Hot off the presses! Feb 01 Nat Methods

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Latest Articles Include:

• All things being equal
  - Nat Methods 9(2):111 (2012)
  Nature Methods | Editorial All things being equal Journal name:Nature MethodsVolume: 9,Page:111Year published:(2012)DOI:doi:10.1038/nmeth.1891Published online 30 January 2012 Direct comparisons of tool or method performance under standardized experimental conditions yield highly valuable information for both method users and developers. View full text Additional data
• The author file: Susan Cox
  - Nat Methods 9(2):113 (2012)
  Nature Methods | This Month The author file: Susan Cox * Monya BakerJournal name:Nature MethodsVolume: 9,Page:113Year published:(2012)DOI:doi:10.1038/nmeth.1866Published online 30 January 2012 Using Bayesian statistics to speed super-resolution microscopy View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Points of view: Networks
  - Nat Methods 9(2):115 (2012)
  Nature Methods | This Month Points of view: Networks * Nils Gehlenborg1 * Bang Wong2 * AffiliationsJournal name:Nature MethodsVolume: 9,Page:115Year published:(2012)DOI:doi:10.1038/nmeth.1862Published online 30 January 2012 We describe graphing techniques to support exploration of networks. View full text Figures at a glance * Figure 1: Node-link diagrams. () A directed graph typical of a biological pathway. () An undirected graph with nodes arranged in a circle. () A spring-embedded layout of data from . * Figure 2: Adjacency matrices. () Nodes are ordered as rows and columns; connections are indicated as filled cells. () A matrix representation of data from Figure 1b. Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Nils Gehlenborg is a research associate at Harvard Medical School and the Broad Institute. * Bang Wong is the creative director of the Broad Institute of the Massachusetts Institute of Technology and Harvard and an adjunct assistant professor in the Department of Art as Applied to Medicine at The Johns Hopkins University School of Medicine. Competing financial interests The authors declare no competing financial interests. Author Details * Nils Gehlenborg Search for this author in: * NPG journals * PubMed * Google Scholar * Bang Wong Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Improved Mos1-mediated transgenesis in C. elegans
  - Nat Methods 9(2):117-118 (2012)
  Nature Methods | Correspondence Improved Mos1-mediated transgenesis in C. elegans * Christian Frøkjær-Jensen1, 2 * M Wayne Davis1 * Michael Ailion1, 3 * Erik M Jorgensen1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:117–118Year published:(2012)DOI:doi:10.1038/nmeth.1865Published online 30 January 2012 To the Editor: The ability to add or delete genes to the genome of genetic model organisms is essential. Previously, we had developed methods based on the Mos1 transposon1 to make targeted transgene insertions (Mos1-mediated single-copy transgene insertions; MosSCI2) and targeted deletions (Mos1-mediated deletions; MosDEL3) in Caenorhabditis elegans, the latter published in Nature Methods. Here we present new reagents that improve the efficiency, facilitate the selection for transgenic strains and expand the set of MosSCI insertion sites (Supplementary Table 1). View full text Subject terms: * Genetics * Molecular Engineering * Model Organisms * Gene Expression Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Howard Hughes Medical Institute, Department of Biology, University of Utah, Salt Lake City, Utah, USA. * Christian Frøkjær-Jensen, * M Wayne Davis, * Michael Ailion & * Erik M Jorgensen * Department of Biomedical Sciences and Danish National Research Foundation Centre for Cardiac Arrhythmia, University of Copenhagen, Copenhagen, Denmark. * Christian Frøkjær-Jensen * Present address: Department of Biochemistry, University of Washington, Seattle, Washington, USA. * Michael Ailion Competing financial interests E.M.J. is an author of a patent covering techniques described in this paper (US patent 7,196,244 and European patent pending). Corresponding author Correspondence to: * Erik M Jorgensen Author Details * Christian Frøkjær-Jensen Search for this author in: * NPG journals * PubMed * Google Scholar * M Wayne Davis Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Ailion Search for this author in: * NPG journals * PubMed * Google Scholar * Erik M Jorgensen Contact Erik M Jorgensen Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (5.2M) Supplementary Figures 1–5, Supplementary Table 1 and Supplementary Methods Additional data
• Generating transgenic nematodes by bombardment and antibiotic selection
  - Nat Methods 9(2):118-119 (2012)
  Nature Methods | Correspondence Generating transgenic nematodes by bombardment and antibiotic selection * Jennifer I Semple1 * Laura Biondini1 * Ben Lehner1, 2 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:118–119Year published:(2012)DOI:doi:10.1038/nmeth.1864Published online 30 January 2012 To the Editor: In an extension of methods we1 and others2 have previously described in Nature Methods, we report here single- or dual-antibiotic selection to isolate transgenic nematodes after microparticle bombardment. The protocol makes it straightforward to generate integrated transgenes in diverse Caenorhabditis strains and species. View full text Subject terms: * Genetics * Genomics * Model Organisms * Gene Expression Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * European Molecular Biology Laboratory Centre for Genomic Regulation Systems Biology Unit, Barcelona, Spain. * Jennifer I Semple, * Laura Biondini & * Ben Lehner * Institució Catalana de Recerca i Estudis Avançats, Centre for Genomic Regulation and Pompeu Fabra University, Barcelona, Spain. * Ben Lehner Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ben Lehner Author Details * Jennifer I Semple Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Biondini Search for this author in: * NPG journals * PubMed * Google Scholar * Ben Lehner Contact Ben Lehner Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (7.3M) Supplementary Figures 1–9, Supplementary Tables 1–4 and Supplementary Methods Additional data
• Structural variation: the genome's hidden architecture
  - Nat Methods 9(2):133-137 (2012)
  Nature Methods | Technology Feature Structural variation: the genome's hidden architecture * Monya Baker1Journal name:Nature MethodsVolume: 9,Pages:133–137Year published:(2012)DOI:doi:10.1038/nmeth.1858Published online 30 January 2012 Next-generation sequencing is uncovering more variants than ever before, but it also faces limitations. View full text Figures at a glance * Figure 1: Structural variation occurs in all forms and sizes. Genome structural variation encompasses polymorphic rearrangements 50 base pairs to hundreds of kilobases in size and affects about 0.5% of the genome of a given individual. * Figure 2: Entries for structural variation are increasing in the scientific literature (a) and in the Database of Genomic Variants (DGV; b), which posts curated data from peer-reviewed studies on human samples. It draws from two other databases (DGVa and dbVAR) that accept open submissions for data. * Figure 3: Several analytic techniques are used to find structural variation. Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Monya Baker is technology editor for Nature and Nature Methods Corresponding author Correspondence to: * Monya Baker Author Details * Monya Baker Contact Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Super resolution for common probes and common microscopes
  - Nat Methods 9(2):139-141 (2012)
  Article preview View full access options Nature Methods | News and Views Super resolution for common probes and common microscopes * Keith A Lidke1Journal name:Nature MethodsVolume: 9,Pages:139–141Year published:(2012)DOI:doi:10.1038/nmeth.1863Published online 30 January 2012 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A sophisticated analysis approach based on the concept of fluorophore localization provides dynamic super-resolution data of GFP-labeled live cells using a common, arc lamp–based wide-field fluorescence microscope. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Methods for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Affiliations * Keith A. Lidke is at the University of New Mexico, Albuquerque, New Mexico, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Keith A Lidke Author Details * Keith A Lidke Contact Keith A Lidke Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Making sense out of nonsense to visualize editing in the fly nervous system
  - Nat Methods 9(2):141-143 (2012)
  Article preview View full access options Nature Methods | News and Views Making sense out of nonsense to visualize editing in the fly nervous system * Chammiran Daniel1 * Marie Öhman1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:141–143Year published:(2012)DOI:doi:10.1038/nmeth.1860Published online 30 January 2012 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In vivo methods to capture processing events such as RNA editing in specific cell types are sparse. Researchers have now developed a method to visualize adenosine-to-inosine editing activity in individual fruit fly neurons using a reverse-engineered fluorescent reporter. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Methods for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Affiliations * Chammiran Daniel and Marie Öhman are in the Department of Molecular Biology and Functional Genomics, Stockholm University, Stockholm, Sweden. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Marie Öhman Author Details * Chammiran Daniel Search for this author in: * NPG journals * PubMed * Google Scholar * Marie Öhman Contact Marie Öhman Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• DNA methylome analysis using short bisulfite sequencing data
  - Nat Methods 9(2):145-151 (2012)
  Nature Methods | Review DNA methylome analysis using short bisulfite sequencing data * Felix Krueger1, 3 * Benjamin Kreck2, 3 * Andre Franke2 * Simon R Andrews1 * Affiliations * Corresponding authorsJournal name:Nature MethodsVolume: 9,Pages:145–151Year published:(2012)DOI:doi:10.1038/nmeth.1828Published online 30 January 2012 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Bisulfite conversion of genomic DNA combined with next-generation sequencing (BS-seq) is widely used to measure the methylation state of a whole genome, the methylome, at single-base resolution. However, analysis of BS-seq data still poses a considerable challenge. Here we summarize the challenges of BS-seq mapping as they apply to both base and color-space data. We also explore the effect of sequencing errors and contaminants on inferred methylation levels and recommend the most appropriate way to analyze this type of data. View full text Subject terms: * Bioinformatics * Genetics * Epigenetics * Genomics Figures at a glance * Figure 1: Effect of bisulfite treatment of DNA. Bisulfite conversion of genomic DNA and subsequent PCR amplification gives rise to two PCR products and up to four potentially different DNA fragments for any given locus. (Hydroxy)methylated cytosine residues are resistant to bisulfite conversion and can be used as a readout of the DNA methylation state. mC, 5-methylcytosine; hmC, 5-hydroxymethylcytosine; OT, original top strand; CTOT, strand complementary to the original top strand; OB, original bottom strand; and CTOB, strand complementary to the original bottom strand. * Figure 2: Performance and accuracy of unbiased base-space and color-space BS-seq alignment tools. () A total of 106 random mouse genomic sequences of different lengths were aligned to the mouse genome (NCBIM37) with Bowtie as an example of methylation-aware mapping (biased) or with Bismark as an example of unbiased mapping (unbiased). Non-unique alignments were discarded. (,) A total of 106 random mouse base-space (Bismark; ) or human color-space (B-SOLANA; ) reads (75 base pairs) were simulated with different rates of bisulfite conversion (context is indicated) and aligned to the mouse (NCBIM37) or human (NCBI37) genomes. Bismark accurately detected various simulated methylation levels at a constant mapping efficiency. Alignment of color-space reads with B-SOLANA was efficient, and methylation calls were accurate only when methylation in non-CpG context was fairly low (ideally less than 5%). H (in CHG and CHH) stands for C, T or A. () Reads as in , were simulated with typical mammalian methylation levels (CpG context, 70%; CHG and CHH context, 3%) using Sherman (http://! www.bioinformatics.bbsrc.ac.uk/projects/sherman/). * Figure 3: Recommended workflow for the primary analysis of BS-seq data. Black arrows depict required steps, gray arrows indicate optional steps. *, only works with base-space data. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Felix Krueger & * Benjamin Kreck Affiliations * Bioinformatics Group, The Babraham Institute, Cambridge, UK. * Felix Krueger & * Simon R Andrews * Institute of Clinical Molecular Biology, Christian Albrechts University, Kiel, Germany. * Benjamin Kreck & * Andre Franke Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Andre Franke or * Simon R Andrews Author Details * Felix Krueger Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin Kreck Search for this author in: * NPG journals * PubMed * Google Scholar * Andre Franke Contact Andre Franke Search for this author in: * NPG journals * PubMed * Google Scholar * Simon R Andrews Contact Simon R Andrews Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (520K) Supplementary Figures 1–3 and Supplementary Table 1 Additional data
• Immunolabeling artifacts and the need for live-cell imaging
  - Nat Methods 9(2):152-158 (2012)
  Nature Methods | Perspective Immunolabeling artifacts and the need for live-cell imaging * Ulrike Schnell1 * Freark Dijk1 * Klaas A Sjollema1 * Ben N G Giepmans1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:152–158Year published:(2012)DOI:doi:10.1038/nmeth.1855Published online 30 January 2012 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Fluorescent fusion proteins have revolutionized examination of proteins in living cells. Still, studies using these proteins are met with criticism because proteins are modified and ectopically expressed, in contrast to immunofluorescence studies. However, introducing immunoreagents inside cells can cause protein extraction or relocalization, not reflecting the in vivo situation. Here we discuss pitfalls of immunofluorescence labeling that often receive little attention and argue that immunostaining experiments in dead, permeabilized cells should be complemented with live-cell imaging when scrutinizing protein localization. View full text Subject terms: * Cell Biology * Microscopy * Imaging * Sensors and Probes Figures at a glance * Figure 1: Fixation and permeabilization can affect epitope accessibility. () Fluorescence images of 293T cells expressing EGFP fixed with 2% PFA and 0.05% glutaraldehyde for 30 min at room temperature (18–22 °C) and permeabilized with 0.05% Triton X-100 for 30 min with subsequent immunostaining with an antibody to GFP (anti-GFP) and mounting in Mowiol. Also shown are merged and differential interference contrast (DIC) images. Scale bar, 20 μm. () Images of 293T cells expressing tight junction protein Claudin-7–EGFP (CLDN7-EGFP) immunostained with an antibody to Claudin-7 (anti-CLDN7) after fixation with 4% PFA (30 min) and permeabilized with either methanol (MeOH; −20 °C) for 1 min or 0.1% Triton X-100 (Triton) for 15 min at room temperature. Scale bar, 10 μm. * Figure 2: Effects of standard fixation and permeabilization protocols on protein localization and epitope accessibility in different cell lines. () Images of 293T and MDCK cells transfected with EGFP-encoding plasmids and imaged live or after fixation with 4% PFA. () Images of 293T and MDCK cells after fixation as in and permeabilizion with 0.05% Triton X-100 (Triton; 15 min) or methanol (MeOH; 1 min −20 °C) and immunostaining with an antibody to GFP (anti-GFP). () Images of 293T cells transfected with plasmids encoding EGFP fusion proteins that localize in inidicated compartments. ER, endoplasmic reticulum. 'Tubulin', MDCK cells stably expressing EYFP–α-tubulin. Cells were recorded live, after fixation with 4% PFA or after fixation and permeabilization with Triton X-100 or MeOH and immunostaining with anti-GFP. Whereas laser intensity and pinhole were the same in each experiment, gain and offset settings were chosen for each condition. All scale bars, 20 μm. * Figure 3: Effects of standard immunostaining methods on protein extraction and EGFP fluorescence. () Images of MDCK cells stably expressing EpCAM-EGFP and fixed with methanol in real time (imaged every 2 min). The initial methanol (first time MeOH) was replaced (second time MeOH) and cells were washed 3× with PBS. Note that EGFP fluorescence is lost during dehydration but recovers after rehydration. (,) Real-time imaging of MDCK cells stably expressing EGFP, fixed with 2% or 4% PFA and permeabilized with 0.1% Triton X-100 (Triton; 15 min) or methanol (MeOH; 1 min at −20 °C when added to the dish). After permeabilization, cells were washed 6× with PBS to mimic the washing steps during the immunostaining procedure. EGFP fluorescence was recorded every 3 min using the same microscope settings for each condition. Scale bars, 20 μm. () Normalized average fluorescence of cells in five different microscope fields. AU, arbitrary units. Error bars, s.d. (n = 5 different fields). * Figure 4: Ultrastructural changes after fixation and permeabilization. (–) Electron micrographs of MDCK cells fixed with 2% or 4% PFA (–), methanol () or glutaraldehyde (). To mimic the immunostaining procedure, PFA-fixed cells were permeabilized with 0.05% Triton X-100 (15 min; ,) or MeOH (1 min at −20 °C; ,). All samples were washed (6× PBS), then fixed with 2% glutaraldehyde for 10 min and processed for electron microscopy. Scale bars, 2 μm. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Cell Biology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. * Ulrike Schnell, * Freark Dijk, * Klaas A Sjollema & * Ben N G Giepmans Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ben N G Giepmans Author Details * Ulrike Schnell Search for this author in: * NPG journals * PubMed * Google Scholar * Freark Dijk Search for this author in: * NPG journals * PubMed * Google Scholar * Klaas A Sjollema Search for this author in: * NPG journals * PubMed * Google Scholar * Ben N G Giepmans Contact Ben N G Giepmans Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–9 and Supplementary Methods Additional data
• Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins
  - Nat Methods 9(2):159-172 (2012)
  Nature Methods | Analysis Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins * Joanna Mattis1, 2, 7 * Kay M Tye1, 7 * Emily A Ferenczi1, 2, 7 * Charu Ramakrishnan1 * Daniel J O'Shea1, 2 * Rohit Prakash1, 2 * Lisa A Gunaydin1, 2 * Minsuk Hyun1 * Lief E Fenno1, 2 * Viviana Gradinaru1, 3 * Ofer Yizhar1, 4 * Karl Deisseroth1, 2, 3, 5, 6 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:159–172Year published:(2012)DOI:doi:10.1038/nmeth.1808Received 09 May 2011 Accepted 10 November 2011 Published online 18 December 2011 Corrected online10 January 2012Corrected online10 January 2012 Abstract * Abstract * Accession codes * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Diverse optogenetic tools have allowed versatile control over neural activity. Many depolarizing and hyperpolarizing tools have now been developed in multiple laboratories and tested across different preparations, presenting opportunities but also making it difficult to draw direct comparisons. This challenge has been compounded by the dependence of performance on parameters such as vector, promoter, expression time, illumination, cell type and many other variables. As a result, it has become increasingly complicated for end users to select the optimal reagents for their experimental needs. For a rapidly growing field, critical figures of merit should be formalized both to establish a framework for further development and so that end users can readily understand how these standardized parameters translate into performance. Here we systematically compared microbial opsins under matched experimental conditions to extract essential principles and identify key parameters for the! conduct, design and interpretation of experiments involving optogenetic techniques. View full text Subject terms: * Neuroscience Figures at a glance * Figure 1: Properties of depolarizing optogenetic tools. () Depolarizing tool classes. White bars indicate mutations. () Construct design and representative image for in vitro characterization. Scale bar, 50 μm. () Normalized representative photocurrents. Scale bars, 400 pA, 200 ms. Horizontal scale bar applies to all traces. Color and shape legend applies throughout the figure. () Action spectra (n = 5–11). () Peak (filled bars) and steady-state (hollow bars) photocurrents to 1 s light (n = 8–27). () Time to peak (n = 8–27) versus τdes (n = 8–50). Traces show normalized representative ChR2 (black) and C1V1TT (red) onset photocurrents. Vertical scale bars represent 200 pA, bars indicate time to peak, and blue arrow indicates ongoing light pulse. () Recovery from desensitization (n = 5–20). Vertical and horizontal scale bars represent 1 nA and 2 s. () Normalized representative traces and summary plots of τoff (n = 8–53). Scale bars, 200 pA and 25 ms. () Peak and steady-state photocurrents across light intensities. In! set, representative ChR2 photocurrents at low (light gray) versus high (dark gray) light intensity. Scale bars, 250 pA and 250 ms. EPD50 for peak (filled bars) and steady state (hollow bars) (n = 5–15). () τoff versus EPD50 and peak and steady-state photocurrents. All population data are plotted as mean ± s.e.m. *P < 0.05, **P < 0.01 and ***P < 0.001. Unless otherwise indicated, C1V1T and C1V1TT were activated with 560-nm light, and all other tools were activated with 470-nm light at ~5 mW mm−2. * Figure 2: Performance of depolarizing tools. () Proportion of successfully evoked spikes (of 40 pulses; 5–100 Hz) at different light intensities (n = 8–18). Colors and shapes apply throughout the figure. () Temporal stationarity at 20 Hz, 2 mW mm−2 (n = 8–18), based on the proportion of successful spikes in each quartile of pulses. Vertical and horizontal scale bars represent 40 mV and 1 s, respectively. () Representative evoked spiking across stimulation frequencies for ChIEF, FR and CatCh with closely matched ~1.5 nA steady-state photocurrents at 6 mW mm−2. Vertical and horizontal scale bars represent 40 mV and 1 s, respectively. () Comparison of spiking performance between ChR2R (n = 19) and CatCh (n = 12) in cell-attached mode at 6 mW mm−2. () Plateau potential across pulse frequencies at 6 mW mm−2 (n = 5–17). () Mean plateau potential for each opsin plotted against τoff, steady-state photocurrents and projected peak photocurrents. All values taken from the 6 mW mm−2 condition. () Latency spread ! across a pulse train, illustrated by representative traces of 40 consecutive ChR2 spikes in a train, aligned to the light pulse and overlaid. Vertical and horizontal scale bars represent 40 mV and 10 ms, respectively. All population data are plotted as mean ± s.e.m. *P < 0.05 and **P < 0.01. C1V1T and C1V1TT were activated with 560-nm light, and all other opsins were activated with 470-nm light. * Figure 3: Properties and performance of ultrafast depolarizing tools. () Schemata and normalized photocurrents for ChETAs and ChR2. White bars indicate mutations. Colors and shapes apply throughout the figure. Scale bars, 500 pA and 500 ms. Horizontal scale bar applies to all traces. () Action spectra (n = 5–12). () Peak (filled bars) and steady-state (hollow bars) photocurrents (n = 9–35). () Recovery from desensitization (n = 8–20). () ChETAA and ChETATR expression in fast-spiking neurons using a Cre recombinase–dependent strategy. Scale bar, 50 μm. () Steady-state photocurrents (n = 9), τoff (n = 7), and consecutively evoked spikes for ChETAA and ChETATR (5 Hz, 2-ms light pulses). Scale bars, 20 mV and 1 ms. () τoff at −70 mV to +50 mV (n = 7–12). () ChETAA and ChIEF expression (scale bar, 50 μm). () Steady-state photocurrents (n = 9–13), τoff (n = 7), and evoked high and low frequency firing (200 Hz and 20 Hz). Scale bars, 25 mV and 25 ms. () ChIEF-expressing neurons with small (190 pA) or large (510 pA) photocurrents, u! nder stringent or permissive conditions (1 ms or 5 ms pulse width). Vertical scale bar, 20 mV. Horizontal scale bars, 50 ms (left) and 10 ms (right). Spiking performance and multiple spike likelihood (under those same conditions) for all cells. All population data is plotted as mean ± s.e.m. *P < 0.05 and ***P < 0.001. Cells were illuminated with 470-nm light at ~5 mW mm−2, unless otherwise specified. * Figure 4: Relationship between off kinetics and light sensitivity of optogenetic tools. Summary plot (on a log-log scale) of the relationship between τoff versus EPD50 for all depolarizing tools from Figures 1 and 3, plus VChR1, SFO(C128S) and SSFO(C128S/D156A). Dashed line represents best fit regression with R2 = 0.83; Spearman correlation coefficient R = −0.93, P < 0.001. Values for SFO and SSFO were estimated from previous publications11, 15 and did not contribute to the regression or correlation calculations. * Figure 5: Properties of hyperpolarizing tools. () NpHR is an inward chloride pump (halorhodopsin type; HR), whereas Arch, ArchT, and Mac are outward proton pumps (bacteriorhodopsin type; BR). The 3.0 versions include the endoplasmic reticulum export sequence (ER) after the fluorophore (which constitutes the 2.0 version) as well as a trafficking sequence (TS) between opsin and fluorophore. () Confocal images of 1.0 (the originally described version of the molecule) and 3.0 versions (green) expressed in culture and immunolabeled with an ER marker (KDEL; red). Scale bar, 25 μm. () Representative traces and raw photocurrents in response to 1 s light for 1.0 (open bars) versus 3.0 versions (closed bars) for Arch (n = 15–19), ArchT (n = 14–16) and Mac (n = 8–12). Vertical and horizontal scale bars represent 500 pA and 500 ms, respectively. Photocurrents were normalized to eNpHR3.0 values from within the same experiment to enable direct comparisons across opsins (n = 8–35). () Action spectra for 3.0 versions (n = 7–2! 0) alongside ChR2 (black). () τon and τoff (n = 7–35). Vertical and horizontal scale bars represent 200 pA and 5 ms, respectively. () EPD50 values for all hyperpolarizing opsins (n = 5–14). Raw photocurrent versus light power density plotted alongside within-experiment eNpHR3.0 (n = 5–14). Population data are plotted as mean ± s.e.m. *P < 0.05, **P < 0.01 and ***P < 0.001. Unless otherwise indicated, eNpHR3.0 was activated with 590-nm light, and all other tools were activated with 560-nm light, both at ~5 mW mm−2. * Figure 6: Performance of hyperpolarizing tools. () Confocal images of eNpHR3.0 and eArch3.0 expression at the injection site in medial prefrontal cortex (mPFC) and the downstream basolateral amygdala (BLA). Scale bars, 250 μm and 25 μm. DAPI staining (white) delineates cell bodies. () Mean input resistances for opsin-expressing cells and eYFP controls (n = 10–22). () Representative traces and mean onset photocurrents for eNpHR3.0 and eArch3.0 in response to 60 s 5 mW mm−2 light pulses (n = 8–10). Vertical and horizontal scale bars represent 400 pA and 10 s, respectively. () Mean peak hyperpolarization generated by eNpHR3.0 and eArch3.0 with 60 s 5 mW mm−2 light pulses (n = 6–10). () Suppression of current injection–evoked spiking in reliably firing cells by 60 s of continuous light in cells expressing eNpHR3.0 or eArch3.0. Cells were illuminated with light power densities set to achieve approximately matched hyperpolarization. Vertical and horizontal scale bars represent 40 mV and 20 s, respectively. () Rela! tionship between hyperpolarization magnitude and cell stability. Post-light recovery of evoked spiking (relative to pre-light performance) and change in resting potential plotted against light-evoked hyperpolarization. Population data are plotted as mean ± s.e.m. *P < 0.05 and **P < 0.01. eNpHR3.0 was activated with 590-nm light, and eArch3.0 was activated with 560-nm light. Accession codes * Abstract * Accession codes * Change history * Author information * Supplementary information Referenced accessions GenBank * ACD70142.1 Change history * Abstract * Accession codes * Change history * Author information * Supplementary informationCorrected online 10 January 2012In the version of this article initially published online, the x-axis labels in Figure 5d were incorrectly labeled. The error has been corrected for the print, PDF and HTML versions of this article.Corrected online 10 January 2012In the version of this article initially published online, in the Discussion the statement "to achieve sufficient activation of cells far from the light source may require excessive hyperpolarization" was incorrect. The error has been corrected for the print, PDF and HTML versions of this article. Author information * Abstract * Accession codes * Change history * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Joanna Mattis, * Kay M Tye & * Emily A Ferenczi Affiliations * Department of Bioengineering, Stanford University, Stanford, California, USA. * Joanna Mattis, * Kay M Tye, * Emily A Ferenczi, * Charu Ramakrishnan, * Daniel J O'Shea, * Rohit Prakash, * Lisa A Gunaydin, * Minsuk Hyun, * Lief E Fenno, * Viviana Gradinaru, * Ofer Yizhar & * Karl Deisseroth * Neuroscience Program, Stanford University, Stanford, California, USA. * Joanna Mattis, * Emily A Ferenczi, * Daniel J O'Shea, * Rohit Prakash, * Lisa A Gunaydin, * Lief E Fenno & * Karl Deisseroth * CNC Program, Stanford University, Stanford, California, USA. * Viviana Gradinaru & * Karl Deisseroth * Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel. * Ofer Yizhar * Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA. * Karl Deisseroth * Howard Hughes Medical Institute, Stanford University, Stanford, California, USA. * Karl Deisseroth Contributions J.M., K.M.T., E.A.F., C.R., R.P., O.Y. and K.D. contributed to study design and data interpretation. J.M. coordinated all experiments and data analysis. J.M., K.M.T., E.A.F., D.J.O., R.P. and L.E.F. contributed to acquisition of electrophysiological data. C.R. cloned all constructs, cultured primary neurons, performed transfections and managed viral packaging processes. D.J.O. wrote custom analysis scripts and analyzed all electrophysiological data. M.H. contributed to data analysis. J.M., K.M.T., C.R., L.A.G. and V.G. contributed to the histological processing and fluorescence imaging. K.D. supervised all aspects of the work. J.M., K.M.T., E.A.F. and K.D. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Karl Deisseroth Author Details * Joanna Mattis Search for this author in: * NPG journals * PubMed * Google Scholar * Kay M Tye Search for this author in: * NPG journals * PubMed * Google Scholar * Emily A Ferenczi Search for this author in: * NPG journals * PubMed * Google Scholar * Charu Ramakrishnan Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel J O'Shea Search for this author in: * NPG journals * PubMed * Google Scholar * Rohit Prakash Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa A Gunaydin Search for this author in: * NPG journals * PubMed * Google Scholar * Minsuk Hyun Search for this author in: * NPG journals * PubMed * Google Scholar * Lief E Fenno Search for this author in: * NPG journals * PubMed * Google Scholar * Viviana Gradinaru Search for this author in: * NPG journals * PubMed * Google Scholar * Ofer Yizhar Search for this author in: * NPG journals * PubMed * Google Scholar * Karl Deisseroth Contact Karl Deisseroth Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–17, Supplementary Tables 1–2 Additional data
• HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
  - Nat Methods 9(2):173-175 (2012)
  Nature Methods | Brief Communication HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment * Michael Remmert1 * Andreas Biegert1 * Andreas Hauser1 * Johannes Söding1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:173–175Year published:(2012)DOI:doi:10.1038/nmeth.1818Received 29 July 2011 Accepted 01 December 2011 Published online 25 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Sequence-based protein function and structure prediction depends crucially on sequence-search sensitivity and accuracy of the resulting sequence alignments. We present an open-source, general-purpose tool that represents both query and database sequences by profile hidden Markov models (HMMs): 'HMM-HMM–based lightning-fast iterative sequence search' (HHblits; http://toolkit.genzentrum.lmu.de/hhblits/). Compared to the sequence-search tool PSI-BLAST, HHblits is faster owing to its discretized-profile prefilter, has 50–100% higher sensitivity and generates more accurate alignments. View full text Subject terms: * Bioinformatics * Structural Biology * Biophysics Figures at a glance * Figure 1: Workflow and benchmark comparison. () HHblits can iteratively search for homologous sequences in large databases such as UniProt. The HHblits database is a clustered version in which each set of full-length alignable sequences is represented by an HMM. Sequences from matched HMMs with a statistically significant E value are added to the query MSA, from which a new HMM is calculated for the next search iteration. A prefilter reduces the number of full HMM-HMM alignments by ~2,500-fold. () Median run times for searches with 100 test sequences through the UniProt or UniProt20 database (the inset shows the test sequence length distribution). () True positive pairs (same SCOP fold) compared to false positive pairs (different SCOP fold) for one and three search iterations in an all-against-all comparison. FDR, false discovery rate. () Mean fraction of correctly aligned residue pairs out of all structurally alignable pairs (sensitivity) compared to the fraction of correctly aligned pairs out of all the aligned pairs! (precision). The parameter mact controls the alignment greediness (Supplementary Fig. 10). * Figure 2: Structure predictions for Pfam families and the modeling of human Pip49 (also known as FAM69B). () Families to which only HHblits and both HHblits and HMMER3 assigned a structural template below a given E value. () Homology model of human Pip49 kinase domain (blue) with the inserted EF hand (green). () Catalytic center showing the conserved residues (red) for protein kinase activity. () EF hand insertion with the conserved residues (magenta) for the predicted Ca2+-dependent activation. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Protein Data Bank * 1RDQ * 3C1V * 1RDQ * 3C1V Author information * Accession codes * Author information * Supplementary information Affiliations * Gene Center and Center for Integrated Protein Science Munich, Ludwig-Maximilians Universität München, Munich, Germany. * Michael Remmert, * Andreas Biegert, * Andreas Hauser & * Johannes Söding Contributions M.R. performed research, J.S. initiated and guided research, A.B. generated the profile-column alphabet, A.H. contributed code for fast file access, and M.R. and J.S. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Johannes Söding Author Details * Michael Remmert Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Biegert Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Hauser Search for this author in: * NPG journals * PubMed * Google Scholar * Johannes Söding Contact Johannes Söding Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–10, Supplementary Tables 1 and 2 Text files * Supplementary Data 1 (57K) 100 random sequences from the nr database used for run time benchmark. * Supplementary Data 2 (90K) List of query-template pairs for alignment benchmark. * Supplementary Data 3 (168K) 3D homology model of PIP49/FAM69B. * Supplementary Data 4 (180K) Training and test set of SCOP domain sequence for sensitivity benchmark. * Supplementary Data 5 (721K) FASTA formatted multiple sequence alignment for human PIP49/FAM69B built by HHblits. Additional data
• Detection of structural variants and indels within exome data
  - Nat Methods 9(2):176-178 (2012)
  Nature Methods | Brief Communication Detection of structural variants and indels within exome data * Emre Karakoc1 * Can Alkan1, 2 * Brian J O'Roak1 * Megan Y Dennis1 * Laura Vives1 * Kenneth Mark1 * Mark J Rieder1 * Debbie A Nickerson1 * Evan E Eichler1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:176–178Year published:(2012)DOI:doi:10.1038/nmeth.1810Received 15 July 2011 Accepted 16 November 2011 Published online 18 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We report an algorithm to detect structural variation and indels from 1 base pair (bp) to 1 Mbp within exome sequence data sets. Splitread uses one end–anchored placements to cluster the mappings of subsequences of unanchored ends to identify the size, content and location of variants with high specificity and sensitivity. The algorithm discovers indels, structural variants, de novo events and copy number–polymorphic processed pseudogenes missed by other methods. View full text Subject terms: * Bioinformatics * Genomics * Sequencing * Genetics Figures at a glance * Figure 1: Splitread definition and analyses. () Schematic diagrams for mapping paired-end sequences in cases of a deletion (red) or an insertion (blue) with respect to the reference sequence. In each case, one end–anchored sequence was used to map one read in a pair. The second (unmapped) read was then decomposed into either two equal subsequences (balanced split) or two unequal subsequences (unbalanced split). () Number of Splitread predictions called by 1000 Genomes versus total number of Splitread predictions using indicated threshold numbers of balanced and unbalanced reads, respectively. () Venn diagram of variants detected by Splitread exome analysis versus whole-genome sequence analysis of NA12891 (black) or all variants within dbSNP130 (red). To intersect, variants must be at same position and within 10 bp of the predicted size. () Length distribution of predicted insertions and deletions mapping within coding region of NA12891. Red, events with multiples of 3 bp; blue, events that would disrupt the frame. ()! Venn diagram of Pindel, GATK and Splitread call sets on NA12891. Black, total number of events; red, events previously detected as part of dbSNP130 and/or the 1000 Genomes Project. * Figure 2: Validation of processed pseudogenes. () Gene models and predicted intron deletions of processed pseudogenes. Primers (red triangles) were designed in coding regions. We detected the expected product size for processed pseudogenes for TMEM5 (), C13orf3 (), ATP9B (), MFF () and TMEM66 () in our PCR experiments. In , we genotyped the processed pseudogenes MFF and TMEM66 within eight HapMap samples; each was amplified only in the predicted sample (boxed in yellow, NA19238 (MFF) and NA12891 (TMEM66)). All PCRs amplified the normal gene (signal on top), with only one sample each amplifying the processed gene. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Sequence Read Archive * SRA039053 Author information * Accession codes * Author information * Supplementary information Affiliations * Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA. * Emre Karakoc, * Can Alkan, * Brian J O'Roak, * Megan Y Dennis, * Laura Vives, * Kenneth Mark, * Mark J Rieder, * Debbie A Nickerson & * Evan E Eichler * Howard Hughes Medical Institute, Seattle, Washington, USA. * Can Alkan & * Evan E Eichler Contributions E.K. designed and implemented the Splitread algorithm; E.K. and C.A. analyzed data; B.J.O., L.V., M.J.R. and D.A.N. generated sequencing data; M.Y.D. and K.M. carried out validation experiments and analyzed processed pseudogenes and E.K., C.A. and E.E.E. wrote the manuscript. Competing financial interests E.E.E. is a member of the Scientific Advisory Board of Pacific Biosciences. Corresponding author Correspondence to: * Evan E Eichler Author Details * Emre Karakoc Search for this author in: * NPG journals * PubMed * Google Scholar * Can Alkan Search for this author in: * NPG journals * PubMed * Google Scholar * Brian J O'Roak Search for this author in: * NPG journals * PubMed * Google Scholar * Megan Y Dennis Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Vives Search for this author in: * NPG journals * PubMed * Google Scholar * Kenneth Mark Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Rieder Search for this author in: * NPG journals * PubMed * Google Scholar * Debbie A Nickerson Search for this author in: * NPG journals * PubMed * Google Scholar * Evan E Eichler Contact Evan E Eichler Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1 and 2, Supplementary Tables 1–6 and Supplementary Note Additional data
• A linear complexity phasing method for thousands of genomes
  - Nat Methods 9(2):179-181 (2012)
  Nature Methods | Brief Communication A linear complexity phasing method for thousands of genomes * Olivier Delaneau1, 2 * Jonathan Marchini2 * Jean-François Zagury1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:179–181Year published:(2012)DOI:doi:10.1038/nmeth.1785Received 15 March 2011 Accepted 17 October 2011 Published online 04 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Human-disease etiology can be better understood with phase information about diploid sequences. We present a method for estimating haplotypes, using genotype data from unrelated samples or small nuclear families, that leads to improved accuracy and speed compared to several widely used methods. The method, segmented haplotype estimation and imputation tool (SHAPEIT), scales linearly with the number of haplotypes used in each iteration and can be run efficiently on whole chromosomes. View full text Figures at a glance * Figure 1: Illustration of the model and the associated graphs for a simple example. (,) In this example, contains K = 8 haplotypes (rows in ) and the individual's genotype contains four heterozygous SNPs (), both defined over M = 8 markers (columns). In , we illustrate how the graph g is built by splitting the haplotypes of between markers 4 and 5, resulting in two segments that each contain J = 3 distinct haplotypes. The nodes of the graph are labeled either with allele 1 or allele 0. Each edge is weighted by the number of haplotypes in that traverse it. A haplotype of and its corresponding path in g is illustrated in magenta. In , we illustrate how the graph g is built by making two segments of five and three SNP markers, each one containing two heterozygous markers in (represented as state 1; state 0 and 2 are wild type and homozygous, respectively). Each segment has four possible haplotypes compatible with . A pair of paths in g compatible with is colored blue and green. * Figure 2: Accuracy and computational burden as a function of the number of conditioning states. (–) Switch error rate plotted against N for all the methods tested on the European, WTCCC2 and Vietnamese datasets, respectively. For SHAPEIT, this is the number of collapsed states (N = J). For Impute2 and MaCH, it is the size of the subset of the K haplotypes used in each iteration. Error rates of Fastphase and Beagle are represented as lines because default settings were used. (–) Running times plotted against N. We fit linear regression to the SHAPEIT running times and quadratic regression to the Impute2 and MaCH running times; these lines are plotted. Dashed lines are extrapolations. Author information * Author information * Supplementary information Affiliations * Chaire de Bioinformatique, Laboratoire Génomique, Bioinformatique, et Applications (Equipe d'accueil 4627), Conservatoire National des Arts et Métiers, Paris, France. * Olivier Delaneau & * Jean-François Zagury * Department of Statistics, University of Oxford, Oxford, UK. * Olivier Delaneau & * Jonathan Marchini Contributions O.D. derived the algorithm and carried out all the experiments. J.-F.Z. supervised the research. J.M. gave advice on experiments and the interpretation of results. O.D., J.-F.Z. and J.M. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jean-François Zagury Author Details * Olivier Delaneau Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Marchini Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-François Zagury Contact Jean-François Zagury Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–3, Supplementary Tables 1–3 and Supplementary Note Additional data
• Blotting protein complexes from native gels to electron microscopy grids
  - Nat Methods 9(2):182-184 (2012)
  Nature Methods | Brief Communication Blotting protein complexes from native gels to electron microscopy grids * Roland Wilhelm Knispel1, 2 * Christine Kofler1, 2 * Marius Boicu1 * Wolfgang Baumeister1 * Stephan Nickell1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:182–184Year published:(2012)DOI:doi:10.1038/nmeth.1840Received 18 May 2011 Accepted 05 December 2011 Published online 08 January 2012 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We report a simple and generic method for the direct transfer of protein complexes separated by native gel electrophoresis to electron microscopy grids. After transfer, sufficient material remains in the gel for identification and characterization by mass spectrometry. The method should facilitate higher-throughput single-particle analysis by substantially reducing the time needed for protein purification, as demonstrated for three complexes from Thermoplasma acidophilum. View full text Subject terms: * Biochemistry * Biophysics * Structural Biology Figures at a glance * Figure 1: Grid-blotting procedure. () Two different protein mixtures containing protein complexes of thermosomes (Ths.; lanes 1 and 1′), 20S proteasomes (20S; lanes 2 and 2′) and VAT (lanes 2 and 2′) were loaded in duplicate with a molecular weight marker (M) on a native electrophoresis gel. Both fractions originated from our protein-complex enrichment scheme and contained smaller molecular weight proteins as impurities. After electrophoresis, the gel was split (dashed line) into 'reference lanes' (M, 1 and 2; stained with Coomassie Blue and then de-stained) and 'blotting lanes' (1′ and 2′; kept at 4 °C in a humid environment). () Then both gel parts were aligned, and nonstained protein bands were localized by manually extrapolating from the position of stained bands across horizontal lines toward the 'blotting lanes'. () At the localized protein spots, the gel surface was roughened. () Protein complexes were blotted by placing the electron microscopy grids directly onto the gel. The grid-gel inter! faces were wetted by applying 5 μl of electrophoresis buffer. After 2 min the grids were removed and either negatively stained or plunge-frozen. * Figure 2: Electron micrographs of protein complexes blotted from native gels. (–) Micrographs of thermosomes (), 20S proteasomes () and VAT () negatively stained with 2% uranyl acetate after grid blotting. (–) Micrographs of vitrified, unstained samples obtained using continuous carbon film with a thickness of ~2 nm; as shown for thermosomes () and for 20S proteasomes (). In the case of holey carbon films, protein complexes tend to adsorb at the carbon support film, thereby depleting the protein concentration in the free ice layer. This effect is illustrated by showing the accumulation of 20S proteasome particles at Lacey carbon bars (). The applied defocus setting was between −2 and −2.6 μm for – and −4.5 μm for . Insets in – show representative class averages for each particle species and have an edge length of 20 nm × 20 nm. Scale bars, 40 nm. Author information * Author information * Supplementary information Affiliations * Max Planck Institute of Biochemistry, Department of Molecular Structural Biology, Martinsried, Germany. * Roland Wilhelm Knispel, * Christine Kofler, * Marius Boicu, * Wolfgang Baumeister & * Stephan Nickell * Present addresses: ChemAxon Kft., Budapest, Hungary (R.W.K.), Tietz Video and Image Processing Systems GmbH, Gauting, Germany (C.K.) and Carl Zeiss NTS GmbH, Oberkochen, Germany (S.N.). * Roland Wilhelm Knispel, * Christine Kofler & * Stephan Nickell Contributions R.W.K., C.K. and S.N. designed and conducted the grid-blotting experiments. R.W.K. and M.B. prepared protein samples. R.W.K. and S.N. acquired electron microscopy data, analyzed images and prepared the figures. R.W.K., W.B. and S.N. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Wolfgang Baumeister Author Details * Roland Wilhelm Knispel Search for this author in: * NPG journals * PubMed * Google Scholar * Christine Kofler Search for this author in: * NPG journals * PubMed * Google Scholar * Marius Boicu Search for this author in: * NPG journals * PubMed * Google Scholar * Wolfgang Baumeister Contact Wolfgang Baumeister Search for this author in: * NPG journals * PubMed * Google Scholar * Stephan Nickell Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–4 and Supplementary Table 1 Additional data
• Dual-objective STORM reveals three-dimensional filament organization in the actin cytoskeleton
  - Nat Methods 9(2):185-188 (2012)
  Nature Methods | Brief Communication Dual-objective STORM reveals three-dimensional filament organization in the actin cytoskeleton * Ke Xu1 * Hazen P Babcock2 * Xiaowei Zhuang1, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:185–188Year published:(2012)DOI:doi:10.1038/nmeth.1841Received 12 October 2011 Accepted 04 December 2011 Published online 08 January 2012 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg By combining astigmatism imaging with a dual-objective scheme, we improved the image resolution of stochastic optical reconstruction microscopy (STORM) and obtained <10-nm lateral resolution and <20-nm axial resolution when imaging biological specimens. Using this approach, we resolved individual actin filaments in cells and revealed three-dimensional ultrastructure of the actin cytoskeleton. We observed two vertically separated layers of actin networks with distinct structural organizations in sheet-like cell protrusions. View full text Subject terms: * Microscopy * Imaging * Cell Biology Figures at a glance * Figure 1: Experimental setup and spatial resolution of dual-objective 3D STORM. () Schematic of setup. Two microscope objectives are placed opposite each other and focused on the same spot of the sample. Astigmatism is introduced into the images collected by both objectives using a cylindrical lens. M, mirror; Obj., objective; LP, long-pass filter; CL, cylindrical lens; BP, band-pass filter. () Localization precision of Alexa Fluor 647 molecules in fixed cells measured with dual-objective STORM. Each molecule gives a cluster of localizations owing to repetitive activation of the same molecule. Localizations from 108 clusters (each containing >10 localizations) are aligned by their center of mass to generate the 3D presentation of the localization distribution. Histograms of distribution in x, y and z are fit to Gaussian functions, and the resultant s.d. (σx, σy and σz) is shown. () Distribution of number of photons detected for individual Alexa Fluor 647 molecules through both objectives (red; average, 10,600) and from a single objective (black; aver! age, 5,200). () Images of activated Alexa Fluor 647 molecules obtained from two objectives in a single frame. A molecule that appears elongated in x through one objective should appear elongated in y through the opposing objective (examples, green and blue arrows). In contrast, if two nearby molecules were mistaken for a single molecule, the images obtained through both objectives would appear elongated in the same direction along the line that connects the two molecules (example, magenta arrows). Scale bar, 2 μm. * Figure 2: Dual-objective 3D STORM resolves individual actin filaments in cells. () Dual-objective STORM image of actin (labeled with Alexa Fluor 647-phalloidin) in a COS-7 cell. The z positions are color coded (violet and red, positions closest to and farthest from substratum, respectively). () Close-up of boxed region in . () STORM image of same area obtained by using only information collected by Objective 1 of dual-objective setup. () Conventional fluorescence image of same area. () Cross-sectional profile of eight filaments aligned by the center of each filament. Red line, Gaussian fit with FWHM of 12 nm. () Cross-sectional profiles for two nearby filaments in , (white arrows). Gray bars, dual-objective images in ; red line, single-objective image in . Scale bars, 2 μm (), 500 nm (–). * Figure 3: Sheet-like cell protrusion comprises two layers of actin networks with distinct structures. () Dual-objective STORM image of actin in a BSC-1 cell. The z positions are color coded (color bar). (,) Vertical cross sections (each 500-nm wide in x or y) of cell in along dotted and dashed lines, respectively. When far from cell edge, z position of dorsal layer increases quickly and falls out of imaging range. (,) The z profiles for two points along vertical section (red and yellow arrows in , respectively). Each histogram is fit to two Gaussians (red curves), yielding apparent thickness of ventral and dorsal layers and peak separation between the two layers. () Quantification of apparent thickness averaged over two layers and dorsal-ventral separation obtained from x–z cross-section profile in . (,) Ventral and dorsal actin layers of cell in . (,) Ventral and dorsal actin layers of a COS-7 cell treated with blebbistatin. (,) Vertical cross sections (each 500-nm wide in x or y) of cell along dotted and dashed lines, respectively. () Actin density of ventral and dorsal ! layers along yellow box in ,, measured by localization density. Scale bars, 2 μm (,–); 100 nm for z and 2 μm for x and y (,,,). Author information * Author information * Supplementary information Affiliations * Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA. * Ke Xu & * Xiaowei Zhuang * Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA. * Hazen P Babcock * Department of Physics, Harvard University, Cambridge, Massachusetts, USA. * Xiaowei Zhuang Contributions K.X., H.P.B. and X.Z. designed research. K.X. did experiments and data analysis. H.P.B. assisted with the optical setup. K.X. and X.Z. prepared the manuscript. X.Z. supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Xiaowei Zhuang Author Details * Ke Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Hazen P Babcock Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaowei Zhuang Contact Xiaowei Zhuang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (24M) Supplementary Figures 1–7, Supplementary Results, Supplementary Discussion and Supplementary Protocols 1–2 Zip files * Supplementary Software (8K) Analysis software Additional data
• Visualizing adenosine-to-inosine RNA editing in the Drosophila nervous system
  - Nat Methods 9(2):189-194 (2012)
  Nature Methods | Article Visualizing adenosine-to-inosine RNA editing in the Drosophila nervous system * James E C Jepson1, 2 * Yiannis A Savva1 * Kyle A Jay1, 3 * Robert A Reenan1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:189–194Year published:(2012)DOI:doi:10.1038/nmeth.1827Received 08 June 2011 Accepted 17 November 2011 Published online 25 December 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Informational recoding by adenosine-to-inosine RNA editing diversifies neuronal proteomes by chemically modifying structured mRNAs. However, techniques for analyzing editing activity on substrates in defined neurons in vivo are lacking. Guided by comparative genomics, here we reverse-engineered a fluorescent reporter sensitive to Drosophila melanogaster adenosine deaminase that acts on RNA (dADAR) activity and alterations in dADAR autoregulation. Using this artificial dADAR substrate, we visualized variable patterns of RNA-editing activity in the Drosophila nervous system between individuals. Our results demonstrate the feasibility of structurally mimicking ADAR substrates as a method to regulate protein expression and, potentially, therapeutically repair mutant mRNAs. Our data suggest variable RNA editing as a credible molecular mechanism for mediating individual-to-individual variation in neuronal physiology and behavior. View full text Subject terms: * Molecular Biology * Molecular Engineering * Neuroscience * Sensors and Probes Figures at a glance * Figure 1: Molecular design of a fluorescent reporter of RNA editing. () Organization of endogenous Syt1 (top) and experimentally verified dsRNA secondary structures8 in the pre-mRNA (bottom). Exonic sequences (black) base pair with intronic sequences (blue) to generate this structure. I/V and I/M refer to amino-acid alterations owing to editing at sites C and D, respectively. () Insertion of Syt1 intron 9 into the open reading frame of GFP in such a way that the second-position guanosine (green) of a tryptophan codon (TGG) is positioned at precisely the same position as the edited adenosine of site D. () Mutations (red) introduced into the construct in restore the secondary structure (but not primary sequence) of the endogenous Syt1 dADAR substrate, and the tryptophan codon has been converted to an amber (UAG) nonsense codon. () Same structure as in , except that the tryptophan codon is wild type (UGG), and the construct (YFPsplice) encodes YFP rather than GFP. * Figure 2: GFPedit–derived fluorescence in Drosophila tissues. (,) Bright-field (top) and epi-fluorescence (excitation at 488 nm; bottom) whole-mount images of heads () and forelegs () of 1–2-day-old males expressing the YFPsplice control (left) or GFPedit in a wild-type (middle) or dAdar5g1 (right) background, driven by tub-Gal4. Lab, labelum; cly, clypeus. Images shown in the middle and on the right were exposed to the same fluorescence intensity. Scale bars, 100 μm. (,) In the adult nervous system, YFPsplice () and GFPedit () were driven by tub-Gal4. YFP and GFP were visualized using an antibody to GFP and a fluorescent Cy5-conjugated secondary antibody. z-dimension stacks show expression in the mushroom body lobes (α, β and γ) and antennal lobes (AL) (top). Confocal slices detail expression in the central complex (bottom; highlighted by arrowheads). Neuropil regions were labeled with an antibody to Bruchpilot (nc82). Scale bars, 20 μm. * Figure 3: Neuron-specific modulation of GFPedit–derived fluorescence by alterations in dADAR auto-editing. () Diagrams of the dAdar locus illustrating engineered mutations introduced using homologous recombination. The dAdarhyp allele is a result of insertion of a mini-white+ (mw+) marker into intron 7 of the dAdar locus, flanked by two LoxP sites (triangles). Excision of the mw+ by Cre recombinase leaves a single LoxP site. dAdarS and dAdarG recombinant flies have targeted mutations in the edited serine codon in exon 7 that either disrupts (dAdarS) or hard-wires (dAdarG) auto-editing. () Representative confocal z-dimension stacks of GFPedit–derived expression in the mushroom bodies (MBs) and antennal lobes (ALs) of hemizygous males of indicated genotypes. Merged confocal images of nc82 (antibody to Bruchpilot) and GFP (top). GFP fluorescence shown in glowscale to illustrate relative intensities (bottom). () Quantification of GFPedit–derived fluorescence in α/β and γ lobes of MBs and ALs in dAdarhyp (n = 14, 12 and 12, respectively), dAdarG (n = 65, 40 and 40) and dAdarS (! n = 66, 42 and 42) males, normalized to dAdarLoxP controls (n = 79, 48 and 48). ***P < 0.0001 relative to dAdarLoxP controls (one-way ANOVA with Dunnett post-hoc test). Error bars, s.e.m. Scale bars, 20 μm. * Figure 4: Variability in dADAR activity between individual male Drosophila. () Whole-head fluorescence images of five individual adult males expressing GFPedit (top; boxed regions are magnified below). Arrowheads highlight variable fluorescence in regions corresponding to the antennal lobes (ALs) and the Kenyon cells. () Examples of GFPedit–derived fluorescence in adult male forelegs. Scale bars, 100 μm. (–) Histograms of YFPsplice–derived (,) or GFPedit–derived (,) fluorescence in the clypeus (,) and in the large segment of adult forelegs (,). Values for the population studied were normalized such that the average fluorescence = 1. * Figure 5: Nonstereotypical patterns of RNA editing in Drosophila neurons. (,) Example images of variation in YFPsplice–derived () and GFPedit–derived () fluorescence in the mushroom bodies (MBs) and antennal lobes (ALs). Scale bars, 20 μm. (–) Histograms of YFPsplice–derived and GFPedit–derived expression in ALs and α/β and γ lobes of the MBs. () Correlation between the sensitivity of ten edited adenosines to changes in dADAR levels and coefficient of variation (CV) values between individuals for editing of each site in 14 male flies. Each edited mRNA was amplified from cDNA derived from individual head and thorax tissue, and the mean editing level calculated from 3–5 PCRs. As a proxy for sensitivity to dADAR expression changes, we used the percentage reduction relative to controls of editing in dAdarhyp thoraxes15. () Normalized mean editing levels in each fly (flies 1–14) relative to the group mean for sites (n = 6) that are reduced in editing in thoraxes of dAdarhyp flies (and also have CV > 0.08). Flies 1, 2 and 6 had signifi! cant deviations from the mean for the combined six editing sites (*P < 0.05, ***P < 0.0005, paired t-test), suggesting that dADAR activity was broadly higher (fly 2) or lower (flies 1 and 6). () For sites that were not reduced in editing in thoraxes of dAdarhyp flies (that is, were insensitive to changes in dADAR expression) (n = 4), only minimal variation between flies was observed, as expected. Error bars, s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA. * James E C Jepson, * Yiannis A Savva, * Kyle A Jay & * Robert A Reenan * Department of Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA. * James E C Jepson * Department of Biochemistry and Biophysics, University of California, San Francisco, California, USA. * Kyle A Jay Contributions J.E.C.J. performed all imaging experiments and analyzed the data. Y.A.S. and J.E.C.J. performed RNA-editing analysis. R.A.R. designed the GFPedit reporter. K.A.J. and R.A.R. cloned the GFPedit and YFPsplice constructs and performed in vitro validation experiments. Y.A.S. and J.E.C.J. generated the recombinant dAdar alleles. J.E.C.J. and R.A.R. wrote the paper, with contributions from Y.A.S. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Robert A Reenan Author Details * James E C Jepson Search for this author in: * NPG journals * PubMed * Google Scholar * Yiannis A Savva Search for this author in: * NPG journals * PubMed * Google Scholar * Kyle A Jay Search for this author in: * NPG journals * PubMed * Google Scholar * Robert A Reenan Contact Robert A Reenan Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–8 and Supplementary Table 1 Additional data
• Bayesian localization microscopy reveals nanoscale podosome dynamics
  - Nat Methods 9(2):195-200 (2012)
  Nature Methods | Article Bayesian localization microscopy reveals nanoscale podosome dynamics * Susan Cox1, 7 * Edward Rosten2, 3, 7 * James Monypenny1 * Tijana Jovanovic-Talisman4 * Dylan T Burnette4 * Jennifer Lippincott-Schwartz4 * Gareth E Jones1 * Rainer Heintzmann1, 5, 6 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:195–200Year published:(2012)DOI:doi:10.1038/nmeth.1812Received 25 December 2010 Accepted 02 November 2011 Published online 04 December 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We describe a localization microscopy analysis method that is able to extract results in live cells using standard fluorescent proteins and xenon arc lamp illumination. Our Bayesian analysis of the blinking and bleaching (3B analysis) method models the entire dataset simultaneously as being generated by a number of fluorophores that may or may not be emitting light at any given time. The resulting technique allows many overlapping fluorophores in each frame and unifies the analysis of the localization from blinking and bleaching events. By modeling the entire dataset, we were able to use each reappearance of a fluorophore to improve the localization accuracy. The high performance of this technique allowed us to reveal the nanoscale dynamics of podosome formation and dissociation throughout an entire cell with a resolution of 50 nm on a 4-s timescale. View full text Subject terms: * Microscopy * Imaging * Cell Biology * Biophysics Figures at a glance * Figure 1: Correlative measurements using PALM imaging and Bayesian localization imaging on tubulin. (,) Wide-field images created by averaging all the frames in the PALM image dataset of PA-GFP–tubulin () and by averaging all of the frames in the Bayesian localization image dataset of mCherry-tubulin (). (,) Super-resolution images generated by analyzing the PALM PA-GFP–tubulin dataset from using a standard PALM analysis5 () and by analyzing the mCherry-tubulin dataset from using a 3B analysis (). () An image generated from the PAGFP dataset from using a 3B analysis. () Overlay of and . Green arrows indicate regions with differences in apparent structure that arise from labeling differences. Linescans corresponding to lines i–iii are shown in –, respectively, with the 3B analysis data shown in blue, PALM data shown in pink, the 3B analysis PALM data shown in green and the wide-field data shown in black. Scale bars, 1 μm. AU, arbitrary units. * Figure 2: A 3B analysis of vinculin in fixed cells containing podosomes and labeled with Alexa 488. () An example of a maximum likelihood estimate for one set of MCMC samples superimposed on a wide-field image created by averaging all 300 images. () A probability map created by combining MAP positions created using different sets of MCMC samples. Scale bars, 500 nm. (,) A whole cell showing a wide-field () and 3B analysis (). The green rectangle corresponds to the enlarged image in , the blue rectangle corresponds to the enlarged image in , and the white rectangle corresponds to the enlarged image in . Scale bars, 500 nm (,); 2 mm (,); and 500 nm (–). * Figure 3: Podosomes, visualized using an mCherry-tagged truncated talin construct, forming and dissociating in a live cell. (,) A podosome being dissociated. Scale bars, 400 nm. () Podosomes being formed. Scale bar, 1 μm. () A steady-state podosome. Scale bar, 400 nm. Each reconstructed frame used 200 frames (4 s), and frames are spaced 600 frames (12 s) apart. Videos of the podosomes shown in – are provided as Supplementary Videos 3,4,5,6, respectively. * Figure 4: Dissociation and formation of groups of podosomes in a motile cell. (,) Dissociation and formation of linked podosomes. () Separated podosomes joining together. Each reconstructed frame used 200 frames (4 s), and frames are spaced 1,000 frames (20 s) apart. A video containing the podosomes shown in – as well as the rest of the cell is provided as Supplementary Video 7. Scale bars, 800 nm. * Figure 5: A 3B analysis of fixed-cell data to determine the colocalization of vinculin and the truncated talin construct in podosomes. () Wide-field image of vinculin labeled with Alexa 488. (,) The individual 3B analysis images shown in glowscale for talin () and vinculin (). () Superposition of images from the 3B analysis showing the truncated talin construct (in cyan) and vinculin 3B data (in magenta). Scale bars, 1 μm. * Figure 6: Simulations showing the performance of the 3B analysis method. (–) Ground truth simulated image data (,) and 3B analysis reconstructions (,). (–) For the simulations, the simulated wide-field image created by averaging all frames (,) and a typical frame (,) are shown. Images in and correspond to the boxed regions in and , respectively. (,) Linescans of the simulations and 3B analysis reconstructions show the 3B analysis method achieving good reproduction of the structure and a resolution of 50 nm. Scale bars, 50 nm (,); otherwise, 200 nm. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Susan Cox & * Edward Rosten Affiliations * Randall Division, King's College London, Guy's Campus, London, UK. * Susan Cox, * James Monypenny, * Gareth E Jones & * Rainer Heintzmann * Department of Engineering, University of Cambridge, Cambridge, UK. * Edward Rosten * Computer Vision Consulting Ltd., Lynton House, Woking, Surrey, UK. * Edward Rosten * National Institutes of Health, Cell Biology and Metabolism Branch, Bethesda, Maryland, USA. * Tijana Jovanovic-Talisman, * Dylan T Burnette & * Jennifer Lippincott-Schwartz * Institute of Physical Chemistry, Friedrich-Schiller University Jena, Jena, Germany. * Rainer Heintzmann * Institute of Photonic Technology, Jena, Germany. * Rainer Heintzmann Contributions S.C., J.M., T.J.–T., D.T.B., J.L.-S., G.E.J. and R.H. conceived of and designed the experiments. S.C. and E.R. conceived of and designed the analysis. J.M. prepared the podosome samples, and T.J.–T. and D.T.B. prepared the samples for correlative measurements. S.C. and J.M. performed live-cell experiments, S.C. carried out fixed-cell experiments on podosomes, and T.J.–T. and D.T.B. carried out the correlative measurements. E.R. and S.C. carried out the data analysis and wrote the manuscript, and all authors revised the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Susan Cox Author Details * Susan Cox Contact Susan Cox Search for this author in: * NPG journals * PubMed * Google Scholar * Edward Rosten Search for this author in: * NPG journals * PubMed * Google Scholar * James Monypenny Search for this author in: * NPG journals * PubMed * Google Scholar * Tijana Jovanovic-Talisman Search for this author in: * NPG journals * PubMed * Google Scholar * Dylan T Burnette Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Lippincott-Schwartz Search for this author in: * NPG journals * PubMed * Google Scholar * Gareth E Jones Search for this author in: * NPG journals * PubMed * Google Scholar * Rainer Heintzmann Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (413K) Supplementary Figures 1–4 and Supplementary Note Movies * Supplementary Video 1 (766K) Raw data video of vinculin in fixed podosome samples. Samples were labeled with Alexa 488 and mounted in PBS with 100 mM mercaptoethanol added as a reducing agent to induce blinking. Sample was illuminated using a laser at 488nm with a nominal power of 1 kW/cm2. A series of 300 images were collected, taken at 50 frames per second. Scalebar is 500 nm. * Supplementary Video 2 (2.5M) Raw data video of live THP-1 cells stably expressing an mCherry tagged, truncated talin construct. Sample was illuminated with a Xenon arc lamp in the wavelength range 615-687 nm. Video shows the first 500 of 8,000 images taken at frame rates of 50 fps. Scalebar is 2 μm. * Supplementary Video 3 (406K) Widefield (left) and 3B (right) video of a podosome being dissociated by unwinding. A truncated talin construct is labeled. This video corresponds to Figure 3a in the main text. Each widefield and 3B image is generated from 200 frames (4 seconds) of raw data, and are spaced 50 frames (1 second) apart. Widefield images are created by averaging. Cells were maintained at 37C during imaging. Scalebar is 1 μm. * Supplementary Video 4 (577K) Widefield (left) and 3B (right) video of a podosome being dissociate by being drawn into its center. A truncated talin construct is labeled. This video corresponds to Figure 3b in the main text. Each widefield and 3B image is generated from 200 frames (4 s) of raw data, and are spaced 50 frames (1 s) apart. Widefield images are created by averaging. Cells were maintained at 37C during imaging. Scalebar is 1 μm. * Supplementary Video 5 (975K) Widefield (left) and 3B (right) video of podosomes being constructed. A truncated talin construct is labeled. This video corresponds to Figure 3c in the main text. Each widefield and 3B image is generated from 200 frames (4 seconds) of raw data, and are spaced 50 frames (1 second) apart. Widefield images are created by averaging. Cells were maintained at 37C during imaging. Scalebar is 1 μm. * Supplementary Video 6 (343K) Widefield (left) and 3B (right) video of a steady state podosome in which a truncated talin construct is labeled. This video corresponds to Figure 3d in the main text. Each widefield and 3B image is generated from 200 frames (4 seconds) of raw data, and are spaced 50 frames (1 second) apart. Widefield images are created by averaging. Cells were maintained at 37C during imaging. Scalebar is 500 nm. * Supplementary Video 7 (5M) Widefield (left) and 3B (right) video reveals podosomes in a motile cell to be highly dynamic. A truncated talin construct is labeled in these cells. This video includes, as a small part, the areas shown in Figure 4a–c in the main text. Each widefield and 3B image is generated from 200 frames (4 seconds) of raw data, and are spaced 100 frames (2 s) apart. Widefield images are created by averaging. Cells were maintained at 37C during imaging. Scalebar is 2 μm. Zip files * Supplementary Software (1.7M) 3B analysis software. Contains source code, test data and instructions for use. Additional data
• Fast two-photon in vivo imaging with three-dimensional random-access scanning in large tissue volumes
  - Nat Methods 9(2):201-208 (2012)
  Nature Methods | Article Fast two-photon in vivo imaging with three-dimensional random-access scanning in large tissue volumes * Gergely Katona1, 5 * Gergely Szalay1, 5 * Pál Maák2, 5 * Attila Kaszás1, 3, 5 * Máté Veress2 * Dániel Hillier4 * Balázs Chiovini1 * E Sylvester Vizi1 * Botond Roska4 * Balázs Rózsa1, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:201–208Year published:(2012)DOI:doi:10.1038/nmeth.1851Received 26 May 2011 Accepted 12 December 2011 Published online 08 January 2012 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The understanding of brain computations requires methods that read out neural activity on different spatial and temporal scales. Following signal propagation and integration across a neuron and recording the concerted activity of hundreds of neurons pose distinct challenges, and the design of imaging systems has been mostly focused on tackling one of the two operations. We developed a high-resolution, acousto-optic two-photon microscope with continuous three-dimensional (3D) trajectory and random-access scanning modes that reaches near-cubic-millimeter scan range and can be adapted to imaging different spatial scales. We performed 3D calcium imaging of action potential backpropagation and dendritic spike forward propagation at sub-millisecond temporal resolution in mouse brain slices. We also performed volumetric random-access scanning calcium imaging of spontaneous and visual stimulation–evoked activity in hundreds of neurons of the mouse visual cortex in vivo. These expe! riments demonstrate the subcellular and network-scale imaging capabilities of our system. View full text Subject terms: * Imaging * Neuroscience * Microscopy Figures at a glance * Figure 1: Design and characterization of the two-photon microscope setup. () Schematics of microscope setup. Material-dispersion compensation was adjusted with a four prism compressor and a Ti:S laser. A Faraday isolator eliminated coherent back reflections. Motorized mirrors (M) stabilized the position of the beam on the surface of two quadrant detectors (Q) before the beam expander. Two AO deflectors optimized for diffraction efficiency controlled the z focusing of the beam (AO z focusing). A 2D AO scanner unit (2D-AO scanning) performed x-y scanning and drift compensation during z scanning. A spherical field lens in the second telecentric lens system (Tc3 and Tc4) provided additional angular dispersion compensation. PMT, photomultiplier tubes. () The maximal field of view (compensated, black bar) is shown when both deflector pairs were used for deflection (no deflector grouping) or when optically rotated deflectors (no acoustic rotation), small aperture objectives (60× objective), no angular dispersion compensation (with angular dispersion) or! small aperture acousto-optic deflectors were used (small aperture). () The compensated PSF size along x axis (PSFx) (central, black bar) at (x, y, z) = (150, 150, 100) μm coordinates (lateral) or when no angular dispersion compensation (with angular dispersion), no electronic compensation (no electric compensation) or reduced AO apertures were applied (no large apertures). () PSFz and PSFx variation as a function of z depth and lateral AO scanning (x shift). Error bars are mean ± s.d., n = 5. () Temporal width of the laser pulse at the laser output (original), and before the objective lens with and without dispersion compensation (prechirp). () Five fluorescent beads (diameter 6 μm; locations, blue points) were repetitively scanned in random-access mode in an 800 μm × 600 μm × 500 μm sample. Image shows bead locations (right). Five overlaid fluorescence measurements are shown (left). * Figure 2: Three-dimensional measurement of BAPs with sub-millisecond resolution. () A 3D view of the dendritic arbor of a CA1 pyramidal cell imaged with 3D AO scanning (top, z stack); spheres represent the measurement locations. Maximum intensity z-projection image of the same neuron (bottom left); recorded dendrites are numbered. Schema of the apical trunk and the dendritic branches of the neuron (bottom right) showing calcium transients recorded near-simultaneously in each of the dendrites, averaged from five traces. Scale bars, 100 μm. () Dendritic Ca2+ transients measured from the same dendritic point of the apical trunk (average of five traces) and corresponding somatic voltage traces (Vm). () Experimental arrangement for signal propagation experiments. Signal propagation speed was measured by somatic whole-cell current-clamp (Vm, black), cell-attached current-clamp (cyan, purple) and 3D two-photon calcium imaging (orange, pink, green and blue). The same color-coding is used in –. () Triggered action potential peak and averaged and normalized bac! kpropagating calcium transients (mean ± s.e.m.; n = 54; top). Linear fits (red dashed lines) define onset latency times. Maximal temporal resolution achieved: 39 μs (SEMT, x-axis projection of s.e.m., inset). Cell-attached somatic voltage recording (cyan) peaked at the maximum of the derivate (δVm / δt) of Vm (brown trace; bottom). Somatic (orange) versus dendritic (green) Ca2+ transients and position-matched cell-attached signals (dendritic, magenta; somatic, cyan). Arrows point to the lag of the Ca2+ signals. () Transients in in extended time scale. () First derivatives of the Ca2+ transients shown in . () Onset latency times (mean ± s.e.m., n = 54) of Ca2+ transients in as a function of dendritic distance. Linear fit: average propagation speed. () Dendritic propagation speeds at different temperatures (mean ± s.e.m., n = 5 cells). * Figure 3: Point-by-point and continuous 3D trajectory scanning of dendritic Ca2+ spike propagation in CA1 pyramidal cells. () Maximum-intensity projection AO image of a CA1 pyramidal cell. Ca2+ transients in dendritic spines (orange and magenta traces) after induction of dendritic Ca2+ spike by focal extracellular stimulation (electrode, yellow). Enlarged views are shown in insets. Purple dots represent scanning points in a dendrite. () Spatially normalized and projected Ca2+ signals in the purple dotted dendritic region in (average of five subthreshold responses). Black dashed line, stimulus onset. Column labeled 'S', somatic Ca2+ response. () Ca2+ transients derived from the color-coded and numbered regions indicated in . Baseline-shifted Ca2+ transients measured in the region contained in the dashed box in (right). Yellow dots, onset latency times at the half-maximum. () Onset latency times at the peak of the derivate (δF(t)/δt) of Ca2+ transients shown in . () Onset latency times as a function of dendritic distance from the soma for somatic subthreshold (d-spike, black) and suprathreshold ! (d-spike+AP, blue) dendritic Ca2+ spikes. () Point-by-point and continuous 3D trajectory scanning of dendritic segments. Schema of the scanning modes (top right; blue, point-by-point scanning; green, continuous scanning). Example of Ca2+ responses measured by point-by-point (top left) and continuous trajectory modes (bottom). Traces were spatially normalized. () Image of a fluorescent bead in continuous trajectory scanning mode. The bead image was elongated because the focal spot was moving during PMT integration. Scale bar, 2 μm. * Figure 4: High-speed 3D calcium imaging of spontaneous neuronal network activity in vivo. () Sketch of in vivo experimental arrangement. Staining by bolus loading (OGB-1-AM and SR-101) in mouse V1. () Five representative planes at different depths imaged with 3D AO scanning. Scale bar, 100 μm. Depths are measured relative to the pia. () Example of an image plane at 200 μm depth showing neurons (green) and glial cells (magenta and white). Scale bar, 100 μm. () Image and intensity profiles of a pre-selected bright glial cell (purple box in ) used to establish the coordinate system. Scale bar, 5 μm. () A 35 μm z-projection of the dashed boxed region marked in (top). Neuronal somata detected with the aid of an algorithm in a subvolume (shown with projections, neurons in white and glial cells in black; bottom). Scale bar, 50 μm. () Maximal intensity side and z projections of the entire z stack (400 μm × 400 μm × 500 μm) with autodetected cell locations (spheres) color-coded in relation to depth following the legend in Figure 2a. The set detection threshold ! yields 532 neurons. Scale bar, 100 μm. () Spontaneous neuronal network activity measured in the 532 cells in . Example of a raw trace in which each line corresponds to a cell (left). Spatially normalized traces (middle) and corresponding Ca2+ transients (right). * Figure 5: 3D measurement of neuronal network activity in vivo in response to visual stimuli. () Sketch of in vivo experimental arrangement. Visual stimulation was induced by moving bars in eight directions of motion in 45° steps. () Maximal intensity side- and z-projection image of the entire z stack (280 μm × 280 μm × 230 μm; bolus loading with OGB-1-AM and SR-101). Spheres represent 375 autodetected neuronal locations color-coded by depth following scale in Figure 2a. Scale bars, 50 μm. () Parallel 3D recording of spontaneous Ca2+ responses from the 375 locations. Rows, single cells measured in random-access scanning mode. Scale bar, 5 s. () Examples of Ca2+ transients showing active neurons in . () Ca2+ responses from the same 375 neuronal locations (visual stimulation, moving bar at −45°). Rows, single cells from a single 3D measurement. Scale bar, 2 s. () Examples of Ca2+ transients from neurons in , preferentially responding to the −45° bar direction. () Stimulation with a 90° oriented stimulus (at −135°) in the same neurons in . () Examples o! f responses from simultaneously recorded neurons in . One nonselective, one orientation–selective and two direction-selective neurons are shown. Top rows, mean ± s.e.m. (n = 4–12 trials per orientation) in black; bottom rows, three color-coded single trials for each direction. Polar plot radius values from top to bottom: 0.2, 0.15, 0.2, 0.2 ΔF/F. () Three-dimensional map of orientation- and direction-selective cells measured in three dimensions in the volume in . Scale bar, 40 μm. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Gergely Katona, * Gergely Szalay, * Pál Maák & * Attila Kaszás Affiliations * Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary. * Gergely Katona, * Gergely Szalay, * Attila Kaszás, * Balázs Chiovini, * E Sylvester Vizi & * Balázs Rózsa * Department of Atomic Physics, Budapest University of Technology and Economics, Budapest, Hungary. * Pál Maák & * Máté Veress * The Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary. * Attila Kaszás & * Balázs Rózsa * Neural Circuit Laboratories, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. * Dániel Hillier & * Botond Roska Contributions Optical design was performed by P.M., G.S. and M.V. Software was written by G.K. In vitro measurements were performed by B.C., A.K., G.S. and Ba.R. In vivo measurements were designed by D.H. and performed by D.H., A.K., G.S. and Ba.R. Analysis was carried out by Ba.R., A.K., G.K. and G.S. This manuscript was written by Ba.R., Bo.R., D.H., G.K., A.K. and P.M., with comments from all authors. Ba.R., Bo.R., E.S.V. and P.M. supervised the project. Competing financial interests G.K., E.S.V. and Ba.R. are owners of Femtonics and the patent WO2010076579. Corresponding author Correspondence to: * Balázs Rózsa Author Details * Gergely Katona Search for this author in: * NPG journals * PubMed * Google Scholar * Gergely Szalay Search for this author in: * NPG journals * PubMed * Google Scholar * Pál Maák Search for this author in: * NPG journals * PubMed * Google Scholar * Attila Kaszás Search for this author in: * NPG journals * PubMed * Google Scholar * Máté Veress Search for this author in: * NPG journals * PubMed * Google Scholar * Dániel Hillier Search for this author in: * NPG journals * PubMed * Google Scholar * Balázs Chiovini Search for this author in: * NPG journals * PubMed * Google Scholar * E Sylvester Vizi Search for this author in: * NPG journals * PubMed * Google Scholar * Botond Roska Search for this author in: * NPG journals * PubMed * Google Scholar * Balázs Rózsa Contact Balázs Rózsa Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (17M) Supplementary Figures 1–14, Supplementary Results 1–3, Supplementary Discussion, Supplementary Notes 1–9 and Supplementary Protocols 1–3 Movies * Supplementary Video 1 (6M) A 3D virtual reality environment for 3D two-photon imaging. This movie shows a surface-fitted pyramidal cell (located in the hippocampal CA1 region) and selected 3D measurement locations used to record the bAP-induced Ca2+ transients shown in Figure 2a. Using the 3D virtual reality environment, the 3D measurement locations can be freely modified or observed from any angle. Head-tracked shutter glasses ensure that the virtual objects maintain a stable, 'fixed' virtual position even when viewed from different viewpoints and angles. That is, the cell's virtual coordinate system is locked in space when the viewer's head position (view angle) changes; however, it can be rotated or shifted by the 3D 'bird' mouse. The bird also allows the 3D measurement points to be picked and repositioned in the virtual 3D space of the cell. * Supplementary Video 2 (1M) Automatic selection of the measurement points for 3D two-photon imaging in vivo. This movie shows a bulk-loaded cell assembly located in the mouse visual cortex, visualized with real-time maximum-intensity projection in the 3D virtual-reality environment. After detecting putative neuron locations from the stack (see Supplementary Note 5), the experimenter can set the selection threshold with real-time control of the number of selected cells and their localization. * Supplementary Video 3 (1M) Millimeter-range image stack captured without mechanical movement. This movie shows a 3D image stack of neurons from a fluorescently labeled invertebrate ganglion also shown in Figure 1g. While capturing the images, the microscope objective was fixed; images were taken by AO z-focusing. The stack dimensions are 717 μm × 717 μm × 1,071 μm; 40 slices. Zip files * Supplementary Software 1 (250K) Use of AD9910. This summary contains information about the usage of the AD9910 DDS chip used to generate frequency signals for the acousto-optic crystals. Wiring to the FPGA, routines used to initialize the chip and Matlab code segments calculating the necessary register values during scanning are incorporated. * Supplementary Software 2 (23M) A 3D interactive workstation module. This program provides a 3D VR environment with an open-source Matlab interface. It is possible to visualize and interact with 3D MIP projected volume data, surfaces and various annotation objects needed for controlling the experiments and for visualizing the results. It can perform mono or anaglyph views or be used in combination with the Leonar3Do virtual reality hardware. * Supplementary Software 3 (20K) Automatic drift-compensation algorithm. Description and code parts used for maintaining scan locations on the cells to measure. * Supplementary Software 4 (500K) Automatic detection of fluorescently labeled cells. Matlab code identifying cell centers using three dimensional two-channel measurement data was developed for combined OGB-1 and SR-101 bolus loading experiments (Supplementary Note 9 and Supplementary Video 2). Accompanying sample data help evaluate the performance of the code. Additional data