pubget—the blog: January 2011

Sunday, January 30, 2011

Hot off the presses! Jan 01 UNKNOWN

The Jan 01 issue of the UNKNOWN is now up on Pubget (About UNKNOWN): if you're at a subscribing institution, just click the link in the latest link at the home page. (Note you'll only be able to get all the PDFs in the issue if your institution subscribes to Pubget.)

Friday, January 28, 2011

Hot off the presses! Feb 01 Nat Meth

The Feb 01 issue of the Nat Meth is now up on Pubget (About Nat Meth): if you're at a subscribing institution, just click the link in the latest link at the home page. (Note you'll only be able to get all the PDFs in the issue if your institution subscribes to Pubget.)

Latest Articles Include:

The right partner
- Nat Meth 8(2):97 (2011)
Nature Methods | Editorial The right partner Journal name:Nature MethodsVolume: 8,Page:97Year published:(2011)DOI:doi:10.1038/nmeth0211-97Published online28 January 2011 For the development, application and dissemination of high-impact methods, interdisciplinary collaboration between experts is vital. View full text Additional data
The author file: Hang Lu
- Nat Meth 8(2):99 (2011)
Nature Methods | This Month The author file: Hang Lu * Monya Baker Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:99Year published:(2011)DOI:doi:10.1038/nmeth0211-99Published online28 January 2011 Practical microsystems are used to monitor flies and worms. View full text Additional data
Points of view: Points of review (part 1)
- Nat Meth 8(2):101 (2011)
Nature Methods | This Month Points of view: Points of review (part 1) * Bang Wong1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:101Year published:(2011)DOI:doi:10.1038/nmeth0211-101Published online28 January 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Methods for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. My goal over the next two months is to show concretely how scientific figures can benefit from design principles. I will review concepts from past columns by applying them to several published figures. In the design of common objects, such as a door, when a handle is used many people will mistakenly pull even if the door is to be opened by pushing. When the handle is replaced with a flat plate, which affords pushing, people will know to push. When dealing with figures, we depend on visual cues. We want our figure's layout to express its underlying meaning. View full text Figures at a glance * Figure 1: Layouts can express meaning. () Diagram of a microscopy system. Reprinted from Nature Methods1. () A sketch using grouping and white space to make the three parts of the system being illustrated more apparent. * Figure 2: Visual structure that matches the message. () Illustration showing a gene expression analysis technique. Reprinted from Genome Biology4. () The same elements organized according to the purpose of the illustration, which is to show a sequence of steps. 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 * Bang Wong is the creative director of the Broad Institute of the Massachusetts Institute of Technology and Harvard and an adjunct assistant professor in the Department of Art as Applied to Medicine at The Johns Hopkins University School of Medicine. Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Methods for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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
Resources for proteomics in mouse embryonic stem cells
- Nat Meth 8(2):103-104 (2011)
Nature Methods | Correspondence Resources for proteomics in mouse embryonic stem cells * Frank Schnütgen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Franziska Ehrmann1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ina Poser2 Search for this author in: * NPG journals * PubMed * Google Scholar * Nina C Hubner3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jens Hansen4 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Floss4 Search for this author in: * NPG journals * PubMed * Google Scholar * Ingrid deVries2 Search for this author in: * NPG journals * PubMed * Google Scholar * Wolfgang Wurst4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony Hyman2 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias Mann3 Search for this author in: * NPG journals * PubMed * Google Scholar * Harald von Melchner1 Contact Harald von Melchner Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:103–104Year published:(2011)DOI:doi:10.1038/nmeth0211-103Published online28 January 2011 To the Editor: A recent publication in Nature Methods described recombinase-mediated cassette exchange (RMCE) for re-engineering gene targeted alleles in mouse embryonic stem cells (ESCs) derived from the International Knock Out Mouse Consortium (IKMC) repositories1. We wish to point out that FlipRosaβgeo gene–trapped ESC lines in the same repositories2 can be engineered to encode proteins with N-terminal protein tags using an RMCE-based approach. As do the IKMC's gene-targeted alleles, the FlipRosaβgeo gene-trap alleles include site-specific recombinase target sequences that enable RMCE3 (Fig. 1a). 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 Affiliations * Department for Molecular Hematology, University of Frankfurt Medical School, Frankfurt am Main, Germany. * Frank Schnütgen, * Franziska Ehrmann & * Harald von Melchner * Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. * Ina Poser, * Ingrid deVries & * Anthony Hyman * Max Planck Institute of Biochemistry, Martinsried, Germany. * Nina C Hubner & * Matthias Mann * Institute for Developmental Genetics Helmholtz Zentrum München, Germany. * Jens Hansen, * Thomas Floss & * Wolfgang Wurst * German Center for Neurodegenerative Diseases, Technische Universität München, Neuherberg, Germany. * Wolfgang Wurst Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Harald von Melchner Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 1 (976K) List of tagging compatible gene trap lines. PDF files * Supplementary Text and Figures (3.1M) Supplementary Figures 1–5, Supplementary Tables 2–3, Supplementary Methods Additional data
Data transformation practices in biomedical sciences
- Nat Meth 8(2):104-105 (2011)
Nature Methods | Correspondence Data transformation practices in biomedical sciences * Mihai Valcu1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Cristina-Maria Valcu2, 3 Contact Cristina-Maria Valcu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:104–105Year published:(2011)DOI:doi:10.1038/nmeth0211-104Published online28 January 2011 To the Editor: In over a century since it was first introduced by William Sealy Gosset (under the pseudonym Student), the t-test has become one of the most common tests in many fields of research1 and is now a basic element in a biologist's toolkit for statistical hypothesis testing. Our screen of the first 2010 issue of medical and biological science journals with an impact factor higher than 15 revealed that in 88 of the 213 research articles, the authors had used t-tests to analyze their data (Supplementary Methods). 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. * Mihai Valcu & * Cristina-Maria Valcu Affiliations * Department of Behavioral Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, Seewiesen, Germany. * Mihai Valcu * Department of Experimental and Molecular Pediatric Cardiology, German Heart Centre, Technical University Munich, Munich, Germany. * Cristina-Maria Valcu Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Cristina-Maria Valcu Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1-3, Supplementary Methods Additional data
One genome, two haplotypes
- Nat Meth 8(2):107 (2011)
Nature Methods | Research Highlights One genome, two haplotypes * Nicole Rusk Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:107Year published:(2011)DOI:doi:10.1038/nmeth0211-107Published online28 January 2011 Two approaches using either fosmid clones or a microfluidic device are used to tackle the challenge of a haplotype-resolved human genome. View full text Subject terms: * Genomics Additional data
Rewiring cellular networks
- Nat Meth 8(2):108-109 (2011)
Nature Methods | Research Highlights Rewiring cellular networks * Erika Pastrana Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Pages:108–109Year published:(2011)DOI:doi:10.1038/nmeth0211-108aPublished online28 January 2011 RNA-based regulatory systems control the behavior of cells in response to endogenous proteins. View full text Subject terms: * Synthetic Biology Additional data
Better living through biochemistry
- Nat Meth 8(2):108-109 (2011)
Nature Methods | Research Highlights Better living through biochemistry * Michael Eisenstein Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Pages:108–109Year published:(2011)DOI:doi:10.1038/nmeth0211-108bPublished online28 January 2011 In-depth mass spectrometric analysis reveals how cells survive stress by coordinating various enzymes that modify RNAs involved in protein synthesis. View full text Subject terms: * Biochemistry Additional data
News in brief
- Nat Meth 8(2):109 (2011)
Nature Methods | Research Highlights News in brief Journal name:Nature MethodsVolume: 8,Page:109Year published:(2011)DOI:doi:10.1038/nmeth0211-109Published online28 January 2011 Read the full article * FREE access with registration Register now * Already have a Nature.com account? Login Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. An RNA crystallization chaperone The crystallization of RNA molecules for structural analysis is even more challenging than protein crystallization owing to the low chemical diversity, flexibility and conformational heterogeneity of RNA. Koldobskaya et al. introduce a chaperone system that stabilizes RNA structure and promotes crystallization. The chaperone is an antigen-binding fragment (Fab) that recognizes an epitope tag that can be installed on any RNA of interest. Koldobskaya, Y.et al. Nat. Struct. Mol. Biol.18, 100–106 (2011). View full text Read the full article * FREE access with registration Register now * Already have a Nature.com account? Login Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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
Differentiation in three dimensions
- Nat Meth 8(2):111 (2011)
Nature Methods | Research Highlights Differentiation in three dimensions * Monya Baker Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:111Year published:(2011)DOI:doi:10.1038/nmeth0211-111Published online28 January 2011 Pluripotent stem cells form intestine-like structures in vitro. View full text Subject terms: * Stem Cells Additional data
A genetic system to study reprogramming
- Nat Meth 8(2):112 (2011)
Nature Methods | Research Highlights A genetic system to study reprogramming * Natalie de Souza Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:112Year published:(2011)DOI:doi:10.1038/nmeth0211-112Published online28 January 2011 Caenorhabditis elegans can be used to probe the mechanistic basis for cell-fate conversion. View full text Subject terms: * Genetics Additional data
Synthesis through sequencing
- Nat Meth 8(2):114 (2011)
Nature Methods | Research Highlights Synthesis through sequencing * Daniel Evanko Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:114Year published:(2011)DOI:doi:10.1038/nmeth0211-114Published online28 January 2011 Next-generation sequencing platforms provide both high-throughput sequencing and DNA production. View full text Subject terms: * Synthetic Biology Additional data
Metabolomics: from small molecules to big ideas
- Nat Meth 8(2):117-121 (2011)
Nature Methods | Technology Feature Metabolomics: from small molecules to big ideas * Monya Baker1 Contact Monya Baker Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Pages:117–121Year published:(2011)DOI:doi:10.1038/nmeth0211-117Published online28 January 2011 The focus of metabolomic studies is shifting from cataloging chemical structures to finding biological stories. 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 * Monya Baker is technology editor for Nature and Nature Methods Corresponding author Correspondence to: * Monya Baker Additional data
Five challenges to bringing single-molecule force spectroscopy into living cells
- Nat Meth 8(2):123-127 (2011)
Nature Methods | Commentary Five challenges to bringing single-molecule force spectroscopy into living cells * Yves F Dufrêne1 Search for this author in: * NPG journals * PubMed * Google Scholar * Evan Evans2 Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Engel3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonne Helenius4 Search for this author in: * NPG journals * PubMed * Google Scholar * Hermann E Gaub5 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel J Müller4 Contact Daniel J Müller Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:123–127Year published:(2011)DOI:doi:10.1038/nmeth0211-123Published online28 January 2011 In recent years, single-molecule force spectroscopy techniques have been used to study how inter- and intramolecular interactions control the assembly and functional state of biomolecular machinery in vitro. Here we discuss the problems and challenges that need to be addressed to bring these technologies into living cells and to learn how cellular machinery is controlled in vivo. View full text Figures at a glance * Figure 1: SMFS of the cell's molecular machinery. () SMFS methods rely on different force probes to quantify interactions: AFM uses ~10–200-micrometer-long cantilevers5, 13, optical and magnetic tweezers use beads4, pressurized microcapsules use single cells or vesicles and microneedles1, 3. F, force. Scale bar, 20 μm. () Examples of using force probes (gray spheres) to quantify biomolecular interactions of single biomolecules in vitro (top to bottom): protein unfolding and folding, DNA-binding proteins, ligand-receptor bonds and cytoskeletal motor proteins. * Figure 2: Force-probing cellular interactions in vivo. SMFS offers exciting opportunities to sense interactions that drive the molecular machinery of the cell, including the dynamic assembly of supramolecular complexes, transport phenomena, protein folding, unfolding and degradation, membrane-protein insertion and folding, membrane shaping and reorganization, DNA-binding proteins, cell adhesion and signaling, signaling pathways or interactions of the cytoskeleton with membrane proteins. 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 * Yves F. Dufrêne is at Universite catholique de Louvain, Institute of Condensed Matter and Nanosciences, Louvain-la-Neuve, Belgium. * Evan Evans is at Boston University, Medical Engineering and Physics, Boston Massachusetts, USA. * Andreas Engel is in the Department of Pharmacology, Case Western Reserve University, Cleveland, Ohio, USA and at the Biozentrum, University of Basel, Basel, Switzerland. * Jonne Helenius and Daniel J. Müller are at Eidgenössische Technische Hochschule Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland. * Hermann E. Gaub is at Ludwig Maximillians University, Applied Physics, Munich, Germany. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Daniel J Müller Additional data
Unrestrained worms bridled by the light
- Nat Meth 8(2):129-130 (2011)
Nature Methods | News and Views Unrestrained worms bridled by the light * André E X Brown1 Search for this author in: * NPG journals * PubMed * Google Scholar * William R Schafer1 Contact William R Schafer Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:129–130Year published:(2011)DOI:doi:10.1038/nmeth0211-129Published online28 January 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Methods for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. Two systems allow precise optogenetic stimulation of specific neurons in freely behaving nematodes. 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 * André E.X. Brown and William R. Schafer are in the Division of Cell Biology, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * William R Schafer Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Methods for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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
New approaches to modeling complex biochemistry
- Nat Meth 8(2):130-131 (2011)
Nature Methods | News and Views New approaches to modeling complex biochemistry * John A Bachman1 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Sorger1 Search for this author in: * NPG journals * PubMed * Google Scholar * AffiliationsJournal name:Nature MethodsVolume: 8,Pages:130–131Year published:(2011)DOI:doi:10.1038/nmeth0211-130Published online28 January 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Methods for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. Combining rule-based descriptions of biochemical reactions with agent-based computer simulation opens new avenues for exploring complex cellular processes. 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 * John A. Bachman and Peter Sorger are in the Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. Competing financial interests The authors declare no competing financial interests. Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Methods for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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
A new way to look at fat
- Nat Meth 8(2):132-133 (2011)
Nature Methods | News and Views A new way to look at fat * Joerg Bewersdorf1 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert V Farese Jr2 Search for this author in: * NPG journals * PubMed * Google Scholar * Tobias C Walther1 Contact Tobias C Walther Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:132–133Year published:(2011)DOI:doi:10.1038/nmeth0211-132Published online28 January 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Methods for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. Stimulated Raman scattering (SRS) microscopy is used to directly visualize lipids in cells and model organisms, and facilitates screening for genes involved in fat storage. 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 * Joerg Bewersdorf and Tobias C. Walther are at the Yale University School of Medicine, Department of Cell Biology, New Haven, Connecticut, USA. * Robert V. Farese Jr. is at the J. David Gladstone Institute of Cardiovascular Disease and the Department of Medicine and Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, USA. Competing financial interests J.B. has financial interest in Vutara, a company that produces fluorescence microscopes. Corresponding author Correspondence to: * Tobias C Walther Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Methods for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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
RNAi screening for fat regulatory genes with SRS microscopy
- Nat Meth 8(2):135-138 (2011)
Nature Methods | Brief Communication RNAi screening for fat regulatory genes with SRS microscopy * Meng C Wang1, 2, 6 Contact Meng C Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Min3, 5, 6 Contact Wei Min Search for this author in: * NPG journals * PubMed * Google Scholar * Christian W Freudiger3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Gary Ruvkun2 Search for this author in: * NPG journals * PubMed * Google Scholar * X Sunney Xie3 Contact X Sunney Xie Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:135–138Year published:(2011)DOI:doi:10.1038/nmeth.1556Received22 September 2010Accepted17 December 2010Published online16 January 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Identification of genes regulating fat accumulation is important for basic and medical research; genetic screening for those genes in Caenorhabditis elegans, a widely used model organism, requires in vivo quantification of lipids. We demonstrated RNA interference screening based on quantitative imaging of lipids with label-free stimulated Raman scattering (SRS) microscopy, which overcomes major limitations of coherent anti-Stokes Raman scattering microscopy. Our screening yielded eight new genetic regulators of fat storage. View full text Figures at a glance * Figure 1: Visualization of cellular fat storage in lipid droplets using SRS microscopy. () Experimental scheme of label-free SRS microscopy for in vivo lipid imaging. PMT, photomultipier tube. (–) YFP fluorescence images (–), SRS signals (–) and merged images (–) of HEK 293 cells expressing YFP-tagged perilipin A (,,), ADFP (,,) and LSDP5 (,,). Scale bars, 1 μm. * Figure 2: Imaging fat accumulation and distribution in C. elegans by SRS microscopy. (,) SRS signals revealed fat storage in intestine (arrow), hypodermis (asterisk), early embryos in the uterus (arrowhead) and cellular nuclei (labeled with "n"). () Subcelluar fat accumulation in a pool of droplets observed by SRS microscopy. (–) Two-photon–excited fluorescence from Nile Red staining (), SRS signals () and the overlaid images of these two signals (). (–) BODIPY staining of two distinct groups of subcellular organelles with weaker (asterisk) and stronger (arrow) fluorescence signals (), (SRS) image of the same organelles (), and overlap of BODIPY and SARS signals (). (,) CARS images showing strong signals in the intestinal cell nuclei owing to nonresonant background () and SRS image of the same worm with dark nuclei in the intestinal cells (). (–) CARS and SRS images of the same worm taken at a Raman shift of 2,845 cm−1 resonant with CH2 stretching mode (,) and a Raman shift of 2,796 cm−1 off-resonant (,). Cross-section profiles of the regions! marked by gray lines in and are shown in . Scale bars, 50 μm. * Figure 3: RNAi screening of new fat storage regulatory genes based on in vivo lipid quantification using label-free SRS microscopy. (–) Fat was visualized by SRS in the wild-type worm (), the daf-2(e1370) mutant () and the transgenic worm intestinally overexpressing the K04A8.5 lipase () under same imaging conditions. () Quantification of fat content by SRS (n = 5 worms) and thin-layer chromatography–gas chromatography (TLC/GC) (n = 5 × 103 worms). () SRS signal increase compared to the control for genes that resulted in a fat content increase of more than 25% when inactivated by RNAi (P < 0.0001, n = 5 worms). Control, worms fed with bacteria containing empty vectors. All the experiments were performed twice independently. Results from one experiment are shown. () Normal fat accumulation as observed in the RNAi hypersensitive strain, nre-1(hd20)lin-15b(hd126), fed with empty vector–containing bacteria (control). (–) SRS images of three candidate worms. Scale bars, 50 μm. Error bars, s.d. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Meng C Wang & * Wei Min Affiliations * Department of Molecular and Human Genetics and Huffington Center on Aging, Baylor College of Medicine, Houston, Texas, USA. * Meng C Wang * Department of Molecular Biology, Massachusetts General Hospital, and Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Meng C Wang & * Gary Ruvkun * Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA. * Wei Min, * Christian W Freudiger & * X Sunney Xie * Department of Physics, Harvard University, Cambridge, Massachusetts, USA. * Christian W Freudiger * Present address: Department of Chemistry, Columbia University, New York, New York, USA. * Wei Min Contributions M.C.W., W.M. and X.S.X. conceived the study; M.C.W. and W.M. designed the experiments; M.C.W., W.M. and C.W.F. performed the experiments; M.C.W. analyzed the data; M.C.W., W.M., G.R. and X.S.X. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * X Sunney Xie or * Meng C Wang or * Wei Min Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1– 4 and Supplementary Table 1 Additional data
Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing
- Nat Meth 8(2):139-142 (2011)
Nature Methods | Brief Communication Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing * Adrian Cheng1, 2, 3, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * J Tiago Gonçalves2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Peyman Golshani2 Search for this author in: * NPG journals * PubMed * Google Scholar * Katsushi Arisaka1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Carlos Portera-Cailliau2, 3 Contact Carlos Portera-Cailliau Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:139–142Year published:(2011)DOI:doi:10.1038/nmeth.1552Received01 October 2010Accepted14 December 2010Published online09 January 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In vivo two-photon calcium imaging would benefit from the use of multiple excitation beams to increase scanning speed, signal-to-noise ratio and field of view or to image different axial planes simultaneously. Using spatiotemporal multiplexing we circumvented light-scattering ambiguity inherent to deep-tissue multifocal two-photon microscopy. We demonstrate calcium imaging at multiple axial planes in the intact mouse brain to monitor network activity of ensembles of cortical neurons in three spatial dimensions. View full text Figures at a glance * Figure 1: Spatiotemporal multiplexing to overcome depth limitations in multifocal 2PLSM. () Layout of the prototype microscope. Laser pulses are emitted with a 12-ns period from a commercial ultrafast Ti:Al203 laser. The beam is divided into four beams, which are delayed by 3 ns each (1 m per 3 ns) and converged on the slow-axis scan mirror aperture, which is then projected onto the objective back aperture. The resulting emitted fluorescence, which is highly scattered, is collected by two hybrid photodetectors. The hybrid photodetector's active area is placed in a demagnified conjugate plane of the objective back aperture to maximize scattered light collection. () Schematic of different beam-scanning patterns at the sample. Time multiplexing removes ambiguity between different imaging planes, allowing both axial and lateral beam distribution. () Time course of detected fluorescence signal for a single beam (top) and four spatiotemporally multiplexed beams (bottom). Overlay of 200 oscilloscope traces and summary histograms of single photoelectron events (using a ! pollen grain). Fluorescence from different time windows (different colors) is associated with different delayed excitation beams. Scale bar, 12 ns. * Figure 2: Multifocal two- and three-dimension in vivo 2PCI of L2/3 neurons in barrel cortex with spatiotemporal multiplexing. () Spatial distribution of four beams in a single image plane (left) and typical field of view (right), an average intensity time projection of a representative calcium imaging movie (3 min, 250 frames s−1) from a P20 mouse using Fluo-4 AM. Scale bar, 50 μm. () Zero-lag cross-correlation image computed from a movie. () Final segmented image of cell bodies obtained through morphological filters (red contours). () Raw calcium traces of 11 different cells (neurophil signal in blue). () Model calcium traces with identified neuronal spiking events (tic marks) of selected cells using a peeling algorithm are shown with relative fluorescence change (ΔF/F). (,) Details of shaded regions shown in and , respectively. () Spatial distribution of four beams arranged axially (left) and field of view for each imaging plane (right). Images are average intensity time projections of a typical movie with Fluo-4 AM (3 min, 60 volumes s−1) with depth spanning from 90 μm to 180 μm below th! e pia (encompassing layers 1 to 3). Scale bar, 50 μm. () Zero-lag cross-correlation image (left) and fully segmented image (right) with cell contours (red). () Selected traces reconstructed by the peeling algorithm, with rows in and corresponding to beams shown in . * Figure 3: Multifocal 2PCI with spatiotemporal multiplexing to assess activity-derived neuronal connectivity in L2/3 of barrel cortex. () Spatial distribution of four scanning beams (left) and representative field of view in two separate imaging planes (right) from an experiment with a P20 mouse using Fluo-4 AM (3 min, 100 frames s−1). Scale bar, 50 μm. () Zero-lag cross-correlation image (left) and segmented image (right) of the same experiment, with rows in corresponding to rows in . Cells are numerically ordered according to their vertical coordinates. () Raster plot showing identified spiking events in cells from . Events shown in red were identified as having participated in a peak of synchrony (bottom trace). () Peak correlation coefficient (over a time lag of ± 1 s) for significantly correlated (P < 0.05; as defined in Online Methods) cells shown in . () Axial (depth) versus radial (lateral) spread of bursts of neuronal firing corresponding to peaks of synchrony identified in several movies (10 movies from 2 mice, 173 peaks of synchrony). A minority of bursts had a spatial organization consistent! with either columnar (top left) or laminar connectivity (bottom right). () Peak correlation coefficients from versus cell pair radial distance (ΔR), for cell pairs in different imaging planes, the same imaging plane and for all pairs (10 movies, 10,262 pairs). Error bars, s.e.m. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Adrian Cheng & * J Tiago Gonçalves Affiliations * Department of Physics and Astronomy, University of California Los Angeles, Los Angeles, California, USA. * Adrian Cheng & * Katsushi Arisaka * Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA. * Adrian Cheng, * J Tiago Gonçalves, * Peyman Golshani & * Carlos Portera-Cailliau * Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA. * Adrian Cheng & * Carlos Portera-Cailliau * California NanoSystems Institute,University of California, Los Angeles, Los Angeles, California, USA. * Katsushi Arisaka Contributions A.C., J.T.G., P.G., K.A. and C.P.-C. conceived the project. A.C. designed and built the microscope and control electronics, and developed the microscope software. J.T.G. performed in vivo multifocal calcium imaging and simultaneous cell-attached recordings. A.C. analyzed the data. A.C., J.T.G. and C.P.-C. wrote the manuscript. K.A. and C.P.-C. supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Carlos Portera-Cailliau Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–8 and Supplementary Note 1 Additional data
A photoprotection strategy for microsecond-resolution single-molecule fluorescence spectroscopy
- Nat Meth 8(2):143-146 (2011)
Nature Methods | Brief Communication A photoprotection strategy for microsecond-resolution single-molecule fluorescence spectroscopy * Luis A Campos1, 2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Jianwei Liu2, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Xiang Wang2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Ravishankar Ramanathan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas S English2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Victor Muñoz1, 2 Contact Victor Muñoz Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:143–146Year published:(2011)DOI:doi:10.1038/nmeth.1553Received27 April 2010Accepted07 December 2010Published online09 January 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Time resolution of current single-molecule fluorescence techniques is limited to milliseconds because of dye blinking and bleaching. Here we introduce a photoprotection strategy that affords microsecond resolution by combining efficient triplet quenching by oxygen and Trolox with minimized bleaching via the oxygen radical scavenger cysteamine. Using this approach we resolved the single-molecule microsecond conformational fluctuations of two proteins: the two-state folder α-spectrin SH3 domain and the ultrafast downhill folder BBL. View full text Figures at a glance * Figure 1: Effect of photoprotectors on the photon throughput of B-DNA10 labeled with A488-A594 at 10-base-pair separation. () We irradiated the DNA with high continuous wave 485-nm laser excitation intensities and collected 100-μs bins with more than 50 total photons (donor plus acceptor) over the background, which we termed high-emission bursts for simplicity. Plotted are the high-emission bursts detected in one minute from free diffusing single molecules. The lines are fits to polynomial functions. Trolox-cysteamine, 1 mM Trolox and 10 mM cysteamine. (–) FRET efficiency histograms calculated from the high-emission bursts collected over a 5-min period with irradiance of 525 kW cm−2. Asterisk, bulk FRET efficiency. () High-emission bursts min−1 at 700 kW cm−2 irradiance for indicated combinations of photoprotectors. Error bars, s.d. (five experiments). OS, enzymatic oxygen scavenger as described in reference 6 and Online Methods. * Figure 2: Microsecond-resolution free-diffusion single-molecule FRET experiments of α-spectrin SH3 domain labeled with A488-A594 on two cysteines introduced at the protein ends. () Examples of FRET efficiency trajectories of α-spectrin SH3 domain single molecules freely diffusing through the observation volume of the confocal microscope. The time resolution was 50 μs, and colors represent the number of photons (n) in each 50-μs bin. Shown are examples of trajectories of folded molecules (~35% of the total; top row); trajectories of unfolded molecules (~40% of the total; second row); trajectories with the acceptor in a dark state (12% of the total; third row); and trajectories that showed bleaching or long-lasting blinking (bottom row). () FRET-efficiency histograms at indicated urea concentrations obtained from 1-ms bins with >200 photons (left) and from 100-μs bins of >50 photons after pruning all single-molecule diffusive trajectories that visited the (dark-acceptor state) (FRET efficiency values below 0.2) at any point (right). () Free energy of unfolding as a function of urea concentration calculated from the relative areas of the folded (~0! .75 FRET efficiency) and unfolded (~0.4 FRET efficiency) peaks compared to the bulk estimate (dark blue line). The red line shows the linear regression to the single-molecule data obtained with 1-ms binning for reference. () Comparison of the two FRET-efficiency histograms at 2 M urea expanded to highlight the artifacts in the intermediate FRET-efficiency region. * Figure 3: Microsecond resolution free diffusion single-molecule FRET of BBL. () A three-dimensional structure of BBL labeled with A488 (cyan) and A594 (red) on the ends of long unstructured tails. () FRET efficiency histograms of labeled BBL in 5 M urea (the denaturation midpoint (Cm) was ~5.3 M) obtained from 50-μs bins (left) and 1-ms bins (right) with > 40 total photons over background. The expected shot-noise width is shown in red and the FRET efficiency value at the Cm is indicated with a black vertical line. () FRET-efficiency autocorrelation function (R) calculated from experimental free diffusion trajectories (blue) and from the maximum likelihood fit to a seven-states model (green). The red curves are fits to an exponential function with the indicated relaxation times. () Examples of free-diffusing BBL trajectories near the denaturation midpoint (cyan circles). The red lines show the fit to the seven-states model for each trajectory. () Cartoon of the stochastic dynamic simulations on a harmonic potential. () FRET-efficiency autocorrelation! function (R) for the downhill simulation. The fit (red curve) to a single exponential function is shown with the relaxation time indicated. () A 10-ms single-molecule trajectory simulated with the stochastic downhill model sketched in . A simulation of shot-noise corresponding to a 0.8 MHz photon count rate (dark blue) and the fit to the seven-states model (red). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Luis A Campos & * Jianwei Liu Affiliations * Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Científicas, Madrid, Spain. * Luis A Campos, * Ravishankar Ramanathan & * Victor Muñoz * Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland, USA. * Luis A Campos, * Jianwei Liu, * Xiang Wang, * Douglas S English & * Victor Muñoz * Present addresses: Department of Pediatrics, Stanford University, Stanford, California, USA (J.L.), Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina, USA (X.W.) and Department of Chemistry, Wichita State University, Wichita, Kansas, USA (D.S.E.). * Jianwei Liu, * Xiang Wang & * Douglas S English Contributions L.A.C. and J.L. prepared samples, identified the oxygen radical scavengers, performed experiments and analyzed data. L.A.C. performed all the additional experiments requested by the reviewers. X.W. acquired and analyzed data. R.R. performed the stochastic simulations of downhill folding. D.S.E. supervised data acquisition and designed research. V.M. designed research, supervised data acquisition, performed and supervised data analysis and simulations, and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Victor Muñoz Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (524K) Supplementary Figures 1–4 and Supplementary Table 1 Additional data
Optogenetic manipulation of neural activity in freely moving Caenorhabditis elegans
- Nat Meth 8(2):147-152 (2011)
Nature Methods | Article Optogenetic manipulation of neural activity in freely moving Caenorhabditis elegans * Andrew M Leifer1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher Fang-Yen1, 2, 4 Contact Christopher Fang-Yen Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Gershow1 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Alkema3 Search for this author in: * NPG journals * PubMed * Google Scholar * Aravinthan D T Samuel1 Contact Aravinthan D T Samuel Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:147–152Year published:(2011)DOI:doi:10.1038/nmeth.1554Received23 August 2010Accepted16 December 2010Published online16 January 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 present an optogenetic illumination system capable of real-time light delivery with high spatial resolution to specified targets in freely moving Caenorhabditis elegans. A tracking microscope records the motion of an unrestrained worm expressing channelrhodopsin-2 or halorhodopsin in specific cell types. Image processing software analyzes the worm's position in each video frame, rapidly estimates the locations of targeted cells and instructs a digital micromirror device to illuminate targeted cells with laser light of the appropriate wavelengths to stimulate or inhibit activity. Because each cell in an unrestrained worm is a rapidly moving target, our system operates at high speed (~50 frames per second) to provide high spatial resolution (~30 μm). To test the accuracy, flexibility and utility of our system, we performed optogenetic analyses of the worm motor circuit, egg-laying circuit and mechanosensory circuits that have not been possible with previous methods. View full text Figures at a glance * Figure 1: High-resolution optogenetic control of freely moving C. elegans. () An individual worm swims or crawls on a motorized stage under red dark-field illumination. A high-speed camera images the worm. Custom software instructs a DMD to reflect laser light onto targeted cells. () Images are acquired and processed at ~50 FPS. Each 1,024 × 768 pixel image is thresholded and the worm boundary is found. Head and tail are located as maxima of boundary curvature (red arrows). Centerline is calculated from the midpoint of line segments connecting dorsal and ventral boundaries (blue bar) and is resampled to contain 100 equally spaced points. The worm is partitioned into segments by finding vectors (green arrows) from centerline to boundary, and selecting one that is most perpendicular to the centerline (orange arrow). Targets defined in worm coordinates are transformed into image coordinates and sent to the DMD for illumination (green bar). () Schematic of body-wall muscles. Anterior, to left; dorsal, to top. Bending wave speed of swimming worm expres! sing Halo/NpHR in its body-wall muscles subjected to green light (10 mW mm−2) outside or inside the worm boundary (n = 5 worms, representative trace). () Schematic of HSN. A swimming worm expressing ChR2 in HSN was subjected to blue light (5 mW mm−2). Histogram, position at which egg-laying occurred when a narrow stripe of light was slowly scanned along the worm's centerline (n = 13 worms). Once an egg was laid, the worm was discarded. * Figure 2: Optogenetic inactivation of muscle cells. () Kymograph of time-varying body curvature along the centerline of a Pmyo3Halo/NpHRCFP transgenic worm. Between 0 s and 4 s, the worm was stimulated with green light (10 mW mm−2) in a region spanning the worm diameter and between 0.38 and 0.6 of the fractional distance along the centerline. () For the kymograph in , time-varying curvature at two points along the worm centerline, both anterior (top) and posterior (bottom) to the illuminated region. * Figure 3: Inhibition of motor neurons. () Schematic of cholinergic DB and VB motor neurons. Anterior, to left; dorsal, to top. Kymograph of time-varying body curvature along the centerline of a Punc-17Halo/NpHRCFP transgenic worm illuminated by a stripe of green light (10 mW mm−2) along its VNC between t = 0 s and 1.6 s. In the dorsal-ventral direction, the stripe width was equal to 50% of the worm diameter and centered on the ventral boundary. In the anterior-posterior direction, the stripe length was between 0.14 and 0.28 of the fractional distance along the body. () For the kymograph in , time-varying curvature at two points along the worm centerline, both anterior (top) and posterior (bottom) to the illuminated region. () Video sequence of worm illuminated by a long stripe of green light (10 mW mm−2) spanning the VNC between t = 0 s and 1.8 s. Scale bar, ~100 μm. () Bending wave speed of a swimming worm illuminated by a long stripe of green light (10 mW mm−2) lasting 1.8 s and spanning the VNC (top) an! d dorsal nerve cord (bottom). * Figure 4: Optogenetic analysis of mechanosensory neurons. () Top, schematic of anterior and posterior touch receptor cells. Anterior, to left; dorsal, to top. Kymographs (left) of time-varying curvature of centerline of worms expressing ChR2 in mechanosensory neurons (Pmec-4ChR2GFP) subjected to rectangles of blue light (5 mW mm−2) targeting different groups of touch receptor neurons. Plots of bending wave speed (right) indicate stimulus-evoked changes in direction or speed. AVM and ALM neurons are subjected to 1.5 s of stimulation. Given a coordinate system where x specifies dorsal-ventral location (–1, dorsal boundary; 0, centerline; 1, ventral boundary) and y defines fractional distance along the worm's centerline (0, head; 1, tail), the rectangle of illumination has corners (x,y) = ((–1.1,0),(1.1,0.46)). () PVM and PLM neurons are subjected to 2.5 s of stimulation with a rectangular illumination (n = 5 worms, representative trace) with corners at (x,y) = ((–1.1,0.62),(1.1,0.99)). () ALM cell body is specifically stimula! ted by illuminating a small rectangle with corners at (x,y) = ((–0.3,0.38), (–0.9,0.46)). () AVM cell body is specifically stimulated by illuminating a small rectangle with corners at (x,y) = ((0.3,0.3),(0.9,0.38)). * Figure 5: Habituation of individual touch receptor neuronal types. (,) Schematic showing anterior and posterior touch receptor neurons (top). Anterior, to left; dorsal, to top. A freely swimming worm expressing Kaede in touch receptor neurons was continuously tracked and illuminated with a small rectangle of 405-nm light (2 mW mm−2) centered on either AVM or ALM (as in Fig. 4c,d) for 60 s. Red and green fluorescence images are shown. Scale bars, 100 μm. () Individual ALM and AVM neurons were repeatedly stimulated with blue light (5 mW mm−2) for 1.5 s every 60 s for ~40 min, either alone () or interleaved within each experiment (; ALM, 30 s; AVM, 30 s; ALM, 30 s; and so on). Fractional response to stimulus of each neuronal type was fit to an exponential, a + b exp(–t/τ), using maximum likelihood estimator. Time constant for habituation, τ, was extracted from each fit. Error bars, s.e.m. Fractional response of ALM when stimulated alone (; n = 7 worms). Fractional response of AVM when stimulated alone (; n = 8 worms). Fractional respo! nse of ALM (left) and AVM (right) during interleaved stimulation of both (; n = 7 worms). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Andrew M Leifer & * Christopher Fang-Yen Affiliations * Department of Physics and Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA. * Andrew M Leifer, * Christopher Fang-Yen, * Marc Gershow & * Aravinthan D T Samuel * Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA. * Christopher Fang-Yen * Department of Neurobiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * Mark J Alkema Contributions C.F.-Y. and A.M.L. designed the hardware setup; A.M.L. wrote the software, with supervision from M.G.; A.M.L., C.F.-Y., M.J.A. and A.D.T.S. designed experiments; A.M.L. carried out experiments; A.M.L. and C.F.-Y. analyzed data with advice from M.G.; A.M.L., C.F.-Y. and A.D.T.S. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Christopher Fang-Yen or * Aravinthan D T Samuel Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (5M) A Pmyo-3::Halo::CFP worm expressing Halorhodopsin in muscle is induced to relax only when the Colbert system illuminates within the worm's body. The movie shows the same individual as shown in Figure 1c. During frames 6707–6771, the entire region outside the worm's boundary is illuminated with green light (10 mW mm−2) and the worm continues locomotion. During frames 6,847–6,917, only the region inside the boundary of the worm is illuminated and the worm relaxes. During frames 7,052–7,117 only the region outside the worm's boundary is illuminated and the worm continues moving normally. The frame number is indicated at the bottom right. Light green shading indicates the area where the system is targeting. Bright green shading and the appearance of the words "DLP ON" indicate that the system is illuminating the targeted area. * Supplementary Video 2 (8M) An Pegl-6::ChR2::GFP worm is induced to lay eggs when a stripe of blue light reaches HSN. The video shows the same individual as in Figure 1d. A narrow stripe of light (5 mW mm−2), 0.02 of the fractional length along the worm centerline and twice the width of the worm, progresses from the worm's head towards its tail. The stripe takes steps of 0.02 fractional worm lengths and illuminates for 4 s at each step. At frame 8,828, the illumination band reaches HSN and the worm lays eggs. The frame number is indicated at the bottom right. * Supplementary Video 3 (960K) The bending waves of a Pmyo-3::Halo::CFP transgenic worm are dampened and the anterior relaxes when a portion of the worm is illuminated with green light. The video shows the same individual as in Figure 2. The illumination is turned on 4 s into the movie. The worm recovers after the illumination is turned off. Light grey shading indicates the area where the system is targeting. . Light green shading indicates the area where the system is targeting. Bright green shading and the appearance of the words "DLP ON" indicate that the system is illuminating the target. * Supplementary Video 4 (3M) The bending waves of an Punc-17::Halo::CFP are abolished when a small ventral region near the worm's head is illuminated. The video shows the same individual as shown in Figure 3a,b. During frames 9,075 to 9,141, the worm is illuminated with green light (10 mW mm−2) and no bending waves are propagated from the head to the tail. On the contrary, the worm is paralyzed posterior to the region of illumination and its curvature is frozen. Only after the stimulation ends, are bending waves again able to propagate from the anterior to posterior of the worm. The frame number is indicated at the bottom right. Light green shading indicates the area where the system is targeting. Bright green shading and the appearance of the words "DLP ON" indicate that the system is illuminating the target. * Supplementary Video 5 (8M) An Punc-17::Halo::CFP transgenic worm is paralyzed only when the ventral nerve cord is illuminated, but not when the dorsal nerve cord is illuminated. The video shows the same 988kindividual as in Figure 3c,d. The ventral nerve cord is illuminated with green light at 10 mW mm−2 (frames 37,909–37,971) and then the the dorsal nerve cord is illuminated (frames 38,233–38,295). Note that during paralysis the worm does not relax to a neutral position. Light green shading indicates the area where the system is targeting. Bright green shading and the appearance of the words "DLP ON" indicate that the system is illuminating the target. * Supplementary Video 6 (988K) The anterior of a Pmec-4::ChR2::GFP worm is illuminated for 1.5s, inducing a reversal. The video shows the same individual as in Figure 4a. During frames 7,645–7,709, the anterior 46% of the worm is illuminated with blue light at 5 mW mm−2, which includes the neurons AVM and ALM and their associated processes. The frame number is indicated in the bottom right hand corner. Light blue shading indicates the area where the system is targeting. Bright blue shading and the appearance of the words "DLP ON" indicate that the system is illuminating the target. * Supplementary Video 7 (2M) The posterior of a Pmec-4::ChR2::GFP worm is illuminated with blue light, inducing forward movement. The video shows the same individual as in Figure 4b. During frames 13,655–13,733, the posterior 38% of the worm, which includes the neurons PVM and PLM and their associated processes is illuminated with blue light (5 mW mm−2) for 1.5 s. The worm, originally in a resting state, moves forward. The frame number is indicated in the bottom right hand corner. Light blue shading indicates the area where the system is targeting. Bright blue shading and the appearance of the words "DLP ON" indicate that the system is illuminating the target. * Supplementary Video 8 (2M) The cell bodies of ALM in a Pmec-4::ChR2::GFP worm are illuminated with blue light, inducing a reversal. The video shows the same individual as in Figure 4c. During frames 2,013−2,079, ALM is illuminated with blue light (5 mW mm−2) for 1.5 s. The worm subsequently reverses. The frame number is indicated in the bottom right hand corner. Light blue shading indicates the area where the system is targeting. Bright blue shading and the appearance of the words "DLP ON" indicate that the system is illuminating the target. * Supplementary Video 9 (896K) The cell body of the single neuron AVM in a Pmec-4::ChR2::GFP is illuminated with blue light, initiating a reversal. The video shows the same individual as in Figure 4d. During frames 1,925–1,994, AVM is illuminated with blue light (5 mW mm−2) for 1.5 and the worm subsequently undergoes a reversal. The frame number is indicated in the bottom right hand corner. Light blue shading indicates the area where the system is targeting. Bright blue shading and the appearance of the words "DLP ON" indicate that the system is illuminating the target. Zip files * Supplementary Software (2M) MindControl is software, written in the C programming language, used to track a worm and create illumination patterns in real time. Documentation is also included. Additional data
Real-time multimodal optical control of neurons and muscles in freely behaving Caenorhabditis elegans
- Nat Meth 8(2):153-158 (2011)
Nature Methods | Article Real-time multimodal optical control of neurons and muscles in freely behaving Caenorhabditis elegans * Jeffrey N Stirman1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew M Crane2 Search for this author in: * NPG journals * PubMed * Google Scholar * Steven J Husson3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Sebastian Wabnig3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Schultheis3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Gottschalk3, 4 Contact Alexander Gottschalk Search for this author in: * NPG journals * PubMed * Google Scholar * Hang Lu1, 2 Contact Hang Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:153–158Year published:(2011)DOI:doi:10.1038/nmeth.1555Received13 August 2010Accepted16 December 2010Published online16 January 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The ability to optically excite or silence specific cells using optogenetics has become a powerful tool to interrogate the nervous system. Optogenetic experiments in small organisms have mostly been performed using whole-field illumination and genetic targeting, but these strategies do not always provide adequate cellular specificity. Targeted illumination can be a valuable alternative but it has only been shown in motionless animals without the ability to observe behavior output. We present a real-time, multimodal illumination technology that allows both tracking and recording the behavior of freely moving C. elegans while stimulating specific cells that express channelrhodopsin-2 or MAC. We used this system to optically manipulate nodes in the C. elegans touch circuit and study the roles of sensory and command neurons and the ultimate behavioral output. This technology enhances our ability to control, alter, observe and investigate how neurons, muscles and circuits ultimat! ely produce behavior in animals using optogenetics. View full text Figures at a glance * Figure 1: Illumination system for live animal tracking and optogenetic stimulation and quantification of behavior elicited by targeted illumination. () Optical configuration for using a projector for illumination. The normal epifluorescence optical train is replaced by a projector and a relay lens. Projector image planes are indicated, and a motorized x-y translational stage is used to track animals. () Modification of the three-color LCD projector to further narrow the spectrum is accomplished by the addition of filters into the individual RGB light paths. () Sequential frames from Supplementary Videos 1 and 2 showing qualitative behavioral responses. Top, use of the dorsal coiling effect to cause a worm to crawl in a triangle; bottom, direct muscular control of a paralyzed worm. Images are false-colored to show illumination pattern. () Illustration of the positions of the six sensory neurons, and a frame from Supplementary Video 3 showing the 20-μm bar of blue light, perpendicular to the worm's longitudinal axis, which was scanned at a rate of 12.5% body length per second (~100 μm s−1). () Two scanning schemes alon! g the A-P axis: head to tail and tail to head. () Histograms showing the distributions of positions along the A-P axis where the blue light elicited a reversal response. Shown are the distribution of positions where accelerations elicited by the tail-to-head scan were observed (28 out of 52 worms showed an increase in speed 2 s.d. greater than the average speed before illumination) and the distributions of the anatomical positions of the touch neurons in pmec-4GFP worms. Scale bars, 100 μm. * Figure 2: Optical stimulation of anterior/posterior mechanosensory neurons or forward/backward command interneurons. () Illustration of the positions of neurons expressing ChR2 in pmec-4ChR2 and pglr-1ChR2 transgenic worms. () The touch circuit, showing receptors, command neurons and the resulting behaviors. () Average velocity plots of pmec-4ChR2 worms under illumination conditions (shown as a blue bar above). n = 13 (posterior illumination); n = 15 (anterior illumination). Error bars, s.e.m. () Average velocity plots of pglr-1ChR2 worms under illumination conditions (shown as a blue bar above). n = 24 (posterior illumination); n = 12 (anterior illumination). Error bars. s.e.m. * Figure 3: Quantification of behavioral responses elicited by different anterior illumination intensities. () Patterns used for illumination location and their intensity. () Velocity plots from pooled data from worms receiving different illumination intensities (also see Supplementary Video 5). NR, no response; Sl/P, a slowing or pausing of the worm with no negative velocity; r, a small reversal; R, a large reversal. n = 40 for each of the three illumination levels. The number of worms showing NR, Sl/P, r and R behaviors were 28, 14, 35 and 43 respectively. Error bars, s.e.m. () Distribution of the four responses observed at the three intensity levels. * Figure 4: Illumination patterns used to explore the integration of anterior and posterior signals and behavior generated from the stimulation. () Illumination locations and plot of the temporal variation of the intensity for the two patterns tested. Normalized intensity of 1 corresponds to blue light of intensity 1.17 mW mm−2. () Histogram distributions of intensity at which worms initiated a reversal under two illumination patterns: anterior alone, and anterior and posterior simultaneously (n = 40 for each illumination scheme). () Distributions among the four response states for anterior illumination alone or simultaneous anterior and posterior illumination at the same intensity (1.17mW mm−2) (n = 40 for each). * Figure 5: Simultaneous two-color illumination. () Illustrations of the two illumination schemes. () Velocity plots of pmec-4ChR2 and pglr-1MACmCherry worms subjected to the illumination schemes in . Error bars, s.e.m.; n = 19 for scheme 1, n = 12 for scheme 2. () The neural gentle touch circuit showing the neurons that are either stimulated or silenced and the resulting behaviors at different points in the two sets of experiments. Author information * Abstract * Author information * Supplementary information Affiliations * School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA. * Jeffrey N Stirman & * Hang Lu * Interdisciplinary Program in Bioengineering, Institute of Biosciences and Bioengineering, Georgia Institute of Technology, Atlanta, Georgia, USA. * Jeffrey N Stirman, * Matthew M Crane & * Hang Lu * Johann Wolfgang Goethe University, Institute of Biochemistry, Biocenter N220, Frankfurt, Germany. * Steven J Husson, * Sebastian Wabnig, * Christian Schultheis & * Alexander Gottschalk * Frankfurt Institute for Molecular Life Sciences, Johann Wolfgang Goethe University, Frankfurt, Germany. * Steven J Husson, * Sebastian Wabnig, * Christian Schultheis & * Alexander Gottschalk Contributions J.N.S., M.M.C., A.G. and H.L. designed the experiments. J.N.S. and M.M.C. wrote the software. J.N.S. constructed the illumination system, performed experiments and analyzed the data. S.J.H., S.W. and C.S. contributed to reagents and provided valuable discussions. J.N.S., M.M.C., S.J.H., A.G. and H.L. prepared the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Alexander Gottschalk or * Hang Lu Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (176K) Using the dorsal coiling effect and head illumination to dictate the locomotive path of an animal expressing ChR2 in the cholinergic motor neurons. False color recreation was overlaid to show stimulation location and timing. * Supplementary Video 2 (664K) Direct muscular control of a paralyzed worm using light. Worms expressing ChR2 in the muscle cells were paralyzed with an ivermectin solution (0.01 mg ml-1) and were controlled using structured illumination. False color recreation was overlaid to show stimulation location and timing. * Supplementary Video 3 (1M) Illumination line scans performed on freely behaving pmec-4::ChR2 animals eliciting acceleration or reversal behavior depending on the location of illumination. The line scan travels from posterior to anterior and, in separate experiments, anterior to posterior at ~100 μm s-1. Simultaneous blue and red light was used in order to visualize the location of illumination. * Supplementary Video 4 (2M) Using localized light stimulation to excite anterior or posterior gentle touch neurons (in animals carrying pmec-4::ChR2), and anterior or posterior command interneurons (in animals carrying pglr-1::ChR2). False color recreation was overlaid to show stimulation location and timing. * Supplementary Video 5 (2M) Examples of four response states ('NR', 'Sl/P', 'r', 'R') to optical stimulation of the anterior gentle touch neurons (pmec-4::ChR2). False color recreation was overlaid to show stimulation location and timing. * Supplementary Video 6 (1M) Demonstration of complex illumination patterns to investigate sensory integration in pmec-4::ChR2 animals: light pulses of gradually increasing intensity were delivered to the anterior touch neurons, and in a separate experiment the same pulses delivered anteriorly while the posterior touch neurons were continuously illuminated. False color recreation was overlaid to show stimulation location and timing. * Supplementary Video 7 (396K) At certain anterior and posterior illumination intensities the behavior response (forward and reverse locomotion) alternate (in animals carrying pmec-4::ChR2). False color recreation was overlaid to show stimulation location and timing. * Supplementary Video 8 (2M) Using two-color, spatially distinct optical stimuli to rapidly curtail touch avoidance behavior in animals carrying pmec-4::ChR2 and plgr-1::MAC::mCherry. Also shown is the inhibition of a spontaneous reversal, demonstrating that natural, and not merely optogenetically generated reversals, can be inhibited. False color recreation was overlaid to show stimulation location and timing. Zip files * Supplementary Software (3M) Software used for tracking and illumination control. PDF files * Supplementary Text and Figures (780K) Supplementary Figures 1–6 and Supplementary Notes 1–2 Additional data
Knocking out multigene redundancies via cycles of sexual assortment and fluorescence selection
- Nat Meth 8(2):159-164 (2011)
Nature Methods | Article Knocking out multigene redundancies via cycles of sexual assortment and fluorescence selection * Yo Suzuki1, 11 Contact Yo Suzuki Search for this author in: * NPG journals * PubMed * Google Scholar * Robert P St Onge2 Search for this author in: * NPG journals * PubMed * Google Scholar * Ramamurthy Mani1 Search for this author in: * NPG journals * PubMed * Google Scholar * Oliver D King1, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Adrian Heilbut3 Search for this author in: * NPG journals * PubMed * Google Scholar * Vyacheslav M Labunskyy4 Search for this author in: * NPG journals * PubMed * Google Scholar * Weidong Chen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Linda Pham1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lan V Zhang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Amy H Y Tong5 Search for this author in: * NPG journals * PubMed * Google Scholar * Corey Nislow6 Search for this author in: * NPG journals * PubMed * Google Scholar * Guri Giaever7 Search for this author in: * NPG journals * PubMed * Google Scholar * Vadim N Gladyshev4 Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Vidal8, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Schow10 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph Lehár3 Search for this author in: * NPG journals * PubMed * Google Scholar * Frederick P Roth1, 8, 11 Contact Frederick P Roth Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:159–164Year published:(2011)DOI:doi:10.1038/nmeth.1550Received18 October 2010Accepted08 December 2010Published online09 January 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 Phenotypes that might otherwise reveal a gene's function can be obscured by genes with overlapping function. This phenomenon is best known within gene families, in which an important shared function may only be revealed by mutating all family members. Here we describe the 'green monster' technology that enables precise deletion of many genes. In this method, a population of deletion strains with each deletion marked by an inducible green fluorescent protein reporter gene, is subjected to repeated rounds of mating, meiosis and flow-cytometric enrichment. This results in the aggregation of multiple deletion loci in single cells. The green monster strategy is potentially applicable to assembling other engineered alterations in any species with sex or alternative means of allelic assortment. To test the technology, we generated a single broadly drug-sensitive strain of Saccharomyces cerevisiae bearing precise deletions of all 16 ATP-binding cassette transporters within clades as! sociated with multidrug resistance. View full text Figures at a glance * Figure 1: Design of the green monster process. () Schematic overview of the process. In yeast, crossing different haploid single mutants generates no-deletion (off-white), one-deletion (light green) and two-deletion (dark green) cells. From this mixture, flow cytometry is used to enrich for two-deletion cells. Higher-order multimutants are assembled via repeated rounds of sexual assortment and enrichment. () In this process a universal GFP deletion cassette replaces KanMX4 in a target gene (yfg) via recombination within Kan subsequences internal to the flanking barcodes. The inducible tetO2 promoter allows titration of GFP expression. Transcriptional terminators (brown) and the Kan promoter (light blue) and terminator (dark blue), each derived from the Ashbya gossypii TEF gene, are shown. The transformation marker is URA3. () GMToolkits, inserted at the CAN1 locus, contain rtTA21 and either KanMX4 and STE2pr-Sp-his5 (GMToolkit-) or NatMX420 and STE3pr-LEU2 (ref. 19) (GMToolkit-α). * Figure 2: Demonstration of the green monster process. () Simulations showing that >99% of a cell population accumulate all 24 deletions in eight (top), 12 (middle) or 19 rounds (bottom), with greater efficiency for lower coefficient of variation (CV) of GFP signal intensity (achievable using internal standard to control for noise). () Simulation showing that 24 linked deletions with the meiotic cross-over probability between adjacent loci of 5% can be assembled in 16 rounds when the GFP signal intensity CV is 50%. () Histograms illustrating the results of cell sorting for no-GFP cells (0Δ), single-GFP cells (1Δ) and a haploid 'meiotic mix' resulting from a cross of two single-GFP strains, with an expected 1:2:1 ratio of no-GFP, one-GFP and two-GFP (2Δ) cells. The brightest 1% of the cells in the meiotic mix were collected (red filled area). () GFP fluorescence intensity (arbitrary units) of multimutants. Histograms are shown for no-GFP, 1-GFP, 2-GFP, 4-GFP, 8-GFP and 16-GFP 'ABC16-monster' cells (isogenic populations). () Fl! uorescence micrographs showing nonmutant cells, double-mutant cells, ABC16-monster cells, and a mixture of double-mutant and ABC16-monster cells. Identical exposure, brightness, and contrast settings were used for images. Scale bar, 10 μm. (). Average deletion numbers for the en masse green monster process from three independent processes are plotted. Error bars, s.d. From Round 1 to Round 5, n = 21, 23, 24, 24, 24 (red); n = 23, 24, 24, 24, 23 (blue); n = 24, 24, 23, 24, 34 (brown). * Figure 3: Hypersensitivity of the ABC16 monster to drugs. () Number of drugs to which the ABC16 monster or the previously described drug-hypersensitive AD strain is sensitive compared to wild type. () IC50 values for single-mutant drug sensitivity and for ABC16-monster drug sensitivity relative to that of the corresponding nonmutant-drug combination as indicated in the legend (top). The minimum value among the relative IC50 values for single mutants is indicated for comparison with the relative IC50 of the ABC16 monster. () Exponential growth rates of the ABC16-monster (green), nonmutant (blue) and single-deletion strains (gray) as a function of tamoxifen, fluconazole and valinomycin concentration. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. * Yo Suzuki, * Ramamurthy Mani, * Oliver D King, * Weidong Chen, * Linda Pham, * Lan V Zhang & * Frederick P Roth * Stanford Genome Technology Center, Palo Alto, California, USA. * Robert P St Onge * Bioinformatics Program, Boston University, Boston, Massachusetts, USA. * Adrian Heilbut & * Joseph Lehár * Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Vyacheslav M Labunskyy & * Vadim N Gladyshev * Genome Research Centre, The University of Hong Kong, Pokfulam, Hong Kong, China. * Amy H Y Tong * Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. * Corey Nislow * Department of Pharmaceutical Sciences, Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. * Guri Giaever * Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Marc Vidal & * Frederick P Roth * Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Marc Vidal * Flow Cytometry Core Facility, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Peter Schow * Present addresses: Department of Synthetic Biology and Bioenergy, J. Craig Venter Institute, San Diego, California, USA (Y.S.), Boston Biomedical Research Institute, Watertown, Massachusetts, USA (O.D.K.) and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto and Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, Ontario, Canada (F.P.R.). * Yo Suzuki, * Oliver D King & * Frederick P Roth Contributions Y.S. and F.P.R. developed the green monster method and prepared the manuscript; R.P.S.O., A.H., J.L. and Y.S. measured drug sensitivity; R.M. analyzed growth curves; O.D.K. simulated the process; L.V.Z., C.N. and G.G. advised on method design; A.H.Y.T., V.M.L., V.N.G. and M.V. provided reagents and advice; W.C. and L.P. provided technical support; P.S. and Y.S. performed flow cytometry. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Yo Suzuki or * Frederick P Roth Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–8 and Supplementary Tables 1–13 Additional data
A transgenic mouse for in vivo detection of endogenous labeled mRNA
- Nat Meth 8(2):165-170 (2011)
Nature Methods | Article A transgenic mouse for in vivo detection of endogenous labeled mRNA * Timothée Lionnet1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin Czaplinski1, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Xavier Darzacq3 Search for this author in: * NPG journals * PubMed * Google Scholar * Yaron Shav-Tal4 Search for this author in: * NPG journals * PubMed * Google Scholar * Amber L Wells1, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey A Chao1 Search for this author in: * NPG journals * PubMed * Google Scholar * Hye Yoon Park1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Valeria de Turris1 Search for this author in: * NPG journals * PubMed * Google Scholar * Melissa Lopez-Jones1 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert H Singer1, 2 Contact Robert H Singer Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:165–170Year published:(2011)DOI:doi:10.1038/nmeth.1551Received23 September 2010Accepted10 December 2010Published online16 January 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 Live-cell single mRNA imaging is a powerful tool but has been restricted in higher eukaryotes to artificial cell lines and reporter genes. We describe an approach that enables live-cell imaging of single endogenous labeled mRNA molecules transcribed in primary mammalian cells and tissue. We generated a knock-in mouse line with an MS2 binding site (MBS) cassette targeted to the 3′ untranslated region of the essential ββ-actin gene. As β-actin–MBS was ubiquitously expressed, we could uniquely address endogenous mRNA regulation in any tissue or cell type. We simultaneously followed transcription from the β-actin alleles in real time and observed transcriptional bursting in response to serum stimulation with precise temporal resolution. We tracked single endogenous labeled mRNA particles being transported in primary hippocampal neurons. The MBS cassette also enabled high-sensitivity fluorescence in situ hybridization (FISH), allowing detection and localization of single ! β-actin mRNA molecules in various mouse tissues. View full text Figures at a glance * Figure 1: Schematic of the Actb-MS2 system for live-cell imaging. () As the gene is transcribed by the RNA polymerase (RNAP), the RNA hairpins form and get bound by the coexpressed MCP-YFP. () In the MBS cassette, a unit containing two MBS sequences and the intervening linkers is repeated 12 times, resulting in 24 MBSs. We designed three FISH probes (named Lk51-1, Lk20 and Lk51-2) that bind each unit at the indicated positions. The MBS array is inserted downstream of the zip code regulatory region (green). () In the construct (top), the long homology arm (LA) encompasses the full Actb gene, including a region 4 kbp upstream of the transcription start site (TSS); the positions of the exons (blue), introns (gray), start and stop codon, and polyadenylation site (poly(A)) are indicated. The short homology arm (SA) extends 1.3 kbp downstream of the Neo cassette (yellow) flanked by the two lox sites (purple triangles). The 24× MBS cassette (red) is inserted in the 3′ UTR in the sixth exon. The resulting genomic locus in the Actb–MBS mouse i! s shown on the bottom. * Figure 2: RNA FISH in sections from various tissues. (–) Merge of DAPI signal (blue) and Cy5 fluorescence from three FISH probes targeting the MBS cassette (red; bandpass data) in the indicated tissues from the indicated strains. Scale bars, 10 μm (–), 5 μm (–; magnification of boxed areas in –). () Quantification of the Actb-MBS allele expression (number of spots counted) in the cerebellum after thresholding the FISH signal. () Average mRNA concentration inside the nucleus (left) and outside the nucleus, displayed as a function of the distance from the nuclear boundary (right). Both concentrations were normalized to their value at the nucleus boundary. AU, arbitrary units. * Figure 3: Actb mRNA localizes to the leading edge of primary fibroblasts isolated from MBS mice. (–) Differential interference contrast image (), DAPI-stained image (), FISH with Cy3-labeled probes to the Actb coding region () and FISH with Cy5-labeled probes to the MBS cassette. () Scale bar, 10 μm. () Time-lapse images of a primary fibroblast migrating on a fibronectin substrate. Cells were infected with lentivirus that expresses NLS-MCP-GFP, and stained with membrane-permeant red cytoplasmic dye. Color bar, NLS-MCP-GFP fluorescence normalized by the red cytoplasmic dye intensity to account for the cell volume. AU, arbitrary units. Scale bar, 20 μm. * Figure 4: Live-cell imaging of serum response in MBS immortalized MEFs. () Images of immortalized MEFs (tetraploids) during serum response taken at indicated times after serum addition (maximum intensity projections of z-dimension stacks). At 0 min, no transcriptional activity was detected, and at subsequent time points all four transcription sites appeared as bright nuclear spots (arrows). Scale bar, 5 μm. () Quantification of the fluorescence intensity at the transcription sites marked in . Black, average response of the four alleles in . Gray, average response over 11 cells. (–) Data from shown separately for each allele. AU, arbitrary units. * Figure 5: Live-cell imaging of mRNP transport in primary hippocampal neurons. (–) Images of neurons transfected with a plasmid encoding MCP-YFP (imaged at 20 frames s−1; shown images are 1 s apart) showing an mRNP moving unidirectionally along a neuronal process. Scale bar, 10 μm. () Trajectories of two particles (circles and squares) observed successively along the process shown in –. () Instantaneous rates for both mRNPs (averages ± s.e.m., 2.95 ± 0.14 μm s−1 (circles) and 2.90 ± 0.13 μm s−1 (squares)). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, New York, USA. * Timothée Lionnet, * Kevin Czaplinski, * Amber L Wells, * Jeffrey A Chao, * Hye Yoon Park, * Valeria de Turris, * Melissa Lopez-Jones & * Robert H Singer * Gruss Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, USA. * Timothée Lionnet, * Hye Yoon Park & * Robert H Singer * Institut de Biologie de l'Ecole Normale Supérieure, Centre National de la Recherche Scientifique Unité Mixte de Recherche 8197, Paris, France. * Xavier Darzacq * The Mina and Everard Goodman Faculty of Life Sciences & Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel. * Yaron Shav-Tal * Present addresses: Department of Biochemistry and Cell Biology, Center for Nervous Systems Disorders, Stony Brook University Stony Brook, New York, USA (K.C.) and Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA (A.L.W.). * Kevin Czaplinski & * Amber L Wells Contributions T.L. performed the biochemistry experiments, the tissue FISH imaging, serum response live-cell imaging, and quantitative mRNA FISH, analyzed the data and wrote the paper. K.C. generated cell lines and performed the neuron live-cell imaging. X.D. and Y.S.-T. generated the mouse line. A.L.W. performed FISH mRNA localization experiments. J.A.C. performed the serum response live-cell imaging. H.Y.P. performed the live-cell localization experiments. V.d.T. generated cell lines. M.L.-J. performed the biochemistry experiments. R.H.S. consulted on the research and helped write the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Robert H Singer Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (1M) Live-cell imaging of a serum-induced primary fibroblast. MCP-GFP-labeled mRNA particles can be detected moving around the cell; the two transcription sites of the primary cell appear as two bright spots in the nucleus. Note that the transcription sites intensity is intentionally saturated to allow visualizing the dimmer single particles. * Supplementary Video 2 (2M) Individual mRNP moving along a neuronal process. * Supplementary Video 3 (2M) Individual mRNP moving along a neuronal process. The process is the same as the one imaged in Supplementary Video 2. * Supplementary Video 4 (236K) Anterograde mRNP motion along a neuronal process. * Supplementary Video 5 (212K) Retrograde mRNP motion along a neuronal process. * Supplementary Video 6 (2M) Bidirectional mRNP motion along a neuronal process. * Supplementary Video 7 (2M) Branching mRNP motion along a neuronal process. * Supplementary Video 8 (928K) Immobile mRNP in a neuronal process. PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–8, Supplementary Note 1 Additional data
A microfluidic array for large-scale ordering and orientation of embryos
- Nat Meth 8(2):171-176 (2011)
Nature Methods | Article A microfluidic array for large-scale ordering and orientation of embryos * Kwanghun Chung1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Yoosik Kim2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jitendra S Kanodia2 Search for this author in: * NPG journals * PubMed * Google Scholar * Emily Gong1 Search for this author in: * NPG journals * PubMed * Google Scholar * Stanislav Y Shvartsman2 Search for this author in: * NPG journals * PubMed * Google Scholar * Hang Lu1 Contact Hang Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:171–176Year published:(2011)DOI:doi:10.1038/nmeth.1548Received04 October 2010Accepted29 November 2010Published online26 December 2010 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 Quantitative studies of embryogenesis require the ability to monitor pattern formation and morphogenesis in large numbers of embryos, at multiple time points and in diverse genetic backgrounds. We describe a simple approach that greatly facilitates these tasks for Drosophila melanogaster embryos, one of the most advanced models of developmental genetics. Based on passive hydrodynamics, we developed a microfluidic embryo-trap array that can be used to rapidly order and vertically orient hundreds of embryos. We describe the physical principles of the design and used this platform to quantitatively analyze multiple morphogen gradients in the dorsoventral patterning system. Our approach can also be used for live imaging and, with slight modifications, could be adapted for studies of pattern formation and morphogenesis in other model organisms. View full text Figures at a glance * Figure 1: Microfluidic embryo-trap array for high-throughput arraying of vertically oriented Drosophila embryos. () Image of an adult Drosophila (left) with dorsal, posterior, ventral and anterior directions indicated. Scale bar, 1 mm. () Micrograph of early embryo stained using antibody to Dl. (Anterior is to the left, and dorsal is at the top.) Scale bar, 100 μm. () Photograph of the device (left) and a micrograph of the boxed region (right). Scale bar, 1 cm (left) and 500 μm (right). () Detail of the embryo-trap array design (top view). () Scanning electron micrograph of the trap structure. Scale bar, 100 μm. () Schematic showing the embryo trapping process: an embryo is guided into the trap (top); the flow around the embryo orients it vertically (middle); the trap contracts and secures the embryo (bottom). The yellow plane represents imaging focal plane. Blue arrows show the direction of bulk flow in the serpentine channel. () Schematic of the imaging setup. Inset, representative confocal image of an embryo stained with antibodies to Dl, Twist and dpERK. () A section of the arra! y with trapped embryos (dark circular object in each trap). Scale bar, 500 μm. * Figure 2: Operating principles of the embryo-trap array. (,) Volumetric flow rate in the serpentine main channel () and through the cross-flow channels () at each trap. Widths of the resistance channels in the optimal design (Fig. 1d), low-resistance design and high resistance design were 40 μm, 80 μm and 20 μm, respectively. Dummy columns are the first and last columns of the device. () Schematic of streamlines plotted from the numerical computational fluid dynamic model as the fluid turns the corner in the main channel. () Optical images at the indicated time points show an embryo (circled) migrating along the wall of the serpentine channel. Scale bar, 800 μm. (–) Three-dimensional characterization of the trap by confocal microscopy at 0 psi (–) and 6 psi (–). Single-frame top view from the middle of the device (,; dotted red circle represents dorsoventral plane of an embryo). Single frame cross-sectional view of the trap opening (,; dotted red ellipse represents vertically oriented embryo). Dotted white lines, locatio! ns where cross-sections of the trap opening (,) were acquired. Scale bars, 100 μm. * Figure 3: Spatial extent of the Dl gradient. () Confocal images of vertically oriented embryos stained for Dl and stained with DAPI. A merge is also shown. (–) Average gradients of nuclear Dl from four representative experiments. Error bars are s.e.m. (number of gradients used is indicated in each plot). The arrow denotes the dorsoventral (DV) position beyond which the nuclear Dl gradient can be considered 'flat'. DV distances are normalized: x = 0 for ventral and x = 1 for dorsal. (,) Early () and late () expression patterns of Dl and zen. () Schematic of regulatory models that can be used to account for the two phases of zen expression (top schematic depicts early expression). U is a uniform activator and pMAD is phosphorylated MAD. (–) Pairwise comparison of Dl gradients in wild-type and mutant backgrounds. Nuclear Dl gradients from the wild-type embryos (), embryos from dl−/+ females (), and average gradients for both genetic backgrounds (). Error bars, s.e.m. (n = 70 for wild type and n = 82 for mutant). * Figure 4: Quantitative characterization of signal transduction and morphogen gradients in dorsoventral patterning. () Schematic of the dorsoventral patterning network, showing the feedforward loops activated by Dl. (–) Confocal images of embryos immunostained for Dl and Twist (Twi) (), Dl and phospho-MAPK (dpERK) (), and Dl and phospho-MAD (pMAD) (). Scale bar, 25 μm. (–) Averaged gradients of pMAD (), Twi () and dpERK (). Error bars, s.e.m. (n = 64 gradients (), 40 gradients () and 38 gradients (), respectively). * Figure 5: Live imaging of embryos using the embryo array. (,) Frames from videos of embryos expressing nuclear histone– GFP undergoing nuclear divisions () or ventral invagination (). For both videos, images were taken 70 μm from the anterior pole. Scale bars, 25 μm. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Kwanghun Chung & * Yoosik Kim Affiliations * School of Chemical and Biomolecular Engineering, and Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA. * Kwanghun Chung, * Emily Gong & * Hang Lu * Department of Chemical and Biological Engineering and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA. * Yoosik Kim, * Jitendra S Kanodia & * Stanislav Y Shvartsman Contributions K.C., E.G. and H.L. designed, fabricated and tested the device. Y.K. tested the device and performed imaging. J.S.K. wrote the image processing and statistical analysis programs for gradient quantification. K.C., Y.K., S.Y.S. and H.L. designed the experiments and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Hang Lu Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (3M) Drosophila embryo trapping. This movie shows trapping of the embryos from an embryo suspension. * Supplementary Video 2 (340K) Contraction of the traps. This movie shows automatic contraction of the traps resulting from the loading pressure being decreased from 6 psi to 0 psi. Notice that embryos in the traps are not secured. * Supplementary Video 3 (3M) Live imaging: early embryo. This movie shows consecutive nuclear divisions in the early embryo. * Supplementary Video 4 (996K) Live imaging: ventral invagination. This movie shows consecutive nuclear divisions in an embryo undergoing ventral invagination. PDF files * Supplementary Text and Figures (508K) Supplementary Figures 1– 4 Additional data
Efficient modeling, simulation and coarse-graining of biological complexity with NFsim
- Nat Meth 8(2):177-183 (2011)
Nature Methods | Article Efficient modeling, simulation and coarse-graining of biological complexity with NFsim * Michael W Sneddon1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * James R Faeder3 Search for this author in: * NPG journals * PubMed * Google Scholar * Thierry Emonet1, 2, 4 Contact Thierry Emonet Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:177–183Year published:(2011)DOI:doi:10.1038/nmeth.1546Received08 September 2010Accepted03 December 2010Published online26 December 2010 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 Managing the overwhelming numbers of molecular states and interactions is a fundamental obstacle to building predictive models of biological systems. Here we introduce the Network-Free Stochastic Simulator (NFsim), a general-purpose modeling platform that overcomes the combinatorial nature of molecular interactions. Unlike standard simulators that represent molecular species as variables in equations, NFsim uses a biologically intuitive representation: objects with binding and modification sites acted on by reaction rules. During simulations, rules operate directly on molecular objects to produce exact stochastic results with performance that scales independently of the reaction network size. Reaction rates can be defined as arbitrary functions of molecular states to provide powerful coarse-graining capabilities, for example to merge Boolean and kinetic representations of biological networks. NFsim enables researchers to simulate many biological systems that were previously ! inaccessible to general-purpose software, as we illustrate with models of immune system signaling, microbial signaling, cytoskeletal assembly and oscillating gene expression. View full text Figures at a glance * Figure 1: Combinatorial complexity in multisite phosphorylation and NFsim performance scaling. () A substrate protein (S, blue) can be phosphorylated (P, red) at multiple independent sites by a kinase (K, yellow). A single rule, indicating that a site named 'p' on 'S' must be in the unphosphorylated state 'U' for the event to occur, can represent 4n–1 different reactions, where n is the number of phosphorylation sites on the substrate protein. () The number of reactions and chemical species that need to be accounted for grows exponentially with n. Rules and parameters grow only linearly with n. () The runtime performance of NFsim (blue filled circles) for this model is compared to the runtime of optimized ODE (triangles) and SSA (open circles) simulators9, 10. Network generation (squares) depicts the computational cost of transforming a rule-based model into a set of reactions that can be simulated by ODE or SSA simulators. * Figure 2: Schematic overview of NFsim. () Rules keep track of the molecular agents that can participate in a reaction (dashed lines) by matching possible reactants to user-defined reactant patterns. At each simulation step, a rule event is generated, reactant molecules are selected and transformed, and the set of possible reactants for each rule is updated (see Online Methods). In addition to the binding and unbinding events, rules can also specify reversible reactions, domain state changes, molecular synthesis and molecular degradation. () Example BNGL file specifying the model depicted in . Lines that begin with # are user comments ignored by NFsim. Parameters and rate constants are defined in the parameters block. Molecule types 'A', 'B' and 'C' are defined with a set of labeled domains 'a', 'b' and 'c'. Observables specify molecular patterns that provide simulation output, such as the pattern 'AB_complex'. Molecular domains are referenced in rules to define how molecules interact. 'A(b!1).B(a!1)' denotes that! a bond connects domain 'b' of molecule 'A' to domain 'a' of molecule 'B'. Here binding of 'A' to 'B' is declared to be independent of the binding of 'A' to 'C', because in the reactant pattern of the rule, the site named 'c' of molecule 'A' is omitted. Reaction rates are given as parameters 'k1', 'k2' and 'k3'. * Figure 3: Simulation performance and parameter estimation for receptor aggregation models. () Schematic of the early events of Fcε receptor (FcεRI) signaling. () The runtime performance of NFsim (filled circles) for a compendium of eight FcεRI signaling models of increasing reaction network size27 as compared to ODE simulation (triangles), SSA simulation (open circles) and reaction network generation (squares). We used an optimized ODE solver10 that can activate sparse-matrix representations and adaptive time-steps, leading to the apparent plateau in ODE performance. The largest model could not be simulated with the SSA because total computer memory (4 GB) was exhausted. () The trivalent-ligand, bivalent-receptor (TLBR) model serves as a simplified representation of FcεRI aggregation and is readily encoded in three BNGL rules, where the syntax 'r!+' indicates that the molecular domain named 'r' must be bound. () We used the NFsim suite of Matlab-based utilities to fit the TLBR model parameters to published flow cytometry data30 that measured steady-state recep! tor-ligand binding. * Figure 4: Tracking molecular connectivity during simulation of cytoskeletal actin polymerization. () Simplified versions of the rules that model actin polymerization, branching and severing reactions. (,) Comparison of simulations with TIRF microscopy experiments that monitored filament branching in flow cells32. () Distributions of the positions of branching events taking place on preexisting mother filaments. Distances are measured relative to the position of the barbed end of the mother filament at the time of Arp2/3 addition. () Distributions of the positions of branching events taking place on portions of mother filaments that elongated after addition of Arp2/3. Distances are measured from the branching point to the position of the growing barbed end of the mother filament at the time of branching. Error bars denote s.e.m. (n = 146 for ; n = 154 for ). () Three-dimensional visualizations of the molecular connectivity generated using NFsim's output options depicting ATP-actin subunits (blue), ADP-Pi actin subunits (cyan), ADP actin subunits (red) and filament ends th! at are capped by capping protein (yellow). Inset shows a close-up of the visualization. (,) Simulated trajectory of the mean filament length of a connected actin structure () and corresponding steady-state distribution (). (,) Simulated trajectory of the mean number of branches in a connected actin structure () and corresponding steady-state distribution (). * Figure 5: Coarse-graining with local and global functions. () Active (on) and inactive (off) conformations of signaling teams of chemoreceptor dimers in the bacterial chemotaxis system. The probability of a signaling team being in the active conformation depends on the methylation level m of each receptor in the complex and the concentration of external ligand l (ref. 12). () Local functions use observable patterns to track the methylation state of individual dimers and calculate the probability of each signaling team (x) being active. () The chemotaxis signaling model captures the observed response of the system to increasing doses (arrows) of the chemoattractant methyl-aspartate. () The flagellar motor is modeled as a two-state system in which states correspond to clockwise (CW) or counter-clockwise (CCW) rotation34. The rate of transitions between states depends on the height of the free energy barrier, which varies in time with the concentration of phosphorylated CheY ([CheY-P]). This model is specified with an observable patter! n that tracks CheY-P numbers and global functions that define the rate at which the motor switches states. () The model simulated with NFsim (line) captures the probability of being in the CW rotational state (CW bias) as a function of [CheY-P] as measured in single cell experiments35 (circles); inset shows the free energy diagram governing the transitions between CW and CCW rotations. * Figure 6: Achieving multiple levels of resolution with conditional and functional rate-law expressions. () Schematic diagram of a biochemical system that can oscillate owing to negative feedback with a time delay that arises from nuclear shuttling and protein synthesis. For the system to oscillate, the negative feedback on the promoter activity must show some nonlinearity. () Nonlinearity represented by a simplified Boolean ON-OFF switch, a piecewise linear response or a Hill function. () Functions written in NFsim describing each of these coarse-grained representations. Conditional expressions, which can be arbitrarily nested as shown in the definition of the linear approximation, are interpreted as "if [Condition], then use [RateExpression1], else [RateExpression2]". () Time courses for mRNA (black solid line) and protein (gray dashed line) levels for the different coarse-grained representations. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, USA. * Michael W Sneddon & * Thierry Emonet * Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA. * Michael W Sneddon & * Thierry Emonet * Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. * James R Faeder * Department of Physics, Yale University, New Haven, Connecticut, USA. * Thierry Emonet Contributions M.W.S. wrote the software and performed all simulations. M.W.S., J.R.F. and T.E. designed the algorithms and research and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Thierry Emonet Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (3M) Unconstrained actin growth simulated with NFsim. Visualizations depict ATP-actin subunits (blue), ADP-Pi actin subunits (cyan), ADP actin subunits (red) and filament ends that are capped by capping protein (yellow). In this simulation, the concentration of the ADF/cofilin severing complex is set to zero, which allows the structure to continue rapid growth throughout the simulation. * Supplementary Video 2 (2M) Actin growth in the presence of ADF/cofilin. Visualizations depict ATP-actin subunits (blue), ADP-Pi actin subunits (cyan), ADP actin subunits (red) and filament ends that are capped by capping protein (yellow). In this simulation, a steady-state regime is achieved where branching and polymerization reactions are compensated by the severing of filaments. Severed ends of the filament are discarded from the simulation so that only a single connected structure is followed over time. Notice that actin structures are typically small and consist of only a few filaments that are capped. Occasional stochastic events, however, allow periods of rapid growth and the transient formation of much larger structures. PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–7, Supplementary Tables 1–7 and Supplementary Notes 1–12 Additional data
Addendum: Mechanical regulation of cell function with geometrically modulated elastomeric substrates
- Nat Meth 8(2):184 (2011)
Nature Methods | Addendum Addendum: Mechanical regulation of cell function with geometrically modulated elastomeric substrates * Jianping Fu Search for this author in: * NPG journals * PubMed * Google Scholar * Yang-Kao Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Michael T Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Ravi A Desai Search for this author in: * NPG journals * PubMed * Google Scholar * Xiang Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Zhijun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher S Chen Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:184Year published:(2011)DOI:doi:10.1038/nmeth0211-184aPublished online28 January 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Methods7, 733–736 (2010); published online 1 August 2010; addendum published after print 28 January 2011. In the version of this article initially published, the implication that it was the first work to decouple substrate rigidity from surface properties was incorrect, as we and others had previously reported the approach. Additional reference to previous work on micropost arrays also should have been included1. Our fabrication process, in which micropost arrays are doubly replica molded from microfabricated silicon masters, scales up production of these substrates and allows replication and distribution of disposable molds to potential users. References * Saez, A., Buguin, A., Silberzan, P. & Ladoux, B.Biophys. J.89, L52–L54 (2005). * ChemPort * ISI * PubMed * Article Download references Additional data
Corrigendum: Two-photon calcium imaging from head-fixed Drosophila during optomotor walking behavior
- Nat Meth 8(2):184 (2011)
Nature Methods | Corrigendum Corrigendum: Two-photon calcium imaging from head-fixed Drosophila during optomotor walking behavior * Johannes D Seelig Search for this author in: * NPG journals * PubMed * Google Scholar * M Eugenia Chiappe Search for this author in: * NPG journals * PubMed * Google Scholar * Gus K Lott Search for this author in: * NPG journals * PubMed * Google Scholar * Anirban Dutta Search for this author in: * NPG journals * PubMed * Google Scholar * Jason E Osborne Search for this author in: * NPG journals * PubMed * Google Scholar * Michael B Reiser Search for this author in: * NPG journals * PubMed * Google Scholar * Vivek Jayaraman Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:184Year published:(2011)DOI:doi:10.1038/nmeth0211-184bPublished online28 January 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Methods7, 535–540 (2010); published online 6 June 2010; corrected after print 10 January 2011. In the version of this article initially published, the units for angular position (degrees) in Figure 3a,b are incorrect. The correct unit should be mm. The error has been corrected in the HTML and PDF versions of the article. Additional data
Erratum: Salience
- Nat Meth 8(2):184 (2011)
Nature Methods | Erratum Erratum: Salience * Bang Wong Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 8,Page:184Year published:(2011)DOI:doi:10.1038/nmeth0211-184cPublished online28 January 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Methods7, 773 (2010); published online 29 September 2010; corrected after print 15 December 2010. In the version of this article initially published, a portion of Figure 1 was missing. The error has been corrected in the HTML and PDF versions of the article. Additional data

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