinct signaling and functions in neurons * Yuanyuan Ji1, 2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Yuan Lu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Feng Yang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Wanhua Shen2 Search for this author in: * NPG journals * PubMed * Google Scholar * Tina Tze-Tsang Tang3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Linyin Feng2 Search for this author in: * NPG journals * PubMed * Google Scholar * Shumin Duan2 Search for this author in: * NPG journals * PubMed * Google Scholar * Bai Lu1, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:302–309Year published:(2010)DOI:doi:10.1038/nn.2505 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Extracellular factors may act on cells in two distinct modes: an acute increase in concentration as a result of regulated secretion, or a gradual increase in concentration when secreted constitutively or from a distant source. We found that cellular responses to brain-derived neurotrophic factor (BDNF) differed markedly depending on how BDNF was delivered. In cultured rat hippocampal neurons, acute and gradual increases in BDNF elicited transient and sustained activation of TrkB receptor and its downstream signaling, respectively, leading to differential expression of Homer1 and Arc. Transient TrkB activation promoted neurite elongation and spine head enlargement, whereas sustained TrkB activation facilitated neurite branch and spine neck elongation. In hippocampal slices, fast and slow increases in BDNF enhanced basal synaptic transmission and LTP, respectively. Thus, the kinetics of TrkB activation is critical for cell signaling and functions. This temporal dimension in ce! llular signaling may also have implications for the therapeutic drug design. View full text Figures at a glance * Figure 1: Transient or sustained TrkB and Erk activation induced by acute or gradual BDNF stimulation. () Protocols for application of BDNF in acute and gradual modes. In the acute protocol, BDNF concentration in culture increased immediately from 0 to 1 nM (25 ng ml−1). Neuronal proteins were extracted at 0, 0.25, 0.5, 1, 2, 4 and 8 h after BDNF application. In the gradual protocol, BDNF concentration increased tenfold every 30 min, starting from 10−4 nM (2.5 pg ml−1), until it reached 1 nM (total of 2 h). Protein extraction started at 30 min after application of 10−2 nM (0.25 ng ml−1) BDNF (1.5 h after the first BDNF application, indicated by arrow). When BDNF concentration reached 1 nM, protein extraction followed the same time course. () Transient and sustained activations of TrkB and Erk induced by acute and gradual stimulation with BDNF, respectively. Representative western blots are shown on top of the quantitative plots (n = 6 independent experiments.). Full-length blots are presented in Supplementary Figure 9. Control, vehicle control; saline, five consecut! ive saline treatments with a 30-min interval as a negative control. () Biotinylation assay of surface TrkB at the indicated time points after acute and gradual BDNF treatment. Data were compared with control by ANOVA. Data are presented as mean ± s.e.m., *P < 0.05, **P < 0.01. * Figure 2: Differential expression of Homer1 and Arc by acute and gradual modes of BDNF stimulation. (,) Representative western blots () and quantitative plots () of the time courses of BDNF-induced Homer1, Arc expression in acute and gradual modes. Actin was used as loading control. Full-length blots are presented in Supplementary Figure 9. Data are presented as mean ± s.e.m., *P < 0.05, **P < 0.01. * Figure 3: Differential effects of acute and gradual modes on neurite growth of young neurons. Representative images of MAP2-stained hippocampal neurons under different conditions. Neurons (3 d in vitro) were treated with BDNF in acute or gradual mode. Neurons were fixed and stained with antibody to MAP2 3 d later. Scale bar represents 20 μm. * Figure 4: Acute and gradual modulation of dendritic spine growth of mature neurons. () Examples showing dendritic spines under different conditions. Acute BDNF stimulation increased the size of the spine head, whereas gradual stimulation caused spine length elongation and induced more filopodia-like protrusions. These responses were completely inhibited when cells were pretreated for 1 h with 100 nM K252a. Scale bars represent 5 μm (low magnification, left) and 1 μm (high magnification, right). () Quantification of spine (left) and filopodia (right) densities (number of spines/filopodia per 10 μm of dendrite length). (,) Quantification of the shape of dendritic spines under the indicated modes. Cumulative frequency plots showing distribution of spine length (μm) and spine head width (μm). The average spine width and length for each group are shown in the insets. Data are presented as mean ± s.e.m., **P < 0.01 compared with control group, #P < 0.05 between acute and gradual, ##P < 0.01 between acute and gradual, ANOVA. * Figure 5: Differential signaling of fast and slow BDNF stimulation in hippocampal slices. (–) Differential kinetics of TrkB and Erk activation induced by fast (240 ml h−1) and slow (25 ml h−1) rates of BDNF (8 nM, 200 ng ml−1) stimulation. Representative western blots () and quantitative plots (,) are shown (n = 3–5 slices per time point; three independent experiments). (–) Differential expression of Homer1 and Arc in adult hippocampal slices induced by slow and fast BDNF perfusion. The experiments were performed as in . Representative western blots () and quantification of data (,) (n = 3 repetitions of pooled slices). The same experiment was repeated using entirely independent samples and the same results were obtained. Data are presented as mean ± s.e.m., *P < 0.05, **P < 0.01, ANOVA. Full-length blots are presented in Supplementary Figure 9. * Figure 6: Differential physiological effects of fast and slow BDNF stimulation in hippocampal slices. () Field EPSPs (fEPSPs) after slow and fast BDNF perfusion in adult (8 week old) hippocampal slices. After a stable baseline was established, BDNF was perfused into slices at either a fast or slow rate throughout the recording (>3 h). In and , insets are example recordings at 0 min (dotted line) and 180 min (solid line). () Effect of transient, fast BDNF perfusion on adult hippocampal slices. The experiments were carried out as in , except that BDNF was transiently perfused at a fast rate as indicated by the horizontal bar. (,) Fast and slow perfusion of BDNF did not alter basal synaptic transmission in neonatal hippocampal slices. (–) Effect of fast or slow BDNF perfusion on neonatal hippocampal slices. The experiments were performed as in , except that postnatal day 12–14 slices were used. () Slow perfusion of BDNF increased E-LTP compared with BSA or co-perfusion of K252a (P < 0.001, ANOVA). () Synaptic fatigue during 3× TBS was attenuated by BDNF as compared with BS! A or co-perfusion of K252a (*P < 0.01, ANOVA). () Fast perfusion of BDNF did not affect early-phase LTP induced by 3× TBS. () Synaptic fatigue during 3× TBS was indistinguishable between BDNF and BSA. In and , insets are sample recordings at 0 min (dotted line) and 120 min (solid line). In and , original data are shown on the left. Data are presented as mean ± s.e.m. * Figure 7: Differential regulation of NMDAR EPSCs and AMPAR/NMDAR ratio by fast and slow BDNF application. Whole-cell, voltage-clamp recordings were performed on CA1 pyramidal neurons of hippocampal slices. BSA or BDNF was perfused at a slow (25 ml h−1) or fast (240 ml h−1) rate. (,,,) Representative traces of NMDAR (upper) and AMPAR (lower) currents evoked by extracellular stimulating presynaptic neurons at 50 μA in 2- and 8-week-old mice. Black indicates individual current traces, orange indicates the average waveform, and the shaded light gray and dark gray rectangular areas indicate where measurements were taken to determine AMPA (at −70 mV) and NMDA (at +50 mV) current amplitudes, respectively. () NMDAR currents in 2-week-old slices. Note that the average amplitude was significantly increased (***P = 0.009) after fast, but not slow, BDNF perfusion. () No differences in NMDAR currents were observed between fast and slow BDNF application in 8-week-old CA1 neurons. () AMPA/NMDA current ratio in 8-week-old CA1 neurons. The ratio was significantly increased (*P < 0.05) aft! er fast, but not slow, BDNF perfusion. Data are presented as mean ± s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Section on Neural Development and Plasticity, National Institute of Child Health and Human Development, and Gene, Cognition and Psychosis Program, National Institute on Mental Health, Bethesda, Maryland, USA. * Yuanyuan Ji, * Yuan Lu, * Feng Yang & * Bai Lu * Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China. * Yuanyuan Ji, * Wanhua Shen, * Linyin Feng & * Shumin Duan * Receptor Biology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA. * Tina Tze-Tsang Tang * Medical Research Council Centre for Synaptic Plasticity, Department of Anatomy, University of Bristol, School of Medical Sciences, Bristol, UK.. * Tina Tze-Tsang Tang * Present address: GlaxoSmithKline, R&D China, Pudong, Shanghai, China. * Yuanyuan Ji & * Bai Lu Contributions Y.J. conducted the biology and morphology experiments and wrote the manuscript. Y.L. prepared hippocampal slices and conducted the extracellular recording in slice. F.Y. conducted the whole-cell recording in slices. W.S. conducted the electrophysiology experiments. T.T.-T.T. prepared neuron culture and conducted morphology experiments. L.F. performed the immunostaining and biology experiments. S.D. supervised the project. B.L. supervised the project, designed the experiments and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bai Lu (bai.b.lu@gsk.com) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (628K) Supplementary Figures 1–9 Additional data - Axonal prion protein is required for peripheral myelin maintenance
- Nature neuroscience 13(3):310-318 (2010)
Nature Neuroscience | Article Axonal prion protein is required for peripheral myelin maintenance * Juliane Bremer1 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Baumann1 Search for this author in: * NPG journals * PubMed * Google Scholar * Cinzia Tiberi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Carsten Wessig2 Search for this author in: * NPG journals * PubMed * Google Scholar * Heike Fischer1 Search for this author in: * NPG journals * PubMed * Google Scholar * Petra Schwarz1 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew D Steele3 Search for this author in: * NPG journals * PubMed * Google Scholar * Klaus V Toyka2 Search for this author in: * NPG journals * PubMed * Google Scholar * Klaus-Armin Nave4 Search for this author in: * NPG journals * PubMed * Google Scholar * Joachim Weis5 Search for this author in: * NPG journals * PubMed * Google Scholar * Adriano Aguzzi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:310–318Year published:(2010)DOI:doi:10.1038/nn.2483 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The integrity of peripheral nerves relies on communication between axons and Schwann cells. The axonal signals that ensure myelin maintenance are distinct from those that direct myelination and are largely unknown. Here we show that ablation of the prion protein PrPC triggers a chronic demyelinating polyneuropathy (CDP) in four independently targeted mouse strains. Ablation of the neighboring Prnd locus, or inbreeding to four distinct mouse strains, did not modulate the CDP. CDP was triggered by depletion of PrPC specifically in neurons, but not in Schwann cells, and was suppressed by PrPC expression restricted to neurons but not to Schwann cells. CDP was prevented by PrPC variants that undergo proteolytic amino-proximal cleavage, but not by variants that are nonpermissive for cleavage, including secreted PrPC lacking its glycolipid membrane anchor. These results indicate that neuronal expression and regulated proteolysis of PrPC are essential for myelin maintenance. View full text Figures at a glance * Figure 1: Peripheral polyneuropathy in Prnpo/o mice. () Toluidine blue–stained semithin cross sections of sciatic nerves of wild-type (Balb/c and 129/Ola wt), Prnpo/o (Balb/c), Prnp+/− (Balb/c), tga20 (B6/129Sv), PrnpEdbg/Edbg (129/Ola) and PrnpGFP/GFP (C57Bl/6) mice, all around 60 weeks of age. () CD68-immunostained longitudinal sections of sciatic nerves. More digestion chambers with macrophages and myelin debris are visible in Prnpo/o (Balb/c) than in wild-type (Balb/c) nerves at 60 and 10 weeks of age, respectively. () Axonal density within nerves (number of axons per mm2) was quantified morphometrically and plotted against the cross-sectional areas (μm2) of axons (axonal density-size distribution). Error bars, s.e.m. () Quantification shows significantly more digestion chambers in sciatic nerves of 30- and 60-week-old Prnpo/o mice than in wild-type mice. All scale bars are 50 μm. * Figure 2: Ultrastructural alterations in Prnpo/o sciatic nerves. (–) Electron microscopy of sciatic nerves of 60-week-old Prnpo/o (,) and wild-type (,) mice (both Balb/c). Cross-sections of wild-type nerves show normally myelinated nerve fibers () and regular unmyelinated axons in Remak bundles (R) (,). Cross-sections of Prnpo/o nerves show thinly myelinated axons, surrounded by onion bulb formations (arrows) (,), axon (A) surrounded by Schwann cell with prominent rough endoplasmic reticulum (arrow) and increased density of other organelles (), as well as loss of unmyelinated axons in Remak bundles. Schwann cells ensheathing unmyelinated axons (A) frequently show abnormal branching of cytoplasmic processes (arrows) (). Longitudinal sections of Prnpo/o nerves reveal macrophages (M) within the axon-glia interface () and focally folded myelin (white arrows) in the vicinity of a node of Ranvier (black arrow; ). The right internode shows thinner myelin than the left one, indicating de- and remyelination. Scale bars: ,, 8 μm; –, 5 μm. * Figure 3: Electrophysiology and behavior of Prnpo/o mice. () NCVs were significantly reduced in Prnpo/o mice at 11 weeks (33.0 ± 3.5 m s−1 (Prnpo/o) versus 42.3 ± 4.4 m s−1 (wild type)), at 28 weeks (37.5 ± 1.5 m s−1 (Prnpo/o) versus 50.5 ± 2.8 m s−1 (wild type)) and at 53 weeks (25.1 ± 0.87 m s−1 (Prnpo/o) versus 54.2 ± 5.6 m s−1 (wild type)). () FWLs were only marginally increased in Prnpo/o mice at 11 weeks (5.6 ± 0.5 ms (Prnpo/o) versus 5.1 ± 0.4 ms (wild type)) and 28 weeks (4.2 ± 0.2 ms (Prnpo/o) versus 4.0 ± 0.3 ms (wild type)), but were significantly prolonged at 53 weeks (6.0 ± 0.5 ms (Prnpo/o) versus 4.4 ± 0.4 ms (wild type)). () Prnpo/o mice showed no difference in performance on the rotarod test when compared to wild-type mice at 12, 30 and 60 weeks of age. There was some training effect, as both groups showed slightly longer latencies to fall in test 2, two days after test 1. () Hot plate test performance was significantly worse at 60 weeks in Prnpo/o mice. () Prnpo/o mice showed significantly l! ower grip strength at 12 and 60 weeks. All mice were kept in the Balb/c background. * Figure 4: Expression of PrPC by neurons is essential for myelin sheath maintenance. (,) Prnp mRNA and PrPC protein content of tgNSE-PrP and tgPLP-PrP sciatic nerves were investigated by western blot () and by real-time PCR (; values on ordinate are normalized against wild-type mRNA). () Normal FWL in 35-week-old tgNSE-PrP mice (4.1 ± 0.09 ms (tgNSE-PrP) and 4.2 ± 0.2 ms (wild type)) and prolonged latencies in age-matched B6/129Sv Prnpo/o (4.5 ± 0.2 ms) and tgPLP-PrP mice (4.3 ± 0.2 ms). () Quantification of cumulative axonal density-size distribution indicates nearly normal distribution in tgNSE-PrP mice and reduced large-axon density in Prnpo/o and tgPLP-PrP mice. Error bars, s.e.m. () PrPC localization was studied by immunofluorescence. (,) Semithin sections of nerves from 60-week-old tgNSE-PrP and tgPLP-PrP mice stained with toluidine blue. () Percentage of fibers with g-ratio > 0.81 and onion bulb formation in wild-type, Prnpo/o, tgNSE-PrP and tgPLP-PrP mice. Error bars, s.d. * Figure 5: Neuron-specific but not Schwann cell–specific depletion of PrPC induces polyneuropathy. Prnpo/o mice carrying a loxP-flanked Prnp transgene, termed tgPrnploxP, were crossed to tgNFH-Cre expressing Cre in neurons or to tgDhh-Cre expressing Cre in Schwann cells. (–) Morphological analysis of sciatic nerves at 60 weeks of age. () Toluidine blue-stained semithin sections showing CDP in tgPrnploxP × tgNFH-Cre mice with neuronal PrPC depletion. () Electron microscopy showing onion bulb formation in a tgPrnploxP × tgNFH-Cre mouse. (,,,) By contrast, tgPrnploxP on a Prnpo/o background and tgPrnploxP × tgDhh-Cre showed normal morphology of sciatic nerves in semithin sections (,) and electron microscopy (,). Scale bars: , 50 μm; , 2 μm. () Quantification of cumulative axonal density-size distribution showing reduction of large axons in tgPrnploxP × tgNFH-Cre as in Prnpo/o nerves. Error bars, s.e.m. () Percentage of fibers with g-ratio > 0.81 and onion bulb formation in tgPrnploxP × tgNFH-Cre mice was significantly increased compared to tgPrnploxP × tgDhh-Cre an! d tgPrnploxP mice. Error bars, s.d. (–) Expression of Prnp mRNA was analyzed by in situ hybridization in 60-week-old tgNFH-Cre and tgPrnploxP × tgNFH-Cre mice using a Prnp antisense probe. TgPrnploxP mice express Prnp in dorsal root ganglia () and spinal cord neurons (). Dashed line: border between spinal white and gray matter. Scale bars: , 200 μm; , 500 μm. After recombination, Prnp was undetectable in ~70% of DRG () and in all spinal cord neurons (). () Western blot of PrPC expression in tgPrnploxP mice and following conditional depletion of Prnp. Samples were treated with PNGase or left untreated; antibody: POM1. * Figure 6: PrPC expression and proteolytic processing in sciatic nerves of wild-type and tgGPI−PrP mice. Western blot analysis comparing PrPC protein expression in the sciatic nerve with that in the brain of wild-type mice, using two different monoclonal antibodies, POM1 and POM3, for detection. () After PNGase treatment of protein lysates, two bands were recognized by POM3 antibody, whereas POM1 detected three bands. () This band pattern is explained by the localization of antibody epitopes and by the existence of three PrP isoforms, each containing an intact C terminus: full-length PrP, C2 fragment and C1 fragment. () TgGPI−PrP mice were analyzed for PrP expression with POM11 antibody, before and after PNGase treatment in comparison to serially diluted Prnp+/− sciatic nerve lysates. () For analysis of PrP processing, we used antibody POM1 which detects all holo-PrPC and all C-terminal fragments. POM1 detected GPI−PrP to a lesser extent than POM11. However, no formation of C-terminal fragments was observed, even after very long exposures (right). * Figure 7: Role of N-terminal domains and lymphocytes in the pathogenesis of Prnpo/o polyneuropathy. () Transgenic constructs, presence of PrP C-terminal fragments in the transgenic mice, presence of peripheral neuropathy, and life expectancy. () Mice expressing deletion mutants of PrP were analyzed for PrP expression and processing in sciatic nerves. Western blots before and after PNGase treatment of lysates are shown. Asterisk marks full-length PrP; hash marks C2 fragment; circle marks C1 fragment. A western blot with better separation of full-length and C2 fragment for tgC4 mice is shown (all lanes are from the same blot, but intervening lanes were deleted for clarity). () DRMs were prepared from sciatic nerves of transgenic and wild-type mice and subjected to step density gradient centrifugation. Fractions containing DRM were analyzed by western blot for presence of PrPC and flotillin as a control. Western blots of the fractions from this experiment are shown in Supplementary Figure 6. (,) Toluidine blue-stained semithin cross sections of sciatic nerves of 60-week-old R! ag1−/− × Prnp+/+ mice showed normal morphology of the sciatic nerve () and peripheral neuropathy in Rag1−/− × Prnpo/o mice (). Author information * Abstract * Author information * Supplementary information Affiliations * Institute of Neuropathology, University Hospital of Zürich, Zürich, Switzerland. * Juliane Bremer, * Frank Baumann, * Cinzia Tiberi, * Heike Fischer, * Petra Schwarz & * Adriano Aguzzi * Department of Neurology, University of Würzburg, Würzburg, Germany. * Carsten Wessig & * Klaus V Toyka * Division of Biology, California Institute of Technology, Pasadena, California, USA. * Andrew D Steele * Department of Neurogenetics, Max-Planck Institute of Experimental Medicine, Göttingen, Germany. * Klaus-Armin Nave * Institute of Neuropathology, Medical Faculty, Rheinisch-Westfälische Technische Hochschule (RWTH) University Aachen, Aachen, Germany. * Joachim Weis Contributions J.B. and A.A. designed the study and wrote the manuscript. J.B., F.B., C.T., C.W., H.F., P.S., A.D.S., K.V.T. and J.W. did the experiments. J.B., F.B., C.T., C.W., H.F., A.D.S., K.V.T., K.-A.N., J.W. and A.A. analyzed the data. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Adriano Aguzzi (adriano.aguzzi@usz.ch) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–15 and Supplementary Table 1 Additional data - CXCR2-positive neutrophils are essential for cuprizone-induced demyelination: relevance to multiple sclerosis
Liu L Belkadi A Darnall L Hu T Drescher C Cotleur AC Padovani-Claudio D He T Choi K Lane TE Miller RH Ransohoff RM - Nature neuroscience 13(3):319-326 (2010)
Nature Neuroscience | Article CXCR2-positive neutrophils are essential for cuprizone-induced demyelination: relevance to multiple sclerosis * LiPing Liu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Abdelmadjid Belkadi2 Search for this author in: * NPG journals * PubMed * Google Scholar * Lindsey Darnall1 Search for this author in: * NPG journals * PubMed * Google Scholar * Taofang Hu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Caitlin Drescher1 Search for this author in: * NPG journals * PubMed * Google Scholar * Anne C Cotleur1 Search for this author in: * NPG journals * PubMed * Google Scholar * Dolly Padovani-Claudio2 Search for this author in: * NPG journals * PubMed * Google Scholar * Tao He1 Search for this author in: * NPG journals * PubMed * Google Scholar * Karen Choi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas E Lane3 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert H Miller2 Search for this author in: * NPG journals * PubMed * Google Scholar * Richard M Ransohoff1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:319–326Year published:(2010)DOI:doi:10.1038/nn.2491 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Multiple sclerosis is an inflammatory demyelinating disorder of the CNS. Recent studies have suggested diverse mechanisms as underlying demyelination, including a subset of lesions induced by an interaction between metabolic insult to oligodendrocytes and inflammatory mediators. For mice of susceptible strains, cuprizone feeding results in oligodendrocyte cell loss and demyelination of the corpus callosum. Remyelination ensues and has been extensively studied. Cuprizone-induced demyelination remains incompletely characterized. We found that mice lacking the type 2 CXC chemokine receptor (CXCR2) were relatively resistant to cuprizone-induced demyelination and that circulating CXCR2-positive neutrophils were important for cuprizone-induced demyelination. Our findings support a two-hit process of cuprizone-induced demyelination, supporting the idea that multiple sclerosis pathogenesis features extensive oligodendrocyte cell loss. These data suggest that cuprizone-induced demyel! ination is useful for modeling certain aspects of multiple sclerosis pathogenesis. View full text Figures at a glance * Figure 1: Cxcr2−/− mice are relatively resistant to cuprizone-induced demyelination. Cxcr2−/− or Cxcr2+/+ mice received cuprizone for 1, 2, 3, 4 or 6 weeks before evaluation of demyelination in matched serial sections. () Black-gold staining showed robust demyelination in Cxcr2+/+ mice (upper panels), but not in Cxcr2−/− mice (lower panels), following 3 and 6 weeks of cuprizone feeding. () Electron microscopy revealed demyelination in the corpus callosum of Cxcr2+/+ mice, but not Cxcr2−/− mice, after 4 weeks of cuprizone feeding. Scale bars represent 250 μm () and 2 μm (). () Quantification of the percentage of demyelinated area in corpus callosum shown in after 6 weeks of cuprizone feeding. () Quantification of myelinated axons shown in in corpus callosum after 4 weeks of cuprizone feeding. **P < 0.01, comparing Cxcr2−/− and Cxcr2+/+ mice. * Figure 2: Myelin protein mRNA expression is transiently reduced in Cxcr2−/− mice after cuprizone feeding and little apoptotic cell death occurs. () Cxcr2−/− and Cxcr2+/+ mice received cuprizone for 1 to 4 weeks, and we analyzed MBP mRNA and CNPase expression, normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Real-time PCR data showed equivalent and severe reduction in both Cxcr2−/− and Cxcr2+/+ mice after 1 or 2 weeks of cuprizone feeding. After 3 and 4 weeks of cuprizone feeding, however, we observed a significant rebound of MBP mRNA expression in the corpus callosum of Cxcr2−/−, but not Cxcr2+/+, mice. () The number of apoptotic cells was significantly elevated in Cxcr2+/+ mice after 3 weeks of cuprizone feeding, as seen by TUNEL staining. We used DAPI to stain the nuclei of TUNEL-positive cells. Scale bar represents 25 μm. () Quantification of TUNEL-positive cells in corpus callosum in mice that were fed cuprizone for 1–3. *P < 0.05, **P < 0.01, comparing Cxcr2−/− and Cxcr2+/+ mice. NS, no significant difference (P > 0.05). * Figure 3: Differential response of cells of the oligodendrocyte lineage cells to cuprizone feeding in Cxcr2+/+ and Cxcr2−/− mice. (–) Matched serial sections of mice were analyzed after 1–3 or 6 weeks of cuprizone feeding with antibodies to GST-π (), Olig2 () or PDGFRα (). (–) Quantification of GST-π– (), Olig2- () and PDGFRα-positive () cells in corpus callosum. *P < 0.05, **P < 0.01, comparing Cxcr2−/− and Cxcr2+/+ mice. Scale bars represent 25 μm (), 50 μm () and 250 μm (). Arrowheads indicate representative quantified cells. * Figure 4: Cxcr2+/−→Cxcr2−/− and Cxcr2+/−→Cxcr2+/+ chimeric mice exhibited equal levels of cuprizone-induced demyelination. (,) Radiation bone-marrow chimeras, Cxcr2+/+, Cxcr2+/− and Cxcr2−/− mice were fed cuprizone and analyzed for demyelination of the corpus callosum. Panels in are representative of corresponding groups shown in . We evaluated the effects of chimerization by comparing Cxcr2+/+ and Cxcr2+/+→Cxcr2+/+ mice. The effects of haploinsufficiency on the response to cuprizone were assessed by comparing Cxcr2+/+ and Cxcr2+/− mice and by comparing Cxcr2+/+→Cxcr2+/+ and Cxcr2+/−→Cxcr2+/+ mice (). Two separate cohorts of Cxcr2+/−→Cxcr2+/+ mice were prepared and analyzed (). Scale bar represents 250 μm in . (–) Cxcr2+/−→Cxcr2−/− mice were susceptible to cuprizone-induced demyelination, which we confirmed using electron microscopy. Cxcr2+/−→Cxcr2+/+ and Cxcr2+/−→Cxcr2−/− mice were fed cuprizone for 4 weeks and we then analyzed the rostral corpus callosum by electron microscopy () and quantified axon myelination and G ratios in the corpus callosum (,).! NS, not significant (P > 0.05). Scale bar represents 2 μm in . * Figure 5: Cxcr2−/−→Cxcr2+/+ mice are resistant to cuprizone-induced demyelination and there is a minimal reduction in the number of GST-π–positive cells or in the extent of the inflammatory reaction in corpus callosum of cuprizone-fed Cxcr2−/−→Cxcr2+/+ mice. (–) Cxcr2−/−→Cxcr2+/+ and Cxcr2+/+→Cxcr2+/+ mice (generated and studied separately from those shown in Fig. 4) were fed cuprizone for 4 weeks and analyzed for demyelination (, top panel in ), mature oligodendrocytes (top panels in , middle panel in ) or infiltrating leukocytes and reactive microglia (lower panels in and ). Arrowheads indicate mature oligodendrocytes. Arrows indicate CD45-positive leukocytes. **P < 0.01. Scale bars represent 250 μm () and 25 μm (). * Figure 6: All Ly6G-positive neutrophils are CXCR2 positive and all CXCR2-positive cells are neutrophils. Gating on live cells (left), blood cells from Cxcr2+/+ mice were stained with Ly6G-FITC and CXCR2-PE and analyzed by flow cytometry (right). These data represent three separate experiments, with four mice in each experiment. * Figure 7: Neutrophils infiltrate into the corpus callosum at the early stages of cuprizone-induced demyelination. Cells from corpus callosum of Cxcr2+/+ mice fed with cuprizone for 3 d, 5 d, 7 d, 9 d and 6 weeks were stained with Ly6G-FITC, F4/80-PE, CD45-PerCP and Gr1-APC antibodies. In comparison with control, the number of CD45int, Ly6G-positive neutrophils increased substantially at day 7 (left). The Ly6G-positive population was uniformly negative for F4/80 (middle of each group). The presence of the myeloid marker Gr1 (Ly6C) was correlated with the presence of Ly6G (right of each group). Each group consisted of two mice. These data represent three separate experiments. * Figure 8: Neutrophils infiltrate in Cxcr2+/+ and Cxcr2−/− mice after 5 d of cuprizone feeding. Although Cxcr2−/− mice had elevated numbers of neutrophils in the CNS at baseline (left), the fold increase of neutrophils over control (about fivefold) and the percent of total CD45int/hi infiltrating cells represented by neutrophils at 5 d (27–30%) were equivalent in the Cxcr2+/+ and Cxcr2−/− mice (right). Each group consisted of two mice. These data represent two separate experiments. Author information * Abstract * Author information * Supplementary information Affiliations * Neuroinflammation Research Center, Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA. * LiPing Liu, * Lindsey Darnall, * Taofang Hu, * Caitlin Drescher, * Anne C Cotleur, * Tao He, * Karen Choi & * Richard M Ransohoff * Centers for Stem Cells and Regenerative Medicine, Translational Neuroscience, Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, Ohio, USA. * Abdelmadjid Belkadi, * Dolly Padovani-Claudio & * Robert H Miller * Department of Molecular Biology & Biochemistry, University of California, Irvine, CA, USA. * Thomas E Lane Contributions L.L. was responsible for day-to-day management of the project, designed and performed experiments, analyzed data, and wrote the initial draft of the manuscript. A.B. and R.H.M. designed and performed the experiments shown in Supplementary Figure 1. L.D., T. Hu and T. He were involved in generating and analyzing Cxcr2−/− mice on the B6 background. They performed experiments and were involved in data analysis and interpretation. C.D. participated in the development and implementation of electron microscopy quantitative methodology. A.C.C. interpreted flow cytometry experiments. D.P.-C. conducted initial LPC studies in Cxcr2−/− mice. K.C. quantified demyelination and assisted in the development of the technique. T.E.L. provided antibodies to CXCR2, participated in experimental design and provided input into the manuscript. R.H.M. participated in all aspects of experimental design and generation of the manuscript. R.M.R. conceived the project, designed the experiments an! d wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Richard M Ransohoff (ransohr@ccf.org) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–10 Additional data - Disrupted-in-Schizophrenia 1 (DISC1) regulates spines of the glutamate synapse via Rac1
Hayashi-Takagi A Takaki M Graziane N Seshadri S Murdoch H Dunlop AJ Makino Y Seshadri AJ Ishizuka K Srivastava DP Xie Z Baraban JM Houslay MD Tomoda T Brandon NJ Kamiya A Yan Z Penzes P Sawa A - Nature neuroscience 13(3):327-332 (2010)
Nature Neuroscience | Article Disrupted-in-Schizophrenia 1 (DISC1) regulates spines of the glutamate synapse via Rac1 * Akiko Hayashi-Takagi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Manabu Takaki1 Search for this author in: * NPG journals * PubMed * Google Scholar * Nick Graziane2 Search for this author in: * NPG journals * PubMed * Google Scholar * Saurav Seshadri1 Search for this author in: * NPG journals * PubMed * Google Scholar * Hannah Murdoch3 Search for this author in: * NPG journals * PubMed * Google Scholar * Allan J Dunlop3 Search for this author in: * NPG journals * PubMed * Google Scholar * Yuichi Makino4 Search for this author in: * NPG journals * PubMed * Google Scholar * Anupamaa J Seshadri1 Search for this author in: * NPG journals * PubMed * Google Scholar * Koko Ishizuka1 Search for this author in: * NPG journals * PubMed * Google Scholar * Deepak P Srivastava5 Search for this author in: * NPG journals * PubMed * Google Scholar * Zhong Xie5 Search for this author in: * NPG journals * PubMed * Google Scholar * Jay M Baraban4 Search for this author in: * NPG journals * PubMed * Google Scholar * Miles D Houslay3 Search for this author in: * NPG journals * PubMed * Google Scholar * Toshifumi Tomoda6 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas J Brandon7 Search for this author in: * NPG journals * PubMed * Google Scholar * Atsushi Kamiya1 Search for this author in: * NPG journals * PubMed * Google Scholar * Zhen Yan2 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Penzes5 Search for this author in: * NPG journals * PubMed * Google Scholar * Akira Sawa1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:327–332Year published:(2010)DOI:doi:10.1038/nn.2487 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Synaptic spines are dynamic structures that regulate neuronal responsiveness and plasticity. We examined the role of the schizophrenia risk factor DISC1 in the maintenance of spine morphology and function. We found that DISC1 anchored Kalirin-7 (Kal-7), regulating access of Kal-7 to Rac1 and controlling the duration and intensity of Rac1 activation in response to NMDA receptor activation in both cortical cultures and rat brain in vivo. These results explain why Rac1 and its activator (Kal-7) serve as important mediators of spine enlargement and why constitutive Rac1 activation decreases spine size. This mechanism likely underlies disturbances in glutamatergic neurotransmission that have been frequently reported in schizophrenia that can lead to alteration of dendritic spines with consequential major pathological changes in brain function. Furthermore, the concept of a signalosome involving disease-associated factors, such as DISC1 and glutamate, may well contribute to the mu! ltifactorial and polygenetic characteristics of schizophrenia. View full text Figures at a glance * Figure 1: Short-term knockdown of DISC1 elicits spine enlargement in rat primary cortical neurons. () Spine changes in mature neurons by short-term knockdown (2 d) of DISC1 using two independent RNAi . Scale bar represents 50 μm. #P < 0.001. () Enhanced surface expression of GluR1 on the spine. Arrowheads indicate GluR1 clustering on spines. GFP, green fluorescent protein. () Increase in the frequency of miniature excitatory postsynaptic currents (mEPSC). Left, representative mEPSC traces. Right, mEPSC amplitude and frequency. Error bars indicate s.e.m. *P < 0.05. * Figure 2: Protein interaction of DISC1 and Kal-7 regulates spine morphology in rat primary cortical neurons. () Endogenous interactions of DISC1 with Kal-7 and PSD-95 (red asterisk) by co-immunoprecipitation (IP) from primary cortical neurons and rat cerebral cortex. DISC1 did not bind Tiam1 or βPIX. Strong interactions of DISC1/Kal-7 and DISC1/PSD-95 were observed in the synaptosomal fractions (double red asterisks). Full-length blots are presented in Supplementary Figure 17. () Spine shrinkage and reduced spine density by overexpression (for 2 d) of DISC-FL, but not by DISC1–ΔKal-7. Both DISC1-FL and DISC1–ΔKal-7 were localized in the dendritic spine (arrowheads). () Normalization of DISC1 knockdown–induced spine enlargement by DISC1-FL, but not by DISC1–ΔKal-7. () Increase in the frequency of mEPSC was normalized by overexpression of DISC1-FLR, but not by overexpression of DISC1–ΔKal-7R. Left, representative mEPSC traces. Right, mEPSC amplitude and frequency. Error bars indicate s.e.m. *P < 0.05, #P < 0.001. * Figure 3: Augmentation of Kal-7–PSD-95 protein binding by DISC1 in rat primary cortical neurons. () Increased Kal-7–PSD-95 protein binding by overexpression of DISC1. Left, immunofluorescent cell staining indicating that the majority of neurons were infected with Sindbis virus expressing DISC1-FL–HA or DISC1–ΔKal-7–HA. Middle and right, increased binding of Kal-7 and PSD-95 by overexpression of DISC1-FL (red asterisk), but not of DISC1–ΔKal-7. Scale bars represent 20 μm. EGFP, enhanced GFP. () Decrease in Kal-7–PSD-95 binding on lentivirus-based DISC1 knockdown. *P < 0.05, #P < 0.001. Full-length blots are presented in Supplementary Figure 17. * Figure 4: Protein interaction of DISC1–Kal-7–PSD-95 influenced by activation of the NMDA-type glutamate receptor. () Decrease in interactions among DISC1, Kal-7 and PSD-95 (red asterisk) 3 min after ECT in rat brains in vivo. Full-length blots are presented in Supplementary Figure 18. () Decrease in the interactions of DISC1–Kal-7 and DISC1–PSD-95 (red asterisk) as well as activation of Rac1 (input, red asterisk) after selective activation of the NMDA receptor by AP5 withdrawal (WD). *P < 0.05. Full-length blots are presented in Supplementary Figure 18. * Figure 5: Regulation of Rac1 activity via signalosome of DISC1–Kal-7. () Inhibition of Rac1 activity (level of GTP-Rac1) by DISC1-FL (red asterisk), but not by DISC1–ΔKal-7, and augmentation of that activity by DISC1 knockdown (green asterisk) in primary cortical neurons. Augmented Rac1 activity was also regulated by phosphorylation of Pak1. Full-length blots are presented in Supplementary Figure 18. () Rac1–Kal-7 binding was reduced by DISC1-FL expression (red asterisk), but not by DISC1–ΔKal-7. () Rac1-DN expression reduced spine enlargement, indicating that Rac1 is important for DISC1 regulation of spine enlargement. Scale bars represent 20 μm. Error bars indicate s.e.m. Significant effects of Rac1-DN compared with mock are shown as *P < 0.05, †P < 0.01 and #P < 0.001. Full-length blots are presented in Supplementary Figure 18. * Figure 6: Long-term disturbance of DISC expression leads to spine shrinkage in rat primary cortical neurons. () Short- or long-term effect of Rac1 on spine morphology in mature neurons. Right, changes in spine size induced by expression of Rac1-CA or Rac1-DN relative to that induced by wild-type Rac1 (Rac1-WT) expression are shown (fold changes). Scale bars represent 10 μm. () Short- or long-term effect of DISC1 knockdown (green) or overexpression (red) in mature neurons. Scale bars represent 10 μm. () Reduced spine size by knockdown of DISC1 for 6 d in primary cortical culture. () Decrease in mEPSC by long-term DISC1 knockdown. () Spine deterioration (decreased spine size) induced by long-term knockdown of DISC1 was normalized by overexpression of DISC1-FL, but not by DISC1–ΔKal-7. () Spine deterioration (decreased mEPSC frequency) induced by long-term knockdown of DISC1 was normalized by overexpression of DISC1-FL, but not by DISC1–ΔKal-7. Error bars indicate s.e.m. *P < 0.05, #P < 0.001. * Figure 7: Long-term suppression of DISC1 leads to spine shrinkage in slices and brains in vivo. () Effect of long-term DISC1 knockdown on spine morphology in rat organotypic cortical culture. Representative image of culture with DISC1 RNAi (green). Scale bar represents 5 mm. Cx, cerebral cortex; Str, striatum. () Long-term effect of DISC1 knockdown on spine size in the medial prefrontal cortex. Error bars indicate s.e.m. #P < 0.001. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Akiko Hayashi-Takagi, * Manabu Takaki, * Saurav Seshadri, * Anupamaa J Seshadri, * Koko Ishizuka, * Atsushi Kamiya & * Akira Sawa * Department of Physiology and Biophysics, University at Buffalo, State University of New York, Buffalo, New York, USA. * Nick Graziane & * Zhen Yan * Neuroscience and Molecular Pharmacology, Faculty of Biomedical and Life Sciences, University of Glasgow, Glasgow, UK. * Hannah Murdoch, * Allan J Dunlop & * Miles D Houslay * Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Yuichi Makino, * Jay M Baraban & * Akira Sawa * Department of Physiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. * Deepak P Srivastava, * Zhong Xie & * Peter Penzes * Beckman Research Institute of the City of Hope, Duarte, California, USA. * Toshifumi Tomoda * Pfizer, Princeton, New Jersey, USA. * Nicholas J Brandon Contributions A.H.-T., M.T., N.G., S.S., H.M., A.J.D. and T.T. conducted the experiments. Y.M., A.J.S., K.I., D.P.S. and Z.X. provided assistance for the experiments. J.M.B., M.D.H., T.T., N.J.B., A.K., Z.Y. and P.P. contributed to experimental design. A.H.-T., M.D.H., N.J.B. and A.S. wrote the manuscript. A.S. led the overall experimental design of the entire project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Akira Sawa (asawa1@jhmi.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–18 Additional data - Regulation of neuronal activity by Cav3-Kv4 channel signaling complexes
Anderson D Mehaffey WH Iftinca M Rehak R Engbers JD Hameed S Zamponi GW Turner RW - Nature neuroscience 13(3):333-337 (2010)
Nature Neuroscience | Article Regulation of neuronal activity by Cav3-Kv4 channel signaling complexes * Dustin Anderson1 Search for this author in: * NPG journals * PubMed * Google Scholar * W Hamish Mehaffey1 Search for this author in: * NPG journals * PubMed * Google Scholar * Mircea Iftinca1 Search for this author in: * NPG journals * PubMed * Google Scholar * Renata Rehak1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jordan D T Engbers1 Search for this author in: * NPG journals * PubMed * Google Scholar * Shahid Hameed1 Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald W Zamponi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ray W Turner1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:333–337Year published:(2010)DOI:doi:10.1038/nn.2493 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Kv4 low voltage–activated A-type potassium channels are widely expressed in excitable cells, where they control action potential firing, dendritic activity and synaptic integration. Kv4 channels exist as a complex that includes K+ channel–interacting proteins (KChIPs), which contain calcium-binding domains and therefore have the potential to confer calcium dependence on the Kv4 channel. We found that T-type calcium channels and Kv4 channels form a signaling complex in rat that efficiently couples calcium influx to KChIP3 to modulate Kv4 function. This interaction was critical for allowing Kv4 channels to function in the subthreshold membrane potential range to regulate neuronal firing properties. The widespread expression of these channels and accessory proteins indicates that the Cav3-Kv4 signaling complex is important for the function of a wide range of electrically excitable cells. View full text Figures at a glance * Figure 1: A-type channels are regulated by calcium influx through T-type channels in cerebellar stellate cells. () Whole-cell voltage-clamp trace of IA for 10-mV steps from a holding potential of −100 mV. Steady-state IA inactivation plots were determined from a holding potential of −120 mV and test potential of −30 mV (500 ms) to activate T-type calcium currents following 500-ms voltage presteps in 5-mV increments. (,) Mibefradil (MIB, 0.5 μM) selectively shifted IAVh by approximately −10 mV (P < 0.001, ) with no effect on Va (P > 0.05, ). () A test potential to +15 mV to recruit HVA calcium influx did not reverse the effect of mibefradil on Vh. () The shift in Vh by mibefradil was not reproduced by SNX-482 (0.6 μM) but was seen in mibefradil-treated perforated-patch (PP) recordings and in whole-cell recordings by bath perfusing low [Ca2+]o medium. The mibefradil-induced shift in Vh persisted in the presence of internal EGTA (10 mM) but is occluded by internal BAPTA (10 mM). *P < 0.05, **P < 0.01, ***P < 0.001. () Normalized plots of peak IA for steps to −30 mV from −80! mV or −120 mV before and after local ejection of 0.1 mM calcium aCSF (1 s, gray bar). Inset, representative current traces for steps from −80 mV in control (1) and low calcium medium (2). Scale bars represent 400 pA and 50 ms. Values are mean ± s.e.m. * Figure 2: Cav3 calcium channels are coupled to the Kv4 potassium channel complex. () Stellate cells expressed KChIP2, KChIP3 and DPL proteins. Scale bars represent 10 μm. () Co-immunoprecipitation of Kv4.2 channels with Cav3.2 and Cav3.3 calcium channels from either whole brain homogenate or lysate of tsA-201 cells co-transfected with Kv4.2 and Cav3.2 or Cav3.3. No co-immunoprecipitation was apparent between Kv4.2 and Cav2.2 calcium channels or on omission of the precipitating antibody (control). The brain homogenate and tsA-201 lysate controls were run on the same western blots as the corresponding co-immunoprecipitations but were subjected to longer exposure to visualize the input bands. Lysate (1) and (2), lysate controls for cells coexpressing Cav3.2 and Kv4.2 or Cav3.3 and Kv4.2, respectively. () Pulldown of Kv4.2 channels from brain homogenate with immobilized GST fusion proteins of the major intracellular regions of Cav3.2 and Cav3.3. () Co-immunoprecipitation of KChIP3 with Cav3.2 and Cav3.3 calcium channels from brain homogenate. () Pulldown of ! KChIP3 protein from tsA-201 lysate with immobilized GST fusion proteins of the C-terminal region of Cav3.2 or Cav3.3. Note that KChIP3 was pulled down by Cav3.2 and Cav3.3 C termini only when coexpressed with Kv4.2 protein. * Figure 3: Cav3-mediated calcium influx modulates Kv4 channel inactivation when coexpressed in tsA-201 cells. () Whole-cell recording from a tsA-201 cell expressing cDNAs for a complement of Cav3 and Kv4 complex subunits representative of those expressed in stellate cells. Currents were evoked in 10-mV increments from a holding potential of −100 mV. () Plots of the effects of mibefradil (MIB, 0.5 μM) application on Kv4.2 Vh when coexpressed with different complex members. Inactivation plots were obtained using a holding potential of −110 mV, conditioning steps (1 s) in 10-mV increments and a test potential of −30 mV. The baseline value for Kv4.2 expressed in isolation is shown and for a Kv4.2 and Cav3.3 combination either alone or when coexpressed with the indicated subunits. The mibefradil-induced shift in Vh was KChIP3-dependent and did not occur when the Cav3.3 subunit was substituted with a calcium-impermeable mutant Cav3.3(m) channel. ***P < 0.001. (,) Inactivation () and activation () plots for the complement of Cav3-Kv4 subunits indicated in with or without mibefradil ! application. () The Vh of a Kv4.2-KChIP3-DPP10c complex was unaffected when coexpressed with the HVA calcium channel Cav2.2 and the calcium current was blocked with ω-conotoxin GVIA (1 μM). Values are mean ± s.e.m. * Figure 4: KChIP3 proteins act as a calcium sensor in stellate cells to modulate IA window current and the gain of spike firing. () Vh of IA in stellate cells in response to mibefradil (MIB, 0.5 μM) or internal perfusion of antibodies to KChIP (1:100). Vh was significantly shifted only with mibefradil application or internal perfusion of pan-KChIP antibodies or either of two different antibodies to KChIP3 (shaded bars), but not by antibodies to KChIP2 or KChIP1 or denatured (boiled) pan-KChIP antibodies. KChIP3 (N), antibody to KChIP3 from Neuromab; KChIP3 (M), antibody to KChIP3 from Millipore. ***P < 0.001. (,) Current and conductance plots illustrating a pan-KChIP antibody–induced shift in IA steady-state inactivation () with no effect on activation (). () Expanded view of the window current available at subthreshold potentials in control (total gray area) and when Vh was left-shifted with internal pan-KChIP antibody perfusion (light gray area; plots taken from and ). Note the reduction in the A-type potassium current available between the trough of the AHP and spike threshold when Vh was shifte! d by internal pan-KChIP antibody. (,) Current-frequency plots () and gain () of stellate cell firing with or without internal perfusion of pan-KChIP or KChIP1 antibodies. *P < 0.05, **P < 0.01. () A reduced-firing model reproduced the increase in gain when Vh was shifted by −10 mV, emphasizing the ability of IA to regulate stellate cell firing dynamics. Values are mean ± s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Departments of Physiology & Pharmacology and Cell Biology & Anatomy, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada. * Dustin Anderson, * W Hamish Mehaffey, * Mircea Iftinca, * Renata Rehak, * Jordan D T Engbers, * Shahid Hameed, * Gerald W Zamponi & * Ray W Turner Contributions R.W.T. and G.W.Z. designed the study. D.A., M.I., J.D.T.E., H.W.M., S.H. and R.R. performed the experiments and analyzed the data. D.A., H.W.M., R.W.T. and G.W.Z. wrote the manuscript. Corresponding author Correspondence to: * Ray W Turner (rwturner@ucalgary.ca) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (480K) Supplementary Figures 1–8 and Supplementary Tables 1 and 2 Additional data - A neuronal role for SNAP-23 in postsynaptic glutamate receptor trafficking
- Nature neuroscience 13(3):338-343 (2010)
Nature Neuroscience | Article A neuronal role for SNAP-23 in postsynaptic glutamate receptor trafficking * Young Ho Suh1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Akira Terashima3 Search for this author in: * NPG journals * PubMed * Google Scholar * Ronald S Petralia4 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert J Wenthold4 Search for this author in: * NPG journals * PubMed * Google Scholar * John T R Isaac3 Search for this author in: * NPG journals * PubMed * Google Scholar * Katherine W Roche1 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul A Roche2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:338–343Year published:(2010)DOI:doi:10.1038/nn.2488 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Regulated exocytosis is essential for many biological processes and many components of the protein trafficking machinery are ubiquitous. However, there are also exceptions, such as SNAP-25, a neuron-specific SNARE protein that is essential for synaptic vesicle release from presynaptic nerve terminals. In contrast, SNAP-23 is a ubiquitously expressed SNAP-25 homolog that is critical for regulated exocytosis in non-neuronal cells. However, the role of SNAP-23 in neurons has not been elucidated. We found that SNAP-23 was enriched in dendritic spines and colocalized with constituents of the postsynaptic density, whereas SNAP-25 was restricted to axons. In addition, loss of SNAP-23 using genetically altered mice or shRNA targeted to SNAP-23 led to a marked decrease in NMDA receptor surface expression and NMDA receptor currents, whereas loss of SNAP-25 did not. SNAP-23 is therefore important for the functional regulation of postsynaptic glutamate receptors. View full text Figures at a glance * Figure 1: SNAP-25 and SNAP-23 are differentially expressed in neurons. (–) Hippocampal neurons were labeled with purified rabbit SNAP-23 and mouse monoclonal SNAP-25 antibodies as indicated. Alexa 488–conjugated secondary antibody to rabbit was used to visualize endogenous SNAP-23 () and Alexa 568–conjugated secondary antibody to mouse to visualize SNAP-25 (). Merged images are shown (). Images were collected with a confocal immunofluorescence microscope with 60× objective and maximal projections are shown as described in the Online Methods. The lower panels show higher-magnification images of the individual processes boxed in the upper panel images. Scale bar represents 20 μm. () Hippocampal neurons were labeled with antibody to SNAP-23 and mouse antibody to MAP2. Alexa 488–conjugated secondary antibody was used to visualize SNAP-23 and Alexa 568–conjugated secondary antibody to visualize MAP2. Scale bar represents 20 μm. () Subcellular fractionation revealed that the distribution of SNAP-23 differed from that of SNAP-25. We loade! d and immunoblotted 20 μg of protein from each subcellular fraction as indicated. LP1, synaptosomal membrane fraction; LP2, crude synaptic vesicle-enriched fraction; LS1, synaptic vesicle and cytosolic supernatant fraction; LS2, synaptosomal-cytosolic fraction; P2, crude synaptosomal fraction; P3, microsomal membrane fraction; S3, cytosolic fraction; SPM, synaptic plasma membrane. () Developmental expression of SNAP-23 in the brain. Crude synaptosomes (P2 fraction) from mouse hippocampus or cortex were solubilized and subjected to immunoblottings with the indicated antibodies. * Figure 2: Endogenous SNAP-23 is enriched at excitatory synapses on dendritic spines. () Hippocampal neurons were stained with purified rabbit antibody to SNAP-23 and Alexa 568–conjugated secondary antibody to rabbit, followed by a 5-min incubation with Phalloidin–Alexa 488 to visualize F-actin. Scale bar represents 20 μm. (–) Hippocampal neurons were labeled with rabbit antibody to SNAP-23 and mouse monoclonal antibodies to PSD-95, synaptobrevin/VAMP-2, gephyrin or NR1, as indicated. Alexa 488–conjugated secondary antibody to rabbit was used to visualize endogenous SNAP-23 and Alexa 568–conjugated secondary antibody to mouse was used for PSD-95 (), synaptobrevin/VAMP-2 (), gephyrin () and NR1 (). Right, higher-magnification images of the individual processes boxed in the larger images. Scale bars represent 20 μm. () Immunogold electron microscopy labeling of endogenous SNAP-23. Gold particles (arrowheads) labeled endogenous SNAP-23 in postsynaptic spines in the CA1 stratum radiatum. p, presynaptic terminal; * indicates PSD. Scale bars represent 1! 00 nm. * Figure 3: NMDA receptor surface expression is reduced in SNAP-23 heterozygous mice. () Targeting strategy to remove Snap23 exon 2 (E2) containing the initiator ATG. The structures of the wild-type (WT) Snap23 gene and the targeting construct are shown. Exons E1 to E5 are represented as black boxes. Using a genomic clone harboring the Snap23 gene, a targeting vector containing the neomycin resistance gene (NEO) flanked by loxP sites and the thymidine kinase (TK) gene was generated. Following homologous recombination in embryonic stem cells, targeted heterozygous mice containing the targeted allele were obtained. Mice harboring the Snap23 E2-deleted allele were generated by breeding with EIIa-cre transgenic mice. () Southern blot analysis of genomic DNA isolated from tails of Snap23 E2-targeted mice. EcoRI-digested genomic DNA was hybridized with the 5′ probe. () Genomic PCR to detect the targeted and wild-type alleles from tail DNA of E2-targeted mice. () Expression of SNAP-23 was analyzed by immunoblotting whole brain lysates of SNAP-23 heterozygous (Het)! mice and wild-type littermates. () Expression of glutamate receptors in the P2 crude synaptosome fraction from hippocampus of SNAP-23 heterozygous and wild-type littermates. () Surface expression of glutamate receptors was analyzed using a surface biotinylation assay in primary cortical neurons from SNAP-23 heterozygous and wild-type littermates. Surface receptors were isolated by precipitation using Streptavidin-agarose beads and immunoblotted with the indicated antibodies. () Quantitation of the immunoblots was performed by measuring the band intensity of the biotinylated fraction compared with the intensity of total input using ImageJ software (US National Institutes of Health). Graphs represent means ± s.e.m. *P < 0.01 (n = 3–5; P = 0.0077 for NR2A, 0.0097 for NR2B, 0.0095 for NR1, 0.4130 for GluR1, 0.7772 for GluR2, 0.1706 for GABA(A) α1 and 0.4740 for mGluR7). * Figure 4: SNAP-23, but not SNAP-25, regulates surface expression of NMDA receptors. () Primary hippocampal neurons (DIV 5–7) were transduced with scrambled, SNAP-23 or SNAP-25 shRNA lentivirus for 7 d. Surface expression of glutamate receptors was evaluated using a surface biotinylation assay. () Quantitation of the immunoblots was performed by measuring the band intensity of the biotinylated fraction compared with the band intensity of total input using ImageJ software. Graphs represent means ± s.e.m. **P < 0.01 and *P < 0.05 (n = 5; P = 0.0004 (NR2A), 0.0078 (NR2B), 0.0004 (NR1), 0.0103 (GluR1) and 0.0176 (GluR2) for SNAP-23 shRNA; P = 0.4526 (NR2A), 0.4727 (NR2B), 0.9965 (NR1), 0.7723 (GluR1) and 0.9977 (GluR2) for SNAP-25 shRNA compared with scrambled shRNA). * Figure 5: SNAP-23 regulates the recycling of the NMDA receptor subunit NR2B. (,) Internalization () and recycling () of NR2B-containing NMDA receptors. Scrambled or SNAP-23 shRNA lentivirus–transduced primary hippocampal neurons were transfected with GFP-NR2B at 12 DIV. Surface, internalized and recycled receptor populations were labeled as described in the Online Methods. The lower panels show higher-magnification images of the individual processes boxed in the upper images. Scale bars represent 20 μm. () Internalized receptors () are presented as the percentage of internalized compared with the total (surface and internalized) fraction. Graphs represent means ± s.e.m. P > 0.5 (n > 45 neurons from 3 independent experiments; P = 0.5709). () Recycled receptors () are presented as the percentage of recycled receptor compared with the total (internalized and recycled) fraction. Graphs represent means ± s.e.m. **P < 0.01 (n > 60 neurons from 4 independent experiments; P = 0.009). * Figure 6: Knockdown of SNAP-23 causes a reduction in NMDA-evoked currents and NMDA EPSCs in CA1 pyramidal neurons. () Mean amplitude of direct current recorded from voltage-clamped CA1 pyramidal neurons in cultured hippocampal slices at a holding potential of −40 mV in response to a 5-min bath application of NMDA (50 μM, indicated by black bar) for cells expressing SNAP-23 shRNA, SNAP-25 shRNA or scrambled shRNA (n = 8 for all). The peak inward current in SNAP-23 shRNA cells was significantly reduced compared with scrambled shRNA (P < 0.05). () Mean amplitude of NMDA EPSCs evoked in CA1 pyramidal neurons expressing SNAP-23 shRNA and from in-slice uninfected control neurons at a holding potential of +40 mV using the same stimulus position and intensity (n = 6, *P < 0.05). Top, example traces. Author information * Abstract * Author information * Supplementary information Affiliations * Receptor Biology Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA. * Young Ho Suh & * Katherine W Roche * Experimental Immunology Branch, National Cancer Institute, Bethesda, Maryland, USA. * Young Ho Suh & * Paul A Roche * Developmental Synaptic Plasticity Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA. * Akira Terashima & * John T R Isaac * National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland, USA. * Ronald S Petralia & * Robert J Wenthold Contributions P.A.R. and K.W.R. designed and supervised the experiments and wrote the manuscript. Immunogold electron microscopy was performed by R.S.P. and R.J.W. The electrophysiology study was carried out by A.T. and J.T.R.I. All other experiments were performed by Y.H.S. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Paul A Roche (paul.roche@nih.gov) or * Katherine W Roche (rochek@ninds.nih.gov) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (12M) Supplementary Figures 1–6 and Supplementary Methods Additional data - Connectivity reflects coding: a model of voltage-based STDP with homeostasis
- Nature neuroscience 13(3):344-352 (2010)
Nature Neuroscience | Article Connectivity reflects coding: a model of voltage-based STDP with homeostasis * Claudia Clopath1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lars Büsing1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Eleni Vasilaki1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Wulfram Gerstner1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:344–352Year published:(2010)DOI:doi:10.1038/nn.2479 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Electrophysiological connectivity patterns in cortex often have a few strong connections, which are sometimes bidirectional, among a lot of weak connections. To explain these connectivity patterns, we created a model of spike timing–dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants. Our model describes several nonlinear effects that are observed in STDP experiments, as well as the voltage dependence of plasticity. We found that, in a simulated recurrent network of spiking neurons, our plasticity rule led not only to development of localized receptive fields but also to connectivity patterns that reflect the neural code. For temporal coding procedures with spatio-temporal input correlations, strong connections were predominantly unidirectional, whereas they were bidirectional under rate-coded input with spatial correlations only. Thus, variable connect! ivity patterns in the brain could reflect different coding principles across brain areas; moreover, our simulations suggested that plasticity is fast. View full text Figures at a glance * Figure 1: Illustration of the model. Synaptic weights react to presynaptic events (top) and postsynaptic membrane potential (bottom). () The synaptic weight was decreased if a presynaptic spike x (green) arrived when the low-pass-filtered value ū− (magenta) of the membrane potential was above θ– (dashed horizontal line). () The synaptic weight was increased if the membrane potential u (black) was above a threshold θ+ and the low-pass-filtered value of the membrane potential ū+ (blue) was higher than a threshold θ– and the presynaptic low-pass filter (orange) was nonzero. () Step current injection made the postsynaptic neuron fire at 50 Hz in the absence of presynaptic stimulation (membrane potential u in black). No weight change was observed. Note the depolarizing spike afterpotential, consistent with experimental data. () Reproduced from ref. 16. (–) Voltage-clamp experiment. A neuron received weak presynaptic stimulation of 2 Hz during 50 s while the postsynaptic voltage was clamped to values bet! ween −60 mV and 0 mV. (–) Schematic drawing of the trace (orange) of the presynaptic spike train (green) as well as the voltage (black) and the synaptic weight (blue) for hyperpolarization (), slight depolarization () and large depolarization (). () The weight change as a function of clamped voltage using the standard set of parameters for visual cortex data (dashed blue line, voltage paired with 25 spikes at the synapse). With a different set of parameters, the model fit the experimental data (red circles) in hippocampal slices10 (see Online Methods for details). * Figure 2: Fitting the model to experimental data. (,) Simulated STDP experiments. () Spike timing–dependent learning window: synaptic weight change for different time intervals T between pre- and postsynaptic firing using 60 pre-post-pairs at 20 Hz. () Weight change as a function of pairing repetition frequency ρ using pairings with a time delay of +10 ms (pre-post, blue) and −10 ms (post-pre, red). Dots represent data from ref. 16 and lines represent our plasticity model. (–) Interaction of voltage and STDP. (–) Schematic induction protocols (green, presynaptic input; black, postsynaptic current; blue, evolution of synaptic weight). () Low-frequency potentiation is rescued by depolarization16. Low-frequency (0.1 Hz) pre-post spike pairs yielded LTP if a 100-ms-long depolarized current was injected around the pairing. () LTP failed if an additional brief hyperpolarized pulse was applied 14 ms before postsynaptic firing so that voltage is brought to rest. () Hyperpolarization preceding action potential prevents pote! ntiation that normally occurred at 40 Hz16. () The simulated postsynaptic voltage u (black) is shown after using the protocol described in , together with temporal averages ū− (magenta) and ū+ (blue). The presynaptic spike time is indicated by the green arrow. Using the model (equation (3)) with this setting yielded potentiation. () Data are presented as in but using the protocol described in . No weight change was measured. () Data are presented as in but using the protocol described in . No weight change was measured. () Histogram summarizing the normalized synaptic weight of the simulation (bar) and the experimental data16 (dot, blue bar indicates variance) for 0.1-Hz pairing (control 1), 0.1-Hz pairing with the depolarization (protocol ), 0.1-Hz pairing with the depolarization and brief hyperpolarization (protocol ), 40-Hz pairing (control 2), and 40-Hz pairing with the constant hyperpolarization (protocol ); parameters are described in Table 1. * Figure 3: Burst timing–dependent plasticity. One presynaptic spike was paired with a burst of postsynaptic spikes. This pairing was repeated 60 times at 0.1 Hz. () Normalized weight as a function of the number of postsynaptic spikes (1, 2, 3) at 50 Hz (dots represent data from ref. 30, crosses represent simulation). The presynaptic spike was paired +10 ms before the first postsynaptic spike (blue) or −10 ms after (red). () Normalized weight as a function of the frequency between the three postsynaptic action potentials (dots indicate data, lines indicate simulation, blue indicates pre-post, red indicates post-pre). () Normalized weight as a function of the timing between the presynaptic spike and the first postsynaptic spike of a three-spike burst at 50 Hz (dot indicates data, black lines indicate simulation). A hard upper bound was set to 250% normalized weight. The dashed line and crosses and the dotted line and stars represent simulations with alternative sets of parameters, ALTD = 21 ×10−5 mV−1, ALTP = 50 ×! 10—4 mV−2, τx = 143 ms, τ– = 6 ms, τ+ = 5 ms and ALTD = 21 ×10—5 mV−1, ALTP = 67 ×10—4 mV−2, τx = 5 ms, τ– = 8 ms, τ+ = 5 ms, respectively. Shading indicates reachable data points generated by the model with different parameters. * Figure 4: Weight evolution in an all-to-all connected network of ten neurons. () Rate code. Neurons fired at different frequencies, neuron 1 at 2 Hz, neuron 2 at 4 Hz, neuron 10 at 20 Hz. The weights (bottom) averaged over 100 s indicate that neurons with high firing rates developed strong bidirectional connections (light blue, weak connections (under 2/3 of the maximal value); yellow, strong unidirectional connections (above 2/3 of the maximal value); brown, strong bidirectional connections). The cluster is schematically represented (after). () Temporal code. Neurons fired successively every 20 ms (neuron 1, followed by neuron 2 20 ms later, followed by neuron 3 20 ms later, etc). Connections (bottom) were unidirectional with strong connections from presynaptic neuron with index n (vertical axis) to postsynaptic neuron with index n + 1, n + 2 and n + 3, leading to a ring-like topology. (,) Data are presented as in and , but we used a standard STDP rule12, 14, 19. Bidirectional connections are impossible. * Figure 5: Plasticity during rate coding. () A network of ten excitatory (light blue) and three inhibitory neurons (red) received feedforward inputs from 500 Poisson spike trains with a Gaussian profile of firing rates. The center of the Gaussian was shifted randomly every 100 ms (schematic network before (left) and after the plasticity experiment (right)). The temporal evolution of the weights are shown (top, small amplitudes of plasticity; bottom, normal amplitudes of plasticity; left, feedforward connections onto neuron 1; right, recurrent connections onto neuron 1). (–) Learning with small amplitudes. We used the parameters detailed in Table 1b (visual cortex) except for the amplitudes ALTP and ALTD, which were reduced by a factor 100. () Mean feedforward weights (left) and recurrent excitatory weights (right) averaged over 100 s. The feedforward weights (left) indicate that the neurons developed localized receptive fields (light gray). The recurrent weights (right) were classified as weak (less than 2/3 of th! e maximal weight, light blue), strong unidirectional (more than 2/3 of the maximal weight, yellow) or strong reciprocal (brown) connections. The diagonal is white, as self-connections do not exist in the model. () Data are presented as in , but the neuron index was reordered. () Three snapshots of the recurrent connections taken 5 s apart indicate that recurrent connections were stable. () Histogram of reciprocal, unidirectional and weak connections in the recurrent network averaged over 100 s, as shown in (fluc, fluctuations). The total number of weight fluctuations during 100 s was zero. The histogram shows an average of ten repetitions (error bars represent s.d.). (–) Rate code during learning with normal amplitudes. We used the network described above but with a standard set of parameters (Table 1b, visual cortex). () Receptive fields were localized. () Reordering showed clusters of neurons with bidirectional coupling. These clusters were stable when averaged over 100! s. () Connections were able to change from one time step to t! he next. () The percentage of reciprocal connections was high, but because of fluctuations, more than 1,000 transitions between strong unidirectional to strong bidirectional or back occurred during 100 s. * Figure 6: Temporal-coding procedure. The same parameters were used as in Figure 5 (Table 1b, visual cortex), but input patterns were moved successively every 20 ms, corresponding to a step-wise motion of the Gaussian stimulus profile across the input neurons. () The schematic figure shows the network before and after the plasticity experiment. Shown are the temporal evolution of the weights (top panels: amplitude of synaptic plasticity for feedforward connections reduced by a factor of 100; left, feedforward weights onto neuron 6; right, lateral connections onto neuron 6; bottom panels: normal amplitude of plasticity; left, feedforward connections onto neuron 1; right, temporal evolution of asymmetry index of connection pattern (gray line indicates asymmetrical index for simulation; Fig. 5). Positive values indicate the weights from neurons n to n + k are stronger than those from n to n − k for 1 ≤ k ≤ 3). () Receptive fields are localized (left). The recurrent network developed a ring-like structure with! strong unidirectional connections from neuron 8 (vertical axis) to neurons 9 and 10 (horizontal axis), etc. (small amplitudes of plasticity). () Data are presented as in , but normal plasticity values were used. () Some of the strong unilateral connections appeared or disappeared from one time step to the next, but the ring-like network structure persisted, as the lines just above the diagonal are much more populated than the line below the diagonal. () Reciprocal connections are absent, but unidirectional connections fluctuated several times between weak and strong during 100 s. * Figure 7: Receptive fields development. () A small patch of 16 × 16 pixels was chosen from the whitened natural images benchmark35. The patch was selected randomly and was presented as input to 512 neurons for 200 ms. The positive part of the image was used as the firing rate to generate Poisson spike trains of the 256 ON inputs and the negative one for the 256 OFF inputs. () The weights after convergence are shown for the ON inputs and the OFF inputs rearranged on a 16 × 16 image. The filter was calculated by subtracting the OFF weights from the ON weights. The filter was localized and bimodal, corresponding to an oriented receptive field. () Temporal evolution of the weights shown in the red dashed box in . () Nine different neurons. () Two different neurons receiving presynaptic input with varying firing rates from 0–25 Hz (top), 0–37.5 Hz (middle) and 0–75 Hz (bottom). Author information * Abstract * Author information * Supplementary information Affiliations * Laboratory of Computational Neuroscience, Brain-Mind Institute and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. * Claudia Clopath, * Lars Büsing, * Eleni Vasilaki & * Wulfram Gerstner * Present address: Institut für Grundlagen der Informationsverarbeitung, Graz University of Technology, Austria (L.B.), and Department of Computer Science, University of Sheffield, Sheffield, UK (E.V.). * Lars Büsing & * Eleni Vasilaki Contributions C.C. developed the model and carried out the experiments. L.B. and E.V. participated in discussions. W.G. supervised the project and wrote most of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Claudia Clopath (claudia.clopath@epfl.ch) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (804K) Supplementary Figures 1–3 and Supplementary Methods Additional data - Functional organization and population dynamics in the mouse primary auditory cortex
- Nature neuroscience 13(3):353-360 (2010)
Nature Neuroscience | Article Functional organization and population dynamics in the mouse primary auditory cortex * Gideon Rothschild1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Israel Nelken1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Adi Mizrahi1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:353–360Year published:(2010)DOI:doi:10.1038/nn.2484 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Cortical processing of auditory stimuli involves large populations of neurons with distinct individual response profiles. However, the functional organization and dynamics of local populations in the auditory cortex have remained largely unknown. Using in vivo two-photon calcium imaging, we examined the response profiles and network dynamics of layer 2/3 neurons in the primary auditory cortex (A1) of mice in response to pure tones. We found that local populations in A1 were highly heterogeneous in the large-scale tonotopic organization. Despite the spatial heterogeneity, the tendency of neurons to respond together (measured as noise correlation) was high on average. This functional organization and high levels of noise correlations are consistent with the existence of partially overlapping cortical subnetworks. Our findings may account for apparent discrepancies between ordered large-scale organization and local heterogeneity. View full text Figures at a glance * Figure 1: In vivo two-photon calcium imaging from dozens of neurons simultaneously in A1. () In vivo two-photon micrograph of a single optical plane in A1 after bolus loading of Fluo-4 a.m. (green) and SR101 (red). This optical plane is 381 μm below the pia. Scale bar represents 10 μm. () A drawing of the path of the line scan that was chosen to image 27 cells from . () Relative changes in fluorescence (ΔF/F) of the 27 neurons shown in and during presentation of a stimuli series (not shown). Scale bars represent 1 s and 50% ΔF/F. () A single calcium transient enlarged from (red dotted box). Scale bars represents 250 ms and 10% ΔF/F. () Left, a fluorescent micrograph of a coronal slice following electroporation of dextran-rhodamine into A1 (green arrow). Middle, micrograph of a coronal slice of the medial geniculate body (MGB) from the same mouse. Labeled axons project to the ventral MGB (yellow arrow). Right, high-resolution micrograph of axonal projections in the ventral MGB shown in the middle panel. Scale bar represents 1,200 μm (left), 600 μm (middle) ! and 20 μm (right). * Figure 2: Identification of spike-induced calcium transients. () In vivo two-photon micrograph of a patch pipette loaded with Alexa 594 attached to a Fluo-4–loaded neuron. Scale bar represents 10 μm. () Simultaneous imaging and loose-patch traces of spontaneous activity in vivo (upper and middle traces, respectively). Bottom ticks indicate the events identified by the algorithm as being spike evoked. Scale bars represent 1 s and 20% ΔF/F. () A plot of transient amplitude as a function of the number of evoking spikes (n = 120). Means are marked in red (paired t test, **P < 0.01, ns indicates not significant, P > 0.05). () A single calcium transient labeled with the parameters used by the algorithm: transient amplitude (vertical line) and transient area (dashed area). () Top, distributions of transient amplitude and transient area. Scale bars represent 20% ΔF/F and 10 arbitrary units. Bottom, examples of raw calcium traces taken from the distribution above. Scale bars represent 30% ΔF/F and 2 s. Distributions and traces are shown f! or an astrocyte, neuropil, nonactive neuron and an active neuron. Red circles denote the events identified as being spike triggered by the algorithm. Black dots in the distribution correspond to black dots in the traces. () A representative example of a calcium signal (top) with its corresponding weighted events (bottom). Scale bars represent 20% ΔF/F and 600 ms. * Figure 3: Single-trial and mean response profiles to pure tones. () A two-photon micrograph of Fluo-4–loaded neurons from a single optical plane. Scale bar represents 10 μm. () A 15-s representative example of relative change in fluorescence from five neurons imaged simultaneously (neurons in indicated by arrows). Dots mark tone presentations. Color codes for frequency of stimulation. Scale bars represent 1 s and 30% ΔF/F. () FRAs of the five neurons shown in and . () Single trial responses of the five neurons (same neurons as in –) to four different stimuli. Columns mark neurons 1–5 from left to right and rows mark different stimuli. Each panel indicates the responses to all trials of the specific stimulus. Black lines mark the stimulus. Scale bars represent 100 ms and 20% ΔF/F. Insets show the weighted events that were identified by the algorithm for these traces. SPL, sound pressure level. * Figure 4: Functional micro-architecture in A1 is heterogeneous. () Two examples of FRA maps from different mice. Each map is constructed from a single optical plane of neurons that were imaged simultaneously. Each FRA is drawn at the location of the neuron from which it was derived. Scale bar represents 10 μm. () A post-stimulus time histogram matrix representation of two FRAs marked as 1 and 2 in showing that nearby neurons can have very similar response profiles. Each cell in the matrix is the sum of all events that occurred following the presentations of the corresponding stimulus. () Data are presented as in for the FRAs marked 3 and 4 in showing that close by neurons can have very different response profiles. () Two additional FRA maps as in from two different experiments. Scale bar represents 10 μm. * Figure 5: Local populations in A1 are not organized tonotopically. () Distribution histogram of best frequencies from all neurons in the dataset with a clear best frequency (n = 241 neurons). () Diagrams of neuronal locations coded by their best frequency. Rows correspond to two different experiments. Left and center panels show the positions of the neurons, color coded according to their best frequency in a side view and a top view, respectively. Right, plots of the neurons best frequencies and their relative distances along the rostro-caudal axis (CR). LM, lateral-medial. () Data are presented as in , but for a mouse with two injection sites, covering a larger cortical surface. () Difference in best frequency as a function of distance between pairs of neurons, data grouped from all experiments (n = 3,783 pairs). Distance is measured in the two-dimensional rostro-caudal, lateral-medial plane. * Figure 6: Signal correlations between neurons in local networks are low on average and are variable and decrease with distance. () Distribution histogram of the signal correlation values between all pairs in the dataset of simultaneously imaged neurons (gray bars) and for shuffled FRAs (black line) (n = 3,926 pairs). () Schematic presentation of signal correlation values between a representative group of neurons from one mouse in one optical plane. Each FRA is drawn at the location of the neuron from which it was derived. Color of lines between each pair of FRAs codes the signal correlation value. Scale bar represents 20 μm. SC, signal correlation. () Signal correlation values as a function of distance between neurons from a single experiment (blue dots and line, n = 835 pairs). Red line is the best linear fit to the data from all the pooled data (n = 3926 pairs, n = 11 mice). () Data are presented as in but after randomly shuffling each FRA. * Figure 7: Noise correlations in local networks are high on average and variable and decrease with distance. () Raster plots of the calcium event responses of two pairs of neurons to all 760 stimuli. Left, a pair of neurons with low noise correlation (−0.03). Right, a pair of neurons with high noise correlation (0.51). Each row corresponds to −100 ms to +400 ms after stimulus onset. Calcium events are marked by ticks (red or black for the two neurons). Gray bars mark the time window considered a response. () Distribution histogram of noise correlation values between all simultaneously imaged neurons (n = 3,926 pairs). () A plot of noise correlation during ongoing activity as a function of noise correlation during auditory stimulation for all pairs of simultaneously imaged neurons (n = 3,926 pairs). The red line indicates the best linear fit to the data. () A plot of noise correlation as a function of distance between neurons from a single experiment (blue dots and line, n = 835 pairs). The red line indicates the best linear fit to the data from all the pooled data. () A plot of! noise correlation as a function of signal correlation for all pairs in a single experiment (blue dots and line, n = 835 pairs). The red line indicates the best linear fit of all the pooled data. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurobiology, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel. * Gideon Rothschild, * Israel Nelken & * Adi Mizrahi * The Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem, Israel. * Gideon Rothschild, * Israel Nelken & * Adi Mizrahi Contributions G.R., I.N. and A.M. designed the experiments together and wrote the paper together. G.R. performed the experiments and analyzed the data. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Adi Mizrahi (mizrahia@cc.huji.ac.il) Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Movie 1 (6M) Image stack from A1 following Fluo4-AM and SR101 loading. PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–7 and Supplementary Discussion Additional data - Dichotomy of functional organization in the mouse auditory cortex
- Nature neuroscience 13(3):361-368 (2010)
Nature Neuroscience | Article Dichotomy of functional organization in the mouse auditory cortex * Sharba Bandyopadhyay1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Shihab A Shamma2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick O Kanold1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:361–368Year published:(2010)DOI:doi:10.1038/nn.2490 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The sensory areas of the cerebral cortex possess multiple topographic representations of sensory dimensions. The gradient of frequency selectivity (tonotopy) is the dominant organizational feature in the primary auditory cortex, whereas other feature-based organizations are less well established. We probed the topographic organization of the mouse auditory cortex at the single-cell level using in vivo two-photon Ca2+ imaging. Tonotopy was present on a large scale but was fractured on a fine scale. Intensity tuning, which is important in level-invariant representation, was observed in individual cells, but was not topographically organized. The presence or near absence of putative subthreshold responses revealed a dichotomy in topographic organization. Inclusion of subthreshold responses revealed a topographic clustering of neurons with similar response properties, whereas such clustering was absent in supra-threshold responses. This dichotomy indicates that groups of nearby ! neurons with locally shared inputs can perform independent parallel computations in the auditory cortex. View full text Figures at a glance * Figure 1: Functional two-photon Ca2+ imaging in mouse ACX. (,) Confirmation of craniotomy and imaging site in ACX by anterograde labeling. Choleratoxin-B (CTB) was injected into the medial geniculate body (MGB) stereotactically (). A craniotomy was performed at our imaging locations. Fluorescently labeled terminals were imaged at three depths of 205, 310 and 410 μm from the cortical surface (). Following imaging of terminals in vivo, slices were cut to confirm tracer injection in the MGB (). () Images taken at different depths in two mice are superimposed. Note that most signals originated at a depth of 300–400 μm, indicating the thalamo-recipient layer. Thus, our imaging location was in A1. Scale bars represent 100 μm (,). () Images of bulk-loading ACX with OGB-1 (left) and Fluo-4 (right). The area over which cells were loaded varied in experiments from 200-μm- to 1-mm-diameter regions. Scale bars represent 50 μm (left) and 10 μm (right). () Single trial (black) and mean (red) fluorescence changes (ΔF/F) with SAM broadband! noise from four different cells. Error bars represent 95% confidence intervals, indicating a significant fluorescence change. Frame timing is shown below the fluorescence trace. * Figure 2: ACX Ca2+ responses are unreliable. () Traces show mean fluorescence changes (ΔF/F) in one cell for SAM noise of varying intensity. Significant responses at 0–30 dB attenuations were seen in this case. Error bars represent 95% confidence intervals. () Thresholded fluorescence changes (ΔF/F) (color bar to right) in all individual trials for the same cell. Gray backgrounds depict stimulation period. Note the unreliability, but larger ΔF/F, of single trials. () Cumulative distribution of mean reliability in all imaged cells with Fluo-4 (black) and OGB-1 (green). Mean reliability was defined as the fraction of trials with significant responses (Online Methods) in each cell for a set of stimuli (either different intensities of SAM noise or different frequencies of SAM tone or noise or tone pips). () Cumulative distribution of the fraction of responsive cells in each imaged field (same mouse as in ). A responsive cell was defined as a cell that responded to at least one of the presented stimuli significantly (9! 5% confidence interval). () Cumulative distribution of maximum responses in each field. The mean maximum response was higher with OGB-1 (4.8%) than Fluo-4 (3.3%, **P < 0.05, t test, same mouse as in and ). * Figure 3: Large-scale organization of ACX probed with single-cell resolution. () Traces show responses (ΔF/F) of single neurons in A1 to SAM tones of various stimulus frequencies (duration indicated by gray area). Plotted is the mean response with 95% confidence intervals. Right, plotting peak ΔF/F versus stimulation frequency (tuning curve) showed unimodal tuning (characteristic frequency (CF) = 32 kHz, *). () Tuning curves from eight cells imaged in one mouse (locations indicated in ). Characteristic frequency progresses from cell 4 to 8. () Reconstructions of the large-scale organization of ACX in one mouse by imaging multiple sites. The relative distances (approximate) between centers of imaged sites (gray boxes) are indicated. Relative positions not to scale. Color denotes characteristic frequency (color bar in ) based on peak ΔF/F and luminance response strength. For example, cells tuned to 38 kHz are shown in orange. Strongly responding cells are bright orange, whereas weakly responding cells are dark orange. Different regions of ACX were id! entified on the basis of characteristic frequency and tuning curve shape (): A1, dorsal posterior (DP) and ultra frequency (UF). () Progression of cell characteristic frequency as a function of rostro-caudal position. Red line indicates best fit. () Large-scale organization of mouse ACX redrawn from ref. 22. Box indicates putative location of imaging site in A1 (). Because of inter-animal variation22 exact positions of ultra frequency and dorsal posterior relative to A1 in are slightly different. This is a rough depiction, as clear demarcations of the different regions are lacking. Scale bar represents 250 μm. () Post-hoc verification of A1 imaging site () by 1,1′-dioctadecyl-3,3,3′3′-tetramethylindocarbocyanine perchlorate (DiI) injection. MGB was retrogradely labeled. Damage on imaging site was from DiI crystal insertion after imaging. Scale bar represents 1 mm. * Figure 4: Tonotopy exists in A1 and AAF on a large scale, but not on small spatial scales. () Reconstruction of four imaging sites (relative positions are approximate) in A1 in one mouse. Cell characteristic frequency is indicated by color and increases from caudal (~10 kHz) to rostral (~23 kHz). Cells at opposite A1 ends can have similar characteristic frequencies (arrows). Positive characteristic frequency gradient (inset) indicates that the imaging site was in A1. () Reconstruction of five imaging sites (relative positions are approximate) in ACX in one mouse (different from ). A negative characteristic frequency gradient (maximum slope after rotation and exclusion of neurons that were in secondary region, inset) indicates that the imaging site was in AAF. () Slope of characteristic frequency gradient in rostro-caudal direction from fits to characteristic frequencies and their respective cells' location (see , and Fig. 3d). As a result of inter-animal variability and slight differences in animal position, the characteristic frequency gradient that was fit could! occur at an angle from the rostro-caudal axis. Positive or negative characteristic frequency slopes indicate A1 or AAF, respectively. Because the entire extent of ACX was not covered in each mouse, characteristic frequency slopes are an estimate of the large-scale characteristic frequency progression. () Cumulative distribution of characteristic frequency variability (s.d. of characteristic frequency normalized by number of cells in imaged field) in ACX. Variability (median 0.025 octaves per cell, 34 cells per site, average of 0.85 octaves per ~100 μm2) was similar for OGB-1 and Fluo-4 (P > 0.1; OGB-1, n = 15; Fluo-4, n = 24 mice). () d′ analysis of sharpness of tonotopy. d′ is the mean characteristic frequency difference at different A1 locations normalized by the s.d. d′ = 1 indicates that the mean characteristic frequencies can be discriminated. * Figure 5: High local variability in bandwidth. () Color-coded plot of bandwidth variation in A1 for the leftmost field in Figure 3c shows varied bandwidth in nearby cells. Scale bar represents 20 μm. () Large-scale reconstruction of bandwidths is shown for the area imaged for the example in Figure 4a; this case shows similar heterogeneity of bandwidths as in . () Cumulative distribution of variability of bandwidth in imaging sites showed that variability was lower for cells imaged with OGB-1 than with Fluo-4 (0.007 and 0.01, **P < 10−4, n = 15 animals with OGB-1 and 24 animals with Fluo-4). Variability was measured as the s.d. of bandwidths normalized by the number of cells in the field of view. * Figure 6: Intensity tuning and local heterogeneity in noise responses. () Traces show maximum mean fluorescence changes to SAM noise stimuli with increasing intensity (decreasing attenuation) in three cells. Cells had monotonic (top) and nonmonotonic (middle and bottom) intensity-tuning curves. Nonmonotonic tuning curves were identified by a significant decrease in ΔF/F at higher intensities. Error bars represent 95% confidence intervals. () Cells in one imaging site (top left) that responded to noise at a particular intensity (activation plot). Intensity levels are given as dB attenuation. The brightness of the circles indicates the response strength (maximum mean ΔF/F) (color bar, right). Note that different populations of cells responded at each intensity. Scale bar represents 20 μm. () The number of cells activated at various intensities, indicating a nonmonotonic population representation of sound intensity. () Superposition of three of the activation plots in , identified by colored squares. Colors of cells depict which combination of ! the three (red, green and blue squares) intensities a cell responded to (for example, white indicates all three intensities and cyan indicates the green and blue intensities). Note that the response properties of nearby cells were heterogeneous. * Figure 7: Lack of organized intensity maps. () Reconstruction of five imaging sites in ACX (boxes) of one mouse depicting the best intensities of cells in response to SAM broadband noise. The colors of the circles indicate the preferred intensity (color bar). There was no clear pattern of organization of best intensities. The traces on the left show intensity functions of nine cells indicated in the reconstruction. * denotes the best intensity. Note that some cells had monotonic intensity functions, whereas others had nonmonotonic intensity functions. () Cumulative distribution of best intensity variability in an imaging site. Variability was lower for cells imaged with OGB-1 than those imaged with Fluo-4 (**P < 10−5, rank sum, n = 15 animals with OGB-1 and 24 animals with Fluo-4). Values were normalized by the number of cells in the field of view. () Cumulative population distribution of average percentage of cells in five neighboring cells in a field of view that had the same preferred intensity as the central cel! l. OGB-1 had a higher (P < 10−10) percentage of cells on average (30%) with the same preferred intensity as the central cell than did Fluo-4 (15%). Cartoon on the left shows how the local percentages were computed (same population as in ). * Figure 8: ACX cells receive shared inputs, but respond differentially. () Mean response characteristics (centroids) of four clusters formed in (lower right image, label color indicates cluster). () Examples of cluster formation to SAM tone or noise sets with OGB-1 (upper) and Fluo-4 (lower). Cells in each cluster are shown in the same color. The within- to inter-cluster distance ratios indicating the degree of cluster separation were 0.358 and 0.988 for OGB-1 and 0.871 and 0.590 for Fluo-4, respectively. () Cumulative distributions of cluster number and size (**P < 0.05, n = 15 mice with OGB-1, n = 24 with Fluo-4). () Fraction of neighbors in same cluster (numbers of cells in the same cluster from the five nearest neighbors). Analysis is shown (top) for two cells in (bottom left, black circles). Cumulative distributions (bottom) were different (Kolmogorov-Smirnov test, P < 10−8, same mouse as ). () In vitro current-clamp recordings and two-photon imaging of two cells filled with Fluo-4 and OGB-1, respectively. Inset shows image of one recorde! d cell (P, patch pipette). Traces show the membrane voltage (Vm, upper), current (Im, middle) and significant (outside 95% confidence interval) mean ΔF/F (ten repeats, lower). Red traces show the respective subthreshold depolarization (left) in suprathreshold traces. () Distributions of in vitro ΔF/F for the two categories for each cell (OGB-1, n = 10; Fluo-4, n = 7 cells): highest subthreshold depolarization tested and spiking. Means are indicated by red lines. Peak depolarization (22 ± 5 mV and 23 ± 7 mV for OGB-1 and Fluo-4, respectively, P = 0.65) and membrane charging times for OGB-1 and Fluo-4 were similar (P > 0.5). () Traces show Vm (top) and mean ΔF/F (bottom, 20 repeats) during electrical stimulation (four pulses, red lines) of horizontal inputs (cartoon). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Biology, University of Maryland, College Park, Maryland, USA. * Sharba Bandyopadhyay & * Patrick O Kanold * Institute for Systems Research, University of Maryland, College Park, Maryland, USA. * Sharba Bandyopadhyay, * Shihab A Shamma & * Patrick O Kanold * Department for Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA. * Shihab A Shamma Contributions S.B. performed the in vivo studies. S.B. and P.O.K. carried out the in vitro studies. P.O.K. planned and supervised the project. S.B., S.A.S. and P.O.K. contributed to the experimental design, discussed the results and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Patrick O Kanold (pkanold@umd.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–6 Additional data - Stimulus onset quenches neural variability: a widespread cortical phenomenon
Churchland MM Yu BM Cunningham JP Sugrue LP Cohen MR Corrado GS Newsome WT Clark AM Hosseini P Scott BB Bradley DC Smith MA Kohn A Movshon JA Armstrong KM Moore T Chang SW Snyder LH Lisberger SG Priebe NJ Finn IM Ferster D Ryu SI Santhanam G Sahani M Shenoy KV - Nature neuroscience 13(3):369-378 (2010)
Nature Neuroscience | Article Stimulus onset quenches neural variability: a widespread cortical phenomenon * Mark M Churchland1, 2, 16 Search for this author in: * NPG journals * PubMed * Google Scholar * Byron M Yu1, 2, 3, 16 Search for this author in: * NPG journals * PubMed * Google Scholar * John P Cunningham1 Search for this author in: * NPG journals * PubMed * Google Scholar * Leo P Sugrue2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Marlene R Cohen2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Greg S Corrado2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * William T Newsome2, 4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew M Clark6 Search for this author in: * NPG journals * PubMed * Google Scholar * Paymon Hosseini6 Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin B Scott6 Search for this author in: * NPG journals * PubMed * Google Scholar * David C Bradley6 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew A Smith7 Search for this author in: * NPG journals * PubMed * Google Scholar * Adam Kohn8, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * J Anthony Movshon9 Search for this author in: * NPG journals * PubMed * Google Scholar * Katherine M Armstrong2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Tirin Moore2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Steve W Chang10 Search for this author in: * NPG journals * PubMed * Google Scholar * Lawrence H Snyder10 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen G Lisberger11 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas J Priebe12 Search for this author in: * NPG journals * PubMed * Google Scholar * Ian M Finn13 Search for this author in: * NPG journals * PubMed * Google Scholar * David Ferster13 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen I Ryu1, 14 Search for this author in: * NPG journals * PubMed * Google Scholar * Gopal Santhanam1 Search for this author in: * NPG journals * PubMed * Google Scholar * Maneesh Sahani3 Search for this author in: * NPG journals * PubMed * Google Scholar * Krishna V Shenoy1, 2, 15 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:369–378Year published:(2010)DOI:doi:10.1038/nn.2501 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property! of cortex, that its state is stabilized by an input. View full text Figures at a glance * Figure 1: Schematic illustration of possible types of across-trial firing rate variability. (–) We suppose that the same stimulus is delivered four times (four trials) yielding four different responses. and were constructed to have the same mean response across the four trials. Stimulus-driven decline in variability is shown in . Stimulus-driven rise in variability is shown in . Stimulus-driven decline in variability with little change in mean rate is shown in . * Figure 2: Analysis of intracellularly recorded membrane potential from cat V1. Stimuli were drifting sine-wave gratings presented at different orientations and frequencies. Spikes were removed before further analysis. Analysis employed a 50-ms sliding window (box filter) to match the 50-ms window used for the Fano factor analysis. Similar results were obtained with a shorter (5-ms) or longer (100-ms) window. () Data from one example neuron. Vm for individual trials (black) is plotted on top of the mean (gray). Data are shown when no stimulus was delivered, for a nonpreferred stimulus and for a preferred stimulus. The arrow marks stimulus onset. () Similar plot for a second example neuron. () The mean and variance of Vm across all 52 neurons and all stimuli. Flanking traces give s.e.m. * Figure 3: Changes in firing-rate variability for ten datasets (one per panel). Insets indicate stimulus type. Data are aligned on stimulus onset (arrow). For the two bottom panels (MT area/direction and MT speed), the dot pattern appeared at time zero (first arrow) and began moving at the second arrow. The mean rate (gray) and the Fano factor (black with flanking s.e.) were computed using a 50-ms sliding window. For OFC, where response amplitudes were small, a 100-ms window was used to gain statistical power. Analysis included all conditions, including nonpreferred. The Fano factor was computed after mean matching (Fig.4). The resulting stabilized means are shown in black. The mean number of trials per condition was 100 (V1), 24 (V4), 15 (MT plaids), 88 (MT dots), 35 (LIP), 10 (PRR), 31 (PMd), 106 (OFC), 125 (MTdirection and area) and 14 (MT speed). * Figure 4: Illustration of how the mean-matched Fano factor was computed. Data are from the MT plaids dataset. () Spike rasters for the 46 trials (one per line) recorded from one MT neuron (127) for one stimulus condition (upwards-moving plaid). Shaded areas show four locations of the sliding window, which moved in 10-ms increments. For each window location, the spike count was computed for each trial. The mean and variance (across trials) of that count then contributed one data point to the subsequent analysis. () The Fano factor was computed from scatter plots of the spike-count variance versus mean. Each scatter plot corresponds to a window in . Each point plots data for one neuron and condition (red indicates the neuron and condition from ). The orange line has unity slope, the expected variance-to-mean relationship for Poisson spiking. Data above the orange line is consistent with the presence of underlying-rate variability. Gray dots show all data. Gray lines are regression fits to all data (constrained to pass through zero, weighted accordi! ng to the estimated s.e.m. of each variance measurement). Gray distributions are of mean counts. These appear to have different areas because of the vertical log scale. Black points are those preserved by mean matching (Online Methods). Black distributions are thus identical to within bin resolution. Black lines are regression slopes for the mean-matched data. () The Fano factor versus time. Arrows indicate time points from the panels above. The gray trace (with flanking s.e.) plots the raw Fano factor, the slope of the gray lines from . The black trace plots the mean-matched Fano factor, the slope of the black lines. * Figure 5: Changes in the Fano factor after restricting the analysis to combinations of neuron and condition with little change in mean rate (for example, nonpreferred conditions). (–) The raw Fano factor for four datasets, computed based on nonresponsive conditions. Of the original neuron conditions (the response of one neuron to one condition), this analysis preserved 28% (MT, ), 49% (PRR, ), 27% (PMd, ) and 41% (MT-speed, ). A 100-ms window (rather than 50 ms) was employed to regain lost statistical power. The trace at the top of each panel shows the mean rate, averaged across all included neurons and conditions. The trace with flanking s.e. shows the Fano factor, computed with no further mean matching. Arrows indicate stimulus onset. For the MT speed dataset () the stimulus appeared at the very start of the record (first arrow) and began moving 256 ms later (second arrow). * Figure 6: Application of factor analysis to data from V1 and PMd. () Factor analysis was applied to covariance matrices (number of neurons × number of neurons) of spike counts, taken in an analysis window that either ended at stimulus onset (prestimulus) or began just after stimulus onset (stimulus). The measured covariance matrix was approximated as the sum of a network covariance matrix and a diagonal matrix of private noise. To produce the plots in –, we averaged network variances across the subset of neuron and condition combinations (48% and 30% for V1, 74% and 79% for PMd) whose distribution of mean rates was matched before and after stimulus onset (similar to Fig.4). () Estimated variances for one V1 dataset. Network variability declined more than private variability in both absolute (P < 10−7) and relative (percent of initial value, P < 10−7) terms (paired t tests across conditions). () Similar plot for a V1 dataset from a second monkey (P < 0.002, absolute; P < 0.002, relative). () Summary comparison for V1. Changes in vari! ability (stimulus–prestimulus) were expressed in percentage terms. Data to the left of zero indicate that network variability underwent the larger decline. The distribution includes all conditions and both datasets. The mean and s.e. are given by the black symbol at top (P < 10−7 compared with zero, paired t test). Gray symbols give individual means for each dataset. () Data are presented as in and but for one PMd dataset (G20040123). Network variability declined more in absolute (P < 0.005) and relative (P < 0.001) terms. () Similar plot for a second PMd dataset (G20040122; P < 0.05, absolute; P < 0.02, relative). () Summary comparison for PMd (distribution mean <0, P < 10−4). () Relationship between mean firing rate and network level (shared firing rate) variance. Data (same dataset as ) were binned by mean rate and the average network variance (± s.e.) was computed for each bin. This was done both before stimulus (gray) and after stimulus onset (black). The averag! e was taken across neurons and conditions (each datum being av! eraged was, for one condition, one element of the blue diagonal in ). Distributions of mean rates are shown at bottom. The analysis in was based on the overlapping (mean matched) portion of these distributions. () Similar plot for PMd (same dataset as ). See Online Methods and Supplementary Figure 6 for a description of datasets. * Figure 7: Individual-trial neural trajectories computed using GPFA. () Projections of PMd activity into a two-dimensional state space. Each black point represents the location of neural activity on one trial. Gray traces show trajectories from 200 ms before target onset until the indicated time. The stimulus was a reach target (135°, 60 mm distant), with no reach allowed until a subsequent go cue. 15 (of 47) randomly selected trials are shown. The dataset is the same as in Figure 6e. () Trajectories were plotted until movement onset. Blue dots indicate 100 ms before stimulus (reach target) onset. No reach was allowed until after the go cue (green dots), 400–900 ms later. Activity between the blue and green dots thus relates to movement planning. Movement onset (black dots) was ~300 ms after the go cue. For display, 18 randomly selected trials are plotted, plus one hand-selected trial (red, trialID 211). Covariance ellipses were computed across all 47 trials. This is a two-dimensional projection of a ten-dimensional latent space. In the fu! ll space, the black ellipse is far from the edge of the blue ellipse. This projection was chosen to accurately preserve the relative sizes (on a per-dimension basis) of the true ten-dimensional volumes of the ellipsoids. Data are from the G20040123 dataset. () Data are presented as in , with the same target location, but for data from another day's dataset (G20040122; red trial, trialID 793). * Figure 8: Projections of V1 activity into a two-dimensional space using GPFA. Blue, black and red traces show activity before, during and after stimulus presentation (a drifting 45° grating). Data are from the dataset used in Figure 6c. () The mean trajectory and three trials picked by hand. The gray spot shows the average location of prestimulus activity. In a few cases (for example, upper left portion of the rightmost panel), traces were moved very slightly apart to make it clear that they traveled in parallel rather than crossed. () Trajectories after data were shuffled to remove correlated variability. 25 randomly selected trials are plotted (lighter traces) along with the mean (saturated traces). () Data are presented as in but for the original unshuffled data. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Mark M Churchland & * Byron M Yu Affiliations * Department of Electrical Engineering, Stanford University School of Medicine, Stanford University, Stanford, California, USA. * Mark M Churchland, * Byron M Yu, * John P Cunningham, * Stephen I Ryu, * Gopal Santhanam & * Krishna V Shenoy * Neurosciences Program, Stanford University School of Medicine, Stanford University, Stanford, California, USA. * Mark M Churchland, * Byron M Yu, * Leo P Sugrue, * Marlene R Cohen, * Greg S Corrado, * William T Newsome, * Katherine M Armstrong, * Tirin Moore & * Krishna V Shenoy * Gatsby Computational Neuroscience Unit, University College London, London, UK. * Byron M Yu & * Maneesh Sahani * Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford University, Stanford, California, USA. * Leo P Sugrue, * Marlene R Cohen, * Greg S Corrado & * William T Newsome * Department of Neurobiology, Stanford University School of Medicine, Stanford University, Stanford, California, USA. * William T Newsome, * Katherine M Armstrong & * Tirin Moore * Department of Psychology and Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, USA. * Andrew M Clark, * Paymon Hosseini, * Benjamin B Scott & * David C Bradley * Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Matthew A Smith * Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, USA. * Adam Kohn * Center for Neural Science, New York University, New York, New York, USA. * Adam Kohn & * J Anthony Movshon * Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri, USA. * Steve W Chang & * Lawrence H Snyder * Howard Hughes Medical Institute, W.M. Keck Foundation Center for Integrative Neuroscience and Department of Physiology, University of California San Francisco, San Francisco, California, USA. * Stephen G Lisberger * Section of Neurobiology, School of Biological Sciences, University of Texas at Austin, Austin, Texas, USA. * Nicholas J Priebe * Department of Neurobiology and Physiology, Northwestern University, Evanston, Illinois, USA. * Ian M Finn & * David Ferster * Department of Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford, California, USA. * Stephen I Ryu * Department of Bioengineering, Stanford University, Stanford, California, USA. * Krishna V Shenoy Contributions M.M.C. wrote the manuscript, performed the Fano factor and factor analyses and created the figures. GPFA was developed by B.M.Y., J.P.C., M.S. and K.V.S. This application of factor analysis was devised by M.M.C. and B.M.Y. The mean-matched Fano factor was developed by M.M.C. and K.V.S. The conception for the study arose from conversations between M.M.C., K.V.S., B.M.Y., D.C.B., M.R.C., W.T.N. and J.A.M. V1 data (extracellular) were collected in the laboratory of J.A.M. by M.A.S. and A.K. and in the laboratory of A.K. V4 data were collected in the laboratory of T.M. by K.M.A. MT (plaid) data were collected in the laboratory of D.C.B. by A.M.C., P.H. and B.B.S. MT (dots) data were collected in the laboratory of W.T.N. by M.R.C. LIP and OFC data were collected in the laboratory of W.T.N. by L.P.S. using an experimental design developed by L.P.S. and G.S.C. PRR data were collected in the laboratory of L.H.S. by S.W.C. PMd data were collected in the laboratory of K.V.S. by B.M.Y.! , S.I.R., G.S. and M.M.C. MT (direction/area and speed) data were collected by N.J.P. and M.M.C. in the laboratory of S.G.L. Intracellularly recorded V1 data were collected by N.J.P. and I.M.F. in the laboratory of D.F. All authors contributed to manuscript revisions and editing, particularly J.A.M., W.T.N., L.P.S., D.F., J.P.C., B.M.Y. and K.V.S. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mark M Churchland (church@stanford.edu) Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (492K) A movie version of Figure 7a. Data are from PMd, and show the decline in across-trial variance after the onset of the stimulus (a reach target). The movie spans 750 ms, beginning 400 ms before stimulus onset and ending 350 ms after. The movie ends before the go cue is given. Each black dot shows the state of PMd on one trial. Fifteen randomly-chosen trials are shown. Dots turn blue for a brief moment at the time of stimulus onset. Note the subsequent drop in the variance of the dot locations (i.e., a drop in firing-rate variance). This feature of the response is at least as clear as the change in mean dot location (i.e., the change in mean firing rates). G20040123 dataset. * Supplementary Video 2 (1M) As in Supplementary Video 1, but more time is shown and the trajectory of the RT-outlier trial is now included (red). The movie spans ~1500 ms. This time-span differs slightly across trials, as they have different go-cue and movement-onset times. At the time of the go cue, each dot turns green and further progress is halted. Progress resumes once all trials have passed the time of their respective go cues. This re-aligns the data to the go cue, much as is commonly done in PSTH's. Traces end at movement onset. * Supplementary Video 3 (508K) As in Supplementary Video 1, but for the G20040122 PMd dataset (that shown in Figure 7c). * Supplementary Video 4 (1M) As in Supplementary Video 2, but for the G20040122 PMd dataset (that shown in Figure 7c). PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–8 and Supplementary Notes 1–3 Additional data - Perceptual decision making in less than 30 milliseconds
Stanford TR Shankar S Massoglia DP Costello MG Salinas E - Nature neuroscience 13(3):379-385 (2010)
Nature Neuroscience | Article Perceptual decision making in less than 30 milliseconds * Terrence R Stanford1 Search for this author in: * NPG journals * PubMed * Google Scholar * Swetha Shankar1 Search for this author in: * NPG journals * PubMed * Google Scholar * Dino P Massoglia1 Search for this author in: * NPG journals * PubMed * Google Scholar * M Gabriela Costello1 Search for this author in: * NPG journals * PubMed * Google Scholar * Emilio Salinas1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:379–385Year published:(2010)DOI:doi:10.1038/nn.2485 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In perceptual discrimination tasks, a subject's response time is determined by both sensory and motor processes. Measuring the time consumed by the perceptual evaluation step alone is therefore complicated by factors such as motor preparation, task difficulty and speed-accuracy tradeoffs. Here we present a task design that minimizes these confounding factors and allows us to track a subject's perceptual performance with unprecedented temporal resolution. We find that monkeys can make accurate color discriminations in less than 30 ms. Furthermore, our simple task design provides a tool for elucidating how neuronal activity relates to sensory as opposed to motor processing, as demonstrated with neural data from cortical oculomotor neurons. In these cells, perceptual information acts by accelerating and decelerating the ongoing motor plans associated with correct and incorrect choices, as predicted by a race-to-threshold model, and the time course of these neural events paralle! ls the time course of the subject's choice accuracy. View full text Figures at a glance * Figure 1: Sequence of events in the compelled-saccade task. A trial is correct if the subject makes an eye movement to the peripheral location that matches the color of the fixation spot (red in this example). The subject must initiate a response (left or right) when the fixation spot disappears (Go), although target and distracter are revealed after a gap of 50–250 ms (Cue). * Figure 2: Oculomotor execution during the compelled-saccade task. (,) Eye velocity () and eye position () as functions of time for 30 saccades performed by monkey S in short-gap (50–100 ms) trials. Only horizontal components are shown. Black lines are single-trial traces; gray lines are averages. Numbers shown are mean peak velocity and mean width at half-height ± s.e.m. (,) Eye velocity () and eye position () as functions of time for 30 saccades performed by monkey S in long-gap (200–250 ms) trials. (–) As in –, except for 30 short-gap (,) and 30 long-gap (,) trials performed by monkey G. * Figure 3: Behavioral and model performance in the compelled-saccade task. () Percentage of correct responses as a function of time gap (psychometric curve). Numbers of trials per point are 568 ≤ n ≤ 598 for monkey S and 702 ≤ n ≤ 777 for monkey G. () Mean reaction time (RT) ± 1 s.d. as a function of gap (chronometric curve). Each point includes both correct and incorrect trials. () Distributions of ePT values for correct (black and blue bars) and incorrect (magenta lines) trials. () Percentage of correct responses as a function of ePT (tachometric curve). () Distributions of reaction time values at five gaps. Gap values are indicated on upper left corners. RTs for correct (black and blue bars) and incorrect (magenta lines) trials are shown. In and , bin width is 20 ms; in it is 40 ms. All results labeled 'Model' are from simulated trials generated with identical parameter values for each monkey. * Figure 4: Five trials of the race-to-threshold model. Each plot shows the decision variables xL (green) and xR (red) as functions of time. Black triangles and vertical lines mark when the go signal is given (Go) and when the saccade is initiated (Sac); the interval between them is the reaction time. In these examples, xL and xR start racing 60 ms (afferent delay) after the go signal and a saccade is produced 30 ms (efferent delay) after threshold (dotted line) is crossed. Initially, build-up rates are drawn randomly and remain constant during the gap period (gray shade), but once the cue information becomes available (end of gray shade), the build-up rate for the target side (xR in these examples) starts increasing and that for the distracter side starts decreasing. (–) Three trials with a 100-ms gap. (,) Two trials with a 250-ms gap. In all examples the target was red and was on the right, so , and are correct and and are incorrect trials. Horizontal bars at the bottom indicate the ePT period in each trial; ePT values are po! sitive (black) for races that are influenced by the sensory information (–) and negative (gray) for races that end before the cue information becomes available (,). * Figure 5: Behavioral and model performance in the motor-bias experiment. Trials are sorted according to choices, either toward the high-reward side (black) or the low-reward side (orange). () Fractions of saccades made to the high- and low-reward sides as functions of gap (330 ≤ n ≤ 361 trials per gap). () Percentages of correct choices as functions of gap. () Mean reaction times (RTs) ± 1 s.d. as functions of gap. () Distributions of ePT values for correct (black bars) and incorrect (gray lines) responses toward the high-reward side. () Distributions of ePT values for correct (orange bars) and incorrect responses (gray lines) toward the low-reward side. () Percentages of correct responses as functions of ePT for high- (black lines) and low- (orange lines) reward trials. () For each ePT, the curves show the fraction of all saccades (orange lines) or of all correct saccades (gray lines) made to the low-reward side. ePT bin size is 20 ms. * Figure 6: Oculomotor activity during the compelled-saccade task. () Tachometric curve obtained from all recording sessions; n = 7,282 trials. The x axis is rPT (rPT = reaction time – gap). Shaded areas indicate short- and long-rPT groups. () Mean time courses of the decision variables xL and xR synchronized on threshold crossing (dashed line), with saccades assumed to occur 30 ms later (triangles and vertical lines). Correct model responses into (red) and away from (green) the movement field are shown for short (left side) and long (right side) rPTs. () Responses of a single FEF neuron during correct trials into the movement field, for short (left side) and long (right side) rPTs. Each panel shows spike trains from 30 trials synchronized on saccade onset (triangles and vertical lines). Firing rates as functions of time (red traces) were obtained by convolving the spikes with a Gaussian of σ = 6 ms. The key on the right indicates the positions of the movement field (gray patch), target (filled circle) and distracter (open circle). () Re! sponses from the same cell as in but for correct saccades away from the movement field. () Average firing rates of 30 FEF neurons as functions of time. For each cell, activity was normalized by the firing rate at saccade onset in short-rPT trials into the movement field. Light colors indicate ± 1 s.e.m. Note differences between short- (left side) and long-rPT (right side) responses. * Figure 7: Sensory information accelerates oculomotor activity. () The mean convexity c between two points on a curve is computed as the average difference between the line that joins those points (dotted line) and the curve values (continuous traces). (,) Mean convexity of the trajectories of the model variables xL and xR as a function of rPT. Red and green lines indicate correct trials into and away from the movement field, respectively. Insets above show mean trajectories obtained in two rPT bins (keyed by a circle and a diamond). Shaded areas indicate the interval used to calculate convexity. () Tachometric curve from all recording sessions. Arrow indicates the transition point at which the curve starts increasing. Horizontal line across arrow indicates error s.d. (from jackknife; see Online Methods). (,) Mean convexity of the FEF population activity as a function of rPT, for correct trials into (red) and away from (green) the movement field. Light colors indicate ± 1 s.e.m. Arrows indicate transition points and horizontal lines are! error s.d. (from jackknife). Before averaging across cells, rates were normalized as in Figure 6e. Other conventions as in ,. rPT bin size is 40 ms. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA. * Terrence R Stanford, * Swetha Shankar, * Dino P Massoglia, * M Gabriela Costello & * Emilio Salinas Contributions T.R.S. conceived the task, supervised all experiments and data analyses, and co-wrote the manuscript; S.S. contributed to the collection, analysis and modeling of the behavioral data; D.P.M. contributed to the design of the experiments and to the collection of behavioral data; M.G.C. contributed to the collection of behavioral data and to the collection and analysis of neural data; E.S. developed the race model, contributed to the experimental design and data analysis and co-wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Emilio Salinas (esalinas@wfubmc.edu) Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Movie 1 (5M) PDF files * Supplementary Text and Figures (224K) Supplementary Figures 1–5 and Supplementary Notes 1–7 Additional data - Ultrafast optogenetic control
- Nature neuroscience 13(3):387-392 (2010)
Nature Neuroscience | Technical Report Ultrafast optogenetic control * Lisa A Gunaydin1, 2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Ofer Yizhar1, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * André Berndt3, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Vikaas S Sohal1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Karl Deisseroth1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Hegemann3 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:387–392Year published:(2010)DOI:doi:10.1038/nn.2495 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Channelrhodopsins such as channelrhodopsin-2 (ChR2) can drive spiking with millisecond precision in a wide variety of cells, tissues and animal species. However, several properties of this protein have limited the precision of optogenetic control. First, when ChR2 is expressed at high levels, extra spikes (for example, doublets) can occur in response to a single light pulse, with potential implications as doublets may be important for neural coding. Second, many cells cannot follow ChR2-driven spiking above the gamma (~40 Hz) range in sustained trains, preventing temporally stationary optogenetic access to a broad and important neural signaling band. Finally, rapid optically driven spike trains can result in plateau potentials of 10 mV or more, causing incidental upstates with information-processing implications. We designed and validated an engineered opsin gene (ChETA) that addresses all of these limitations (profoundly reducing extra spikes, eliminating plateau potentials! and allowing temporally stationary, sustained spike trains up to at least 200 Hz). View full text Figures at a glance * Figure 1: Rational design of a fast channelrhodopsin. () Whole-cell current-clamp recordings from a parvalbumin interneuron strongly expressing wild-type ChR2; note the extra spikes, the missed spike later in the train, and the prolonged depolarizations observed after termination of each 2-ms light pulse. () Left, homology model of ChR2 based on the bacteriorhodopsin X-ray structure (1KGB, RSCB protein data bank). The retinal is shown in violet, conserved residues in green and substitutions in ChR2 in gray. Oxygen is red, nitrogen is blue and sulfur is yellow. Right, formation of the complex RSB counterion was predicted to be disturbed by replacement of E123 with threonine (right). Candidate hydrogen bonds are shown as dotted lines. () Recovery of peak current in wild-type (WT) ChR2 and E123 mutants on stimulation with a second light pulse after a variable dark period, recorded in oocytes. Inset, peak recovery of wild-type ChR2 after 2 s in darkness at −75 mV in standard solution (100 mM NaCl, pH 7.5). The ratio of ΔI2 to Δ! I1 yields the recovery in percent. Values for wild-type ChR2 (black), E123Q (blue), E123T (red) and E123D (green) are plotted versus the dark interval between the two flashes; data points were fit by a mono-exponential decay (colored lines). () Voltage-clamp recordings of wild-type ChR2, E123D, E123Q and E123T under stimulation with a 1-s light pulse (blue bars, 500 ± 25 nm, 50 mW cm−2) at −100 mV and 100 mM NaCl in the external solution (pH 7.5). τ values characterize the mono-exponential transition from peak to stationary currents (τin) and current kinetics (τoff) after the light was switched off. Vertical black bars represent 10 nA. * Figure 2: Photocurrent properties of E123T in oocytes and cultured neurons. () Oocyte current recordings. Excitation was delivered with a 5-ns laser flash (470 nm, 0.5 mW cm−2, blue arrow) at −100 mV. Photocurrent decay was fitted mono-exponentially with τ values shown for wild-type ChR2 (black) and E123T (red) and traces were normalized to highlight differences in decay kinetics. Vertical bars correspond to 2.5 nA (wild type, black; E123T, red). () The action spectrum of E123T (red, n = 6) is shown in comparison with spectra of wild-type ChR2 (black, n = 6), E123D (green, n = 3) and E123Q (blue, n = 4). Current amplitudes were measured at the different wavelengths, normalized to respective maximum values and corrected for fluctuations of the flash intensity. () Current-voltage relationships of wild-type ChR2 and E123T at different external pH values in presence of 100 mM Na+. Approximate current amplitudes at time zero (I0, left) and steady state (I∞, right) were normalized to the steady-state value for −100 mV at pH 7.5. Note that the pH ! dependence of the I0 was larger for E123T. () Photocurrents evoked by 2-ms pulses of 470-nm light recorded from cultured hippocampal neurons expressing wild-type ChR2 (black) and ChR2(E123T) (red). Vertical bars correspond to 20 pA (wild type, black; E123T, red). () Left, summary of photocurrent amplitudes recorded as in . Right, summary of steady state–to-peak current ratio recorded from cultured hippocampal neurons stimulated with 1-s, 470-nm light pulses at −70 mV. * Figure 3: Frequency-response performance: spiking to 200 Hz. () Schematic of the adeno-associated virus (AAV) vector construct carrying a loxP/lox2722-flanked opsin gene. The resulting virus was injected into Pvalb-cre mice to achieve parvalbumin-specific opsin gene expression. () Confocal images of wild type (left) and ChETA (right) expression in prefrontal parvalbumin interneurons. Scale bars represent 20 μm. () Left, photocurrents evoked by a 1-s pulse of 472-nm blue light in prefrontal cortical parvalbumin interneurons. Traces are normalized to the peak photocurrent amplitude to illustrate the increase in the steady state–to-peak current ratio in ChETA (red) compared with wild-type ChR2 (black). Right, summary of steady-state current amplitudes in response to 1-s blue light pulses measured in parvalbumin interneurons that also expressed wild-type ChR2 (black) and ChETA (red), represented as mean ± s.e.m. (n = 11 and 8 for wild type and ChETA, respectively). () Expanded photocurrents from normalized to steady-state current had ! accelerated off-kinetics in ChETA (red). () Whole-cell current-clamp recordings from parvalbumin interneurons that also expressed wild-type ChR2 (black) or ChETA (red) in response to 20-Hz light stimulation (472-nm, 2-ms pulse widths); note the higher percentage and temporal stationarity of successful spikes evoked with ChETA. () Summary of the percentage of successful spikes evoked over a range of light-pulse frequencies from 5–200 Hz in parvalbumin interneurons expressing wild-type ChR2 (black) or ChETA (red). () Summary of the percentage of successful spikes evoked with 1-, 2- and 5-ms light pulse widths, plotted at 10, 60 and 100 Hz from . To determine whether light pulses were significantly more effective at eliciting spiking in ChETA-expressing cells than in wild-type ChR2-expressing cells, we computed the two-way ANOVA for each pulse width, comparing the percentage of successful spikes from many cells and using genotype (ChETA versus wild type) and frequency (5–2! 00 Hz) as factors. We found a significant effect of genotype o! n the percentage of successful spikes fired, with ChETA outperforming wild-type ChR2 for both light-pulse widths (P < 0.001 for 2-ms and 5-ms pulse widths, n = 11 wild-type and 8 ChETA cells). * Figure 4: Multiple dimensions of enhanced ChETA performance. () Whole-cell current-clamp recordings from parvalbumin interneurons expressing wild-type ChR2 (black) or ChETA (red) in response to 10-, 80- and 200-Hz light stimulation (rows 1, 2 and 3, respectively; all 472-nm, 2-ms light pulse widths). Note the brisker repolarizations and reduced extra spikes in ChETA-expressing interneurons. Scale bars apply to all traces. () Summary of extra spikes evoked by trains of 2-ms light pulses (total of 40 pulses) in parvalbumin interneurons expressing wild-type ChR2 (black) or ChETA (red), data averaged from cells that spiked to 100% to light pulses ranging from 5–200Hz (n = 5 and 7 for wild type and ChETA, respectively). () Summary of plateau potential amplitude in parvalbumin neurons that also expressed wild-type ChR2 or ChETA, measured from baseline to the trough between light pulses in the middle of the train (2-ms light pulses). () Spike waveforms in interneurons expressing wild-type ChR2 (black) and ChETA (red) evoked by Poisson puls! e trains consisting of 2-ms light pulses delivered at a mean rate of 40 Hz. Traces were chosen to match the resting membrane potential. Arrows denote extra spikes. Scale bars apply to both traces. () Responses of interneurons expressing wild-type ChR2 (black) and ChETA (red) to extended Poisson pulse trains with the same parameters as in ; note the reduced extra spikes, larger spike amplitudes and rapid repolarization. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Lisa A Gunaydin, * Ofer Yizhar & * André Berndt Affiliations * Department of Bioengineering, Stanford University, Stanford, California, USA. * Lisa A Gunaydin, * Ofer Yizhar, * Vikaas S Sohal & * Karl Deisseroth * Neuroscience Program, Stanford University, Stanford, California, USA. * Lisa A Gunaydin * Institute of Biology, Experimental Biophysics, Humboldt-University, Berlin, Germany. * André Berndt & * Peter Hegemann * Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA. * Vikaas S Sohal & * Karl Deisseroth Contributions All authors conceived and designed the experiments. L.A.G., O.Y., A.B. and V.S.S. conducted the experiments and contributed to the writing and analysis. K.D. and P.H. contributed to the writing and analysis, and supervised all aspects of the work. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Peter Hegemann (hegemape@rz.hu-berlin.de) or * Karl Deisseroth (deissero@stanford.edu.) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1 and 2 Additional data - Active flight increases the gain of visual motion processing in Drosophila
Maimon G Straw AD Dickinson MH - Nature neuroscience 13(3):393-399 (2010)
Nature Neuroscience | Technical Report Active flight increases the gain of visual motion processing in Drosophila * Gaby Maimon1 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew D Straw1 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael H Dickinson1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:393–399Year published:(2010)DOI:doi:10.1038/nn.2492 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We developed a technique for performing whole-cell patch-clamp recordings from genetically identified neurons in behaving Drosophila. We focused on the properties of visual interneurons during tethered flight, but this technique generalizes to different cell types and behaviors. We found that the peak-to-peak responses of a class of visual motion–processing interneurons, the vertical-system visual neurons (VS cells), doubled when flies were flying compared with when they were at rest. Thus, the gain of the VS cells is not fixed, but is instead behaviorally flexible and changes with locomotor state. Using voltage clamp, we found that the passive membrane resistance of VS cells was reduced during flight, suggesting that the elevated gain was a result of increased synaptic drive from upstream motion-sensitive inputs. The ability to perform patch-clamp recordings in behaving Drosophila promises to help unify the understanding of behavior at the gene, cell and circuit levels. View full text Figures at a glance * Figure 1: Patch-clamp recordings in tethered, flying Drosophila. () Apparatus. A schematic cutaway of the flight stage is shown. () Cartoon of the right side of the fly's brain with VS cells highlighted in green. () Immuno-amplified GFP signal in a fly expressing GFP driven by the Gal4-3a promoter (maximal z projection of a confocal stack). Only the lobula plate is shown. Scale bar represents 20 μm. () Immuno-amplified GFP signal (green) and a recorded, biocytin-filled VS1 neuron (red; maximal z projection of a two-photon stack). Scale bar is approximately 20 μm. * Figure 2: Behavioral measurements of wing stroke amplitude during tethered flight. () Wing stroke envelopes were visible in an infrared image from below the fly (left). Using image analysis, we extracted the maximum stroke amplitude of the two wings in each frame (yellow lines; Online Methods). () Sample traces of simultaneously acquired wing stroke amplitude measurements and a whole-cell patch-clamp recording. Black membrane voltage trace shows a digitally low pass–filtered version of the gray trace (fourth-order Butterworth, 25-Hz cutoff). () Mean stroke-amplitude responses to down and up moving gratings. These traces are drawn from the dataset in Figure 6 (45 flies were tested: 42 with both up and down motion, 1 with only down motion and 2 with only up motion). We only included cases in which the flies flew continuously throughout the trial. We averaged 752 traces for up motion and 293 traces for down motion. * Figure 3: Visual responses of VS cells are boosted and the resting potential depolarizes during flight. () Membrane voltage of a VS cell before, during and after flight. This fly stopped flying twice during the flight epoch shown (breaks in the infrared sensor trace), but immediately restarted each time following delivery of an air puff. The post-flight trace shares the same y axis scale and offset as the pre-flight/flight trace. D, down; DL, down-left; DR, down-right; L, left; R, right; U, up; UL, up-left; UR, up-right. () Mean responses of 33 VS neurons from the right lobula plate to the eight directions of grating motion. Note that the membrane potential before (red) and after (black) flight was quite similar, even though these data were collected ≥8 min apart. This stability of the membrane potential was typical of our recordings. () Tuning curves. Left, the mean voltage (±s.e.m.) for the eight stimuli in the final 2.8 s of the stimulus presentation period for the single cell from (top) and for the population (bottom). Middle, baseline-subtracted mean voltage responses ! to the eight stimuli (±s.e.m.). Standard errors of post-flight curves were very similar in magnitude to pre-flight curves and are not shown for clarity. Right, difference between baseline-subtracted responses in flight and pre-flight conditions, one point per cell, per stimulus. Distributions whose mean significantly differs from zero are shown in black (t test, Bonferroni-corrected threshold of P < 0.01). * Figure 4: During flight, passive membrane-resistance decreases and membrane voltage and current fluctuations increase. () Signal-averaged current traces measured before flight (n = 3 traces per voltage step), during flight (n = 3) and after flight (n = 4) in response to the family of voltage steps shown below (Online Methods). Arrows indicate unclamped action potentials. () Current-voltage (I-V) curves generated from the data in . We averaged the current and voltage traces over the final 25 ms to generate the steady-state values that are plotted. We fit a line based on the four most hyperpolarized voltage steps to generate estimates of the passive membrane resistance (1/slope = Rm) for pre-flight, flight and post-flight conditions. For clarity, only fits for pre-flight and flight are shown (dotted lines). This cell's Rm was 209 MΩ pre-flight, 187 MΩ during flight and 209 MΩ post-flight. () Left, mean (±s.e.m.) membrane resistances for pre-flight, flight and post-flight epochs (n = 12 cells). Right, distribution of the cell-by-cell differences in Rm between flight and pre-flight condit! ions. () Sample baseline Vm traces from a single neuron during flight and non-flight. () Using the dataset from Figure 6, we measured the s.d. of Vm in the 1-s baseline period preceding each stimulus and compared this value between pre-flight, flight and post-flight epochs (right). We required that the fly was flying for the entire duration of the 1-s baseline period, 3-s stimulus period and 1-s post-stimulus period for an s.d. measurement to be taken. At least one such trial was procured from 40 of the 45 cells. () Sample current traces from a single neuron held at −93 mV. () Mean s.d. (±s.e.m.) of current traces before, during and after flight. For each cell, we measured the s.d. of Im in the final 25 ms of hyperpolarized voltage steps (−123 to −73 mV) and averaged these values in each condition (n = 12 cells). * Figure 5: Other neurons in the central brain are not depolarized during flight. () Membrane voltage of a descending interneuron before, during and after flight. // indicates a break in the trace of the specified duration. This fly flew continuously for 17 min and the cell was tonically hyperpolarized during flight. The flight-induced hyperpolarization eliminated all spiking activity. The observation that some cells hyperpolarize with flight thus acts as a control to show that the tonic depolarization of VS cells is unlikely to be a trivial property of all brain cells during flight (or a mechanical artifact), but is instead a specific property of VS-cell circuitry. Biocytin fill (green) and nc-82 neuropil stain (magenta) are shown on the right. This cell sent its axon down to the thoracic ganglion (maximal intensity confocal projection). Scale bar represents 50 μm. () Membrane voltage of a cell that did not show a strong modulation as a result of flight. Top, cell at its natural resting potential of approximately −60 mV, at which it did not fire spont! aneous action potentials. This cell's membrane voltage was not strongly altered by flight. The large, saturating infrared sensor signal at the start of flight was a result of the sensor's gain set slightly too high; we lowered the gain mid-way through the flight bout. Bottom, we injected current to depolarize the neuron by 20 mV at the soma, which caused the cell to spike at ~2 Hz. When we induced flight, we observed no obvious change in spike rate. Biocytin fill (green) and nc-82 neuropil stain (magenta) are shown on the right (maximal intensity confocal projection). Scale bar represents 50 μm. * Figure 6: The baseline depolarization and visual-response boost have different recovery dynamics at the cessation of flight. () Responses of one VS cell (top) and mean responses of a population of 43 VS cells (bottom) to repeated presentations of downward moving grating (3-s stimulus, 2-s intertrial) before, during and after flight. Highlighted in light gray is the flight epoch, beginning with the start of the first grating presentation during flight and terminating with the end of the last grating presentation during flight. Digitally low pass–filtered traces (fourth-order Butterworth, 25-Hz cutoff) are shown in black. Periods of flight and nonflight, as well as the instances of air puffs (gray arrows), are indicated below the single-cell trace. The probability of flies flying (a value of 1 indicates all flies flew 100% of the time in that bin) and the puffing rate are indicated below the population-averaged trace. There is a discontinuity in the x axis because different flies flew for different lengths of time between the first four and last four stimuli (Online Methods). () Data are presented! as in , except we repeatedly presented an upward moving grating. The same example neuron is shown in and . () Quantification of the visual boost and baseline shift over time. For each stimulus, we measured the mean Vm in the final 2.8 s of the stimulus presentation period and subtracted the mean voltage from the 1 s immediately preceding the stimulus. The baseline-subtracted response over time (±s.e.m.) for downward and upward gratings is shown on the left. For the first stimulus presented during flight, we subtracted the baseline voltage following, not preceding, the stimulus; this was necessary because flight initiation was often induced only part way through the preceding intertrial period. For the final stimulus during flight we could not get an accurate estimate of the response strength (isolated black dot) because the flies typically stopped flying on their own at a variable time point in the last stimulus/intertrial period. The mean baseline voltage in the 1 s prec! eding each stimulus (±s.e.m.) is shown on the right. Author information * Abstract * Author information * Supplementary information Affiliations * Divisions of Biology and Engineering and Applied Sciences, California Institute of Technology, Pasadena, California, USA. * Gaby Maimon, * Andrew D Straw & * Michael H Dickinson Contributions G.M., A.D.S. and M.H.D. designed the experiments. G.M. and M.H.D. wrote the paper. G.M. developed the preparation, conducted the experiments and analyzed the data. A.D.S. designed the software and hardware system for tracking wing beat amplitudes in real time. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Gaby Maimon (maimon@caltech.edu) Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (4M) Fly flying during a patch-clamp recording. This video shows footage of a flying fly during an electrophysiological recording session. The green lines represent the estimates of the left and right wingbeat amplitudes. PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–4 Additional data
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