Hot off the presses! Aug 01 Nat Methods

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• The year of the chemist
  - Nat Methods 8(8):607 (2011)
  Nature Methods | Editorial International Year of Chemistry Special Feature issue: August 2011 Volume 8, No 8 * * Commentary * Chemistry Methods * Technology Feature * * Contents * Editorial * Historical Commentary The year of the chemist Journal name:Nature MethodsVolume: 8,Page:607Year published:(2011)DOI:doi:10.1038/nmeth.1667Published online28 July 2011 The year 2011 has been designated the International Year of Chemistry. Nature Methods joins in the celebration with a special feature in this issue. View full text Additional data
• The author file: Carl Hansen
  - Nat Methods 8(8):609 (2011)
  Nature Methods | This Month The author file: Carl Hansen * Monya BakerJournal name:Nature MethodsVolume: 8,Page:609Year published:(2011)DOI:doi:10.1038/nmeth.1655Published online28 July 2011 A million picoliter PCR chambers give quick, precise answers. View full text Additional data
• Points of view: Simplify to clarify
  - Nat Methods 8(8):611 (2011)
  Article preview View full access options Nature Methods | This Month Points of view: Simplify to clarify * Bang Wong1Journal name:Nature MethodsVolume: 8,Page:611Year published:(2011)DOI:doi:10.1038/nmeth.1660Published online28 July 2011 In the past two columns I have focused on making information accessible. I discussed ways to avoid color and shift color hues to make them discernible by individuals with color vision deficiencies. In this column I focus on ways to make information apparent by simplifying its presentation. Simplification can lead to greater clarity. In the marketplace, simplicity is the capital used to develop clear brand identity. Apple prides itself on making things simple and on offering products that are easy to use. In science, value is placed on communications that are accurate and concise. Edward Tufte wrote about the data:ink ratio as a call to reduce the proportion of a graphic that is used for decorative purposes or that can be erased without loss of data information1. The best way to simplify is to reduce the number of elements on the page. Every picture and bit of text stimulates the visual senses and contributes to the intricacy of the presentation. The aim is to use the fewest possible 'marks' to convey the message without sacrificing sophistication. Our general tendency is to fill white space with more information. Thus, the judicious removal of material is typically not a natural part of the authoring workflow. But the opportunity lost from including less is gained in greater emphasis on what is shown. I find it helpful to focus on the primary goal of a figure or slide as a guide to pare it down to its constituent parts. I assess every component against this measure to create a hierarchy of information, eliminating extraneous elements and refining the remainder to support the message. In Figure 1, an inversion event that results in two fusion genes is shown. The process as initially illustrated is unnecessarily complicated (Fig. 1a). The diagram can be simplified by combining the first two steps of the process and using fewer arrows to indicate movement (Fig. 1b). These modifications effectively improve the communication by simplifying the design. Figure 1: Simplifying illustrations. () Initial diagram shows chromosomal inversion in three steps with the distal chromosomal ends exchanging places as indicated by arrows. () A simplified version of the diagram in with fewer steps and a single arrow depicting the rotation of the center part of the chromosome. * Full size image (34 KB) * Figures index * Next figure Simple should not be mistaken for simplistic. By simplifying, we take advantage of the way people see and process information. The Gestalt psychologists favored the theoretical approach that explains phenomena of perceptual organization in terms of maximizing simplicity. Simplified presentations with well-ordered layouts and clean lines are more engaging to read and are likely better understood. Figures at a glance * Figure 1: Simplifying illustrations. () Initial diagram shows chromosomal inversion in three steps with the distal chromosomal ends exchanging places as indicated by arrows. () A simplified version of the diagram in with fewer steps and a single arrow depicting the rotation of the center part of the chromosome. * Figure 2: Reducing redundant elements. Words repeated in several labels (magenta boxes) can be pulled out as headers. Using the smallest number of examples to convey a concept will make ideas easier to understand (magenta circles). Grouping labels that describe transformations between steps with arrows and starting or ending products with images (magenta arrows) will add meaningful structure to layouts. Reprinted from Nature Methods2. 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
• OpenFreezer: a reagent information management software system
  - Nat Methods 8(8):612-613 (2011)
  Nature Methods | Correspondence OpenFreezer: a reagent information management software system * Marina Olhovsky1 * Kelly Williton1 * Anna Yue Dai1 * Adrian Pasculescu1 * John Paul Lee1 * Marilyn Goudreault1 * Clark D. Wells2 * Jin Gyoon Park1, 5 * Anne-Claude Gingras1, 3 * Rune Linding4 * Tony Pawson1, 3 * Karen Colwill1 * Affiliations * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:612–613Year published:(2011)DOI:doi:10.1038/nmeth.1658Published online28 July 2011 To the Editor: The rapid growth of large-scale reagent collections that are necessary for systems-level biological approaches has brought about a concomitant need for extensible and flexible tracking systems to manage such resources. To meet this need, we developed OpenFreezer, an open-source, web-based enterprise software application, which maintains detailed and standardized documentation on common laboratory reagents. OpenFreezer tracks both large-scale reagent collections and individual reagents in a laboratory via a centralized repository that allows for easy access, sharing and management of data across projects and research groups. As such, OpenFreezer will be of interest to researchers in a broad range of laboratories and institutes of varying magnitude. View full text Subject terms: * Molecular Biology * Proteomics Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. * Marina Olhovsky, * Kelly Williton, * Anna Yue Dai, * Adrian Pasculescu, * John Paul Lee, * Marilyn Goudreault, * Jin Gyoon Park, * Anne-Claude Gingras, * Tony Pawson & * Karen Colwill * Departments of Biochemistry and Molecular Biology, University of Indiana School of Medicine, Indianapolis, Indiana, USA. * Clark D. Wells * Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Anne-Claude Gingras & * Tony Pawson * Cellular Signal Integration Group (C-SIG), Center for Biological Sequence Analysis (CBS), Department of Systems Biology, Technical University of Denmark (DTU), Lyngby, Denmark. * Rune Linding * Present address: Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, Arizona, USA. * Jin Gyoon Park Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Karen Colwill or * Tony Pawson Author Details * Marina Olhovsky Search for this author in: * NPG journals * PubMed * Google Scholar * Kelly Williton Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Yue Dai Search for this author in: * NPG journals * PubMed * Google Scholar * Adrian Pasculescu Search for this author in: * NPG journals * PubMed * Google Scholar * John Paul Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Marilyn Goudreault Search for this author in: * NPG journals * PubMed * Google Scholar * Clark D. Wells Search for this author in: * NPG journals * PubMed * Google Scholar * Jin Gyoon Park Search for this author in: * NPG journals * PubMed * Google Scholar * Anne-Claude Gingras Search for this author in: * NPG journals * PubMed * Google Scholar * Rune Linding Search for this author in: * NPG journals * PubMed * Google Scholar * Tony Pawson Contact Tony Pawson Search for this author in: * NPG journals * PubMed * Google Scholar * Karen Colwill Contact Karen Colwill Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (5.5M) Supplementary Figures 1–15, Supplementary Tables 1–2 Additional data
• Thwarting amyloid fibers
  - Nat Methods 8(8):615 (2011)
  Nature Methods | Research Highlights Thwarting amyloid fibers * Irene KaganmanJournal name:Nature MethodsVolume: 8,Page:615Year published:(2011)DOI:doi:10.1038/nmeth0811-615Published online28 July 2011 Two structure-driven studies of the culprits behind diseases associated with amyloid fibers give clues to stopping these agents in their tracks. View full text Subject terms: * Structural Biology Additional data Author Details * Irene Kaganman Search for this author in: * NPG journals * PubMed * Google Scholar
• Predicting neurogenesis
  - Nat Methods 8(8):616-617 (2011)
  Nature Methods | Research Highlights Predicting neurogenesis * Natalie de SouzaJournal name:Nature MethodsVolume: 8,Pages:616–617Year published:(2011)DOI:doi:10.1038/nmeth0811-616aPublished online28 July 2011 Expression of a microRNA cluster predicts whether or not a particular human pluripotent stem cell line will differentiate well into neurons. View full text Subject terms: * Stem Cells Additional data Author Details * Natalie de Souza Search for this author in: * NPG journals * PubMed * Google Scholar
• Swift, flexible knockouts
  - Nat Methods 8(8):616-617 (2011)
  Nature Methods | Research Highlights Swift, flexible knockouts * Monya BakerJournal name:Nature MethodsVolume: 8,Pages:616–617Year published:(2011)DOI:doi:10.1038/nmeth0811-616bPublished online28 July 2011 Researchers produce a mouse embryonic stem cell library along with convenient vectors. View full text Subject terms: * Stem Cells Additional data Author Details * Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar
• News in brief
  - Nat Methods 8(8):617 (2011)
  Nature Methods | Research Highlights News in brief Journal name:Nature MethodsVolume: 8,Page:617Year published:(2011)DOI:doi:10.1038/nmeth0811-617Published online28 July 2011 Model organisms * Model organisms * Mass spectrometry * Structural biology * Molecular enginering * Imaging Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Zinc-finger nucleases for gene correction in vivo There a great deal of interest in the use of zinc-finger nucleases for tailored genome engineering, but they have not yet been used for genome modification in vivo. Li et al. now use zinc-finger nuclease–mediated targeting of a promoter-less DNA fragment to correct mutations in a mouse model of hemophilia B. They intraperitoneally injected a hepatotropic adeno-associated viral vector to deliver the nuclease and the therapeutic fragment, and observed sufficiently effective gene targeting to restore functional clotting in the mouse. Li, H.et al. Nature advance online publication (26 June 2011). Mass spectrometry * Model organisms * Mass spectrometry * Structural biology * Molecular enginering * Imaging Introducing the Q Exactive Michalski et al. introduce the Q Exactive, a benchtop mass spectrometer with many beneficial advantages for proteomics research. This instrument combines quadrupole and Orbitrap analyzers, allowing multiplexed operation for single-stage and tandem mass spectrometry. Compared to current top-of-the-line Orbitrap instruments, the Q Exactive also offers high mass spectrometric resolution, identifies more peptides in a single run and is faster and easier to use. Michalski, A.et al. Mol. Cell. Proteomics advance online publication (3 June 2011). Structural biology * Model organisms * Mass spectrometry * Structural biology * Molecular enginering * Imaging Combined solution and solid-state NMR spectroscopy Bertini et al. report a method for investigating the structure of large proteins by nuclear magnetic resonance (NMR) spectroscopy both in solution and in solid state without changing the sample tube. They first performed solution-state NMR measurements on the protein apoferritin. Then, by spinning the sample tube at ultracentrifugation speeds, the protein sedimented on the tube walls, allowing them to make solid-state measurements. The method is applicable to proteins larger than about 100 kilodaltons. Bertini, I.et al. Proc. Natl. Acad. Sci. USA108, 10396–10399 (2011). Molecular enginering * Model organisms * Mass spectrometry * Structural biology * Molecular enginering * Imaging A minimalist nuclear pore Disordered Phe-Gly domains of nucleporins are thought to constitute the selectivity filter at the nuclear pore. Kowalczyk et al. report a biomimetic nuclear pore complex capable of selective protein transport. The minimalist structure consisted of a silicon-based nanopore coated with nucleoporin Phe-Gly domains. Stringency of selectivity depended both on nanopore diameter and the nucleoporin of choice, revealing intrinsic differences between nucleoporin function at the selectivity barrier. Kowalczyk, S.W.et al. Nat. Nanotechnol.6, 433–438 (2011). Imaging * Model organisms * Mass spectrometry * Structural biology * Molecular enginering * Imaging Fluorescent cell biolasers Lasers emit light through optical amplification of input electromagnetic energy. This is achieved through stimulated photon emission by an appropriate "gain medium" inside a highly reflective optical resonator. By pumping single fluorescent cells with brief optical pulses in a mirrored biconcave microcavity, Gather and Yun could stimulate the emission of bright directional laser beams without affecting cell viability. The concept could enable new techniques for cellular and tissue imaging. Gather, M.C. & Yun, S.H.Nat. Photonics5, 406–410 (2011). Additional data
• Next-generation protein binding
  - Nat Methods 8(8):619 (2011)
  Nature Methods | Research Highlights Next-generation protein binding * Daniel EvankoJournal name:Nature MethodsVolume: 8,Page:619Year published:(2011)DOI:doi:10.1038/nmeth0811-619Published online28 July 2011 A next-generation sequencing instrument allows deep quantitative measurement of protein-DNA binding affinity. View full text Subject terms: * Gene Expression Additional data Author Details * Daniel Evanko Search for this author in: * NPG journals * PubMed * Google Scholar
• An affinity for motifs
  - Nat Methods 8(8):620 (2011)
  Nature Methods | Research Highlights An affinity for motifs * Allison DoerrJournal name:Nature MethodsVolume: 8,Page:620Year published:(2011)DOI:doi:10.1038/nmeth0811-620Published online28 July 2011 Antibodies targeting short sequence motifs found in multiple proteins can be used in a discovery array–based platform. View full text Subject terms: * Proteomics Additional data Author Details * Allison Doerr Search for this author in: * NPG journals * PubMed * Google Scholar
• Taming crystals' whimsy
  - Nat Methods 8(8):622 (2011)
  Nature Methods | Research Highlights Taming crystals' whimsy * Petya V KrastevaJournal name:Nature MethodsVolume: 8,Page:622Year published:(2011)DOI:doi:10.1038/nmeth0811-622Published online28 July 2011 Molecularly imprinted polymers act as 'smart' nucleants for protein crystallization. View full text Subject terms: * Structural Biology Additional data Author Details * Petya V Krasteva Search for this author in: * NPG journals * PubMed * Google Scholar
• Protein engineering: navigating between chance and reason
  - Nat Methods 8(8):623-626 (2011)
  Nature Methods | Technology Feature International Year of Chemistry Special Feature issue: August 2011 Volume 8, No 8 * * Commentary * Chemistry Methods * Technology Feature * * Contents * Editorial * Historical Commentary Protein engineering: navigating between chance and reason * Monya Baker1Journal name:Nature MethodsVolume: 8,Pages:623–626Year published:(2011)DOI:doi:10.1038/nmeth.1654Published online28 July 2011 Researchers use large libraries, focused libraries and rational design to engineer useful proteins. 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
• From journal articles to computational models: a new automated tool
  - Nat Methods 8(8):627-628 (2011)
  Article preview View full access options Nature Methods | News and Views From journal articles to computational models: a new automated tool * Tom M. Mitchell1Journal name:Nature MethodsVolume: 8,Pages:627–628Year published:(2011)DOI:doi:10.1038/nmeth.1661Published online28 July 2011 Automated methods can now extract brain-image coordinates appearing in hundreds of publications in targeted topic areas and then integrate these data to form computational models that classify new brain-image data. 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 * Tom M. Mitchell is in the Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Tom M. Mitchell Author Details * Tom M. Mitchell Contact Tom M. Mitchell Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Seeing the light: integrating genome engineering with double-strand break repair
  - Nat Methods 8(8):628-630 (2011)
  Article preview View full access options Nature Methods | News and Views Seeing the light: integrating genome engineering with double-strand break repair * Matthew Porteus1Journal name:Nature MethodsVolume: 8,Pages:628–630Year published:(2011)DOI:doi:10.1038/nmeth.1656Published online28 July 2011 The two-color traffic light reporter reads out what pathway is used to repair a DNA break and will increase insights into genome engineering. 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 * Matthew Porteus is in the Department of Pediatrics at Stanford University, Stanford, California, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Matthew Porteus Author Details * Matthew Porteus Contact Matthew Porteus Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Simply quantifying ubiquitin complexity
  - Nat Methods 8(8):630-631 (2011)
  Article preview View full access options Nature Methods | News and Views Simply quantifying ubiquitin complexity * Eric J Bennett1 * J Wade Harper1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:630–631Year published:(2011)DOI:doi:10.1038/nmeth.1651Published online28 July 2011 An absolute quantification approach combined with differential affinity capture provides a means by which to accurately measure distinct pools of ubiquitin in cells or tissues. 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 * Eric J. Bennett and J. Wade Harper are in the Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * J Wade Harper Author Details * Eric J Bennett Search for this author in: * NPG journals * PubMed * Google Scholar * J Wade Harper Contact J Wade Harper Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• A century of mass spectrometry: from atoms to proteomes
  - Nat Methods 8(8):633-637 (2011)
  Nature Methods | Historical Commentary International Year of Chemistry Special Feature issue: August 2011 Volume 8, No 8 * * Commentary * Chemistry Methods * Technology Feature * * Contents * Editorial * Historical Commentary A century of mass spectrometry: from atoms to proteomes * John R Yates III1Journal name:Nature MethodsVolume: 8,Pages:633–637Year published:(2011)DOI:doi:10.1038/nmeth.1659Published online28 July 2011 Long before mass spectrometry became an important tool for cell biology, it was yielding scientific insights in physics and chemistry. Here is a brief history of how the technology has expanded from a tool for studying atomic structure and characterizing small molecules to its current incarnation as the most powerful technique for analyzing proteomes. View full text Subject terms: * Mass Spectrometry * Chemistry * Proteomics Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * John R. Yates III is in the Department of Chemical Physiology, The Scripps Research Institute, La Jolla, California, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * John R Yates III Author Details * John R Yates III Contact John R Yates III Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (40k) Supplementary Figure 1 Additional data
• Bringing chemistry to life
  - Nat Methods 8(8):638-642 (2011)
  Nature Methods | Commentary International Year of Chemistry Special Feature issue: August 2011 Volume 8, No 8 * * Commentary * Chemistry Methods * Technology Feature * * Contents * Editorial * Historical Commentary Bringing chemistry to life * Michael Boyce1 * Carolyn R Bertozzi2 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:638–642Year published:(2011)DOI:doi:10.1038/nmeth.1657Published online28 July 2011 Bioorthogonal chemistry allows a wide variety of biomolecules to be specifically labeled and probed in living cells and whole organisms. Here we discuss the history of bioorthogonal reactions and some of the most interesting and important advances in the field. 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 * Michael Boyce is in the Department of Chemistry, University of California, Berkeley, California, USA. * Carolyn R. Bertozzi is in the Departments of Chemistry and Molecular and Cell Biology, and Howard Hughes Medical Institute, University of California, Berkeley, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Carolyn R Bertozzi Author Details * Michael Boyce Search for this author in: * NPG journals * PubMed * Google Scholar * Carolyn R Bertozzi Contact Carolyn R Bertozzi Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Fluorescent probes for sensing and imaging
  - Nat Methods 8(8):642-645 (2011)
  Nature Methods | Commentary International Year of Chemistry Special Feature issue: August 2011 Volume 8, No 8 * * Commentary * Chemistry Methods * Technology Feature * * Contents * Editorial * Historical Commentary Fluorescent probes for sensing and imaging * Tasuku Ueno1 * Tetsuo Nagano1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:642–645Year published:(2011)DOI:doi:10.1038/nmeth.1663Published online28 July 2011 A diverse array of small molecule–based fluorescent probes is available for many different types of biological experiments. Here we examine the history of these probes and discuss some of the most interesting applications. 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 * Tasuku Ueno and Tetsuo Nagano are at the Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Tetsuo Nagano Author Details * Tasuku Ueno Search for this author in: * NPG journals * PubMed * Google Scholar * Tetsuo Nagano Contact Tetsuo Nagano Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Chemistry Methods
  - Nat Methods 8(8):646-647 (2011)
  Nature Methods | Chemistry Methods International Year of Chemistry Special Feature issue: August 2011 Volume 8, No 8 * * Commentary * Chemistry Methods * Technology Feature * * Contents * Editorial * Historical Commentary Chemistry Methods Journal name:Nature MethodsVolume: 8,Pages:646–647Year published:(2011)DOI:doi:10.1038/nmeth0811-646 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A fun selection of 'chemistry' papers published in Nature Methods Cell surface protein labeling Chen, I., Howarth, M., Lin, W. & Ting, A.Y.Nat. Methods2, 99–104 (2005). Smaller probes are usually better when it comes to labeling cell-surface proteins because bulky fluorescent proteins can interfere with biological function. However, it has been a challenge to specifically and covalently label proteins on cells with small probes. Alice Ting and colleagues introduced the use of the Escherichia coli enzyme biotin ligase to site-specifically attach a biotin containing a reactive ketone handle to a short acceptor peptide sequence. Kinase profiling with chemical genetics Zhang, C.et al. Nat. Methods2, 435–441 (2005). Allen, J.J.et al. Nat. Methods4, 511–516 (2007). Protein kinases can be engineered to be sensitive to inhibitors and ATP analogs that are not recognized by their wild-type counterparts. However, not all kinases will tolerate modifications to the ATP binding site. Kevan Shokat and colleagues described an approach to identify second-site suppressor mutations to rescue mutant kinase activity. In another paper, Shokat and colleagues traced the activity of kinases via a semisynthetic approach to affinity-tag their substrates for identification. Glycan derivatization Xia, B.et al. Nat. Methods2, 845–850 (2005). In this work, Richard Cummings and colleagues described a method to generate fluorescently labeled glycans by derivatizing them with 2,6-diaminopyridine. This straightforward approach allowed both natural and synthetic glycans to be prepared for glycan arrays for functional glycomics analyses. Sweet! A new caging chromophore Momotake, A., Lindegger, N., Niggli, E., Barsotti, R.J., Ellis-Davies, G.C.Nat. Methods3, 35–40 (2006). Photoactivatable 'caged' bioactive molecules are powerful probes for studying cellular signaling dynamics. Graham Ellis-Davies and colleagues introduced a new caging group based on the nitrobenzofuran chromophore. This caging group had greatly improved efficiency, compared to existing compounds, for both UV-light and two-photon photolysis. Genetic incorporation of unnatural amino acids Ryu, Y. & Schultz, P.G.Nat. Methods3, 263–265 (2006). Liu, W., Brock, A., Chen, S., Chen, S. & Schultz, P.G.Nat. Methods4, 239–244 (2007). Unnatural amino acids carrying fluorescent probes, cross-linkers or defined post-translational modifications can be site-specifically incorporated into proteins using orthogonal tRNA-aminoacyl tRNA synthetase pairs. In two papers, Peter Schultz and colleagues introduced methods for substantially improving the efficiency of unnatural amino acid incorporation in Escherichia coli and in mammalian cells. Monovalent streptavidin, monovalent quantum dots Howarth, M.et al. Nat. Methods3, 267–273 (2006). Howarth, M.et al. Nat. Methods5, 397–399 (2008). The extremely strong streptavidin-biotin interaction is widely exploited for many biochemistry and cell biology applications. But streptavidin is a tetramer, containing four binding sites, which complicates fluorescence tracking of biotinylated biomolecules on the cell. Alice Ting and colleagues engineered a monovalent streptavidin variant that overcomes these issues and later used the variant to generate monovalent, reduced-size quantum dots. Phosphoproteome enrichment Bodenmiller, B., Mueller, L.N., Mueller, M., Domon, B. & Aebersold, R.Nat. Methods4, 231–237 (2007). In this comparative analysis, Ruedi Aebersold and colleagues assessed the performance of three common phosphopeptide enrichment methods (phosphoramidate chemistry, immobilized metal affinity chromatography and titanium dioxide). They found that each of the methods enriched different portions of the phosphoproteome, suggesting that no single method is sufficient for comprehensive phosphoproteomics analysis. Probes for metabolite enrichment Carlson, E.E. & Cravatt, B.F., Nat. Methods4, 429–435 (2007). Although a wide variety of methods have been developed to study the genome and the proteome, tools for investigating the metabolome have been lacking. Erin Carlson and Benjamin Cravatt presented chemoselective probes to tag, enrich and profile various chemical classes of metabolites by liquid chromatography-mass spectrometry. Macrocycles for enzyme assays Hennig, A., Bakirci, H. & Nau, W.M.Nat. Methods4, 629–632 (2007). Macrocycles have been known to form complexes with biomolecules, but Werner Nau and colleagues took this a step further, showing that macrocycles could be used in label-free enzyme activity assays. They selected a macrocycle that strongly binds an enzyme product but not the substrate. As the product is generated, it displaces a fluorescent dye from the macrocycle, producing a convenient readout. Polymers for prion-strain discrimination Sigurdson, C.J.et al. Nat. Methods4, 1023–1030 (2007). In this work, Adriano Aguzzi and colleagues introduced luminescent conjugated polymers for prion-strain discrimination. These polymers contain a swiveling thiophene backbone; the geometry of the backbone modulates their fluorescence such that when they bind to different prion aggregates, they emit distinct fluorescence spectra. Calcium indicators Mank, M.et al. Nat. Methods5, 805–811 (2008). Tian, L.et al. Nat. Methods6, 875–881 (2009). Horikawa, K.et al. Nat. Methods7, 729–732 (2010). Fluorescent calcium indicators provide a visual readout of changes in calcium in living cells and are the most widely used sensors in fluorescence microscopy. But performance limitations of existing sensors necessitate development of new variants. Several improved calcium indicators—some of which are listed below, from the groups of Oliver Griesbeck, Loren Looger and Takeharu Nagai—have been published in Nature Methods over the years, and we expect this number to grow. Labeling glycoproteins on living cells Zeng, Y., Ramya, T.N.C., Dirksen, A., Dawson, P.E. & Paulson, J.C.Nat. Methods6, 207–209 (2009). In this work, James Paulson and colleagues introduced a chemical method for efficiently labeling cell-surface glycans containing sialic acid. By applying mild periodate oxidation, the polyhydroxy side chain of sialic acid can be selectively oxidized to generate an aldehyde, which then serves as a handle for aniline-catalyzed oxime ligation with a suitable tag. Amphiphiles for membrane protein crystallization Chae, P.S.et al. Nat. Methods7, 1003–1008 (2010). Membrane protein crystallization is a notoriously challenging problem in structural biology. With a family of maltose-neopentyl glycol amphiphiles introduced by Samuel Gellman, Brian Kobilka, Bernadette Byrne and colleagues, structural biologists now have new tools for stabilizing membrane proteins, which, in some cases, can lead to their successful crystallization. Chemical imaging Nasse, M.J.et al. Nat. Methods8, 413–416 (2011). Fourier-transform infrared (FTIR) microscopy is a label-free imaging technique based on the detection of characteristic chemical bond vibration signals. Seeking to dramatically improve FTIR imaging resolution and image acquisition speed, Carol Hirschmugl, Rohit Bhargava and colleagues combined the use of multiple synchrotron beams with wide-field detection. Fluorogenic sequencing Sims, P.A., Greenleaf, W.J., Duan, H. & Xie, X.S.Nat. Methods8, 575–580 (2011). The wide variety of DNA sequencing methods owes much to chemistry. In this work, Sunney Xie and colleagues reported a multiplex sequencing-by-synthesis approach in which the incorporation of a terminal-phosphate-labeled fluorogenic nucleotide by DNA polymerase generates a fluorescent product that is trapped in a sealed microreactor. This simple approach relies on single-color fluorescence detection and does not require a real-time readout. Additional data
• Megapixel digital PCR
  - Nat Methods 8(8):649-651 (2011)
  Nature Methods | Brief Communication Megapixel digital PCR * Kevin A Heyries1, 2 * Carolina Tropini1, 2, 6 * Michael VanInsberghe1, 2 * Callum Doolin1, 2 * Oleh I Petriv1, 2 * Anupam Singhal1, 2, 3, 4 * Kaston Leung1, 2, 5 * Curtis B Hughesman3, 4 * Carl L Hansen1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:649–651Year published:(2011)DOI:doi:10.1038/nmeth.1640Received24 January 2011Accepted26 May 2011Published online03 July 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We present a microfluidic 'megapixel' digital PCR device that uses surface tension–based sample partitioning and dehydration control to enable high-fidelity single DNA molecule amplification in 1,000,000 reactors of picoliter volume with densities up to 440,000 reactors cm−2. This device achieves a dynamic range of 107, single-nucleotide-variant detection below one copy per 100,000 wild-type sequences and the discrimination of a 1% difference in chromosome copy number. View full text Subject terms: * Gene Expression * Single Molecule * Lab-on-a-chip * Genomics Figures at a glance * Figure 1: Megapixel digital PCR using planar emulsion arrays. () Schematic of megapixel digital PCR device, with insets showing the array and chamber geometries. Hydration channels surrounding the array are shown in red. Scale bar, 3 mm. () Schematic of the layered device structure, showing the position of the embedded parylene C layer. () Optical micrograph of reaction chambers filled with blue dye (top) and after oil partitioning (arrow). Scale bar, 50 μm. () Expanded view of a section of the device showing 342 chambers. The detection of HLCS and RPPH1 sequences from human genomic DNA is visible in green and blue, respectively. Separate fluorescence channels (middle) are shown from boxed region at the top. Intensity profile across the highlighted strip of five chambers is shown at the bottom. Scale bars, 50 μm. () Histograms of normalized fluorescence intensities over 100,000 chambers. The total number of positive counts as well as the normalized mean and s.d. of fluorescence intensity (arbitrary units) are listed. The red line ind! icates the threshold used to classify 'positive' and 'negative' chambers. * Figure 2: Dynamic range, sensitivity and precision of megapixel digital PCR. () Digital PCR response for the RPPH1 gene measured from dilutions of DNA template and synthetic fragment of RPPH1 gene. Solid lines show fit to expected binomial distribution for synthetic fragment (R2 = 0.9999) and genomic DNA (R2 = 0.9978). Inset, the measured λ values for the synthetic fragment of RPPH1 (molecules per chamber) at high fill factors (values indicated in the graph) plotted against expected values as determined by dilutions of the stock solution. Solid line shows linear regression (y = 1.08x, R2 = 0.9992). () Digital PCR measurements of serial dilutions of two plasmids containing either the wild-type sequence or sequence encoding V617F JAK2 at relative dilutions ranging from 1:1 to 1:100,000. Measurements were on subarrays of 105 chambers or 5 × 105 chambers. Rate of false positive SNV detection owing to polymerase errors is indicated by dashed line. () Replicate measurements of the abundance of the RPPH1 and HLCS genes from a single sample of normal human! genomic DNA (100,000 chambers) are plotted in the order of relative position across the array. Error bars represent theoretical noise calculated by propagating the binomial noise of each allele (one s.d.) through the ratio. () Ratios of the RPPH1 and HLCS gene for samples of normal human genomic DNA spiked with 2%, 4% or 6% T21 genomic DNA. All ratios are normalized by that obtained from a matched unspiked sample. Expected values are indicated (red lines). Error bars, theoretical precision defined as 1 s.d. of binomial noise in HLCS and RPPH1 measurements propagated through the ratio. Author information * Author information * Supplementary information Affiliations * Centre for High-Throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada. * Kevin A Heyries, * Carolina Tropini, * Michael VanInsberghe, * Callum Doolin, * Oleh I Petriv, * Anupam Singhal, * Kaston Leung & * Carl L Hansen * Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada. * Kevin A Heyries, * Carolina Tropini, * Michael VanInsberghe, * Callum Doolin, * Oleh I Petriv, * Anupam Singhal, * Kaston Leung & * Carl L Hansen * Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia, Canada. * Anupam Singhal & * Curtis B Hughesman * Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada. * Anupam Singhal & * Curtis B Hughesman * Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada. * Kaston Leung * Present address: Biophysics Program, Stanford University, Stanford, California, USA. * Carolina Tropini Contributions K.A.H., C.T., M.V., C.D., A.S., K.L. and C.L.H. developed and optimized the device design. K.A.H. and C.D. developed fabrication protocols and fabricated devices. K.A.H., C.T. and C.D. performed the on-chip digital PCR experiments. K.A.H., O.I.P. and C.B.H. performed the off-chip qPCR experiments. M.V. and C.D. developed image analysis software. K.A.H., C.T., M.V. and C.D. performed data analysis. C.L.H. designed research. K.A.H., C.T., M.V. and C.L.H. wrote the manuscript. Competing financial interests C.L.H. has a financial interest in Fluidigm Corporation, which has products related to the subject matter of this study. Corresponding author Correspondence to: * Carl L Hansen Author Details * Kevin A Heyries Search for this author in: * NPG journals * PubMed * Google Scholar * Carolina Tropini Search for this author in: * NPG journals * PubMed * Google Scholar * Michael VanInsberghe Search for this author in: * NPG journals * PubMed * Google Scholar * Callum Doolin Search for this author in: * NPG journals * PubMed * Google Scholar * Oleh I Petriv Search for this author in: * NPG journals * PubMed * Google Scholar * Anupam Singhal Search for this author in: * NPG journals * PubMed * Google Scholar * Kaston Leung Search for this author in: * NPG journals * PubMed * Google Scholar * Curtis B Hughesman Search for this author in: * NPG journals * PubMed * Google Scholar * Carl L Hansen Contact Carl L Hansen Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–9, Supplementary Tables 1–2 and Supplementary Notes 1–9 Additional data
• CREST maps somatic structural variation in cancer genomes with base-pair resolution
  - Nat Methods 8(8):652-654 (2011)
  Nature Methods | Brief Communication CREST maps somatic structural variation in cancer genomes with base-pair resolution * Jianmin Wang1 * Charles G Mullighan2 * John Easton3 * Stefan Roberts3, 4, 5 * Sue L Heatley2 * Jing Ma1 * Michael C Rusch4, 4 * Ken Chen6, 7 * Christopher C Harris6 * Li Ding6, 7 * Linda Holmfeldt2 * Debbie Payne-Turner2 * Xian Fan6 * Lei Wei2, 4 * David Zhao1 * John C Obenauer1 * Clayton Naeve1 * Elaine R Mardis6, 7 * Richard K Wilson6, 7 * James R Downing2 * Jinghui Zhang4 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:652–654Year published:(2011)DOI:doi:10.1038/nmeth.1628Received16 November 2010Accepted19 May 2011Published online12 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We developed 'clipping reveals structure' (CREST), an algorithm that uses next-generation sequencing reads with partial alignments to a reference genome to directly map structural variations at the nucleotide level of resolution. Application of CREST to whole-genome sequencing data from five pediatric T-lineage acute lymphoblastic leukemias (T-ALLs) and a human melanoma cell line, COLO-829, identified 160 somatic structural variations. Experimental validation exceeded 80%, demonstrating that CREST had a high predictive accuracy. View full text Subject terms: * Bioinformatics * Sequencing * Genomics Figures at a glance * Figure 1: Mapping SV breakpoints using soft-clipped reads. () Illustration of SV analysis using discordantly mapped paired-end reads versus mapping using soft-clipping reads. Red and blue lines mark two discontinuous genomic regions. () An example of using soft-clipping signature to identify an interchromosomal translocation. The shown region is base pairs 206579705–206579789 on chromosome 2. Reference genome sequence is at the top and next-generation sequencing reads are shown below it with highlighted mismatches to the reference (red letters) and soft-clipping subsequences not aligned to the reference (gray letters). Upper-case letters represent high-quality bases (phred score ≥20) and the darkness of shading correlates to lower quality score. In this example, the soft-clipping subsequences map to chromosome X, revealing a chromosome 2 to chromosome X translocation. () The five-step CREST algorithm: extraction of soft-clipped reads in the binary alignment/map (bam) file; assembly of soft-clipped reads at a putative breakpoint ! into a contig; mapping of the contig against the reference genome to identify candidate partner breakpoints; identification of all possible soft-clipped reads and assembly into a contig; and alignment of the contig derived from the partner back to the reference genome. A match to the initial breakpoint is considered a SV. * Figure 2: SV validation result for one T-ALL sample (SJTALL003). () PCR amplification of 28 SV breakpoints predicted by CREST. All putative SVs except for those tested in lanes marked in blue were validated by Sanger sequencing. Lanes marked in red indicate amplicons listed in –. () Schematic of complex inter-chromosomal translocations involving chromosomes (chr.) 1, 4, 5 and 10. Blue segments on chromosomes 4 and 10 are deletion segments identified by next-generation sequencing coverage analysis. (–) Sanger sequencing data for the four breakpoints involved in the complex rearrangement marked in red in . Author information * Author information * Supplementary information Affiliations * Department of Information Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * Jianmin Wang, * Jing Ma, * David Zhao, * John C Obenauer & * Clayton Naeve * Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * Charles G Mullighan, * Sue L Heatley, * Linda Holmfeldt, * Debbie Payne-Turner, * Lei Wei & * James R Downing * Pediatric Cancer Genome Project, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * John Easton & * Stefan Roberts * Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * Stefan Roberts, * Michael C Rusch, * Lei Wei & * Jinghui Zhang * Department of Medical Engineering, Washington University, St. Louis, Missouri, USA. * Stefan Roberts * The Genome Center at Washington University, St. Louis, Missouri, USA. * Ken Chen, * Christopher C Harris, * Li Ding, * Xian Fan, * Elaine R Mardis & * Richard K Wilson * Department of Genetics, Washington University, St. Louis, Missouri, USA. * Ken Chen, * Li Ding, * Elaine R Mardis & * Richard K Wilson Contributions J.Z. conceived and designed the CREST algorithm. J.W. implemented the algorithm. J.R.D. and C.G.M. designed the experiment. J.W., J.Z., S.R., J.M., M.C.R., K.C., C.C.H., L.D., X.F. and L.W. analyzed the data. C.G.M., J.E., S.L.H., L.H. and D.P.-T. performed validation assay. E.R.M. and R.K.W. supervised whole-genome sequencing data generation. D.Z., J.C.O. and C.N. set up the computing infrastructure. J.R.D. and J.Z. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jinghui Zhang Author Details * Jianmin Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Charles G Mullighan Search for this author in: * NPG journals * PubMed * Google Scholar * John Easton Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Roberts Search for this author in: * NPG journals * PubMed * Google Scholar * Sue L Heatley Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Michael C Rusch Search for this author in: * NPG journals * PubMed * Google Scholar * Ken Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher C Harris Search for this author in: * NPG journals * PubMed * Google Scholar * Li Ding Search for this author in: * NPG journals * PubMed * Google Scholar * Linda Holmfeldt Search for this author in: * NPG journals * PubMed * Google Scholar * Debbie Payne-Turner Search for this author in: * NPG journals * PubMed * Google Scholar * Xian Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Lei Wei Search for this author in: * NPG journals * PubMed * Google Scholar * David Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * John C Obenauer Search for this author in: * NPG journals * PubMed * Google Scholar * Clayton Naeve Search for this author in: * NPG journals * PubMed * Google Scholar * Elaine R Mardis Search for this author in: * NPG journals * PubMed * Google Scholar * Richard K Wilson Search for this author in: * NPG journals * PubMed * Google Scholar * James R Downing Search for this author in: * NPG journals * PubMed * Google Scholar * Jinghui Zhang Contact Jinghui Zhang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 2 (41K) Summary of CREST SV analysis results for COLO-829. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–10, Supplementary Tables 1, 3–4, Supplementary Data 1–3, Supplementary Discussion Additional data
• Large-scale phosphosite quantification in tissues by a spike-in SILAC method
  - Nat Methods 8(8):655-658 (2011)
  Nature Methods | Brief Communication Large-scale phosphosite quantification in tissues by a spike-in SILAC method * Mara Monetti1 * Nagarjuna Nagaraj1 * Kirti Sharma1 * Matthias Mann1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:655–658Year published:(2011)DOI:doi:10.1038/nmeth.1647Received12 January 2011Accepted09 June 2011Published online10 July 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Despite progress in mass spectrometry (MS)-based phosphoproteomics, large-scale in vivo analyses remain challenging. Here we report a 'spike-in' stable-isotope labeling with amino acids in cell culture (SILAC) methodology using standards derived from labeled mouse liver cell lines, using which we analyzed insulin signaling. With this approach we identified 15,000 phosphosites and quantitatively compared 10,000 sites in response to insulin treatment, creating a very large, accurately quantified in vivo phosphoproteome dataset. View full text Subject terms: * Proteomics * Mass Spectrometry * Signal Transduction * Model Organisms Figures at a glance * Figure 1: Liver quantitative phosphoproteomic analyses. () Experimental design for the analyses of control (PBS-treated) and insulin-treated mice. A mixture of insulin-treated and untreated Hepa1-6 cells labeled via SILAC serves as an internal standard. The mixtures are digested, and the resulting peptides are analyzed by high-resolution LC-MS/MS. Ratios from control and insulin-treated samples are then compared ('ratio of ratios'). SCX, strong cation exchange. (,) Distribution of the ratios between the phosphopeptides of the control livers and of the Hepa1-6 cells () and between phosphopeptides of insulin-treated livers and of Hepa1-6 cells () (labeled liver/Hepa1-6). (,) 'Ratio of ratios' histogram () and boxplot of the phosphosite ratios between insulin-treated and control (labeled insulin/PBS) liver samples () (representative example of one technical replicate; triplicate distribution is even narrower). * Figure 2: Insulin signaling pathway members activated in liver upon insulin treatment. After interacting with the insulin receptor tyrosine kinase, insulin activates mediators that regulate the metabolic and mitogenic effects of insulin. Phosphorylation changes after insulin treatment of mice are shown. The pathway in the figure summarizes the literature11, and the phosphosites identified as regulated in this study are indicated by colored circles. Author information * Author information * Supplementary information Affiliations * Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany. * Mara Monetti, * Nagarjuna Nagaraj, * Kirti Sharma & * Matthias Mann Contributions M. Monetti designed and performed the experiments, analyzed the data and wrote the paper. N.N. contributed to experiments, data analysis and writing the manuscript. K.S. assisted with data analysis and writing the manuscript. M. Mann supervised the work and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthias Mann Author Details * Mara Monetti Search for this author in: * NPG journals * PubMed * Google Scholar * Nagarjuna Nagaraj Search for this author in: * NPG journals * PubMed * Google Scholar * Kirti Sharma Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias Mann Contact Matthias Mann Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 2 (9M) List of all class I sites quantified in at least two experiments with details including the coefficient of variation and directional variability of the ratios. * Supplementary Table 3 (1M) List of regulated class I sites and sites that are exclusively quantified either in insulin- or PBS- treated samples. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–3, Supplementary Table 1 and Supplementary Note Additional data
• A public genome-scale lentiviral expression library of human ORFs
  - Nat Methods 8(8):659-661 (2011)
  Nature Methods | Brief Communication A public genome-scale lentiviral expression library of human ORFs * Xiaoping Yang1, 10 * Jesse S Boehm2, 10 * Xinping Yang3, 4, 5, 10 * Kourosh Salehi-Ashtiani3, 4, 5, 9, 10 * Tong Hao3, 4, 5, 10 * Yun Shen3, 4, 5, 10 * Rakela Lubonja1, 10 * Sapana R Thomas2 * Ozan Alkan1 * Tashfeen Bhimdi1 * Thomas M Green1 * Cory M Johannessen2, 6, 7 * Serena J Silver1 * Cindy Nguyen1 * Ryan R Murray3, 4, 5 * Haley Hieronymus2, 8, 9 * Dawit Balcha3, 4, 5 * Changyu Fan3, 4, 5 * Chenwei Lin3, 4, 5 * Lila Ghamsari3, 4, 5 * Marc Vidal3, 4, 5 * William C Hahn2, 3, 6, 7 * David E Hill3, 4, 5 * David E Root1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:659–661Year published:(2011)DOI:doi:10.1038/nmeth.1638Received17 December 2010Accepted13 May 2011Published online26 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Functional characterization of the human genome requires tools for systematically modulating gene expression in both loss-of-function and gain-of-function experiments. We describe the production of a sequence-confirmed, clonal collection of over 16,100 human open-reading frames (ORFs) encoded in a versatile Gateway vector system. Using this ORFeome resource, we created a genome-scale expression collection in a lentiviral vector, thereby enabling both targeted experiments and high-throughput screens in diverse cell types. View full text Subject terms: * Cell Biology * Molecular Biology * Genetics * Systems Biology Figures at a glance * Figure 1: Overview of hORFeome V8.1. () Schematic of hORFeome V8.1 creation. () Sequencing outcomes for 19,281 ORF samples. We fully sequenced 14,524 ORFs (complete, accepted). We sequenced 198 ORFs but rejected them because of a missing start codon (complete, rejected). Another 825 ORFs obtained sequences with undetermined nucleotides (partial), and 823 isoform ORFs were made clonal but were intentionally not sequenced (not attempted). () Alignment of the 14,524 completely sequenced clones with MGC templates. Of these, 12,736 clones had identical sequence as templates or had one synonymous error only (perfect) and 1,788 clones had additional mutations (mutant). () Alignment of the 14,524 completely sequenced clones with NCBI RefSeq transcripts. Of these, 10,216 ORFs represent full-length coding sequences with >99% homology (full), 1,545 ORFs were partial-length coding sequences with >85% homology (partial) and 2,763 clones fell into other categories. * Figure 2: Performance of the CCSB-Broad lentiviral expression library. () Micrographs show A549 lung cancer cell lines stained with an antibody to the V5 epitope after lentiviral infection and 3 d of growth in blasticidin. Wells in which no virus was added are outlined in yellow. () Distribution of ORF sizes and average viral titer as a function of ORF size. () ORF expression as a function of ORF size. Background was assessed from cells expressing a control vector without V5 expression. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Xiaoping Yang, * Jesse S Boehm, * Xinping Yang, * Kourosh Salehi-Ashtiani, * Tong Hao, * Yun Shen & * Rakela Lubonja Affiliations * RNA interference (RNAi) Platform, Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA. * Xiaoping Yang, * Rakela Lubonja, * Ozan Alkan, * Tashfeen Bhimdi, * Thomas M Green, * Serena J Silver, * Cindy Nguyen & * David E Root * Cancer Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. * Jesse S Boehm, * Sapana R Thomas, * Cory M Johannessen, * Haley Hieronymus & * William C Hahn * Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Xinping Yang, * Kourosh Salehi-Ashtiani, * Tong Hao, * Yun Shen, * Ryan R Murray, * Dawit Balcha, * Changyu Fan, * Chenwei Lin, * Lila Ghamsari, * Marc Vidal, * William C Hahn & * David E Hill * Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Xinping Yang, * Kourosh Salehi-Ashtiani, * Tong Hao, * Yun Shen, * Ryan R Murray, * Dawit Balcha, * Changyu Fan, * Chenwei Lin, * Lila Ghamsari, * Marc Vidal & * David E Hill * Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Xinping Yang, * Kourosh Salehi-Ashtiani, * Tong Hao, * Yun Shen, * Ryan R Murray, * Dawit Balcha, * Changyu Fan, * Chenwei Lin, * Lila Ghamsari, * Marc Vidal & * David E Hill * Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Cory M Johannessen & * William C Hahn * Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Cory M Johannessen & * William C Hahn * Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA. * Haley Hieronymus * Present addresses: New York University Abu Dhabi, Abu Dhabi, United Arab Emirates and Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA (K.S.-A.) and Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA (H.H.). * Kourosh Salehi-Ashtiani & * Haley Hieronymus Contributions J.S.B., Xia. Y. and D.E.R. wrote the manuscript and, together with D.E.H., K.S.-A. and M.V., designed and supervised the process of creating clonal sequenced ORF collections. Xin. Y., D.B., L.G., J.S.B., S.R.T., H.H., R.R.M., K.S.-A. and C.M.J. generated the starting collections of ORFs. R.L., O.A., J.S.B., C.N., Xia. Y., S.J.S., S.R.T. and C.M.J. created the final libraries, DNA, viruses and pLX vectors, and evaluated expression. T.H., Y.S., C.F., C.L., J.S.B., T.B., T.M.G., Xia. Y. and D.E.R. performed bioinformatic analyses. M.V., W.C.H., D.E.H. and D.E.R. supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Marc Vidal or * William C Hahn or * David E Hill or * David E Root Author Details * Xiaoping Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Jesse S Boehm 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 * Tong Hao Search for this author in: * NPG journals * PubMed * Google Scholar * Yun Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Rakela Lubonja Search for this author in: * NPG journals * PubMed * Google Scholar * Sapana R Thomas Search for this author in: * NPG journals * PubMed * Google Scholar * Ozan Alkan Search for this author in: * NPG journals * PubMed * Google Scholar * Tashfeen Bhimdi Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas M Green Search for this author in: * NPG journals * PubMed * Google Scholar * Cory M Johannessen Search for this author in: * NPG journals * PubMed * Google Scholar * Serena J Silver Search for this author in: * NPG journals * PubMed * Google Scholar * Cindy Nguyen Search for this author in: * NPG journals * PubMed * Google Scholar * Ryan R Murray Search for this author in: * NPG journals * PubMed * Google Scholar * Haley Hieronymus Search for this author in: * NPG journals * PubMed * Google Scholar * Dawit Balcha Search for this author in: * NPG journals * PubMed * Google Scholar * Changyu Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Chenwei Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Lila Ghamsari Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Vidal Contact Marc Vidal Search for this author in: * NPG journals * PubMed * Google Scholar * William C Hahn Contact William C Hahn Search for this author in: * NPG journals * PubMed * Google Scholar * David E Hill Contact David E Hill Search for this author in: * NPG journals * PubMed * Google Scholar * David E Root Contact David E Root 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 1–12, Supplementary Tables 1–4; Supplementary Notes 1–8 Additional data
• Functional ultrasound imaging of the brain
  - Nat Methods 8(8):662-664 (2011)
  Nature Methods | Brief Communication Functional ultrasound imaging of the brain * Emilie Macé1 * Gabriel Montaldo1 * Ivan Cohen2 * Michel Baulac2 * Mathias Fink1 * Mickael Tanter1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:662–664Year published:(2011)DOI:doi:10.1038/nmeth.1641Received03 December 2010Accepted27 May 2011Published online03 July 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We present functional ultrasound (fUS), a method for imaging transient changes in blood volume in the whole brain at better spatiotemporal resolution than with other functional brain imaging modalities. fUS uses plane-wave illumination at high frame rate and can measure blood volumes in smaller vessels than previous ultrasound methods. fUS identifies regions of brain activation and was used to image whisker-evoked cortical and thalamic responses and the propagation of epileptiform seizures in the rat brain. View full text Subject terms: * Imaging * Neuroscience Figures at a glance * Figure 1: Principles for performing fUS in the rat brain. () Schematic setup depicting the ultrasonic probe, cranial window and a schema of a coronal slice from the rat brain. The principles of ultrasound imaging are schematized. () fUS is performed by emitting 17 planar ultrasonic waves tilted with different angles into the rat brain. The ultrasonic echoes produce 17 images of 2 cm × 2 cm (amplitudes are in decibels, dB). Summing up these images results in a compound image acquired in 1 ms. The entire fUS sequence consists of acquiring 200 compound images in 200 ms. () Temporal variation s(t) of the backscattered ultrasonic amplitude in one pixel (normalized by the maximum amplitude). The blood signal sB is extracted by applying a high-pass filter (same scale in the two graphs). () Frequency spectrum of sB (top left). Two parameters are extracted from this spectrum: the central frequency fD, which is proportional to the axial blood velocity with respect to the z axis and gives rise to the axial velocity image (below left); and th! e intensity (power Doppler), which is proportional to the cerebral blood volume and gives rise to the power Doppler image (right). fUS is based on power Doppler images. Scale bars, 2 mm. * Figure 2: Applications of fUS imaging. (–) fUS imaging of task-evoked brain activation in the rat brain. () Power Doppler (PD) fUS images are acquired every 3 s during whisker stimulation (probe in the coronal plane, stereotaxic coordinates β −2.5 mm). The whisker stimulation pattern (red line) consisted of 32 s on and 64 s off repeated 10 times (7 cycles shown). The PD is plotted in percentage relative to the baseline (n = 6). () Representative example of an activation map obtained when stimulating the left whiskers. We calculated maps as the correlation coefficient between the power Doppler signal and the stimulus pattern. S1, primary somatosensory barrel cortex; VPM, ventral posterior medial nucleus. S1 and VPM regions were delineated from a rat brain atlas. () Representative example of an activation map obtained when stimulating a single whisker. (–) fUS imaging of transient brain activity in a rat model of epilepsy. () Schematic setup for the imaging of epileptiform seizures. We injected 4-AP focally ! in the cortex and implanted cortical electrodes for EEG recordings (n = 4). () Spatiotemporal spreading of epileptiform activity for two selected ictal events. The power Doppler signal (in percentage relative to baseline) is superimposed on a control power Doppler image. () Comparison between electrical recordings (EEG; green line) and the power Doppler signal (PD, blue line) at the site of 4-AP injection. The two events in the shaded region are zoomed in on the graph at right. () Maps of the propagation delay of blood volume changes from the focus to other regions (propagation delay in seconds is color coded following the legend on the right: onset is indicated in blue; and blue to red indicates delay increases). Arrows represent the direction of propagation. Scale bars, 2 mm. Author information * Author information * Supplementary information Affiliations * Institut Langevin, Ecole Supérieure de Physique et de Chimie Industrielles Paris Tech, Centre National de la Recherche Scientifique (CNRS) UMR7587, Institut National de la Santé et de la Recherche Médicale (INSERM) U979, Université Paris VII, Paris, France. * Emilie Macé, * Gabriel Montaldo, * Mathias Fink & * Mickael Tanter * Centre de Recherche Institut du Cerveau et de la Moelle Epinière, INSERM UMRS 975, CNRS UMR7225, Centre Hospitalier Universitaire Pitié Salpêtrière, Paris, France. * Ivan Cohen & * Michel Baulac Contributions M.B., M.F. and M.T. conceived and initiated the project; M.T. supervised the project. E.M., G.M. and I.C. designed and performed experiments; E.M., G.M. and M.T. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mickael Tanter Author Details * Emilie Macé Search for this author in: * NPG journals * PubMed * Google Scholar * Gabriel Montaldo Search for this author in: * NPG journals * PubMed * Google Scholar * Ivan Cohen Search for this author in: * NPG journals * PubMed * Google Scholar * Michel Baulac Search for this author in: * NPG journals * PubMed * Google Scholar * Mathias Fink Search for this author in: * NPG journals * PubMed * Google Scholar * Mickael Tanter Contact Mickael Tanter Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Movies * Supplementary Video 1 (3M) Video showing changes in cerebral blood volume during induced epileptiform activity (4AP injection) imaged by fUS. The epileptiform activity is recorded during 1 hour with a short control time at the beginning (baseline). The video displays the variation in Power Doppler relative to the baseline in a color scale ranging from - 50% (blue) to + 50% (red), superimposed on a control Power Doppler image. fUS acquisitions are performed every 3s. The injection site is represented by a green circle and the time relative to injection is displayed in the video. The blood volume response to onset and propagation of hyperactivity is visualized from the injection site to other brain regions. Zip files * Supplementary Software (5M) Implementation of fUS. A guide describes ultrasound sequences and signal processing. Code and data example are also provided. PDF files * Supplementary Text and Figures (572K) Supplementary Figures 1–3 and Supplementary Notes 1 and 2 Additional data
• Large-scale automated synthesis of human functional neuroimaging data
  - Nat Methods 8(8):665-670 (2011)
  Nature Methods | Article Large-scale automated synthesis of human functional neuroimaging data * Tal Yarkoni1 * Russell A Poldrack2, 3, 4 * Thomas E Nichols5, 6 * David C Van Essen7 * Tor D Wager1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:665–670Year published:(2011)DOI:doi:10.1038/nmeth.1635Received24 January 2011Accepted24 May 2011Published online26 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale. View full text Subject terms: * Neuroscience * Imaging Figures at a glance * Figure 1: Schematic overview of NeuroSynth framework and applications. () Outline of the NeuroSynth approach. The full text of a large corpus of articles is retrieved and terms of scientific interest are stored in a database. Articles are retrieved from the database on the basis of a user-entered search string (for example, 'pain') and peak coordinates from the associated articles are extracted from tables. A meta-analysis of the peak coordinates is automatically performed, producing a whole-brain map of the posterior probability of the term given activation at each voxel (P(pain|activation)). () Outlines of forward and reverse inference in brain imaging. Given a known psychological manipulation, one can quantify the corresponding changes in brain activity and generate a forward inference, but given an observed pattern of activity, drawing a reverse inference about associated cognitive states is more difficult because multiple cognitive states could have similar neural signatures. () Given meta-analytic posterior probability maps for multiple t! erms (for example, working memory, emotion and pain), one can classify a new activation map by identifying the class with the highest probability, P, given the new data (in this example, pain). * Figure 2: Comparison of previous meta-analysis results with forward and reverse inference maps produced automatically using the NeuroSynth framework. () Meta-analytic maps produced manually in previous studies14, 15, 16. () Automatically generated forward inference maps showing the probability of activation given the presence of the term (P(act.|term)). () Automatically generated reverse inference maps showing the probability of the term given observed activation (P(term|act.)). Meta-analyses were carried out for working memory (top), emotion (middle) and physical pain (bottom) and mapped to the PALS-B12 atlas30. Regions in were consistently associated with the term and regions in were selectively associated with the term. To account for base differences in term frequencies, reverse inference maps assumed uniform priors (equal 50% probabilities of 'term' and 'no term'). Activation in orange or red regions implies a high probability that a term is present, and activation in blue regions implies a high probability that a term is not present. Values for all images are shown only for regions that survived a test of associatio! n between term and activation, with a whole-brain correction for multiple comparisons (false discovery rate was 0.05). DLPFC, dorsolateral prefrontal cortex; DACC, dorsal anterior cingulate cortex; AI, anterior insula. * Figure 3: Comparison of forward and reverse inference in regions of interest. () Labeled regions of interest shown on lateral and medial brain surfaces. () Comparison of forward inference (probability of activation given term P(act.|term)) and reverse inference (probability of term given activation P(term|act.)) for the domains of working memory, emotion and pain as marked. * denotes results at a false discovery rate threshold of 0.05; (whole-brain false discovery rate, (q) = 0.05). DACC, dorsal anterior cingulate cortex (stereotactic coordinates in Montreal Neurological Institute space: +2, +8, +50); AI, anterior insula (+36, +16, +2); IFJ, inferior frontal junction (−50, +8, +36); PI, posterior insula (+42, −24, +24); APFC, anterior prefrontal cortex (−28, +56, +8); VMPFC, ventromedial prefrontal cortex (0, +32, −4). Dashed lines indicate even odds of a term being used (P(term|act.) = 0.5). * Figure 4: Three-way classification of working memory, emotion and pain. () Naive Bayes classifier performance when cross-validated on studies in the database (left) or applied to individual subjects from studies not in the database (right). () Whole-brain maximum posterior probability map; each voxel is colored by the term with the highest associated probability. () Whole-brain maps showing the proportion of individual subjects in the three pain studies (n = 79 subjects total) who showed activation at each voxel (P < 0.05, uncorrected), averaged separately for subjects who were classified correctly (n = 51 subjects; top) or incorrectly (n = 28 subjects; bottom). Regions are color-coded according to the proportion of subjects in the sample who showed activation at each voxel. * Figure 5: Accuracy of the naive Bayes classifier when discriminating between all possible pairwise combinations of 25 key terms. Each cell represents a cross-validated binary classification between the intersecting row and column terms. Off-diagonal values reflect accuracy averaged across the two terms. Diagonal values reflect the mean classification accuracy for each term. Terms were ordered using the first two factors of a principal components analysis. All accuracy rates above 58% and 64% are statistically significant at P < 0.05 and P < 0.001, respectively. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA. * Tal Yarkoni & * Tor D Wager * Imaging Research Center, University of Texas at Austin, Austin, Texas, USA. * Russell A Poldrack * Department of Psychology, University of Texas at Austin, Austin, Texas, USA. * Russell A Poldrack * Department of Neurobiology, University of Texas at Austin, Austin, Texas, USA. * Russell A Poldrack * Department of Statistics, University of Warwick, Coventry, UK. * Thomas E Nichols * Warwick Manufacturing Group, University of Warwick, Coventry, UK. * Thomas E Nichols * Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri, USA. * David C Van Essen Contributions T.Y. conceived the project and carried out most of the software implementation, data analysis and writing. R.A.P. provided data and performed analyses. T.E.N. provided statistical advice, reviewed all statistical procedures and contributed to the implementation of the naive Bayes classifier. D.C.V.E. provided data, contributed to automated data extraction and coordinated data validation. T.D.W. conceived the classification analyses, wrote part of the software, provided data and suggested and performed analyses. All authors contributed to writing and editing the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Tal Yarkoni Author Details * Tal Yarkoni Contact Tal Yarkoni Search for this author in: * NPG journals * PubMed * Google Scholar * Russell A Poldrack Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas E Nichols Search for this author in: * NPG journals * PubMed * Google Scholar * David C Van Essen Search for this author in: * NPG journals * PubMed * Google Scholar * Tor D Wager Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–10 and Supplementary Note Additional data
• Tracking genome engineering outcome at individual DNA breakpoints
  - Nat Methods 8(8):671-676 (2011)
  Nature Methods | Article Tracking genome engineering outcome at individual DNA breakpoints * Michael T Certo1, 2 * Byoung Y Ryu2 * James E Annis3 * Mikhail Garibov2 * Jordan Jarjour2, 4, 6 * David J Rawlings2, 4, 5 * Andrew M Scharenberg2, 4, 5 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:671–676Year published:(2011)DOI:doi:10.1038/nmeth.1648Received06 April 2011Accepted07 June 2011Published online10 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Site-specific genome engineering technologies are increasingly important tools in the postgenomic era, where biotechnological objectives often require organisms with precisely modified genomes. Rare-cutting endonucleases, through their capacity to create a targeted DNA strand break, are one of the most promising of these technologies. However, realizing the full potential of nuclease-induced genome engineering requires a detailed understanding of the variables that influence resolution of nuclease-induced DNA breaks. Here we present a genome engineering reporter system, designated 'traffic light', that supports rapid flow-cytometric analysis of repair pathway choice at individual DNA breaks, quantitative tracking of nuclease expression and donor template delivery, and high-throughput screens for factors that bias the engineering outcome. We applied the traffic light system to evaluate the efficiency and outcome of nuclease-induced genome engineering in human cell lines and i! dentified strategies to facilitate isolation of cells in which a desired engineering outcome has occurred. View full text Subject terms: * Molecular Engineering * Molecular Biology * Genetics * Synthetic Biology Figures at a glance * Figure 1: The traffic light reporter. () Diagram of the TLR. Arrow represents promoter and initial eGFP start codon. Reading frames relative to the initial eGFP start codon are indicated in parentheses. () Schematic depicting different engineering outcomes after the induction of a site specific double-strand break (DSB). If the break is resolved through the HDR pathway, the full eGFP sequence will be reconstituted, and cells will fluoresce green; if the break undergoes mutNHEJ, eGFP will be translated out of frame (gibberishFP, +3 reading frame) and the T2A and mCherry sequences are rendered in frame to produce red fluorescent cells. () Flow cytometric analysis of HEK293T TLR-Sce cells 72 h after transduction with the indicated lentiviral constructs. Numbers shown inside plots indicate percentages of live cells. FI, relative fluorescence intensity reported in arbitrary units. * Figure 2: Titration of nuclease and donor template. () Representative flow plot after transduction of HEK293T TLR-Sce cells with indicated amounts of I-SceI plus donor lentivirus (LV). p24 values indicate the amount of lentiviral capsid protein added to cells. Numbers inside plots indicate percentages of live cells. FI, relative fluorescence intensity reported in arbitrary units. () Quantification of data from . Bar graphs represent a minimum of three independent experiments performed in duplicate, with s.e. shown. () Ratio of HDR to mutNHEJ based on data in . () Representative flow plot after titration of donor integrase-deficient lentivirus in fivefold increments with constant I-SceI lentiviral vector dose in HEK293T TLR-Sce cells. () Quantification of data from . Bars represent a minimum of three independent experiments performed in duplicate, with s.e. shown. () Ratio of HDR to mutNHEJ in . NA, not applicable. * Figure 3: Four-color system to track nuclease and donor template delivery simultaneously with the TLR. () Representative flow plot 72 h after transduction of HEK293T TLR-Sce cells with I-SceI-T2A-IFP lentivirus and Donor-T2A-BFP integration-deficient lentivirus. Shown are nuclease and donor tracking (top), and engineering outcome (bottom). Numbers inside plots indicate percentages of live cells. () Control gating analysis of HEK293T TLR-Sce cells transduced with both I-SceI-T2A-IFP lentivirus and Donor-T2A-BFP integration-deficient lentivirus. Inset, flow plots showing nuclease and donor template expression levels as indicated by mean fluorescence intensity (FI). Main plots show readout from the TLR as a function of the gate shown in the inset. () Quantification of TLR readout when applying a nuclease titration gating analysis in cells transduced with both I-SceI-T2A-IFP and Donor-T2A-BFP. Bars represent the amount of gene targeting and mutNHEJ present in the indicated inset gates (low, middle and high mean fluorescence intensity values) normalized to the HDR and mutNHEJ valu! es for the total ungated population. Average data of three independent experiments are shown with s.e. () Quantification of TLR readout when applying donor template titration gating analysis as indicated above. Error bars, s.e. (n = 3). * Figure 4: Effect of single versus double-strand DNA breaks on engineering outcome. () Representative flow plots showing TLR readout of HEK293T TLR-Ani cells transduced with either I-AniY2-T2A-BFP lentivirus (cleavase) or I-AniIK227M-T2A-BFP lentivirus (nickase). Insets, gating for nuclease expression to control for transduction levels. () Quantification of data shown in from three independent experiments in duplicate. Percentage measured events have had the background rates from cells transduced with donor alone subtracted to control for the low numbers. Error bars, s.e. () Comparison of the ratio of HDR to mutNHEJ between cleavase- and nickase-induced engineering. () Gating analysis showing TLR readout across nickase expression levels. Error bars, s.e. (three independent experiments performed in duplicate). Inset, BFP histogram gated for relative nickase expression. * Figure 5: High-throughput siRNA kinome screen to identify modifiers of engineering outcome. () Scatter plot of gene targeting and mutNHEJ Z scores obtained from the siRNA screen. Library data are in gray. Control siRNA values are an average for at least three independent transfections. () Gating analysis comparing TLR readout from control and PRKDC siRNA treatment as a function of nuclease expression 72 h after transduction of HEK293T TLR-Sce cells transduced with I-SceI-T2A-IFP lentivirus and donor integration-deficient lentivirus. Inset, nuclease expression gates. Data are derived from three independent experiments performed in duplicate. Error bars, s.e. Author information * Abstract * Author information * Supplementary information Affiliations * Program in Molecular and Cellular Biology, University of Washington, Seattle, Washington, USA. * Michael T Certo * Center of Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, Washington, USA. * Michael T Certo, * Byoung Y Ryu, * Mikhail Garibov, * Jordan Jarjour, * David J Rawlings & * Andrew M Scharenberg * Quellos High Throughput Core, Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, Washington, USA. * James E Annis * Department of Immunology, University of Washington, Seattle, Washington, USA. * Jordan Jarjour, * David J Rawlings & * Andrew M Scharenberg * Department of Pediatrics, University of Washington, Seattle, Washington, USA. * David J Rawlings & * Andrew M Scharenberg * Present address: Precision Genome Engineering Inc., Seattle, Washington, USA. * Jordan Jarjour Contributions M.T.C. designed and performed experiments, analyzed data and wrote the paper; B.Y.R., J.E.A. and M.G. performed experiments; J.J. and D.J.R. designed experiments; and A.M.S. designed experiments and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Andrew M Scharenberg or * David J Rawlings Author Details * Michael T Certo Search for this author in: * NPG journals * PubMed * Google Scholar * Byoung Y Ryu Search for this author in: * NPG journals * PubMed * Google Scholar * James E Annis Search for this author in: * NPG journals * PubMed * Google Scholar * Mikhail Garibov Search for this author in: * NPG journals * PubMed * Google Scholar * Jordan Jarjour Search for this author in: * NPG journals * PubMed * Google Scholar * David J Rawlings Contact David J Rawlings Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew M Scharenberg Contact Andrew M Scharenberg Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–8, Supplementary Tables 1–2 and Supplementary Notes 1–2 Additional data
• A large-scale method to measure absolute protein phosphorylation stoichiometries
  - Nat Methods 8(8):677-683 (2011)
  Nature Methods | Article A large-scale method to measure absolute protein phosphorylation stoichiometries * Ronghu Wu1 * Wilhelm Haas1 * Noah Dephoure1 * Edward L Huttlin1 * Bo Zhai1 * Mathew E Sowa1 * Steven P Gygi1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:677–683Year published:(2011)DOI:doi:10.1038/nmeth.1636Received23 February 2011Accepted11 May 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The functional role of protein phosphorylation is impacted by its fractional stoichiometry. Thus, a comprehensive strategy to study phosphorylation dynamics should include an assessment of site stoichiometry. Here we report an integrated method that relies on phosphatase treatment and stable-isotope labeling to determine absolute stoichiometries of protein phosphorylation on a large scale. This approach requires the measurement of only a single ratio relating phosphatase-treated and mock-treated samples. Using this strategy we determined stoichiometries for 5,033 phosphorylation sites in triplicate analyses from Saccharomyces cerevisiae growing through mid-log phase. We validated stoichiometries at ten sites that represented the full range of values obtained using synthetic phosphopeptides and found excellent agreement. Using bioinformatics, we characterized the biological properties associated with phosphorylation sites with vastly differing absolute stoichiometries. View full text Subject terms: * Proteomics * Mass Spectrometry * Cell Biology * Systems Biology Figures at a glance * Figure 1: Principle of the method for phosphatase-based, absolute stoichiometry measurements. Two identical aliquots of a proteolyzed protein lysate are either mock- or phosphatase-treated followed by differential chemical labeling with stable isotopes. After mixing, fractional occupancy is encoded in a single ratio comparing the nonphosphorylated form of a peptide with and without phosphatase treatment. P, phosphorylation site. Peptide sequences obtained are then examined against a database of known sites from the literature. We term these overlapping, nonphosphorylated forms ODPs. Based on the ratio of the ODPs, stoichiometries are calculated. * Figure 2: Absolute site stoichiometries for 5,033 events in exponentially growing yeast. () Three biological yeast replicates were grown to mid-log phase. Lysates were proteolyzed with endoproteinase lys-C. An identical aliquot of each proteolyzed lysate was either mock- or phosphatase-treated as illustrated in Figure 1. ODPs (5,033 peptides) were identified based on the overlap with sites in five published studies7, 10, 31, 32, 33. Shown is a scatter plot of all ODP ratios. Ratios directly encode fractional occupancies (orange lines). S/N, signal-to-noise ratio. CIP, calf intestinal phosphatase. () Example of an ODP (EAENDEDS*EVNAK, where the asterisk indicates the phosphorylated serine) from protein Bbc1, with signals from CIP-treated (yellow) and untreated (blue) samples highlighted. () Site stoichiometry distribution for 5,033 events from wild-type yeast undergoing exponential growth. * Figure 3: An example of validation of a site stoichiometry by AQUA. (,) Synthetic peptides were generated representing heavy phosphorylated and nonphosphorylated versions of the peptide. The synthetic peptides were spiked into proteolyzed lysates and separated by LC-MS/MS techniques. Using the heavy peptides as internal standards, the light versions were quantified and stoichiometries were calculated. An example of the measured amount of peptide (RIIEHSDVENENVK) and phosphopeptide (RIIEHS*DVENENVK, where the asterisk indicates the phosphorylation site) by AQUA in the protein UBP1 (). The calculated stoichiometry was 69.2% (= 46.0/(20.5 + 46.0) × 100). An example of phosphopeptide identification (RIIEHS*DVENENVK) by MS/MS (). Xcorr is the primary score from the Sequest algorithm. * Figure 4: Bioinformatic analyses of site stoichiometry with respect to kinase motifs and gene ontology. () Analysis of phosphorylation events in indicated motifs. () Analysis of phosphorylation events in ordered and disordered regions. (,) Clustering of the phosphoproteins according to their highest stoichiometry site based on their enrichment in specific cell compartments () and biological processes (). Categories without a P value were assigned a conservative value of 1. The P values were log-transformed and then z-transformed. Phosphoproteins were then grouped based on their z scores via hierarchical clustering. * Figure 5: Evolutionary conservation of the site residues across 25 yeast species. Sites were subgrouped by fractional occupancy into high, medium and low sets, and then clustered based on overall conservation levels. Each column represents a single site residue. If a homolog was identified and the site residue was conserved, the corresponding cell is yellow otherwise it is blue. If no homolog was identified, the cell is black. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA. * Ronghu Wu, * Wilhelm Haas, * Noah Dephoure, * Edward L Huttlin, * Bo Zhai, * Mathew E Sowa & * Steven P Gygi Contributions S.P.G. and R.W. designed the research. R.W., W.H., N.D., E.L.H., B.Z., M.E.S. and S.P.G. participated in the data generation, analysis and interpretation. R.W. and S.P.G. wrote the manuscript and all authors edited it. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Steven P Gygi Author Details * Ronghu Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Wilhelm Haas Search for this author in: * NPG journals * PubMed * Google Scholar * Noah Dephoure Search for this author in: * NPG journals * PubMed * Google Scholar * Edward L Huttlin Search for this author in: * NPG journals * PubMed * Google Scholar * Bo Zhai Search for this author in: * NPG journals * PubMed * Google Scholar * Mathew E Sowa Search for this author in: * NPG journals * PubMed * Google Scholar * Steven P Gygi Contact Steven P Gygi Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Table 2 (14M) Peptides identified in experiment 1. * Supplementary Table 3 (142M) Peptides identified in experiment 2. * Supplementary Table 4 (15M) Peptides identified in experiment 3. * Supplementary Table 6 (588K) Site stoichiometries obtained in biological triplicate experiments. * Supplementary Table 7 (24K) Site stoichiometries for events described as Cdk1substrates. * Supplementary Table 8 (12K) Examples of phosphorylation sites with high stoichiometries. PDF files * Supplementary Text and Figures (384K) Supplementary Figures 1–4, Supplementary Tables 1 and 5 Additional data
• Two-photon polarization microscopy reveals protein structure and function
  - Nat Methods 8(8):684-690 (2011)
  Nature Methods | Article Two-photon polarization microscopy reveals protein structure and function * Josef Lazar1, 2, 3, 4 * Alexey Bondar1, 2 * Stepan Timr5 * Stuart J Firestein4 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:684–690Year published:(2011)DOI:doi:10.1038/nmeth.1643Received13 April 2010Accepted17 May 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Membrane proteins are a large, diverse group of proteins, serving a multitude of cellular functions. They are difficult to study because of their requirement of a lipid membrane for function. Here we show that two-photon polarization microscopy can take advantage of the cell membrane requirement to yield insights into membrane protein structure and function, in living cells and organisms. The technique allows sensitive imaging of G-protein activation, changes in intracellular calcium concentration and other processes, and is not limited to membrane proteins. Conveniently, many suitable probes for two-photon polarization microscopy already exist. View full text Subject terms: * Imaging * Biophysics * Sensors and Probes * Neuroscience Figures at a glance * Figure 1: Mathematical models. (,) Simulated images of a fluorescently labeled spherical cell () and cylindrical cell () shown as projections of a single-photon confocal z-dimension stack, for fluorophore tilt angle α0 values 0°, 22.5°, 45°, 67.5° and 90° (left to right). (,) Simulated images for a spherical cell () and cylindrical cell () showing fluorescence excited by horizontally and vertically polarized light (Fh and Fv) colored magenta and green, respectively. Nongray color (excess of magenta or green) indicates presence of LD. Direction of polarization and coloring of corresponding fluorescence is indicated by double-headed arrows. Orientation of the fluorophore with respect to the cell membrane (tilt angle α0) is indicated by the schematics in bottom right corners of individual images. () LD, expressed as r = Fh/Fv and log2(r), as a function of mean fluorophore tilt angle α0, for different widths (described by σ) of distribution of α, for the cylindrical cell in . () Fractional changes i! n dichroic ratio (Δr/r) of the cylindrical cell in and upon a change in mean tilt angle α0 by 1°, for a range of starting tilt angles α0 and tilt angle σ. * Figure 2: Proof of principle. () Schematic of the dleGFP construct. (–) Single-photon confocal images of a dleGFP-expressing cell. Direction of polarization and coloring of corresponding fluorescence is indicated as in Figure 1. Shown are projections of z-dimension stacks acquired with excitation light polarized horizontally () and vertically (); a composite of images in and colored magenta and green, respectively, without any color lookup table (LUT) adjustment (); a single confocal slice of the same cell (); and the same image as in , but after application of an LUT suitable for displaying the range of dichroic ratio r in the image (1–2.5; pixels exceeding this range appear pure magenta or pure green; only a small number of such pixels are visible, indicating that rmax = (Fh/Fv)max = ~2.5) (). (–) Images as in – but acquired using two-photon excitation. () Schematic of the ileGFP construct. () A two-photon section of an ileGFP-expressing cell, processed as in but with a color scale covering a n! arrower range of values as indicated. () Schematic of the cleGFP construct. () A two-photon section of a cleGFP-expressing cell, processed as in but with a different color scale as indicated. All scale bars, 5 μm. * Figure 3: 2PPM imaging of G-protein complexes. (–) Images of cells expressing fluorescently tagged Gα subunits GAP43-CFP-Gαi2 (), Gαi2-Leu91-YFP (), Gαo-Leu91-YFP () and Gαi1-Leu91-YFP (). (–) Images of the same Gα subunits as in – but expressed together with Gβ1 and Gγ2. Coloring is as in Figure 2. Scale bars, 5 μm. * Figure 4: 2PPM imaging of G-protein activation. () Cyan fluorescence of a cell expressing GAP43-CFP-Gαi2, Gβ1, Gγ2 and α2a-adrenergic receptor-YFP before addition of norepinephrine (left), after addition of norepinephrine (center) and after removal of norepinephrine (right). Coloring is as in Figures 2 and 3. () Plot of LD (expressed as r and log2(r)) of the GAP43-CFP-Gαi2–expressing cell in , as a function of time. Triangles and squares denote data from the indicated horizontally and vertically oriented sections of the membrane, respectively. Dashed traces indicate s.e.m., n = 110–160 pixels. The 10-s period of presence of norepinephrine is indicated by a bar (top left). () Yellow fluorescence of a cell expressing Gαo-Leu91-YFP, Gβ1, Gγ2 and α2a–adrenergic receptor–CFP before addition of norepinephrine (left), after addition of norepinephrine (center) and after removal of norepinephrine (right). () Plot as in but for the Gαo-Leu91-YFP–expressing cell in (n = 90–160 pixels). All scale bars, 5 μm. * Figure 5: 2PPM imaging of intracellular calcium concentration through conformational changes in the calcium sensor lynD3cpV. () CFP signal of lynD3cpV. () cpVenus fluorescence before application of ATP (left), during application of ATP (center) and after ATP removal (right). Coloring is as in Figures 2, 3 and 4. All scale bars, 5 μm. () Plot of cpVenus LD (expressed as r and log2(r)) of the outlined sections (inset) of the cell shown in and , as a function of time. Triangles and squares denote data from the indicated horizontally and vertically oriented sections of the membrane, respectively. Dashed traces indicate s.e.m., n = 160–200 pixels. The 40-s period of presence of ATP is indicated by a bar. () LD of cpVenus and FRET of lynD3cpV as a function of intracellular calcium concentration. Error bars, s.e.m.; n = 15–30 cells. The curve shown is a prediction for intermediate values of Kd (0.68 μM) and Hill coefficient (2.8) obtained from LD and FRET measurements. Top inset, intracellular calcium concentrations for six typical cells stimulated by ATP, determined by FRET (horizontal axis) and 2! PPM (vertical axis). Error bars, s.e.m., n = 60–400 pixels with |θ| < 3° and n = 10,000–13,000 pixels used for FRET measurements. Bottom inset, experiment in , interpreted in terms of intracellular calcium concentration. Author information * Abstract * Author information * Supplementary information Affiliations * Laboratory of Cell Biology, Institute of Nanobiology and Structural Biology, Global Change Research Centre, Academy of Sciences of the Czech Republic, Nove Hrady, Czech Republic. * Josef Lazar & * Alexey Bondar * Department of Systems Biology, Institute of Physical Biology, University of South Bohemia, Nove Hrady, Czech Republic. * Josef Lazar & * Alexey Bondar * Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of South Bohemia, Ceske Budejovice, Czech Republic. * Josef Lazar * Department of Biological Sciences, Columbia University, New York, New York, USA. * Josef Lazar & * Stuart J Firestein * Department of Physics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic. * Stepan Timr Contributions J.L. conceived the idea, carried out mathematical modeling and analyses, performed initial microscopy experiments, developed image-processing software, directed the project and wrote the manuscript. A.B. performed microscopy experiments, prepared constructs, analyzed data and devised experimental strategies. S.T. developed software for quantitative analysis. S.J.F. contributed inspiration, consultations and funding. Competing financial interests A patent #302233, covering the described method, device and applications, has been awarded by the Industrial Property Office of the Czech Republic (J.L.). A Patent Cooperation Treaty (PCT) application has been filed (J.L.). J.L. is a founder and owner of Innovative Bioimaging, L.L.C. Corresponding author Correspondence to: * Josef Lazar Author Details * Josef Lazar Contact Josef Lazar Search for this author in: * NPG journals * PubMed * Google Scholar * Alexey Bondar Search for this author in: * NPG journals * PubMed * Google Scholar * Stepan Timr Search for this author in: * NPG journals * PubMed * Google Scholar * Stuart J Firestein Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (713K) A three-dimensional reconstruction of a cell expressing dleGFP, imaged using two-photon polarization microscopy. A rotating view of the cell allowed visualization of LD of the whole cell surface. Color-coding and scale are the same as in Figure 2i. * Supplementary Video 2 (352K) Monitoring G-protein activation: GAP43-CFP-Gαi2. An HEK293 cell expressing GAP43-CFP-Gαi2, Gβ1, Gγ2 and α2a-adrenergic receptor–YFP, imaged by two-photon polarization microscopy (excitation at 800 nm, 0.20 f.p.s., shown at 10× speed), showing distinct LD. Upon exposure to an agonist (norepinephrine; presence indicated by an asterisk), LD disappeared, only to slowly reappear after agonist removal. Color-coding and scale are the same as in Figure 4a. * Supplementary Video 3 (766K) Monitoring G-protein activation: Gαo-Leu91-YFP. An HEK293 cell expressing Gαo-Leu91-YFP, Gβ1, Gγ2 and α2a-adrenergic receptor–CFP, imaged by two-photon polarization microscopy (excitation at 960 nm, 0.33 f.p.s., shown at 10× speed) shows LD. Upon exposure to an agonist (norepinephrine; presence indicated by an asterisk), LD disappeared, only to slowly reappear after agonist removal. Color-coding and scale are the same as in Figure 4c. * Supplementary Video 4 (467K) Monitoring calcium concentration using lynD3cpV. HEK293 cells expressing lynD3cpV, imaged by two-photon polarization microscopy (excitation at 960 nm, 0.33 f.p.s., shown at 10× speed), showed little LD in resting state. Exposure to ATP (presence indicated by an asterisk) caused an increase of LD. Removal of ATP led to gradual decrease of LD to initial levels. Color-coding and scale are the same as in Figure 5. PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–6, Supplementary Tables 1–3, Supplementary Note and Supplementary Discussion Additional data
• Protein standard absolute quantification (PSAQ) method for the measurement of cellular ubiquitin pools
  - Nat Methods 8(8):691-696 (2011)
  Nature Methods | Article Protein standard absolute quantification (PSAQ) method for the measurement of cellular ubiquitin pools * Stephen E Kaiser1 * Brigit E Riley1 * Thomas A Shaler2 * R Sean Trevino1 * Christopher H Becker2 * Howard Schulman2 * Ron R Kopito1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:691–696Year published:(2011)DOI:doi:10.1038/nmeth.1649Received29 November 2010Accepted03 June 2011Published online10 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The protein ubiquitin is an important post-translational modifier that regulates a wide variety of biological processes. In cells, ubiquitin is apportioned among distinct pools, which include a variety of free and conjugated species. Although maintenance of a dynamic and complex equilibrium among ubiquitin pools is crucial for cell survival, the tools necessary to quantify each cellular ubiquitin pool have been limited. We have developed a quantitative mass spectrometry approach to measure cellular concentrations of ubiquitin species using isotope-labeled protein standards and applied it to characterize ubiquitin pools in cells and tissues. Our method is convenient, adaptable and should be a valuable tool to facilitate our understanding of this important signaling molecule. View full text Subject terms: * Biochemistry * Cell Biology * Mass Spectrometry Figures at a glance * Figure 1: Ubiquitin pools and assay overview. () Ubiquitin (Ub) is present in eukaryotic cells as a mixture of free ubiquitin, monoubiquitinated substrates, polyubiquitin chains and activated species linked to enzymes by thioester bonds. DUBs, deubiquitinating enzymes. E1, ubiquitin activating enzyme. E2, ubiquitin transfer enzyme. SH, catalytic cysteine residues; S, catalytic cysteine residues conjugated via covalent thioester linkages to ubiquitin. () Cartoon representations of the interactions of the protein-affinity reagents used in the Ub-PSAQ assay with ubiquitin. The hP2 UBA domain binds the Ile44-centered hydrophobic patch on ubiquitin (patch is obscured on the left and is shown in green on the right) and preferentially binds ubiquitin chains without bias for specific ubiquitin-ubiquitin linkages8, 23, 24. The BUZ domain of IsoT binds the C-terminal Gly-Gly of free ubiquitin species (shown in blue on the left)13. () Schematic of the Ub-PSAQ assay. Isotope-labeled protein standards are added to lysates containing! sample derived ubiquitin species. Half of the sample is treated with the deubiquitinating enzyme usp2cc, free ubiquitin species are bound to the BUZ affinity reagent, washed, eluted and then trypsinized before quantification of total sample–derived ubiquitin relative to the 13C-labeled free-ubiquitin protein standard by LC-ESI TOF MS. Free ubiquitin species in the untreated half of the sample are similarly captured by the BUZ affinity reagent and quantified by LC-ESI MS. Next, the untreated half of the sample is incubated with the hP2 UBA affinity reagent to capture polyubiquitin chains, which are quantified relative to the polyubiquitin chain standard. * Figure 2: Assay validation. () Superimposed serial dilution curves of ubiquitin detection by BUZ-enriched PSAQ and ELISA comparing the linear dynamic range and the lower limit of detection. Mass spectrometry signal intensity is expressed in arbitrary units (a.u.) after normalization to the spiked 13C-labeled free-ubiquitin standard. A450 nm, absorbance at 450 nm. Data are represented as mean ± s.d. (n = 8 for ELISA and n = 3 for PSAQ). (–) Measurement of defined mixtures composed of unlabeled free ubiquitin, autoubiquitinated Rsp5 and ubiquitin-GFP mock monoubiquitinated substrate containing similar amounts of input free ubiquitin and ubiquitin-GFP substrate (), higher levels of ubiquitin-GFP and ubiquitin chains (chain) than free ubiquitin (), high levels of mock monoubiquitinated substrate (mono) () and higher levels of free ubiquitin (). Error bars (–), ± s.d. (n = 3). * Figure 3: Effect of acute proteasome inhibition on ubiquitin pools. (,) Ub-PSAQ analysis of lysates from HEK293 () and MEF () cell lines treated with either DMSO (vehicle) or the proteasome inhibitor MG-132 (1 μM) over 12 h. Error bars, ± s.d. (n = 3). *P < 0.05; **P < 0.01; and ***P < 0.005 (unpaired t-test). MonoUb, monoubiquitin conjugates. () Representation of ubiquitin pool components in HEK293 and MEF cell lines. () Ubiquitin western blots with antibodies FK2 and A100 showing ubiquitin-immunoreactive material for HEK293 cells treated with either DMSO (vehicle) or MG-132 (1 μM) over 12 h. Bars on top right of blots denote high-molecular-weight ubiquitin conjugates (HMW Ub conjugates), and ubiquitin-modified histone H2A (UH) and free ubiquitin are indicated. () Distribution of monoubiquitinated substrates in cytosolic and histone-enriched fractions from HEK293 cells treated with either DMSO or MG-132 (10 μM) for 6 h. * Figure 4: Ub-PSAQ analysis of ubiquitin pools in cytosolic and histone-enriched fractions from HEK293 cells. () Representation of ubiquitin pool components in cytosolic and histone-enriched fractions. () Distribution of ubiquitin pool components in cytosolic and histone-enriched fractions. Bar graphs show concentration of the indicated species per milligram of total protein in each fraction. Pie charts show the same data after normalization to show the distribution of each species. () Distribution of ubiquitin chain linkages in cytosolic and histone-enriched fractions. Bar graphs show the concentration of each species within each fraction, and pie charts show the distribution of each species between cytosolic and histone-enriched fractions. Error bars (,), means ± s.d. (n = 3). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Biology, Stanford University, Stanford, California, USA. * Stephen E Kaiser, * Brigit E Riley, * R Sean Trevino & * Ron R Kopito * Caprion Proteomics U.S., LLC, Menlo Park, California, USA. * Thomas A Shaler, * Christopher H Becker & * Howard Schulman Contributions S.E.K. and R.R.K. devised the Ub-PSAQ strategy with contributions from B.E.R. and T.A.S.; S.E.K. prepared all protein affinity reagents and standards, performed the experiments and analyzed the data with input from B.E.R. and T.A.S.; B.E.R. prepared all cellular samples; T.A.S. performed all mass spectrometry analyses; and R.S.T. contributed to mass spectrometry data analysis. S.E.K. and R.R.K. wrote the manuscript, and B.E.R. contributed to figure preparation. B.E.R., T.A.S. and R.S.T. contributed to editing. C.H.B. and H.S. contributed to conceptual and experimental design. All authors discussed the results and manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ron R Kopito Author Details * Stephen E Kaiser Search for this author in: * NPG journals * PubMed * Google Scholar * Brigit E Riley Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas A Shaler Search for this author in: * NPG journals * PubMed * Google Scholar * R Sean Trevino Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher H Becker Search for this author in: * NPG journals * PubMed * Google Scholar * Howard Schulman Search for this author in: * NPG journals * PubMed * Google Scholar * Ron R Kopito Contact Ron R Kopito Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Data 1 (233K) Overview of Ub-PSAQ analysis using the samples described in Figure 3b as an example. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–7 and Supplementary Table 1 Additional data