Latest Articles Include:
- NUAP (no unnecessary acronyms please)
- Nat Methods 8(7):521 (2011)
Nature Methods | Editorial NUAP (no unnecessary acronyms please) Journal name:Nature MethodsVolume: 8,Page:521Year published:(2011)DOI:doi:10.1038/nmeth.1646Published online29 June 2011 Acronyms that refer to methods are often useful but not always appropriate. Here are some guidelines. View full text Additional data - The author file: Xiaoliang Sunney Xie
- Nat Methods 8(7):523 (2011)
Nature Methods | This Month The author file: Xiaoliang Sunney Xie * Monya BakerJournal name:Nature MethodsVolume: 8,Page:523Year published:(2011)DOI:doi:10.1038/nmeth.1639Published online29 June 2011 Single-molecule studies lead to high-throughput sequencing. View full text Additional data - Points of view: Avoiding color
- Nat Methods 8(7):525 (2011)
Article preview View full access options Nature Methods | This Month Points of view: Avoiding color * Bang Wong1Journal name:Nature MethodsVolume: 8,Page:525Year published:(2011)DOI:doi:10.1038/nmeth.1642Published online29 June 2011 Last month I wrote about color blindness and ways to make information accessible to individuals with color vision deficiencies. I would like to continue by considering graphical alternatives to color that could improve the overall clarity and utility of data displays. The primary use of color in research is to convey information. When used effectively, color can simplify a complex analysis task. When misused, it can bias a reader's perception of the underlying data. For example, when color gradients indicating relative quantity contain abrupt transitions, specific numerical ranges can be preferentially accentuated (Fig. 1a). Edward Tufte advises us that color used poorly is worse than no color at all; his motto is: "Above all, do no harm"1. Color can cause the wrong information to stand out and make meaningful information difficult to see. Furthermore, the overuse of color can produce visual clutter akin to signage in Times Square or Piccadilly Circus with countless elements competing for our attention. Figure 1: Color can mask data. () Color scale with sharp transition in hue and value (arrow) can exaggerate specific data ranges. () Juxtaposing colors highly varying in saturation and value can make aspects of the data appear under-represented (light blue). * Full size image (83 KB) * Figures index * Next figure In addition to limiting accessibility, there are several other disadvantages to using color to present data. I showed how the visual phenomenon resulting from the interaction of color can cause the same color in heatmaps to appear different2. Color is a relative medium. When we pair hues varying greatly in saturation or value (lightness), we can unintentionally produce presentations that are lopsided. In Figure 1b, the light blue bands appear under-represented partially because they are lighter than the other colors as evident by looking at the key in grayscale (Fig. 1b). Color can also elicit size biases; some people find equal areas filled with vibrant colors seem to be more dissimilar than when less saturated colors are used. Figures at a glance * Figure 1: Color can mask data. () Color scale with sharp transition in hue and value (arrow) can exaggerate specific data ranges. () Juxtaposing colors highly varying in saturation and value can make aspects of the data appear under-represented (light blue). * Figure 2: Color can limit accessibility and hinder analysis. () Heatmap representation of time series data for species A and B. () Filled line charts of data from facilitate profile comparison. () Color hue indicates correlation score for metabolites in glycolysis (boxes). Enzymes are shown as arrows. () Replacing color encoding from with bar length for metabolites and position of circles on the x axis for enzymes increases data density and makes rank ordering easy. Color indicates directionality of enzymatic activity. Visualization technique is from reference 3. Article preview Read the full article * Instant access to this article: US$32 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 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Bang Wong is the creative director of the Broad Institute of the Massachusetts Institute of Technology & Harvard and an adjunct assistant professor in the Department of Art as Applied to Medicine at The Johns Hopkins University School of Medicine. Author Details * Bang Wong Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Identification of clustering artifacts in photoactivated localization microscopy
- Nat Methods 8(7):527-528 (2011)
Nature Methods | Correspondence Identification of clustering artifacts in photoactivated localization microscopy * Paolo Annibale1, 3 * Stefano Vanni2, 3 * Marco Scarselli1, 3 * Ursula Rothlisberger2 * Aleksandra Radenovic1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:527–528Year published:(2011)DOI:doi:10.1038/nmeth.1627Published online12 June 2011 To the Editor: Fluorescent proteins are known to display in certain cases an on-off blinking and switching behavior1. We could not yet find a report investigating the impact of this phenomenon on super-resolution techniques that are based on the sequential photoactivation and bleaching of individual emitters such as photoactivated localization microscopy (PALM)2. In our hands, even monomeric (m)Eos2, one of the most promising photoconvertible fluorescent proteins reported in this journal3, had non-negligible light-induced fluorescence recovery4 (Supplementary Figs. 1 and 2). This is particularly important for imaging membrane receptors, for which phenomena such as oligomerization and clustering of proteins can be properly identified only if the number of their constituents is correctly estimated. View full text 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 Primary authors * These authors contributed equally to this work. * Paolo Annibale, * Stefano Vanni & * Marco Scarselli Affiliations * Laboratory of Nanoscale Biology, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. * Paolo Annibale, * Marco Scarselli & * Aleksandra Radenovic * Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. * Stefano Vanni & * Ursula Rothlisberger Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Aleksandra Radenovic Author Details * Paolo Annibale Search for this author in: * NPG journals * PubMed * Google Scholar * Stefano Vanni Search for this author in: * NPG journals * PubMed * Google Scholar * Marco Scarselli Search for this author in: * NPG journals * PubMed * Google Scholar * Ursula Rothlisberger Search for this author in: * NPG journals * PubMed * Google Scholar * Aleksandra Radenovic Contact Aleksandra Radenovic Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (884K) Supplementary Figures 1–3, Supplementary Methods Additional data - PSICQUIC and PSISCORE: accessing and scoring molecular interactions
- Nat Methods 8(7):528-529 (2011)
Nature Methods | Correspondence PSICQUIC and PSISCORE: accessing and scoring molecular interactions * Bruno Aranda1, 27 * Hagen Blankenburg2, 27 * Samuel Kerrien1 * Fiona S L Brinkman3 * Arnaud Ceol4, 5 * Emilie Chautard6, 7 * Jose M Dana1 * Javier De Las Rivas8 * Marine Dumousseau1 * Eugenia Galeota5, 9 * Anna Gaulton1 * Johannes Goll10 * Robert E W Hancock11 * Ruth Isserlin12 * Rafael C Jimenez1 * Jules Kerssemakers13 * Jyoti Khadake1 * David J Lynn14 * Magali Michaut12 * Gavin O'Kelly1 * Keiichiro Ono15 * Sandra Orchard1 * Carlos Prieto8, 16 * Sabry Razick17, 18 * Olga Rigina19 * Lukasz Salwinski20 * Milan Simonovic21 * Sameer Velankar1 * Andrew Winter22 * Guanming Wu7 * Gary D Bader12 * Gianni Cesareni5, 9 * Ian M Donaldson17, 23 * David Eisenberg20, 24, 25 * Gerard J Kleywegt1 * John Overington1 * Sylvie Ricard-Blum6 * Mike Tyers22, 26 * Mario Albrecht2 * Henning Hermjakob1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:528–529Year published:(2011)DOI:doi:10.1038/nmeth.1637Published online29 June 2011 To the Editor: To study proteins in the context of a cellular system, it is essential that the molecules with which a protein interacts are identified and the functional consequence of each interaction is understood. A plethora of resources now exist to capture molecular interaction data from the many laboratories generating such information, but whereas such databases are rich in information, the sheer number and variability of such databases constitutes a substantial challenge in both data access and quality assessment to the researchers interested in a specific biological domain. View full text Subject terms: * Bioinformatics * Proteomics 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 Primary authors * These authors contributed equally to this work. * Bruno Aranda & * Hagen Blankenburg Affiliations * European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK. * Bruno Aranda, * Samuel Kerrien, * Jose M Dana, * Marine Dumousseau, * Anna Gaulton, * Rafael C Jimenez, * Jyoti Khadake, * Gavin O'Kelly, * Sandra Orchard, * Sameer Velankar, * Gerard J Kleywegt, * John Overington & * Henning Hermjakob * Max Planck Institute for Informatics, Saarbrücken, Germany. * Hagen Blankenburg & * Mario Albrecht * Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada. * Fiona S L Brinkman * Institute for Research in Biomedicine, Barcelona, Spain. * Arnaud Ceol * Department of Biology, University of Rome Tor Vergata, Rome, Italy. * Arnaud Ceol, * Eugenia Galeota & * Gianni Cesareni * Institut de Biologie et Chimie des Protéines, Unité Mixte de Recherche 5086, Centre National de la Recherche Scientifique–Université Lyon 1, Lyon, France. * Emilie Chautard & * Sylvie Ricard-Blum * Ontario Institute for Cancer Research, Toronto, Ontario, Canada. * Emilie Chautard & * Guanming Wu * Cancer Research Center, Centro de Investigación de Cáncer–Instituto de Biología Molecular y Celular del Cáncer, Consejo Superior de Investigaciones Científicas, Universidad de Salamanca, Salamanca, Spain. * Javier De Las Rivas & * Carlos Prieto * Istituto di Ricovero e Cura a Carattere Scientifico, Fondazione S. Lucia, Rome, Italy. * Eugenia Galeota & * Gianni Cesareni * J. Craig Venter Institute, Rockville, Maryland, USA. * Johannes Goll * Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, British Columbia, Canada. * Robert E W Hancock * The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. * Ruth Isserlin, * Magali Michaut & * Gary D Bader * Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. * Jules Kerssemakers * Animal and Bioscience Research Department, Animal and Grassland Research Innovation Centre, Teagasc, Ireland. * David J Lynn * University of California, Trey Ideker Lab, San Diego, School of Medicine, La Jolla, California, USA. * Keiichiro Ono * Institute of Biotechnology of León, León, Spain. * Carlos Prieto * The Biotechnology Centre of Oslo, University of Oslo, Oslo, Norway. * Sabry Razick & * Ian M Donaldson * Biomedical Research Group, Department of Informatics, University of Oslo, Oslo, Norway. * Sabry Razick * Center for Biological Sequence Analysis, BioCentrum, Technical University of Denmark, Kongens Lyngby, Denmark. * Olga Rigina * University of California, Los Angeles, Department of Energy Institute for Genomics and Proteomics, Los Angeles, California, USA. * Lukasz Salwinski & * David Eisenberg * Faculty of Science, Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland. * Milan Simonovic * Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland. * Andrew Winter & * Mike Tyers * Department for Molecular Biosciences, University of Oslo, Oslo, Norway. * Ian M Donaldson * Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, USA. * David Eisenberg * Howard Hughes Medical Institute, University of California, Los Angeles, California, USA. * David Eisenberg * Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. * Mike Tyers Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bruno Aranda Author Details * Bruno Aranda Contact Bruno Aranda Search for this author in: * NPG journals * PubMed * Google Scholar * Hagen Blankenburg Search for this author in: * NPG journals * PubMed * Google Scholar * Samuel Kerrien Search for this author in: * NPG journals * PubMed * Google Scholar * Fiona S L Brinkman Search for this author in: * NPG journals * PubMed * Google Scholar * Arnaud Ceol Search for this author in: * NPG journals * PubMed * Google Scholar * Emilie Chautard Search for this author in: * NPG journals * PubMed * Google Scholar * Jose M Dana Search for this author in: * NPG journals * PubMed * Google Scholar * Javier De Las Rivas Search for this author in: * NPG journals * PubMed * Google Scholar * Marine Dumousseau Search for this author in: * NPG journals * PubMed * Google Scholar * Eugenia Galeota Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Gaulton Search for this author in: * NPG journals * PubMed * Google Scholar * Johannes Goll Search for this author in: * NPG journals * PubMed * Google Scholar * Robert E W Hancock Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth Isserlin Search for this author in: * NPG journals * PubMed * Google Scholar * Rafael C Jimenez Search for this author in: * NPG journals * PubMed * Google Scholar * Jules Kerssemakers Search for this author in: * NPG journals * PubMed * Google Scholar * Jyoti Khadake Search for this author in: * NPG journals * PubMed * Google Scholar * David J Lynn Search for this author in: * NPG journals * PubMed * Google Scholar * Magali Michaut Search for this author in: * NPG journals * PubMed * Google Scholar * Gavin O'Kelly Search for this author in: * NPG journals * PubMed * Google Scholar * Keiichiro Ono Search for this author in: * NPG journals * PubMed * Google Scholar * Sandra Orchard Search for this author in: * NPG journals * PubMed * Google Scholar * Carlos Prieto Search for this author in: * NPG journals * PubMed * Google Scholar * Sabry Razick Search for this author in: * NPG journals * PubMed * Google Scholar * Olga Rigina Search for this author in: * NPG journals * PubMed * Google Scholar * Lukasz Salwinski Search for this author in: * NPG journals * PubMed * Google Scholar * Milan Simonovic Search for this author in: * NPG journals * PubMed * Google Scholar * Sameer Velankar Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew Winter Search for this author in: * NPG journals * PubMed * Google Scholar * Guanming Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Gary D Bader Search for this author in: * NPG journals * PubMed * Google Scholar * Gianni Cesareni Search for this author in: * NPG journals * PubMed * Google Scholar * Ian M Donaldson Search for this author in: * NPG journals * PubMed * Google Scholar * David Eisenberg Search for this author in: * NPG journals * PubMed * Google Scholar * Gerard J Kleywegt Search for this author in: * NPG journals * PubMed * Google Scholar * John Overington Search for this author in: * NPG journals * PubMed * Google Scholar * Sylvie Ricard-Blum Search for this author in: * NPG journals * PubMed * Google Scholar * Mike Tyers Search for this author in: * NPG journals * PubMed * Google Scholar * Mario Albrecht Search for this author in: * NPG journals * PubMed * Google Scholar * Henning Hermjakob Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2.2M) Supplementary Table 1, Supplementary Notes 1–3 Additional data - A flow cytometry revolution
- Nat Methods 8(7):531 (2011)
Nature Methods | Research Highlights A flow cytometry revolution * Allison DoerrJournal name:Nature MethodsVolume: 8,Page:531Year published:(2011)DOI:doi:10.1038/nmeth0711-531Published online29 June 2011 A new flow cytometry technology based on mass detection allows a very large number of parameters to be simultaneously measured in single cells. View full text Subject terms: * Immunology Additional data Author Details * Allison Doerr Search for this author in: * NPG journals * PubMed * Google Scholar - Guilt by phenotypic association
- Nat Methods 8(7):532-533 (2011)
Nature Methods | Research Highlights Guilt by phenotypic association * Natalie de SouzaJournal name:Nature MethodsVolume: 8,Pages:532–533Year published:(2011)DOI:doi:10.1038/nmeth0711-532aPublished online29 June 2011 Phenotypic screens of a complex in vivo structure predict function for essential genes in the worm. View full text Subject terms: * Genetics Additional data Author Details * Natalie de Souza Search for this author in: * NPG journals * PubMed * Google Scholar - Profiling the sixth base
- Nat Methods 8(7):532-533 (2011)
Nature Methods | Research Highlights Profiling the sixth base * Nicole RuskJournal name:Nature MethodsVolume: 8,Pages:532–533Year published:(2011)DOI:doi:10.1038/nmeth0711-532bPublished online29 June 2011 Two complementary methods facilitate genome-wide enrichment of hydroxymethylated DNA. View full text Subject terms: * Epigenetics Additional data Author Details * Nicole Rusk Search for this author in: * NPG journals * PubMed * Google Scholar - News in brief
- Nat Methods 8(7):533 (2011)
Article preview View full access options Nature Methods | Research Highlights News in brief Journal name:Nature MethodsVolume: 8,Page:533Year published:(2011)DOI:doi:10.1038/nmeth0711-533Published online29 June 2011 Improving molecular replacement Molecular replacement is a standard approach for reconstructing three-dimensional protein structures from crystallography data, but this method usually fails for proteins with less than 30% sequence identity to the nearest homologous structure. DiMaio et al. show that the Rosetta structural modeling program, used in combination with algorithms for crystallographic structure determination, could generate high-resolution structures for several proteins that could not be solved using traditional methods. DiMaio, F.et al. Nature473, 540–543 (2011). Article preview Read the full article * FREE access with registration Register now * Already have a Nature.com account? Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Single molecules made simple
- Nat Methods 8(7):535 (2011)
Nature Methods | Research Highlights Single molecules made simple * Daniel EvankoJournal name:Nature MethodsVolume: 8,Page:535Year published:(2011)DOI:doi:10.1038/nmeth0711-535Published online29 June 2011 A single-molecule pull-down method provides a simple way for biologists to examine their favorite protein at the single-molecule level. View full text Subject terms: * Single Molecule Additional data Author Details * Daniel Evanko Search for this author in: * NPG journals * PubMed * Google Scholar - The dynamic RNA world
- Nat Methods 8(7):536 (2011)
Nature Methods | Research Highlights The dynamic RNA world * Natalie de SouzaJournal name:Nature MethodsVolume: 8,Page:536Year published:(2011)DOI:doi:10.1038/nmeth0711-536Published online29 June 2011 A combination of techniques is used to measure and model RNA dynamics in dendritic cells. View full text Subject terms: * Systems Biology Additional data Author Details * Natalie de Souza Search for this author in: * NPG journals * PubMed * Google Scholar - Turning the lights on a few molecules at a time
- Nat Methods 8(7):538 (2011)
Nature Methods | Research Highlights Turning the lights on a few molecules at a time * Tal NawyJournal name:Nature MethodsVolume: 8,Page:538Year published:(2011)DOI:doi:10.1038/nmeth0711-538Published online29 June 2011 Researchers adapt split-GFP complementation to single-molecule imaging. View full text Subject terms: * Single Molecule Additional data Author Details * Tal Nawy Search for this author in: * NPG journals * PubMed * Google Scholar - Coding your way out of a problem
- Nat Methods 8(7):541-543 (2011)
Nature Methods | Technology Feature Coding your way out of a problem * Jeffrey M. Perkel1Journal name:Nature MethodsVolume: 8,Pages:541–543Year published:(2011)DOI:doi:10.1038/nmeth.1631Published online29 June 2011 Forays into information technology can make lab work easier to manage. 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 Affiliations * Jeffrey M. Perkel is a freelance writer based in Pocatello, Idaho, USA Corresponding author Correspondence to: * Jeffrey M. Perkel Author Details * Jeffrey M. Perkel Contact Jeffrey M. Perkel Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Generating high-quality protein binders: a large screening effort pays off
- Nat Methods 8(7):545-546 (2011)
Article preview View full access options Nature Methods | News and Views Generating high-quality protein binders: a large screening effort pays off * Peter Nollau1Journal name:Nature MethodsVolume: 8,Pages:545–546Year published:(2011)DOI:doi:10.1038/nmeth.1632Published online29 June 2011 A large-scale, multifaceted screening and validation strategy combining traditional and recombinant antibody technologies yielded a broad spectrum of validated protein binders with high binding affinity and specificity applicable to many fields in proteome research. 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 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Peter Nollau is at University Medical Center Hamburg–Eppendorf, Institute of Clinical Chemistry, Hamburg, Germany. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Peter Nollau Author Details * Peter Nollau Contact Peter Nollau Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Spectral archives: a vision for future proteomics data repositories
- Nat Methods 8(7):546-548 (2011)
Article preview View full access options Nature Methods | News and Views Spectral archives: a vision for future proteomics data repositories * Henry Lam1Journal name:Nature MethodsVolume: 8,Pages:546–548Year published:(2011)DOI:doi:10.1038/nmeth.1633Published online29 June 2011 A method for clustering billions of unidentified tandem mass spectra from shotgun proteomics experiments offers new ways of storing, organizing and analyzing proteomics data, with potential benefits to the entire proteomics community. 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 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Henry Lam is in the Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Henry Lam Author Details * Henry Lam Contact Henry Lam Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Fluorogenic pyrosequencing in microreactors
- Nat Methods 8(7):548-549 (2011)
Article preview View full access options Nature Methods | News and Views Fluorogenic pyrosequencing in microreactors * Jason A Steen1 * Matthew A Cooper1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:548–549Year published:(2011)DOI:doi:10.1038/nmeth.1634Published online29 June 2011 A technique that combines the speed of pyrosequencing with the sensitivity of fluorescent detection may lead to faster sequencing with smaller quantities of DNA. 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 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Jason A. Steen and Matthew A. Cooper are at the Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthew A Cooper Author Details * Jason A Steen Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew A Cooper Contact Matthew A Cooper Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - A roadmap to generate renewable protein binders to the human proteome
- Nat Methods 8(7):551-558 (2011)
Nature Methods | Analysis A roadmap to generate renewable protein binders to the human proteome * Karen Colwill1 * Renewable Protein Binder Working Group2 * Susanne Gräslund3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:551–558Year published:(2011)DOI:doi:10.1038/nmeth.1607Received21 May 2010Accepted11 April 2011Published online15 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Despite the wealth of commercially available antibodies to human proteins, research is often hindered by their inconsistent validation, their poor performance and the inadequate coverage of the proteome. These issues could be addressed by systematic, genome-wide efforts to generate and validate renewable protein binders. We report a multicenter study to assess the potential of hybridoma and phage-display technologies in a coordinated large-scale antibody generation and validation effort. We produced over 1,000 antibodies targeting 20 SH2 domain proteins and evaluated them for potency and specificity by enzyme-linked immunosorbent assay (ELISA), protein microarray and surface plasmon resonance (SPR). We also tested selected antibodies in immunoprecipitation, immunoblotting and immunofluorescence assays. Our results show that high-affinity, high-specificity renewable antibodies generated by different technologies can be produced quickly and efficiently. We believe that this wo! rk serves as a foundation and template for future larger-scale studies to create renewable protein binders. View full text Subject terms: * Immunology * Molecular Biology * Proteomics * Systems Biology Figures at a glance * Figure 1: Flowchart showing methodologies used to systematically produce and validate renewable antibodies. Validation steps are outlined in yellow. * Figure 2: Antibody validation by immunoprecipitation. () mCitrine-Crk was expressed in HEK 293T cells. Whole-cell lysate (WCL) or immunoprecipitates (IP) using either CRK_SS_Fab_9 (Fab), CRK_SD_scFv_2 (scFv) or CRK_AS_mAb_1 (monoclonal antibody mAb 1) were resolved by SDS-PAGE and subjected to immunoblot analysis. The combined signals of anti-GFP (mCitrine was detected with a polyclonal antibody to GFP) and anti-Crk are shown. Amounts of antibodies used per immunoprecipitation are indicated below the blots. () The experiment from was repeated without overexpressed Crk protein. mAb 1 was tested using both 4 μg and 20 μg for the immunoprecipitation. () GRB2_SK_Fab_3 (Fab), GRB2_JM_scFV_3 (scFv) and monoclonal antibody GRB2_AS_mAb_3 (mAb 3) were tested for their ability to immunoprecipitate Myc-tagged Grb2 expressed in HEK 293T cells. () HEK 293T WCL or IP using Fab GRB2_SK_Fab_3 resolved by SDS-PAGE and subjected to immunoblot analysis. The presence of ectopic Flag-tagged Grb2 (Flag-Grb2) is indicated below the blot. *, light c! hain from the anti-Flag M2. () scFv SHC1_JM_scFv_1 and monoclonal antibodies (mAb 1–3) SHC1_AS_mAb_1, SHC1_AS_mAb_2 and SHC1_AS_mAb_3 were tested for their ability to recognize endogenous Shc1 in HEK 293T cells. The amount of antibody used in each immunoprecipitate is indicated below the gel. Three isoforms of Shc1, p46, p52 and p66, are visible at 46, 52 and 66 kDa, respectively. A monoclonal antibody to Crk (CRK_AS_mAb_1) was used as a control to identify background signal in and . PSM, prestained marker. * Figure 3: Anti-Lyn Fab (LYN_SS_Fab_2) recognizes ectopic Lyn expressed in HEK 293T cells. () Schematic of the antibodies used in the immunoblot analysis. () Lysates from HEK 293T cells, with (labeled mCitrine-Lyn) or without (labeled HEK 293T) mCitrine-tagged Lyn, were probed with antibodies indicated below the blots. Overlay, signal for anti-GFP and anti-Lyn Fab with the intermediary anti-Flag M2. () As a control, the same lysates as those described in were probed with anti-GFP and anti-Flag M2. For mCitrine detection, blots were probed with a polyclonal antibody to GFP (anti-GFP). * Figure 4: An scFv recognizes Shc1 in MDCK cells. (,) Confocal images of fixed MDCK cells expressing CFP-ErbB2 and YFP-Shc1 that were stimulated with EGF for the times indicated. In overlay images, shown are signals for CFP-ErbB2 (blue), YFP-Shc1 (green) and anti-Shc1 (red; scFv -SHC1_JM_scFv_1); overlap of all channels appears white. In , cells were stained with biotin-labeled anti-Shc1 scFv (labeled as Biotin-scFv_Shc1) and streptavidin-Cy3 (top) or streptavidin-Cy3 only (bottom). In , cells were incubated with anti-Shc1 scFv (labeled as Flag-scFv-Shc1), anti-Flag M2 that recognizes the Flag-tagged anti-Shc1 scFv and Alexa555 goat anti-mouse IgG (top) or with anti-Flag M2 and Alexa555 goat anti-mouse IgG only (bottom). Scale bars, 20 μm. Author information * Abstract * Author information * Supplementary information Affiliations * Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. * Karen Colwill, * Anna Dai, * Oliver Rocks, * Kelly Williton, * Frederic A Fellouse, * Kadija Hersi & * Tony Pawson * A full list of authors appears at the end of this paper. * Renewable Protein Binder Working Group * Structural Genomics Consortium, Department of Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. * Lars-Göran Dahlgren, * Alex Flores, * Ida Johansson, * Johan Weigelt & * Susanne Gräslund * Banting and Best Department of Medical Research, Department of Molecular Genetics, and the Terrence Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. * Helena Persson, * Nicholas E Jarvik & * Sachdev Sidhu * Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, USA. * Arkadiusz Wyrzucki, * John Wojcik, * Akiko Koide, * Anthony A Kossiakoff & * Shohei Koide * Department of Biochemistry, University of Cambridge, Downing Site, Cambridge, UK. * Michael R Dyson, * Aneesh Karatt-Vellatt, * Darren J Schofield & * John McCafferty * Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA. * Kritika Pershad, * John D Pavlovic & * Brian K Kay * Technische Universität Braunschweig, Institute of Biochemistry and Biotechnology, Braunschweig, Germany. * Michael Mersmann, * Doris Meier, * Jana Mersmann, * Saskia Helmsing, * Michael Hust & * Stefan Dübel * Monash Antibody Technologies Facility, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia. * Susie Berkowicz, * Alexia Freemantle, * Michael Spiegel, * Alan Sawyer, * Daniel Layton & * Edouard Nice * Department of Molecular and Medical Genetics, University of Toronto, Ontario, Canada. * Tony Pawson * Department of Proteomics, School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden. * Peter Nilsson, * Mårten Sundberg, * Ronald Sjöberg, * Åsa Sivertsson, * Jochen M Schwenk, * Jenny Ottosson Takanen, * Sophia Hober & * Mathias Uhlén * Structural Genomics Consortium, Toronto, Ontario, Canada. * Lissette Crombet, * Peter Loppnau, * Ivona Kozieradzki, * Doug Cossar, * Cheryl H Arrowsmith & * Aled M Edwards * Present addresses: Department of Immunotechnology, Lund University, Lund, Sweden (H.P.); European Molecular Biology Laboratory Monoclonal Core Facility, Monterotondo-Scalo, Lazio, Italy (A.S.); School of Biological Sciences, Nanyang Technological University, Lab 7-01 Institute of Molecular and Cell Biology, Proteos, Singapore (L.-G.D.). * Helena Persson, * Alan Sawyer & * Lars-Göran Dahlgren Consortia * Renewable Protein Binder Working Group * In vitro Antibody Consortium * Helena Persson, * Nicholas E Jarvik, * Arkadiusz Wyrzucki, * John Wojcik, * Akiko Koide, * Anthony A Kossiakoff, * Shohei Koide, * Sachdev Sidhu, * Michael R Dyson, * Kritika Pershad, * John D Pavlovic, * Aneesh Karatt-Vellatt, * Darren J Schofield, * Brian K Kay, * John McCafferty, * Michael Mersmann, * Doris Meier, * Jana Mersmann, * Saskia Helmsing, * Michael Hust & * Stefan Dübel * Monash Antibody Technologies Facility * Susie Berkowicz, * Alexia Freemantle, * Michael Spiegel, * Alan Sawyer, * Daniel Layton & * Edouard Nice * Pawson Laboratory * Anna Dai, * Oliver Rocks, * Kelly Williton, * Frederic A Fellouse, * Kadija Hersi & * Tony Pawson * Human Protein Atlas * Peter Nilsson, * Mårten Sundberg, * Ronald Sjöberg, * Åsa Sivertsson, * Jochen M Schwenk, * Jenny Ottosson Takanen, * Sophia Hober & * Mathias Uhlén * Structural Genomics Consortium * Lars-Göran Dahlgren, * Alex Flores, * Ida Johansson, * Johan Weigelt, * Lissette Crombet, * Peter Loppnau, * Ivona Kozieradzki, * Doug Cossar, * Cheryl H Arrowsmith & * Aled M Edwards Contributions K.P., J.D.P., A.K.-V., D.J.S., N.E.J., A.W., J.Wo., A.K., M.M., D.M., J.M., S.H., S.B., A.F., M.S., K.W., A.D., K.H., M.S., R.S., J.M.S., A.S., J.O., S.H., L.-G.D., A.F., I.J., L.C., P.L., I.K., D.L. and F.A.F. designed and performed experiments; M.R.D., M.H., H.P., O.R., P.N., E.N. and D.C. conceived, designed and performed experiments and wrote the paper; J.We., C.H.A., B.K.K., A.A.K. and M.U. oversaw the project; J.Mc., S.K., S.S., S.D., A.S., T.P. and A.M.E. conceived and oversaw the project and wrote the paper; K.C. and S.G. conceived, designed and performed experiments, oversaw the project and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Karen Colwill or * Susanne Gräslund Author Details * Karen Colwill Contact Karen Colwill Search for this author in: * NPG journals * PubMed * Google Scholar * Susanne Gräslund Contact Susanne Gräslund Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Table 1 (74K) Summary of the results at each step in this study. * Supplementary Table 3 (164K) Construction and sequence of SH2 domains. PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–5 and Supplementary Table 2 Additional data - A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins
- Nat Methods 8(7):559-564 (2011)
Nature Methods | Analysis A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins * Shivendra Kishore1, 3 * Lukasz Jaskiewicz1, 3 * Lukas Burger2, 3 * Jean Hausser1 * Mohsen Khorshid1 * Mihaela Zavolan1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:559–564Year published:(2011)DOI:doi:10.1038/nmeth.1608Received10 December 2010Accepted21 April 2011Published online15 May 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Cross-linking and immunoprecipitation (CLIP) is increasingly used to map transcriptome-wide binding sites of RNA-binding proteins. We developed a method for CLIP data analysis, and applied it to compare CLIP with photoactivatable ribonucleoside–enhanced CLIP (PAR-CLIP) and to uncover how differences in cross-linking and ribonuclease digestion affect the identified sites. We found only small differences in accuracies of these methods in identifying binding sites of HuR, which binds low-complexity sequences, and Argonaute 2, which has a complex binding specificity. We found that cross-link–induced mutations led to single-nucleotide resolution for both PAR-CLIP and CLIP. Our results confirm the expectation from original CLIP publications that RNA-binding proteins do not protect their binding sites sufficiently under the denaturing conditions used during the CLIP procedure, and we show that extensive digestion with sequence-specific RNases strongly biases the recovered bindi! ng sites. This bias can be substantially reduced by milder nuclease digestion conditions. View full text Subject terms: * Bioinformatics * Small RNAs * Molecular Biology * Systems Biology Figures at a glance * Figure 1: Relationship between the affinity and enrichment of HuR-binding sites obtained with different CLIP methods. () Pearson correlation coefficients (R) between the enrichment in reads (relative to mRNA abundance) and the predicted affinity of HuR binding sites identified by the indicated CLIP and PAR-CLIP methods. (–) Correlation between the estimated affinity of a 7-mer motif for HuR and its enrichment relative to 3′ untranslated regions (UTRs) in the indicated CLIP samples (A and B indicate replicates). R, Pearson correlation coefficient. * Figure 2: Location of the ten 7-mers with highest affinity for HuR relative to the most frequently cross-linked nucleotide in the binding site. (–) Frequency of 7-mer matches in the top 1,000 sites as a function of the distance to the cross-linked nucleotide was determined for CLIP (), PAR-CLIP (), PAR-CLIP with MNase treatment () and PAR-CLIP with mild RNase T1 treatment (). The position of the cross-linked nucleotide is indicated by a dashed line and a match is counted at the location where the fourth nucleotide of the indicated 7-mer occurs. Numbers indicate nucleotide positions in 41-nucleotide cross-link-centered regions. * Figure 3: Predicted affinities of HuR binding sites isolated based on different measures. () Enrichment relative to the abundance of the mRNA in the total RNA. () Coverage of the binding site by reads. () Density of cross-link-diagnostic mutations in a given site. For each measure and each sample, sites were sorted and divided into non-overlapping bins of 1,000 sites. Error bars, s.e.m.; A and B indicate replicates. * Figure 4: Proportion of Ago2 binding sites matching the seed regions of the ten most abundantly expressed miRNA families. (–) Ago2 binding sites were sorted based on enrichment (), coverage by reads () or density of cross-link-diagnostic mutations () and divided in the indicated bins. Error bars, s.e.m.; A and B indicate replicates. * Figure 5: Location and frequency of miRNA seed-complementary regions relative to the most frequently cross-linked nucleotide in the binding site. The cross-linked site is indicated by a dashed line. We used the ten most abundant miRNA families and the top 1,000 Ago2 sites obtained with the methods indicated in –. Numbers indicate nucleotide positions in the 41-nucleotide-long cross-link-centered regions. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE28865 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Shivendra Kishore, * Lukasz Jaskiewicz & * Lukas Burger Affiliations * Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland. * Shivendra Kishore, * Lukasz Jaskiewicz, * Jean Hausser, * Mohsen Khorshid & * Mihaela Zavolan * Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. * Lukas Burger Contributions S.K. designed and performed the experiments, L.J. designed and performed the experiments and wrote the paper, L.B. analyzed the data, J.H. analyzed the data, M.K. developed the annotation tools and analyzed the data and M.Z. designed and supervised the experiments, analyzed the data and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mihaela Zavolan Author Details * Shivendra Kishore Search for this author in: * NPG journals * PubMed * Google Scholar * Lukasz Jaskiewicz Search for this author in: * NPG journals * PubMed * Google Scholar * Lukas Burger Search for this author in: * NPG journals * PubMed * Google Scholar * Jean Hausser Search for this author in: * NPG journals * PubMed * Google Scholar * Mohsen Khorshid Search for this author in: * NPG journals * PubMed * Google Scholar * Mihaela Zavolan Contact Mihaela Zavolan Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (11M) Supplementary Figures 1–14 and Supplementary Table 1 Additional data - Single-tube linear DNA amplification (LinDA) for robust ChIP-seq
- Nat Methods 8(7):565-567 (2011)
Nature Methods | Brief Communication Single-tube linear DNA amplification (LinDA) for robust ChIP-seq * Pattabhiraman Shankaranarayanan1 * Marco-Antonio Mendoza-Parra1 * Mannu Walia1 * Li Wang2 * Ning Li2 * Luisa M Trindade3 * Hinrich Gronemeyer1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:565–567Year published:(2011)DOI:doi:10.1038/nmeth.1626Received14 February 2011Accepted05 May 2011Published online05 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Genome-wide profiling of transcription factors based on massive parallel sequencing of immunoprecipitated chromatin (ChIP-seq) requires nanogram amounts of DNA. Here we describe a high-fidelity, single-tube linear DNA amplification method (LinDA) for ChIP-seq and reChIP-seq with picogram DNA amounts obtained from a few thousand cells. This amplification technology will facilitate global analyses of transcription-factor binding and chromatin with very small cell populations, such as stem or cancer-initiating cells. View full text Subject terms: * Molecular Biology * Sequencing * Genomics Figures at a glance * Figure 1: Linear amplification of DNA. () Schematic of the LinDA protocol. pT7, T7 polymerase promoter; inv.pT7, inverse sequence. () Estrogen-induced ERα binding to target genes at 1 h determined by qPCR of ChIPed DNA (3 ng) and LinDA-prepared 30 pg aliquot thereof; inductions are relative to DPP10. GREB1(2) and GREB1(3) refer to the second and third binding site of ERα to the GREB1 locus. () Binding of RXRα to target sites determined by qPCR from LinDA-amplified RXRα ChIPs of ATRA-treated mouse F9 cells. Indicated amounts of ChIPed DNA were amplified and target loci were quantified by qPCR (fold occupancy relative to Gapdh). Error bars in and , s.e.m. (n = 3). () Comparison of ERα ChIP-seq from 2 million H3396 breast cancer cells with LinDA-ChIP-seq analyses of 100,000, 10,000 and 5,000 cells. () Quantitative comparison of signal intensities of the RXR ChIP-seqs of unamplified and LinDA-amplified samples by seqMINER15 across 1-kb bins around MACS-identified (P = 10−5) peaks. r, Pearson correlation coeffi! cient. () RXRα ChIP-seq profiles of the Hoxa1 locus for biological replicates RXRα(1) and RXRα(2) as well as the corresponding LinDA–ChIP-seq profile of an equal amount of 100-fold-diluted RXRα(1). In and , the x axis gives RefSeq gene annotation. * Figure 2: ReChIP of RARγ-RXRα heterodimer using LinDA. () qPCR validation of established RXR targets from LinDA-amplified reChIPs (ChIP with antibody to RXRα; reChIP with antibody to RARγ) and the corresponding qPCRs from 50% of the unamplified reChIP sample. The second half of the unamplified sample was used for LinDA. Error bars, s.e.m. (n = 3). () Overlap of the peaks predicted by MACS (P = 10−5) for the indicated ChIP-seq datasets. () Genomic display of the ChIP-seq profiles obtained for RXRα, RARγ with the LinDA-amplified reChIP (ChIP, RXRα; reChIP, RARγ) profile at the indicated loci. (,) Similar display as in but revealing the binding of only RXRα () or only RARγ () to these sites. In –, the x axis gives RefSeq gene annotation. Author information * Author information * Supplementary information Affiliations * Department of Cancer Biology, Institute of Genetics and Molecular and Cellular Biology, Strasbourg-Illkirch, France. * Pattabhiraman Shankaranarayanan, * Marco-Antonio Mendoza-Parra, * Mannu Walia & * Hinrich Gronemeyer * Beijing Genome Institute, Guangdong, China. * Li Wang & * Ning Li * Wageningen University and Research Center–Plant Breeding, Wageningen, The Netherlands. * Luisa M Trindade Contributions P.S., L.M.T. and H.G. designed and optimized LinDA. M.-A.M.-P. performed the F9-cell experiments and did the bioinformatics analysis with P.S., and M.W. did the H3396-cell experiments. N.L. and L.W. performed the Illumina HiSeq 2000 sequencing and mapping to hg19 human genome assembly. H.G. and P.S. wrote the main text of the manuscript, which M.-A.M.-P. and L.M.T. corrected and improved; P.S. and L.M.T. wrote the Online Methods section. Competing financial interests H.G., P.S. and L.M.T. filed a patent application describing the methods presented here (European patent EP11305531.3). Corresponding author Correspondence to: * Hinrich Gronemeyer Author Details * Pattabhiraman Shankaranarayanan Search for this author in: * NPG journals * PubMed * Google Scholar * Marco-Antonio Mendoza-Parra Search for this author in: * NPG journals * PubMed * Google Scholar * Mannu Walia Search for this author in: * NPG journals * PubMed * Google Scholar * Li Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Ning Li Search for this author in: * NPG journals * PubMed * Google Scholar * Luisa M Trindade Search for this author in: * NPG journals * PubMed * Google Scholar * Hinrich Gronemeyer Contact Hinrich Gronemeyer Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (884K) Supplementary Figures 1–6 and Supplementary Tables 1–2 Additional data - Near-infrared branding efficiently correlates light and electron microscopy
- Nat Methods 8(7):568-570 (2011)
Nature Methods | Brief Communication Near-infrared branding efficiently correlates light and electron microscopy * Derron Bishop1 * Ivana Nikić2 * Mary Brinkoetter1 * Sharmon Knecht1 * Stephanie Potz2, 3 * Martin Kerschensteiner2, 5 * Thomas Misgeld3, 4, 5 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:568–570Year published:(2011)DOI:doi:10.1038/nmeth.1622Received19 January 2011Accepted22 April 2011Published online05 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The correlation of light and electron microscopy of complex tissues remains a major challenge. Here we report near-infrared branding (NIRB), which facilitates such correlation by using a pulsed, near-infrared laser to create defined fiducial marks in three dimensions in fixed tissue. As these marks are fluorescent and can be photo-oxidized to generate electron contrast, they can guide re-identification of previously imaged structures as small as dendritic spines by electron microscopy. View full text Subject terms: * Imaging * Neuroscience * Microscopy * Cell Biology Figures at a glance * Figure 1: Defined induction of fiducial marks by NIRB. (–) Two-photon images of NIRB marks in glutaraldehyde-fixed vibratome sections of mouse cerebral cortex. Maximum intensity projection with overlay of transillumination image (grayscale) and NIRB fluorescence (magenta) (). Pseudocolored projection of three planes (inset, x–z projection) (); the letters N and R were burned at a depth of 15 μm, whereas I and B were burned at a depth of 30 μm; an intermediate depth is shown in red. Erythrocytes show autofluorescence (asterisk). Three-dimensional rendering () showing the position of the 15 μm (left) and 30 μm (right) planes relative to the orthogonal projections. () Images of NIRB marks made with line scans of increasing percentages of laser power (top) and increasing numbers of laser swipes (bottom); scan line length, 106 μm. () Fluorescence emission spectrum (λem) of NIRB marks compared to tissue autofluorescence after excitation with a 488-nm laser (normalized intensity as percentage of maximum). () Transmitted light! images of an NIRB mark before (left) and after (right) photo-oxidation using diaminobenzidine. (,) NIRB marks of a kidney tubule (; nuclei, blue) or a macrophage in a lymph node (; GFP labeling in a Cx3cr1GFP/+ mouse, green). Scale bars, 25 μm (,), 50 μm () and 25 μm (,). Scale cube in has a border length of 25 μm. * Figure 2: NIRB allows re-identification and electron microscopic reconstruction of dendritic spines. (,) Confocal projections of apical dendrites in a cortical vibratome section of a Thy1-GFPS mouse, before () and after () NIRB marking. Asterisk indicates autofluorescent lipofuscin granules. (,) Magnified and contrast-inverted views of the boxed regions in and (arrowheads indicate the targeted spine) before () and after () NIRB marking. The inner NIRB box with a border length of 6.5 μm is shown by the dashed magenta line. () Low-power electron micrograph of the target region after switching from thick sectioning to ultrathin sectioning. The outer NIRB box is easily detected. The inner box is surrounded by a magenta outline. () Ultrathin section through the targeted spine (arrowhead) and its dendrite (pseudocolored green). The inner NIRB box is evident. () Three-dimensional rendering of spine (arrowhead) based on tracings in serial electron micrographs. Scale bars, 10 μm (,), 5 μm (,), 25 μm (), 2 μm () and scale cube in has a border length of 1 μm. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Martin Kerschensteiner & * Thomas Misgeld Affiliations * Department of Physiology, Indiana University School of Medicine–Muncie, Muncie, Indiana, USA. * Derron Bishop, * Mary Brinkoetter & * Sharmon Knecht * Research Unit Therapy Development, Institute of Clinical Neuroimmunology, Ludwig Maximilians University, Munich, Germany. * Ivana Nikić, * Stephanie Potz & * Martin Kerschensteiner * Biomolecular Sensors and Center for Integrated Protein Sciences (Munich) at the Institute of Neuroscience, Technical University Munich, Munich, Germany. * Stephanie Potz & * Thomas Misgeld * Technical University Munich Institute for Advanced Study, Munich, Germany. * Thomas Misgeld Contributions D.B., M.K. and T.M. conceived the experiments. I.N., S.P., M.K. and T.M. performed in vivo imaging and near-infrared branding experiments. M.B., S.K. and D.B. performed correlated serial electron microscopy. M.K., T.M. and D.B. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Thomas Misgeld or * Martin Kerschensteiner Author Details * Derron Bishop Search for this author in: * NPG journals * PubMed * Google Scholar * Ivana Nikić Search for this author in: * NPG journals * PubMed * Google Scholar * Mary Brinkoetter Search for this author in: * NPG journals * PubMed * Google Scholar * Sharmon Knecht Search for this author in: * NPG journals * PubMed * Google Scholar * Stephanie Potz Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Kerschensteiner Contact Martin Kerschensteiner Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Misgeld Contact Thomas Misgeld Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Movies * Supplementary Video 1 (2M) Two-photon stack of the NIRB marks burned at different depths (letters N and R were burned at a depth of 15 μm and the letters I and B at a depth of 30 μm) in a cortex section derived from a wild-type mouse as shown in Figure 1a–c. * Supplementary Video 2 (2M) Time-lapse of NIRB marking in a cortex section derived from a wild-type mouse using a line scan and interspersed scanning with a second laser (trans-illumination image with superimposed NIRB fluorescence, orange). * Supplementary Video 3 (5.2M) Movie sequence of the dendritic spine (shown in Figure 2) that illustrates the correlation between the confocal light microscopic image and ssTEM. PDF files * Supplementary Text and Figures (572K) Supplementary Figures 1–3 Additional data - Sharper low-power STED nanoscopy by time gating
- Nat Methods 8(7):571-573 (2011)
Nature Methods | Brief Communication Sharper low-power STED nanoscopy by time gating * Giuseppe Vicidomini1, 3, 4 * Gael Moneron1, 4 * Kyu Y Han1, 3, 4 * Volker Westphal1 * Haisen Ta1 * Matthias Reuss2 * Johann Engelhardt2 * Christian Eggeling1 * Stefan W Hell1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:571–573Year published:(2011)DOI:doi:10.1038/nmeth.1624Received25 January 2011Accepted13 May 2011Published online05 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Applying pulsed excitation together with time-gated detection improves the fluorescence on-off contrast in continuous-wave stimulated emission depletion (CW-STED) microscopy, thus revealing finer details in fixed and living cells using moderate light intensities. This method also enables super-resolution fluorescence correlation spectroscopy with CW-STED beams, as demonstrated by quantifying the dynamics of labeled lipid molecules in the plasma membrane of living cells. View full text Subject terms: * Microscopy * Imaging Figures at a glance * Figure 1: Principle of g-STED. () Microscope setup with pulsed excitation and CW-STED lasers, whose beams are combined by dichroic mirrors (gray) and which form diffraction-limited Gaussian and doughnut-shaped focal intensity distribution, respectively (inset scale bars, 200 nm). Fluorescence light (magenta) is detected by the objective lens and imaged onto a single-photon-counting (SPC) detector, whose detection events are time-gated with respect to the excitation pulses (Trigger) and registered by a computer. () Time-correlated single-photon counting histograms of the fluorescence of a single isolated NV center in bulk diamond for indicated CW-STED beam power (PSTED) with experimental time sequence (top) of excitation, stimulated emission (STED) and signal detection. The time-gated detection is characterized by the time delay Tg and detection period ΔT. () Fluorescence (arbitrary units; a.u.) detected from a single isolated NV center as a function of PSTED for different time gates Tg (mean ± s.d.; n =! 4) and as a function of Tg for PSTED = 5.1 mW (inset, mean ± s.d.; n = 4). Solid lines show theoretical fittings. (–) Fluorescence images of a single isolated NV center for confocal (left) and CW-STED (right, PSTED = 47 mW) (), g-STED (right, Tg = 15 ns) and a fluorescence lifetime image for the CW-STED recording (left) () and normalized intensity profiles through the centers of the images (). Scale bars, 200 nm (,). Excitation, 532 nm; repetition rate and average power 10 MHz and 10 μW (,) and 20 MHz and 11 μW (,), respectively. STED, 740 nm; diffraction-limited Gaussian (,) and doughnut-shaped spot (,). * Figure 2: g-STED fluorescence nanoscopy. (–) Images of 40-nm-diameter yellow-green beads (), keratin fused to the fluorescent protein citrine in a living PtK2 cell () and vimentin filaments in a fixed PtK2 cell labeled by immunocytochemistry with the organic dye Alexa Fluor 488 (). Shown are CW-STED, g-STED and confocal (top right corners) recordings as well as normalized intensity (arbitrary units; a.u.) profiles along the dashed lines. Scale bars, 1 μm. Insets show magnified views of the marked areas, renormalized in signal intensity. Excitation, 485 nm, 80 MHz and 11 μW. STED, 592 nm and PSTED = 370 mW () and 200 mW (); gated detection: Tg = 2 ns () and 1.5 ns () and ΔT = 8 ns. * Figure 3: g-STED-FCS. () Dependence of the lateral focal transit time txy on PSTED of an Atto647N-labeled phosphoethanolamine lipid in a supported lipid bilayer, determined by FCS for indicated Tg values (mean + s.d.; n = 6). The right axis reports the FWHM of the E-PSF calculated from √(txy(PSTED)/txy(0)). Lines show theoretical fittings that match g-STED but not CW-STED nanoscopy data. () Dependence of the anomaly coefficient α of the same data on Tg for indicated PSTED values. α≈ 1 for Gaussian E-PSFs as for Tg > 1 ns and α < 1 for Gaussian-Lorentzian shapes as for nongated CW-STED (Tg < 0 ns, shaded area; dashed line, time point of maximum of excitation pulse). () Dependence of the ratio of the lateral focal transit time txy (n = 6; median + s.e.m. ≈ 10%) of fluorescent lipid analogs of sphingomyelin (SM) and phosphoethanolamine (PE) in the plasma membrane of living PtK2 on PSTED for different Tg values and CW-STED beam power. The dashed and dotted lines report the values determined ! from previous pulsed STED-FCS data for ~70 nm and 40–50 nm large focal spots, respectively4. Excitation, 635 nm, 80 MHz and 12 μW (,) and 8 μW (), STED, 770 nm; gated detection, ΔT = 8 ns. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Giuseppe Vicidomini, * Gael Moneron & * Kyu Y Han Affiliations * Max Planck Institute for Biophysical Chemistry, Department of NanoBiophotonics, Göttingen, Germany. * Giuseppe Vicidomini, * Gael Moneron, * Kyu Y Han, * Volker Westphal, * Haisen Ta, * Christian Eggeling & * Stefan W Hell * German Cancer Research Center, Optical Nanoscopy Division, Heidelberg, Germany. * Matthias Reuss, * Johann Engelhardt & * Stefan W Hell * Present addresses: Italian Institute of Technology, Department of Nanophysics, Genoa, Italy (G.V.) and Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA (K.Y.H.). * Giuseppe Vicidomini & * Kyu Y Han Contributions G.V., G.M., J.E., C.E. and S.W.H. conceived and designed the study. G.V. performed theoretical studies. V.W. designed electronic components. G.V., G.M., K.Y.H., H.T. and M.R. performed experiments. G.V., G.M., K.Y.H. and C.E. analyzed data. G.V., G.M., C.E. and S.W.H. wrote the manuscript. All authors discussed the conceptual and practical implications of the method at all stages. Competing financial interests G.V., G.M., K.Y.H, V.W., M.R., J.E., C.E. and S.W.H. have filed a patent application on the method presented. Corresponding author Correspondence to: * Stefan W Hell Author Details * Giuseppe Vicidomini Search for this author in: * NPG journals * PubMed * Google Scholar * Gael Moneron Search for this author in: * NPG journals * PubMed * Google Scholar * Kyu Y Han Search for this author in: * NPG journals * PubMed * Google Scholar * Volker Westphal Search for this author in: * NPG journals * PubMed * Google Scholar * Haisen Ta Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias Reuss Search for this author in: * NPG journals * PubMed * Google Scholar * Johann Engelhardt Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Eggeling Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan W Hell Contact Stefan W Hell Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–8 and Supplementary Note 1 Additional data - Fluorogenic DNA sequencing in PDMS microreactors
- Nat Methods 8(7):575-580 (2011)
Nature Methods | Article Fluorogenic DNA sequencing in PDMS microreactors * Peter A Sims1, 2 * William J Greenleaf1, 2 * Haifeng Duan1 * X Sunney Xie1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:575–580Year published:(2011)DOI:doi:10.1038/nmeth.1629Received24 February 2011Accepted20 May 2011Published online12 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We developed a multiplex sequencing-by-synthesis method combining terminal phosphate–labeled fluorogenic nucleotides (TPLFNs) and resealable polydimethylsiloxane (PDMS) microreactors. In the presence of phosphatase, primer extension by DNA polymerase using nonfluorescent TPLFNs generates fluorophores, which are confined in the microreactors and detected. We immobilized primed DNA templates in the microreactors, then sequentially introduced one of the four identically labeled TPLFNs, sealed the microreactors and recorded a fluorescence image after template-directed primer extension. With cycle times of <10 min, we demonstrate 30 base reads with ~99% raw accuracy. Our 'fluorogenic pyrosequencing' offers benefits of pyrosequencing, such as rapid turnaround, one-color detection and generation of native DNA, along with high detection sensitivity and simplicity of parallelization because simultaneous real-time monitoring of all microreactors is not required. View full text Subject terms: * Sequencing * Lab-on-a-chip * Microscopy * Genetics Figures at a glance * Figure 1: Fluorogenic pyrosequencing chemistry and workflow. () A nonfluorescent TPLFN is incorporated by DNA polymerase into a DNA primer-template, releasing a labeled, nonfluorescent polyphosphate, which is digested by phosphatase to generate a fluorescent product. () Absorption and fluorescence emission spectra of the TPLFN and fluorescent product. () DNA is immobilized on beads in PDMS microreactors, which are loaded with a reaction mixture containing DNA polymerase, phosphatase and one of four TPLFNs at low temperature, sealed and heated to trigger primer extension. Fluorophores are generated in microreactors that contain DNA templates in which the base adjacent to the primer is complementary to the introduced TPLFN. The array is imaged with a fluorescence microscope, unsealed and washed before the cycle is repeated. * Figure 2: Schematic of the fluorogenic pyrosequencing system. The sequencer comprises three modules: imaging, fluidics and temperature control. The imaging module is a one-color epifluorescence microscope composed of a laser, halogen lamp, CCD camera, microscope frame, filter set and a 20× air objective with some additional optics (phase scrambler and tophat diffuser) to flatten the field of view. The lamp provides bright-field transmission images of the microreactor array that are used for maintaining the proper focal plane. A rotary selector valve, hydraulic and pneumatic valves, a nitrogen line and a vacuum line comprise the fluidics module, which controls sealing, reagent flow and washing. The vacuum line is used to both deliver reagents to the sample and also seal the PDMS microreactor array against the lower surface of the flow cell. The thermoelectric temperature controller cools the microreactor array while reagents are loaded into the reactors to avoid substantial nucleotide incorporation before sealing. This module then heat! s the array after sealing to allow rapid primer extension. A schematic of a PDMS microreactor flow cell and a bright-field image of the PDMS microreactors is shown at top middle. The microreactors are ~5 μm in diameter, 3.8 μm in height with center-to-center distance of 7.5 μm. Some of the microreactors contain 1-μm polystyrene beads. Scale bars, 1 cm (left) and 7.5 μm (right). * Figure 3: Fluorogenic pyrosequencing images and homopolymer analysis. () Sequential fluorescence images of a PDMS microreactor array obtained during the first 13 cycles of a 60-cycle fluorogenic pyrosequencing run. Signal heterogeneity between reactors is due to either the presence of multiple beads or a homopolymeric region of the DNA template, which leads to the generation of more than one fluorophore per DNA copy. Scale bar, 74 μm. () Representative sequence of homopolymeric DNA. The colored bars represent a corrected (Online Methods) sequencing trace from a single microreactor containing the HL sequence. Dashed lines mark the intensity ranges used to determine homopolymer lengths. Signal intensities expected from this template sequence (black dots) show that this sequencing trace is error-free. The average and s.d. of the intensities for all analyzed traces (n = 15) fell within the expected intensity range. Intensity levels for all sequencing cycles from the analyzed HL traces were plotted as a histogram (right). * Figure 4: Sequencing traces of quasi-random sequences and analysis of global errors. (–) Corrected sequencing traces from individual microreactors containing the R1 (), R2 () and R3 () DNA templates. Dashed lines mark the intensity ranges used to determine homopolymer lengths, and expected signals from the template sequence are shown as black dots. () Global analysis of accuracy as a function of read length for 36 analyzed R1, R2 and R3 traces (n = 36). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Peter A Sims & * William J Greenleaf Affiliations * Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA. * Peter A Sims, * William J Greenleaf, * Haifeng Duan & * X Sunney Xie Contributions P.A.S., W.J.G. and X.S.X. conceived the fluorogenic pyrosequencing concept. P.A.S. and W.J.G. constructed the sequencing apparatus. H.D. synthesized the TPLFNs. P.A.S. collected and analyzed the data. W.J.G. carried out the microfabrication. P.A.S., W.J.G., H.D. and X.S.X. wrote the manuscript. Competing financial interests Harvard University has filed patent applications based on this work. Corresponding author Correspondence to: * X Sunney Xie Author Details * Peter A Sims Search for this author in: * NPG journals * PubMed * Google Scholar * William J Greenleaf Search for this author in: * NPG journals * PubMed * Google Scholar * Haifeng Duan Search for this author in: * NPG journals * PubMed * Google Scholar * X Sunney Xie Contact X Sunney Xie Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (536K) Supplementary Figures 1–6, Supplementary Table 1 and Supplementary Notes 1–3 Additional data - High-throughput analysis of single hematopoietic stem cell proliferation in microfluidic cell culture arrays
- Nat Methods 8(7):581-586 (2011)
Nature Methods | Article High-throughput analysis of single hematopoietic stem cell proliferation in microfluidic cell culture arrays * Véronique Lecault1, 3 * Michael VanInsberghe2 * Sanja Sekulovic4 * David J H F Knapp4 * Stefan Wohrer4 * William Bowden1, 2, 5 * Francis Viel2 * Thomas McLaughlin2, 5 * Asefeh Jarandehei1, 3 * Michelle Miller4 * Didier Falconnet2 * Adam K White2 * David G Kent4 * Michael R Copley4 * Fariborz Taghipour3 * Connie J Eaves4, 6 * R Keith Humphries4, 7 * James M Piret1, 3 * Carl L Hansen2, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:581–586Year published:(2011)DOI:doi:10.1038/nmeth.1614Received13 December 2010Accepted15 April 2011Published online22 May 2011Corrected online03 June 2011 Abstract * Abstract * Change history * 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 Heterogeneity in cell populations poses a major obstacle to understanding complex biological processes. Here we present a microfluidic platform containing thousands of nanoliter-scale chambers suitable for live-cell imaging studies of clonal cultures of nonadherent cells with precise control of the conditions, capabilities for in situ immunostaining and recovery of viable cells. We show that this platform mimics conventional cultures in reproducing the responses of various types of primitive mouse hematopoietic cells with retention of their functional properties, as demonstrated by subsequent in vitro and in vivo (transplantation) assays of recovered cells. The automated medium exchange of this system made it possible to define when Steel factor stimulation is first required by adult hematopoietic stem cells in vitro as the point of exit from quiescence. This technology will offer many new avenues to interrogate otherwise inaccessible mechanisms governing mammalian cell grow! th and fate decisions. View full text Subject terms: * Lab-on-a-chip * Cell Biology * Stem Cells * Microscopy Figures at a glance * Figure 1: Iso-osmotic perfusion microfluidic cell culture array. () Schematic of the device with micrographs as insets. The cell culture layer contains 1,600 chambers (pink) connected by flow channels (gray). Hydration lines are located on each side of the array to minimize edge effects. The control lines (blue) consist of an isolation valve and a peristaltic pump to control cell loading and perfusion rates. Arrows point at single cells. Scale bars, 1 mm (left) and 100 μm (right). () Schematic of the layers that are assembled during device fabrication. () The inverted configuration of the device features control lines (blue) pushing down on flow channels (gray) located at the top of culture chambers (pink). This geometry allows medium exchange over suspension cells that are sequestered at the bottom of the chambers by gravity. () Numerical simulation of flow profile through a culture chamber. The chamber dimensions and volume are shown. () Lineage tracing of three ND13 clones cultured in the microfluidic device. Cells were imaged every 5! min and medium was exchanged every 6 h. Cell lineages were built by manual inspection of the videos. Circles represent cells, and crosses represent cell death. * Figure 2: Robust clonal cell culture in the microfluidic array. () Time-lapse automated imaging of clonal ND13 cell expansion in a chamber. Scale bar, 100 μm. () Average clonal growth rates of ND13 cells in the indicated culture conditions: two experiments for microfluidic culture with iso-osmotic bath, one experiment for microfluidic culture without iso-osmotic bath, three experiments for macroscale culture and three experiments for single cells plated in 96-well plates. Error bars, s.d. Arrows denote medium exchange. () Percentage of dividing cells over time for mouse HSCs cultured in the microfluidic array under high SF concentration (300 ng ml−1). Single cells were imaged every 4 min, and the times for the first, second and third divisions were identified by manual inspections of the videos (for 46 cells). () Number of cells counted by automated image analysis for individual ND13 clones at the indicated times in the microfluidic array. The apparent decline in cell counts for large clones at the end of the experiment is due to cell! s growing in multiple layers, leading to undercounting by the algorithm. () Distribution of average doubling times of clonal ND13 cultures in the indicated culture conditions, after 72 h (see Online Methods for calculation of average doubling times). Cells marked 'no proliferation' (52%) did not divide during this period or died. * Figure 3: Clonal heterogeneity of ND13 cells. (,) Individual growth curves based on time-lapse imaging of single lin+ () and lin− () cells. () Micrographs of ND13 clones originating from lin− single cells (arrows) immunostained live for lin markers (B220, Gr-1 and Mac-1) after 72 h of culture in the microfluidic array. Scale bar, 100 μm. () Number of colonies formed in a methylcellulose assay, per 100 ND13 cells, cultured in the microfluidic device or in conventional macroculture for 72 h. Error bars, s.d. (n = 3 for each culture condition). * Figure 4: Maintenance of functional HSCs in microfluidic culture. () Outline of the experimental protocol used to test for HSC function after culture in the microfluidic array. () The plot shows the fraction of donor (GFP+) WBCs in recipient mice 16 weeks after transplantation of NA10hd-transduced cells that had been cultured in a 96-well-plate macroscale control or in the microfluidic array. Injected fractions equivalent to 1/1,520th and 1/15,200th of the initial starting cells correspond to a minimal HSC expansion of 60× and 600×, respectively. () Distribution of myeloid and lymphoid donor (GFP+) cells in recipient mice 16 weeks after transplantation of NA10hd-transduced cells that were cultured as indicated. Error bars, s.d. (n = 6 mice for each culture condition). () Distribution of average clonal doubling times for NA10hd cells after 60 h in culture, as in Figure 2e. * Figure 5: Culture of primary HSCs under dynamic conditions in microfluidic arrays. () Quiescent adult mouse HSCs (E-SLAM cells) were exposed to 20 ng ml−1 IL-11 plus the indicated amounts of SF for the durations shown. Cells were cultured in a device containing 6,144 chambers, and medium was exchanged every 2 h for all conditions. () Differences in cell survival during microfluidic culture in the indicated conditions compared to the high [SF] condition. Cells were imaged every 12 min, and survival curves were normalized to a third-order polynomial fit for the high [SF] condition. () Cumulative division kinetics of primary HSCs that are cycling (excluding dead and quiescent cells) in the indicated in vitro conditions for the first and second divisions. Change history * Abstract * Change history * Author information * Supplementary informationCorrigendum 03 June 2011In the version of this article initially published online, Michelle Miller's name was incorrect. The errors have been corrected for the PDF and HTML versions of this article. Author information * Abstract * Change history * Author information * Supplementary information Affiliations * Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada. * Véronique Lecault, * William Bowden, * Asefeh Jarandehei & * James M Piret * Centre for High-Throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada. * Michael VanInsberghe, * William Bowden, * Francis Viel, * Thomas McLaughlin, * Didier Falconnet, * Adam K White & * Carl L Hansen * Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia, Canada. * Véronique Lecault, * Asefeh Jarandehei, * Fariborz Taghipour & * James M Piret * Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada. * Sanja Sekulovic, * David J H F Knapp, * Stefan Wohrer, * Michelle Miller, * David G Kent, * Michael R Copley, * Connie J Eaves & * R Keith Humphries * Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada. * William Bowden, * Thomas McLaughlin & * Carl L Hansen * Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada. * Connie J Eaves * Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada. * R Keith Humphries Contributions V.L., S.S., M.M., D.J.H.F.K., S.W., C.J.E., R.K.H., J.M.P. and C.L.H. designed the research. V.L. performed microscale experiments. V.L., S.S., D.J.H.F.K., S.W. and M.M. performed macroscale cultures. S.S. performed in vivo assays. V.L., T.M., W.B. and C.L.H. designed and fabricated microfluidic cell culture arrays. V.L., M.V. and W.B. developed automated image acquisition. M.V., W.B. and F.V. wrote image and data analysis scripts. V.L., S.S., M.M., M.V., D.J.H.F.K., S.W. and W.B. analyzed data. A.J., J.M.P. and F.T. did modeling studies. V.L., T.M., W.B., D.F., A.K.W. and C.L.H. contributed to technology development. M.M., S.S., D.J.H.F.K., S.W., D.G.K., M.R.C., C.J.E. and R.K.H. provided cells, reagents and analytical tools. V.L., S.S., M.V., A.J., W.B., C.J.E., R.K.H., J.M.P. and C.L.H. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Carl L Hansen Author Details * Véronique Lecault Search for this author in: * NPG journals * PubMed * Google Scholar * Michael VanInsberghe Search for this author in: * NPG journals * PubMed * Google Scholar * Sanja Sekulovic Search for this author in: * NPG journals * PubMed * Google Scholar * David J H F Knapp Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Wohrer Search for this author in: * NPG journals * PubMed * Google Scholar * William Bowden Search for this author in: * NPG journals * PubMed * Google Scholar * Francis Viel Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas McLaughlin Search for this author in: * NPG journals * PubMed * Google Scholar * Asefeh Jarandehei Search for this author in: * NPG journals * PubMed * Google Scholar * Michelle Miller Search for this author in: * NPG journals * PubMed * Google Scholar * Didier Falconnet Search for this author in: * NPG journals * PubMed * Google Scholar * Adam K White Search for this author in: * NPG journals * PubMed * Google Scholar * David G Kent Search for this author in: * NPG journals * PubMed * Google Scholar * Michael R Copley Search for this author in: * NPG journals * PubMed * Google Scholar * Fariborz Taghipour Search for this author in: * NPG journals * PubMed * Google Scholar * Connie J Eaves Search for this author in: * NPG journals * PubMed * Google Scholar * R Keith Humphries Search for this author in: * NPG journals * PubMed * Google Scholar * James M Piret Search for this author in: * NPG journals * PubMed * Google Scholar * Carl L Hansen Contact Carl L Hansen Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Change history * Author information * Supplementary information Movies * Supplementary Video 1 (9M) Medium exchange does not disturb the spatial position of the cells. Cells were imaged continuously (1 frame s−1) during 10 min with and without medium exchange. * Supplementary Video 2 (7M) Recovery of individual colonies. Colonies of ND13 cells were recovered from the microfluidic cell culture array using a micropipette after 72 h in culture. Zip files * Supplementary Software 1 (20K) Image analysis scripts. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–11, Supplementary Notes 1–4 Additional data - Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra
- Nat Methods 8(7):587-591 (2011)
Nature Methods | Article Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra * Ari M Frank1 * Matthew E Monroe2 * Anuj R Shah2 * Jeremy J Carver1 * Nuno Bandeira1, 3 * Ronald J Moore2 * Gordon A Anderson2 * Richard D Smith2 * Pavel A Pevzner1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:587–591Year published:(2011)DOI:doi:10.1038/nmeth.1609Received05 April 2010Accepted13 April 2011Published online15 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Tandem mass spectrometry (MS/MS) experiments yield multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra they generate. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about peptide spectra that are common across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with new ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ~1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archiv! es rather than be analyzed as disparate datasets, as is mostly the case today. View full text Subject terms: * Proteomics * Bioinformatics * Mass Spectrometry Figures at a glance * Figure 1: Clustering of the PNNL dataset. () The 581 million spectra from the PNNL dataset that passed quality filtration were assigned into 299 million clusters of different sizes as indicated. () Distribution of multiclusters according to the number of organisms whose spectra were included in each cluster for each of 21.5 million multiclusters. * Figure 2: Identification of peptides across different species. Distribution of the number of peptide identifications made with the S. oneidensis data using three methods: no clustering (standard MS/MS search), single-species clustering followed by MS/MS search and multispecies clustering followed by MS/MS search. Results were processed to maintain a 2% FDR. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA. * Ari M Frank, * Jeremy J Carver, * Nuno Bandeira & * Pavel A Pevzner * Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA. * Matthew E Monroe, * Anuj R Shah, * Ronald J Moore, * Gordon A Anderson & * Richard D Smith * Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA. * Nuno Bandeira Contributions A.M.F. designed and implemented the algorithms, designed and ran the experiments and wrote the paper. P.A.P. designed the algorithms and the experiments and wrote the paper. R.D.S. developed the measurement capabilities. R.J.M. was responsible for the measurements. M.E.M. and G.A.A. developed protocols and did the proteomics data acquisition and processing. A.R.S. assisted in designing the experiments. J.J.C. and N.B. designed and implement the web-based archive searching tool. All authors discussed, commented and contributed to writing the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Pavel A Pevzner Author Details * Ari M Frank Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew E Monroe Search for this author in: * NPG journals * PubMed * Google Scholar * Anuj R Shah Search for this author in: * NPG journals * PubMed * Google Scholar * Jeremy J Carver Search for this author in: * NPG journals * PubMed * Google Scholar * Nuno Bandeira Search for this author in: * NPG journals * PubMed * Google Scholar * Ronald J Moore Search for this author in: * NPG journals * PubMed * Google Scholar * Gordon A Anderson Search for this author in: * NPG journals * PubMed * Google Scholar * Richard D Smith Search for this author in: * NPG journals * PubMed * Google Scholar * Pavel A Pevzner Contact Pavel A Pevzner Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (561K) Supplementary Tables 1–2 and Supplementary Notes 1–6 Additional data - High-throughput behavioral analysis in C. elegans
- Nat Methods 8(7):592-598 (2011)
Nature Methods | Article High-throughput behavioral analysis in C. elegans * Nicholas A Swierczek1, 5 * Andrew C Giles2, 3, 5 * Catharine H Rankin3, 4 * Rex A Kerr1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:592–598Year published:(2011)DOI:doi:10.1038/nmeth.1625Received08 November 2010Accepted25 April 2011Published online05 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We designed a real-time computer vision system, the Multi-Worm Tracker (MWT), which can simultaneously quantify the behavior of dozens of Caenorhabditis elegans on a Petri plate at video rates. We examined three traditional behavioral paradigms using this system: spontaneous movement on food, where the behavior changes over tens of minutes; chemotaxis, where turning events must be detected accurately to determine strategy; and habituation of response to tap, where the response is stochastic and changes over time. In each case, manual analysis or automated single-worm tracking would be tedious and time-consuming, but the MWT system allowed rapid quantification of behavior with minimal human effort. Thus, this system will enable large-scale forward and reverse genetic screens for complex behaviors. View full text Subject terms: * Neuroscience * Genetics * Imaging Figures at a glance * Figure 1: Accuracy and performance of the MWT. () The major components of the MWT system. CCD, charge-coupled device. () Examples of full-field images, the regions selected for analysis by the MWT and visualization in Choreography after capture. Scale bars, 1 cm (left), 2 mm (middle), 0.4 mm (right). () Probability that a given number of frames will be dropped for each frame processed, with indicated numbers of worms. () Accuracy of detection and selection of worms for analysis. A false positive is either an object that is not a worm or a worm scored by hand as partially obscured. Each data point corresponds to a different choice for time and distance threshold and with or without the following criteria as indicated in the figure: worms must be in a region of interest away from the edges of the plate and worms must move one body length before being quantified to avoid image processing artifacts. Arrow marks our typical choice of parameters. () Effect of crowding on fraction of worms detected or selected for analysis (usi! ng parameters chosen in ). Ideal, all worms detected and analyzed. () Effect of crowding on duration one worm can be followed (identity is lost upon collision). (,) Effect of pixel size and frame rate on estimate of worm position (; the orange × indicates typical parameters for a non–real time tracker, Parallel Worm Tracker) and of worm speed (; movement averaged over 0.5 s). * Figure 2: Worm movement on food. () Plot of worm speeds over time for selected worms, individual plates, and the mean of eight plates (~30 tracked worms per plate). (,) Plot of worm speed for the indicated mutants and strains after being placed on the tracker (; mean of four plates, ≥15 worms per plate) and for different Caenorhabditis species (; mean of four plates, ≥20 worms per plate). () Intensity map of first two principal components of worm shape computed from the reference dataset; darker colors indicate more observations. The ring shape of the map corresponds to the worm's sinusoidal movement cycle; the blue arrow indicates forward movement or an increase in phase φ. () Example of phase progression: snapshots correspond to sine-like and cosine-like postures; the worm's path is colored by phase. () Intensity map as in at the indicated times after worms were placed on the tracker (~20 tracked worms on one plate, strain XJ1). () Worm centroid movement in body lengths is plotted against estimated n! umber of body bends (phase advance). Data for individual movements and best linear fit are shown. () The probability of omega turn initiation is plotted against reversal distance (number of body bends) for the indicated strains. Error bars, s.e.m., n = ~400 omega turns per strain across all distances. * Figure 3: Analysis of chemotaxis. () Chemotaxis effectiveness of wild-type worms for eight plates with ~8 worms tracked per plate. Preference score is the Bayesian estimate of the probability that a worm will travel into the food spot instead of the control spot. () Pirouette frequency of the indicated strains near food. Reversal bias is fT / (fT + fA), where f is the frequency of reversals when moving toward (fT) or away (fA) from food or control spot. Error bars, s.e.m. from Monte Carlo simulation (n ≥ 3 plates with ≥6 worms each). *P < 0.05 that reversals are equally common when traveling toward versus away from food (χ2 test). () Weathervane chemotaxis to NaCl in the absence of food. The mean curve of the worm's path is plotted against its bearing relative to the gradient; signs were chosen so that positive curve at positive bearing is a turn up the gradient. Error bars, s.e.m.; wild-type (N2) n = 30 plates of 10 worms, mutants (che-2, osm-6 and tax-2) n = 5 plates of 10 worms each. () Analysis of t! he strength of weathervane chemotaxis over time. Positive curve is up the gradient, and results were averaged over quadrants around ± 90° bearing. P < 0.001 (t-test) that the first 10 min and the next 20 are the same. * Figure 4: Analysis of tap habituation. (,) Probability of reversing (, Bayesian estimate) or reversal distance () after a tap are plotted against the number of tap stimuli. Data for six plates (~30 worms per plate) of wild-type (XJ1) worms were plotted in different shades of gray. (,) Reversal probability () and distance () for mechanosensory and chemosensory mutants plotted against the number of tap stimuli. Error bars are s.e.m.; n = 3 plates for mutants and N2, 6 plates for XJ1, ≥10 worms per plate. () Probability of response to first tap of various mutants and wild-type controls; Z-score was normalized by wild-type distribution. () Habituated response probabilities (at stimuli 28–30). Rejected mutants were those with abnormal initial response. (,) Probability of reversal after tap plotted for the loss-of-habituation mutant adp-1 () and the hyper-habituation mutant tom-1 (). Error bars, s.e.m.; n = 3 plates with ~30 tracked worms each. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Nicholas A Swierczek & * Andrew C Giles Affiliations * Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA. * Nicholas A Swierczek & * Rex A Kerr * Graduate Program in Neuroscience, University of British Columbia, Vancouver, British Columbia, Canada. * Andrew C Giles * Brain Research Center, University of British Columbia, Vancouver, British Columbia, Canada. * Andrew C Giles & * Catharine H Rankin * Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada. * Catharine H Rankin Contributions N.A.S. and R.A.K. designed the MWT system, built the hardware and wrote the software. A.C.G., C.H.R. and R.A.K. designed the experiments. A.C.G. and R.A.K. conducted the experiments and analyzed data. R.A.K. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Rex A Kerr Author Details * Nicholas A Swierczek Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew C Giles Search for this author in: * NPG journals * PubMed * Google Scholar * Catharine H Rankin Search for this author in: * NPG journals * PubMed * Google Scholar * Rex A Kerr Contact Rex A Kerr Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Zip files * Supplementary Software 1 (39M) The MultiWorm Tracker (MWT) consists of real-time image-processing software, MWT, and offline analysis software, Choreography. Additional data - High-content behavioral analysis of Caenorhabditis elegans in precise spatiotemporal chemical environments
- Nat Methods 8(7):599-605 (2011)
Nature Methods | Article High-content behavioral analysis of Caenorhabditis elegans in precise spatiotemporal chemical environments * Dirk R Albrecht1 * Cornelia I Bargmann1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:599–605Year published:(2011)DOI:doi:10.1038/nmeth.1630Received21 December 2010Accepted06 May 2011Published online12 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To quantitatively understand chemosensory behaviors, it is desirable to present many animals with repeatable, well-defined chemical stimuli. To that end, we describe a microfluidic system to analyze Caenorhabditis elegans behavior in defined temporal and spatial stimulus patterns. A 2 cm × 2 cm structured arena allowed C. elegans to perform crawling locomotion in a controlled liquid environment. We characterized behavioral responses to attractive odors with three stimulus patterns: temporal pulses, spatial stripes and a linear concentration gradient, all delivered in the fluid phase to eliminate variability associated with air-fluid transitions. Different stimulus configurations preferentially revealed turning dynamics in a biased random walk, directed orientation into an odor stripe and speed regulation by odor. We identified both expected and unexpected responses in wild-type worms and sensory mutants by quantifying dozens of behavioral parameters. The devices are inexpen! sive, easy to fabricate, reusable and suitable for delivering any liquid-borne stimulus. View full text Subject terms: * Neuroscience * Genetics * Model Organisms * Lab-on-a-chip Figures at a glance * Figure 1: Worm behavioral arena and stimulus patterns. () Photo of the microfluidic device, configured such that continuous flow of three streams (1–3) produces a stable pattern of dye stripes in the arena. Worm barriers prevent passage out of the arena (white arrowheads). () Top view showing arena geometry and a young adult worm crawling through fluid-filled channels (gray) between cylindrical microposts (white). Arrows indicate flow direction. Also shown are an oblique view of the micropost array before device assembly (bottom left) and a cross-section schematic, indicating the glass substrate, polydimethylsiloxane (PDMS) top surface and posts, worm (W) and stimulus fluid (Fl). Scale bars, 500 μm. () Stimuli flow by gravity from elevated reservoirs through external valves into the device. Worms were loaded with a syringe. (–) Upstream channels are tailored to each stimulus pattern: inlet channels converge to a distribution tree for stimulus pulses (), remain separated for stimulus stripes () or pass though a mixing tree t! o create linear or sigmoidal concentration profiles (). Graphs on the right show the corresponding odor patterns, estimated by measured dye concentration with respect to time or position in the device. * Figure 2: Locomotory behavior in structured arenas. () Representative tracks from a single worm demonstrating the indicated behavioral states. Arrows and shading show the direction of travel. () The path taken by a representative wild-type worm over 1 h shows navigation across the entire buffer-filled arena (top) and magnification of the boxed region (bottom). Dots indicate the worm centroid at 0.5-s intervals. () The centroid path for the worm in , colored according to behavioral state. Swimming behavior at the upstream and downstream barriers was removed from analysis, and path breaks indicate temporary collisions with other worms. Specific reversal (R1–4) and pirouette (Pir 1 and Pir 2) behaviors are labeled in the magnified view below. () Time course of morphological parameters and speed for the magnified worm path in . The corresponding behavioral state (colored and labeled as in ) was automatically determined from these parameters for each video frame; for example, reversals were bounded by high angular velocity, and ! omega turns were identified by high aspect ratio and sharp reorientation (dashed lines). Scale bars, 500 μm ( and , bottom) and 2 mm (, top). * Figure 3: Odor pulse assay. () Odor concentration estimated from dye absorbance during one cycle of isoamyl alcohol (IAA) pulses (top) and corresponding instantaneous forward locomotion speed (bottom, n = 48 worms) are shown. () Ethogram showing the instantaneous behavioral state of worms subjected to the odor pattern in . Each row represents one worm, and four 15-min cycles are shown stacked for a total of 192 rows. Speed traces and ethograms for each worm were aligned in time to the odor step it experienced. () Instantaneous behavioral state probability from , excluding collisions and worms near the barriers. Scale bar, 50% behavioral-state probability. () Initiation of individual omega turns from (black points at the top) and average omega turn rate per worm (mean ± s.d.; n = 48 worms). () Average stimulus (top) and behavioral dynamics (state probability) for repeated odor removals (shading indicates 95% confidence for odor and s.e.m. for state probability, n = 24 pulses). Peak response times (arro! wheads) are indicated. Dotted lines show buffer-buffer control switches. () Average probability of response to odor addition and removal (mean ± s.e.m., n = 6–24 pulses). Numbers indicate percent probability. *P < 0.01 versus wild-type odor-free probability of forward (gray), reverse (black) or pirouette (red) responses. () Ethogram showing a ± 10 s window around odor removal number 24 (black box in ), sorted by the predominant behavioral state after odor removal. () Responses of 23 individual worms to 24 repeated odor removal steps, colored as in . *P < 0.05; NS, not significant versus the population mean by the mass function of the trinomial distribution and the Benjamini–Hochberg–Yekutieli correction for false discovery rate. Arrowheads mark the midpoints of concentration changes during odor removal steps. * Figure 4: Odor stripe assay. () The image shows the arena with two odor stripes containing 0.92 or 1.84 μM isoamyl alcohol (IAA) and dye, surrounded by buffer. () Relative x–y residence of 24 worms over 80 min (a value of 1 represents a uniform distribution). () Time course of worm residence along the y axis over time, with odor added at 3 min (arrowhead). () Histograms of residence relative to different spatial odor patterns (shading in bottom graphs). () Heat maps of wild-type, mutant and rescued worm residence in odor or buffer stripes over time, as in . () Spatially averaged behavioral parameters within 1.8 mm surrounding each odor edge (arrowheads). Shading indicates odor. Directional parameters are shown for worms traveling downward before the event, with top and bottom plots representing inward and outward movement relative to odor, respectively. Dot indicates worm head, and event locations correspond to the worm centroid at event initiation (circle). Data are mean ± s.e.m., n = 2–10 experi! ments for turn rates, n = 8–24 odor edges for other parameters, averaged over 37–230 worms per condition. () Spatial plot and histogram of upward (blue) and downward (red) 'surf' curves occurring within 0.5 mm of the odor edge (dotted lines), by 25 wild-type worms over 1 h. () Mean chemotaxis index (top) from 20 min to 80 min, defined as (worm density in odor – density out of odor)/(total density); and peak outward forward speed relative to speed in odor (bottom). Error bars as in , with significance assessed by ANOVA and Bonferroni's correction for multiple comparisons. **P < 0.001; *P < 0.01; NS, not significant compared with wild-type no-odor control. * Figure 5: Turning responses at sharp spatial odor gradients. () A wild-type worm track in a 0.92 μM isoamyl alcohol (IAA) odor stripe (gray) shows turning events (circled) initiated within 0.5 mm of the odor edge (dotted lines) or odor edge crossings. () Ethogram shows −30 s to +15 s relative to the eight numbered edge encounter events in . () Events were grouped according to the direction of edge approach (inward, 'surfing' (horizontal) or outward) and by the response outcome (correctly remaining in the odor (C) or incorrectly leaving the odor (I)). All eight numbered events in and were correct. () Outward edge event rates for wild-type experiments with odor (WT) or buffer stripes (no odor) and tax-4 with odor over 120 min, and average forward speed. () Correctness (top) and type (bottom) of all edge-triggered responses over 120 min. () Representative wild-type worm paths shown for correct and incorrect outward responses. Shading indicates odor. () Behavior rasters for wild-type worms heading outward, grouped by correctness and re! sponse category (2,208 responses from 159 worms over 80 min). () Relative forward speed for wild-type worms that did not pirouette, curve or reverse upon exit of odor or buffer stripes. () Probabilities of outward responses for worms exiting odor and buffer stripes, indicating the percentage correct for each category (mean ± s.e.m., n = 2–10 experiments). †, no significant difference from random (50% correct), P > 0.2 via two-tailed t-test. * Figure 6: Odor gradient assay. () Odor concentration gradient from 0 μM to 1.84 μM isoamyl alcohol (IAA) in the arena, visualized with dye. () Relative x–y residence of 25 worms tracked over 120 min in the gradient; (a value of 1 represents a uniform distribution). () Time course of worm residence in the odor gradient, established at 5 min (arrowhead). () Distribution of wild-type worms over 120 min (average of 5 experiments) subjected to a spatial odor gradient (red plot and gray shading). () Odor concentration gradient calculated from odor profile in (mean ± s.d., n = 6 points per 1.67 mm wide y-bin). () Location in the arena of surf curves directed up (blue) or down (red) the odor gradient. Bracket marks the region of constant odor gradient analyzed for turning bias. () Relative prevalence of surf curves, other curves and pirouettes directed up (positive) or down (negative) odor gradients from 0–5 μM mm−1 IAA as diagramed. Indices on the y axis are defined as (number of events up – number o! f events down)/(total events per y bin) averaged over the gradient region (bracketed in ,). Bars indicate mean ± s.e.m., n = 5–9 y bins averaged over 6–10 experiments. **P < 0.001; *P < 0.01; +P < 0.05; NS, not significant compared with wild-type no-odor control. Data for sharp gradients (≥2.5 μM mm−1) were obtained with stripe device (Fig. 4). () Upward bias of forward speed, defined as (speed up gradient – speed down gradient)/(mean speed per y bin); statistics as in . Author information * Abstract * Author information * Supplementary information Affiliations * Howard Hughes Medical Institute and Laboratory of Neural Circuits and Behavior, The Rockefeller University, New York, New York, USA. * Dirk R Albrecht & * Cornelia I Bargmann Contributions D.R.A. designed and fabricated the devices, wrote the analysis code, performed the experiments and analyzed data. D.R.A. and C.I.B. designed the experiments and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Dirk R Albrecht Author Details * Dirk R Albrecht Contact Dirk R Albrecht Search for this author in: * NPG journals * PubMed * Google Scholar * Cornelia I Bargmann Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (872K) Worm locomotion in the structured microfluidic environment. Real-time video shows a wild-type young adult worm crawling forward through the arena, pausing briefly (at 8 s), then performing two pirouettes: long reversals (at 12 s and 20 s) followed by a sharp turns (at 17 s and 25 s). Video shows a 2.3 mm × 1.7 mm region. All videos are encoded with the Xvid codec (available at http://www.xvidmovies.com/codec). * Supplementary Video 2 (2M) Loading worms into a device. Video shows 18 wild-type (right arena) and 22 tax-4 (left arena) worms loaded simultaneously into a single device containing two 16 mm A 15 mm arenas. Wild-type worms paused and reversed frequently for about 20 min, whereas tax-4 mutants quickly dispersed. tax-4 worms did not respond to the central stripe containing 0.92 μM isoamyl alcohol (IAA) odor and dye for visualization. Video is accelerated 15×. * Supplementary Video 3 (8M) Annotated video of an odor pulse assay. Video shows tracking and analysis of 23 wild-type worms responding to repeated 0.92 μM IAA odor stimulation according to the pulse sequence in Figure 3a. Each worm is numbered in the arena and labeled with its current centroid (circle and green +) and a line indicating 30-s of centroid history. Line color indicates behavioral history according to the legend, for example, white indicates forward movement and red indicates the pirouette state. The ethogram (right) shows a scrolling 45-s window of worm behavior, time-adjusted such that each worm's response lines up with the odor pulse pattern. Green arrowheads on the ethogram indicate the position of each worm at the current video frame. Tick marks represent 15 s. Video is accelerated 15×. * Supplementary Video 4 (3M) Dynamic odor stripe assay. Video shows the locomotor responses of 25 worms to two attractive odor stripes (0.92 μM and 1.84 μM IAA, top to bottom) separated by odor-free buffer. Flow is left to right, and odor stripes contain dye for visualization. Worms experience a sharp odor gradient at the stripe edges but no mechanical or flow rate discontinuity. Near the end of the video, odor stripes are turned off by closing external valves. Video is accelerated 15×. * Supplementary Video 5 (332K) Example behavioral responses upon odor stripe exit. Odor (0.92 μM IAA) is present in the lower half of the video (gray). Three outward encounters of the odor stripe edge are shown; each response is shown twice. The first worm responds with a correct forward turn, the second with a correct pirouette and the third with an incorrect pirouette. 'Correct' refers to the worm remaining in the attractive odor stripe after the response. Video shows a 3.9 mm × 3.3 mm region and is accelerated 15×. Zip files * Supplementary Software 1 (2M) Software to identify instantaneous behavioral states from video, and to summarize behavior and speed data over space and time. PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–12, Supplementary Table 1, Supplementary Notes 1–4 * Supplementary Data 1 (1M) Microfluidic channel pattern file. Additional data - Corrigendum: A bright and photostable photoconvertible fluorescent protein
- Nat Methods 8(7):606 (2011)
Nature Methods | Corrigendum Corrigendum: A bright and photostable photoconvertible fluorescent protein Journal name:Nature MethodsVolume: 8,Page:606Year published:(2011)DOI:doi:10.1038/nmeth0711-606aPublished online29 June 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Sean A McKinney, Christopher S Murphy, Kristin L Hazelwood, Michael W Davidson & Loren L LoogerNat. Methods6, 131–133 (2009). In the version of the supplementary information file originally posted online, the mitotic phases were mislabeled in ; during amendment of the figure legend, two of the mitotic phases (metaphase and prophase) could not be tracked back to the original data. The images have been replaced, and the labeling of mitotic phases has been corrected in the supplementary information file as of 10 May 2011. Additional data - Erratum: Stem cells in culture: defining the substrate
- Nat Methods 8(7):606 (2011)
Nature Methods | Erratum Erratum: Stem cells in culture: defining the substrate Journal name:Nature MethodsVolume: 8,Page:606Year published:(2011)DOI:doi:10.1038/nmeth0711-606bPublished online29 June 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Monya BakerNat. Methods8, 293–297 (2011); published online 30 March 2011; corrected after print 6 April 2011. In the version of this article initially published, a reference was incomplete. The error has been corrected in the HTML and PDF versions of the article. Additional data - Erratum: Stem cells in culture: defining the substrate
- Nat Methods 8(7):606 (2011)
Nature Methods | Erratum Erratum: Stem cells in culture: defining the substrate Journal name:Nature MethodsVolume: 8,Page:606Year published:(2011)DOI:doi:10.1038/nmeth0711-606cPublished online29 June 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Monya BakerNat. Methods8, 293–297 (2011); published online 30 March 2011; corrected after print 6 April 2011 and 16 May 2011. In the version of this article initially published, the image descriptions were swapped. The error has been corrected in the HTML and PDF versions of the article. Additional data
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