Latest Articles Include:
- Method of the Year 2011
- Nat Methods 9(1):1 (2012)
Nature Methods | Editorial Method of the Year 2011 Journal name:Nature MethodsVolume: 9,Page:1Year published:(2012)DOI:doi:10.1038/nmeth.1852Published online 28 December 2011 The ability to introduce targeted, tailored changes into the genomes of several species will make it feasible to ask more precise biological questions. View full text Additional data - The author file: Khalid Salaita
- Nat Methods 9(1):3 (2012)
Nature Methods | This Month The author file: Khalid Salaita * Monya BakerJournal name:Nature MethodsVolume: 9,Page:3Year published:(2012)DOI:doi:10.1038/nmeth.1816Published online 28 December 2011 Measuring single-molecule forces with light. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Points of view: Data exploration
- Nat Methods 9(1):5 (2012)
Nature Methods | This Month Points of view: Data exploration * Noam Shoresh1 * Bang Wong2 * AffiliationsJournal name:Nature MethodsVolume: 9,Page:5Year published:(2012)DOI:doi:10.1038/nmeth.1829Published online 28 December 2011 Enhancement of pattern discovery through graphical representation of data. View full text Figures at a glance * Figure 1: Anscombe's quartet. () The four sets of numbers that form Anscombe's quartet. () The highly distinctive graphs that result from plotting the data in . * Figure 2: Small multiples. () A stack graph showing the relative proportions of 24 cell lines over time. () Individual growth curves for the data graphed in . 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 * Noam Shoresh is a senior computational biologist at the Broad Institute. * Bang Wong is the creative director of the Broad Institute of the Massachusetts Institute of Technology and Harvard and an adjunct assistant professor in the Department of Art as Applied to Medicine at The Johns Hopkins University School of Medicine. Competing financial interests The authors declare no competing financial interests. Author Details * Noam Shoresh Search for this author in: * NPG journals * PubMed * Google Scholar * Bang Wong Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - GeneProf: analysis of high-throughput sequencing experiments
- Nat Methods 9(1):7-8 (2012)
Nature Methods | Correspondence GeneProf: analysis of high-throughput sequencing experiments * Florian Halbritter1 * Harsh J Vaidya1 * Simon R Tomlinson1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:7–8Year published:(2012)DOI:doi:10.1038/nmeth.1809Published online 28 December 2011 To the Editor: The huge volume and complexity of data produced by high-throughput sequencing make it difficult for researchers in many laboratories to fully harness the potential of these data for the study of biological processes and human disease. Data processing rather than generation is now often the bottleneck for biological experiments1, and the efficient use of high-throughput sequencing data submitted to public databases such as the Sequence Read Archive remains a challenging goal for many. Workflow-based software2, 3 offers an attractive approach for dealing with complex data because it allows the visual organization of software components into ordered 'workflows' (Supplementary Note). This enables complicated analyses without any need to write custom computer scripts. However, workflow engines focus on the mechanics of the computational processes involved; the primary goal is to achieve computation rather than a particular biological result. Therefore, setting up a workflow can b! e a daunting task for many life scientists, especially those lacking experience in the visual programming paradigm. Existing tools are hence not sufficient to make high-throughput sequencing data fully accessible to the entire research community. View full text Subject terms: * Bioinformatics * Gene Expression * Epigenetics * Sequencing 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 * Institute for Stem Cell Research, Centre for Regenerative Medicine, School of Biological Sciences, University of Edinburgh, Edinburgh, UK. * Florian Halbritter, * Harsh J Vaidya & * Simon R Tomlinson Competing financial interests Edinburgh Research and Innovation (University of Edinburgh) is currently investigating the commercial potential of GeneProf. Corresponding author Correspondence to: * Simon R Tomlinson Author Details * Florian Halbritter Search for this author in: * NPG journals * PubMed * Google Scholar * Harsh J Vaidya Search for this author in: * NPG journals * PubMed * Google Scholar * Simon R Tomlinson Contact Simon R Tomlinson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (5.2M) Supplementary Figures 1–9, Supplementary Note, Supplementary Discussion, Supplementary Methods, Supplementary Data Additional data - Gene expression deconvolution in linear space
- Nat Methods 9(1):8-9 (2012)
- Reply to "Gene expression deconvolution in linear space"
- Nat Methods 9(1):9 (2012)
Nature Methods | Correspondence Gene expression deconvolution in linear space * Yi Zhong1, 2 * Zhandong Liu1, 2 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:8–9Year published:(2012)DOI:doi:10.1038/nmeth.1830Published online 28 December 2011 To the Editor: In the April 2010 issue, Shen-Orr et al. proposed an algorithm for cell type–specific significance analysis of microarrays (csSAM) that can deconvolve cell type–specific expression profiles from complex tissues1. The authors tested the relationships between computationally reconstructed signals from pure cell types and the signal measured from physically mixed samples. They found that a large fraction of the reconstructed (~10%) and deconvolved (~25%) signals deviated from the true gene-expression values1. The authors mixed complementary RNA from the tissues and observed similar off-diagonal effects. They concluded that the off-diagonal effects are due to technical reasons, such as nonlinear sample amplification or probe cross-hybridization, rather than statistical deconvolution. View full text Subject terms: * Gene Expression * Bioinformatics * Genomics * Systems Biology 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 of Pediatrics, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA. * Yi Zhong & * Zhandong Liu * Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA. * Yi Zhong & * Zhandong Liu Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Zhandong Liu Author Details * Yi Zhong Search for this author in: * NPG journals * PubMed * Google Scholar * Zhandong Liu Contact Zhandong Liu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3.3M) Supplementary Figures 1–3 Additional data - Optimal enzymes for amplifying sequencing libraries
- Nat Methods 9(1):10-11 (2012)
Nature Methods | Correspondence Gene expression deconvolution in linear space * Yi Zhong1, 2 * Zhandong Liu1, 2 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:8–9Year published:(2012)DOI:doi:10.1038/nmeth.1830Published online 28 December 2011 To the Editor: In the April 2010 issue, Shen-Orr et al. proposed an algorithm for cell type–specific significance analysis of microarrays (csSAM) that can deconvolve cell type–specific expression profiles from complex tissues1. The authors tested the relationships between computationally reconstructed signals from pure cell types and the signal measured from physically mixed samples. They found that a large fraction of the reconstructed (~10%) and deconvolved (~25%) signals deviated from the true gene-expression values1. The authors mixed complementary RNA from the tissues and observed similar off-diagonal effects. They concluded that the off-diagonal effects are due to technical reasons, such as nonlinear sample amplification or probe cross-hybridization, rather than statistical deconvolution. View full text Subject terms: * Gene Expression * Bioinformatics * Genomics * Systems Biology 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 of Pediatrics, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA. * Yi Zhong & * Zhandong Liu * Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA. * Yi Zhong & * Zhandong Liu Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Zhandong Liu Author Details * Yi Zhong Search for this author in: * NPG journals * PubMed * Google Scholar * Zhandong Liu Contact Zhandong Liu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3.3M) Supplementary Figures 1–3 Additional data - Gene-editing nucleases
- Nat Methods 9(1):23-26 (2012)
Nature Methods | Correspondence Gene expression deconvolution in linear space * Shai S Shen-Orr1 * Robert Tibshirani2 * Atul J Butte3 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Page:9Year published:(2012)DOI:doi:10.1038/nmeth.1831Published online 28 December 2011 Shen-Orr et al. reply: We appreciate the comments made by Zhong and Liu and their hard work on the proof1. Indeed, removing unneeded normalization methods, including log transformation, can yield even better linearity results, optimizing the use of deconvolution methods. View full text Subject terms: * Gene Expression * Bioinformatics * Genomics * Systems Biology 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 of Immunology, Rappaport Faculty of Medicine, Technion, Haifa, Israel. * Shai S Shen-Orr * Health Research and Policy and Department of Statistics, Stanford University School of Medicine, Stanford, California, USA. * Robert Tibshirani * Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA. * Atul J Butte Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Shai S Shen-Orr Author Details * Shai S Shen-Orr Contact Shai S Shen-Orr Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Tibshirani Search for this author in: * NPG journals * PubMed * Google Scholar * Atul J Butte Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (455K) Supplementary Figure 1 Additional data - Primer: genome editing with engineered nucleases
- Nat Methods 9(1):27 (2012)
Nature Methods | Correspondence Optimal enzymes for amplifying sequencing libraries * Michael A Quail1 * Thomas D Otto1 * Yong Gu1 * Simon R Harris1 * Thomas F Skelly1 * Jacqueline A McQuillan1 * Harold P Swerdlow1 * Samuel O Oyola1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:10–11Year published:(2012)DOI:doi:10.1038/nmeth.1814Published online 28 December 2011 To the Editor: PCR amplification introduces bias into Illumina sequencing libraries1. Although amplification-free library preparation solves this, micrograms of starting material are usually required. Most researchers follow standard protocols using Phusion polymerase, which has processivity and fidelity advantages over most polymerases. Yet for genomics applications, our demands on DNA amplification systems often surpass their specification. Thermostable DNA polymerases such as Phusion are used to amplify mixtures of fragments, albeit with variable efficiency. Typically, (G+C)-neutral fragments are amplified with higher efficiency than extremely (G+C)-rich or (A+T)-rich fragments. The accumulation of these slight differences in amplification over multiple cycles often results in profound bias. There have been reports of using alternative DNA polymerases for Illumina library construction2, 3, 4, but these are infrequent, and comprehensive analyses are lacking. To reduce bias, we investigat! ed many thermostable DNA polymerases and alternate reaction conditions for amplification of adapter-ligated fragments for Illumina sequencing. We expect this comparison to be relevant to other applications that involve simultaneous amplification of complex fragment mixtures. View full text Subject terms: * Genomics * Sequencing * Bioinformatics * Molecular Biology 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 * Wellcome Trust Sanger Institute, Hinxton, UK. * Michael A Quail, * Thomas D Otto, * Yong Gu, * Simon R Harris, * Thomas F Skelly, * Jacqueline A McQuillan, * Harold P Swerdlow & * Samuel O Oyola Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Michael A Quail Author Details * Michael A Quail Contact Michael A Quail Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas D Otto Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Gu Search for this author in: * NPG journals * PubMed * Google Scholar * Simon R Harris Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas F Skelly Search for this author in: * NPG journals * PubMed * Google Scholar * Jacqueline A McQuillan Search for this author in: * NPG journals * PubMed * Google Scholar * Harold P Swerdlow Search for this author in: * NPG journals * PubMed * Google Scholar * Samuel O Oyola Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2.2M) Supplementary Figures 1–3, Supplementary Table 1, Supplementary Methods Excel files * Supplementary Table 2 (25K) Enzymes and conditions used for amplification step in Illumina library construction. * Supplementary Table 3 (29K) Four-genome alternative enzyme study. Rank order. * Supplementary Table 4 (119K) Four-genome alternative enzyme study. Performance ranking based on coverage and fidelity. Additional data - Gene editing: not just for translation anymore
- Nat Methods 9(1):28-31 (2012)
Nature Methods | News Feature Gene-editing nucleases * Monya Baker1Journal name:Nature MethodsVolume: 9,Pages:23–26Year published:(2012)DOI:doi:10.1038/nmeth.1807Published online 28 December 2011 Precise ways to modify the genome arose from unexpected places. Monya Baker reports. 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 Author Details * Monya Baker Contact Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Affiliations * Monya Baker is technology editor for Nature and Nature Methods Corresponding author Correspondence to: * Monya Baker Author Details * Monya Baker Contact Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar - Zinc-finger nucleases: how to play two good hands
- Nat Methods 9(1):32-34 (2012)
Nature Methods | Primer Primer: genome editing with engineered nucleases * Natalie de SouzaJournal name:Nature MethodsVolume: 9,Page:27Year published:(2012)DOI:doi:10.1038/nmeth.1848Published online 28 December 2011 A brief description of tools for targeted cleavage and tailored modification of genomes is presented. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Natalie de Souza Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Single-cell methods
- Nat Methods 9(1):35 (2012)
Nature Methods | Commentary Gene editing: not just for translation anymore * Moira A McMahon1 * Meghdad Rahdar1 * Matthew Porteus1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:28–31Year published:(2012)DOI:doi:10.1038/nmeth.1811Published online 28 December 2011 Engineered nucleases have advanced the field of gene therapy with the promise of targeted genome modification as a treatment for human diseases. Here we discuss why engineered nucleases are an exciting research tool for gene editing and consider their applications to a range of biological questions. View full text Figures at a glance * Figure 1: Genome editing using a single pair of engineered nucleases. An array of genome modifications can be designed to result after creating a double-strand break. Gene editing can be done in cell lines, in primary cells (including somatic and pluripotent stem cells) and in fertilized oocytes for the generation of transgenic animals. * Figure 2: Genome editing using two pairs of engineered nucleases. Gross chromosomal rearrangements can be generated by using two pairs of engineered nucleases to create double-stranded breaks (DSBs). These rearrangments are designed to occur at specific target sites for the nucleases and should be distinguished from inadvertent rearrangements that might occur from 'off-target' cutting. Rearrangements could be generated in cell lines, in primary cells (including somatic and pluripotent stem cells) and in generating transgenic animals. 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 * Moira A. McMahon, Meghdad Rahdar and Matthew Porteus are at Stanford University, Stanford, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthew Porteus Author Details * Moira A McMahon Search for this author in: * NPG journals * PubMed * Google Scholar * Meghdad Rahdar Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Porteus Contact Matthew Porteus Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Functional genomic resources
- Nat Methods 9(1):35 (2012)
Nature Methods | Commentary Gene editing: not just for translation anymore * Moira A McMahon1 * Meghdad Rahdar1 * Matthew Porteus1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:28–31Year published:(2012)DOI:doi:10.1038/nmeth.1811Published online 28 December 2011 Engineered nucleases have advanced the field of gene therapy with the promise of targeted genome modification as a treatment for human diseases. Here we discuss why engineered nucleases are an exciting research tool for gene editing and consider their applications to a range of biological questions. View full text Figures at a glance * Figure 1: Genome editing using a single pair of engineered nucleases. An array of genome modifications can be designed to result after creating a double-strand break. Gene editing can be done in cell lines, in primary cells (including somatic and pluripotent stem cells) and in fertilized oocytes for the generation of transgenic animals. * Figure 2: Genome editing using two pairs of engineered nucleases. Gross chromosomal rearrangements can be generated by using two pairs of engineered nucleases to create double-stranded breaks (DSBs). These rearrangments are designed to occur at specific target sites for the nucleases and should be distinguished from inadvertent rearrangements that might occur from 'off-target' cutting. Rearrangements could be generated in cell lines, in primary cells (including somatic and pluripotent stem cells) and in generating transgenic animals. 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 * Moira A. McMahon, Meghdad Rahdar and Matthew Porteus are at Stanford University, Stanford, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthew Porteus Author Details * Moira A McMahon Search for this author in: * NPG journals * PubMed * Google Scholar * Meghdad Rahdar Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Porteus Contact Matthew Porteus Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Glycoproteomics
- Nat Methods 9(1):36 (2012)
Nature Methods | Commentary Zinc-finger nucleases: how to play two good hands * Mark Isalan1Journal name:Nature MethodsVolume: 9,Pages:32–34Year published:(2012)DOI:doi:10.1038/nmeth.1805Published online 28 December 2011 Zinc-finger nuclease dimers are more difficult to engineer than single DNA-binding domains, but the development of new methods could help. View full text Figures at a glance * Figure 1: Overcoming the challenges for engineering functional zinc-finger nucleases. The schematic shows two four-finger pairs binding to DNA in a canonical mode and highlights the main engineering constraints and considerations. Zinc-finger binding sites are shaded in blue (darker for the main contacted DNA bases). * Figure 2: Screening for full zinc-finger nuclease activity. () In a scheme developed by the Kim group23, the fluorescence of an out-of-frame GFP is restored by a functional zinc-finger nuclease (ZFN). This was originally designed as an indirect marker for FACS of ZFN-modified cells. However, it could be adapted to screen candidates from ZFN libraries in mammalian cells, as schematized here. () For screening larger ZFN libraries in a eukaryotic chromatin environment, the yeast one-hybrid system we recently described16 could be adapted. ZFN cleavage of a negative selection marker (URA3 + 5-FOA) would be required for growth on a plate. NHEJ, nonhomologous end joining. Fokl+ and Fokl− are obligate heterodimer variants19. 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 * EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation and UPF Barcelona, Spain. * Mark Isalan Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Mark Isalan Author Details * Mark Isalan Contact Mark Isalan Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Causal mutations in a haploid landscape
- Nat Methods 9(1):36 (2012)
Nature Methods | Methods to Watch Single-cell methods * Natalie de SouzaJournal name:Nature MethodsVolume: 9,Page:35Year published:(2012)DOI:doi:10.1038/nmeth.1819Published online 28 December 2011 Improved single-cell methods are helping to unravel biological complexity. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Natalie de Souza Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Imaging life with thin sheets of light
- Nat Methods 9(1):37 (2012)
Nature Methods | Methods to Watch Functional genomic resources * Nicole RuskJournal name:Nature MethodsVolume: 9,Page:35Year published:(2012)DOI:doi:10.1038/nmeth.1820Published online 28 December 2011 Tools to manipulate murine genes on a genome-wide scale and to phenotype their effects in animals are maturing. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Nicole Rusk Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Non–model organisms
- Nat Methods 9(1):37 (2012)
Nature Methods | Methods to Watch Glycoproteomics * Allison DoerrJournal name:Nature MethodsVolume: 9,Page:36Year published:(2012)DOI:doi:10.1038/nmeth.1821Published online 28 December 2011 Methods for tackling the enormously complex glycoproteome are sorely needed. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Allison Doerr Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Light-based electrophysiology
- Nat Methods 9(1):38 (2012)
Nature Methods | Methods to Watch Causal mutations in a haploid landscape * Nicole RuskJournal name:Nature MethodsVolume: 9,Page:36Year published:(2012)DOI:doi:10.1038/nmeth.1822Published online 28 December 2011 Sequencing a haploid genome and understanding the impact of its variants requires technical and computational improvements. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Nicole Rusk Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - RNA structures
- Nat Methods 9(1):38 (2012)
Nature Methods | Methods to Watch Imaging life with thin sheets of light * Erika PastranaJournal name:Nature MethodsVolume: 9,Page:37Year published:(2012)DOI:doi:10.1038/nmeth.1823Published online 28 December 2011 The revival of light-sheet microscopy opens new possibilities for the imaging of living 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 Author Details * Erika Pastrana Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Quantitative data: learning to share
- Nat Methods 9(1):39-41 (2012)
Nature Methods | Methods to Watch Non–model organisms * Tal NawyJournal name:Nature MethodsVolume: 9,Page:37Year published:(2012)DOI:doi:10.1038/nmeth.1824Published online 28 December 2011 Next-generation sequencing is broadening the application of genetic and genomic studies to the panoply of life. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Tal Nawy Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Running in reverse: rhodopsins sense voltage
- Nat Methods 9(1):43-44 (2012)
Nature Methods | Methods to Watch Light-based electrophysiology * Erika PastranaJournal name:Nature MethodsVolume: 9,Page:38Year published:(2012)DOI:doi:10.1038/nmeth.1825Published online 28 December 2011 Genetically encoded voltage sensors are finally measuring up. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Erika Pastrana Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Tracking genomic hydroxymethylation by the base
- Nat Methods 9(1):45-46 (2012)
Nature Methods | Methods to Watch RNA structures * Petya V KrastevaJournal name:Nature MethodsVolume: 9,Page:38Year published:(2012)DOI:doi:10.1038/nmeth.1826Published online 28 December 2011 Accurate methods for RNA-structure determination are being developed. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Petya V Krasteva Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Power tools for gene expression and clonal analysis in Drosophila
- Nat Methods 9(1):47-55 (2012)
Nature Methods | Technology Feature Quantitative data: learning to share * Monya Baker1Journal name:Nature MethodsVolume: 9,Pages:39–41Year published:(2012)DOI:doi:10.1038/nmeth.1815Published online 28 December 2011 Adaptive technologies are helping researchers combine and organize experimental results. 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 Author Details * Monya Baker Contact Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - A guide to analysis of mouse energy metabolism
- Nat Methods 9(1):57-63 (2012)
Article preview View full access options Nature Methods | News and Views Tracking genomic hydroxymethylation by the base * Gilles Salbert1 * Michael Weber2 * Affiliations * Corresponding authorsJournal name:Nature MethodsVolume: 9,Pages:45–46Year published:(2012)DOI:doi:10.1038/nmeth.1813Published online 28 December 2011 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A method uses single-molecule, real-time DNA sequencing to detect the modified base 5-hydroxymethylcytosine, an epigenetic mark recently suspected of having essential roles in genome regulation. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Methods for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Affiliations * Gilles Salbert is at the Centre National de la Recherche Scientifique (CNRS) Unité Mixte de Recherche (UMR) 6026, Molecular and Cellular Interactions, University of Rennes 1, France. * Michael Weber is at the CNRS UMR 7242, Biotechnology and Cell Signalling, University of Strasbourg, Illkirch, France. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Gilles Salbert or * Michael Weber Author Details * Gilles Salbert Contact Gilles Salbert Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Weber Contact Michael Weber Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Visualizing mechanical tension across membrane receptors with a fluorescent sensor
- Nat Methods 9(1):64-67 (2012)
Nature Methods | Review Power tools for gene expression and clonal analysis in Drosophila * Alberto del Valle Rodríguez1, 2 * Dominic Didiano1, 2 * Claude Desplan1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:47–55Year published:(2012)DOI:doi:10.1038/nmeth.1800Published online 28 December 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The development of two-component expression systems in Drosophila melanogaster, one of the most powerful genetic models, has allowed the precise manipulation of gene function in specific cell populations. These expression systems, in combination with site-specific recombination approaches, have also led to the development of new methods for clonal lineage analysis. We present a hands-on user guide to the techniques and approaches that have greatly increased resolution of genetic analysis in the fly, with a special focus on their application for lineage analysis. Our intention is to provide guidance and suggestions regarding which genetic tools are most suitable for addressing different developmental questions. View full text Figures at a glance * Figure 1: Controlling expression patterns. () Two-component expression systems such as GAL4-UAS, LexA-lexAop or QF-QUAS consist of a transcriptional activator expressed in a specific pattern and a transgene under the control of a promoter that is largely silent in the absence of the transcriptional activator. These systems can be repressed by specific molecules such as GAL80 or QS. () In intersectional strategies for the restriction of transgene expression, GAL80 and Flp are used to restrict GAL4-driven expression. GAL80 and Flp are expressed using two different promoters that partially overlap with the expression of GAL4. GFP from the UASFRT-stop-FRT-GFP construct is expressed only in cells that express both GAL4 and Flp, but not GAL80 (left and center). Split-GAL4 can be used with GAL80. Only cells expressing both hemi-drivers but not GAL80 show expression (right). () In split-molecule technology, activation domain and DNA-binding domain are fused to leucine-zipper motifs that reconstitute a functional transcriptio! nal activator only in those cells that express both subdomains. * Figure 2: Convertible enhancer trap strategy. The InSITE system allows GAL4 to be replaced by any effector sequence (Eff). The mini-white marker (white) is removed from the original enhancer trap using Cre recombinase. ΦC31 integrase allows recombination between the attB site on the donor Eff plasmid and the attP site of the original enhancer trap insertion, allowing replacement of GAL4 by Eff. The Cre recombinase and ΦC31 integrase two-step process is simplified in the figure. Adapted from ref. 22. * Figure 3: Genetic system for clonal analysis. () MARCM and QMARCM. MARCM combines the Flp-FRT system with the suppressible ability of GAL80 over the GAL4-UAS binary system. The QF-QUAS system can similarly be used for MARCM with the transcriptional activator QF and its repressor QS. () Techniques for labeling cell clones with different colors. The TSG allows for two-color labeling through marker reconstitution of N-GFP–C-GFP and N-RFP–C-RFP domains after the recombination of FRT sites. Both transgenes are expressed under the actin 5 (Act5C) promoter. In TS-MARCM the expression of the membrane-bound markers (CD8GFP or CD2RFP) requires the release of the microRNA suppressors (microRNA to CD2 (miR-CD2) or microRNA to GFP (miR-GFP)) through FRT site recombination. The G-TRACE reports real time expression (CD2RFP) and stable inherited expression (GFP) of a gene of interest. Ubi, ubiquitous promoter. () Multicolor systems. In the dBrainbow technique Cre recombinase can generate multicolor labeling by randomly recombining ! matching loxP sites (represented by trapeze-shaped motifs of the same color). The Flybow method uses the flipase to induce inversions (arrow) and excisions, generating color diversity. Trapeze-shaped motifs represent mFRT7.1 recognition sequences, and triangle-shaped boxes represent FRT sequences. The fluorescent proteins pointing to the right represent their correct orientation. * Figure 4: Lineage models for clonal analysis systems. () The MARCM system can be used to generate single-cell clones when heat shock–induced recombination occurs on a ganglion mother cell (GMC) that divides into two neurons (N). Recombination in a neuroblast (NB) can generate sister-cell clones (1) or multicellular clones (2). () TSG and TS-MARCM can be used to generate sister-cell clones of two different colors when the recombination occurs in a GMC. NB clones can generate two cells with one of the markers (magenta in this example) and a multicellular clone with the other marker (green). () The G-TRACE allows for RFP labeling in cells expressing a gene in real time; GFP expression indicates the progeny of RFP expressing precursor cells. Cells in magenta will eventually express GFP and become yellow, but are shown in magenta to indicate GFP expression delay prior to Flp expression and excision of stop cassette. Green cells expressed RFP in the past but no longer do. () In dBrainbow and Flybow2.0 systems, the progeny of each c! ell clone (here a neuroblast, NB) is labeled in one color. The figure represents consecutive heat shocks (hs1–hs3) inducing recombination in different NBs, resulting in several lineages being labeled with distinct colors. The models represent NBs dividing asymmetrically. Author information * Abstract * Author information Primary authors * These authors contributed equally to this work. * Alberto del Valle Rodríguez & * Dominic Didiano Affiliations * Department of Biology, New York University, New York, New York, USA. * Alberto del Valle Rodríguez, * Dominic Didiano & * Claude Desplan Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Claude Desplan Author Details * Alberto del Valle Rodríguez Search for this author in: * NPG journals * PubMed * Google Scholar * Dominic Didiano Search for this author in: * NPG journals * PubMed * Google Scholar * Claude Desplan Contact Claude Desplan Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Cyanine fluorophore derivatives with enhanced photostability
- Nat Methods 9(1):68-71 (2012)
Nature Methods | Perspective A guide to analysis of mouse energy metabolism * Matthias H Tschöp1, 20 * John R Speakman2, 3, 20 * Jonathan R S Arch4 * Johan Auwerx5 * Jens C Brüning6 * Lawrence Chan7 * Robert H Eckel8 * Robert V Farese Jr9 * Jose E Galgani10 * Catherine Hambly2 * Mark A Herman11 * Tamas L Horvath12 * Barbara B Kahn11 * Sara C Kozma13 * Eleftheria Maratos-Flier11 * Timo D Müller1 * Heike Münzberg14 * Paul T Pfluger1 * Leona Plum15 * Marc L Reitman16 * Kamal Rahmouni17 * Gerald I Shulman18 * George Thomas13 * C Ronald Kahn19 * Eric Ravussin14 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 9,Pages:57–63Year published:(2012)DOI:doi:10.1038/nmeth.1806Published online 28 December 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We present a consolidated view of the complexity and challenges of designing studies for measurement of energy metabolism in mouse models, including a practical guide to the assessment of energy expenditure, energy intake and body composition and statistical analysis thereof. We hope this guide will facilitate comparisons across studies and minimize spurious interpretations of data. We recommend that division of energy expenditure data by either body weight or lean body weight and that presentation of group effects as histograms should be replaced by plotting individual data and analyzing both group and body-composition effects using analysis of covariance (ANCOVA). View full text Subject terms: * Physiology * Genetics * Systems Biology Figures at a glance * Figure 1: Problems of analysis and interpretation of energy expenditure demonstrated on hypothetical data. () Experimental genotype (W, white) is compared to the wild-type genotype (B, black). Study A and study B are different experimental manipulations of genotype. Energy expenditure per whole animal (kJ day−1) is plotted against lean body mass (top). In all panels, lines show fitted regressions. Average of raw data across the individuals for each genotype plotted in a histogram (bottom). () Energy expenditure divided by lean body mass versus lean body mass is plotted for each data point (top) and shown as histogram (bottom). Values represent means ± s.e.m. * Figure 2: A practical example of the use of different approaches to the analysis of energy metabolism in the mouse. () Twenty control mice were fed ad libitum (ad lib) and 58 mice were fed a calorie-restricted diet (60% of ad libitum) for three months44. Raw data with the average metabolic rate were calculated without any correction for body mass. Note that the mice fed a calorie-restricted diet had a significantly (one-way ANOVA: P = 0.01) lower metabolic rate than the ones fed ad lib. () Metabolic rate expressed per gram of BW. Note that the opposite result was found: the mice on calorie-restricted diet had significantly (one-way ANOVA: P < 0.001) higher metabolic rates. () Resting metabolic rate (RMR) data as a function of body mass. Note that there is some overlap between the groups and a general positive trend of greater RMR at higher body masses (BM). () RMR versus fat-free mass (FFM) (measured by dual energy X-ray absorptiometry) shows a much greater overlap between the groups. () Dividing RMR by FFM revealed no significant effect of the treatment group on RMR (one-way ANOVA: P = 0! .275). Values represent means ± s.e.m. * Figure 3: Flowchart for mouse energy metabolism phenotype analysis for mouse models. () Analysis with higher BW in comparison with wild-type littermate controls. () Analysis for mouse models with lower BW in comparison with wild-type littermate controls. EI, energy intake; EE, energy expenditure; FI, food intake; KO, knockout. *For discussion of advantages and pitfalls of pair-feeding, see Supplementary Note 3. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Matthias H Tschöp & * John R Speakman Affiliations * Institute for Diabetes and Obesity, Helmholz Centre Munich, Department of Medicine, Technical University of Munich, Munich, Germany. * Matthias H Tschöp, * Timo D Müller & * Paul T Pfluger * Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, Scotland, UK. * John R Speakman & * Catherine Hambly * Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, People's Republic of China. * John R Speakman * Clore Laboratory, University of Buckingham, Buckingham, UK. * Jonathan R S Arch * Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. * Johan Auwerx * Max Planck Institute for Neurological Research, Cologne, Germany. * Jens C Brüning * Departments of Medicine and Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA. * Lawrence Chan * Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado–Denver School of Medicine, Aurora, Colorado, USA. * Robert H Eckel * Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, California, USA. * Robert V Farese Jr * Department of Nutrition, Faculty of Medicine, University of Chile, Santiago, Chile. * Jose E Galgani * Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA. * Mark A Herman, * Barbara B Kahn & * Eleftheria Maratos-Flier * Program on Integrative Cell Signaling and Neurobiology of Metabolism, Section of Comparative Medicine, Yale University School of Medicine, New Haven, Connecticut, USA. * Tamas L Horvath * Metabolic Diseases Institute, Department of Cancer and Cell Biology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. * Sara C Kozma & * George Thomas * Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA. * Heike Münzberg & * Eric Ravussin * Naomi Berrie Diabetes Center and Department of Medicine, Columbia University, New York, New York, USA. * Leona Plum * Diabetes, Endocrinology and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, US National Institutes of Health, Bethesda, Maryland, USA. * Marc L Reitman * Center on Functional Genomics of Hypertension, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA. * Kamal Rahmouni * Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA. * Gerald I Shulman * Research Division, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts, USA. * C Ronald Kahn Contributions M.H.T., J.R.S., C.R.K. and E.R. conceptualized and wrote the manuscript. J.R.S.A. provided original data and biomathematics advice. J.A. edited the manuscript and co-wrote sections on environment-genetics interactions. J.C.B. and T.L.H. edited aspects of the manuscript relevant for neuronal control of energy metabolism. L.C., R.H.E., G.I.S. and R.V.F. Jr. wrote and edited sections on nutrient partitioning and energy metabolism measurements. J.E.G. generated the table. M.A.H., B.B.K. and E.M.,-F. contributed to the sections on quantification of BC, locomotor activity and food intake. C.H. provided the data for Figure 2. T.D.M. wrote sections on housing and husbandry, and integrated all references. H.M. contributed advice and sections on study design and diets. P.T.P. generated the flowcharts together with M.H.T. and co-wrote sections on practical aspects of calorimetry and study design. L.P. co-edited the manuscript and added practical examples and calculations. M.L.R. and K.! R. contributed advice on sections regarding BC and thermogenesis. J.A., S.C.K. and G.T. added input regarding relevant pitfalls arising based on the use of mouse genetics. All authors edited and agreed on the final version of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Eric Ravussin or * C Ronald Kahn Author Details * Matthias H Tschöp Search for this author in: * NPG journals * PubMed * Google Scholar * John R Speakman Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan R S Arch Search for this author in: * NPG journals * PubMed * Google Scholar * Johan Auwerx Search for this author in: * NPG journals * PubMed * Google Scholar * Jens C Brüning Search for this author in: * NPG journals * PubMed * Google Scholar * Lawrence Chan Search for this author in: * NPG journals * PubMed * Google Scholar * Robert H Eckel Search for this author in: * NPG journals * PubMed * Google Scholar * Robert V Farese Jr Search for this author in: * NPG journals * PubMed * Google Scholar * Jose E Galgani Search for this author in: * NPG journals * PubMed * Google Scholar * Catherine Hambly Search for this author in: * NPG journals * PubMed * Google Scholar * Mark A Herman Search for this author in: * NPG journals * PubMed * Google Scholar * Tamas L Horvath Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara B Kahn Search for this author in: * NPG journals * PubMed * Google Scholar * Sara C Kozma Search for this author in: * NPG journals * PubMed * Google Scholar * Eleftheria Maratos-Flier Search for this author in: * NPG journals * PubMed * Google Scholar * Timo D Müller Search for this author in: * NPG journals * PubMed * Google Scholar * Heike Münzberg Search for this author in: * NPG journals * PubMed * Google Scholar * Paul T Pfluger Search for this author in: * NPG journals * PubMed * Google Scholar * Leona Plum Search for this author in: * NPG journals * PubMed * Google Scholar * Marc L Reitman Search for this author in: * NPG journals * PubMed * Google Scholar * Kamal Rahmouni Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald I Shulman Search for this author in: * NPG journals * PubMed * Google Scholar * George Thomas Search for this author in: * NPG journals * PubMed * Google Scholar * C Ronald Kahn Contact C Ronald Kahn Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Ravussin Contact Eric Ravussin Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (594K) Supplementary Table 1 and Supplementary Notes 1–5 Additional data - Counting absolute numbers of molecules using unique molecular identifiers
- Nat Methods 9(1):72-74 (2012)
Nature Methods | Perspective A guide to analysis of mouse energy metabolism * Matthias H Tschöp1, 20 * John R Speakman2, 3, 20 * Jonathan R S Arch4 * Johan Auwerx5 * Jens C Brüning6 * Lawrence Chan7 * Robert H Eckel8 * Robert V Farese Jr9 * Jose E Galgani10 * Catherine Hambly2 * Mark A Herman11 * Tamas L Horvath12 * Barbara B Kahn11 * Sara C Kozma13 * Eleftheria Maratos-Flier11 * Timo D Müller1 * Heike Münzberg14 * Paul T Pfluger1 * Leona Plum15 * Marc L Reitman16 * Kamal Rahmouni17 * Gerald I Shulman18 * George Thomas13 * C Ronald Kahn19 * Eric Ravussin14 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 9,Pages:57–63Year published:(2012)DOI:doi:10.1038/nmeth.1806Published online 28 December 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We present a consolidated view of the complexity and challenges of designing studies for measurement of energy metabolism in mouse models, including a practical guide to the assessment of energy expenditure, energy intake and body composition and statistical analysis thereof. We hope this guide will facilitate comparisons across studies and minimize spurious interpretations of data. We recommend that division of energy expenditure data by either body weight or lean body weight and that presentation of group effects as histograms should be replaced by plotting individual data and analyzing both group and body-composition effects using analysis of covariance (ANCOVA). View full text Subject terms: * Physiology * Genetics * Systems Biology Figures at a glance * Figure 1: Problems of analysis and interpretation of energy expenditure demonstrated on hypothetical data. () Experimental genotype (W, white) is compared to the wild-type genotype (B, black). Study A and study B are different experimental manipulations of genotype. Energy expenditure per whole animal (kJ day−1) is plotted against lean body mass (top). In all panels, lines show fitted regressions. Average of raw data across the individuals for each genotype plotted in a histogram (bottom). () Energy expenditure divided by lean body mass versus lean body mass is plotted for each data point (top) and shown as histogram (bottom). Values represent means ± s.e.m. * Figure 2: A practical example of the use of different approaches to the analysis of energy metabolism in the mouse. () Twenty control mice were fed ad libitum (ad lib) and 58 mice were fed a calorie-restricted diet (60% of ad libitum) for three months44. Raw data with the average metabolic rate were calculated without any correction for body mass. Note that the mice fed a calorie-restricted diet had a significantly (one-way ANOVA: P = 0.01) lower metabolic rate than the ones fed ad lib. () Metabolic rate expressed per gram of BW. Note that the opposite result was found: the mice on calorie-restricted diet had significantly (one-way ANOVA: P < 0.001) higher metabolic rates. () Resting metabolic rate (RMR) data as a function of body mass. Note that there is some overlap between the groups and a general positive trend of greater RMR at higher body masses (BM). () RMR versus fat-free mass (FFM) (measured by dual energy X-ray absorptiometry) shows a much greater overlap between the groups. () Dividing RMR by FFM revealed no significant effect of the treatment group on RMR (one-way ANOVA: P = 0! .275). Values represent means ± s.e.m. * Figure 3: Flowchart for mouse energy metabolism phenotype analysis for mouse models. () Analysis with higher BW in comparison with wild-type littermate controls. () Analysis for mouse models with lower BW in comparison with wild-type littermate controls. EI, energy intake; EE, energy expenditure; FI, food intake; KO, knockout. *For discussion of advantages and pitfalls of pair-feeding, see Supplementary Note 3. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Matthias H Tschöp & * John R Speakman Affiliations * Institute for Diabetes and Obesity, Helmholz Centre Munich, Department of Medicine, Technical University of Munich, Munich, Germany. * Matthias H Tschöp, * Timo D Müller & * Paul T Pfluger * Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, Scotland, UK. * John R Speakman & * Catherine Hambly * Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, People's Republic of China. * John R Speakman * Clore Laboratory, University of Buckingham, Buckingham, UK. * Jonathan R S Arch * Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. * Johan Auwerx * Max Planck Institute for Neurological Research, Cologne, Germany. * Jens C Brüning * Departments of Medicine and Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA. * Lawrence Chan * Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado–Denver School of Medicine, Aurora, Colorado, USA. * Robert H Eckel * Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, California, USA. * Robert V Farese Jr * Department of Nutrition, Faculty of Medicine, University of Chile, Santiago, Chile. * Jose E Galgani * Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA. * Mark A Herman, * Barbara B Kahn & * Eleftheria Maratos-Flier * Program on Integrative Cell Signaling and Neurobiology of Metabolism, Section of Comparative Medicine, Yale University School of Medicine, New Haven, Connecticut, USA. * Tamas L Horvath * Metabolic Diseases Institute, Department of Cancer and Cell Biology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. * Sara C Kozma & * George Thomas * Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA. * Heike Münzberg & * Eric Ravussin * Naomi Berrie Diabetes Center and Department of Medicine, Columbia University, New York, New York, USA. * Leona Plum * Diabetes, Endocrinology and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, US National Institutes of Health, Bethesda, Maryland, USA. * Marc L Reitman * Center on Functional Genomics of Hypertension, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA. * Kamal Rahmouni * Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA. * Gerald I Shulman * Research Division, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts, USA. * C Ronald Kahn Contributions M.H.T., J.R.S., C.R.K. and E.R. conceptualized and wrote the manuscript. J.R.S.A. provided original data and biomathematics advice. J.A. edited the manuscript and co-wrote sections on environment-genetics interactions. J.C.B. and T.L.H. edited aspects of the manuscript relevant for neuronal control of energy metabolism. L.C., R.H.E., G.I.S. and R.V.F. Jr. wrote and edited sections on nutrient partitioning and energy metabolism measurements. J.E.G. generated the table. M.A.H., B.B.K. and E.M.,-F. contributed to the sections on quantification of BC, locomotor activity and food intake. C.H. provided the data for Figure 2. T.D.M. wrote sections on housing and husbandry, and integrated all references. H.M. contributed advice and sections on study design and diets. P.T.P. generated the flowcharts together with M.H.T. and co-wrote sections on practical aspects of calorimetry and study design. L.P. co-edited the manuscript and added practical examples and calculations. M.L.R. and K.! R. contributed advice on sections regarding BC and thermogenesis. J.A., S.C.K. and G.T. added input regarding relevant pitfalls arising based on the use of mouse genetics. All authors edited and agreed on the final version of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Eric Ravussin or * C Ronald Kahn Author Details * Matthias H Tschöp Search for this author in: * NPG journals * PubMed * Google Scholar * John R Speakman Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan R S Arch Search for this author in: * NPG journals * PubMed * Google Scholar * Johan Auwerx Search for this author in: * NPG journals * PubMed * Google Scholar * Jens C Brüning Search for this author in: * NPG journals * PubMed * Google Scholar * Lawrence Chan Search for this author in: * NPG journals * PubMed * Google Scholar * Robert H Eckel Search for this author in: * NPG journals * PubMed * Google Scholar * Robert V Farese Jr Search for this author in: * NPG journals * PubMed * Google Scholar * Jose E Galgani Search for this author in: * NPG journals * PubMed * Google Scholar * Catherine Hambly Search for this author in: * NPG journals * PubMed * Google Scholar * Mark A Herman Search for this author in: * NPG journals * PubMed * Google Scholar * Tamas L Horvath Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara B Kahn Search for this author in: * NPG journals * PubMed * Google Scholar * Sara C Kozma Search for this author in: * NPG journals * PubMed * Google Scholar * Eleftheria Maratos-Flier Search for this author in: * NPG journals * PubMed * Google Scholar * Timo D Müller Search for this author in: * NPG journals * PubMed * Google Scholar * Heike Münzberg Search for this author in: * NPG journals * PubMed * Google Scholar * Paul T Pfluger Search for this author in: * NPG journals * PubMed * Google Scholar * Leona Plum Search for this author in: * NPG journals * PubMed * Google Scholar * Marc L Reitman Search for this author in: * NPG journals * PubMed * Google Scholar * Kamal Rahmouni Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald I Shulman Search for this author in: * NPG journals * PubMed * Google Scholar * George Thomas Search for this author in: * NPG journals * PubMed * Google Scholar * C Ronald Kahn Contact C Ronald Kahn Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Ravussin Contact Eric Ravussin Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (594K) Supplementary Table 1 and Supplementary Notes 1–5 Additional data - Sensitive and specific single-molecule sequencing of 5-hydroxymethylcytosine
- Nat Methods 9(1):75-77 (2012)
Nature Methods | Brief Communication Visualizing mechanical tension across membrane receptors with a fluorescent sensor * Daniel R Stabley1 * Carol Jurchenko1 * Stephen S Marshall1 * Khalid S Salaita1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:64–67Year published:(2012)DOI:doi:10.1038/nmeth.1747Received 13 May 2011 Accepted 27 September 2011 Published online 30 October 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We report a fluorescence-based turn-on sensor for mapping the mechanical strain exerted by specific cell-surface proteins in living cells. The sensor generates force maps with high spatial and temporal resolution using conventional fluorescence microscopy. We demonstrate the approach by mapping mechanical forces during the early stages of regulatory endocytosis of the ligand-activated epidermal growth factor receptor (EGFR). View full text Subject terms: * Microscopy * Chemical Biology * Biophysics * Sensors and Probes Figures at a glance * Figure 1: Design and response of the EGFR tension sensor. () Schematic of the EGF-PEGx (x = 12, 24 or 75) tension sensor, comprised of a PEG polymer of length x that is flanked by fluorescently labeled (Alexa Fluor 647) EGF ligand and a biotin moiety for surface immobilization via streptavidin capture. EGF crystal structure adapted from Protein Data Bank (identifier IJL9). Residues in red in the crystal structure represent lysine and the N terminus, which are the available sites for PEG and fluorophore modification. () Schematic of the mechanism of sensor function. When EGFR exerts a force on its ligand, the flexible PEG linker extends. The displacement of the EGF ligand results in an increase in the measured fluorescence intensity, thus reporting the transmission of mechanical tension through the EGF-EGFR complex. hν, emission of a photon. () Representative brightfield, reflection interference contrast microscopy (RICM) and EGFR tension sensor TIRF response of HCC1143 cells plated onto sensor surfaces at 37 °C for the indicated ! time points (t represents the start of imaging). Images on the bottom show magnification of the boxed regions. Colored line scans represent 34 pixel profiles through the indicated region; the color of each line corresponds to the graph shown below each set of frames. The white, red and blue arrows highlight fluorescent spots that persisted for 90 s, 60 s and 30 s, respectively. Black scale bar, 20 μm; red scale bar, 4 μm. Fluorescence intensity is given in arbitrary units (a.u.). () Histograms of the areas (n = 82) and the durations (n = 68) of fluorescent points under a cell that was observed for 10 min. * Figure 2: Characterization and quantification of the EGFR tension sensor. () Role of the flexible linker (alkyl, 2.2 nm or PEG75, 26 nm) and the quencher in the EGFR tension sensor response. Error bars, s.e.m. (n = 77 cells). () Representative brightfield, reflection interference contrast microscopy (RICM) and EGFR tension sensor response (epifluorescence (epi) 640 nm) channels for cells treated with latrunculin B (LatB) or control (DMSO). Scale bar, 5 μm. () Measured EGF force response (normalized fluorescence intensity) between LatB-treated (n = 33 cells) and untreated (n = 32 cells). Error bars, s.e.m. () Representative dual channel TIRF microscopy images of a CLC-eGFP–transfected cell engaged to the force-sensing surface. Overlay channel shows colocalization of CLC-eGFP and the EGF-force response. Scale bar, 5 μm. () Representative brightfield, RICM and fluorescence response for a cell engaged to an EGF-PEG24 force sensor surface. The sensor fluorescence response was converted into a force map by using the extended WLC model for PEG24. Sca! le bars, 10 μm (3.2 μm in the magnified image). Author information * Author information * Supplementary information Affiliations * Department of Chemistry, Emory University, Atlanta, Georgia, USA. * Daniel R Stabley, * Carol Jurchenko, * Stephen S Marshall & * Khalid S Salaita Contributions D.R.S. adapted the FRET surface sensor for use with human cells expressing the EGFR and performed the majority of the human cell experiments. C.J. developed the force sensor and performed the quantitative characterization of the zero-force sensor conformation and its components. S.S.M. optimized and performed the CLC-eGFP transfections. K.S.S. devised the overall experimental strategy. D.R.S., C.J. and K.S.S. wrote and edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Khalid S Salaita Author Details * Daniel R Stabley Search for this author in: * NPG journals * PubMed * Google Scholar * Carol Jurchenko Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen S Marshall Search for this author in: * NPG journals * PubMed * Google Scholar * Khalid S Salaita Contact Khalid S Salaita Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (10M) Supplementary Figures 1–13 Movies * Supplementary Video 1 (3M) Animation showing the mechanism of sensor function. * Supplementary Video 2 (528K) Movie showing cell activation of the force sensor. * Supplementary Video 3 (5M) Movie showing clathrin colocalization with force sensor activation. Additional data - Decoding cell lineage from acquired mutations using arbitrary deep sequencing
- Nat Methods 9(1):78-80 (2012)
Nature Methods | Brief Communication Cyanine fluorophore derivatives with enhanced photostability * Roger B Altman1 * Daniel S Terry2, 7 * Zhou Zhou1, 7 * Qinsi Zheng3 * Peter Geggier4 * Rachel A Kolster4 * Yongfang Zhao4 * Jonathan A Javitch4, 5 * J David Warren6 * Scott C Blanchard1, 2, 3, 6 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:68–71Year published:(2012)DOI:doi:10.1038/nmeth.1774Received 19 May 2011 Accepted 14 October 2011 Published online 13 November 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Fluorescence applications requiring high photostability often depend on the use of solution additives to enhance fluorophore performance. Here we demonstrate that the direct or proximal conjugation of cyclooctatetraene (COT), 4-nitrobenzyl alcohol (NBA) or Trolox to the cyanine fluorophore Cy5 dramatically enhanced fluorophore photostability without otherwise affecting its native spectral characteristics. Such conjugation is a powerful means of improving the robustness of fluorescence-based applications demanding long-lived, nonblinking fluorescence emission. View full text Subject terms: * Imaging * Biophysics * Chemistry * Chemical Biology * Microscopy * Sensors and Probes Figures at a glance * Figure 1: Enhancement of Cy5 photophysical properties through direct coupling to TSQs. () General schematic of TSQ-conjugated fluorophore derivatives. () Average dwell times in the on state (τon) with individual TSQs in solution (TSQ, solution) or directly conjugated to Cy5 (Cy5-TSQ). Error bars, s.d. (n ≥ 6 movies from at least two independent experiments). () Representative traces of Cy5 fluorescence under direct excitation in the absence of TSQ (Cy5) and with COT, NBA or Trolox directly coupled to the dye. * Figure 2: Role of proximity in the enhancement of Cy5 fluorescence with indirectly coupled TSQs. () Schematic of the TSQ proximity experiment. DNA duplexes were created with one strand labeled with Cy5 (red) at the 5′ end and TSQs (blue) 2, 5 and 8 nucleotides from the 3′ end on the complementary strand. Views from the side (top) and above (bottom) are shown. () τon for constructs in which an individual TSQ was linked proximally to the Cy5 fluorophore 2 base pairs distal to the terminus of the DNA duplex (TSQ-2) (top). τon examined with Trolox attached at positions 2, 5 and 8 nucleotides away from the terminus of the DNA duplex (bottom). Error bars, s.d. (n = 3 movies). () Spatial distributions sampled by Cy5 and proximal TSQs determined using molecular dynamics simulations of DNA duplexes with Cy5 (red) labeled at the 5′ end of one strand and one molecule of Trolox at one of three locations on the DNA helix: 2, 5 and 8 nucleotides (nt) from the 3′ end of the complementary strand. Isosurfaces of the spatial distribution sampled by Cy5 and Trolox are shown as t! ranslucent clouds about the DNA helix. * Figure 3: Photostability of Cy5 and Cy5-TSQ conjugates in the presence of oxygen and in living cells. (,) Photostability of Cy5- and Cy5-TSQ–conjugated DNA duplexes surface-immobilized at a saturating density and in the presence () and absence () of an oxygen-scavenging system. Decay curves were fit to a single exponential process, and the time constants are reported in Supplementary Table 1. (–) Single-molecule total internal reflection fluorescence image sequences of living CHO cells containing dopamine D2 receptors labeled with Cy5 (), labeled with Cy5-COT () and labeled with Cy5-COT and imaged in deoxygenated solution containing 1 mM PCA and 50 nM PCD (). Scale bar, 5 μm. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Daniel S Terry & * Zhou Zhou Affiliations * Department of Physiology and Biophysics, Weill Medical College of Cornell University, New York, New York, USA. * Roger B Altman, * Zhou Zhou & * Scott C Blanchard * Tri-Institutional Training Program in Computational Biology and Medicine, Weill Medical College of Cornell University, New York, New York, USA. * Daniel S Terry & * Scott C Blanchard * Tri-Institutional Training Program in Chemical Biology, Weill Medical College of Cornell University, New York, New York, USA. * Qinsi Zheng & * Scott C Blanchard * Department of Psychiatry, Center for Molecular Recognition, College of Physicians and Surgeons, Columbia University, New York, New York, USA. * Peter Geggier, * Rachel A Kolster, * Yongfang Zhao & * Jonathan A Javitch * Department of Pharmacology, College of Physicians and Surgeons, Columbia University, New York, New York, USA. * Jonathan A Javitch * Department of Biochemistry, Weill Medical College of Cornell University, New York, New York, USA. * J David Warren & * Scott C Blanchard Contributions S.C.B., R.B.A. and D.S.T. designed in vitro experiments. Z.Z. and J.D.W. synthesized TSQ-conjugated fluorophores. R.B.A. made and purified complexes. R.B.A. performed single-molecule and bulk imaging experiments. Q.Z. performed bulk fluorescence and singlet-oxygen measurements. D.S.T. performed simulations. D.S.T., R.B.A. and S.C.B. analyzed in vitro data. S.C.B., J.A.J., and P.G. designed live-cell imaging experiments. Y.Z. and R.A.K. designed and constructed receptor constructs. P.G. performed the in vivo imaging and analyzed data. R.B.A., D.S.T. and P.G. designed figures. R.B.A. and S.C.B. wrote the manuscript, which all authors edited. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Scott C Blanchard Author Details * Roger B Altman Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel S Terry Search for this author in: * NPG journals * PubMed * Google Scholar * Zhou Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Qinsi Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Geggier Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel A Kolster Search for this author in: * NPG journals * PubMed * Google Scholar * Yongfang Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan A Javitch Search for this author in: * NPG journals * PubMed * Google Scholar * J David Warren Search for this author in: * NPG journals * PubMed * Google Scholar * Scott C Blanchard Contact Scott C Blanchard Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (594K) Supplementary Figures 1–8, Supplementary Table 1 and Supplementary Note 1 Movies * Supplementary Video 1 (27M) Photostability of Cy5-TSQ conjugates when linked to DNA duplexes. Shown are single-molecule TIRF movies of Cy5 (top left), Cy5-COT (top right), Cy5-NBA (bottom left), Cy5-Trolox (bottom right) taken at 10 frames s−1 with oxygen scavenging (as in Fig. 1). The movie is played at five times actual imaging speed. The elapsed time (in min:s) is displayed in the top left corner. * Supplementary Video 2 (6M) Photostability of Cy5 and Cy5-COT–labeled dopamine D2 receptors (D2Rs) in living CHO cells (top left). Single-molecule TIRF video sequence of Cy5 labeled D2Rs. In a deoxygenated environment Cy5-labeled D2 receptors show enhanced photostability at the cost of higher blinking rates (top right). A direct linkage of COT to Cy5 improves the photostability (bottom left). The greatest photostability of D2Rs was observed by labeling with Cy5-COT and imaging in a deoxygenated environment (bottom right). All image sequences were recorded at a rate of 25 frames s−1. Additional data - Controlled gene expression in primary Lgr5 organoid cultures
- Nat Methods 9(1):81-83 (2012)
Nature Methods | Brief Communication Counting absolute numbers of molecules using unique molecular identifiers * Teemu Kivioja1, 2, 3, 5 * Anna Vähärautio1, 3, 5 * Kasper Karlsson4 * Martin Bonke1 * Martin Enge3 * Sten Linnarsson4 * Jussi Taipale3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 9,Pages:72–74Year published:(2012)DOI:doi:10.1038/nmeth.1778Received 10 March 2011 Accepted 27 September 2011 Published online 20 November 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Counting individual RNA or DNA molecules is difficult because they are hard to copy quantitatively for detection. To overcome this limitation, we applied unique molecular identifiers (UMIs), which make each molecule in a population distinct, to genome-scale human karyotyping and mRNA sequencing in Drosophila melanogaster. Use of this method can improve accuracy of almost any next-generation sequencing method, including chromatin immunoprecipitation–sequencing, genome assembly, diagnostics and manufacturing-process control and monitoring. View full text Subject terms: * Biochemistry * Genomics * Genetics * Sequencing Figures at a glance * Figure 1: UMIs can be generated by adding oligonucleotide labels, fragmenting, taking a small enough aliquot or a combination thereof. () Three different DNA species (green, blue and black lines) are labeled with a collection of random labels (colored filled circles). Two green molecules are originally present (top), corresponding to two different UMIs (red, blue) among the sequenced molecules (green; bottom). Information about the original number of molecules (top) is preserved in the number of different UMIs detected by sequencing a sample of the amplified and normalized library (bottom). Even if some UMIs are not observed, the original number of molecules can be estimated using count statistics. () The original molecule is randomly fragmented, and a short unique sequence from the resulting fragments constitutes each UMI; here only the fragment adjacent to the poly(A) sequence (red vertical bars) is amplified. () An aliquot is taken from a sample that has many identical molecules such that on average, less than one copy of each molecule remains. * Figure 2: Digital karyotyping by counting the absolute number of molecules. () Standard digital karyotype based on genomic DNA from a boy with trisomy 21 and from his mother, mixed 1:1. () Standard digital karyotype of a sample from a male with a normal chromosome count. () The same sample as in was analyzed by UMI counting (CV = 3.0%). Arrow highlights uniformly elevated copy number of regions in chromosome 21. () Simulated sample by uniform random sampling of 1.28 million molecules in silico from the NCBI human genome build 37 (CV = 2.2%). Number of reads and of molecules aligned to each 5-megabase-pair window is indicated. Chromosomes 21 and X are indicated by shading (the Y chromosome was excluded because its sequence was too repetitive for reliable alignments). * Figure 3: Accuracy of mRNA-seq can be improved by the UMI method. () mRNA-seq libraries were generated by fragmenting total RNA and reverse-transcribing it to cDNA using an oligo(dT) primer with an Illumina linker (blue) and a 5′ template switch adaptor containing another Illumina linker (magenta), a 10-base-pair random label (N10) and an index sequence (green). The combination of label sequence and the 5′ mapped position of the RNA fragment forms the UMI. (,) Measurements of expression of the same set of genes after 15 (x axes) and 25 (y axes) PCR amplification cycles were obtained using total read counts () or the UMI method (). Most individual transcript total read counts obtained in the two measurements are far from each other and from diagonal (dashed gray line), and this effect is corrected by the UMI method (). Genes whose mean in the two measurements deviated more than 5% from the fitted line (gray, solid) are in red. () A density plot shows average copy number of UMIs after 15 PCR cycles as a function of the average G+C conten! t of the fragments for each measured gene from and . Red line in indicates a least-squares fit, for which a P value and adjusted R2 value are given. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Teemu Kivioja & * Anna Vähärautio Affiliations * Genome-Scale Biology Program, Institute of Biomedicine, University of Helsinki, Helsinki, Finland. * Teemu Kivioja, * Anna Vähärautio & * Martin Bonke * Department of Computer Science, University of Helsinki, Helsinki, Finland. * Teemu Kivioja * Science for Life Laboratory, Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden. * Teemu Kivioja, * Anna Vähärautio, * Martin Enge & * Jussi Taipale * Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. * Kasper Karlsson & * Sten Linnarsson Contributions S.L., J.T., A.V., T.K. and M.E. conceived and designed experiments. A.V., K.K. and M.B. performed biological experiments. S.L., J.T., A.V. and T.K. analyzed data. J.T., A.V., T.K., M.E. and S.L. wrote the paper. Competing financial interests S.L. and J.T. have submitted a patent application on the absolute molecule counting method (UK patent application 1016608.0). Corresponding authors Correspondence to: * Jussi Taipale or * Sten Linnarsson Author Details * Teemu Kivioja Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Vähärautio Search for this author in: * NPG journals * PubMed * Google Scholar * Kasper Karlsson Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Bonke Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Enge Search for this author in: * NPG journals * PubMed * Google Scholar * Sten Linnarsson Contact Sten Linnarsson Search for this author in: * NPG journals * PubMed * Google Scholar * Jussi Taipale Contact Jussi Taipale Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–7, Supplementary Table 1, Supplementary Note Additional data - Global profiling of dynamic protein palmitoylation
- Nat Methods 9(1):84-89 (2012)
Nature Methods | Brief Communication Sensitive and specific single-molecule sequencing of 5-hydroxymethylcytosine * Chun-Xiao Song1, 3 * Tyson A Clark2, 3 * Xing-Yu Lu1 * Andrey Kislyuk2 * Qing Dai1 * Stephen W Turner2 * Chuan He1 * Jonas Korlach2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 9,Pages:75–77Year published:(2012)DOI:doi:10.1038/nmeth.1779Received 05 April 2011 Accepted 28 September 2011 Published online 20 November 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We describe strand-specific, base-resolution detection of 5-hydroxymethylcytosine (5-hmC) in genomic DNA with single-molecule sensitivity, combining a bioorthogonal, selective chemical labeling method of 5-hmC with single-molecule, real-time (SMRT) DNA sequencing. The chemical labeling not only allows affinity enrichment of 5-hmC–containing DNA fragments but also enhances the kinetic signal of 5-hmC during SMRT sequencing. We applied the approach to sequence 5-hmC in a genomic DNA sample with high confidence. View full text Subject terms: * Sequencing * Biophysics * Chemical Biology * Single Molecule Figures at a glance * Figure 1: Principle of selective chemical labeling of 5-hmC followed by SMRT DNA sequencing. * Figure 2: Effects of 5-hmC modifications on polymerase kinetics in SMRT DNA sequencing. (–) Using synthetic DNA templates with known positions of 5-hmC (triangles), the graphs show IPD ratios, at each template position, of the modified DNA template over a control DNA template of identical sequence but lacking 5-hmC. Conditions were 5-hmC untreated (), upon coupling with glucose azide () and upon additional coupling with the disulfide-containing biotin linker, followed by cleavage of the disulfide bond (). * Figure 3: Example of 5-hmC detection by SMRT sequencing from mESC genomic DNA. () The raw SMRT sequencing read. A.u., arbitrary units. () Sequencing subreads from the SMRTbell template, mapped onto mouse chromosome 1 over the sequencing time course. Pauses appear as discontinuities as the polymerase temporarily stops progressing along the DNA template. F, forward strand reads; R, reverse strand reads. () Subreads are grouped and annotated by IPD values in a heat-map scale, identifying a hemi-hydroxymethylated CG position in this DNA molecule. Arrows indicate consistent pausing of the polymerase (that is, large IPD value) at the same genomic position across multiple intramolecular subreads, indicating the presence of a 5-hmC adduct. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Chun-Xiao Song & * Tyson A Clark Affiliations * Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois, USA. * Chun-Xiao Song, * Xing-Yu Lu, * Qing Dai & * Chuan He * Pacific Biosciences, Menlo Park, California, USA. * Tyson A Clark, * Andrey Kislyuk, * Stephen W Turner & * Jonas Korlach Contributions C.H., C.-X.S., J.K. and S.W.T. designed experiments. C.-X.S. performed the labeling and pulldown of synthetic template and mESC sample. T.A.C. prepared library constructs and conducted the sequencing experiments. A.K. analyzed data. Q.D., C.-X.S. and X.-Y.L. carried out the chemical synthesis. X.-Y.L. validated mESC hits. C.H., C.-X.S. and J.K. wrote the paper. Competing financial interests T.A.C., S.W.T. and J.K. are employees of Pacific Biosciences, which commercializes single-molecule, real-time sequencing technologies. Corresponding authors Correspondence to: * Chuan He or * Jonas Korlach Author Details * Chun-Xiao Song Search for this author in: * NPG journals * PubMed * Google Scholar * Tyson A Clark Search for this author in: * NPG journals * PubMed * Google Scholar * Xing-Yu Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Andrey Kislyuk Search for this author in: * NPG journals * PubMed * Google Scholar * Qing Dai Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen W Turner Search for this author in: * NPG journals * PubMed * Google Scholar * Chuan He Contact Chuan He Search for this author in: * NPG journals * PubMed * Google Scholar * Jonas Korlach Contact Jonas Korlach Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–3 and Supplementary Tables 1–3 Additional data - Optical recording of action potentials in mammalian neurons using a microbial rhodopsin
- Nat Methods 9(1):90-95 (2012)
Nature Methods | Brief Communication Decoding cell lineage from acquired mutations using arbitrary deep sequencing * Cheryl A Carlson1, 3 * Arnold Kas1 * Robert Kirkwood1 * Laura E Hays1, 3 * Bradley D Preston1 * Stephen J Salipante2 * Marshall S Horwitz1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:78–80Year published:(2012)DOI:doi:10.1038/nmeth.1781Received 03 May 2011 Accepted 31 October 2011 Published online 27 November 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Because mutations are inevitable, the genome of each cell in a multicellular organism becomes unique and therefore encodes a record of its ancestry. Here we coupled arbitrary single primer PCR with next-generation DNA sequencing to catalog mutations and deconvolve the phylogeny of cultured mouse cells. This study helps pave the way toward construction of retrospective cell-fate maps based on mutations accumulating in genomes of somatic cells. View full text Subject terms: * Genetics * Genomics * Bioinformatics * Model Organisms Figures at a glance * Figure 1: Cell lineages. () A single mouse fibroblast was seeded onto a Petri dish. After approximately 20 doublings, a single cell was used to seed each of the next tier of dishes and so on. () Lineage reconstructed from 592 mutations identified from sequencing of single‐primer 'arbitrary PCR' products from DNA extracted from all 15 dishes. () Simplified lineage tree, similar to that in but showing only the terminal nodes. () Lineage reconstructed from the 667 mutations present in only the terminal nodes. Numbers in deduced trees are Bayesian posterior probabilities. Numbers inside boxes identify unique nodes; colors are arbitrary. * Figure 2: Sample-to-sample reproducibility. Total quantity of unique genomic sequence in common for all samples, at various minimum depths of coverage, as number of samples increased from 1 to 15 (for example, at ≥1× depth of coverage, there were ~10,000,000 unique genomic positions that were sequenced in common for all 15 samples). * Figure 3: Genome Browser snapshot. For a representative example, shown are histogram plots of an ~3 kb amplicon on chromosome 1 corresponding to mapped reads from arbitrary PCR for the first six samples (bottom six graphs). Other tracks include (from top to bottom): known genes (coverage overlaps with exons and introns of Il19), position of identified mutations (vertical red line at right end of plots) that are found in at least one sample, minimum fold coverage common to all 15 samples and mean fold coverage for all 15 samples. Note that PCR results were highly consistent from one sample to the next. Also note low depth of coverage reads unique to each sample (flanking the amplicon). Author information * Author information * Supplementary information Affiliations * Department of Pathology, University of Washington School of Medicine, Seattle, Washington, USA. * Cheryl A Carlson, * Arnold Kas, * Robert Kirkwood, * Laura E Hays, * Bradley D Preston & * Marshall S Horwitz * Departments of Laboratory Medicine and Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA. * Stephen J Salipante * Present addresses: Division of Hematology/Oncology, Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA (C.A.C.) and Division of Hematology/Medical Oncology, Department of Medicine, Oregon Health and Science University, Portland, Oregon, USA (L.E.H.). * Cheryl A Carlson & * Laura E Hays Contributions C.A.C., B.D.P., L.E.H., S.J.S. and M.S.H. designed the experiments. C.A.C., S.J.S. and L.E.H. carried out the experiments. C.A.C., A.K., R.K. and M.S.H. contributed to analyzing the data. M.S.H., with input from other authors, wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Marshall S Horwitz Author Details * Cheryl A Carlson Search for this author in: * NPG journals * PubMed * Google Scholar * Arnold Kas Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Kirkwood Search for this author in: * NPG journals * PubMed * Google Scholar * Laura E Hays Search for this author in: * NPG journals * PubMed * Google Scholar * Bradley D Preston Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen J Salipante Search for this author in: * NPG journals * PubMed * Google Scholar * Marshall S Horwitz Contact Marshall S Horwitz Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–6, Supplementary Tables 1,3,4 Excel files * Supplementary Table 2 (172K) Compilation of identified mutations. Zip files * Supplementary Software (12K) PrimerDesign.pl is a Perl program for evaluation of arbitrary primers; ChromosomePrep.pl is a Perl program to prepare mouse reference sequence for PrimerDesign.pl; and Geno.pl is a Perl program to perform mutational analysis. Additional data - mGRASP enables mapping mammalian synaptic connectivity with light microscopy
- Nat Methods 9(1):96-102 (2012)
Nature Methods | Brief Communication Controlled gene expression in primary Lgr5 organoid cultures * Bon-Kyoung Koo1, 3 * Daniel E Stange1, 3 * Toshiro Sato1, 2 * Wouter Karthaus1 * Henner F Farin1 * Meritxell Huch1 * Johan H van Es1 * Hans Clevers1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:81–83Year published:(2012)DOI:doi:10.1038/nmeth.1802Received 13 June 1011 Accepted 04 November 2011 Published online 04 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The study of gene function in endodermal epithelia such as of stomach, small intestine and colon relies heavily on transgenic approaches. Establishing such animal models is laborious, expensive and time-consuming. We present here a method based on Cre recombinase–inducible retrovirus vectors that allows the conditional manipulation of gene expression in primary mouse organoid culture systems. View full text Subject terms: * Molecular Biology * Stem Cells Figures at a glance * Figure 1: An inducible organoid transduction system. () General outline. () Vector scheme for inducible overexpression and knockdown cassettes. LTR, long terminal repeat; puro, puromycin-resistance gene pac. () The micrographs show intestinal organoids transduced with the dsRed-eGFP–containing retrovirus before and after 4-OHT treatment. The dotted line outlines the central lumen with autofluorescence. Scale bars, 100 μm. * Figure 2: Notch pathway inhibition in intestinal organoids. () The micrographs show representative organoids with Math1 or DN-MAML1 overexpression, or cells after Hes1 knockdown. Shown are bright-field images (top), staining for the proliferation marker Ki67 (middle) and staining for PAS, a marker of the goblet cell lineage (bottom). The control sample was a tamoxifen-induced organoid transduced with the dsRed-eGFP overexpression vector. Scale bars, 20 μm. () Results of quantitiative PCR analysis of the indicated markers of secretory cell lineages (Gob5 for goblet cells and Lyz2 for Paneth cells) in control and infected organoids. Error bars, s.d. (n = 3). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Bon-Kyoung Koo & * Daniel E Stange Affiliations * Hubrecht Institute for Developmental Biology and Stem Cell Research, and University Medical Centre, Utrecht, The Netherlands. * Bon-Kyoung Koo, * Daniel E Stange, * Toshiro Sato, * Wouter Karthaus, * Henner F Farin, * Meritxell Huch, * Johan H van Es & * Hans Clevers * Department of Gastroenterology, School of Medicine, Keio University, Tokyo, Japan. * Toshiro Sato Contributions B.-K.K., D.E.S. and H.C. conceived the project and wrote the manuscript. B.-K.K., T.S. and M.H. developed the infection protocol and optimized the culture conditions. B.-K.K.,D.E.S. and H.F.F. designed and constructed retroviral vectors. B.-K.K. and J.H.v.E. established the organoids from CreERT2-transgenic mice. W.K. performed the lentiviral transduction. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Hans Clevers Author Details * Bon-Kyoung Koo Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel E Stange Search for this author in: * NPG journals * PubMed * Google Scholar * Toshiro Sato Search for this author in: * NPG journals * PubMed * Google Scholar * Wouter Karthaus Search for this author in: * NPG journals * PubMed * Google Scholar * Henner F Farin Search for this author in: * NPG journals * PubMed * Google Scholar * Meritxell Huch Search for this author in: * NPG journals * PubMed * Google Scholar * Johan H van Es Search for this author in: * NPG journals * PubMed * Google Scholar * Hans Clevers Contact Hans Clevers Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–5 and Supplementary Table 1 Additional data - High-efficiency counterselection recombineering for site-directed mutagenesis in bacterial artificial chromosomes
- Nat Methods 9(1):103-109 (2012)
Nature Methods | Article Global profiling of dynamic protein palmitoylation * Brent R Martin1, 2 * Chu Wang1 * Alexander Adibekian1 * Sarah E Tully1 * Benjamin F Cravatt1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 9,Pages:84–89Year published:(2012)DOI:doi:10.1038/nmeth.1769Received 27 June 2011 Accepted 04 October 2011 Published online 06 November 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The reversible thioester linkage of palmitic acid on cysteines, known as protein S-palmitoylation, facilitates the membrane association and proper subcellular localization of proteins. Here we report the metabolic incorporation of the palmitic acid analog 17-octadecynoic acid (17-ODYA) in combination with stable-isotope labeling with amino acids in cell culture (SILAC) and pulse-chase methods to generate a global quantitative map of dynamic protein palmitoylation events in cells. We distinguished stably palmitoylated proteins from those that turn over rapidly. Treatment with a serine lipase–selective inhibitor identified a pool of dynamically palmitoylated proteins regulated by palmitoyl-protein thioesterases. This subset was enriched in oncoproteins and other proteins linked to aberrant cell growth, migration and cancer. Our method provides a straightforward way to characterize global palmitoylation dynamics in cells and confirms enzyme-mediated depalmitoylation as a crit! ical regulatory mechanism for a specific subset of rapidly cycling palmitoylated proteins. View full text Subject terms: * Proteomics * Cell Biology * Chemical Biology * Sensors and Probes Figures at a glance * Figure 1: Quantitative analysis of protein palmitoylation. () Cells labeled with isotopically heavy and light amino acids via SILAC were metabolically labeled with 17-ODYA, lysed and mixed at a 1:1 ratio for click chemistry ligation to biotin-azide for enrichment and quantitative proteomic analysis. TBTA, Tris((1-benzyl-1H-1,2,3-triazol-4-yl)methyl)amine and TCEP, Tris(2-carboxyethyl)phosphine hydrochloride. MudPIT, multidimensional protein identification technology. () Global distribution of enriched peptide ratios after mixing membrane lysates at predefined ratios. Data are displayed using a log2 scale on the x axis. Only peptides identified as specifically enriched by the criteria described in Online Methods are shown. () Parent ion spectra (MS1) for a specific peptide from the palmitoylated protein HRAS at indicated dilution ratios. Yellow and dashed lines represent the computationally defined peak and baseline, respectively, used for integration and quantification. The defined dilutions and the calculated experimental ratios ar! e displayed below the individual peak spectra, respectively. Highlighted lysine in the peptide sequence represents the isotope-labeled amino acid. * Figure 2: Enhanced assignment of palmitoylated proteins using SILAC 17-ODYA proteomics. () Overlap of high-confidence palmitoylated protein assignments by SILAC and spectral counting from 17-ODYA–labeled T-cell membrane lysates. () Representative mass spectra for six proteins identified only by SILAC quantification. D10BW1364E (FAM108B), TTYH3 (tweety homolog 3), TRAPPC3 (trafficking protein particle complex 3), TM2D2 (beta-amyloid-binding protein-like 1), PRND (prion protein 2) and BET1L (blocked early in transport 1 homolog-like) all show selective enrichment in SILAC samples with 17-ODYA–labeled light or heavy cells. Yellow lines represent computationally defined peak used for quantification. Blue spectra represent heavy peptides and red spectra represent light peptides. Green letters in the peptide sequences represent isotope-labeled amino acids. * Figure 3: HDFP is a lipase-selective inhibitor. () Structures of fluorophosphonate (FP) activity-based probes and the lipase-directed inhibitors HDFP and HDFP-alk. Tetramethyl-6-carboxyrhodamine (rhodamine) is shown as a cartoon in red. () Competitive ABPP to survey serine hydrolase targets of HDFP. A mouse brain membrane proteome was preincubated with the indicated inhibitors for 30 min and then labeled with the indicated activity-based probes for competitive gel-based ABPP. Arrows highlight annotated lipases that were inhibited by HDFP and labeled by HDFP-alk (detected by click chemistry conjugation to an azide-Rh tag). Molecular weight (MW) is indicated. () PPT1 is selectively inhibited by HDFP-alk. Purified human PPT1 (5 ng) was incubated with 5 μM FP-alk of HDFP-alk and then reacted with azide-Rh for fluorescent gel-based analysis. () Competitive ABPP-SILAC to identify serine hydrolase targets of HDFP. Mouse T-cell hybridoma lysates were fractionated into membrane and soluble proteomes for separate analysis. Peptide! s with MS1 peaks passing stringent size and shape thresholds, but that lacked a signal in the HDFP-treated light or heavy sample were assigned a lower-limit ratio of 0.05 (ratio = HDFP/DMSO, where the HDFP-treated sample is the numerator and the DMSO treated sample is the denominator). Red bars represent enzymes inhibited by >75%. Protein identifiers in red and blue font are annotated lipases and unannotated serine hydrolases, respectively. Protein identifiers in black font are annotated as proteases, peptidases or other metabolite hydrolases. Error bars, s.e.m. (n = 4). * Figure 4: Lipase inhibition by HDFP enhances 17-ODYA labeling and prevents palmitoylation turnover. () Pulse-chase labeling of T-cell hybridoma cells to identify proteins that have dynamic palmitoylation events that are rapidly turned over. Cells were pulsed in 17-ODYA for 2 h, then washed and placed in 'chase' medium containing excess palmitic acid for indicated time periods. Arrows highlight protein with rapid palmitoylation turnover observed by pulse-chase labeling. () Cells treated with HDFP show a time-dependent increase in the extent of 17-ODYA labeling in T-cell hybridoma cells. () Labeling of cells with 17-ODYA in the presence or absence of 1-heptadecanol (C17-OH) and HDFP. () Labeling of cells with 17-ODYA pulsed for 2 h, then washed and chased with or without HDFP. Arrows highlight three representative classes of palmitoylated proteins: complete protection by HDFP (1), partial protection by HDFP (2) and stable palmitoylated proteins or palmitoylated proteins unaffected by HDFP (3). Sizes in all gels indicate molecular weight (kDa). * Figure 5: Enzymatically regulated, dynamically cycling palmitoylated proteins. () Representative MS1 spectra from the UBL3 precursor R.LILVSGK.T tryptic peptide extracted from reciprocal experiments reversing the treatment order between heavy and light isotope–labeled cells. Yellow and dashed lines represent the computationally defined peak and baseline for integration, respectively. () SILAC ratios for two experiments. In experiment 1, cells were labeled with 17-ODYA (2 h) and collected or placed in 'chase' medium for an additional 4 h. The ratio is displayed as time (t) = 0 h/t = 4 h, combining three replicates in which each isotopic pair was treated inversely and totaling six biological replicates. In experiment 2, cells were labeled for 2 h with 17-ODYA, then placed in chase medium for 4 h with DMSO or HDFP. The ratio is displayed as HDFP/DMSO treatment values and was pooled, combining four replicates in which each isotopic pair was treated inversely, totaling eight biological replicates. For both experiments, data were restricted to include only! assigned palmitoylated proteins derived from experiments listed in Supplementary Table 1. Reference lines were added to delineate thresholds for assigning dynamically palmitoylated proteins. FAM49B and SCRIB were maximally changed in each experiment (assigned an upper limit ratio of 20) and hence off scale. Proteins labeled in red are considered dynamically palmitoylated and regulated by targets of HDFP (high ratios in experiments 1 and 2). Proteins labeled in green demonstrated rapid palmitoylation turnover in experiment 1 but showed no stabilization upon HDFP treatment in experiment 2. Author information * Abstract * Author information * Supplementary information Affiliations * The Department of Chemical Physiology and The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California, USA. * Brent R Martin, * Chu Wang, * Alexander Adibekian, * Sarah E Tully & * Benjamin F Cravatt * Present address: Department of Chemistry, University of Michigan, Ann Arbor, Michigan, USA. * Brent R Martin Contributions B.R.M. and B.F.C. designed research. B.R.M. performed research. A.A., B.R.M., S.E.T. and C.W. contributed new analytical tools. B.R.M. and B.F.C. analyzed data and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Brent R Martin or * Benjamin F Cravatt Author Details * Brent R Martin Contact Brent R Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Chu Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Adibekian Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah E Tully Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin F Cravatt Contact Benjamin F Cravatt Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–2, Supplementary Note Excel files * Supplementary Table 1 (2M) Palmitoylated protein validation by SILAC 17-ODYA proteomics. Heavy and light isotope–labeled cells were prepared in two groups. In group 1 (runs 1–5), heavy and light isotope–labeled cells were treated overnight with 20 μM palmitic acid or 20 μM 17-ODYA, respectively, overnight in their standard growth media. In group 2 (runs 6–10), the order of labeling is inversed (heavy, palmitic acid and light, 17-ODYA). Ratio values are given as 17-ODYA/palmitic acid data. MS1 peaks present only in light or only in heavy samples that pass additional criteria (Online Methods) were assigned an upper-limit ratio of 20. Only proteins that were identified in both groups with median ratios greater than 1.5 are listed. Peptides that did not meet the stringent quantification criteria are listed with a ratio of zero. * Supplementary Table 2 (864K) Hydroxylamine (NH2OH) sensitivity of putative palmitoylated proteins. Heavy and light isotope–labeled cells were both labeled for 8 h with 20 μM 17-ODYA. In run 1, 'heavy' membrane lysates were treated with 1 M hydroxylamine, precipitated and mixed with 17-ODYA–labeled 'light' membrane lysates. In runs 2–3, light membrane lysates were treated with 1 M hydroxylamine, precipitated and mixed with 17-ODYA–labeled heavy membrane lysates. Mixtures were then reacted with biotin-azide, enriched and digested for proteomics analysis. Ratios are displayed as 17-ODYA/NH2OH data. Data were filtered by putative palmitoylated proteins identified in Supplementary Table 1. * Supplementary Table 3 (74K) Palmitoylated protein validation by spectral counting. Spectral counts were grouped to assigned proteins and filtered to have an average across all replicates equal or greater than 2. The ratio of average spectral counts comparing 17-ODYA and palmitic acid (PA) groups was calculated and filtered to exclude ratios less than 5. Proteins with spectral count averages of 2–5 were defined as 'medium confidence', and those with spectral count averages greater than 5 are classified as 'high confidence'. Proteins in red font contain a consensus myristoylation motif (Met-Gly) at the N terminus, and those assigned by SILAC proteomics are noted. * Supplementary Table 4 (238K) ABPP-SILAC profiling of serine hydrolases inhibited by HDFP. Ratio values are given as DMSO/HDFP data, and all quantified serine hydrolases are listed. Four separate experiments were performed. Runs 1–2 ('light' cells treated with HDFP) and runs 3–4 ('heavy' cells treated with HDFP) are from different cell preparations. Runs 1 and 3 are from soluble proteomes, and runs 2 and 4 are from membrane proteomes. Individual MS1 spectra were manually analyzed to include the additional hydrolases PPT1, PGAP1 and ABHD13 as valid HDFP targets. Peptides that did not meet the stringent quantification criteria are listed with a ratio of zero. * Supplementary Table 5 (131K) Pulse-chase analysis of HDFP-stabilized protein palmitoylation. A summary of three experiments are shown in a single table. Each experiment contains an equal number of biological replicates where either 'heavy' cells or 'light' cells were perturbed. For each experiment, four columns are listed, including the median ratio, the mean ratio, the standard error and the number (N) of quantitated peptides. In experiment 1 (N = 6 biological replicates), cells were labeled with 17-ODYA (2 h) and collected or placed in 'chase' medium for an additional 4 h. In experiment 2 (N = 8 biological replicates), cells were labeled for 2 h with 17-ODYA, then placed in 'chase' medium for 4 h with DMSO or HDFP. In experiment 3 (N = 2 biological replicates), lysates from experiment 1 were directly mixed and digested without enrichment for proteomic analysis of relative protein abundance. Data were filtered to display assigned palmitoylated proteins (Supplementary Table 1). Singleton peptides (defin! ed in Online Methods) were assigned an arbitrary ratio of 20 but were not included in any of the listed calculations (mean, median and others), except when they were the only representative peptides. * Supplementary Table 6 (807K) Protein and peptide ratios for experiment 1 (pulse-chase analysis of t = 0 /t = 4). Quantitative proteomics analysis of proteins undergoing dynamic depalmitoylation. In experiment 1 (N = 6 biological replicates), cells were labeled with 17-ODYA (2 h) and collected or placed in 'chase' medium for an additional 4 h. Calculated peptide ratios are displayed in the column titled 'median', and the median ratio is listed in the first line for each protein. The 'run' column lists the replicate where the given peptide was quantified. In runs 1–3, the heavy isotope–labeled sample was frozen immediately after the 2-h pulse, and the light isotope–labeled cells were placed in chase medium for 4 h. In runs 4–6, the light isotope–labeled cells were immediately frozen after the pulse labeling, and the heavy isotope–labeled cells were placed in chase medium for 4 h. After inverting the ratios for runs 1–3, the data from all experiments were combined and listed. Peptides classif! ied as singletons were assigned a ratio of 20. Peptides listed with a ratio of 0 were assigned a sequence by Sequest, but the MS1 data did not meet the stringent criteria for ratio assignment. * Supplementary Table 7 (332K) Protein and peptide ratios for experiment 3 (unenriched analysis). Samples prepared as described in Supplementary Table 6 were prepared without click chemistry or enrichment and are identified as experiment 3. Unenriched lysates were mixed and digested for direct assessment of protein abundance. Again, the order of isotopic labeling was inverted in the two biological replicates. After inverting the ratios for run 1, the data from all experiments were combined and listed. Peptides classified as singletons are assigned a ratio of 20. Peptides listed with a ratio of 0 were assigned a sequence by Sequest, but the MS1 data did not meet the stringent criteria for ratio assignment. * Supplementary Table 8 (1M) Protein and peptide ratios for experiment 2 (pulse-chase analysis of HDFP/DMSO data). Quantitative proteomics analysis of palmitoylated proteins protected from turnover by addition of HDFP. In experiment 2 (N = 8 biological replicates), cells were labeled with 17-ODYA (2 h) and placed in 'chase' medium for an additional 4 h with or without the addition of HDFP. Calculated peptide ratios are displayed in the column titled 'median', and the median ratio is listed in the first line for each protein. The 'run' column lists the replicate where the given peptide was quantified. In runs 1–4, the heavy isotope–labeled cells were treated with HDFP and the light isotope–labeled cells were treated with DMSO for 4 h in chase medium containing excess palmitic acid. In runs 5–8, the light isotope–labeled cells were treated with HDFP and the heavy isotope–labeled cells were treated with DMSO for 4 h in chase medium containing excess palmitic acid. After inverting the ratios for! runs 1–4, the data from all experiments were combined and listed. Peptides classified as singletons are assigned a ratio of 20. Peptides listed with a ratio of 0 were assigned a sequence by Sequest, but the MS1 data did not meet the stringent criteria for ratio assignment. Additional data - Erratum: Reply to "More on color blindness"
- Nat Methods 9(1):110 (2012)
Nature Methods | Article Optical recording of action potentials in mammalian neurons using a microbial rhodopsin * Joel M Kralj1, 5 * Adam D Douglass2, 5 * Daniel R Hochbaum3, 5 * Dougal Maclaurin4 * Adam E Cohen1, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:90–95Year published:(2012)DOI:doi:10.1038/nmeth.1782Received 10 June 2011 Accepted 25 October 2011 Published online 27 November 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Reliable optical detection of single action potentials in mammalian neurons has been one of the longest-standing challenges in neuroscience. Here we achieved this goal by using the endogenous fluorescence of a microbial rhodopsin protein, Archaerhodopsin 3 (Arch) from Halorubrum sodomense, expressed in cultured rat hippocampal neurons. This genetically encoded voltage indicator exhibited an approximately tenfold improvement in sensitivity and speed over existing protein-based voltage indicators, with a roughly linear twofold increase in brightness between −150 mV and +150 mV and a sub-millisecond response time. Arch detected single electrically triggered action potentials with an optical signal-to-noise ratio >10. Arch(D95N) lacked endogenous proton pumping and had 50% greater sensitivity than wild type but had a slower response (41 ms). Nonetheless, Arch(D95N) also resolved individual action potentials. Microbial rhodopsin–based voltage indicators promise to enable opti! cal interrogation of complex neural circuits and electrophysiology in systems for which electrode-based techniques are challenging. View full text Subject terms: * Microscopy * Neuroscience * Sensors and Probes * Imaging Figures at a glance * Figure 1: Arch is a fluorescent voltage indicator. () Model of Arch as a voltage sensor, in which pH and membrane potential can both alter the protonation of the Schiff base. The cuvettes contain intact E. coli expressing Arch. () Absorption and fluorescence emission spectra of Arch at neutral and high pH. () Fluorescence of Arch (divided by the value at −150 mV) as a function of membrane potential (recorded over six consecutive sweeps). () Dynamic response of Arch to steps in membrane potential between −70 mV and +30 mV. The overshoots on the rising and falling edges were an artifact of electronic compensation circuitry. The smaller amplitude compared to is because background subtraction was not performed in . Data were averaged over 20 cycles. Inset, step response occurred in less than the 500 μs resolution of the imaging system. () Fluorescence micrograph of an HEK 293 cell expressing Arch (top) and pixel-weight matrix showing regions of voltage-dependent fluorescence (bottom). a.u., arbitrary units. Scale bar, 10 μ! m. * Figure 2: Optical recording of action potentials with Arch. () Fluorescence micrograph of a cultured rat hippocampal neuron expressing Arch (composite of two fields of view). () Low-magnification image of the neuron in (left). Whole-field fluorescence (red) during a single-trial recording at 500 frames s−1 (right). The fluorescence was scaled to overlay the electrical recording (blue). () Pixel-by-pixel map of cross-correlation between whole-field and single-pixel intensities (red) overlaid on the average fluorescence (gray) (left). Note that the process extending to the top left of the cell body does not appear in the red channel; it is electrically decoupled from the cell. Pixel-weighted fluorescence (red) and electrical recording (blue) (right). () Pixel-by-pixel map of cross-correlation between electrical recording and single-pixel intensities (red) overlaid on the average fluorescence (gray) (left). Pixel-weighted fluorescence (red) and electrical recording (blue) (right). () Subcellular localization of an action potential in ! regions indicated by colored polygons (left) and time course of an action potential averaged over 98 events (right) in the regions indicated with the corresponding colors. The top black trace is the electrical recording. Optical recordings appear broadened owing to the finite (2 ms) exposure time of the camera. The white arrow indicates a small protrusion that has a substantially delayed action potential relative to the rest of the cell. Vertical scale on fluorescence traces is arbitrary. () Single-trial recordings of action potentials recorded at a frame rate of 2 kHz. The pixel weight matrix was determined from the accompanying electrophysiology recording. Averaged spike response for 269 events in a single cell is shown on top right. () Application of a voltage to a single neuron caused an increase in fluorescence that distinguished a neuron from its neighbors (top). Time-average Arch fluorescence of multiple transfected neurons (left). Same field of view after membrane p! otential was modulated by whole-cell voltage clamp (right). Re! sponsive pixels were identified via cross-correlation of pixel intensity and applied voltage (V, red). Scale bars, 10 μm (,,) and 50 μm (–). * Figure 3: Arch(D95N) shows voltage-dependent fluorescence but no photocurrent. () Photocurrents in Arch and Arch(D95N), expressed in HEK 293 cells clamped at V = 0. Cells were illuminated with pulses of light (λ = 640 nm; I = 1,800 W cm−2). () Fluorescence of Arch(D95N) as a function of membrane potential. Inset, map of voltage sensitivity. Scale bar, 5 μm. () Dynamic response of Arch(D95N) to steps in membrane potential between −70 mV and +30 mV. Data were averaged over 20 cycles. Inset, step response comprised a component faster than 500 μs (20% of the response) and a component with a time constant of 41 ms. () Response of Arch(D95N) to 10-mV steps in membrane potential. * Figure 4: Optical recording of action potentials with Arch(D95N). (,) Electrically recorded membrane potential of a neuron expressing Arch () or Arch(D95N) (), subjected to pulses of current injection and laser (red bars) illumination. () Fluorescence micrograph of a neuron expressing Arch(D95N), showing Arch(D95N) fluorescence (gray) and regions of voltage-dependent fluorescence (red). Scale bar, 10 μm. () Single-trial recording of whole-cell membrane potential (blue) and weighted Arch(D95N) fluorescence (red) during a train of action potentials. * Figure 5: Optical indicators of membrane potential classified by speed and sensitivity. Green marks represent indicators based on fusions of GFP homologs to membrane proteins. Pink marks represent indicators based on microbial rhodopsins. Blue diamonds represent organic dyes and hybrid dye-protein indicators. Extended bars denote indicators where two time constants have been reported. PROPS is homologous to Arch(D95N) but only functions in bacteria. The speeds of most organic dyes are not known precisely; but they respond in less than 500 μs. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Joel M Kralj, * Adam D Douglass & * Daniel R Hochbaum Affiliations * Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA. * Joel M Kralj & * Adam E Cohen * Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA. * Adam D Douglass * Applied Physics Program, School of Engineering and Applied Science, Harvard University, Cambridge, Massachusetts, USA. * Daniel R Hochbaum * Department of Physics, Harvard University, Cambridge, Massachusetts, USA. * Dougal Maclaurin & * Adam E Cohen Contributions A.E.C. conceived the project. J.M.K., A.D.D. and D.R.H. carried out experiments. D.M. designed and built the imaging system used in Figure 2. All authors designed experiments, analyzed data and wrote the paper. Competing financial interests A.E.C., J.M.K. and A.D.D. filed a patent application (PCT/US11/48793) on microbial rhodopsin–based voltage sensors. Corresponding author Correspondence to: * Adam E Cohen Author Details * Joel M Kralj Search for this author in: * NPG journals * PubMed * Google Scholar * Adam D Douglass Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel R Hochbaum Search for this author in: * NPG journals * PubMed * Google Scholar * Dougal Maclaurin Search for this author in: * NPG journals * PubMed * Google Scholar * Adam E Cohen Contact Adam E Cohen Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (500k) Supplementary Figures 1–7 and Supplementary Tables 1–2 Movies * Supplementary Video 1 (700k) Fluorescence from an HEK cell expressing Arch. The cell was subjected to steps in voltage from −100 mV to 100 mV at 1 Hz. The apparent voltage-sensitive pixels inside the cell are due to out-of-focus fluorescence from the upper and lower surfaces of the plasma membrane. Images are unmodified raw data. Movie is shown in real time. * Supplementary Video 2 (6M) Fluorescence from a rat hippocampal neuron expressing Arch, showing single-trial detection of action potentials. The field on the left shows the time-averaged fluorescence; the field on the right shows deviations from the time average. Movie has been slowed 25-fold. * Supplementary Video 3 (700k) Fluorescence from a rat hippocampal neuron expressing Arch, averaged over n = 98 action potentials. Note the delayed rise and fall of the action potential in the small protrusion coming from the process at 7 o'clock relative to the cell body. The time-averaged fluorescence from the cell has been subtracted to highlight the change in fluorescence during an action potential. The background, in gray, shows the time-averaged image. * Supplementary Video 4 (2M) Fluorescence from a HEK cell expressing Arch(D95N) subjected to steps in voltage from −100 mV to 100 mV at 1 Hz. The apparent voltage-sensitive pixels inside the cell are due to out-of-focus fluorescence from the upper and lower surfaces of the plasma membrane. Images are unmodified raw data. Movie is shown in real time. * Supplementary Video 5 (1.1M) Fluorescence from a HEK cell expressing Arch(D95N) subjected to a voltage-clamp triangle wave from −150 mV to 150 mV. The apparent voltage-sensitive pixels inside the cell are due to out-of-focus fluorescence from the upper and lower surfaces of the plasma membrane. The movie is sped up threefold. Images are unmodified raw data. Zip files * Supplementary Software (400k) extractV.m and ApplyWeights.m Matlab routines for analyzing voltage indicator image data. Additional data
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