Hot off the presses! Nov 01 Nat Methods

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Latest Articles Include:

• Ground-truth data cannot do it alone
  - Nat Methods 8(11):885 (2011)
  Nature Methods | Editorial Ground-truth data cannot do it alone Journal name:Nature MethodsVolume: 8,Page:885Year published:(2011)DOI:doi:10.1038/nmeth.1767Published online28 October 2011 Verifying automated analysis methods via ground-truth data remains an essential step of algorithm development. But as datasets increase in size and complexity, this classical test is often insufficient. Integrated editing tools can help. View full text Additional data
• The author file: Paul Tesar
  - Nat Methods 8(11):887 (2011)
  Nature Methods | This Month The author file: Paul Tesar * Monya BakerJournal name:Nature MethodsVolume: 8,Page:887Year published:(2011)DOI:doi:10.1038/nmeth.1746Published online28 October 2011 Pure oligodendrocyte populations can be made without cell sorting. View full text Additional data
• Points of view: Salience to relevance
  - Nat Methods 8(11):889 (2011)
  Article preview View full access options Nature Methods | This Month Points of view: Salience to relevance * Bang Wong1Journal name:Nature MethodsVolume: 8,Page:889Year published:(2011)DOI:doi:10.1038/nmeth.1762Published online28 October 2011 In science communication, it is critical that visual information be interpreted efficiently and correctly. The discordance between components of an image that are most noticeable and those that are most relevant or important can compromise the effectiveness of a presentation. This discrepancy can cause viewers to mistakenly pay attention to regions of the image that are not relevant. Ultimately, the misdirected attention can negatively impact comprehension. Salience is the physical property that sets an object apart from its surroundings. It is particularly important to ensure that salience aligns with relevance in visuals used for slide presentations. In these situations, information transmission needs to be efficient because the audience member is expected to simultaneously listen and read. By highlighting relevant information on a slide, we can direct a viewer's attention to the right information. For example, coloring a row or column of a table will preferentially direct attention to the selected material (Fig. 1a). As information presented as tables typically appears homogenous, it is especially helpful to define what is most important. The same approach can be applied to plots and graphs to delineate segments of data (Fig. 1b). Whereas these techniques are not appropriate for all journal publications, annotating information presented in slides can be an effective mechanism to enable the audience to better grasp what is be! ing said and shown. Figure 1: Matching salience to relevance draws visual attention to important information. () Table with a row highlighted. () Segments of data in a plot emphasized with color. * Full size image (87 KB) * Figures index * Next figure Human vision is highly selective. When multiple stimuli are in a scene they compete for our visual attention. We make sense of the visual field by selecting, in turn, one or few objects for detailed analysis at the expense of all others. Cognitive scientists create 'salience maps' to describe the relative visibility of objects in an image that explain what we might look at first, second and so on1. Figures at a glance * Figure 1: Matching salience to relevance draws visual attention to important information. () Table with a row highlighted. () Segments of data in a plot emphasized with color. * Figure 2: Discordances between salience and relevance can be harmful. () The relative visibility of hues in the color scale is asymmetric, making higher values (represented by deep red) less apparent. () Continuously moving images can be distracting and can compromise the viewer's ability to concentrate on other content. 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
• More on color blindness
  - Nat Methods 8(11):891 (2011)
  Article preview View full access options Nature Methods | Correspondence More on color blindness * Gabriel Landini1 * D Giles Perryer1 * Affiliations * Corresponding authorsJournal name:Nature MethodsVolume: 8,Page:891Year published:(2011)DOI:doi:10.1038/nmeth.1737Published online28 October 2011 To the Editor: In a recent issue of Nature Methods1 Albrecht raised awareness of the need to carefully design graphic data representation so data can also be appropriately perceived by the many individuals who have color perception deficiencies. Appropriate color selection is not only important for effective communication to a wide audience, but there are also equity implications when developing effective teaching methods and communication strategies. The Nature Methods editors in their reply2 suggested using the Vischeck plugins for ImageJ and Photoshop platforms "for recoloring" purposes. By simulating color blindness, the Vischeck plugins are excellent for giving trichromatic ('normal' vision) observers an insight into how dichromatic viewers may see color images. However, on their own, these simulations do not provide a direct solution to the inverse problem of efficiently recoloring images in a way that allows dichromatic observers to discriminate the color-based information. At best, they may help with trial-and-error attempts at recoloring. It is seriously problematic to recolor any arbitrary image so it can be perceived unambiguously by all types of dichromatic viewers simultaneously. However, in images with a limited number of hues, such as two-channel confocal images (additive colors) or two-dye bright-field histological stains (subtractive colors), digital imaging techniques now exist to reposition the color information so it is optimally targeted to hues that can be discriminated by the available functional retinal receptors. Below we provide suggestions on how to design the recoloring to resolve this problem in additive and subtractive color images. Wong3 in a subsequent article on graphics design accounting for those with color vision deficiencies suggests using, for additive color images (typically two-channel confocal images), the method proposed by Okabe and Ito (http://jfly.iam.u-tokyo.ac.jp/color/). This consists of representing the image data as magenta-green pairs instead of the unfortunately common red-green pairs that are difficult for protanope and deuteranope (red and green color-blind; RGCB) observers to perceive. However this recoloring strategy in itself introduces another unexpected problem: RGCB observers tend to perceive the original magenta channel as a bluish color and the original green channel as a yellow-brownish color. That means that although the colors can be discriminated, in the absence of figure legends indicating the actual name of the colors, their descriptions become confusing in communication between trichromatic and RGCB observers. We suggest that instead blue-yellow color pairs are pre! ferable to green-magenta ones because (i) they preserve the discrimination between channels, and (ii) both trichromatic and RGCB observers agree on the color names. This strategy can be easily applied to red-green channel pairs by first copying the green-channel data into the blue channel and duplicating the red channel in the green one. Subject terms: * Imaging * Microscopy 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 * School of Dentistry, University of Birmingham, Birmingham, UK. * Gabriel Landini & * D Giles Perryer Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Gabriel Landini or * D Giles Perryer Author Details * Gabriel Landini Contact Gabriel Landini Search for this author in: * NPG journals * PubMed * Google Scholar * D Giles Perryer Contact D Giles Perryer Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Reply to "More on color blindness"
  - Nat Methods 8(11):891-892 (2011)
  Article preview View full access options Nature Methods | Correspondence Reply to "More on color blindness" * Bang Wong1Journal name:Nature MethodsVolume: 8,Pages:891–892Year published:(2011)DOI:doi:10.1038/nmeth.1738Published online28 October 2011 Wong replies: Making color information accessible to individuals with color vision deficiencies is a beneficent graphic design practice. Although I appreciate the suggestion by Landini and Perryer to use yellow-blue color pairs to preserve discriminability and correspondence between color and name for color-blind individuals, I see shortcomings with this approach as it relates to human perception. At least for confocal images, in which color information is typically displayed on a black background, blue is difficult to see on black and may result in loss of information (Fig. 1). The colors found in red-green and magenta-green images suffer less from this problem because they tend to be brighter. Another challenge with yellow-blue color pairs is in seeing areas where the colors mix to produce white. When this happens, white tends to blend in with yellow and is difficult to see. Whereas overlap between magenta and green also produces white, the resultant color is easier to pick out from its! constituents (Fig. 1). Figure 1: Alternatives to red-green color coding in false color images. Whereas yellow-blue (as well as magenta-green) color pairs make it possible for individuals with red-green color blindness to discriminate the colors, blue is difficult to see against black, and the white produced by the overlap of yellow and blue is nearly indistinguishable from yellow (arrowheads). These brightness and contrast problems are less pronounced with magenta-green color pairs. Image adapted from reference 2. * Full size image (116 KB) I suspect that no single color combination is ideally suitable for both color-blind individuals and those with typical color vision. The advantages of certain color pairs (whether yellow-blue or magenta-green) will likely depend on the context in which they are applied. The value of correspondence such as this one is to raise awareness that as many as 8 percent of men experience the common form of red-green color-blindness, and we need to strive to make information represented by color as widely accessible as possible. 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 * Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA and Department of Art as Applied to Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Bang Wong Corresponding author Correspondence to: * Bang Wong Author Details * Bang Wong Contact Bang Wong Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Increasing the dynamic range of in situ PLA
  - Nat Methods 8(11):892-893 (2011)
  Nature Methods | Correspondence Increasing the dynamic range of in situ PLA * Carl-Magnus Clausson1 * Amin Allalou2 * Irene Weibrecht1 * Salah Mahmoudi3 * Marianne Farnebo3 * Ulf Landegren1 * Carolina Wählby2, 4 * Ola Söderberg1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:892–893Year published:(2011)DOI:doi:10.1038/nmeth.1743Published online28 October 2011 To the Editor: Methods based on in situ rolling circle amplification (RCA), including those that we previously reported in Nature Methods such as in situ proximity ligation assays (PLA)1 and padlock probes2, 3, have a limited dynamic range over which detected molecules may be quantified. To provide efficient detection, the RCA products are generally large with diameters of ~0.8 μm. The formation of more than a hundred such RCA products per cell causes the signals to coalesce, limiting the dynamic range for digital quantification of target molecules. Here we present an approach to increase the dynamic range of in situ PLA. View full text Subject terms: * Biochemistry * Sensors and Probes * Imaging * Molecular Engineering Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden. * Carl-Magnus Clausson, * Irene Weibrecht, * Ulf Landegren & * Ola Söderberg * Centre for Image Analysis, Science for Life Laboratory, Uppsala University, Uppsala, Sweden. * Amin Allalou & * Carolina Wählby * Department of Oncology-Pathology, Cancercentrum Karolinska, Karolinska Institutet, Stockholm, Sweden. * Salah Mahmoudi & * Marianne Farnebo * Imaging Platform, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Carolina Wählby Competing financial interests U.L. is a founder of Olink Bioscience, which commercializes the in situ PLA technology. Corresponding author Correspondence to: * Ola Söderberg Author Details * Carl-Magnus Clausson Search for this author in: * NPG journals * PubMed * Google Scholar * Amin Allalou Search for this author in: * NPG journals * PubMed * Google Scholar * Irene Weibrecht Search for this author in: * NPG journals * PubMed * Google Scholar * Salah Mahmoudi Search for this author in: * NPG journals * PubMed * Google Scholar * Marianne Farnebo Search for this author in: * NPG journals * PubMed * Google Scholar * Ulf Landegren Search for this author in: * NPG journals * PubMed * Google Scholar * Carolina Wählby Search for this author in: * NPG journals * PubMed * Google Scholar * Ola Söderberg Contact Ola Söderberg Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (6.6M) Supplementary Figures 1–3, Supplementary Table 1, Supplementary Methods Additional data
• Neurons from reprogrammed cells
  - Nat Methods 8(11):905-909 (2011)
  Nature Methods | Technology Feature Neurons from reprogrammed cells * Monya Baker1Journal name:Nature MethodsVolume: 8,Pages:905–909Year published:(2011)DOI:doi:10.1038/nmeth.1741Published online28 October 2011 The potential is vast—but so is the uncertainty. 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
• Taming the isobaric tagging elephant in the room in quantitative proteomics
  - Nat Methods 8(11):911-913 (2011)
  Article preview View full access options Nature Methods | News and Views Taming the isobaric tagging elephant in the room in quantitative proteomics * Andy Christoforou1 * Kathryn S Lilley1 * Affiliations * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:911–913Year published:(2011)DOI:doi:10.1038/nmeth.1736Published online28 October 2011 Isobaric tagging methods allow multiplexed quantitative analysis of a wide variety of proteome samples but have been severely limited by problems of accuracy. Two groups now explore this issue and provide complementary solutions to address the problem. Subject terms: * Mass Spectrometry * Proteomics 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 * Andy Christoforou and Kathryn S. Lilley are at the Cambridge Centre for Proteomics, Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, Cambridge, UK. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Andy Christoforou or * Kathryn S Lilley Author Details * Andy Christoforou Contact Andy Christoforou Search for this author in: * NPG journals * PubMed * Google Scholar * Kathryn S Lilley Contact Kathryn S Lilley Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• High-throughput stem-cell niches
  - Nat Methods 8(11):915-916 (2011)
  Article preview View full access options Nature Methods | News and Views High-throughput stem-cell niches * Jason A Burdick1 * Fiona M Watt2 * Affiliations * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:915–916Year published:(2011)DOI:doi:10.1038/nmeth.1745Published online28 October 2011 A hydrogel microdevice speeds up the investigation of microenvironmental parameters that direct stem-cell differentiation. Subject terms: * Stem Cells Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Methods for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Jason A. Burdick is in the Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA. * Fiona M. Watt is in the Wellcome Trust Centre for Stem Cell Research, University of Cambridge and Cancer Research UK, Cambridge Research Institute, Cambridge, UK. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jason A Burdick or * Fiona M Watt Author Details * Jason A Burdick Contact Jason A Burdick Search for this author in: * NPG journals * PubMed * Google Scholar * Fiona M Watt Contact Fiona M Watt Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Characterizing RNA dynamics at atomic resolution using solution-state NMR spectroscopy
  - Nat Methods 8(11):919-931 (2011)
  Nature Methods | Review Characterizing RNA dynamics at atomic resolution using solution-state NMR spectroscopy * Jameson R Bothe1 * Evgenia N Nikolova2 * Catherine D Eichhorn2 * Jeetender Chugh3 * Alexandar L Hansen4, 5, 6 * Hashim M Al-Hashimi1, 3 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:919–931Year published:(2011)DOI:doi:10.1038/nmeth.1735Published online28 October 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 Many recently discovered noncoding RNAs do not fold into a single native conformation but sample many different conformations along their free-energy landscape to carry out their biological function. Here we review solution-state NMR techniques that measure the structural, kinetic and thermodynamic characteristics of RNA motions spanning picosecond to second timescales at atomic resolution, allowing unprecedented insights into the RNA dynamic structure landscape. From these studies a basic description of the RNA dynamic structure landscape is emerging, bringing new insights into how RNA structures change to carry out their function as well as applications in RNA-targeted drug discovery and RNA bioengineering. View full text Subject terms: * Biochemistry * Structural Biology Figures at a glance * Figure 1: NMR spectroscopy techniques and site-specific probes for characterizing motional modes that carry RNA structure along various regions of the dynamic structure landscape. In the dynamic structure landscape (top), transition free energies corresponding to typical timescales of interconversion were estimated using transition state theory at 25 °C. In NMR spectroscopy experiments (middle), solid lines indicate the timescales at which each NMR spectroscopy experiment is optimally suited for, and dashed lines indicate timescales that are difficult to probe. For probes (bottom), nuclei most commonly used for RNA dynamics measurements: protonated carbons (blue), imino nitrogens (green) and backbone phosphorus (red). * Figure 2: Characterization of pico- to nanosecond motions using spin relaxation. () Reorientation of bond vectors leads to an oscillating local field, Blocal(t), at the nucleus of interest that influences relaxation. Shown is an example involving a 13C-1H dipolar interaction. () Model free analysis of longitudinal (R1) and transverse (R2) spin relaxation data yield an order parameter (S2) describing the amplitude of motion and a constant (τeff) describing its timescale. RF, radiofrequency. (–) Example applications of spin relaxation in studies of RNA dynamics; site-specific U-U-C-G tetraloop dynamics (sugar and nucleobase order parameters17, 32 were determined at 298K whereas phosphate-backbone order parameters18 were determined at 310K; ), redistribution of motional modes in the catalytic domain 5 RNA of a group II intron upon addition of Mg2+ (; adapted from ref. 33 with permission from Elsevier), and RNA interhelical motions persist when bound to the human U1A protein (; reprinted from ref. 39 with permission from the American Chemical Society). * Figure 3: Characterizing motions over sub-millisecond timescales using residual dipolar couplings. () Partial alignment of RNA using Pf1 phage shown as gray rods (left) (adapted from ref. 43). () Domain-elongation for decoupling internal and overall motions allows measurement of bond vector dynamics relative to the elongated helix. An isotopic labeling strategy is used to render elongation residues (R-Y) NMR-invisible (adapted from ref. 52). () Spatially correlated interhelical motions observed using RDCs involving correlated changes in the interhelical twist (α and γ) and bend (β) angles (adapted from ref. 52). () Combining domain-elongation RDCs and molecular dynamics simulations in the construction of atomic-resolution dynamic ensembles of TAR reveals conformations (gray) very similar to those observed in ligand bound states (orange) (adapted from ref. 55 with permission from Oxford University Press). * Figure 4: Characterizing microsecond to millisecond exchange by relaxation dispersion. () Exchange between ground (G) and excited (E) states leads to broadening of the ground signal and disappearance of the excited signal. () Fanning out of bulk magnetization due to exchange can be suppressed by application of radiofrequency fields. () Characteristic relaxation dispersion curve showing the power dependence of Rex which can be used to extract in favorable cases the populations (p), exchange rates (k) and chemical shifts (ω) of ground and excited states. (–) Examples of conformational exchange characterized by a CPMG and R1p carbon relaxation dispersion. Sugar repuckering in a GCAA tetraloop using selectively labeled C2′ and C4′ (green) probes61 (). Transition toward an excited state structure in the U6 RNA involving a C·A+ base pair and looping out of a uridine bulge68 (). Transition to Hoogsteen (HG) G·C+ base pairs in canonical duplex DNA (; adapted from ref. 70). * Figure 5: Characterizing conformational transitions occurring at millisecond and longer timescales by ZZ-exchange and time-resolved NMR spectroscopy. (,) Schematic of () ZZ-exchange and () time-resolved NMR spectroscopy experiment. () Visualizing a multistep ligand-induced conformational transition in the guanine sensing riboswitch using laser induced time-resolved NMR spectroscopy (adapted from ref. 76 with permission from the National Academy of Sciences, USA). () Characterizing slow and fast RNA refolding rates by time-resolved and ZZ-exchange NMR spectroscopy, respectively. * Figure 6: Characterizing base-pair-opening dynamics by imino proton exchange. () Schematic of a two-step (direct or water-mediated) imino proton exchange for an RNA base pair catalyzed by a proton acceptor B, followed by reversible base-pair formation. H exchange experiments can be used to measure rate constants in each step. () Imino proton exchange study of the wild-type (left) Salmonella FourU RNA thermometer and its translationally inactive GA-to-GC mutant (right), showed a correlation between changes in local base-pair stabilities and changes in global melting profiles of the wild-type and mutant constructs91. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Chemistry, The University of Michigan, Ann Arbor, Michigan, USA. * Jameson R Bothe & * Hashim M Al-Hashimi * Chemical Biology Doctoral Program, The University of Michigan, Ann Arbor, Michigan, USA. * Evgenia N Nikolova & * Catherine D Eichhorn * Department of Biophysics, The University of Michigan, Ann Arbor, Michigan, USA. * Jeetender Chugh & * Hashim M Al-Hashimi * Department of Chemistry, The University of Toronto, Toronto, Ontario, Canada. * Alexandar L Hansen * Department of Biochemistry, The University of Toronto, Toronto, Ontario, Canada. * Alexandar L Hansen * Department of Molecular Genetics, The University of Toronto, Toronto, Ontario, Canada. * Alexandar L Hansen Competing financial interests H.M.A.-H. is an advisor to and holds an ownership interest in Nymirum, an RNA-based drug-discovery company. Corresponding author Correspondence to: * Hashim M Al-Hashimi Author Details * Jameson R Bothe Search for this author in: * NPG journals * PubMed * Google Scholar * Evgenia N Nikolova Search for this author in: * NPG journals * PubMed * Google Scholar * Catherine D Eichhorn Search for this author in: * NPG journals * PubMed * Google Scholar * Jeetender Chugh Search for this author in: * NPG journals * PubMed * Google Scholar * Alexandar L Hansen Search for this author in: * NPG journals * PubMed * Google Scholar * Hashim M Al-Hashimi Contact Hashim M Al-Hashimi Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (115K) Supplementary Table 1 Additional data
• Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging
  - Nat Methods 8(11):933-935 (2011)
  Nature Methods | Brief Communication Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging * Craig D Wenger1 * M Violet Lee1, 2 * Alexander S Hebert2, 3 * Graeme C McAlister1 * Douglas H Phanstiel1 * Michael S Westphall2 * Joshua J Coon1, 2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:933–935Year published:(2011)DOI:doi:10.1038/nmeth.1716Received17 March 2011Accepted19 August 2011Published online02 October 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 describe a mass spectrometry method, QuantMode, which improves accuracy of isobaric tag–based quantification by alleviating the pervasive problem of precursor interference, simultaneous isolation and fragmentation of impurities, through gas-phase purification. QuantMode analysis of a yeast sample 'contaminated' with interfering human peptides showed substantially improved quantitative accuracy compared to a standard scan, with a small loss of spectral identifications. This technique enables large-scale, multiplexed quantitative proteomics using isobaric tagging. View full text Subject terms: * Proteomics * Mass Spectrometry * Systems Biology Figures at a glance * Figure 1: Analysis of the precursor purity model and quantitative accuracy model samples with either HCD MS/MS or QuantMode. () Distribution of precursor purity as determined by examining abundance of reporter tag signal at m/z of 126 (yeast) and 131 (human) for yeast-identified sequences using either HCD MS/MS or QuantMode. () Analysis of quantitative accuracy via HCD MS/MS, HCD MS/MS with postacquisition filtering (PAF), and QuantMode. Shown is the true ratio of 10:1 (dashed red line), and boxplots indicate the median (stripe), 25th–75th percentile (interquartile range, box), 1.5 times the interquartile range (whiskers) and outliers (open circles). The number of quantified yeast PSMs (top) and median ratio (bottom) are given for each method. * Figure 2: Overview of QuantMode. () A triply charged precursor at m/z 595.72 was isolated with a 3 Th window. The precursor isotopic cluster occupies only 49% of the total ion current in this region. () QuantMode begins with PTR. () Isolation of the charge-reduced precursor (+2 charge state) purifies this target to 85%; HCD converts these purified precursors to reporter ions; resonant-excitation CAD follows reinjection and reisolation of the triply charged precursor. The HCD and CAD products are combined in the c-trap before orbitrap mass analysis. (,) The conventional HCD MS/MS scan for this impure precursor () is juxtaposed against this QuantMode scan (). Insets are close-ups of the gray shaded regions, showing the reporter-ion regions (identical intensity scale) and the quantitative accuracy achieved by both approaches for the 10:1 ratio. Author information * Author information * Supplementary information Affiliations * Department of Chemistry, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Craig D Wenger, * M Violet Lee, * Graeme C McAlister, * Douglas H Phanstiel & * Joshua J Coon * Genome Center of Wisconsin, University of Wisconsin–Madison, Madison, Wisconsin, USA. * M Violet Lee, * Alexander S Hebert, * Michael S Westphall & * Joshua J Coon * Department of Biomolecular Chemistry, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Alexander S Hebert & * Joshua J Coon Contributions C.D.W. designed and performed research, and wrote the paper; M.V.L., A.S.H. and G.C.M. designed and performed research; D.H.P. designed research; M.S.W. and J.J.C. designed research and wrote the paper. Competing financial interests Two patent applications, in part related to this manuscript, are pending: US 13/086638 (C.D.W., D.H.P. and J.J.C. are the inventors) and US 61/471461 (J.J.C. and M.S.W. are the inventors). Corresponding author Correspondence to: * Joshua J Coon Author Details * Craig D Wenger Search for this author in: * NPG journals * PubMed * Google Scholar * M Violet Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander S Hebert Search for this author in: * NPG journals * PubMed * Google Scholar * Graeme C McAlister Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas H Phanstiel Search for this author in: * NPG journals * PubMed * Google Scholar * Michael S Westphall Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua J Coon Contact Joshua J Coon Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–9, Supplementary Table 1, Supplementary Note, Supplementary Protocol Additional data
• MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics
  - Nat Methods 8(11):937-940 (2011)
  Nature Methods | Brief Communication MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics * Lily Ting1 * Ramin Rad1 * Steven P Gygi1 * Wilhelm Haas1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:937–940Year published:(2011)DOI:doi:10.1038/nmeth.1714Received21 March 2011Accepted24 August 2011Published online02 October 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Quantitative mass spectrometry–based proteomics is highly versatile but not easily multiplexed. Isobaric labeling strategies allow mass spectrometry–based multiplexed proteome quantification; however, ratio distortion owing to protein quantification interference is a common effect. We present a two-proteome model (mixture of human and yeast proteins) in a sixplex isobaric labeling system to fully document the interference effect, and we report that applying triple-stage mass spectrometry (MS3) almost completely eliminates interference. View full text Subject terms: * Proteomics * Mass Spectrometry Figures at a glance * Figure 1: Isobaric labeling, interference and interference modeling. () Quantitative mass spectrometry–based proteomics experiments with stable isotope–containing isobaric tags (TMT or isobaric tags for relative and absolute quantification) produce differentially labeled peptide ions that are indistinguishable in full mass spectra. Quantification is based on low m/z reporter fragment ion intensities after a target ion is isolated and fragmented in an MS2 experiment. () Both accuracy and precision of peptide quantification are affected when contaminating peptide ions are isolated together with the target peptide ion (interference effect). () Outline of accurate measurement of the interference effect by a two-proteome model using TMT sixplex labeled human cell line and S. cerevisiae Lys-C digests. The first three channels model interference, where yeast peptides were combined to create three different ratios (10:1, 4:1 and 2.5:1) among unchanging interfering human peptides (1:1:1). In the last three channels, yeast peptides were combined in! the same ratios (mirrored relative to the first three channels) without any human peptide interference. () An ideal yeast peptide MS2 spectrum without human peptide interference in the first three channels would have identical and mirrored TMT reporter ion intensities in the last three channels. () A typical yeast peptide has some interference in the first three reporter ion channels (red), which results in ratio distortion toward 1:1:1. * Figure 2: Evaluation and attempted removal of the interference effect. () An example of strong distortion of reporter ion intensities for a yeast peptide ion by interference of human peptide ions (left) and the effect of interference by yeast peptides on the reporter ion intensities of a human peptide (right; signals in channels 129, 130 and 131). () Ratio distributions (log2) of yeast peptides in channels with human peptide interference (Y interference), without human peptide interference (Y no interference) and human peptides only (H) for the indicated predicted ratios. LC-MS2 data were collected after separating the yeast and human whole cell lysate digest into 20 fractions using SCX chromatography. () Averaged normalized relative intensities for each TMT reporter ion channel for yeast peptides (left) and human peptides (right) from the dataset described in . Error bars, s.d. for 20,272 and 81,180 yeast and human peptides, respectively. Reporter ion intensity ratios are given with arrows pointing from the numerator to the denominator. () Ana! lysis of improvement of yeast peptide ratios in channels 126, 127 and 128 with human peptide interference after post-acquisition data filtering of isolation specificity (IS), fractionation and precursor isolation width. Expected ratios were divided by measured ratios, where perfect ratios would have a value of 1 for all three axes. The greater the distance of each tested condition from the red perfect ratio triangle, the greater the influence of human peptide interference on yeast peptide ratio distortion. * Figure 3: An MS3-based method eliminates the interference effect. () Precursor ions for MS2 spectra were selected from a high-resolution full mass spectrum acquired on the Orbitrap. Fast LTQ-CID-MS2 experiments were used for peptide sequence assignments, followed by the selection of the most intense fragment ion from MS2 (green) for HCD-MS3, where TMT reporter ion intensities are measured in the Orbitrap. () Normalized intensities for each TMT reporter ion channel for yeast peptides (left), and human peptides (right). Almost no interference effect was measured, except for forward false positives (1.5%). Error bars, s.d. for 8,919 and 65,595 yeast and human peptides, respectively. Reporter ion intensity ratios are given with arrows pointing from the numerator to the denominator. () Yeast peptide ratios from dataset combining 20 SCX chromatography fractions. Error bars, s.d. for 29,813, 22,640 and 7,173 peptides for average, MS2 and MS3 analyses, respectively. () Ratio distribution (log2 scale) of yeast peptides in channels with human peptid! e interference (Y interference), without human peptide interference (Y no interference) and for human peptides (H) at the indicated ratios. Author information * Author information * Supplementary information Affiliations * Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA. * Lily Ting, * Ramin Rad, * Steven P Gygi & * Wilhelm Haas Contributions L.T., S.P.G. and W.H. designed experiments, analyzed data and wrote the paper. L.T. and W.H. performed experiments. R.R. developed software for data analysis. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Wilhelm Haas or * Steven P Gygi Author Details * Lily Ting Search for this author in: * NPG journals * PubMed * Google Scholar * Ramin Rad 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 * Wilhelm Haas Contact Wilhelm Haas Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (852K) Supplementary Figures 1–6 and Supplementary Table 1 Excel files * Supplementary Data (59M) All yeast and human identification and quantification data. Additional data
• Surrogate reporters for enrichment of cells with nuclease-induced mutations
  - Nat Methods 8(11):941-943 (2011)
  Nature Methods | Brief Communication Surrogate reporters for enrichment of cells with nuclease-induced mutations * Hyojin Kim1 * Eunji Um2 * Sung-Rae Cho3 * Chorong Jung4 * Hyongbum Kim4 * Jin-Soo Kim1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:941–943Year published:(2011)DOI:doi:10.1038/nmeth.1733Received27 April 2011Accepted31 August 2011Published online09 October 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Zinc-finger nucleases (ZFNs) and TAL-effector nucleases (TALENs) are powerful tools for creating genetic modifications in eukaryotic cells and organisms. But wild-type and mutant cells that contain genetic modifications induced by these programmable nucleases are often phenotypically indistinguishable, hampering isolation of mutant cells. Here we show that transiently transfected episomal reporters encoding fluorescent proteins can be used as surrogate genes for the efficient enrichment of endogenous gene-modified cells by flow cytometry. View full text Subject terms: * Biochemistry * Genetics * Molecular Biology * Molecular Engineering Figures at a glance * Figure 1: Principle of the use of surrogate reporters for enrichment of nuclease-modified cells. () The reporter consists of the mRFP gene, the programmable nuclease's target sequence (left and right half-sites) and the eGFP gene. mRFP is constitutively expressed from the CMV promoter (PCMV), whereas functional eGFP is not expressed because its sequence is out of frame in the absence of programmable nuclease activity. When a double-strand break is introduced into the target sequence by programmable nucleases, the break is repaired by nonhomologous end-joining (NHEJ), which often causes frameshift mutations. Such mutations can render eGFP in frame with mRFP, inducing the expression of the mRFP-eGFP fusion protein. () Schematic illustrates enrichment of nuclease-induced mutations in mRFP+eGFP+ cells sorted by flow cytometry. Reporter plasmids and chromosomal target loci are illustrated. Mutations are shown as black spots. * Figure 2: A surrogate reporter enriches for TP53-disrupted cells. () Flow cytometry of HEK293 cells 3 d after transfection with the TP53-targeting ZFN and the reporter plasmids. Percentage of cells that express both mRFP and eGFP is indicated. () ZFN-driven mutations detected by the T7E1 assay. Arrows indicate the expected positions of DNA bands cleaved by mismatch-sensitive T7E1. The numbers at the bottom of the gel indicate mutation percentages measured by band intensities. () ZFN-driven mutation rates measured by fluorescence PCR. Arrows indicate amplified DNA peaks that correspond to small insertions. Tallest peaks correspond to wild-type amplicons. Mutation rates (percentages) were calculated by measuring the peak area. () DNA sequences of the TP53 wild-type (WT) and mutant clones, with ZFN recognition sites underlined, deleted bases indicated by dashes and inserted bases in lower case. The number of occurrences is shown in parentheses; X1 and X5 are the numbers of each clone. Mutation frequencies were calculated by dividing the numbe! r of mutant clones by the number of total clones. Author information * Author information * Supplementary information Affiliations * Department of Chemistry, Seoul National University, Gwanak-gu, Seoul, South Korea. * Hyojin Kim & * Jin-Soo Kim * Cha Stem Cell Institute, Cha University, Seoul, South Korea. * Eunji Um * Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, South Korea. * Sung-Rae Cho * Graduate School of Biomedical Science and Engineering/College of Medicine, Hanyang University, Sungdong-gu, Seoul, South Korea. * Chorong Jung & * Hyongbum Kim Contributions Hyojin K., E.U., S.-R.C. and C.J. performed the experiments. Hyongbum K. and J.-S.K. wrote the paper. Competing financial interests J.-S.K. filed a patent application (US 61/445,346) based on this work. Corresponding authors Correspondence to: * Jin-Soo Kim or * Hyongbum Kim Author Details * Hyojin Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Eunji Um Search for this author in: * NPG journals * PubMed * Google Scholar * Sung-Rae Cho Search for this author in: * NPG journals * PubMed * Google Scholar * Chorong Jung Search for this author in: * NPG journals * PubMed * Google Scholar * Hyongbum Kim Contact Hyongbum Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Jin-Soo Kim Contact Jin-Soo Kim Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–10, Supplementary Tables 1–4 and Supplementary Notes 1–2 Additional data
• Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain
  - Nat Methods 8(11):945-947 (2011)
  Nature Methods | Brief Communication Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain * Alexandre Kuhn1, 3 * Doris Thu1 * Henry J Waldvogel2 * Richard L M Faull2 * Ruth Luthi-Carter1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:945–947Year published:(2011)DOI:doi:10.1038/nmeth.1710Received23 May 2011Accepted04 August 2011Published online09 October 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Human diseases are often accompanied by histological changes that confound interpretation of molecular analyses and identification of disease-related effects. We developed population-specific expression analysis (PSEA), a computational method of analyzing gene expression in samples of varying composition that can improve analyses of quantitative molecular data in many biological contexts. PSEA of brains from individuals with Huntington's disease revealed myelin-related abnormalities that were undetected using standard differential expression analysis. View full text Subject terms: * Gene Expression * Bioinformatics * Neuroscience * Genomics Figures at a glance * Figure 1: PSEA of human brain samples. () Schematic of changes in brain-cell populations during neurodegeneration. () Expression of NEFL (neurofilament, light polypeptide) in three sample groups, as measured by the Affymetrix probe set indicated. Horizontal lines indicate average expression in each sample group. () Steps of the PSEA. () Multiple linear regression of gene PPT1 (Affymetrix probe set 200975_at) on reference expression signals showed dependence on neuronal (N) and astrocytic (A) reference signals but not on oligodendrocytic (O) and microglial (M) signals. CR, component plus residual14. Population-specific expressions with P < 0.05 are indicated. () Linear regression of the expression of three genes on the neuronal reference signal. βN, slope of the regression and relative neuron-specific expression. * Figure 2: Comparison of population-specific and total expression change in tissue samples with cell-composition changes. (,) Neuron-specific (, modeled as identical in the two sample groups) and total (, log fold change −0.5) expression of WASF1. (,) Neuron-specific (, log fold change −0.3) and total (, log fold change −0.8) expression of PPP3CA. (,) Neuron-specific (, log fold change 0.2) and total (, log fold change −0.2) expression of ATP6V1A. (,) Oligodendrocyte-specific (, modeled as identical in the two sample groups) and total () expression of LITAF. (,) Oligodendrocyte-specific (, log fold change 0.6) and total (, log fold change 0.8) expression of SEMA6A. (,) Oligodendrocyte-specific (, log fold change −0.8) and total (, log fold change −0.2) expression of SCD. Huntington's disease (HD) samples and controls were compared. Gene expression was measured by the Affymetrix probe sets indicated in parentheses. Reference expression N and O are the neuronal and oligodendrocytic reference expression signals, respectively. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE19380 Author information * Accession codes * Author information * Supplementary information Affiliations * Laboratory of Functional Neurogenomics, Ecole Polytechnique Fédérale de Lausanne, Switzerland. * Alexandre Kuhn, * Doris Thu & * Ruth Luthi-Carter * Department of Anatomy with Radiology and Centre for Brain Research, University of Auckland, New Zealand. * Henry J Waldvogel & * Richard L M Faull * Present address: Laboratory of Microfluidics Systems Biology, Institute of Materials Research and Engineering, Singapore. * Alexandre Kuhn Contributions A.K. developed and applied PSEA, performed experiments with cultured cells, and wrote the manuscript, with input from R.L.-C; D.T., H.J.W. and R.L.M.F. performed immunohistochemical experiments with brain sections; and R.L.-C. conceptualized the project and directed the study. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Alexandre Kuhn Author Details * Alexandre Kuhn Contact Alexandre Kuhn Search for this author in: * NPG journals * PubMed * Google Scholar * Doris Thu Search for this author in: * NPG journals * PubMed * Google Scholar * Henry J Waldvogel Search for this author in: * NPG journals * PubMed * Google Scholar * Richard L M Faull Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth Luthi-Carter Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (8M) Supplementary Figures 1–21, Supplementary Tables 1–15,17–18, Supplementary Data 1–7, Supplementary Discussion Excel files * Supplementary Table 16 (3.5M) Standard differential expression analysis showing differential total expression (HD grade 1 versus control samples). Additional data
• Artificial niche microarrays for probing single stem cell fate in high throughput
  - Nat Methods 8(11):949-955 (2011)
  Nature Methods | Article Artificial niche microarrays for probing single stem cell fate in high throughput * Samy Gobaa1 * Sylke Hoehnel1 * Marta Roccio1 * Andrea Negro1 * Stefan Kobel1 * Matthias P Lutolf1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:949–955Year published:(2011)DOI:doi:10.1038/nmeth.1732Received08 March 2011Accepted19 August 2011Published online09 October 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To understand the regulatory role of niches in maintaining stem-cell fate, multifactorial in vitro models are required. These systems should enable analysis of biochemical and biophysical niche effectors in a combinatorial fashion and in the context of a physiologically relevant cell-culture substrate. We report a microengineered platform comprised of soft hydrogel microwell arrays with modular stiffness (shear moduli of 1–50 kPa) in which individual microwells can be functionalized with combinations of proteins spotted by robotic technology. To validate the platform, we tested the effect of cell-cell interactions on adipogenic differentiation of adherent human mesenchymal stem cells (MSCs) and the effect of substrate stiffness on osteogenic MSC differentiation. We also identified artificial niches supporting extensive self-renewal of nonadherent mouse neural stem cells (NSCs). Using this method, it is possible to probe the effect of key microenvironmental perturbations on! the fate of any stem cell type in single cells and in high throughput. View full text Subject terms: * Stem Cells * Lab-on-a-chip * Cell Biology * Imaging Figures at a glance * Figure 1: Production of artificial niche microarrays. () First a DNA spotter with solid pins was used to spot, in multiple rounds, different protein solutions (represented by different colors) on micropillars of a microfabricated silicon stamp. Then the printed stamps were pressed against a thin, partially cross-linked layer of PEG hydrogel. Finally, the stamp was demolded from patterned hydrogel layer. t, time. The obtained artificial niche microarray can be seeded with either adherent or nonadherent stem cells. () Representative example of two full arrays (combined in mosaic fashion from individual images) spotted with two fluorescently labeled model proteins. Six concentrations of FITC-BSA (green) and six concentrations of rhodamine-BSA (magenta) were printed either in 12 × 12 random motifs (left) or as overlapping gradients (right), all in the context of a topographically patterned gel substrate. Scale bars, 2 mm. A three-dimensional reconstruction of confocal stacks showing microwells with a diameter of 450 μm (bottom ri! ght). Scale bar, 500 μm. * Figure 2: Modulation of MSC seeding concentration and its effect on cell fate. () Distribution of cells per microwell on day 0 after the indicated number of MSCs were seeded onto each array. For the Poisson distribution (blue lines), the Poisson number (λ) was determined by the amount of seeded cells. () Phase contrast micrographs (left) illustrate the variability of the MSC initial density per microwell. Fluorescence micrographs (right) show Nile Red staining (pseudocolored), 4′,6-diamidino-2-phenylindole (DAPI) staining and phalloidin–Alexa Fluor 488 (green) after 11 d of culture in adipogenic medium. These stains were used to quantify indexes of differentiation, proliferation and cell shape (surface) in each microwell. Scale bars, 100 μm. In the false-color scale, highest adipogenic differentiation is marked by red and white pixels. (–) Measured indexes from plotted against initial cell density. Horizontal colored bars indicate means. Error bars, s.e.m. for all the observations made on three arrays. a.u., arbitrary units. * Figure 3: Biochemical recapitulation of the cell density effect on MSC adipogenic differentiation. (,) Micrographs () show immunostaining with the indicated antibodies of microwells functionalized both with N-cadherin and with N-cadherin and FN9-10. Plots () show quantification of the signal after immunostaining microwells containing variable amounts of N-cadherin and equal amounts of FN9-10. Negative control, PEG. () Phase contrast (phase), phalloidin–Alexa Fluor 488 (Phalloidin) and Nile Red images of MSCs exposed to indicated concentrations of N-cadherin (11 d). All scale bars, 100 μm. (,) Adipogenic differentiation () and cell-surface index () plotted against indicated N-cadherin concentrations. NIAD, normalized index of adipogenic differentiation. () Adipogenic differentiation as a function of N-cadherin concentration for microwells with indicated number of initial cells. () Dependence of adipogenic differentiation on cell density at varying N-cadherin concentrations. The red dashed line represents the average adipogenic differentiation across all microwells. (,) ! Effect of blocking N-cadherin on adipogenic differentiation for varying initial cell densities () and N-cadherin concentrations (). IgG, immunoglobulin. Error bars, s.e.m. as computed after a multivariate analysis (generalized linear model) on n = 5,167 (gain of function experiments) and n = 5,860 (loss of function experiments) observations. ***P < 0.001, **P < 0.01 and *P < 0.05. a.u., arbitrary units. * Figure 4: Effect of matrix stiffness on MSC osteogenic differentiation. () Dependence of shear moduli G′ of substrates on PEG precursor percentage. Error bars, s.e.m. (n = 4 measurements). () Immunostaining of eight identical microwells of different stiffness and functionalized with immobilized fibronectin. () Antibody to alkaline phosphatase (α-ALP), phalloidin and DAPI stains of MSCs cultured for 11 d on various stiffnesses in osteogenic medium. All scale bars, 100 μm. (,) Dependence of osteogenic differentiation () and cell shape () on hydrogel stiffness. Error bars, s.e.m. (n = 3,905 observations). ***P < 0.001. () Dependence of osteogenic differentiation on FN9-10 concentration for indicated stiffness values. a.u., arbitrary units. * Figure 5: Screening for the effect of niche proteins on NSC fate. () Selected phase-contrast images from a time-lapse experiment showing proliferation, quiescence or death of single cells in microwells. Arrows indicate the initial single cell. Insets, magnification of single cells or neurospheres. (,) Confocal microscopy images on neurospheres cultured on the artificial niche arrays and stained for nestin (yellow), laminin 1 (red) and Hes5 (green) (progenitor markers, ) and for βIII-tubulin (yellow) (differentiation marker, ). All scale bars, 50 μm. () Number of neurosphere forming cells plotted against total cells in the presence of the indicated recombinant proteins. NS, not significant. Blank, microwells containing no protein. Proteins represented by red triangles significantly favor NSC proliferation (for example, CNTF and Notch ligands). () Average neurosphere size as a function of the spotted proteins. rJag, rat Jagged 1; hJag, human Jagged 1. () Effect of spotted Jagged 1 and laminin 1 concentrations on NSC fate. () Effect of Jagg! ed and laminin combinations on NSC fate. ***P < 0.001, **P < 0.01 and *P < 0.05. For neurosphere size data, significance is given by a multivariate analysis (generalized linear model). Error bars, s.e.m. for n = 564 neurospheres. For frequency data, significance is given by a χ2 test. Error bars, s.e.m. (n = 3 arrays). Author information * Abstract * Author information * Supplementary information Affiliations * Laboratory of Stem Cell Bioengineering and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. * Samy Gobaa, * Sylke Hoehnel, * Marta Roccio, * Andrea Negro, * Stefan Kobel & * Matthias P Lutolf Contributions S.G. and M.P.L. conceived the study, interpreted results and wrote the manuscript; S.G. conducted most experimental work (mask and press design, microwell array production, image acquisition, cell counts) and performed statistical analyses; S.H. contributed to stem cell culture, fate analyses and microfabrication; M.R. contributed to stem cell culture and fate analyses; A.N. contributed to technology development (design of press) and wrote image-analysis scripts; S.K. contributed to technology development (mask design and stamp production) and image acquisition scripts. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthias P Lutolf Author Details * Samy Gobaa Search for this author in: * NPG journals * PubMed * Google Scholar * Sylke Hoehnel Search for this author in: * NPG journals * PubMed * Google Scholar * Marta Roccio Search for this author in: * NPG journals * PubMed * Google Scholar * Andrea Negro Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Kobel Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias P Lutolf Contact Matthias P Lutolf Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–11, Supplementary Tables 1–2 Additional data
• Rapid and robust generation of functional oligodendrocyte progenitor cells from epiblast stem cells
  - Nat Methods 8(11):957-962 (2011)
  Nature Methods | Article Rapid and robust generation of functional oligodendrocyte progenitor cells from epiblast stem cells * Fadi J Najm1 * Anita Zaremba2 * Andrew V Caprariello2 * Shreya Nayak1 * Eric C Freundt3 * Peter C Scacheri1 * Robert H Miller2 * Paul J Tesar1, 2, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:957–962Year published:(2011)DOI:doi:10.1038/nmeth.1712Received24 June 2011Accepted22 August 2011Published online25 September 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 Myelin-related disorders such as multiple sclerosis and leukodystrophies, for which restoration of oligodendrocyte function would be an effective treatment, are poised to benefit greatly from stem cell biology. Progress in myelin repair has been constrained by difficulties in generating pure populations of oligodendrocyte progenitor cells (OPCs) in sufficient quantities. Pluripotent stem cells theoretically provide an unlimited source of OPCs, but current differentiation strategies are poorly reproducible and generate heterogenous populations of cells. Here we provide a platform for the directed differentiation of pluripotent mouse epiblast stem cells (EpiSCs) through defined developmental transitions into a pure population of highly expandable OPCs in 10 d. These OPCs robustly differentiate into myelinating oligodendrocytes in vitro and in vivo. Our results demonstrate that mouse pluripotent stem cells provide a pure population of myelinogenic oligodendrocytes and offer a t! ractable platform for defining the molecular regulation of oligodendrocyte development and drug screening. View full text Subject terms: * Stem Cells * Neuroscience Figures at a glance * Figure 1: Efficient differentiation of epiblast stem cells into region-specific neuroepithelial cells in 5 d. (–) Phase-contrast micrographs (,) and fluorescence images (,) of pluripotent EpiSCs, which express Oct3/4 (,), and of EpiSCs treated with factors to block both BMP and activin-nodal signaling, which results in their progression to neural rosettes expressing Sox1 and Pax6 in 4 d (,). () Images of cells then treated with retinoic acid and SHH for 1 d, showing patterned neural rosettes expressing the region-specific transcription factors Olig2 and Nkx2.2, which are normally expressed in the ventral ventricular zone of the mouse embryonic spinal cord. () Axial section of mouse embryonic day 11.5 (E11.5) developing spinal cord. Inset, localization of Olig2 and Nkx2.2 proteins. () Results of genome-wide transcriptional profiling during the transition of EpiSCs to patterned neural rosettes, showing a rapid downregulation of pluripotency genes and upregulation of genes specific to the developing neuroectoderm. Scale bars, 100 μm (–) and 50 μm (). * Figure 2: Highly expandable OPCs derived from patterned EpiSC-derived neuroectoderm. (–) Patterned neural rosettes derived from EpiSCs were dissociated and grown on a laminin substrate in the presence of FGF2, PDGF-AA and SHH resulting in the formation of OPCs. Phase contrast image of EpiSC-derived OPCs showing characteristic bipolar morphology (). Immunostaining of OPCs revealed they were highly pure as 89.9% co-expressed transcription factors Olig2 and Nkx2.2 (), and 89.2% expressed Sox10 () without sorting or selection (>600 cells from random fields were manually counted for each marker). Scale bars, 50 μm. () Cumulative EpiSC-derived OPC counts over eight passages (passages denoted p1–p8). () Flow cytometric analyses revealed that both low-passage-number (passage 2) and high-passage-number (passage 12) EpiSC-derived OPCs robustly co-expressed OPC surface markers NG2 and PDGFRα. Percentages of cells expressing both markers are indicated. () Results of genome-wide transcriptional profiling during the transition of EpiSC-derived neural rosettes to OPC! s, showing a rapid downregulation of rosette-specific genes and upregulation of genes specific to OPCs. * Figure 3: EpiSC-derived OPCs differentiated into oligodendrocytes in vitro. (,) Phase constrast () and fluorescence () images of EpiSC-derived OPCs cultured in differentiation-inducing medium for 3 d, which adopt a mature oligodendrocyte morphology and coexpress O4 and MBP. () Results of genome-wide transcriptional profiling during the transition of EpiSC-derived OPCs to oligodendrocytes revealed a rapid downregulation of OPC-specific genes and upregulation of genes specific to oligodendrocytes. () Fluorescence images of EpiSC-derived OPCs seeded on in vitro–cultured embryonic mouse cortical neurons showing extension of MBP+ processes along βIII-tubulin+ nerve axons (arrows). DAPI staining is also shown. All scale bars, 50 μm. * Figure 4: EpiSC-derived OPCs are myelinogenic. () Schematic (left) of a coronal section of early postnatal (postnatal day 2–4) mouse forebrain, with boxed region showing the imaged areas. Sections (right) cultured for 13 d show extensive MBP+ (black) segments in wild-type mice and a lack of MBP+ (black) segments in shiverer (Mbpshi/shi) mutants (lethally hypomyelinated owing to a lack of Mbp). CC, corpus callosum. () Schematic shows injection of EpiSC-derived OPCs into shiverer forebrain sections (left), and images reveal extensive MBP+ (black) myelin sheaths after 10 d. No differences were noted in the myelinogenic capacity of low-passage-number (passage 4) and high-passage-number (passage 10) EpiSC-derived OPCs. () Toluidine blue–stained sections (1 μm) as well as electron microscopy (EM) analysis showed that EpiSC-derived OPCs produce compact myelin. () Fluorescence micrographs collected 21 d after injection of EpiSC-derived OPCs into early postnatal shiverer mutant mice show myelination in the corpus callosum an! d the contralateral striatum. Scale bars, 100 μm (,), 25 μm () and 50 μm (). * Figure 5: Screening for extrinsic signals that regulate the fate of EpiSC-derived OPCs. () Fluorescence images showing the differentiation response of EpiSC-derived OPCs (Olig2+ and O4−) after treatment with oligodendrocyte differentiation medium. (–) Fluorescence images showing response of EpiSC-derived OPCs to additional treatment regimens including activation of the notch signaling cascade (with Jag1) (), inhibition of the canonical Wnt–β-catenin signaling pathway by negative regulation of GSK3β (with CHIR99021) () or stimulation with BMP4 and LIF (). All scale bars, 50 μm. () Quantification of the effect of GSK3β inhibition using CHIR99021 to block the differentiation of OPCs into O4+ oligodendrocytes (>2.4 × 103 cells were manually scored from random fields of triplicate wells from two independent experiments; error bars, s.d.). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Genetics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. * Fadi J Najm, * Shreya Nayak, * Peter C Scacheri & * Paul J Tesar * Department of Neurosciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. * Anita Zaremba, * Andrew V Caprariello, * Robert H Miller & * Paul J Tesar * Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA. * Eric C Freundt * New York Stem Cell Foundation, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. * Paul J Tesar Contributions F.J.N. and P.J.T. derived the EpiSC-OPC lines and carried out in vitro experiments; A.V.C., A.Z., F.J.N. and P.J.T. performed in vivo experiments; A.Z., F.J.N., R.H.M. and P.J.T. performed slice culture experiments; E.C.F. carried out the co-culture experiments; F.J.N., S.N. and P.J.T. performed drug screening experiments; F.J.N., P.C.S. and P.J.T. generated and analyzed gene expression data; and F.J.N., R.H.M. and P.J.T. analyzed data and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Paul J Tesar Author Details * Fadi J Najm Search for this author in: * NPG journals * PubMed * Google Scholar * Anita Zaremba Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew V Caprariello Search for this author in: * NPG journals * PubMed * Google Scholar * Shreya Nayak Search for this author in: * NPG journals * PubMed * Google Scholar * Eric C Freundt Search for this author in: * NPG journals * PubMed * Google Scholar * Peter C Scacheri Search for this author in: * NPG journals * PubMed * Google Scholar * Robert H Miller Search for this author in: * NPG journals * PubMed * Google Scholar * Paul J Tesar Contact Paul J Tesar Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (9M) Supplementary Figures 1–8, Supplementary Tables 1–2 Movies * Supplementary Video 1 (9M) Time-lapse imaging of EpiSC-derived OPCs differentiated to oligodendrocytes. Images were collected every 10 min for 68 h on an inverted microscope outfitted with a precision scanning stage in a live cell incubation chamber and cover at 37 °C and 5% CO2 in medium containing T3, SHH, noggin, cAMP, IGF1 and NT3. Additional data
• The proteomes of transcription factories containing RNA polymerases I, II or III
  - Nat Methods 8(11):963-968 (2011)
  Nature Methods | Article The proteomes of transcription factories containing RNA polymerases I, II or III * Svitlana Melnik1, 3 * Binwei Deng1 * Argyris Papantonis1 * Sabyasachi Baboo1 * Ian M Carr2 * Peter R Cook1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:963–968Year published:(2011)DOI:doi:10.1038/nmeth.1705Received29 November 2010Accepted12 August 2011Published online25 September 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 Human nuclei contain three RNA polymerases (I, II and III) that transcribe different groups of genes; the active forms of all three are difficult to isolate because they are bound to the substructure. Here we describe a purification approach for isolating active RNA polymerase complexes from mammalian cells. After isolation, we analyzed their protein content by mass spectrometry. Each complex represents part of the core of a transcription factory. For example, the RNA polymerase II complex contains subunits unique to RNA polymerase II plus various transcription factors but shares a number of ribonucleoproteins with the other polymerase complexes; it is also rich in polymerase II transcripts. We also describe a native chromosome conformation capture method to confirm that the complexes remain attached to the same pairs of DNA templates found in vivo. View full text Subject terms: * Proteomics * Gene Expression * Molecular Biology * Cell Biology Figures at a glance * Figure 1: Purification procedure. () Strategy. Cartoon shows a chromatin loop with nucleosomes (green circle) tethered to a polymerizing complex (oval) attached to the substructure (brown). The cells are permeabilized and in some cases a run-on is performed in [32P]UTP so that nascent RNA can be tracked. The nuclei are then washed with NP-40, most of the chromatin is detached with a nuclease (here, DNase I), the chromatin-depleted nuclei are resuspended in NLB and polymerizing complexes are released from the substructure with caspases. After pelleting, chromatin associated with polymerizing complexes in the supernatant is degraded with DNase I, and the complexes are partially resolved in two-dimensional (2D) gels (using blue native and native gels in the first and second dimensions, respectively); rough positions of complexes (and a control region, labeled 'C') are shown. Finally, different regions are excised, and their content is analyzed by mass spectrometry. () Recovery of [32P]RNA, after including a run! -on. Fractions correspond to those at the same level in . () Run-on activity assayed later during fractionation (as in , but without run-on at beginning). Different fractions, with names as in , were allowed to extend transcripts by <40 nucleotides in [32P]UTP, and the amount of [32P]RNA per cell was determined by scintillation counting. Fractions '2pellet' and '4pellet' were also resuspended in NLB before run-ons were performed; results indicate that NLB reduces incorporation to half or less. Despite this, '5super' has 25% of the run-on activity of permeabilized cells ('2pellet'), which is equivalent to half of the original (after correction for the effects of NLB). * Figure 2: Resolving different polymerases in native two-dimensional gels (run-ons in [32P]UTP are included). () Resolving complexes II and III with Coomassie blue in the first dimension. The cartoon shows regions selected for mass spectrometry analysis. First, an autoradiograph of the gel was prepared; overlapping spots of (nascent) [32P]RNA are present along the diagonal. The region indicated (dotted outline) contained ~0.03% of the protein, ~0.8% of the DNA and ~5% of the nascent [32P]RNA initially present. After blotting, the membrane was stained with Ponceau S; most protein is present on the diagonal. Next, the membrane was immuno-probed successively for three polymerases (using antibodies against RPA194, RPB1 and RPC62); the three are partially resolved. Note that complex I is destabilized by the Coomassie blue in the first dimension, and so it migrates rapidly. () Resolving complex I (no Coomassie in either dimension). The cartoon shows regions selected for mass spectrometry analysis. First, an autoradiograph was prepared; overlapping spots of (nascent) [32P]RNA are again pre! sent along the diagonal. After staining with Coomassie, spots are seen to overlap regions rich in [32P]RNA. After blotting, the membrane was probed for the polymerases (as above); complex I now runs the slowest. () Proteins in regions indicated in and were resolved on a 4–15% SDS-acrylamide gel and stained with Coomassie. * Figure 3: The content of complexes I, II and III as determined by mass spectrometry. () Numbers of proteins in the different complexes and their overlap. () Many proteins in each complex are associated with the GO term 'gene expression' (GO: 0010467), and complex II contains more with 'transcription from RNA polymerase II promoter' (GO: 0006366) than do complexes I and III. () Most proteins in each complex possess GO terms related to transcript production. Selected GO terms were incorporated into eight groups; for example, 'transcription' includes terms 'RNA polymerase', 'transcription factor' and 'transcription regulation'), and 'other terms' includes those not in the other seven groups. Four additional sets of proteins are included for comparison on the right. Some proteins possess terms in more than one group, and terms in each group are expressed as a fraction of the total in all groups. In each complex, 2% of proteins lacked any GO term, and many proteins in the complexes associated with 'other terms' nevertheless turn out to have a role in transcript p! roduction (for example, actin21 proteasomal constituents17 and nucleoporins22). Each complex has a characteristic pattern, which is distinct from those given by proteins with the terms 'cytoplasm' and 'S100'. * Figure 4: Isolated complexes remain associated with DNA sequences found in vivo. () Strategies for 3C and native 3C. Magenta and blue genes on different chromosomes are co-transcribed by one complex (oval) attached to the substructure (brown). Conventional 3C involves covalently cross-linking (turquoise lines) DNA, cutting (here, with HindIII), dilution, ligation and detection of ligated products by PCR. Note that a is joined to c, even though there was no stable molecular bridge between the two before cross-linking; such products yield an inevitable background. Native 3C omits cross-linking and relies on pre-existing (native) contacts. As most (inactive) cellular DNA is lost during isolation (including fragment c), unwanted background is lower, and wanted 3C products are present in higher concentrations. () Targets of primers (gray arrows) used to monitor interactions 1–6; only the contacts that are due to interactions 1 and 6 (purple lines) are detected by both 3C and native 3C. White arrows show primers used for loading controls. () 3C and native 3C! yield similar bands or contacts (although less template is needed with native 3C). HUVECs were treated with TNFα (0, 30 min), and interactions 1–6 were monitored by 3C and native 3C. Arrowheads indicate relevant 3C bands (all verified by sequencing; additional, nonspecific bands are amplified during the 36 PCR cycles used). 'Intra-GAPDH' 3C and 'loading' controls apply to all panels. Controls (with 13–50 ng template) show that PCR is conducted in the linear amplification range. Author information * Abstract * Author information * Supplementary information Affiliations * Sir William Dunn School of Pathology, University of Oxford, Oxford, UK. * Svitlana Melnik, * Binwei Deng, * Argyris Papantonis, * Sabyasachi Baboo & * Peter R Cook * Leeds Institute of Molecular Medicine, University of Leeds, St. James's Hospital, Leeds, UK. * Ian M Carr * Present address: Division of Molecular Biology of the Cell II, German Cancer Research Center, Heidelberg, Germany. * Svitlana Melnik Contributions Experiments were designed by S.M., B.D., A.P., S.B. and P.R.C. S.M. developed the isolation procedure and carried out many of the validation experiments, S.M. and B.D. performed gel electrophoreses and mass spectrometry, A.P. developed native 3C and carried out RT-PCR, S.B. did the light microscopy, and I.M.C. developed software. All authors wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Peter R Cook Author Details * Svitlana Melnik Search for this author in: * NPG journals * PubMed * Google Scholar * Binwei Deng Search for this author in: * NPG journals * PubMed * Google Scholar * Argyris Papantonis Search for this author in: * NPG journals * PubMed * Google Scholar * Sabyasachi Baboo Search for this author in: * NPG journals * PubMed * Google Scholar * Ian M Carr Search for this author in: * NPG journals * PubMed * Google Scholar * Peter R Cook Contact Peter R Cook 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 1–6, Supplementary Tables 1–3, Supplementary Note Excel files * Supplementary Table 4 (2M) Experiment 1: output from the CPFP (FDRs for complexes I, II and III were 0.84, 0.8 and 0.82% respectively), and results from the SI analysis. * Supplementary Table 5 (106K) Experiment 2: output from the CPFP (FDRs for complexes I, II and III were all 0.8%). * Supplementary Table 6 (250K) Experiment 3: output from the CPFP (FDRs for complexes II and III were 0.75 and 0.65%, respectively), and results from the SI analysis. * Supplementary Table 7 (1M) Comparison of the proteomes seen in all three experiments. Additional data
• Probing protein heterogeneity in the plasma membrane using PALM and pair correlation analysis
  - Nat Methods 8(11):969-975 (2011)
  Nature Methods | Article Probing protein heterogeneity in the plasma membrane using PALM and pair correlation analysis * Prabuddha Sengupta1, 4 * Tijana Jovanovic-Talisman1, 3, 4 * Dunja Skoko1 * Malte Renz1 * Sarah L Veatch2 * Jennifer Lippincott-Schwartz1 * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 8,Pages:969–975Year published:(2011)DOI:doi:10.1038/nmeth.1704Received10 May 2011Accepted16 August 2011Published online18 September 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 Photoactivated localization microscopy (PALM) is a powerful approach for investigating protein organization, yet tools for quantitative, spatial analysis of PALM datasets are largely missing. Combining pair-correlation analysis with PALM (PC-PALM), we provide a method to analyze complex patterns of protein organization across the plasma membrane without determination of absolute protein numbers. The approach uses an algorithm to distinguish a single protein with multiple appearances from clusters of proteins. This enables quantification of different parameters of spatial organization, including the presence of protein clusters, their size, density and abundance in the plasma membrane. Using this method, we demonstrate distinct nanoscale organization of plasma-membrane proteins with different membrane anchoring and lipid partitioning characteristics in COS-7 cells, and show dramatic changes in glycosylphosphatidylinositol (GPI)-anchored protein arrangement under varying pertu! rbations. PC-PALM is thus an effective tool with broad applicability for analysis of protein heterogeneity and function, adaptable to other single-molecule strategies. View full text Subject terms: * Single Molecule * Cell Biology * Microscopy * Imaging Figures at a glance * Figure 1: Single molecules appear multiple times with variable blinking intervals. (–) TfR-PAGFP was expressed transiently at low or high density on the plasma membrane of COS-7 cells and imaged using PALM. Best fit coordinates of peak centers from individual frames were calculated and displayed as single dots in and . Shown are clusters of peaks corresponding to two spatially and temporally well-separated molecules in low expressing cell () and peaks corresponding to molecules that are not well separated in highly expressing cell (). Note that the peaks arising from a single molecule are spatially confined in a region determined by average localization precision of the peaks. Center positions of molecules (asterisks) were determined by grouping peaks in the clusters. Color bars indicate frame number at which the molecules appeared. Graphs in show multiple frame appearances of the single molecules (i) and (ii) from . Frames in the sequence with no signal represent times when the molecule switched to a dark state. * Figure 2: PC-PALM distinguishes random versus clustered distributions. () Diagram showing purified PAGFP molecules randomly immobilized on glass coverslip. () Actual spatial distribution of peak centers of PAGFP molecules in a section of this coverslip. () Plot of calculated autocorrelation function (g(r)peaks) of PAGFP molecules in fit to equation (1). The correlation owing to multiple appearances of a single protein (g(r)stoch) and the protein correlation (g(r)protein) were evaluated from the fit. () Distribution of peak centers of TfR-PAGFP across the plasma membrane of a COS-7 cell. A representative section of the cell used for correlation analysis is marked by a yellow box. () Spatial distribution of peak centers of TfR-PAGFP in the section indicated in . Beside peak clusters associated with individual molecules, there was noticeable grouping of these clusters (red dashed ovals). () Measured correlation function of all peaks (g(r)peaks) in was well fit to equation (2). Corrected protein correlation function (g(r)protein) was evaluated by s! ubtracting the contribution from multiple appearances of single protein (g(r)stoch) from the measured correlation function. Scale bars, 200 nm (,) and 5 μm (). * Figure 3: Steady-state spatial distribution of plasma-membrane proteins assessed by PC-PALM. () Maximum-intensity projection confocal images of mEGFP-labeled proteins expressed in COS-7 cells. mEGFP-GPI, Lyn-mEGFP, Lat-mEGFP and VSVG-mEGFP were uniformly distributed across the plasma membrane (–TX). On extraction with 1% Triton X-100 at 4 °C (+TX), mEGFP-GPI, Lyn-mEGFP and Lat-mEGFP were present in detergent resistant membrane, whereas VSVG-mEGFP was solubilized. Scale bars, 5 μm. () Partitioning of mEGFP-labeled proteins and Rh-DOPE between coexisting liquid phases in GPMVs. (–) Cluster parameters of steady-state distribution of PAGFP-labeled proteins graphed as proteins per cluster (–) and as distribution of cluster radius (–) assessed by PC-PALM (n = 91 for PAGFP-GPI, n = 57 for Lyn-PAGFP, n = 88 for Lat-PAGFP and n = 73 for VSVG-PAGFP). () Density of proteins in clusters for PAGFP-GPI, Lyn-PAGFP, Lat-PAGFP and VSVG-PAGFP. Error bars, s.e.m. (n = 91 for PAGFP-GPI, n = 57 for Lyn-PAGFP, n = 88 for Lat-PAGFP and n = 73 for VSVG-PAGFP). () Model of steady-! state spatial distribution of the four proteins based on PC-PALM analysis. * Figure 4: Reorganization of PAGFP-GPI induced by perturbations of plasma membrane evaluated by PC-PALM. () Local density of PAGFP-GPI in clusters under steady state and after perturbations. Error bars, s.e.m. (n = 91 for untreated, n = 60 for no cholesterol, n = 58 with cholesterol treatment, n = 58 with sphingomyelinase (SMase), n = 59 with STxB and n = 53 with cytochalasin B). () Distribution of PAGFP-GPI molecules per cluster. () Distribution of correlation lengths of PAGFP-GPI under steady state and upon perturbations. * Figure 5: Actin-PAmCh localizes with clusters of antibody–cross-linked PAGFP-GPI. () Local density of PAGFP-GPI evaluated by PC-PALM in clusters at steady state at 37 °C (s.e.m.; n = 91), at 4 °C (s.e.m.; n = 29) and upon antibody-induced cross-linking of PAGFP-GPI at 4 °C (s.e.m.; n = 35). () Distribution of PAGFP-GPI molecules per cluster at 37 °C, 4 °C and after antibody cross-linking at 4 °C. () Distribution of PAGFP-GPI cluster sizes at 37 °C, 4 °C and upon antibody cross-linking at 4 °C. () Section of a COS-7 cell coexpressing PAGFP-GPI and actin-PAmCh in absence of cross-linking antibodies at 37 °C. Centers of peaks are shown. () Section of a cell expressing PAGFP-GPI and actin-PAmCh following antibody cross-linking of PAGFP-GPI at 4 °C. () Cross-correlation between PAGFP-GPI and actin-PAmCh with (at 4 °C; s.e.m., n = 61) and without (at 4 °C and 37 °C; s.e.m., n = 39 for 4 °C, n = 34 for 37 °C) antibody cross-linking. Scale bars, 200 nm. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Prabuddha Sengupta & * Tijana Jovanovic-Talisman Affiliations * The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA. * Prabuddha Sengupta, * Tijana Jovanovic-Talisman, * Dunja Skoko, * Malte Renz & * Jennifer Lippincott-Schwartz * Department of Biophysics, University of Michigan, Ann Arbor, Michigan, USA. * Sarah L Veatch * Present address: Department of Chemistry, University of Hawaii at Manoa, Honolulu, Hawaii, USA. * Tijana Jovanovic-Talisman Contributions P.S. and T.J-T. conceived and designed experiments, developed experimental techniques, performed experiments, developed the analytical method, analyzed data and wrote the paper; D.S. helped develop the analytical method; M.R. contributed to characterization of PA-FP and imaging regime; S.L.V. contributed equations used to analyze data; J.L-S. conceived and designed the experiments, and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jennifer Lippincott-Schwartz Author Details * Prabuddha Sengupta Search for this author in: * NPG journals * PubMed * Google Scholar * Tijana Jovanovic-Talisman Search for this author in: * NPG journals * PubMed * Google Scholar * Dunja Skoko Search for this author in: * NPG journals * PubMed * Google Scholar * Malte Renz Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah L Veatch Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Lippincott-Schwartz Contact Jennifer Lippincott-Schwartz Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–7 and Supplementary Notes 1–2 Additional data
• Mining the O-glycoproteome using zinc-finger nuclease–glycoengineered SimpleCell lines
  - Nat Methods 8(11):977-982 (2011)
  Nature Methods | Article Mining the O-glycoproteome using zinc-finger nuclease–glycoengineered SimpleCell lines * Catharina Steentoft1, 2 * Sergey Y Vakhrushev1, 2 * Malene B Vester-Christensen1 * Katrine T-B G Schjoldager1 * Yun Kong1 * Eric Paul Bennett1 * Ulla Mandel1 * Hans Wandall1 * Steven B Levery1 * Henrik Clausen1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 8,Pages:977–982Year published:(2011)DOI:doi:10.1038/nmeth.1731Received04 April 2011Accepted09 September 2011Published online09 October 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 Zinc-finger nuclease (ZFN) gene targeting is emerging as a versatile tool for engineering of multiallelic gene deficiencies. A longstanding obstacle for detailed analysis of glycoproteomes has been the extensive heterogeneities in glycan structures and attachment sites. Here we applied ZFN targeting to truncate the O-glycan elongation pathway in human cells, generating stable 'SimpleCell' lines with homogenous O-glycosylation. Three SimpleCell lines expressing only truncated GalNAcα or NeuAcα2-6GalNAcα O-glycans were produced, allowing straightforward isolation and sequencing of GalNAc O-glycopeptides from total cell lysates using lectin chromatography and nanoflow liquid chromatography–mass spectrometry (nLC-MS/MS) with electron transfer dissociation fragmentation. We identified >100 O-glycoproteins with >350 O-glycan sites (the great majority previously unidentified), including a GalNAc O-glycan linkage to a tyrosine residue. The SimpleCell method should facilitate an! alyses of important functions of protein glycosylation. The strategy is also applicable to other O-glycoproteomes. View full text Subject terms: * Proteomics * Biochemistry * Mass Spectrometry * Cell Biology Figures at a glance * Figure 1: Flow diagram depicting the use of SimpleCell lines to map the O-glycoproteome. () ZFN targeting of the core-1 synthesis step by knockout of COSMC simplifies all O-glycosylation to GalNAc (Tn) and NeuAc-GalNAc (STn), which allows isolation of GalNAc glycopeptides released in a total tryptic digests of cells by VVA lectin chromatography. The STn glycophenotype depends on cell-specific expression of ST6GalNAc-I and does not pose a problem in the presented glycoproteomic strategy because sialic acids can be efficiently removed by pretreatment with neuraminidase. nLC-MS/MS coupled with ETD is used to sequence glycopeptides and determine sites of O-glycans. Symbols for monosaccharides GalNAc, Gal, GlcNAc and sialic acid are indicated. () Fluorescence micrographs showing immunocytochemical staining of the SimpleCell lines and corresponding wild-type cell line (Tn, monoclonal antibody 5F4 plus neuraminidase (Neu); T, monoclonal antibody 3C9 plus Neu; and C1GalT1 enzyme, monoclonal antibody 5B6 and 2G8). Scale bars, 20 μm. () Sequences of target areas of COSMC! gene, highlighting ZFN-introduced mutations in two cell lines. Two different alleles were detected in K562 SimpleCell. Capan-1 was not sequenced. Cyt., cytoplasmic domain; TM; transmembrane domain; WT, wild type; SC, SimpleCell. * Figure 2: Characterization of wild-type and SimpleCell Capan-1 isogenic cells by immunocytology with monoclonal antibodies to C1GalT enzyme and the resulting glycans. () Fluorescence micrographs showing staining of Capan-1 wild type and SimpleCells with monoclonal antibodies to elongated (T) and truncated (STn or Tn) glycosylation as well as a monoclonal antibody to the C1GalT1 enzyme. () The expression of unrelated glycosyltransferases, GalNAc-T2, GalNAc-T3 and β4Gal-T1 in Capan-1 wild-type and SimpleCell lines (GalNAc-T2, monoclonal antibody UH4 (4C4); GalNAc T3, monoclonal antibody UH5 (2D10); β4Gal T1, monoclonal antibody 2F5). Scale bars, 20 μm. * Figure 3: ESI-Orbitrap-ETD-MS2 spectra of O-glycopeptides. (–) Spectra identified from apolipoprotein E (; residues 301–317; precursor m/z 676.3242, z = +3), dystroglycan (; residues 429–454; precursor m/z 841.9080, z = +6) and amyloid beta A4 protein (; residues 649–670; precursor m/z 786.4005, z = +4). Deduced glycosylated residues are denoted by Consortium for Functional Glycomics (CFG) standard yellow squares. * Figure 4: ESI-Orbitrap-MS2 spectra of O-glycopeptide identified from nucleobindin 2 (residues 387–399; precursor m/z 626.3097, z = +3). () HCD-MS2 spectrum. () ETD-MS2 spectrum. Deduced glycosylated residue is denoted by CFG standard yellow square. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Catharina Steentoft & * Sergey Y Vakhrushev Affiliations * Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark. * Catharina Steentoft, * Sergey Y Vakhrushev, * Malene B Vester-Christensen, * Katrine T-B G Schjoldager, * Yun Kong, * Eric Paul Bennett, * Ulla Mandel, * Hans Wandall, * Steven B Levery & * Henrik Clausen Contributions C.S., S.Y.V., S.B.L. and H.C. designed and performed experiments, analyzed data and wrote the paper. K.T.-B.G.S. and H.W. designed experiments. M.B.V.-C., Y.K., E.P.B. and U.M. performed experiments. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Steven B Levery or * Henrik Clausen Author Details * Catharina Steentoft Search for this author in: * NPG journals * PubMed * Google Scholar * Sergey Y Vakhrushev Search for this author in: * NPG journals * PubMed * Google Scholar * Malene B Vester-Christensen Search for this author in: * NPG journals * PubMed * Google Scholar * Katrine T-B G Schjoldager Search for this author in: * NPG journals * PubMed * Google Scholar * Yun Kong Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Paul Bennett Search for this author in: * NPG journals * PubMed * Google Scholar * Ulla Mandel Search for this author in: * NPG journals * PubMed * Google Scholar * Hans Wandall Search for this author in: * NPG journals * PubMed * Google Scholar * Steven B Levery Contact Steven B Levery Search for this author in: * NPG journals * PubMed * Google Scholar * Henrik Clausen Contact Henrik Clausen 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 1–3, Supplementary Tables 1–2 and Supplementary Results Additional data