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- Training the kit generation
- Nat Methods 8(12):983 (2011)
Nature Methods | Editorial Training the kit generation Journal name:Nature MethodsVolume: 8,Page:983Year published:(2011)DOI:doi:10.1038/nmeth.1804Published online29 November 2011 Young scientists must learn not just how to use a kit, but how it works. View full text Additional data - The author file: Ernst Bamberg
- Nat Methods 8(12):985 (2011)
Nature Methods | This Month The author file: Ernst Bamberg * Monya BakerJournal name:Nature MethodsVolume: 8,Page:985Year published:(2011)DOI:doi:10.1038/nmeth.1789Published online29 November 2011 Fusing light-activated proteins for precise optogenetic control View full text Additional data - Points of view: The design process
- Nat Methods 8(12):987 (2011)
Article preview View full access options Nature Methods | This Month Points of view: The design process * Bang Wong1Journal name:Nature MethodsVolume: 8,Page:987Year published:(2011)DOI:doi:10.1038/nmeth.1783Published online29 November 2011 The primary tenets of design are utility and function. Just as objects are intuitive to use when they are well-designed, thoughtfully conceived scientific figures, slides and posters can be easy to interpret and understand. Whereas industrial design focuses on things people use, graphic design is concerned with designs people read. The design process helps us develop a visual literacy to construct presentations that are appealing and convincing. Design is a requirement, not a cosmetic addition. Objects that are well-designed provide visible clues to their underlying function. For example, a vegetable peeler has a handle and a blade that telegraphs how it should it be used (Fig. 1a). The example shown is a classic that has simple form and is highly proficient at peeling. In contrast, despite some obvious features, my office telephone is not so easy to access (Fig. 1b). Making a simple conference call is a bewildering and cryptic process. There is a button marked "conference" but otherwise no hint as to how to enact the function. Poorly mapped visual cues can thwart the normal process of interpretation and understanding. Figure 1: Visual clues should communicate a product's functions and features. () A vegetable peeler with easily interpretable function. () An office phone with poor visual cues to indicate its operation. * Full size image (163 KB) * Figures index * Next figure Good design balances self expression with the need to satisfy an audience in a logical manner and finds the best possible solutions to problems with known objectives and constraints. The effectiveness of a design is determined by the perceiver's ability to decode the visual scheme. Figures at a glance * Figure 1: Visual clues should communicate a product's functions and features. () A vegetable peeler with easily interpretable function. () An office phone with poor visual cues to indicate its operation. * Figure 2: A scientific poster depicting the 'connectivity map' inspired by a flow chart by Charles Minard (inset; source, Wikipedia). Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Methods for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Bang Wong is the creative director of the Broad Institute of the Massachusetts Institute of Technology & Harvard and an adjunct assistant professor in the Department of Art as Applied to Medicine at The Johns Hopkins University School of Medicine. Author Details * Bang Wong Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - The Human Epigenome Browser at Washington University
- Nat Methods 8(12):989-990 (2011)
Nature Methods | Correspondence The Human Epigenome Browser at Washington University * Xin Zhou1 * Brett Maricque1 * Mingchao Xie1 * Daofeng Li1 * Vasavi Sundaram1 * Eric A Martin1 * Brian C Koebbe1 * Cydney Nielsen2 * Martin Hirst2 * Peggy Farnham3 * Robert M Kuhn4 * Jingchun Zhu4 * Ivan Smirnov5 * W James Kent4 * David Haussler4, 6 * Pamela A F Madden7 * Joseph F Costello5 * Ting Wang1 * Affiliations * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:989–990Year published:(2011)DOI:doi:10.1038/nmeth.1772Published online29 November 2011 To the Editor: Advances in next-generation sequencing have reshaped the landscape of genomic and epigenomic research. Large consortia such as the Encyclopedia of DNA Elements, the Roadmap Epigenomics Mapping Consortium and The Cancer Genome Atlas have generated tens of thousands of sequencing-based genome-wide datasets, creating a reference and resource for the scientific community. Small groups of researchers now can rapidly obtain huge volumes of genomic data, which need to be placed in the context of the consortium data for comparison. These data are often accompanied by rich metadata describing the sample and experiment, which is critical for their interpretation. Visualizing, navigating and interpreting such data in a meaningful way is a daunting challenge1. View full text Subject terms: * Bioinformatics * Epigenetics * Genomics * 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 * Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, USA. * Xin Zhou, * Brett Maricque, * Mingchao Xie, * Daofeng Li, * Vasavi Sundaram, * Eric A Martin, * Brian C Koebbe & * Ting Wang * British Columbia Cancer Agency, Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada. * Cydney Nielsen & * Martin Hirst * Department of Biochemistry and Molecular Biology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA. * Peggy Farnham * Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California, USA. * Robert M Kuhn, * Jingchun Zhu, * W James Kent & * David Haussler * Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, Santa Cruz, California, USA. * Ivan Smirnov & * Joseph F Costello * Howard Hughes Medical Institute, Santa Cruz, California, USA. * David Haussler * Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. * Pamela A F Madden Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Ting Wang or * Joseph F Costello Author Details * Xin Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Brett Maricque Search for this author in: * NPG journals * PubMed * Google Scholar * Mingchao Xie Search for this author in: * NPG journals * PubMed * Google Scholar * Daofeng Li Search for this author in: * NPG journals * PubMed * Google Scholar * Vasavi Sundaram Search for this author in: * NPG journals * PubMed * Google Scholar * Eric A Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Brian C Koebbe Search for this author in: * NPG journals * PubMed * Google Scholar * Cydney Nielsen Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Hirst Search for this author in: * NPG journals * PubMed * Google Scholar * Peggy Farnham Search for this author in: * NPG journals * PubMed * Google Scholar * Robert M Kuhn Search for this author in: * NPG journals * PubMed * Google Scholar * Jingchun Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Ivan Smirnov Search for this author in: * NPG journals * PubMed * Google Scholar * W James Kent Search for this author in: * NPG journals * PubMed * Google Scholar * David Haussler Search for this author in: * NPG journals * PubMed * Google Scholar * Pamela A F Madden Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph F Costello Contact Joseph F Costello Search for this author in: * NPG journals * PubMed * Google Scholar * Ting Wang Contact Ting Wang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3.2M) Supplementary Figures 1–7, Supplementary Notes, Supplementary Methods, Supplementary Protocol. Additional data - Benchmarking a luciferase complementation assay for detecting protein complexes
- Nat Methods 8(12):990-992 (2011)
Nature Methods | Correspondence Benchmarking a luciferase complementation assay for detecting protein complexes * Patricia Cassonnet1, 5 * Caroline Rolloy1, 5 * Gregory Neveu1 * Pierre-Olivier Vidalain2 * Thibault Chantier3 * Johann Pellet3 * Louis Jones4 * Mandy Muller1 * Caroline Demeret1 * Guillaume Gaud1 * Françoise Vuillier1 * Vincent Lotteau3 * Fréderic Tangy2 * Michel Favre1 * Yves Jacob1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:990–992Year published:(2011)DOI:doi:10.1038/nmeth.1773Published online29 November 2011 To the Editor: Mapping protein-protein interactions (PPIs) has proven instrumental in functional proteomics. Large PPI databases have been established by literature mining or using high-throughput screening methods; this information requires cross-validation to increase coverage and accuracy. Previously published papers in Nature Methods have argued for the use of defined reference sets for the benchmarking of screening methods for PPIs1, 2. View full text Subject terms: * Systems Biology * Proteomics * Molecular Biology Figures at a glance * Figure 1: Benchmarking a luciferase complementation assay against reference sets. () Schematic of the G. princeps luciferase–based protein complementation assay. A and B are bait and prey proteins, and GL1 and GL2 are inactive fragments of G. princeps luciferase. () The NLR for a given interacting protein pair A-B corresponds to luminescence activity in cells expressing GL1-A and GL2-B divided by the sum of the luminescence measured in control wells as indicated. () Frequency distribution for log(NLR) values for the PRS and the RRS with corresponding superimposed fitted Gaussian curves. Theoretical r.m.s. deviation with 97.5% confidence interval obtained from Gaussian fits on RRS corresponds to a value of 3.2; the positive threshold of 3.5 used in the network and table below is indicated in red. () Cytoscape representation of the PRS interactome network. Proteins of NF-κB, IFN-α/β and TGF-β pathways are shown in orange, yellow and blue, respectively. Interactions with a NLR > 3.5 are shown as blue edges, and unvalidated interactions are shown as gra! y dashed edges. Gray circles indicate ternary complexes subsequently studied by protein complementation assay capture. * Figure 2: Detection of ternary protein complexes. () Schematic of the ternary complex detection assay. A third protein, C, is anchored to the plate by biotin-streptavidin affinity capture to recruit the protein dimer generating the luminescence signal, normalized as described in Figure 1. In parallel, a control NLR was determined for 3×Flag-tagged C protein. () Test and control NLR for the indicated protein complexes. Error bars, s.e.m. based on four experimental replicates; *P = 0.00004 and **P = 0.00002 (Student's t-test). Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Primary authors * These authors contributed equally to this work. * Patricia Cassonnet & * Caroline Rolloy Affiliations * Unité de Génétique, Papillomavirus et Cancer Humain, Institut Pasteur, Paris, France. * Patricia Cassonnet, * Caroline Rolloy, * Gregory Neveu, * Mandy Muller, * Caroline Demeret, * Guillaume Gaud, * Françoise Vuillier, * Michel Favre & * Yves Jacob * Unité de Génomique Virale et Vaccination, Institut Pasteur, Centre National de la Recherche Scientifique, Unité de Recherche Associée 3015, Paris, France. * Pierre-Olivier Vidalain & * Fréderic Tangy * Infection Mapping Project Team, Université Lyon 1, Institut Fédératif de Recherche 128, Institut National de la Santé et de la Recherche Médicale, Unité 851, Lyon, France. * Thibault Chantier, * Johann Pellet & * Vincent Lotteau * Groupe Projets et Développements en Bioinformatique, Institut Pasteur, Paris, France. * Louis Jones Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Yves Jacob Author Details * Patricia Cassonnet Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline Rolloy Search for this author in: * NPG journals * PubMed * Google Scholar * Gregory Neveu Search for this author in: * NPG journals * PubMed * Google Scholar * Pierre-Olivier Vidalain Search for this author in: * NPG journals * PubMed * Google Scholar * Thibault Chantier Search for this author in: * NPG journals * PubMed * Google Scholar * Johann Pellet Search for this author in: * NPG journals * PubMed * Google Scholar * Louis Jones Search for this author in: * NPG journals * PubMed * Google Scholar * Mandy Muller Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline Demeret Search for this author in: * NPG journals * PubMed * Google Scholar * Guillaume Gaud Search for this author in: * NPG journals * PubMed * Google Scholar * Françoise Vuillier Search for this author in: * NPG journals * PubMed * Google Scholar * Vincent Lotteau Search for this author in: * NPG journals * PubMed * Google Scholar * Fréderic Tangy Search for this author in: * NPG journals * PubMed * Google Scholar * Michel Favre Search for this author in: * NPG journals * PubMed * Google Scholar * Yves Jacob Contact Yves Jacob Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3.6M) Supplementary Figures 1–4, Supplementary Tables 1–7, Supplementary Methods Additional data - Faster frames, clearer pictures
- Nat Methods 8(12):1005-1009 (2011)
Nature Methods | Technology Feature Faster frames, clearer pictures * Monya Baker1Journal name:Nature MethodsVolume: 8,Pages:1005–1009Year published:(2011)DOI:doi:10.1038/nmeth.1777Published online29 November 2011 Better-performing imaging sensors have arrived, but putting them to use is not easy. 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 - The Lego-logic of optogenetics
- Nat Methods 8(12):1011-1013 (2011)
Article preview View full access options Nature Methods | News and Views The Lego-logic of optogenetics * Thomas G Oertner1Journal name:Nature MethodsVolume: 8,Pages:1011–1013Year published:(2011)DOI:doi:10.1038/nmeth.1771Published online29 November 2011 Daisy-chaining light-sensitive ion channels, pumps and fluorescent proteins extends the possibilities for control of neuronal activity. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Methods for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Thomas G. Oertner is at the Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Thomas G Oertner Author Details * Thomas G Oertner Contact Thomas G Oertner Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Recognizing heart cells in a crowd
- Nat Methods 8(12):1013-1016 (2011)
Article preview View full access options Nature Methods | News and Views Recognizing heart cells in a crowd * Timothy J Kamp1Journal name:Nature MethodsVolume: 8,Pages:1013–1016Year published:(2011)DOI:doi:10.1038/nmeth.1780Published online29 November 2011 A cardiac-specific reporter genetically engineered into human embryonic stem cells allows the optimization of differentiation protocols and the identification of cell-surface markers—a welcome new tool to help isolate and define cardiac cell lineages. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Methods for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Timothy J. Kamp is at the University of Wisconsin School of Medicine and Public Health, Department of Medicine and Division of Cardiovascular Medicine, Madison, Wisconsin, USA. Competing financial interests T.J.K. is a co-founder and consultant for Cellular Dynamics International, a company that uses human pluripotent stem cells and their derivatives for research, drug discovery and drug testing. Corresponding author Correspondence to: * Timothy J Kamp Author Details * Timothy J Kamp Contact Timothy J Kamp Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Exciting times: bountiful data to facilitate studies of cis-regulatory control
- Nat Methods 8(12):1016-1017 (2011)
Article preview View full access options Nature Methods | News and Views Exciting times: bountiful data to facilitate studies of cis-regulatory control * Anil Ozdemir1 * Angelike Stathopoulos1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1016–1017Year published:(2011)DOI:doi:10.1038/nmeth.1795Published online29 November 2011 Chromatin immunoprecipitation and yeast one-hybrid systems are complementary approaches to identify protein-DNA interactions. Improvements to these methods now make them more versatile and high-throughput, and should lead to the generation of rich datasets for the study of gene 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 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Anil Ozdemir and Angelike Stathopoulos are in the Division of Biology, California Institute of Technology, Pasadena, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Angelike Stathopoulos Author Details * Anil Ozdemir Search for this author in: * NPG journals * PubMed * Google Scholar * Angelike Stathopoulos Contact Angelike Stathopoulos Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Directed molecular evolution to design advanced red fluorescent proteins
- Nat Methods 8(12):1019-1026 (2011)
Nature Methods | Perspective Directed molecular evolution to design advanced red fluorescent proteins * Fedor V Subach1, 2 * Kiryl D Piatkevich1, 2 * Vladislav V Verkhusha1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1019–1026Year published:(2011)DOI:doi:10.1038/nmeth.1776Published online29 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 Fluorescent proteins have become indispensable imaging tools for biomedical research. Continuing progress in fluorescence imaging, however, requires probes with additional colors and properties optimized for emerging techniques. Here we summarize strategies for development of red-shifted fluorescent proteins. We discuss possibilities for knowledge-based rational design based on the photochemistry of fluorescent proteins and the position of the chromophore in protein structure. We consider advances in library design by mutagenesis, protein expression systems and instrumentation for high-throughput screening that should yield improved fluorescent proteins for advanced imaging applications. View full text Figures at a glance * Figure 1: Steps in the directed molecular evolution of fluorescent probes. Vertical arrows indicate the typical order of steps. Horizontal arrows represent possible transitions between the steps of molecular evolution, which can be repeated several times in different order. * Figure 2: Major chemical transformations of the chromophores in red fluorescent proteins. (–) Transformations in fluorescent protein subfamilies derived from red fluorescent protein (), mCherry () and TagRFP (). The colored shading of the chemical structures () and chromophore numbers (,) correspond to the spectral range of the chromophore fluorescence emission. Gray shading denotes the nonfluorescent state; [H] denotes reduction; and [O] denotes oxidation. The chromo states (structures 5, 10 and 13) are not necessarily caused by a cis-trans chromophore isomerization but may result from modifications of the chromophore environment of the same isoform that decrease quantum yield. hv, photon. * Figure 3: Methods that could improve molecular evolution of fluorescent proteins. (–) Schematics depict eukaryotic cell–based mutagenesis methods (,) and advanced protein expression systems (–). Cylinders denote fluorescent protein molecules. Error-prone replication of virus () causes point mutations in the viral genome containing a target fluorescent protein gene; after several rounds of replication, the cell expresses mutated fluorescent protein genes. Somatic hypermutations and gene conversion in eukaryotic cells () allow for creation of large random mutant gene libraries during cell proliferation (note that only one type of fluorescent protein mutant is produced per cell). Expression of fluorescent protein libraries () in thermophilic bacteria for selection of more stable fluorescent proteins. Surface display () of fluorescent protein libraries could facilitate screening for fluorescent protein stability under different environmental conditions or for fluorescent protein–based biosensors. In vitro compartmentalization () of bacteria in water-o! il-water or water-agarose-water droplets should enable screening for fluorescent protein–based biosensors. * Figure 4: Possible FACS-based screening approaches for red-shifted fluorescent proteins. The respective red fluorescent proteins expected to result from each method are listed on the right. The schematic depicts cells or other hosts expressing fluorescent proteins being mixed with ligand, substrate or metabolite with different delays before fluorescence screening. A standard one-photon laser excites flowing cells, and the resultant fluorescent signal is dispersed with a diffraction grating (triangle) and projected onto an array detector (rectangle) for recording of a complete emission spectrum. A two-photon laser excites the cells with two low-energy photons (hv1) and the resulting fluorescence emission (hv2) is detected. Linearly polarized laser excitation and the emitted fluorescence signals have different degrees of polarization depending on the size of fluorescent molecules and FRET efficiency between them. Cylinders denote fluorescent proteins in monomeric and dimeric states. Modulated excitation (yellow sinusoid) results in a phase shift, Δφ, between the! fluorescence emission (red) and side-scattered excitation light (SSC; yellow), which is used to compute the average fluorescence lifetime of fluorescent proteins in a cell. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Fedor V Subach & * Kiryl D Piatkevich Affiliations * Department of Anatomy and Structural Biology, and Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, USA. * Fedor V Subach, * Kiryl D Piatkevich & * Vladislav V Verkhusha Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Vladislav V Verkhusha Author Details * Fedor V Subach Search for this author in: * NPG journals * PubMed * Google Scholar * Kiryl D Piatkevich Search for this author in: * NPG journals * PubMed * Google Scholar * Vladislav V Verkhusha Contact Vladislav V Verkhusha Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (340K) Supplementary Figure 1, Supplementary Tables 1–4 and Supplementary Note Additional data - Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging
- Nat Methods 8(12):1027-1036 (2011)
Nature Methods | Analysis Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging * Graham T Dempsey1, 6 * Joshua C Vaughan2, 3, 6 * Kok Hao Chen3, 6 * Mark Bates4 * Xiaowei Zhuang2, 3, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1027–1036Year published:(2011)DOI:doi:10.1038/nmeth.1768Received01 June 2011Accepted05 October 2011Published online06 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 One approach to super-resolution fluorescence imaging uses sequential activation and localization of individual fluorophores to achieve high spatial resolution. Essential to this technique is the choice of fluorescent probes; the properties of the probes, including photons per switching event, on-off duty cycle, photostability and number of switching cycles, largely dictate the quality of super-resolution images. Although many probes have been reported, a systematic characterization of the properties of these probes and their impact on super-resolution image quality has been described in only a few cases. Here we quantitatively characterized the switching properties of 26 organic dyes and directly related these properties to the quality of super-resolution images. This analysis provides guidelines for characterization of super-resolution probes and a resource for selecting probes based on performance. Our evaluation identified several photoswitchable dyes with good to excell! ent performance in four independent spectral ranges, with which we demonstrated low–cross-talk, four-color super-resolution imaging. View full text Subject terms: * Imaging * Microscopy * Single Molecule Figures at a glance * Figure 1: Principle of single-molecule localization–based super-resolution imaging and modes of switching used for this imaging method. () A structure (here a ring-like object) smaller than the diffraction-limited resolution is densely labeled with switchable fluorophores. When the fluorophores are imaged simultaneously, the spatial features of the structure are obscured by the overlapping fluorescence images of individual molecules. However, the positions of individual molecules may be determined with high precision when the molecules are activated and imaged sequentially (fluorescent image indicated as a red circle whose center position is marked as a yellow '+'). By repeatedly activating and localizing different molecules labeling the structure, the sub-diffraction-limited spatial features can be resolved. () This principle can be performed by using photoswitching or non-photoswitching modes. The first mode includes reversibly switchable or irreversibly activatable fluorophores. The non-photoswitching mode can be achieved, for example, through reversible binding of fluorescently labeled ligands or chemica! l quenching of fluorescence. * Figure 2: Quantitative probe characterization for STORM imaging. (–) The effect of number of detected photons per on-switching event and the on-off duty cycle (fraction of time in the on state) on STORM image quality for an example structure (a ring-like object). A fluorophore with high photon number and low duty cycle produces a hollow, ring-like image with high localization precision and sufficient density (). A fluorophore with low photon number and low duty cycle maintains a large number of localizations but suffers reduced localization accuracy, obscuring the ring-like structure (). A fluorophore with high on-off duty cycle requires reduction in the density of fluorescent probes to allow single-molecule localization, which in turn reduces the number of localizations and adversely affects the overall resolution (). (–) Single-molecule fluorescence time traces measured in the presence of βME and an oxygen-scavenging system for () Alexa Fluor 647, () Atto 655 and () Cy5.5. These dyes represent the scenarios in –. (–) From trace! s such as those shown in –, the number of detected photons was determined for each switching event, and a histogram was constructed from many events from hundreds of molecules (,,). The indicated mean value was derived from the single exponential fit of the distribution (red curves). The on-off duty cycle value was calculated for each dye and plotted versus time (red curve; ,,) to show how each value begins high when most molecules are in the fluorescent state and reaches a quasi-equilibrium later. Reported values are the average duty cycle measured between 400–600 s (gray box). The fraction of molecules that survived photobleaching was plotted together with the duty cycle (blue squares). (–) Images of CCPs in three dimensions using Alexa Fluor 647 (–), Atto 655 (–) and Cy5.5 (–). Shown are 2D projection images () and, for CCPs marked by yellow dashed boxes, x–y (,,) and x–z (,,) cross-sections. Composite x–y cross-sections for ten CCPs aligned to their re! spective centers of mass are shown along with the radial densi! ty distributions of localizations derived from the composite x–y cross-sections (,,). Scale bars, 500 nm (,,) and 100 nm (–,–,–). * Figure 3: Alexa Fluor 647 and Dyomics 654 resolve the hollow structure of immunostained microtubules. (,) STORM images of microtubules immunostained with Alexa Fluor 647 () and Dyomics 654 () and partially overlaid conventional fluorescence images (top left). (,) Transverse profiles of localizations corresponding to regions boxed in yellow in and , respectively. Fitting of the profile by two Gaussian functions (red lines) gave the expected distances between the two peaks. Scale bars, 250 nm. * Figure 4: Four-color STORM imaging of in vitro assembled microtubule filaments and cross-talk analysis. () Four-color STORM image of in vitro assembled microtubules labeled with each of the four dyes, Atto 488 (green), Cy3B (magenta), Alexa Fluor 647 (cyan) and DyLight 750 (white). () Spectral separation of the four dyes, with the black vertical lines representing the excitation wavelength used and the gray regions highlighting the emission filter range for each of the dyes. (–) STORM images in each of the spectral regions for the boxed region in . () Cross-talk between channels measured from control microtubule samples (undetectable in channels marked by an asterisk). Scale bars, 2 μm () and 500 nm (–). * Figure 5: Four-color STORM imaging of cellular structures. (–) Individual channels of a four-color image of Alexa Fluor 647–labeled ER (cyan), Cy3B-labeled mitochondria (magenta), Atto 488–labeled microtubules (green) and DyLight 750–labeled acetylated tubulin (white) in a single fixed cell. () Magnification of the Cy3B and Alexa Fluor 647 channels of the boxed region in – showing extensive contact between mitochondria and the ER. () Magnification of the Atto 488 and DyLight 750 channels of the boxed regions in – showing overlap of acetylated tubulin with a subset of microtubule filaments. Scale bars, 1 μm (–) and 500 nm (,). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Graham T Dempsey, * Joshua C Vaughan & * Kok Hao Chen Affiliations * Graduate program in Biophysics, Harvard University, Cambridge, Massachusetts, USA. * Graham T Dempsey * Howard Hughes Medical Institute, Harvard University, Cambridge, Massachusetts, USA. * Joshua C Vaughan & * Xiaowei Zhuang * Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA. * Joshua C Vaughan, * Kok Hao Chen & * Xiaowei Zhuang * School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA. * Mark Bates * Department of Physics, Harvard University, Cambridge, Massachusetts, USA. * Xiaowei Zhuang Contributions G.T.D., J.C.V., K.H.C. and X.Z. designed the experiments. G.T.D., J.C.V., K.H.C. and M.B. performed the experiments. G.T.D., J.C.V. and K.H.C. performed the data analysis and interpretation. G.T.D., J.C.V. and X.Z. wrote the manuscript. X.Z. supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Xiaowei Zhuang Author Details * Graham T Dempsey Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua C Vaughan Search for this author in: * NPG journals * PubMed * Google Scholar * Kok Hao Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Bates Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaowei Zhuang Contact Xiaowei Zhuang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (25M) Supplementary Figures 1–35 Additional data - NKX2-5eGFP/w hESCs for isolation of human cardiac progenitors and cardiomyocytes
- Nat Methods 8(12):1037-1040 (2011)
Nature Methods | Brief Communication NKX2-5eGFP/w hESCs for isolation of human cardiac progenitors and cardiomyocytes * David A Elliott1 * Stefan R Braam2 * Katerina Koutsis1 * Elizabeth S Ng1 * Robert Jenny1 * Ebba L Lagerqvist3 * Christine Biben4 * Tanya Hatzistavrou1 * Claire E Hirst1 * Qing C Yu1 * Rhys J P Skelton1 * Dorien Ward-van Oostwaard2 * Sue Mei Lim1 * Ouda Khammy5 * Xueling Li1 * Susan M Hawes1 * Richard P Davis2, 6 * Adam L Goulburn1 * Robert Passier2 * Owen W J Prall4 * John M Haynes3 * Colin W Pouton3 * David M Kaye5 * Christine L Mummery2, 6 * Andrew G Elefanty1, 7 * Edouard G Stanley1, 7 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:1037–1040Year published:(2011)DOI:doi:10.1038/nmeth.1740Received14 July 2011Accepted14 September 2011Published online23 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 NKX2-5 is expressed in the heart throughout life. We targeted eGFP sequences to the NKX2-5 locus of human embryonic stem cells (hESCs); NKX2-5eGFP/w hESCs facilitate quantification of cardiac differentiation, purification of hESC-derived committed cardiac progenitor cells (hESC-CPCs) and cardiomyocytes (hESC-CMs) and the standardization of differentiation protocols. We used NKX2-5 eGFP+ cells to identify VCAM1 and SIRPA as cell-surface markers expressed in cardiac lineages. View full text Subject terms: * Stem Cells * Cell Biology * Gene Expression Figures at a glance * Figure 1: Characterization of cardiomyocytes generated from NKX2-5eGFP/w hESCs. () Schematic of wild-type and targeted NKX2-5 alleles. H, HindIII site. (,) Green fluorescence (eGFP; ) and overlayed green fluorescence and brightfield (BFeGFP; ) of day-14 embryoid bodies derived from NKX2-5eGFP/w hESCs. () Ratio (eGFP+/eGFP−) of relative gene expression for the indicated genes in sorted eGFP+ and eGFP− cells. (–) Green fluorescence (), NKX2-5 immunodetection (), α-actinin-1 immunodetection () and merged () images of purified eGFP+ cells. (–) Merged images of eGFP+ cells showing expression of the cardiac markers MYL7 (regulatory myosin light chain 7), ISL1, MYL2 (ventricular myosin light chain 2), GATA4 and α-actinin-1. Scale bars, 100 μm (,), 50 μm (–) and 10 μm (–). () Representative action potential from END2-derived eGFP+ cardiomyocytes. * Figure 2: NKX2-5eGFP/w hESCs facilitate real-time monitoring of cardiac differentiation. () Schematic representation of cardiac differentiation protocols. BVSAW, medium containing BMP4, VEGF, SCF, ACT-A and WNT3a; MG, Matrigel. () Heat map of flow cytometric quantification of eGFP+ cells from day-10 NKX2-5eGFP/w embryoid bodies generated with indicated BMP4 and activin A concentrations. FACS plots (right) show percentages of eGFP+ cells observed in embryoid bodies under selected conditions. A.u., arbitrary units. () Time course of eGFP expression during embryoid body and monolayer differentiation. Numbers indicate percentage of eGFP+ cells. () Quantification of percentage eGFP+ cells during embryoid body differentiation (n = 4; error bars, s.e.m.). (,) Green fluorescence (eGFP; ) and overlayed green fluorescence and brightfield (BFeGFP; ) images showing syncytium of eGFP+ contracting cells in monolayer cultures. Scale bars, 100 μm. * Figure 3: Expression profiling of NKX2-5 eGFP+ cells identifies cardiac cell-surface markers. () Flow cytometric analysis of the cell populations used for array analysis. A.u., arbitary units. () Clustering of cardiac samples (red lines) based on unsupervised hierarchical clustering of microarray gene expression profiling. DN, double negative; GFP−, eGFP-negative; E2, END2 co-culture; P+, PDGFRα+; P+G+, PDGFRα+eGFP+; FH, fetal heart. (–) Co-expression of VCAM1 and SIRPA in day-14 eGFP+ cells by flow cytometry () and immunofluorescence (–) in embryoid bodies. (–) Vcam1 myocardial expression at 9.5 d.p.c. () and 14.5 d.p.c. (,). LA, left atrium; RA, right atrium; IVS, interventricular septum; LV, left ventricle; RV, right ventricle. () Expression of lineage markers in eGFP−, eGFP+SIRPA+ and eGFP+SIRPA+VCAM1+ cells (n = 3; error bars, s.e.m.). (,) Contractile syncytia formed from SIRPA+VCAM1+ cells. Scale bars, 25 μm (–) and 100 μm (–,,). () Flow cytometry of cultured SIRPA+ and SIRPA+VCAM1+ populations. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions ArrayExpress * E-MEXP-3371 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Andrew G Elefanty & * Edouard G Stanley Affiliations * Monash Immunology and Stem Cell Laboratories, Monash University, Clayton, Victoria, Australia. * David A Elliott, * Katerina Koutsis, * Elizabeth S Ng, * Robert Jenny, * Tanya Hatzistavrou, * Claire E Hirst, * Qing C Yu, * Rhys J P Skelton, * Sue Mei Lim, * Xueling Li, * Susan M Hawes, * Adam L Goulburn, * Andrew G Elefanty & * Edouard G Stanley * Department of Anatomy and Embryology, Leiden University Medical Center, Leiden, The Netherlands. * Stefan R Braam, * Dorien Ward-van Oostwaard, * Richard P Davis, * Robert Passier & * Christine L Mummery * Monash Institute of Pharmaceutical Science, Monash University, Victoria, Australia. * Ebba L Lagerqvist, * John M Haynes & * Colin W Pouton * The Walter and Eliza Hall Institute, Royal Melbourne Hospital, Parkville, Victoria, Australia. * Christine Biben & * Owen W J Prall * Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia. * Ouda Khammy & * David M Kaye * Netherlands Proteomics Institute. Utrecht, The Netherlands. * Richard P Davis & * Christine L Mummery Contributions D.A.E., A.G.E., E.G.S. and C.L.M. designed the study. D.A.E., S.R.B., K.K., E.S.N., R.J., E.L.L., C.B., T.H., R.J.P.S., O.K., D.W.O., X.L., S.M.H., S.M.L., R.P., R.P.D., A.L.G., O.W.J.P., A.G.E., X.L., S.M.H., J.M.H., C.W.P. and D.M.K. performed and analyzed experiments. C.E.H. and Q.C.Y. performed bioinformatics analyses. D.A.E., C.L.M., A.G.E. and E.G.S. wrote the manuscript. Competing financial interests D.A.E., A.G.E. and E.G.S. have applied for a patent (US provisional USSN 61/492,099) in relation to results described in this paper. Corresponding authors Correspondence to: * Edouard G Stanley or * Andrew G Elefanty Author Details * David A Elliott Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan R Braam Search for this author in: * NPG journals * PubMed * Google Scholar * Katerina Koutsis Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth S Ng Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Jenny Search for this author in: * NPG journals * PubMed * Google Scholar * Ebba L Lagerqvist Search for this author in: * NPG journals * PubMed * Google Scholar * Christine Biben Search for this author in: * NPG journals * PubMed * Google Scholar * Tanya Hatzistavrou Search for this author in: * NPG journals * PubMed * Google Scholar * Claire E Hirst Search for this author in: * NPG journals * PubMed * Google Scholar * Qing C Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Rhys J P Skelton Search for this author in: * NPG journals * PubMed * Google Scholar * Dorien Ward-van Oostwaard Search for this author in: * NPG journals * PubMed * Google Scholar * Sue Mei Lim Search for this author in: * NPG journals * PubMed * Google Scholar * Ouda Khammy Search for this author in: * NPG journals * PubMed * Google Scholar * Xueling Li Search for this author in: * NPG journals * PubMed * Google Scholar * Susan M Hawes Search for this author in: * NPG journals * PubMed * Google Scholar * Richard P Davis Search for this author in: * NPG journals * PubMed * Google Scholar * Adam L Goulburn Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Passier Search for this author in: * NPG journals * PubMed * Google Scholar * Owen W J Prall Search for this author in: * NPG journals * PubMed * Google Scholar * John M Haynes Search for this author in: * NPG journals * PubMed * Google Scholar * Colin W Pouton Search for this author in: * NPG journals * PubMed * Google Scholar * David M Kaye Search for this author in: * NPG journals * PubMed * Google Scholar * Christine L Mummery Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew G Elefanty Contact Andrew G Elefanty Search for this author in: * NPG journals * PubMed * Google Scholar * Edouard G Stanley Contact Edouard G Stanley Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–8, Supplementary Tables 1–3 Movies * Supplementary Video 1 (795K) NKX2-5eGFP/w derived embryoid bodies express eGFP in contractile areas. Brightfield and green fluorescence of a day-14 NKX2-5eGFP/w embryoid body. This video demonstrates that contractile areas of the embryoid body express eGFP. * Supplementary Video 2 (3M) NKX2-5eGFP/w hESC cultured with the END2 endodermal cell line express eGFP in contractile areas. Movie shows a Z-dimension stack through the beating area demonstrating that eGFP is expressed with contracting clusters. * Supplementary Video 3 (2M) Calcium flux across a contraction cycle. Green fluorescence (top left). Red fluorescence (top right). Brightfield (bottom left). Pseudo-colored video showing calcium flux (bottom right). * Supplementary Video 4 (963K) Monolayer differentiation with NKX2-5eGFP/w hESCs. Brightfield and green fluorescence shows that beating foci are eGFP+. Additional data - Rapid empirical discovery of optimal peptides for targeted proteomics
- Nat Methods 8(12):1041-1043 (2011)
Nature Methods | Brief Communication Rapid empirical discovery of optimal peptides for targeted proteomics * Andrew B Stergachis1 * Brendan MacLean1 * Kristen Lee1 * John A Stamatoyannopoulos1, 2 * Michael J MacCoss1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:1041–1043Year published:(2011)DOI:doi:10.1038/nmeth.1770Received07 July 2011Accepted11 October 2011Published online06 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 report a method for high-throughput, cost-efficient empirical discovery of optimal proteotypic peptides and fragment ions for targeted proteomics applications using in vitro–synthesized proteins. We demonstrate the approach using human transcription factors, which are typically difficult, low-abundance targets and empirically derived proteotypic peptides for 98% of the target proteins. We show that targeted proteomic assays developed using our approach facilitate robust in vivo quantification of human transcription factors. View full text Subject terms: * Mass Spectrometry * Proteomics * Biochemistry * Bioinformatics Figures at a glance * Figure 1: Development of targeted proteomics assays using enriched in vitro–synthesized full-length proteins. () Transcription factor family membership for the proteins for which targeted assays were built. () Schematic of the synthesis, enrichment, digestion and analysis of proteins to identify proteotypic peptides and their fragmentation patterns. () Target protein enrichment and purity were analyzed for 46 samples by immunodetection with an antibody to schistosomal GST (left) and silver staining (right). () SRM chromatographic traces from the NFIA peptide EDFVLTVTGK. Insert, magnification of the chromatographic peak marked by the arrowhead. () EWSR1 peptide intensities (arbitrary units). * Figure 2: Targeted assays can be efficiently developed using in vitro–synthesized proteins and applied to measure proteins in vivo. () Absolute quantity of each in vitro–synthesized protein sample, as measured using a tryptic peptide contained in the C-terminal schistosomal GST tag. () Number of peptides per protein empirically assessed with salient features to accurately detect and quantify the target proteins (peptides with a quality score of either 1 or 2). () Proteotypic peptides identified using in vitro–synthesized CTCF were monitored in K562 nuclear extracts. The relative contribution of each fragment ion to each peptide peak is displayed as different colors. () For each CTCF proteotypic peptide, the relative signal intensity observed using in vitro–synthesized protein is displayed alongside the relative signal intensity observed using K562 nuclear extract peptides not observed (n.o.) in K562 nuclear extracts are indicated. () Measured relative abundance of four transcription factors between the fibroblast (BJ), hepatic carcinoma (HepG2), erythroleukemia (K562) and neuroblastoma (SKNSH) huma! n cell lines. Error bars, s. d. (n = 6). Author information * Author information * Supplementary information Affiliations * Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA. * Andrew B Stergachis, * Brendan MacLean, * Kristen Lee, * John A Stamatoyannopoulos & * Michael J MacCoss * Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA. * John A Stamatoyannopoulos Contributions A.B.S., J.A.S. and M.J.M. conceived and designed the experiments, and wrote the paper. A.B.S. and K.L. performed the wet laboratory experiments. A.B.S. and B.M. analyzed the data. Competing financial interests The authors received financial support from ThermoFisher Scientific. Corresponding authors Correspondence to: * John A Stamatoyannopoulos or * Michael J MacCoss Author Details * Andrew B Stergachis Search for this author in: * NPG journals * PubMed * Google Scholar * Brendan MacLean Search for this author in: * NPG journals * PubMed * Google Scholar * Kristen Lee Search for this author in: * NPG journals * PubMed * Google Scholar * John A Stamatoyannopoulos Contact John A Stamatoyannopoulos Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J MacCoss Contact Michael J MacCoss 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 1–7, Supplementary Data 1–2, Supplementary Note 1 Additional data - Super-resolution 3D microscopy of live whole cells using structured illumination
- Nat Methods 8(12):1044-1046 (2011)
Nature Methods | Brief Communication Super-resolution 3D microscopy of live whole cells using structured illumination * Lin Shao1 * Peter Kner2 * E Hesper Rego1, 3 * Mats G L Gustafsson1, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1044–1046Year published:(2011)DOI:doi:10.1038/nmeth.1734Received03 August 2011Accepted21 September 2011Published online16 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 Three-dimensional (3D) structured-illumination microscopy (SIM) can double the lateral and axial resolution of a wide-field fluorescence microscope but has been too slow for live imaging. Here we apply 3D SIM to living samples and record whole cells at up to 5 s per volume for >50 time points with 120-nm lateral and 360-nm axial resolution. We demonstrate the technique by imaging microtubules in S2 cells and mitochondria in HeLa cells. View full text Subject terms: * Imaging * Cell Biology * Microscopy Figures at a glance * Figure 1: Live 3D SIM and conventional wide-field microscopy images of a Drosophila S2 cell expressing α-tubulin–EGFP. (,) Two x–y planes at z = 0.16 μm () and z = 2.24 μm () of the 3D volume at time 0 of 150 time points. Each SIM volume was acquired in 5.04 s (that is, 18-ms exposure time × 18 axial planes × 15 patterns + 18 axial planes × 10-ms z-stage settle time). () A single x–z cross-section cut through the dashed line shown in . All scale bars, 2 μm. * Figure 2: Live 3D SIM and conventional wide-field microscopy images of a HeLa cell stained with MitoTracker Green. () Maximum-intensity projection along z dimension through the cell volume at time 0 of a 50-time-point series. Each SIM volume was acquired in 20.33 s (that is, 35-ms exposure time × 38 axial planes × 15 patterns + 38 axial planes × 10-ms z-stage settle time). () One x–z cross-section of the same volume sliced through the dashed line shown in . (–) Single-plane x–y slices corresponding to the boxed regions in . () Eight time frames of the region boxed with dashed line in . Each frame is a maximum-intensity projection along z over a 1.3 μm thickness that contains the featured 'Y'-shaped mitochondrion. Scale bars, 2 μm (,) and 1 μm (–). Author information * Author information * Supplementary information Affiliations * Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. * Lin Shao, * E Hesper Rego & * Mats G L Gustafsson * Faculty of Engineering, University of Georgia, Athens, Georgia, USA. * Peter Kner * Graduate Program in Biophysics, University of California, San Francisco, San Francisco, California, USA. * E Hesper Rego * Deceased. * Mats G L Gustafsson Contributions L.S. and P.K. built the optical hardware and wrote the software for the control system and image acquisition. P.K. built the electronics circuit that interfaces the digital signal processing board with the rest of the system. L.S. prepared the samples, and acquired and processed data. E.H.R. provided guidance on biological applications. M.G.L.G. made the conceptual design. L.S. and E.H.R. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Lin Shao Author Details * Lin Shao Contact Lin Shao Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Kner Search for this author in: * NPG journals * PubMed * Google Scholar * E Hesper Rego Search for this author in: * NPG journals * PubMed * Google Scholar * Mats G L Gustafsson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–8, Supplementary Tables 1–2, Supplementary Note, Supplementary Discussion Movies * Supplementary Video 1 (9M) Maximum-intensity projection from different angles of a live 3D SIM time series of a Drosophila S2 cell stably expressing α-tubulin–EGFP (Fig. 1). Volume thickness, 2.88 μm. Number of time points, 150. Time needed per 3D volume, 5 s. Time inserted between time points, 5.5 s. Ratio between this video's frame rate and the real-time frame rate, ~62. Scale bar, 2 μm. * Supplementary Video 2 (8M) Maximum-intensity projection from different angles of a live 3D SIM time series of a HeLa cell stained with 50 nM MitoTracker Green (Fig. 2). Volume thickness, 6.1 μm. Number of time points, 50. Time needed per 3D volume, 20.3 s. Time inserted between time points, 6 s. Scale bar, 2 μm. * Supplementary Video 3 (8M) Maximum-intensity projection along z axis of a live 3D SIM time series of a HeLa cell stained with 50 nM MitoTracker Green (Fig. 2). Volume thickness, 6.1 μm. Number of time points, 50. Time needed per 3D volume, 20.3 s. Time inserted between time points, 6 s. Scale bar, 2 μm. * Supplementary Video 4 (262K) HeLa cell mitochondria dynamics under live 3D SIM. Each frame was cropped from one axial plane at each time point of the same dataset shown in Figure 2 and Supplementary Video 2, and contains interesting events of mitochondrial fission-fusion and other morphological changes. Scale bar, 1 μm. * Supplementary Video 5 (401K) Dynamics of fine internal mitochondrial structures of a HeLa cell under live 3D SIM. Each frame is a maximum-intensity projection over a thickness of 1.3 μm for a subregion at each time point of the same dataset shown in Figure 2 and Supplementary Video 2 that contains the featured 'Y'-shaped mitochondrion. Besides the overall motion, one can observe the dynamics of the fine internal mitochondrial structures that resemble cristae. Scale bar, 1 μm. * Supplementary Video 6 (10M) Maximum-intensity projection from different angles of another live 3D SIM time series of a HeLa cell stained with 50 nM MitoTracker Green. Volume thickness, 6.4 μm. Number of time points, 75. Time needed per 3D volume, 20.8 s. Time inserted between time points, 2.5 s. Scale bar, 2 μm. * Supplementary Video 7 (901K) An example of motion artifact. This is a mitochondrion from the same dataset shown in Fig. 2 and Supplementary Video 2. Scale bar: 1 μm. * Supplementary Video 8 (3M) Variation in zero-order illumination over time owing to insufficient time-averaging of the speckle pattern in the laser output from the multimode fiber. To generate spatially incoherent and smooth illumination light in 3D SIM, a mechanical fiber vibrator was used for shaking the fiber rapidly (Online Methods). This dataset, taken with only the zero-order diffraction beam to simulate uniform (that is, not structured) illumination, is for determining the non-uniformity in the nominally uniform illumination. Sample, thin film (< 300 nm thick) of fluorescein. Exposure time, 10 ms. The bright spots in the video were presumably where fluorescein molecules aggregated. It was determined (Supplementary Discussion) that the non-uniformity of the illumination using this exposure time was much less than 5%. Scale bar, 1 μm. Additional data - Live-cell 3D super-resolution imaging in thick biological samples
- Nat Methods 8(12):1047-1049 (2011)
Nature Methods | Brief Communication Live-cell 3D super-resolution imaging in thick biological samples * Francesca Cella Zanacchi1 * Zeno Lavagnino1, 2 * Michela Perrone Donnorso1 * Alessio Del Bue1 * Laura Furia3 * Mario Faretta3 * Alberto Diaspro1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1047–1049Year published:(2011)DOI:doi:10.1038/nmeth.1744Received20 January 2011Accepted03 October 2011Published online09 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 demonstrate three-dimensional (3D) super-resolution live-cell imaging through thick specimens (50–150 μm), by coupling far-field individual molecule localization with selective plane illumination microscopy (SPIM). The improved signal-to-noise ratio of selective plane illumination allows nanometric localization of single molecules in thick scattering specimens without activating or exciting molecules outside the focal plane. We report 3D super-resolution imaging of cellular spheroids. View full text Subject terms: * Microscopy * Biophysics * Single Molecule * Imaging Figures at a glance * Figure 1: IML-SPIM 3D super-resolution imaging of nanocapsules. () Conventional () and IML-SPIM () images of polyelectrolyte nanocapsules labeled with photoactivatable caged FITC. IML-SPIM image () was generated by analyzing 10,000 frames (33 frames s−1). The total number of events was 30,000. Activation and excitation laser wavelengths and intensities were 405 nm at 0.04 kW cm−2 and 488 nm at 9 kW cm−2, respectively. Total acquisition time was 5 min. Insets show magnification of boxed regions. () A comparison of 2D () and 3D () super-resolution images of a nanocapsule obtained using an astigmatic point spread function. Activation and readout laser intensities in and were 0.05 kW cm−2 and 8 kW cm−2, respectively; total acquisition time was 2.5 min (33 frames s−1). A.u., arbitrary units. Scale bars, 1 μm () and 500 nm (insets in ). () The localization precision () and the distribution of photons per single molecule () for the image in . Events corresponding to more than 30 photons are considered. () The z-dimension resolution! of IML-SPIM. * Figure 2: IML-SPIM super-resolution imaging of human mammary MCF10A cell spheroids expressing H2B-PAmCherry. () Conventional SPIM image obtained by adding the total signal from all the frames () and IML-SPIM image () of the entire field of view. The final image was reconstructed after the collection and localization of 71,000 events. The whole spheroid diameter is ~ 150 μm. Imaging depth was 100 μm. () Magnification of the boxed regions in and , respectively. () Localization precision for the image in . The activation and excitation laser wavelengths intensities were 405 nm at 0.06 kW cm−2 and 561 nm at 12 kW cm−2, respectively. Images were acquired with a frame rate of 33 frames s−1. Total acquisition time for was 3 min. Scale bars, 10 μm () and 1 μm (). * Figure 3: IML-SPIM imaging of cell spheroids expressing connexin 43–PAmCherry. () Conventional 2D SPIM image of the entire field of view () and a selected cell (). A.u., arbitrary units. Green lines indicate cell boundaries. () A 3D IML-SPIM image with the axial positions color-coded as indicated. () The individual z-dimension sections taken at different depths to demonstrate the capability to perform super-resolution continuous imaging in large samples and used to create the image in . Imaging depth in the series of planes is ~60 μm. z values in indicate axial distance of the images relative to the first image. The activation and excitation laser wavelengths intensities were 405 nm at 0.06 kW cm−2 and 561 nm at 5 kW cm−2, respectively. Images were acquired with a frame rate of 25 frames s−1. Total acquisition time for was ~2.5 min. Scale bars, 10 μm (), 5 μm () and 1 μm (). Author information * Author information * Supplementary information Affiliations * Istituto Italiano di Tecnologia, Genova, Italia. * Francesca Cella Zanacchi, * Zeno Lavagnino, * Michela Perrone Donnorso, * Alessio Del Bue & * Alberto Diaspro * Istituto Fondazione italiana ricerca sul cancro di Oncologia Molecolare, MicroScoBio Dipartimento di Fisica Università di Genova, Genova, Italia. * Zeno Lavagnino & * Alberto Diaspro * Department of Experimental Oncology, European Institute of Oncology, Milan, Italy. * Laura Furia & * Mario Faretta Contributions F.C.Z. and A.D. conceived the IML-SPIM imaging concept, conceived the study, designed experiments and wrote the manuscript. F.C.Z. and Z.L. realized the optical set-up and data acquisition. F.C.Z. realized imaging and data analysis. M.P.D. and F.C.Z. realized polyelectrolyte nanocapsules. M.F. and L.F. prepared biological samples. A.D.B. wrote the software tool for 3D analysis. F.C.Z., Z.L., M.F. and A.D. refined the manuscript. A.D. supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Alberto Diaspro Author Details * Francesca Cella Zanacchi Search for this author in: * NPG journals * PubMed * Google Scholar * Zeno Lavagnino Search for this author in: * NPG journals * PubMed * Google Scholar * Michela Perrone Donnorso Search for this author in: * NPG journals * PubMed * Google Scholar * Alessio Del Bue Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Furia Search for this author in: * NPG journals * PubMed * Google Scholar * Mario Faretta Search for this author in: * NPG journals * PubMed * Google Scholar * Alberto Diaspro Contact Alberto Diaspro Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1.7M) Supplementary Figures 1–9, Supplementary Results 1–4 Movies * Supplementary Video 1 (277K) Axial optical sectioning in a spheroid using IML-SPIM. IML-SPIM provides three-dimensional super-resolution images of nuclei in human mammary MCF10A cell spheroids expressing H2B-PAmCherry (experimental details are provided in Supplementary Fig. 6). The movie steps through x-y slices with 116 nm z separation. Scale bar, 10 μm. Additional data - Yeast one-hybrid assays for gene-centered human gene regulatory network mapping
- Nat Methods 8(12):1050-1052 (2011)
Nature Methods | Brief Communication Yeast one-hybrid assays for gene-centered human gene regulatory network mapping * John S Reece-Hoyes1, 2, 3 * A Rasim Barutcu2, 4 * Rachel Patton McCord1, 2, 4 * Jun Seop Jeong5, 6, 7, 8, 9, 10 * Lizhi Jiang5, 6, 7, 8, 9, 10 * Andrew MacWilliams11, 12 * Xinping Yang11, 12 * Kourosh Salehi-Ashtiani11, 12 * David E Hill11, 12 * Seth Blackshaw5, 6, 7, 8, 9, 10 * Heng Zhu5, 6, 7, 8, 9, 10 * Job Dekker1, 2, 4, 11 * Albertha J M Walhout1, 2, 3, 11 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:1050–1052Year published:(2011)DOI:doi:10.1038/nmeth.1764Received20 June 2011Accepted22 August 2011Published online30 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 Gateway-compatible yeast one-hybrid (Y1H) assays provide a convenient gene-centered (DNA to protein) approach to identify transcription factors that can bind a DNA sequence of interest. We present Y1H resources, including clones for 988 of 1,434 (69%) predicted human transcription factors, that can be used to detect both known and new interactions between human DNA regions and transcription factors. View full text Subject terms: * Systems Biology * Gene Expression * Genomics * Molecular Biology Figures at a glance * Figure 1: Human gene-centered Y1H assays. () Summary of the human transcription factor (hTF) collection. () Examples of the detection of two known interactions in Y1H assays. Only HIS3 activation is shown as the DNA baits exhibit high background levels of LacZ expression (+, yeast expressing the TF; −, yeast lacking the TF). () Detection of PRS interactions in different Y1H configurations. Diploid, diploids by mating; haploids, haploids by transformation; interactions not tested are shown in gray; and undetected interactions are shown in white. () Example of eY1H readout plate with the HBG1 promoter as DNA bait. Positive interactions are marked. Scale bar, 1 cm. * Figure 2: eY1H data analysis. () DNA binding domain analysis of the transcription factor (TF) compendium, the transcription factor prey yeast array, and the transcription factors detected in Y1H interactions. ZF, zinc finger; C2H2, Cys-Cys-His-His; bHLH, basic region helix-loop-helix; bZip, basic leucine zipper; NHR, nuclear hormone receptor; WH, winged helix; HMG, high mobility group. () Receiver operating characteristic (ROC) curve of DNA binding site analysis for the HBG2 promoter. Binding sites in the DNA bait sequence were ranked from best to worst (in terms of match to position weight matrix) along the x axis; only the 'best' binding site match for each transcription factor was used. As the curve progresses along the x axis, it steps up only for binding sites of transcription factors detected by eY1H. If the binding sites provided no information regarding eY1H interactions (that is, no match between interactions predicted and detected), the curve would be largely below the diagonal. () Transcriptio! n factor–DNA interactions detected by eY1H depicted in a gene regulatory network. The DNA bait 5′ HS5 is depicted as a clear box because it had no interactions. Unless otherwise noted, colors indicate transcription factor families as in . MH1, MAD homology 1 domain; DHHC, Asp-His-His-Cys; and uDBP, unconventional DNA-binding protein. The outgoing degree k(out) (number of DNA baits bound per transcription factor) is indicated. Green edges indicate detected PRS interactions. Author information * Author information * Supplementary information Affiliations * Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * John S Reece-Hoyes, * Rachel Patton McCord, * Job Dekker & * Albertha J M Walhout * Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * John S Reece-Hoyes, * A Rasim Barutcu, * Rachel Patton McCord, * Job Dekker & * Albertha J M Walhout * Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * John S Reece-Hoyes & * Albertha J M Walhout * Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * A Rasim Barutcu, * Rachel Patton McCord & * Job Dekker * Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Jun Seop Jeong, * Lizhi Jiang, * Seth Blackshaw & * Heng Zhu * Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Jun Seop Jeong, * Lizhi Jiang, * Seth Blackshaw & * Heng Zhu * Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Jun Seop Jeong, * Lizhi Jiang, * Seth Blackshaw & * Heng Zhu * Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Jun Seop Jeong, * Lizhi Jiang, * Seth Blackshaw & * Heng Zhu * Center for High-Throughput Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Jun Seop Jeong, * Lizhi Jiang, * Seth Blackshaw & * Heng Zhu * Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Jun Seop Jeong, * Lizhi Jiang, * Seth Blackshaw & * Heng Zhu * Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Andrew MacWilliams, * Xinping Yang, * Kourosh Salehi-Ashtiani, * David E Hill, * Job Dekker & * Albertha J M Walhout * Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Andrew MacWilliams, * Xinping Yang, * Kourosh Salehi-Ashtiani & * David E Hill Contributions A.J.M.W. conceived the project; J.D., J.S.R.-H. and A.J.M.W. designed the project; J.S.R.-H. and A.R.B. performed the experiments; J.S.R.-H. and A.J.M.W. analyzed the data; J.S.J., L.J. and A.M. picked transcription factor ORF clones; J.S.J. and L.J. assisted A.R.B. and J.S.R.-H. with Gateway cloning. R.P.M. performed the binding-site analysis; H.Z., S.B., K.S.-A., X.Y., A.M. and D.E.H. provided transcription factor ORFeome clones; J.S.R.-H. and A.J.M.W. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * John S Reece-Hoyes or * Albertha J M Walhout Author Details * John S Reece-Hoyes Contact John S Reece-Hoyes Search for this author in: * NPG journals * PubMed * Google Scholar * A Rasim Barutcu Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel Patton McCord Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Seop Jeong Search for this author in: * NPG journals * PubMed * Google Scholar * Lizhi Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew MacWilliams Search for this author in: * NPG journals * PubMed * Google Scholar * Xinping Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Kourosh Salehi-Ashtiani Search for this author in: * NPG journals * PubMed * Google Scholar * David E Hill Search for this author in: * NPG journals * PubMed * Google Scholar * Seth Blackshaw Search for this author in: * NPG journals * PubMed * Google Scholar * Heng Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Job Dekker Search for this author in: * NPG journals * PubMed * Google Scholar * Albertha J M Walhout Contact Albertha J M Walhout Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–3 and Supplementary Tables 1–9 Additional data - Enhanced Y1H assays for Arabidopsis
- Nat Methods 8(12):1053-1055 (2011)
Nature Methods | Brief Communication Enhanced Y1H assays for Arabidopsis * Allison Gaudinier1 * Lifang Zhang2 * John S Reece-Hoyes3 * Mallorie Taylor-Teeples1 * Li Pu1 * Zhijie Liu2 * Ghislain Breton4 * Jose L Pruneda-Paz4 * Dahae Kim1 * Steve A Kay4 * Albertha J M Walhout3 * Doreen Ware2, 5 * Siobhan M Brady1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1053–1055Year published:(2011)DOI:doi:10.1038/nmeth.1750Received01 April 2011Accepted30 August 2011Published online30 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 present an Arabidopsis thaliana full-length transcription factor resource of 92% of root stele–expressed transcription factors and 74.5% of root-expressed transcription factors. We demonstrate its use with enhanced yeast one-hybrid (eY1H) screening for rapid, systematic mapping of plant transcription factor–promoter interactions. We identified 158 interactions with 13 stele-expressed promoters, many of which occur physically or are regulatory in planta. View full text Subject terms: * Plant Sciences * Systems Biology * Molecular Biology Figures at a glance * Figure 1: An Arabidopsis eY1H transcription factor resource. () Schematic of root stele cell types. () Stele cell-type expression profiles of the transcription factors present in our collection. Colors are as in . () Family categorization of transcription factors within the resource. 'Other' are families with fewer than eight members (Supplementary Table 1). GRAS, gibberellic acid–insensitive, repressor of GA1 and scarecreow; DOF, DNA-binding with one finger; GATA, GATA DNA motif; HSF, heat shock factor; CO-like, Constans-like; ARF, auxin response factor; MADS, MCM1, Agamous, Deficiens and SRF box; HB, homeobox; and BZIP, basic leucine zipper. * Figure 2: Using the transcription factor resource in an eY1H assay. () Reporter assay readout with the REV promoter and 94 transcription factors. Empty wells (orange) and negative control (green) are marked. Scale bar, 4 mm. () Relative expression of REV in the 35S-OBP2–glucocorticoid receptor line after induction with dexamethasone. Error bars, s.e.m. (n = 3; ~100 plants per biological replicate). () The stele gene regulatory network for the 13 screened promoters. Yellow circles are transcription factors, green diamonds are in the screen as both transcription factors and promoters, and blue squares are promoters. Author information * Author information * Supplementary information Affiliations * Department of Plant Biology and Genome Center, University of California Davis, Davis, California, USA. * Allison Gaudinier, * Mallorie Taylor-Teeples, * Li Pu, * Dahae Kim & * Siobhan M Brady * Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA. * Lifang Zhang, * Zhijie Liu & * Doreen Ware * Program in Gene Function and Expression and Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * John S Reece-Hoyes & * Albertha J M Walhout * Section of Cell and Developmental Biology, Division of Biological Sciences, University of California San Diego, La Jolla, California, USA. * Ghislain Breton, * Jose L Pruneda-Paz & * Steve A Kay * US Department of Agriculture, Agricultural Research Service, Ithaca, New York, USA. * Doreen Ware Contributions S.M.B., D.W., A.J.M.W. and J.S.R.-H. conceived the project; J.S.R.-H. and A.J.M.W. provided the eY1H mating protocol; A.G. and D.K. performed the yeast experiments; A.G. and L.Z. cloned the de novo transcription factors; L.Z., M.T.-T., A.G., L.P. and Z.L. recombined the transcription factors; L.Z., G.B., J.L.P.-P. and S.A.K. accumulated and curated transcription factor entry clones; and S.M.B., A.G., D.W., L.Z., A.J.M.W. and J.S.R.-H. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Siobhan M Brady Author Details * Allison Gaudinier Search for this author in: * NPG journals * PubMed * Google Scholar * Lifang Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * John S Reece-Hoyes Search for this author in: * NPG journals * PubMed * Google Scholar * Mallorie Taylor-Teeples Search for this author in: * NPG journals * PubMed * Google Scholar * Li Pu Search for this author in: * NPG journals * PubMed * Google Scholar * Zhijie Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Ghislain Breton Search for this author in: * NPG journals * PubMed * Google Scholar * Jose L Pruneda-Paz Search for this author in: * NPG journals * PubMed * Google Scholar * Dahae Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Steve A Kay Search for this author in: * NPG journals * PubMed * Google Scholar * Albertha J M Walhout Search for this author in: * NPG journals * PubMed * Google Scholar * Doreen Ware Search for this author in: * NPG journals * PubMed * Google Scholar * Siobhan M Brady Contact Siobhan M Brady Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (913K) Supplementary Figures 1–3, Supplementary Tables 1–3 and Supplementary Notes Additional data - Embryonic stem cell–based mapping of developmental transcriptional programs
- Nat Methods 8(12):1056-1058 (2011)
Nature Methods | Brief Communication Embryonic stem cell–based mapping of developmental transcriptional programs * Esteban O Mazzoni1 * Shaun Mahony2 * Michelina Iacovino3 * Carolyn A Morrison1 * George Mountoufaris1 * Michael Closser1 * Warren A Whyte4, 5 * Richard A Young4, 5 * Michael Kyba3 * David K Gifford2 * Hynek Wichterle1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1056–1058Year published:(2011)DOI:doi:10.1038/nmeth.1775Received08 June 2011Accepted19 October 2011Published online13 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 The study of developmentally regulated transcription factors by chromatin immunoprecipitation and deep sequencing (ChIP-seq) faces two major obstacles: availability of ChIP-grade antibodies and access to sufficient number of cells. We describe versatile genome-wide analysis of transcription-factor binding sites by combining directed differentiation of embryonic stem cells and inducible expression of tagged proteins. We demonstrate its utility by mapping DNA-binding sites of transcription factors involved in motor neuron specification. View full text Subject terms: * Stem Cells * Cell Biology * Gene Expression * Genomics Figures at a glance * Figure 1: Generation of inducible cell lines. () Schematic of the cloning strategy to generate inducible lines with epitope sequence–tagged genes. Coding sequences lacking the stop codon were tagged to encode V5 at the C terminus. R1, R2, L1 and L2, recombination sites for LR-Clonase enzyme (Invitrogen); TRE, tetracycline response element, and pA, polyadenylation signal. () Overview of ESC-directed differentiation. Differentiating cells become motor neuron progenitors (pMNs) on day 4 and produce motor neurons (MN) on days 5–7. Doxycyline (Dox) was added late on day 3 or 4 to mimic the expression pattern of the endogenous Olig2 and Hoxc9, respectively. Olig2-V5 is analyzed at day 4 and Hoxc9-V5 or FlagB-Hoxc9 at day 5. ES, embryonic stem cell; PE, primitive ectoderm; NP, neural precursor; RA, retinoic acid; and Hh, hedgehog. () Nkx2.2 staining in control or Olig2-V5 expressing cells on day 4 of differentiation under high Hh concentration (500 nM). () Pax6 staining in control or Olig2-V5 expressing cells at day 4 of d! ifferentiation under low Hh (5 nM). () iHoxc9-V5 and iFlagB-Hoxc9–expressing day 5 embryoid bodies stained with antibodies to Hoxc4, V5 and Flag as indicated. Scale bar, 100 μm. * Figure 2: Native- and tagged-protein ChIP comparisons. () ChIP signal tracks over Nkx2-2 genomic loci for endogenous and V5-tagged Olig2. Red peaks represent significant (P < 0.01) enrichment over control. Genomic loci with coordinates are shown at the bottom. () The most overrepresented motifs discovered under ChIP-seq peaks for native Olig2 and Olig2-V5 ChIP experiments. () A comparison of read enrichment from native and V5-tagged Olig2 ChIP-seq experiments at all detected peaks (left). Blue dots in the scatterplot represent peaks significantly (P < 0.01) differently enriched in one experiment over the other. The pie chart shows the distribution of sites differently enriched between native Olig2 and V5-tagged protein ChIP-seq. (–) Same analysis as in – over the Hoxc5 genomic locus for V5- and Flag-tagged Hoxc9 experiments. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE30882 Author information * Accession codes * Author information * Supplementary information Affiliations * Departments of Pathology, Neurology and Neuroscience, Center for Motor Neuron Biology and Disease and Columbia Stem Cell Initiative, Columbia University Medical Center, New York, New York, USA. * Esteban O Mazzoni, * Carolyn A Morrison, * George Mountoufaris, * Michael Closser & * Hynek Wichterle * Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Shaun Mahony & * David K Gifford * Lillehei Heart Institute and Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA. * Michelina Iacovino & * Michael Kyba * Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Warren A Whyte & * Richard A Young * Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA. * Warren A Whyte & * Richard A Young Contributions E.O.M. and G.M. generated the transcription factor inducible lines; E.O.M. performed phenotypic analysis of the derived lines. E.O.M., W.A.W. and C.A.M. performed ChIP experiments. M.I. and M.K. developed the ICE cell lines and vectors. E.O.M. performed expression analysis. E.O.M. and M.C. performed the western blot and protein-binding to immobilized DNA. S.M. analyzed ChIP-seq data. E.O.M., R.A.Y., D.K.G .and H.W. designed the experiments. E.O.M., S.M. and H.W. wrote the manuscript; D.K.G. revised the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Hynek Wichterle Author Details * Esteban O Mazzoni Search for this author in: * NPG journals * PubMed * Google Scholar * Shaun Mahony Search for this author in: * NPG journals * PubMed * Google Scholar * Michelina Iacovino Search for this author in: * NPG journals * PubMed * Google Scholar * Carolyn A Morrison Search for this author in: * NPG journals * PubMed * Google Scholar * George Mountoufaris Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Closser Search for this author in: * NPG journals * PubMed * Google Scholar * Warren A Whyte Search for this author in: * NPG journals * PubMed * Google Scholar * Richard A Young Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Kyba Search for this author in: * NPG journals * PubMed * Google Scholar * David K Gifford Search for this author in: * NPG journals * PubMed * Google Scholar * Hynek Wichterle Contact Hynek Wichterle Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–3 and Supplementary Table 1 Additional data - Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping
- Nat Methods 8(12):1059-1064 (2011)
Nature Methods | Article Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping * John S Reece-Hoyes1, 2, 3 * Alos Diallo1, 2, 3 * Bryan Lajoie1, 2, 4 * Amanda Kent1, 2, 3 * Shaleen Shrestha1, 2, 3 * Sreenath Kadreppa1, 2, 3 * Colin Pesyna5 * Job Dekker1, 2, 4 * Chad L Myers5 * Albertha J M Walhout1, 2, 3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:1059–1064Year published:(2011)DOI:doi:10.1038/nmeth.1748Received20 June 2011Accepted19 August 2011Published online30 October 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 A major challenge in systems biology is to understand the gene regulatory networks that drive development, physiology and pathology. Interactions between transcription factors and regulatory genomic regions provide the first level of gene control. Gateway-compatible yeast one-hybrid (Y1H) assays present a convenient method to identify and characterize the repertoire of transcription factors that can bind a DNA sequence of interest. To delineate genome-scale regulatory networks, however, large sets of DNA fragments need to be processed at high throughput and high coverage. Here we present enhanced Y1H (eY1H) assays that use a robotic mating platform with a set of improved Y1H reagents and automated readout quantification. We demonstrate that eY1H assays provide excellent coverage and identify interacting transcription factors for multiple DNA fragments in a short time. eY1H assays will be an important tool for mapping gene regulatory networks in Caenorhabditis elegans and oth! er model organisms as well as in humans. View full text Subject terms: * Systems Biology * Gene Expression * Model Organisms * Genomics Figures at a glance * Figure 1: eY1H assays. () Schematic of Y1H assays by mating and by transformation. prom, promoter (or DNA bait); AD, Gal4 transcription activation domain; TF, transcription factor. () Schematic of steps in the eY1H pipeline. SC, synthetic complete medium; –Trp, without tryptophan; –Ura, without uracil; –His, without histidine; YAPD, rich yeast medium with adenine. () Example of eY1H readout plate ('1,536 image' in ). Each transcription factor was tested in quadruplicate. (i) Control quads that lack yeast indicate plate identity and orientation (blue square), whereas yeast that contain an empty AD plasmid serve as a background above which interacting factors are detected (green squares); (ii) strong eY1H positive in which all four spots of a TF quad score positively and had bleed-over; (iii) weak eY1H positive in which all four spots of a TF quad score positively; (iv) medium eY1H positive in which three spots of a TF quad score positively; (v) very weak eY1H positive in which two spots of a ! TF quad score positively. We considered only quads in which at least two of the four colonies are positive because such an interaction is by definition retested. * Figure 2: Sampling sensitivity and reproducibility of eY1H assays. () Two promoters, Pvha-15 and Pcog-1, were screened four times against the worm transcription factor array; the cumulative number of times an interaction was detected is indicated. In a single experiment we detected 89% of all interactions collectively detected in four experiments. In a single experiment we detected 90% of the interactions detected in a second experiment. () Bar graph indicating the number of interactions detected using Pvha-15 as a DNA bait. Trafo, transformation. () Venn diagram of the interactions graphed in . The label 26 + 9 indicates 26 transcription factors found exclusively by eY1H assays and 9 factors newly detected with clones that were heretofore not available, but that we cloned based on improved gene models. * Figure 3: eY1H assays with 50 previously published C. elegans gene promoters as DNA baits. () Pie chart of TF quad performance in eY1H assays. The number (2, 3 or 4) indicates the number of colonies in a TF quad that were scored positively. We did not consider transcription factors for which only a single colony was scored. () Venn diagram illustrating overlap between published and eY1H interactions. () Proportion of interactions detected in which all four colonies scoring positively were weak. () Percentage NHRs and uDBPs detected in different subsets of the data. * Figure 4: Automated quantification of eY1H assays using SpotOn. () Example of readout plate after cropping. () Graph of ranked colony intensities calculated from image in . () Normalization of the colony intensity data for the plate in . Bluest grid cells have the highest intensity. () Visualization of positives identified by SpotOn for image in . () Graph of false calls versus missed calls for the 50 DNA baits screened with eY1H assays. A Z-score threshold with 5% false calls resulted in 17% missed calls. With limited manual curation, the false-call rate was reduced to 1%. () Missed calls in SpotOn mostly correspond to very weak interactions that are barely detectable by eye. Percentage indicates proportion correctly found by SpotOn; n indicates the total number of interactions in each category. Author information * Abstract * Author information * Supplementary information Affiliations * Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * John S Reece-Hoyes, * Alos Diallo, * Bryan Lajoie, * Amanda Kent, * Shaleen Shrestha, * Sreenath Kadreppa, * Job Dekker & * Albertha J M Walhout * Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * John S Reece-Hoyes, * Alos Diallo, * Bryan Lajoie, * Amanda Kent, * Shaleen Shrestha, * Sreenath Kadreppa, * Job Dekker & * Albertha J M Walhout * Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * John S Reece-Hoyes, * Alos Diallo, * Amanda Kent, * Shaleen Shrestha, * Sreenath Kadreppa & * Albertha J M Walhout * Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * Bryan Lajoie & * Job Dekker * Department of Computer Science and Engineering, University of Minnesota–Twin Cities, Minneapolis, Minnesota, USA. * Colin Pesyna & * Chad L Myers Contributions J.S.R.-H. and A.J.M.W. conceived the project; A.K. and S.S. performed the eY1H assays; A.D. created the tools for automated eY1H assay quantification in collaboration with B.L., C.P., J.D., J.S.R.-H. and C.L.M.; S.K. cloned additional transcription factor–encoding ORFs; J.S.R.-H. and A.J.M.W. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Albertha J M Walhout or * John S Reece-Hoyes Author Details * John S Reece-Hoyes Contact John S Reece-Hoyes Search for this author in: * NPG journals * PubMed * Google Scholar * Alos Diallo Search for this author in: * NPG journals * PubMed * Google Scholar * Bryan Lajoie Search for this author in: * NPG journals * PubMed * Google Scholar * Amanda Kent Search for this author in: * NPG journals * PubMed * Google Scholar * Shaleen Shrestha Search for this author in: * NPG journals * PubMed * Google Scholar * Sreenath Kadreppa Search for this author in: * NPG journals * PubMed * Google Scholar * Colin Pesyna Search for this author in: * NPG journals * PubMed * Google Scholar * Job Dekker Search for this author in: * NPG journals * PubMed * Google Scholar * Chad L Myers Search for this author in: * NPG journals * PubMed * Google Scholar * Albertha J M Walhout Contact Albertha J M Walhout Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (95M) Supplementary Figs. 1–6, Supplementary Tables 1–9 Additional data - Automated protein-DNA interaction screening of Drosophila regulatory elements
- Nat Methods 8(12):1065-1070 (2011)
Nature Methods | Article Automated protein-DNA interaction screening of Drosophila regulatory elements * Korneel Hens1 * Jean-Daniel Feuz1 * Alina Isakova1 * Antonina Iagovitina1 * Andreas Massouras1 * Julien Bryois1 * Patrick Callaerts2, 3 * Susan E Celniker4 * Bart Deplancke1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1065–1070Year published:(2011)DOI:doi:10.1038/nmeth.1763Received24 June 2011Accepted23 August 2011Published online30 October 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 Drosophila melanogaster has one of the best characterized metazoan genomes in terms of functionally annotated regulatory elements. To explore how these elements contribute to gene regulation, we need convenient tools to identify the proteins that bind to them. Here we describe the development and validation of a high-throughput yeast one-hybrid platform, which enables screening of DNA elements versus an array of full-length, sequence-verified clones containing over 85% of predicted Drosophila transcription factors. Using six well-characterized regulatory elements, we identified 33 transcription factor–DNA interactions of which 27 were previously unidentified. To simultaneously validate these interactions and locate the binding sites of involved transcription factors, we implemented a powerful microfluidics-based approach that enabled us to retrieve DNA-occupancy data for each transcription factor throughout the respective target DNA elements. Finally, we biologically valid! ated several interactions and identified two new regulators of sine oculis gene expression and hence eye development. View full text Subject terms: * Biophysics * Molecular Engineering * Neuroscience Figures at a glance * Figure 1: Workflow underlying the generation of the Drosophila transcription factor (TF) ORF clone resource and the Drosophila Y1H AD transcription factor library. Of 755 predicted Drosophila transcription factors, 501 were available as cDNA clones from the Berkeley Drosophila Genome Project (BDGP). The remaining transcription factors were targeted for de novo cloning. Transcription factor ORFs were PCR-amplified and cloned into the pDONR221 Entry vector. The resulting Entry clones were sequence-verified by high-throughput sequencing and categorized according to the quality and the coverage of the sequencing into three classes: gold for fully sequence–verified clones, silver for 5′ and 3′ end-sequenced clones, and bronze for partially sequenced clones. All nonrejected clones were transferred into the Y1H-compatible AD vectors pAD-DEST-ARS/CEN and pAD-DEST-2μ by Gateway cloning. * Figure 2: Drosophila high-throughput Y1H platform. A yeast DNA-bait strain was distributed over a 384-well plate. Each well of this plate was then transformed with a different AD transcription factor clone from the Drosophila Y1H AD transcription factor library by a robotic yeast transformation platform, which additionally spotted the 384 individually transformed yeast strains on a permissive agar plate. A colony-pinning robot then transferred the yeast colonies onto a permissive and a selective plate, quadruplicating each colony in a square pattern in the process. Transcription factor–DNA bait interactions were identified based on growth on a selective, 3-amino-1,2,4-triazole–containing yeast plate. * Figure 3: Overview of the TIDY program. () Flowchart of TIDY program steps. () Screenshot of the TIDY output upon image analysis of a selective plate from a Y1H screen. In this example, five interactions were observed (green circles). A different threshold was used for plate-interior and plate-exterior yeast colonies. * Figure 4: DNA occupancy analysis of Y1H-identified transcription factors by MARE. (–) Analysis of the so10 element for binding of EY (), TOY (), CG9797 () and TTK () and of the yp1-1 element for binding of DSX () and TJ (). Bound DNA levels normalized over surface-immobilized protein amounts are plotted for each 12-nucleotide stretch and as an interpolated curve. Peaks are indicated with a red line, peak maxima are indicated with a red dot. Peaks found in both replicates are indicated with an asterisk. Where available, DNase I footprinting data and PWM-based binding site predictions are indicated. Overlapping DNase I footprinting data and PWM-based binding site predictions are indicated. Note that as DNA occupancy is plotted as a relative signal normalized for the protein level in the microfluidics chamber, the scale of the y axis may vary between replicates. * Figure 5: In vivo effects of RNAi-mediated knockdown of Y1H-identified transcription factors binding the so10 element. (,) Bright-field microscopy images of adult eyes, lateral view of OK107>CG9797-RNAiTRiP () and OK107>UAS-mCD8-GFP () flies. (,) Bright-field microscopy images of adult eyes, frontal view of OK107>ttk-RNAiVDRC () and OK107>UAS-mCD8-GFP () flies. Scale bars, 100 μm. (,) Quantitative real-time PCR analysis of so expression in third instar eye-antennal discs of OK107>CG9797-RNAiVDRC and OK107>CG9797-RNAiTRiP flies () and in the indicated tissues of OK107>ttk-RNAiVDRC flies (). Values are relative to the corresponding controls. Error bars, s.e.m. (n = 3). *P < 0.05. Author information * Abstract * Author information * Supplementary information Affiliations * Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. * Korneel Hens, * Jean-Daniel Feuz, * Alina Isakova, * Antonina Iagovitina, * Andreas Massouras, * Julien Bryois & * Bart Deplancke * Laboratory of Developmental Genetics, Vlaams Instituut voor Biotechnologie, Leuven, Belgium. * Patrick Callaerts * Laboratory of Developmental Genetics, Department of Human Genetics, Catholic University of Leuven, Leuven, Belgium. * Patrick Callaerts * Department of Genome Dynamics, Berkeley Drosophila Genome Project, Lawrence Berkeley National Laboratory, Berkeley, California, USA. * Susan E Celniker Contributions B.D. supervised the study. K.H. and B.D. designed the study. K.H. and J.B. built the transcription factor clone collection. K.H. and J.-D.F. performed Y1H screens. K.H. performed in vivo validations. A. Iagovitina developed image analysis software. A. Isakova performed MARE analyses. A.M. analyzed high-throughput sequencing data. P.C. provided cDNA clones and financial support. S.E.C. identified transcription factors with sequence-specific DNA-binding domains used in this study and provided transcription factor cDNA clones. K.H. and B.D. provided the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bart Deplancke Author Details * Korneel Hens Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Daniel Feuz Search for this author in: * NPG journals * PubMed * Google Scholar * Alina Isakova Search for this author in: * NPG journals * PubMed * Google Scholar * Antonina Iagovitina Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Massouras Search for this author in: * NPG journals * PubMed * Google Scholar * Julien Bryois Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Callaerts Search for this author in: * NPG journals * PubMed * Google Scholar * Susan E Celniker Search for this author in: * NPG journals * PubMed * Google Scholar * Bart Deplancke Contact Bart Deplancke Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–18, Supplementary Tables 2, 4–6, Supplementary Data Excel files * Supplementary Table 1 (1M) Predicted transcription factors in the Drosophila genome and their cloning status. * Supplementary Table 3 (25K) CRMs used in this study. Additional data - A homozygous mutant embryonic stem cell bank applicable for phenotype-driven genetic screening
- Nat Methods 8(12):1071-1077 (2011)
Nature Methods | Article A homozygous mutant embryonic stem cell bank applicable for phenotype-driven genetic screening * Kyoji Horie1, 2 * Chikara Kokubu1, 3 * Junko Yoshida1 * Keiko Akagi4, 5 * Ayako Isotani6 * Akiko Oshitani3 * Kosuke Yusa1, 9 * Ryuji Ikeda1, 9 * Yue Huang7, 8 * Allan Bradley7 * Junji Takeda1, 3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:1071–1077Year published:(2011)DOI:doi:10.1038/nmeth.1739Received15 April 2011Accepted19 August 2011Published online23 October 2011 Abstract * Abstract * Accession codes * 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 Genome-wide mutagenesis in mouse embryonic stem cells (ESCs) is a powerful tool, but the diploid nature of the mammalian genome hampers its application for recessive genetic screening. We have previously reported a method to induce homozygous mutant ESCs from heterozygous mutants by tetracycline-dependent transient disruption of the Bloom's syndrome gene. However, we could not purify homozygous mutants from a large population of heterozygous mutant cells, limiting the applications. Here we developed a strategy for rapid enrichment of homozygous mutant mouse ESCs and demonstrated its feasibility for cell-based phenotypic analysis. The method uses G418-plus-puromycin double selection to enrich for homozygotes and single-nucleotide polymorphism analysis for identification of homozygosity. We combined this simple approach with gene-trap mutagenesis to construct a homozygous mutant ESC bank with 138 mutant lines and demonstrate its use in phenotype-driven genetic screening. View full text Subject terms: * Genetics * Molecular Biology * Genomics * Stem Cells Figures at a glance * Figure 1: Experimental design. () Construction of the mutant ESC bank. () Modification of the Blm and Rosa26 loci in ESCs. Top, the tetracycline-controlled transactivator (tTA) and the tetracycline operator–minimal promoter (tetO-Pm) unit were knocked into both alleles of the second exon of the Blm gene. Bottom, the ERT2-iCre-ERT2 fusion gene was knocked into the Rosa26 locus. A single copy of the loxP or F3 site is left in the genome after removal of the selection cassettes used during the knock-in procedure. SA, splice acceptor; pA, polyadenylation signal; Dox, doxycycline. () Structure of the gene-trap vectors. Arrows indicate orientation of the neo (N) and puΔtk (P) genes. Pr, Pgk1 promoter. Note that two lox2272 sequences are inversely oriented and flipped upon Cre recombinase–mediated recombination, as depicted in . For the retroviral vector, the structure of the provirus in which the 3′ FRT site is copied into the 5′ long terminal repeat (LTR) is presented. TIR, terminal inverted repeat. (! ) Selection of homozygous mutants. Homozygous mutations are obtained by doxycycline-induced Blm suppression. The 4HT treatment activates Cre recombinase and inverts the orientation of neo and puΔtk genes. As a result, a fraction of homozygous mutants express both neo and puΔtk, allowing for double selection. The SNPs between 129 and C57BL/6 strains are rendered homozygous at the distal region of each vector insertion site, and this SNP homozygosity was used as a reference for the identification of homozygous mutation at the vector insertion site. One telomeric SNP was selected from each chromosome. Note that only the neo-puΔtk selection cassette is presented for the gene-trap vector. R, resistant; S, sensitive. * Figure 2: Regulation of the cNP cassette. () Experimental scheme. Regulation of the cNP cassette was analyzed in a mixed population of approximately 200 clones. () Inversion frequency was determined by the ratio of puromycin-resistant colonies to the total number of cells plated. The number of colonies in the absence of puromycin was used for normalization of plating efficiency. * Figure 3: Isolation and characterization of homozygous mutants. () Representative SNP analysis. PCR amplifications of C57BL/6 and 129 alleles were monitored for each double-resistant (G418 plus puromycin) clone with allele-specific SNP probes, which were labeled with different fluorescent reporter dyes (VIC or FAM). Both the homozygotes and non-homozygotes were derived from the same heterozygous clone. Genomic DNAs extracted from C57BL/6 and 129 mice were used as controls. () Summary of the screening for homozygous clones. () Representative results of locus-specific PCR analysis. Clones showing homozygosity by SNP analysis (as in ) were further analyzed at each gene-trap locus. Note that both homozygous (lane 2) and non-homozygous (lane 1) clones were observed at the Rap1b locus. p1–p3, primers; +/+, wild type/wild type; m/+, mutation/wild type. () Western blot analysis of homozygous mutant cells with insertions of the retroviral gene-trap vector. β-actin (Actb) was used as an internal control. m/m, mutation/mutation. * Figure 4: Phenotypic analyses of Dgcr8 and Ptpn11 homozygous mutant cells. () Scatter plots of the microRNA expression data from microarray analysis of wild-type and Dgcr8 homozygous mutant cells. Green lines indicate limits of the twofold change from the equivalent expression levels depicted by the red line. m/m, mutation/mutation. () Growth retardation of Dgcr8 homozygous mutant cells. Wild-type and Dgcr8 homozygous mutant cells were plated sparsely for clonal growth on feeder cells and cultured for 5 d. Doubling time of each cell type is shown with s.d. (n = 4). Scale bars, 500 μm. () Reduction of spontaneous differentiation in Ptpn11 homozygous mutant cells. ESCs were plated sparsely for clonal growth on gelatin-coated dishes in the absence of feeder cells and maintained for 7 d. Boxed areas in the top images are magnified in the bottom images. Large, flat-shaped differentiated cells were observed at the edge of the wild-type colonies, whereas no such cells were seen in the Ptpn11 homozygous mutant colonies. Scale bars, 500 μm (top) and 100 �! �m (bottom). () Top, sustained expression of Oct3/4 in embryoid bodies derived from Ptpn11 homozygous mutant cells. Embryoid bodies were immunostained after a 9-day suspension culture. Scale bars, 500 μm. Bottom, expression level of Oct3/4 mRNA was quantified by quantitative RT-PCR and normalized to β-actin mRNA expression level. () Continuous expression of SSEA-1 in Ptpn11 homozygous mutant cells. Embryoid bodies were dissociated into single cells and subjected to flow-cytometry analysis together with undifferentiated ESCs. * Figure 5: Phenotypic analyses of homozygous mutant ESC lines. (–) Defective neural differentiation of the Axin1 homozygous mutant cells. m/+, mutation/wild type; m/m, mutation/mutation. () Impaired neurosphere formation from Axin1 homozygous mutant ESCs. Scale bars, 500 μm. () Removal of mutagenic vector sequences by FLPo recombinase. Axin1r, revertant allele. Asterisk indicates nonspecific PCR bands. (,) Differential expression of lineage-specific markers in Axin1 mutants. The monolayer culture was stained with antibodies to Nestin () and TuJ-1 () 7 d and 14 d after induction of neural differentiation, respectively. Scale bars, 100 μm. (,) Expression of Nestin and TuJ-1 mRNAs was examined by quantitative RT-PCR and normalized by β-actin expression level. () Impaired self-renewal of Csnk2b and Ilf2 homozygous mutant ESCs. These clones were identified from the screen in Supplementary Figure 5. ESCs were cultured in 2i medium (MEK inhibitor and GSK3 inhibitor) for 5 d in the absence of feeder cells. Revertant clones (Csnk2br/r and I! lf2r/r) were obtained by the same procedure shown in . Doubling time of each cell type is shown with s.d. (n = 3). Scale bars, 500 μm. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE20268 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan. * Kyoji Horie, * Chikara Kokubu, * Junko Yoshida, * Kosuke Yusa, * Ryuji Ikeda & * Junji Takeda * Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan. * Kyoji Horie * Center for Advanced Science and Innovation, Osaka University, Suita, Osaka, Japan. * Chikara Kokubu, * Akiko Oshitani & * Junji Takeda * Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, USA. * Keiko Akagi * Comprehensive Cancer Center, Ohio State University, Columbus, Ohio, USA. * Keiko Akagi * Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan. * Ayako Isotani * The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Yue Huang & * Allan Bradley * National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Department of Medical Genetics, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, China. * Yue Huang * Present addresses: The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK (K.Y.) and Osaka Isen College of Medical Care & Welfare, Osaka, Osaka, Japan (R.I.). * Kosuke Yusa & * Ryuji Ikeda Contributions K.H. designed experiments, constructed vectors, performed ESC culture and phenotypic analyses of mutant ESCs, and wrote the manuscript. C.K. performed bioinformatics analyses and contributed to writing the manuscript. J.Y. conducted vector construction and ESC culture. K.A. performed bioinformatics analyses and constructed the database. A.I. generated chimeric mice. A.O. conducted ESC culture. K.Y. performed gene targeting of ESCs. R.I. conducted ESC culture and PCR genotyping of mutant ESCs. Y.H. and A.B. contributed to the vector design for the selection of homozygous mutants. J.T. conducted vector construction and gene targeting of ESCs, and contributed to writing the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Kyoji Horie or * Junji Takeda Author Details * Kyoji Horie Contact Kyoji Horie Search for this author in: * NPG journals * PubMed * Google Scholar * Chikara Kokubu Search for this author in: * NPG journals * PubMed * Google Scholar * Junko Yoshida Search for this author in: * NPG journals * PubMed * Google Scholar * Keiko Akagi Search for this author in: * NPG journals * PubMed * Google Scholar * Ayako Isotani Search for this author in: * NPG journals * PubMed * Google Scholar * Akiko Oshitani Search for this author in: * NPG journals * PubMed * Google Scholar * Kosuke Yusa Search for this author in: * NPG journals * PubMed * Google Scholar * Ryuji Ikeda Search for this author in: * NPG journals * PubMed * Google Scholar * Yue Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Allan Bradley Search for this author in: * NPG journals * PubMed * Google Scholar * Junji Takeda Contact Junji Takeda Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figs. 1-5, Supplementary Tables 1-6 Excel files * Supplementary Data (504K) Vector insertion sites. Additional data - A scalable pipeline for highly effective genetic modification of a malaria parasite
- Nat Methods 8(12):1078-1082 (2011)
Nature Methods | Article A scalable pipeline for highly effective genetic modification of a malaria parasite * Claudia Pfander1 * Burcu Anar1 * Frank Schwach1 * Thomas D Otto1 * Mathieu Brochet1 * Katrin Volkmann1 * Michael A Quail1 * Arnab Pain1, 2 * Barry Rosen1 * William Skarnes1 * Julian C Rayner1 * Oliver Billker1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:1078–1082Year published:(2011)DOI:doi:10.1038/nmeth.1742Received23 May 2011Accepted29 September 2011Published online23 October 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 In malaria parasites, the systematic experimental validation of drug and vaccine targets by reverse genetics is constrained by the inefficiency of homologous recombination and by the difficulty of manipulating adenine and thymine (A+T)-rich DNA of most Plasmodium species in Escherichia coli. We overcame these roadblocks by creating a high-integrity library of Plasmodium berghei genomic DNA (>77% A+T content) in a bacteriophage N15–based vector that can be modified efficiently using the lambda Red method of recombineering. We built a pipeline for generating P. berghei genetic modification vectors at genome scale in serial liquid cultures on 96-well plates. Vectors have long homology arms, which increase recombination frequency up to tenfold over conventional designs. The feasibility of efficient genetic modification at scale will stimulate collaborative, genome-wide knockout and tagging programs for P. berghei. View full text Subject terms: * Molecular Biology * Microbiology * Genetics Figures at a glance * Figure 1: Characterization of the P. berghei large insert genomic DNA library PbG01. () Schematic of the phage N15–derived pJAZZ vector used to generate the genomic library, showing hairpin telomeres (black), telomerase gene (telN), replication factor and origin (repA) and kanamycin-resistance gene (aph). Pb, P. berghei. () Distribution of insert sizes. () PbG01 inserts mapped on 60 kbp of chromosome 9. () Observed genome coverage by actual library inserts compared with modeled coverage by random inserts. Percentage of genes covered to at least 50% is shown. * Figure 2: Modification of PbG01 inserts in E. coli by lambda Red recombineering and site-specific recombinase. () A two-stage strategy for gene deletion. Primer extensions homologous to 3′ and 5′ P. berghei target sequence are shown in magenta and green. () The strategy for 3′ tagging. GOI, gene of interest. () Step-by-step verification of vector product by PCR genotyping. Typical primer locations are indicated in and . *, clones that did not undergo the final Gateway reaction. Arrowhead, colony in which unreacted plasmid persisted in addition to the desired product. * Figure 3: Knock-out vector production in 96 parallel liquid cultures. Steps 1–3 and 5 take place in E. coli; steps 4 and 6 use purified vector in vitro. Cloning and genotyping is deferred until step 6. After introduction of the recombinase plasmid, bacteria are cultured at a permissive temperature of 30 °C (step 1). GOI, gene of interest; kan, kanamycin; tet, tetracycline; TB, terrific broth medium. Recombinase expression is induced by arabinose (step 2), and bacteria are electroporated with PCR products containing the zeo-PheS cassette flanked by 50 bp homologous to the chosen target locus (step 3). An in vitro Gateway reaction (step 4) switches the bacterial marker to one for P. berghei. Plasmids are retransformed into E. coli and plated on p-chlorophenylalanine (YEG-Cl) to select for recombination products lacking pheS (step 5). Colonies are picked for PCR verification (step 6). Percentages are average efficiencies of individual steps. * Figure 4: Validation of recombineered vectors in P. berghei ANKA. () Primary genotyping of resistant parasite pools by Southern hybridization of separated chromosomes. The probe recognizes two copies of the dhfr-ts 3′ UTR in the targeting vector (variable band) and additionally highlights chromosome 7 (endogenous dhfr-ts) and chromosome 3 (gfp transgene integrated into the p230p locus, PBANKA_030600). The expected chromosomal location of target genes is given by the first two digits of the gene identifier, as defined by the P. berghei genome project at the Wellcome Trust Sanger Institute (http://www.genedb.org/Homepage/Pberghei). Asterisk indicates recombinant genotype is in the minority as judged by band intensity. HA, hemagglutinin. () Western blot analysis showing expression of HA-tagged proteins in lysates from schizonts and gametocytes. Arrows point to stage-specifically expressed proteins. () Immunolocalization of HA-tagged proteins (with antibody to hemagglutinin tag; anti-HA) showing localization to the cytosol (PBANKA_082340, PG! K) or a peripheral staining pattern consistent with localization to the inner membrane complex (PBANKA_143660, alveolin 3, IMC1h). Fixed and permeabilized ookinetes were counterstained with Hoechst for DNA and with a monoclonal antibody to the major surface protein P28 (anti-28). Scale bar, 10 μm. * Figure 5: Effect of homology arm length on targeting frequency. () Deletion vectors for the pdeδ gene. The restriction enzymes shown were used to modify lengths of homology arms. () Transfection efficiency is plotted against the sum of both homology arms. Error bars, s.d. (n = 3 transfections). Author information * Abstract * Author information * Supplementary information Affiliations * The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Claudia Pfander, * Burcu Anar, * Frank Schwach, * Thomas D Otto, * Mathieu Brochet, * Katrin Volkmann, * Michael A Quail, * Arnab Pain, * Barry Rosen, * William Skarnes, * Julian C Rayner & * Oliver Billker * Computational Bioscience Research Center, Chemical Life Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia. * Arnab Pain Contributions J.C.R and O.B. initiated and directed the research. C.P., A.P., B.R., W.S., J.C.R. and O.B. designed experiments. F.S., T.D.O., M.A.Q. and A.P. generated, sequenced, mapped and quality controlled the PbG01 library. C.P. and B.A. carried out experiments to develop the recombineering pipeline. C.P., M.B. and K.V. carried out experiments to validate the vectors. C.P. and O.B. wrote the manuscript. All authors analyzed data and edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Oliver Billker or * Julian C Rayner Author Details * Claudia Pfander Search for this author in: * NPG journals * PubMed * Google Scholar * Burcu Anar Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Schwach Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas D Otto Search for this author in: * NPG journals * PubMed * Google Scholar * Mathieu Brochet Search for this author in: * NPG journals * PubMed * Google Scholar * Katrin Volkmann Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Quail Search for this author in: * NPG journals * PubMed * Google Scholar * Arnab Pain Search for this author in: * NPG journals * PubMed * Google Scholar * Barry Rosen Search for this author in: * NPG journals * PubMed * Google Scholar * William Skarnes Search for this author in: * NPG journals * PubMed * Google Scholar * Julian C Rayner Contact Julian C Rayner Search for this author in: * NPG journals * PubMed * Google Scholar * Oliver Billker Contact Oliver Billker Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (647K) Supplementary Figure 1, Supplementary Tables 1–2, Supplementary Protocols 1–3 Additional data - A gene-fusion strategy for stoichiometric and co-localized expression of light-gated membrane proteins
- Nat Methods 8(12):1083-1088 (2011)
Nature Methods | Article A gene-fusion strategy for stoichiometric and co-localized expression of light-gated membrane proteins * Sonja Kleinlogel1 * Ulrich Terpitz1, 4 * Barbara Legrum1 * Deniz Gökbuget1, 4 * Edward S Boyden2 * Christian Bamann1 * Phillip G Wood1 * Ernst Bamberg1, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:1083–1088Year published:(2011)DOI:doi:10.1038/nmeth.1766Received07 April 2011Accepted19 September 2011Published online06 November 2011 Abstract * Abstract * Accession codes * 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 precise co-localization and stoichiometric expression of two different light-gated membrane proteins can vastly improve the physiological usefulness of optogenetics for the modulation of cell excitability with light. Here we present a gene-fusion strategy for the stable 1:1 expression of any two microbial rhodopsins in a single polypeptide chain. By joining the excitatory channelrhodopsin-2 with the inhibitory ion pumps halorhodopsin or bacteriorhodopsin, we demonstrate light-regulated quantitative bi-directional control of the membrane potential in HEK293 cells and neurons in vitro. We also present synergistic rhodopsin combinations of channelrhodopsin-2 with Volvox carteri channelrhodopsin-1 or slow channelrhodopsin-2 mutants, to achieve enhanced spectral or kinetic properties, respectively. Finally, we demonstrate the utility of our fusion strategy to determine ion-turnovers of as yet uncharacterized rhodopsins, exemplified for archaerhodopsin and CatCh, or to correct! pump cycles, exemplified for halorhodopsin. View full text Subject terms: * Biophysics * Molecular Engineering * Neuroscience Figures at a glance * Figure 1: Design of protein chimeras and functional evaluation of ChR2-EYFP-βbR, ChR2-EYFP-βNphR and hChR2(H134R)-mKate-hβbR. () Schematic drawing of the ChR2-EYFP-βbR construct after ligation of ChR2-EYFP with βbR. βHK, β helix derived from the H+,K+-ATPase β subunit; bR, bacteriorhodopsin. Blue and orange arrows indicate light spectrum that preferentially activates each protein. () Diagrams of the tandem expression cassette variants presented in this paper. h indicates human codon-optimized sequence; bp, base pairs. () Typical photocurrents measured at −40 mV in ChR2-EYFP-βbR– and ChR2-EYFP-βNphR–expressing HEK293 cells evoked by sequential equiphoton blue and orange 1-s illumination (bars). () Representative current-to-voltage relationships of ChR2 (inward rectifying) and bacteriorhodopsin or NphR (linear) in ChR2-EYFP-βbR and ChR2-EYFP-βNphR, normalized to the ChR2 current at −100 mV. Vr, reversal potential; Vh, membrane holding potential; I, current. () Western blot analyses of transiently transfected HEK293 cells. Cell lysates comprising 30 μg of protein were analyzed with a! monoclonal ChR2 antibody (anti-ChR2) and with an antibody to GFP (anti-GFP). The 100-kDa bands represent the full-length fusion proteins, the 40-kDa band of hChR2(H134R)-mKate-hβbR represents an additional proteolytic fragment. * Figure 2: Expression and functionality of bidirectional rhodopsin tandem proteins in cultured hippocampal neurons. () Apotome (left) and confocal (middle and right) images of hippocampal neurons expressing ChR2-EYFP-βbR, ChR2-EYFP-βNphR or hChR2(H134R)-mKate-hβbR. Scale bars, 50 μm. () Representative voltage-clamp recording of neurons expressing ChR2-EYFP-βbR exposed to 473-nm (blue bar) and 593-nm (orange bar) light (−60 mV, J473/593 1.4 × 1019 photons s−1 cm−2). () Representative current-clamp recording of hippocampal neurons expressing ChR2-EYFP-βbR exposed to 473-nm (blue bar) and 593-nm (orange bar) light (J473/593 1.4 × 1019 photons s−1 cm−2). () Representative current-clamp recording of neurons simultaneously exposed to 473-nm (6.4 × 1017 photons s−1 cm−2) and 593-nm light (1.1 × 1019 photons s−1 cm−2). () Representative current-clamp recordings of neurons exposed to 10-ms blue light pulses (J473 2.25 × 1018 photons s−1 cm−2) and simultaneous 593-nm light pulses (top; J593 2.25 × 1018 photons s−1 cm−2) or 593-nm background illumination (bottom! ; J593 1.6 × 1019 photons s−1 cm−2). Bars indicate periods of illumination. * Figure 3: Properties of rhodopsin tandem variants measured in HEK293 cells and hippocampal neurons. () Orange-light–induced off-switching of hChR2(D156A) and hChR2(D156A)-mKate-hβbR expressed in transiently transfected HEK293 cells and activated with a 5-ms blue light pulse (blue). Measurements were conducted at −60 mV; orange light onset is indicated by a black arrow. () Action spectra of hVChR1-mKate (n = 5), hChR2-mKate (n = 5) and hVChR1-mKate-βhChR2 (n = 5) determined in HEK293 cells based on 1-ms light pulses (2.5 × 1017 photons s−1 cm−2, −60 mV). The black dashed curve was calculated from I = IChR2 + 1.7 × IhVChR1. () hVChR1-mKate-βhChR2 expressed in hippocampal pyramidal cells. Typical photocurrents evoked by 1-s 473-nm (blue bar), 593-nm (orange bar) and 532-nm (green bar) light pulses in hippocampal pyramidal cells expressing hVChR1-mKate-βhChR2. Representative whole-cell current-clamp recording (bottom, J473/593 2.2 × 1019 photons s−1 cm−2, J532 1.2 × 1019 photons s−1 cm−2). () Typical ChR2-EYFP-βArch photocurrents (J473/593 7.6 × 101! 8 photons s−1 cm−2) measured at −40 mV in HEK293 cells. () Overlay of representative responses of hVChR1-mKate-βhChR2 and hVChR1-mKate-βhChR2(L132C) to 1-s pulses of 473-nm (blue bar) and 593-nm (orange bar) light (1.7 × 1019 photons s−1 cm−2). Currents were normalized to the respective hVChR1 responses. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * JN836740 * JN836741 * JN836742 * JN836743 * JN836744 * JN836745 * JN836746 * JN836747 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Max Planck Institute of Biophysics, Department of Biophysical Chemistry, Frankfurt am Main, Germany. * Sonja Kleinlogel, * Ulrich Terpitz, * Barbara Legrum, * Deniz Gökbuget, * Christian Bamann, * Phillip G Wood & * Ernst Bamberg * The Massachusetts Institute of Technology Media Laboratory, Synthetic Neurobiology Group and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Edward S Boyden * Chemical and Pharmaceutical Sciences Department, Johann Wolfgang Goethe University Frankfurt, Frankfurt am Main, Germany. * Ernst Bamberg * Present addresses: Department of Biotechnology and Biophysics, Julius Maximilians University Würzburg, Biocenter Am Hubland, Würzburg, Germany (U.T.) and Eidgenössische Technische Hochschule Zürich, Institute of Cell Biology, Zurich, Switzerland (D.G.). * Ulrich Terpitz & * Deniz Gökbuget Contributions E.B., S.K. and P.G.W. conceived the molecular and electrophysiological experiments, and S.K., B.L. and U.T. carried them out. D.G. and C.B. performed the antibody screening. S.K., U.T. and C.B. designed and carried out the data analysis. S.K. and E.B. wrote the paper. E.S.B. supplied the Arch plasmid and contributed to writing. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ernst Bamberg Author Details * Sonja Kleinlogel Search for this author in: * NPG journals * PubMed * Google Scholar * Ulrich Terpitz Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara Legrum Search for this author in: * NPG journals * PubMed * Google Scholar * Deniz Gökbuget Search for this author in: * NPG journals * PubMed * Google Scholar * Edward S Boyden Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Bamann Search for this author in: * NPG journals * PubMed * Google Scholar * Phillip G Wood Search for this author in: * NPG journals * PubMed * Google Scholar * Ernst Bamberg Contact Ernst Bamberg Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–6 and Supplementary Notes 1–5 Additional data - Sorting out sequencing data
- Nat Methods 8(12):1089 (2011)
Nature Methods | Erratum Sorting out sequencing data * Monya BakerJournal name:Nature MethodsVolume: 8,Page:1089Year published:(2011)DOI:doi:10.1038/nmeth1211-1089Published online29 November 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Methods8, 799–803 (2011); corrected after print 28 October 2011. In the version of this article initially published, a figure was incorrectly attributed. It is reprinted from reference 3. The error has been corrected in the HTML and PDF versions of the article. Additional data Author Details * Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar
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