Hot off the presses! Dec 01 Nat Meth

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Latest Articles Include:

• Brain observations
  - Nat Meth 7(12):937 (2010)
  Nature Methods | Editorial Brain observations Journal name:Nature MethodsVolume: 7,Page:937Year published:(2010)DOI:doi:10.1038/nmeth1210-937Published online29 November 2010 New tools are improving the prospects for transcranial light-based neuroscience, but better methods for using them are needed before they can reach their full potential. View full text Additional data
• The author file: Chandra Tucker
  - Nat Meth 7(12):939 (2010)
  Nature Methods | This Month The author file: Chandra Tucker * Monya Baker Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Page:939Year published:(2010)DOI:doi:10.1038/nmeth1210-939Published online29 November 2010 Blue light pulls proteins together. View full text Additional data
• Points of View: Gestalt principles (Part 2)
  - Nat Meth 7(12):941 (2010)
  Our visual system attempts to structure what we see into patterns to make sense of information. The Gestalt principles describe different ways we organize visual data.
• Multiple displacement amplification compromises quantitative analysis of metagenomes
  - Nat Meth 7(12):943-944 (2010)
  Nature Methods | Correspondence Multiple displacement amplification compromises quantitative analysis of metagenomes * Suzan Yilmaz1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Allgaier1, 2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Philip Hugenholtz1, 2, 3p.hugenholtz@uq.edu.au Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 7,Pages:943–944Year published:(2010)DOI:doi:10.1038/nmeth1210-943Published online29 November 2010 To the Editor: Nucleic acid concentration is often a limiting factor for 'omic' characterization of low-biomass habitats or small-sized samples. Multiple displacement amplification (MDA) with phi29 DNA polymerase is increasingly being used to overcome this hurdle1, 2, 3. Despite its enormous potential, MDA has several recognized limitations, including amplification bias that may compromise subsequent quantitative analysis3. MDA of picogram quantities of genomic DNA introduced pronounced skewing of most members of an eight-species synthetic microbial community based on small-subunit ribosomal RNA gene profiling4. We found that MDA also introduced a bias in natural and more complex communities. View full text Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Primary authors * These authors contributed equally to this work. * Suzan Yilmaz & * Martin Allgaier Affiliations * Microbial Ecology Program, Department of Energy Joint Genome Institute, Walnut Creek, California, USA. * Suzan Yilmaz, * Martin Allgaier & * Philip Hugenholtz * Deconstruction Division, Joint BioEnergy Institute, Emeryville, California, USA. * Martin Allgaier & * Philip Hugenholtz * Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Australia. * Philip Hugenholtz Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Philip Hugenholtz (p.hugenholtz@uq.edu.au) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1.1Mb) Supplementary Figures 1–2, Supplementary Tables 1–4, Supplementary Methods Additional data
• Noncoding transcripts as expression boosters
  - Nat Meth 7(12):947 (2010)
  Nature Methods | Research Highlights Noncoding transcripts as expression boosters * Nicole Rusk Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Page:947Year published:(2010)DOI:doi:10.1038/nmeth1210-947Published online29 November 2010 Some long noncoding RNAs act as enhancers for genes in their vicinity. View full text Subject terms: * Genomics Additional data
• Neuroscience in a virtual world
  - Nat Meth 7(12):948-949 (2010)
  Nature Methods | Research Highlights Neuroscience in a virtual world * Erika Pastrana Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Pages:948–949Year published:(2010)DOI:doi:10.1038/nmeth1210-948aPublished online29 November 2010 Using a virtual reality setup and a deep window into the brain, researchers can image the activity of neurons as mice navigate virtual environments. View full text Subject terms: * Neuroscience Additional data
• iPS cell aberrations
  - Nat Meth 7(12):948-949 (2010)
  Nature Methods | Research Highlights iPS cell aberrations * Natalie de Souza Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Pages:948–949Year published:(2010)DOI:doi:10.1038/nmeth1210-948bPublished online29 November 2010 Gene expression profiles identify chromosomal aberrations in human induced pluripotent stem cell lines. View full text Subject terms: * Stem Cells Additional data
• News in brief
  - Nat Meth 7(12):949 (2010)
  Sequencing Chemistry Sensors and probes Neuroscience Biochemistry
• Following the fold
  - Nat Meth 7(12):950 (2010)
  Nature Methods | Research Highlights Following the fold * Allison Doerr Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Page:950Year published:(2010)DOI:doi:10.1038/nmeth1210-950Published online29 November 2010 A specialized supercomputer allows molecular dynamics simulations to be carried out for much longer periods of time than previously possible, yielding new insights into protein folding and dynamics. View full text Subject terms: * Biophysics Additional data
• Peptides made by walkin'
  - Nat Meth 7(12):952 (2010)
  Nature Methods | Research Highlights Peptides made by walkin' * Irene Kaganman Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Page:952Year published:(2010)DOI:doi:10.1038/nmeth1210-952Published online29 November 2010 A DNA walker–based system enables ordered, multistep synthesis of a peptide in a single solution. View full text Subject terms: * Chemical biology Additional data
• Warming up to a glow
  - Nat Meth 7(12):954 (2010)
  Nature Methods | Research Highlights Warming up to a glow * Allison Doerr Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Page:954Year published:(2010)DOI:doi:10.1038/nmeth1210-954Published online29 November 2010 New dyes emit near-infrared chemiluminescence when warmed to body temperature, allowing deep-tissue imaging in mice. View full text Subject terms: * Imaging Additional data
• Nanotechnology imaging probes: smaller and more stable
  - Nat Meth 7(12):957-962 (2010)
  Nature Methods | Technology Feature Nanotechnology imaging probes: smaller and more stable * Monya Baker1techfeatures@nature.com Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Pages:957–962Year published:(2010)DOI:doi:10.1038/nmeth1210-957Published online29 November 2010 Quantum dots, nanodiamonds and other nanomaterials broaden researchers' tools for watching biology. 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 (techfeatures@nature.com) Additional data
• Pulling it together in three dimensions
  - Nat Meth 7(12):963-965 (2010)
  The most abundant proteins in our cells are there to generate mechanical forces, and measurement of these forces has just become possible.
• The RNA structurome: high-throughput probing
  - Nat Meth 7(12):965-967 (2010)
  Novel deep-sequencing strategies are used to monitor, at the genomic scale, the structure of cellular RNAs using enzymatic probing.
• Defined substrates for pluripotent stem cells: are we there yet?
  - Nat Meth 7(12):967-968 (2010)
  Synthetic surfaces displaying a glycosaminoglycan-binding peptide derived from vitronectin support long-term culture of human pluripotent stem cells.
• Measurement of mechanical tractions exerted by cells in three-dimensional matrices
  - Nat Meth 7(12):969-971 (2010)
  Nature Methods | Brief Communication Measurement of mechanical tractions exerted by cells in three-dimensional matrices * Wesley R Legant1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jordan S Miller1 Search for this author in: * NPG journals * PubMed * Google Scholar * Brandon L Blakely1 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel M Cohen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Guy M Genin2 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher S Chen1chrischen@seas.upenn.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 7,Pages:969–971Year published:(2010)DOI:doi:10.1038/nmeth.1531Received07 July 2010Accepted19 October 2010Published online14 November 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Quantitative measurements of cell-generated forces have heretofore required that cells be cultured on two-dimensional substrates. We describe a technique to quantitatively measure three-dimensional traction forces exerted by cells fully encapsulated in well-defined elastic hydrogel matrices. Using this approach we measured traction forces for several cell types in various contexts and revealed patterns of force generation attributable to morphologically distinct regions of cells as they extend into the surrounding matrix. View full text Figures at a glance * Figure 1: Cell-induced hydrogel deformations and construction of a discretized Green's function. () Volume rendering of an EGFP-expressing NIH 3T3 fibroblast spreading into a 3D hydrogel containing fluorescent beads (red). Scale bar, 50 μm (10 μm in inset). () Bead displacement trajectories color coded by magnitude. Scale bar, 50 μm. () Two-dimensional slices through the volume showing the magnitude of the peak principal strain in the hydrogel surrounding the cell. () Plots of bead displacements and hydrogel strain as a function of distance from the cell surface. () Schematic outlining the use of the finite element method to reconstruct the Green's function. Surface traction () applied to the highlighted facet induced displacements of the surrounding beads (gij, inset). When repeated over all facets and beads, these relationships describe a discretized Green's function that can be used to calculate the tractions applied by the cell. The subscript indices of and g represent the Cartesian components of the bead displacement in direction i in response to an applied surf! ace traction in direction j. * Figure 2: Measurement of tractions exerted by live cells. () Contour plot of the tractions (magnitude) exerted by a cell. () Magnification of sections outlined in , showing the individual traction vectors on each facet. () Boxplot of the traction magnitudes as a function of the normalized distance from the center of mass of the cell. This normalized distance was ~1 for the most spread regions (such as tips of long, slender extensions) and ~0 for the central cell body. () Mean traction at a given angle for cells encapsulated in 978 ± 228 Pa hydrogels. Inset, the angle (θ) was computed between the traction vector () and the position vector () of the cell facet with respect to the cell's center of mass (COM). Data shown in and are for 12 cells for each condition. * Figure 3: Measurement of dynamic tractions exerted by spreading cells. () Contour plot of the tractions (magnitude) exerted by a cell as it invades into the surrounding hydrogel at indicated times relative to the beginning of measurement. Scale bar, 20 μm. () Tractions exerted by extensions labeled in as a function of distance from the center of mass of the cell. Author information * Author information * Supplementary information Affiliations * Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA. * Wesley R Legant, * Jordan S Miller, * Brandon L Blakely, * Daniel M Cohen & * Christopher S Chen * Department of Mechanical, Aerospace and Structural Engineering, Washington University in St. Louis, St. Louis, Missouri, USA. * Guy M Genin Contributions W.R.L., G.M.G. and C.S.C. conceived and initiated the project. W.R.L., J.S.M., B.L.B. and D.M.C. designed and performed experiments. C.S.C. supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Christopher S Chen (chrischen@seas.upenn.edu) Supplementary information * Author information * Supplementary information Movies * Supplementary Movie 1 (10M) Volume rendering of encapsulated cell and surrounding beads. An EGFP-expressing fibroblast (green) was encapsulated within a PEGDA hydrogel containing fluorescent beads (red). After spreading into the surrounding hydrogel, the cell was imaged using confocal microscopy. For clarity, the cell was rendered in front of the beads and only one half of all beads were rendered. * Supplementary Movie 2 (9M) A 3D wall-eyed stereogram of bead displacements. Volume rendering showing the discretized surface mesh of the cell and tracked bead displacements, color-coded by magnitude, obtained from bead locations before and after cell lysis. * Supplementary Movie 3 (4M) Cell lysis and hydrogel relaxation. A 2D confocal section showing an EGFP-expressing fibroblast and surrounding fluorescent beads during cell lysis with SDS. * Supplementary Movie 4 (8M) A 3D wall-eyed stereogram rendering of cellular tractions. Tractions are color-coded by magnitude. For clarity, the surface mesh was made transparent to permit visualization of traction vectors directed inward from the cell surface. PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–10, Supplementary Notes 1–3 Additional data
• Rapid blue-light–mediated induction of protein interactions in living cells
  - Nat Meth 7(12):973-975 (2010)
  Nature Methods | Brief Communication Rapid blue-light–mediated induction of protein interactions in living cells * Matthew J Kennedy1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert M Hughes2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Leslie A Peteya2 Search for this author in: * NPG journals * PubMed * Google Scholar * Joel W Schwartz1 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael D Ehlers1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Chandra L Tucker2chandra@duke.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 7,Pages:973–975Year published:(2010)DOI:doi:10.1038/nmeth.1524Received21 May 2010Accepted01 October 2010Published online31 October 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Dimerizers allowing inducible control of protein-protein interactions are powerful tools for manipulating biological processes. Here we describe genetically encoded light-inducible protein-interaction modules based on Arabidopsis thaliana cryptochrome 2 and CIB1 that require no exogenous ligands and dimerize on blue-light exposure with subsecond time resolution and subcellular spatial resolution. We demonstrate the utility of this system by inducing protein translocation, transcription and Cre recombinase–mediated DNA recombination using light. View full text Figures at a glance * Figure 1: Mapping of interacting domains of CRY2 and CIB1. () Schematics of constructs, with numbers indicating amino acid positions. () β-galactosidase activity of CRY2 and CIB1 constructs tested for interaction in the dark or in blue light (461 nm, 1.9 mW, 4 h). The Gal4 binding domain (GalBD-X) and Gal4 activation domain (GalAD-Y) fusions used are indicated. The empty vector control was pGBKT7rec containing no insert. Error bars, s.d. (n = 3 samples). Inset, immunoblot analysis of Gal4BD fusion proteins in yeast. * Figure 2: Light-triggered translocation of CRY2 in mammalian cells. () Schematic showing fusion proteins. CIBN-pmEGFP contains a CaaX box prenylation motif for targeting to the plasma membrane. () Fluorescence images of CIBN-pmEGFP and CRY2-mCh coexpressed in HEK293T cells. CRY2-mCh was imaged before light excitation and 20 s after a 100-ms pulse of blue light (488 nm, 25 μW). Scale bar, 5 μm. () Time course of CRY2-mCh recruitment to the plasma membrane after a single 100-ms pulse of 488-nm light (25 μW). CIBN-pmEGFP localization is shown on the left. Scale bar, 2 μm. () CRY2-mCh translocation kinetics after a 100-ms pulse of 488-nm light (arrow). The distribution of CIBN-pmEGFP and the line used to generate the CRY2-mCh kymograph is shown in the upper left image. Scale bar, 1 μm. The graph shows quantification of CRY2-mCh in the cytoplasm and at the plasma membrane, using the regions shown in by the dotted and solid lines, respectively. Each fraction was normalized between 0 and 1. () Fluorescence images of cells expressing the indica! ted constructs before and after delivery of two 100-ms pulses of blue light (25 μW) spaced 12.5 min apart (top). Quantification of cytoplasmic CRY2PHR-mCh, with light pulses (arrows) delivered at 0 and 12.5 min (bottom). * Figure 3: Light-induced activation of transcription and DNA recombination. () Schematic of split Gal4 modules expressed in yeast cells containing a gene encoding a hemagglutinin (HA)-tagged reporter protein under control of a galactose-inducible promoter. UAS, upstream activating sequence. () Immunoblot analysis of the HA-tagged reporter (top) in response to blue-light pulses (10 s pulses, 1.7 mW, 8 min apart). Control, lysates from cells expressing only the reporter. Quantification of western blot bands is shown below. () Schematic showing the two split Cre recombinase constructs (CIBN-CreC and CRY2-CreN) and the reporter construct. IRES, internal ribosome entry site. () Cre reporter recombination measured 48 h after transfection of HEK293T cells with the Cre reporter and indicated constructs. Cells were exposed to blue-light pulses (450 nm and 4.5 mW) for the indicated durations or kept in the dark (−). Error bars, s.d. (n = 3) from three independent experiments. () EGFP fluorescence images from samples containing both CRY2-CreN and CIBN-CreC t! hat were exposed to 24 h of blue light or maintained in the dark. Scale bar, 20 μm. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Matthew J Kennedy & * Robert M Hughes Affiliations * Department of Neurobiology, Duke University Medical Center, Durham North Carolina, USA. * Matthew J Kennedy, * Joel W Schwartz & * Michael D Ehlers * Department of Biology, Duke University, Durham North Carolina, USA. * Robert M Hughes, * Leslie A Peteya & * Chandra L Tucker * Pfizer Global Research and Development, Neuroscience Research Unit, Groton, Connecticut, USA. * Michael D Ehlers Contributions C.L.T. conceived the idea and directed the work. M.J.K., R.M.H. and C.L.T. designed experiments. M.J.K., R.M.H., L.A.P., J.W.S. and C.L.T. performed experiments. M.D.E. supervised microscopy experiments. M.J.K., R.M.H. and C.L.T. wrote the manuscript. M.D.E. and C.L.T. edited the manuscript and reviewed the data. Competing financial interests M.D.E. is an employee of Pfizer, Inc. A provisional patent application has been filed by Duke University on behalf of this technology. Corresponding author Correspondence to: * Chandra L Tucker (chandra@duke.edu) Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 2 (48K) Plasmids and oligos used in CRY2-CIB experiments Movies * Supplementary Video 1 (312K) Light-triggered translocation of CRY2-mCh to the plasma membrane. HEK293T cells expressing CRY2-mCh were exposed to a 100-ms pulse of 488-nm light at t = 0. The mCherry channel was recorded at 3 Hz. The dimensions of the movie are 35 μm × 35 μm. PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–4 and Supplementary Table 1 Additional data
• Magnetic torque tweezers: measuring torsional stiffness in DNA and RecA-DNA filaments
  - Nat Meth 7(12):977-980 (2010)
  Nature Methods | Brief Communication Magnetic torque tweezers: measuring torsional stiffness in DNA and RecA-DNA filaments * Jan Lipfert1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacob W J Kerssemakers1 Search for this author in: * NPG journals * PubMed * Google Scholar * Tessa Jager1 Search for this author in: * NPG journals * PubMed * Google Scholar * Nynke H Dekker1n.h.dekker@tudelft.nl Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 7,Pages:977–980Year published:(2010)DOI:doi:10.1038/nmeth.1520Received16 June 2010Accepted23 September 2010Published online17 October 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We introduce magnetic torque tweezers, which enable direct single-molecule measurements of torque. Our measurements of the effective torsional stiffness C of dsDNA indicated a substantial force dependence, with C = ~40 nm at low forces up to C = ~100 nm at high forces. The initial torsional stiffness of RecA filaments was nearly twofold larger than that for dsDNA, yet at moderate torques further build-up of torsional strain was prevented. View full text Figures at a glance * Figure 1: Principle of magnetic torque tweezers and their operation. () The MTT setup consists of a DNA tethered magnetic bead with a smaller, nonmagnetic bead attached as a fiducial marker, a surface-attached reference bead that was used to correct for mechanical drift, a cylindrical permanent magnet to apply force and a smaller side magnet to apply torques. Magnet north and south poles are labeled N and S, respectively. () Schematic showing that after overwinding (or underwinding) the DNA tether by N turns, the DNA exerts a restoring torque on the bead that leads to a shift in the equilibrium angular position from θ0 to θ. () CCD image of a 1.4-μm-radius magnetic bead with a 0.5-μm-radius fiducial marker in focus. Scale bar, 3 μm. () Out-of-focus image of the magnetic bead with a fiducial marker. Scale bar, 3 μm. () Radial section of the image in used for angular tracking. () Transformation of the image in from Cartesian (x,y) coordinates into polar (r,θ) coordinates. () Traces of rotational fluctuations obtained from analysis of CCD! images for a surface-attached bead, for a DNA-tethered bead held in conventional magnetic tweezers and for a tethered bead in the MTT (each shown schematically on the right) at 0 turns (torsionally relaxed DNA), and for the same DNA after introducing 40 turns. The stretching force was 3.5 pN for all traces. * Figure 2: Torque measurements for a 7.9 kbp dsDNA molecule in PBS buffer. () The s.d. of the angular fluctuations as a function of applied turns. (,) The shift in the mean rotation angle as a function of applied turns and corresponding torques. () Simultaneously monitored DNA tether extension as a function of applied turns. Representative traces at indicated stretching forces are shown in –. () Buckling torque as a function of F determined from the plateaus in the torque versus turn data at positive turns. At 6.5 pN, the critical torque for the B-DNA to supercoiled P-DNA transition is shown (red square). Also shown are data obtained at intermediate stretching forces using optical torque tweezers9 at low stretching forces using a magnetic nanorod probe5 and by indirect measurements18. Predictions of a simple model of DNA elasticity11 (dashed line; reduced χ2 = 2.45, P < 0.05) and a fit to the model by Marko12 with the torsional stiffness of the plectonemic state determined from the fit to be P = 25.2 ± 2 nm (solid line; reduced χ2 = 0.74, P = ! 0.75) are shown. () The effective twist persistence length C as a function of force determined from linear fits of the torque versus applied turns data in the elastic twist regime (MTT data). For comparison, values for C from fluorescence polarization anisotropy measurements14, 15, 16, data obtained using a rotor bead assay6 and indirect measurements18 are shown. Selected points over a larger force range are shown in the inset. The line is the Moroz-Nelson model17. Error bars in and indicate s.e.m. from six to ten independent measurements. * Figure 3: Measurements of RecA-DNA heteroduplex filaments. () Rotation-extension data for RecA-dsDNA filaments at F values of 2, 3, 4 and 5 pN and for bare dsDNA at 2 pN. Dashed line indicates the center of the RecA rotation response, which was shifted to −330 turns with respect to relaxed bare DNA, as expected for full RecA coverage of our 7.9 kbp DNA construct given the observed unwinding by −15° per bp upon RecA binding21. () Torque response of a RecA filament at F = 3.5 pN. Turns were measured with respect to the torsionally relaxed RecA filament (offset by +330 turns). The effective torsional stiffness was determined from a linear fit to the initial slope of the torque versus applied turns response (black line). Inset, crystallographic structure of the RecA-DNA heteroduplex filament, with RecA in blue, DNA in brown and ATP analogs bound at the RecA monomer interfaces in red (Protein Data Bank: 3CMT; ref. 21). Author information * Author information * Supplementary information Affiliations * Department of Bionanoscience, Kavli Institute of Nanoscience, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands. * Jan Lipfert, * Jacob W J Kerssemakers, * Tessa Jager & * Nynke H Dekker Contributions J.L., J.W.J.K. and N.H.D. designed the study, J.L. and T.J. performed the experiments, J.W.J.K. wrote the angular tracking routine and J.L. and N.H.D. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Nynke H Dekker (n.h.dekker@tudelft.nl) Supplementary information * Author information * Supplementary information Zip files * Supplementary Software (196K) PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–16 Additional data
• Chronic optical access through a polished and reinforced thinned skull
  - Nat Meth 7(12):981-984 (2010)
  Nature Methods | Brief Communication Chronic optical access through a polished and reinforced thinned skull * Patrick J Drew1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Andy Y Shih1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan D Driscoll1 Search for this author in: * NPG journals * PubMed * Google Scholar * Per Magne Knutsen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Pablo Blinder1 Search for this author in: * NPG journals * PubMed * Google Scholar * Dimitrios Davalos4 Search for this author in: * NPG journals * PubMed * Google Scholar * Katerina Akassoglou4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Philbert S Tsai1 Search for this author in: * NPG journals * PubMed * Google Scholar * David Kleinfeld1, 6, 7dk@physics.ucsd.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 7,Pages:981–984Year published:(2010)DOI:doi:10.1038/nmeth.1530Received25 June 2010Accepted18 October 2010Published online21 October 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We present a method to form an optical window in the mouse skull that spans millimeters and is stable for months without causing brain inflammation. This enabled us to repeatedly image blood flow in cortical capillaries of awake mice and determine long-range correlations in speed. We also repeatedly imaged dendritic spines, microglia and angioarchitecture, as well as used illumination to drive motor output via optogenetics and induce microstrokes via photosensitizers. View full text Figures at a glance * Figure 1: PoRTS window procedure, optical properties and basic capabilities. () Window schematic. The thinned and polished skull is protected with a cover glass attached with cyanoacrylate cement. Dental cement is used to seal the edge of the cover glass and provide support for the meniscus. The thinned area is typically 1–2 mm on edge. () Point spread functions for TPLSM determined by imaging 0.2-μm fluorescent beads embedded in 1% (w/v) agarose with a 40×, 0.8 numerical aperture (NA) water-dipping objective. The profiles show integrated radial intensity along the optical axis (z axis; depth), and integrated axial intensity along the radial axis (radius displacement). The reported axial resolution (Δz) and radial width (Δr) are the full widths that encompass half of the integrated intensity. The two sets of PoRTS data are for separate beads imaged through an excised PoRTS window. Scale bar, 1 μm. () Representative fluorescence images of dendrites and spines of thy1-YFP neurons taken 30 μm below the surface 2 d and 30 d (average of 5 frames o! wing to reduction in YFP signal) after PoRTS window implantation (dwell time, 6 μs pixel−1; average incident power through the objective, 35–70 mW). Scale bars, 10 μm (left image for each time point) and 1 μm (close-up images of areas marked by arrows; right). () Maximum z-axis projections across 65 μm of fluorescein-conjugated dextran–filled vasculature through a PoRTS window 90 d after surgery. Each image is the average of 6 frames; z-step, 2 μm; dwell time, 3 μs pixel−1; and average power, 25–120 mW. Scale bars, 100 μm. () Maximum projections across 20–70 μm below the PoRTS window at indicated times after window implantation (average of 5 frames; dwell time, 6 μs pixel−1; and average power, 30–45 mW). Scale bars, 100 μm. * Figure 2: Long-range coherence of RBC-flow velocity in capillaries in the cortex of awake head-fixed mice through a PoRTS window. () Maximal z-dimension projection over 90 μm of images of fluorescein-conjugated dextran–labeled vasculature, based on frames separated by 10 μm, 2 d after surgery (top), and a single plane in the same mouse at a different location 51 d after surgery (bottom). Colored lines show the laser-focus scan path: green and orange are constant velocity segments along capillaries and purple are minimum time segments between capillaries. Scale bars, 100 μm () Space-time plots of one segment of line scan data for each of two capillaries, with the calculated instantaneous velocity for the entire 300 s run (bottom) for each constant-velocity segment shown in . Scale bars, 10 μm. () Power spectra for the two velocity traces in (0.083 Hz bandwidth; top) and magnitude of the spectral coherence between the velocities of the two capillaries as a function of frequency (0.1 Hz bandwidth; bottom). () Spontaneous velocity coherence between capillaries obtained at various times after surgery;! 0.1 Hz bandwidth. Representative data in – are from the same mouse obtained using a 10×, 0.3 NA lens and averaged over 5–10 frames. Scale bars, 100 μm. Lines in graphs in and denote P < 0.004 (inverse of 2× degrees of freedom). () Mean coherence as a function of distance in the 0.1–1.0 Hz vasomotor band (top), for the heart rate (positive control for the maximum possible coherence; middle) and in the 2–6 Hz band (null hypothesis; bottom). Fifty vessel pairs in nine mice were analyzed. Colored lines correspond to fits with exponential functions to the corresponding spectral band. * Figure 3: Examples of cortical physiology evoked through the PoRTS window. () Sample frame from video of an awake, head-fixed ChR2-carrying mouse with a PoRTS window fabricated over vibrissa motor cortex 150 d earlier. Green line marks the position of the caudal vibrissa that is tracked over time. () Rostral-caudal motion of a caudal vibrissa in response to 467-nm light pulses (5 Hz, 100-ms; blue lines) from an LED. Larger angles indicate retraction. () Maximal projection through a 200-μm depth of a Cx3cr1EGFP/+ mouse cortex before () and 100 min after () occlusion of a single penetrating arteriole using targeted optical activation of Rose Bengal (arrow) made 1 d after implantation of the window. Images are averages of four frames with a dwell time of 3 μs per pixel and 1-μm steps along the z axis. Dashed lines indicate the boundaries of the penetrating artery in which flow was blocked. () Extent of the infarct, for the same mouse as in and , visualized 2 d after the optically generated stroke. Note the invasion of EGFP-labeled microglia into th! e cyst. Scale bars, 5 mm (), 100 μm () and 1 mm (). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Physics, University of California at San Diego, San Diego, California, USA. * Patrick J Drew, * Andy Y Shih, * Jonathan D Driscoll, * Per Magne Knutsen, * Pablo Blinder, * Philbert S Tsai & * David Kleinfeld * Center for Neural Engineering, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, USA. * Patrick J Drew * Department of Neurosurgery, The Pennsylvania State University, University Park, Pennsylvania, USA. * Patrick J Drew * Gladstone Institute of Neurological Disease, University of California, San Francisco, San Francisco, California, USA. * Dimitrios Davalos & * Katerina Akassoglou * Department of Neurology, University of California, San Francisco, San Francisco, California, USA. * Katerina Akassoglou * Graduate Program in Neurosciences, University of California at San Diego, San Diego, California, USA. * David Kleinfeld * Center for Neural Circuits and Behavior, University of California at San Diego, San Diego, California, USA. * David Kleinfeld Contributions P.J.D., A.Y.S. and P.S.T. conceived the PoRTS window; J.D.D. and D.K. developed the imaging tools; K.A., D.D., P.J.D., D.K., A.Y.S. and P.S.T. designed the experiments; P.J.D., P.M.K., A.Y.S. and P.S.T. carried out the experiments; P.B., P.J.D., D.K. and P.S.T. analyzed data; and P.J.D., D.K. and A.Y.S. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David Kleinfeld (dk@physics.ucsd.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1.67Mb) Supplementary Figures 1-10, Supplementary Note 1 Additional data
• Three-dimensional cellular ultrastructure resolved by X-ray microscopy
  - Nat Meth 7(12):985-987 (2010)
  Nature Methods | Brief Communication Three-dimensional cellular ultrastructure resolved by X-ray microscopy * Gerd Schneider1gerd.schneider@helmholtz-berlin.de Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Guttmann1 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Heim1 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Rehbein1 Search for this author in: * NPG journals * PubMed * Google Scholar * Florian Mueller2 Search for this author in: * NPG journals * PubMed * Google Scholar * Kunio Nagashima3 Search for this author in: * NPG journals * PubMed * Google Scholar * J Bernard Heymann4 Search for this author in: * NPG journals * PubMed * Google Scholar * Waltraud G Müller2 Search for this author in: * NPG journals * PubMed * Google Scholar * James G McNally2mcnallyj@exchange.nih.gov Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 7,Pages:985–987Year published:(2010)DOI:doi:10.1038/nmeth.1533Received02 August 2010Accepted20 October 2010Published online14 November 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We developed an X-ray microscope using partially coherent object illumination instead of previously used quasi-incoherent illumination. The design permitted the incorporation of a cryogenic tilt stage, enabling tomography of frozen-hydrated, intact adherent cells. We obtained three-dimensional reconstructions of mouse adenocarcinoma cells at ~36-nm (Rayleigh) and ~70-nm (Fourier ring correlation) resolution, which allowed us to visualize the double nuclear membrane, nuclear pores, nuclear membrane channels, mitochondrial cristae and lysosomal inclusions. View full text Figures at a glance * Figure 1: X-ray microscope designs. () Schematic of the design for quasi-incoherent imaging, in which the X-ray source produces a divergent X-ray beam containing small coherently illuminated areas (dim yellow), which are a tiny fraction of the total beam and condenser diameter (~10 mm). The numerical apertures (NA) of the condenser and the objective (both of which are zone plates) were matched (NAcondenser / NAobjective = 1), and a 50-nm zone plate objective is used. () Schematic of the design for partially coherent imaging, in which the X-ray source produces a more collimated photon beam containing ~100× more coherent light (bright yellow) with a coherent beam diameter that is a substantial fraction of the condenser diameter (2 mm). The numerical aperture of the objective is more than twice that of the condenser (NAcondenser / NAobjective = 0.43). A 25-nm zone plate objective is used. () Contrast transfer functions are plotted for partially coherent and quasi-incoherent microscope designs. The curves were ca! lculated theoretically using the optical parameters of the two designs. () Photograph of the tilted flat sample holder (tilt stage) inside the microscope chamber. Scale bar, 1 mm. * Figure 2: X-ray images of a cell. (–) The 3D partially coherent X-ray tomograms of mouse adenocarcinoma cells show many subcellular organelles including mitochondria (M), lyosomes (L), endoplasmic reticulum (ER), vesicles (V), the plasma membrane (PM), the nuclear membrane (NM), nuclear pores (NP), nucleoli (Nu) and nuclear membrane channels (NMC). All images were acquired with a 25-nm zone plate at 510 eV photon energy, except for the image shown in , which was acquired with a 40-nm zone plate. Pixel sizes and slice thicknesses are 9.8 nm (,–) and 15.6 nm (). Scale bars, 0.39 μm. * Figure 3: Volumetric rendering of cell cytoplasm. () The 3D data corresponding to the image in Figure 2b was segmented to visualize the association of the nuclear membrane channels with the nuclear membrane. (,) The 3D data corresponding to the image in Figure 2a were segmented, yielding x-y () and x-z () views of the cytoplasm. Percentages indicate the volume fraction occupied by different organelles measured in the 3D subvolume delineated by the white rectangles. Author information * Author information * Supplementary information Affiliations * Helmholtz Zentrum Berlin für Materialien und Energie GmbH, Wilhelm-Conrad-Röntgen Campus, Berlin, Germany. * Gerd Schneider, * Peter Guttmann, * Stefan Heim & * Stefan Rehbein * Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. * Florian Mueller, * Waltraud G Müller & * James G McNally * Electron Microscopy Laboratory, Science Applications International Corporation (SAIC)-Frederick, Inc., National Cancer Institute, National Institutes of Health, Frederick, Maryland, USA. * Kunio Nagashima * Laboratory of Structural Biology, National Institute of Arthritis, Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA. * J Bernard Heymann Contributions G.S. directed the X-ray microscopy project and with his colleagues designed and built the microscope. P.G. contributed to the design and construction of the microscope and collected all tomographic data. S.H. assisted with microscope construction, wrote the software to control the microscope and helped with some data collection. S.R. assisted with microscope construction and designed and constructed the zone plate objectives. F.M. performed most of the tomographic reconstructions. K.N. performed electron microscopy. J.B.H. adapted his software package Bsoft to permit reconstruction of the X-ray tomographic datasets and helped interpret the reconstructed images. W.G.M. prepared all of the specimens, collected the light microscopy data, helped collect the X-ray data and developed methods to grow the mouse cells on grids and preserve them by cryofixation. J.G.M. directed the cell biology project, helped design the study and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Gerd Schneider (gerd.schneider@helmholtz-berlin.de) or * James G McNally (mcnallyj@exchange.nih.gov) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–7 and Supplementary Protocol Additional data
• A defined glycosaminoglycan-binding substratum for human pluripotent stem cells
  - Nat Meth 7(12):989-994 (2010)
  Nature Methods | Article A defined glycosaminoglycan-binding substratum for human pluripotent stem cells * Joseph R Klim1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lingyin Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul J Wrighton3 Search for this author in: * NPG journals * PubMed * Google Scholar * Marian S Piekarczyk4 Search for this author in: * NPG journals * PubMed * Google Scholar * Laura L Kiessling1, 2, 3kiessling@chem.wisc.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 7,Pages:989–994Year published:(2010)DOI:doi:10.1038/nmeth.1532Received10 March 2010Accepted21 October 2010Published online14 November 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To exploit the full potential of human pluripotent stem cells for regenerative medicine, developmental biology and drug discovery, defined culture conditions are needed. Media of known composition that maintain human embryonic stem (hES) cells have been developed, but finding chemically defined, robust substrata has proven difficult. We used an array of self-assembled monolayers to identify peptide surfaces that sustain pluripotent stem cell self-renewal. The effective substrates displayed heparin-binding peptides, which can interact with cell-surface glycosaminoglycans and could be used with a defined medium to culture hES cells for more than 3 months. The resulting cells maintained a normal karyotype and had high levels of pluripotency markers. The peptides supported growth of eight pluripotent cell lines on a variety of scaffolds. Our results indicate that synthetic substrates that recognize cell-surface glycans can facilitate the long-term culture of pluripotent stem cel! ls. View full text Figures at a glance * Figure 1: Strategy for the identification of peptide-substituted surfaces for hES cell adhesion and survival. () H9 cells bound to a representative array presenting the shown bioactive peptides at indicated surface densities (indicated as percentage of peptide-substituted alkanethiol in the mixed self-assembled monolayer). Cells were fixed, immunostained for Oct-4 (red) or SSEA-4 (green) and counterstained with DAPI (blue). (–) Representative higher magnification images of H9 cells (–) and H1 cells (,) on surfaces presenting the fibroblast growth factor receptor–binding peptide GGGEVYVVAENQQGKSKA and the integrin-binding peptide KGRGDS (), KGRGDS and another bioactive peptide derived from fibronectin, KPHSRN (), the laminin-derived peptide GSDPGYIGSR () or on surfaces presenting heparin-binding peptides GKKQRFRHRNRKG from vitronectin (), GWQPPRARI from fibronectin () or FHRRIKA from bone sialoprotein (). Scale bars, 1 mm () and 200 μm (–). * Figure 2: Surfaces displaying heparin-binding peptides support hES cell adhesion and self-renewal. () Percentage of cells binding to the indicated surfaces as measured by a luminescence assay (average ratio of the mean luminescence of cell lysates plated in the presence of heparin versus those without heparin). Error bars, s.d. (n = 3). () Percentage of cells expressing the indicated markers after culture for three passages in mTeSR with ROCK inhibitor on surfaces presenting the indicated peptides. (–) Micrographs showing the morphology of cells cultured on surfaces presenting the indicated peptides. Scale bars, 200 μm. * Figure 3: Synthetic surfaces support the long-term culture of pluripotent stem cells. () Immunostaining of H9 hES cells cultured for three months in mTeSR with ROCK inhibitor on the synthetic surface for Oct-4 and SSEA-4, and stained with DAPI. Scale bar, 200 μm. (,) Immunostaining of DF19-9 7T iPS cells cultured for 2.5 months in mTeSR with ROCK inhibitor on the synthetic surface for nanog and E-cadherin () or for Sox2 and Tra-1-60 (). Scale bars, 100 μm. () Percentage of H9 cells staining positive for the indicated markers as measured by flow cytometry after three months (17 passages). Cells were cultured in mTeSR with ROCK inhibitor on the synthetic surface or in mTeSR on Matrigel. Data represent the average (± s.d.) of three consecutive passages. () Relative expression of the indicated genes as measured by real-time qPCR analysis of H13 hES cells maintained in long-term culture (14 passages) in mTeSR with ROCK inhibitor on a surface displaying the peptide GKKQRFRHRNRKG and cells cultured concurrently in mTeSR on Matrigel. POU5F1 encodes Oct-4. Error ba! rs, s.d. (n = 3). * Figure 4: Pluripotent stem cells grown on synthetic surfaces maintain their ability to differentiate. () Micrographs illustrating in vitro differentiation of DF19-9 7T human iPS cells maintained in mTeSR with ROCK inhibitor on surfaces presenting the heparin-binding peptide GKKQRFRHRNRKG for 3 months. Differentiated cells were stained for markers of ectoderm (β-III tubulin), endoderm (α-fetoprotein (AFP)) and mesoderm (α-smooth muscle actin (SMA)) and counterstained with DAPI. Scale bar, 100 μm. (–) Micrographs showing teratoma formation by H9 cells maintained on self-assembled monolayers presenting the heparin-binding peptide GKKQRFRHRNRKG for 3 months in mTeSR with ROCK inhibitor. Teratomas contained a mixture of tissues resembling the neural tube (), the gut () and the mesenchyme (). Scale bars, 200 μm. (–) Fold induction (relative gene expression) of lineage-specific genes representing () ectoderm, () endoderm and () mesoderm after directed differentiation of H14 hES cells maintained in long-term cultures (17 passages) on surfaces presenting GKKQRFRHRNRKG in mTe! SR with ROCK inhibitor, compared to Matrigel culture. Expression was analyzed via real-time qPCR. Error bars, s.d. (n = 3). On day 0, FOXF1 was not detected after 40 amplification cycles. * Figure 5: Streptavidin-coated surfaces presenting heparin-binding peptides support robust adhesion and self-renewal. () H9 hES cell adhesion to the indicated surfaces as measured by a luminescence assay. Error bars, s.d. (n = 3). () Flow-cytometric analysis with phycoerythrin (PE)-conjugated antibodies to Oct-4 (anti-Oct-4–PE) and Alexa Fluor 647–conjugated antibodies to SSEA-4 (anti-SSEA-4–Alexa Fluor 647) of H14 hES cells (orange) cultured for one month (ten passages) on surfaces presenting a combination of GKKQRFRHRNRKG and cyclic RGD peptide in mTeSR alone. Data from partially differentiated cells also are shown (gray) and were used to set gates between positive and negative staining. () Micrographs of H9 hES cells cultured in mTeSR for two months (17 passages) on a combination of GKKQRFRHRNRKG and cyclic RGD peptide and immunostained for Oct-4 (red), SSEA-4 (green) and counterstained with DAPI (blue). Scale bar, 100 μm. Author information * Abstract * Author information * Supplementary information Affiliations * Cell and Molecular Biology Program, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Joseph R Klim & * Laura L Kiessling * Department of Chemistry, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Lingyin Li & * Laura L Kiessling * Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Paul J Wrighton & * Laura L Kiessling * WiCell Research Institute, Madison, Wisconsin, USA. * Marian S Piekarczyk Contributions J.R.K., L.L., P.J.W. and L.L.K. conceived the experiments and interpreted the results. J.R.K. performed the in vitro experiments. L.L. synthesized and purified the molecules used to fabricate the surfaces. J.R.K. and M.S.P. conducted the teratoma assay, and P.J.W. conducted the directed differentiation assays. J.R.K. and L.L.K. wrote the manuscript. Competing financial interests L.L.K. is an author on a patent on self-assembled monolayers for stem cell culture (US patent 2007/0207543). L.L.K., J.R.K. and L.L. are authors on a pending patent that describes surfaces for the long-term culture of pluripotent cells (US patent application 20100087004). Corresponding author Correspondence to: * Laura L Kiessling (kiessling@chem.wisc.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (36M) Supplementary Figures 1–11 and Supplementary Tables 1–2 Additional data
• FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing
  - Nat Meth 7(12):995-1001 (2010)
  Nature Methods | Article FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing * Jason G Underwood1, 2, 6, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew V Uzilov3, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Sol Katzman3, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Courtney S Onodera3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacob E Mainzer4 Search for this author in: * NPG journals * PubMed * Google Scholar * David H Mathews5 Search for this author in: * NPG journals * PubMed * Google Scholar * Todd M Lowe3 Search for this author in: * NPG journals * PubMed * Google Scholar * Sofie R Salama1, 2, 3ssalama@soe.ucsc.edu Search for this author in: * NPG journals * PubMed * Google Scholar * David Haussler1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature MethodsVolume: 7,Pages:995–1001Year published:(2010)DOI:doi:10.1038/nmeth.1529Received16 August 2010Accepted13 October 2010Published online07 November 2010 Abstract * Abstract * Accession codes * 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 Classical approaches to determine structures of noncoding RNA (ncRNA) probed only one RNA at a time with enzymes and chemicals, using gel electrophoresis to identify reactive positions. To accelerate RNA structure inference, we developed fragmentation sequencing (FragSeq), a high-throughput RNA structure probing method that uses high-throughput RNA sequencing of fragments generated by digestion with nuclease P1, which specifically cleaves single-stranded nucleic acids. In experiments probing the entire mouse nuclear transcriptome, we accurately and simultaneously mapped single-stranded RNA regions in multiple ncRNAs with known structure. We probed in two cell types to verify reproducibility. We also identified and experimentally validated structured regions in ncRNAs with, to our knowledge, no previously reported probing data. View full text Figures at a glance * Figure 1: Overview of the FragSeq method. () Preparation of FragSeq libraries for sequencing. RNA 5′ and 3′ end chemistry is shown to highlight PNK and nuclease treatment products; when RNA end chemistry is not shown, it denotes any possible end chemistry. Only clonable RNA fragments are shown at and after the size-selection step. Arrowheads mark the specific ligation events at each end of the RNA fragment. () Overview of the FragSeq algorithm. * Figure 2: Visual representation of data at progressive stages in the FragSeq algorithm, from genome-mapped reads to cutting scores. (–) Data tracks in the UCSC Genome Browser (mm9 mouse genome assembly) showing spliceosomal snRNA U1a (); data from mouse undifferentiated embryonic stem cell samples (UESC) (–) are processed to obtain cutting scores, which are compared to cutting scores from cells differentiated into neural precursor cells (). Ignored sites are denoted in as areas for which no nuclease data are shown (for example, the sequence GUG in the Sm region). (,) Sequence of U1a () and cutting scores from a mouse undifferentiated embryonic stem cell sample superimposed on the known secondary structure (); green and yellow subsequences are expected to be single-stranded. Noncanonical base pairs in interior loops of stem 2 are shown as unpaired. The 2′-O-methylated positions are not depicted. SL, stem-loop; IL, interior loop; and MBL, multibranch loop. U1a structure is from several sources. * Figure 3: Comparison of FragSeq with previous probing experiments. (–) Probing results for human U3 () and U5 () purified from HeLa cells and FragSeq cutting scores for mouse U3b () and U5 (). Reactivities in and were taken verbatim from references 15 and 16, respectively; structures and other annotations were compiled from multiple sources. Only bases downstream of the primer in were probed in reference 16. The 2′-O-methylated positions are not depicted. * Figure 4: FragSeq cutting scores and coverage for ncRNAs with known structures and long C/D box snoRNAs. () Cutting scores compared to ssRNA regions greater than three bases long (dark green boxes) for ncRNAs with published structure models. Light green boxes indicate regions in which the in vitro structure of a single, naked RNA is uncertain. Coverage (mean reads per nucleotide) is shown for nuclease and control treatments. SL, stem-loop; Sm, Sm protein binding site; BP, splicing branch-point binding site; flank, flanking ssRNA region of a nearby motif; IL, interior loop; hinge, ssRNA region connecting two RNA domains; and Kturn, kink-turn RNA motif containing noncanonical base pairs. () Cutting scores for all long (>120 nt) C/D box snoRNAs considered for follow-up probing. Asterisks mark RNAs chosen for follow-up probing. * Figure 5: FragSeq probing versus conventional nuclease probing of U15b C/D box snoRNA. () FragSeq ssRNA cutting scores and band quantification readouts (SAFA software counts) based on the gel resolving 5′ end–labeled probing products. Gray boxes indicate regions outside the quantifiable area on the gel. Parentheses denote Watson-Crick base pairs, and dots denote ssRNA. Triangles denote bases where a nuclease can cut: T1, gray triangles at guanine; RNase A, black triangles for cytosine and red triangles for uracil. () Follow-up probing data superimposed on our structure model, with probing enzymes color-coded as in . Enzyme activity was inferred from manual inspection of the gel and SAFA software–based quantification from . () FragSeq cutting scores superimposed on the same structure model as in . Boxes (green) and the putative region that base-pairs with target rRNA (orange) are highlighted, with the base opposite of the methylated position26 in red. Highlighting is as in . Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE24622 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Jason G Underwood & * Andrew V Uzilov Affiliations * Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, California, USA. * Jason G Underwood, * Sofie R Salama & * David Haussler * Center for Biomolecular Science and Engineering, Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, California, USA. * Jason G Underwood, * Sofie R Salama & * David Haussler * Department of Biomolecular Engineering, Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, California, USA. * Andrew V Uzilov, * Sol Katzman, * Courtney S Onodera, * Todd M Lowe, * Sofie R Salama & * David Haussler * Department of Physics and Astronomy, University of Rochester, Rochester, New York, USA. * Jacob E Mainzer * Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, New York, USA. * David H Mathews * Present affiliations: Pacific Biosciences, Inc., Menlo Park, California, USA (J.G.U.) and Center for Biomolecular Science and Engineering, Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, California, USA (S.K.). * Jason G Underwood & * Sol Katzman Contributions J.G.U. designed and carried out the experiments. A.V.U. designed and carried out the bioinformatics analysis, except for preparing the read mappings, which S.K. did, with C.S.O. contributing data. J.E.M. programmed additional features in the RNAstructure software. J.G.U., A.V.U. and S.R.S. wrote the manuscript. S.R.S., D.H.M., T.M.L. and D.H. directed the research. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Sofie R Salama (ssalama@soe.ucsc.edu) Supplementary information * Abstract * Accession codes * Author information * Supplementary information Zip files * Supplementary Data 1 (4KB) Stockholm-format (machine-readable) multiple alignment of U15b C/D box snoRNA homologs, containing structure models that were evaluated. See file for detailed comments. * Supplementary Data 2 (4KB) Stockholm-format (machine-readable) multiple alignment of U22 C/D box snoRNA homologs, containing structure models that were evaluated. See file for detailed comments. * Supplementary Data 3 (4KB) Stockholm-format (machine-readable) multiple alignment of U97 C/D box snoRNA homologs, containing structure models that were evaluated. See file for detailed comments. * Supplementary Data 4 (4KB) FASTA-format file of sequences used for filtering out sequencing reads prior to mapping to genome (see Methods). * Supplementary Data 5 (4KB) Six-column BED-format file containing genomic coordinates (mm9 genome assembly) of all RNAs examined in this study. This can be uploaded to the UCSC Genome Browser as a custom track. * Supplementary Software (48K) FragSeq algorithm implementation, configuration files and Readme. All FragSeq algorithm software, scripts and configuration files needed to reproduce the analysis in this paper are provided. The Readme file contains complete instructions on how to rerun our analysis. However, read mappings are not provided owing to their large size and have to be downloaded from the GEO (accession number is listed in the paper; see the Readme file). The script dpToVarna.py is also provided (Supplementary Note 3). PDF files * Supplementary Text and Figures (9.5M) Supplementary Figures 1–12, Supplementary Table 1, Supplementary Notes 1–3, Supplementary Discussion Additional data
• Maltose–neopentyl glycol (MNG) amphiphiles for solubilization, stabilization and crystallization of membrane proteins
  - Nat Meth 7(12):1003-1008 (2010)
  Nature Methods | Article Maltose–neopentyl glycol (MNG) amphiphiles for solubilization, stabilization and crystallization of membrane proteins * Pil Seok Chae1 Search for this author in: * NPG journals * PubMed * Google Scholar * Søren G F Rasmussen2 Search for this author in: * NPG journals * PubMed * Google Scholar * Rohini R Rana3 Search for this author in: * NPG journals * PubMed * Google Scholar * Kamil Gotfryd4 Search for this author in: * NPG journals * PubMed * Google Scholar * Richa Chandra5 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Goren6 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew C Kruse2 Search for this author in: * NPG journals * PubMed * Google Scholar * Shailika Nurva5 Search for this author in: * NPG journals * PubMed * Google Scholar * Claus J Loland4 Search for this author in: * NPG journals * PubMed * Google Scholar * Yves Pierre7 Search for this author in: * NPG journals * PubMed * Google Scholar * David Drew3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Luc Popot7 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Picot7 Search for this author in: * NPG journals * PubMed * Google Scholar * Brian G Fox6, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Lan Guan5 Search for this author in: * NPG journals * PubMed * Google Scholar * Ulrik Gether4 Search for this author in: * NPG journals * PubMed * Google Scholar * Bernadette Byrne3b.byrne@imperial.ac.uk Search for this author in: * NPG journals * PubMed * Google Scholar * Brian Kobilka2kobilka@stanford.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Samuel H Gellman1gellman@chem.wisc.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 7,Pages:1003–1008Year published:(2010)DOI:doi:10.1038/nmeth.1526Received25 May 2010Accepted30 September 2010Published online31 October 2010Corrected online09 November 2010 Abstract * Abstract * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The understanding of integral membrane protein (IMP) structure and function is hampered by the difficulty of handling these proteins. Aqueous solubilization, necessary for many types of biophysical analysis, generally requires a detergent to shield the large lipophilic surfaces of native IMPs. Many proteins remain difficult to study owing to a lack of suitable detergents. We introduce a class of amphiphiles, each built around a central quaternary carbon atom derived from neopentyl glycol, with hydrophilic groups derived from maltose. Representatives of this maltose–neopentyl glycol (MNG) amphiphile family show favorable behavior relative to conventional detergents, as manifested in multiple membrane protein systems, leading to enhanced structural stability and successful crystallization. MNG amphiphiles are promising tools for membrane protein science because of the ease with which they may be prepared and the facility with which their structures may be varied. View full text Figures at a glance * Figure 1: Chemical structures of MNG amphiphiles (MNG-1, MNG-2 and MNG-3) and their linear counterparts (MPA-1, MPA-2, MPA-3, MPA-4, DM, UDM, DDM and TDM). The CMC value for each agent, measured via hydrophobic dye solubilization, is indicated in parentheses. * Figure 2: GPCR stability in MNG amphiphiles or conventional detergents. () Tm values of β2AR-T4L plotted in terms of wt % of the MNG amphiphiles (MNG-1, MNG-2 and MNG-3) or conventional detergents (MPA-1, MPA-3, DM, DDM and TDM). β2AR-T4L with bound carazolol (an inverse agonist) was incubated with various agents at the various concentrations at indicated temperatures for 5 min before fluorescence emission measurements. Normalized results are expressed as mean ± s.e.m. (n = 3, 4 or 5). () Specific activities (pmol mg−1) of M3AchR in DDM and MNG-3. The activity of the protein was evaluated after the protein was washed and eluted with buffer including DDM or MNG-3, but without CHS, via a binding assay involving the antagonist [3H]N-methylscopolamine, in the absence (t = 0 h, − CHS; first bar) or presence of CHS (t = 0 h, + CHS; second bar). The DDM- and MNG-3–purified M3AchR samples were stored at 4 °C for 15 h, and then activities were measured again in the presence of CHS (t = 15 h, + CHS; third bar). Results are expressed as mean ± s! .d. (n = 3). * Figure 3: SDS-12% PAGE analysis and western blot detection of MelB. MelB samples were subjected to SDS-PAGE analysis, and MelB was detected by western blotting using anti–histidine tag antibody. Each sample contained 10 μg membrane proteins. For extracts generated with each detergent or amphiphile, one sample was subjected to ultracentrifugation (+) and a comparison sample was not (−). As a control, an untreated membrane sample (no ultracentrifugation) was included in each gel. * Figure 4: Stability of SQR solubilized with MNG amphiphiles or conventional detergents. () Results of CPM assays for SQR solubilized with MNG amphiphiles (MNG-1, MNG-2 and MNG-3) or conventional detergents (MPA-4, DDM, DM and SDS) at 10× CMC. The unfolding of the each protein was monitored at 40 °C for 130 min using a microplate spectrofluorometer. (,) Gel filtration analysis of SQR in DDM () or MNG-3 () at 10× CMC. SQR in DDM or MNG-3 was incubated for 120 min at 40 °C (AU, absorbance unit). () Time course of SQR activity in MNG-3 or DDM. Each agent was used at 10× CMC (0.01 wt % for MNG-3, 0.087 wt % for DDM) and 50× CMC (0.05 wt % for MNG-3, 0.44 wt % for DDM). Note that 50× CMC MNG-3 is comparable to DDM at 10× CMC in terms of wt %. The catalytic rate constant (kcat) is plotted as a function of incubation time. Data at t = 0 correspond to the activity of SQR following thermal activation performed at 30 °C for 20 min. Protein solubilized with each agent was incubated at 40 °C for a further 120 min, and activity of the protein was measured at the de! signated times. The kcat values at each time point were calculated by analyzing reaction data according to Michaelis-Menten kinetics. * Figure 5: Long-term stability of LeuT and R. capsulatus superassembly in MNG amphiphiles or conventional detergents. () Time course of activity ([3H]leucine binding) assay for LeuT solubilized with MNG amphiphiles (MNG-1, MNG-2 and MNG-3) and DDM at 0.026 wt % above the critical micelle concentration (CMC) (total concentrations: 0.035 wt % DDM, 0.028 wt % MNG-1, 0.027 wt % MNG-2 and 0.027 wt % MNG-3). LeuT activity was monitored at indicated time points, using a scintillation proximity assay (SPA), for protein stored at the room temperature. Results are expressed as % activity relative to the appropriate day 0 measurement. Normalized results are expressed as mean ± s.e.m. (n = 2). () Time course of stability of R. capsulatus superassembly purified with MNG amphiphiles (MNG-1, MNG-2 and MNG-3) or conventional detergents (MPA-3 and DDM) at 1 × CMC. The absorbance ratios (A875/A680) of the detergent or amphiphile samples were followed as a function of time. * Figure 6: Image and X-ray diffraction pattern from crystals of cytochrome b6f–MNG-3 complexes. X-ray diffraction by a cytochrome b6f crystal obtained in the presence of MNG-3. Left panel represents a portion of the pattern (0.5° oscillation range). Resolution limits are marked with arrows (the white cross is due to the tiling of the detector). Top right, enlargement of the yellow square with two strong spots near the resolution limit. A section through the two strong spots is shown in the lower right corner (a.u., arbitrary unit). Change history * Abstract * Change history * Author information * Supplementary informationCorrigendum 09 November 2010In the version of this article initially published online, Figure 1 contained errors (an incorrect number of carbons were drawn in the molecules). The error has been corrected for the print, PDF and HTML versions of this article. Author information * Abstract * Change history * Author information * Supplementary information Affiliations * Department of Chemistry, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Pil Seok Chae & * Samuel H Gellman * Molecular and Cellular Physiology, Stanford University, Stanford, California, USA. * Søren G F Rasmussen, * Andrew C Kruse & * Brian Kobilka * Division of Molecular Biosciences, Department of Life Sciences, Imperial College London, London, UK. * Rohini R Rana, * David Drew & * Bernadette Byrne * Molecular Neuropharmacology Group, Department of Neuroscience and Pharmacology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark. * Kamil Gotfryd, * Claus J Loland & * Ulrik Gether * Department of Cell Physiology and Molecular Biophysics, Center for Membrane Protein Research, Texas Tech University Health Sciences Center, Lubbock, Texas, USA. * Richa Chandra, * Shailika Nurva & * Lan Guan * Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Michael A Goren & * Brian G Fox * Laboratoire de Biologie Physico-Chimique des Protéines Membranaires, Centre National de la Recherche Scientifique/Université Paris-7 Unité Mixte de Recherche 7099, Institut de Biologie Physico-Chimique, Paris, France. * Yves Pierre, * Jean-Luc Popot & * Daniel Picot * Center for Eukaryotic Structural Genomics, University of Wisconsin–Madison, Madison, Wisconsin, USA. * Brian G Fox Contributions P.S.C. designed the MNG amphiphiles, with contributions from S.G.F.R., B.K. and S.H.G. P.S.C. synthesized the amphiphiles. P.S.C., S.G.F.R., R.R.R., K.G., R.C., M.A.G., A.C.K., S.N., Y.P. and D.P. designed and performed the research and interpreted the data. C.J.L., D.D., B.G.F., L.G., U.G., J.-L.P., B.B., B.K. and S.H.G. contributed to experimental design and data interpretation. P.S.C. and S.H.G. wrote the manuscript, with oversight from S.G.F.R., R.R.R., K.G., R.C., M.A.G., A.C.K., S.N., C.J.L., Y.P., D.D., J.-L.P., D.P., B.G.F., L.G., U.G., B.B. and B.K. Competing financial interests P.S.C, S.G.F.R., B.K. and S.H.G. are co-inventors on a patent application that covers the MNG amphiphiles. Corresponding authors Correspondence to: * Samuel H Gellman (gellman@chem.wisc.edu) or * Brian Kobilka (kobilka@stanford.edu) or * Bernadette Byrne (b.byrne@imperial.ac.uk) Supplementary information * Abstract * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (868K) Supplementary Figures 1–9, Supplementary Tables 1 and 2, and Supplementary Note Additional data
• Analysis and design of RNA sequencing experiments for identifying isoform regulation
  - Nat Meth 7(12):1009-1015 (2010)
  Nature Methods | Article Analysis and design of RNA sequencing experiments for identifying isoform regulation * Yarden Katz1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric T Wang2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Edoardo M Airoldi4airoldi@fas.harvard.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher B Burge2, 5cburge@mit.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 7,Pages:1009–1015Year published:(2010)DOI:doi:10.1038/nmeth.1528Received18 August 2010Accepted08 October 2010Published online07 November 2010 Abstract * Abstract * Accession codes * 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 Through alternative splicing, most human genes express multiple isoforms that often differ in function. To infer isoform regulation from high-throughput sequencing of cDNA fragments (RNA-seq), we developed the mixture-of-isoforms (MISO) model, a statistical model that estimates expression of alternatively spliced exons and isoforms and assesses confidence in these estimates. Incorporation of mRNA fragment length distribution in paired-end RNA-seq greatly improved estimation of alternative-splicing levels. MISO also detects differentially regulated exons or isoforms. Application of MISO implicated the RNA splicing factor hnRNP H1 in the regulation of alternative cleavage and polyadenylation, a role that was supported by UV cross-linking–immunoprecipitation sequencing (CLIP-seq) analysis in human cells. Our results provide a probabilistic framework for RNA-seq analysis, give functional insights into pre-mRNA processing and yield guidelines for the optimal design of RNA-seq e! xperiments for studies of gene and isoform expression. View full text Figures at a glance * Figure 1: More accurate inference of splicing levels using MISO. () Generative process for MISO model. White, alternatively spliced exon; gray and black, flanking constitutive exons. RNA-seq reads aligning to the alternative exon body (white) or to splice junctions involving this exon support the inclusive isoform, whereas reads joining the two constitutive exons (black-gray exon junction) support the exclusive isoform. Reads aligning to the constitutive exons are common to both isoforms. () The estimate uses splice-junction and alternative exon–body reads only. () The MISO estimate, (derived here analytically), also uses constitutive reads and paired-end read information; orange lines connect reads in a pair; the insert length distribution is shown at right. () Comparison of and estimates from simulated data. Reads were sampled at varying coverage, measured in RPK, from the gene structure shown at top right, with underlying true Ψ = 0.5. Mean values from 3,000 simulations are shown (±s.d.) for each coverage value. Percentiles of gene! expression values are shown for a data set assuming 25 million mapped paired-end (PE) read pairs (25M PE; blue, extrapolating from an Illumina GA2 run that yielded 15 million mapped read pairs) and for a data set of 78 million mapped read pairs from an Illumina HiSeq 2000 instrument (78M PE; red), both obtained from human heart tissue. * Figure 2: MISO CIs for Ψ values and qRT-PCR validation. qRT-PCR measurements from ref. 13 for a set of 52 alternatively skipped exons were compared to MISO posterior mean estimates of Ψ, denoted . Full listing of events is given in Supplementary Table 1. () The Ψ posterior distributions obtained by sampling and 95% CIs are shown for two representative exons, one with a wide (NFYA, exon 3) and one with a narrower (ZNF207, exon 6) CI. qRT-PCR Ψ measurements are indicated in red. () Scatterplot of MISO and qRT-PCR Ψ estimates for the full set of 52 events. () Scatterplot of MISO and qRT-PCR estimates for the subset of 23 high-confidence events, for which CI width <0.25. One exon was excluded from this plot because of expressed sequence tag (EST) evidence of an alternative isoform expected to confound the qRT-PCR analysis (Supplementary Fig. 6). * Figure 3: Bayes factor analysis of hnRNP H regulation of exon splicing. () CLIP tag density (H CLIP; green) and RNA-seq read densities in hnRNP H–knockdown and control conditions (H KD and H Ctrl; light and dark blue, respectively) for an alternative exon in human C17orf49. Number of guanines in poly(G) runs in upstream and downstream introns is shown. () Model of hnRNP H function in splicing regulation: binding of poly(G) runs (Gn) adjacent to an exon enhances the exon's splicing (+ arrows); binding in exon body represses splicing (− arrow). A 250-nt window in flanking introns was used to count CLIP tags in analyses. () BF for exon 2 of PRMT2 gene. Gray dashed line, distribution over ΔΨ under the null hypothesis; black solid line, posterior distribution. () Cumulative distribution of BFs using hnRNP H RNA-seq data for exons with sufficiently high read coverage. Inset, fraction of differentially regulated exons (ΔΨ ≥ 0.15 by qRT-PCR), grouping exons by BF (n = 25 exons). () Percentage of exons enhanced by hnRNP H (ΔΨ > 0), plotted ag! ainst increasing BF thresholds, for exons with CLIP tags in downstream or upstream introns but not in exon body (red and orange curves), for exons with CLIP tags in exon body but not in flanking introns (blue curve) and for exons with no CLIP tags (dotted black line). () Guanines in poly(G) runs in downstream intron, plotted against increasing BFs. * Figure 4: Bayes factor analysis implicates hnRNP H in alternative cleavage and polyadenylation. () CLIP tag density (H CLIP; green) and RNA-seq read densities in hnRNP H control and knockdown conditions (H Ctrl and H KD; light and dark blue, respectively) along the 3′ UTR of the NFATC4 gene. Core and extension poly(A) sites for NFATC4 are shown, with a model illustrating the effect of hnRNP H effect on poly(A) site selection. () Number of CLIP tags per kilobase normalized by expression (RPKM) for exons with shortened and lengthened UTRs between hnRNP H control and knockdown conditions (red and blue curves, respectively). Values plotted are averages of subsampled mean densities (n = 100 subsamplings) where exons were matched for expression (RPKM). Error bars show s.e.m. CLIP tag density for UTRs not differentially regulated (BF < 1), as shown by dotted gray line. * Figure 5: Improved estimation of isoform abundance using paired-end reads. () Representative gene model with 100-nt first exon, 100-nt skipped exon (exon 5, in white), 150-nt constitutive exons and 600-nt last exon. () We simulated reads from the two-isoform gene model shown in while varying the mean, μ, of the insert length distribution, setting the s.d. to adjust for the higher variability expected in the size selection for longer fragments. Fraction of 1-bit (assignable to only one isoform) paired and single-end reads is plotted (±s.d.). () Distribution of errors for paired-end and single-end estimation as coverage increases (measured in RPK). () Histogram shows library insert length distribution computed from read pairs mapped to long constitutive 3′ UTRs in a human testes RNA-seq data set. In the example exon trio shown (similar to that in Fig. 1d), the insert length distribution assigns a higher probability to the top (inclusion) isoform than to the bottom (exclusion) isoform, for which the inferred insert length is improbably small. () ! Fraction of assignable 2-bit and 1-bit reads (±s.d.) for paired-end and single-end reads as a function of the number of intervening constitutive exons, k. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE23694 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA. * Yarden Katz * Department of Biology, MIT, Cambridge, Massachusetts, USA. * Yarden Katz, * Eric T Wang & * Christopher B Burge * Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA. * Eric T Wang * Department of Statistics and FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, USA. * Edoardo M Airoldi * Department of Biological Engineering, MIT, Cambridge, Massachusetts, USA. * Christopher B Burge Contributions Y.K., development of MISO model and software, analyses involving MISO, writing of main text and methods; E.T.W., hnRNP H CLIP-seq experiments and associated computational analyses, CUGBP1 knockdown RNA-seq experiments and associated computational analyses; E.M.A., development of model and statistical analysis, writing of methods; C.B.B., development of MISO model, contributions to computational analyses, writing of main text. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Edoardo M Airoldi (airoldi@fas.harvard.edu) or * Christopher B Burge (cburge@mit.edu) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–12, Supplementary Tables 1 and 2, Supplementary Note Additional data
• Quantitative analysis of fitness and genetic interactions in yeast on a genome scale
  - Nat Meth 7(12):1017-1024 (2010)
  Nature Methods | Article Quantitative analysis of fitness and genetic interactions in yeast on a genome scale * Anastasia Baryshnikova1, 2, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Costanzo1, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Yungil Kim3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Huiming Ding1 Search for this author in: * NPG journals * PubMed * Google Scholar * Judice Koh1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kiana Toufighi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ji-Young Youn1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Jiongwen Ou5 Search for this author in: * NPG journals * PubMed * Google Scholar * Bryan-Joseph San Luis1 Search for this author in: * NPG journals * PubMed * Google Scholar * Sunayan Bandyopadhyay3 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Hibbs6 Search for this author in: * NPG journals * PubMed * Google Scholar * David Hess7 Search for this author in: * NPG journals * PubMed * Google Scholar * Anne-Claude Gingras8 Search for this author in: * NPG journals * PubMed * Google Scholar * Gary D Bader1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Olga G Troyanskaya9 Search for this author in: * NPG journals * PubMed * Google Scholar * Grant W Brown5 Search for this author in: * NPG journals * PubMed * Google Scholar * Brenda Andrews1, 2brenda.andrews@utoronto.ca Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Boone1, 2charlie.boone@utoronto.ca Search for this author in: * NPG journals * PubMed * Google Scholar * Chad L Myers3cmyers@cs.umn.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature MethodsVolume: 7,Pages:1017–1024Year published:(2010)DOI:doi:10.1038/nmeth.1534Received28 July 2010Accepted14 October 2010Published online14 November 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles. View full text Figures at a glance * Figure 1: The SGA score for measuring quantitative genetic interactions. () An SGA experiment crossing a strain carrying a query mutation to an input array of single mutants, each of which carries a wild-type copy of the query gene and a unique array strain mutation. A final output array of double mutants is generated after several SGA selection steps, photographed and processed using software that measures colony areas in terms of pixels. Relative colony size, determined by measuring deviation of individual colonies from the median size for the same colony across 1,712 different experiments7, is shown. () Schematic depiction of the five factors that contribute to experimental variance of colony size. () Relative colony size after normalization. Single-mutant fitness (WA, WB) and double-mutant fitness (WAB) derived from normalized colony size measurements were used to identify and measure genetic interactions (SGA score; ε). * Figure 2: Evaluation of single-mutant fitness measures. () Comparison of single-mutant fitness or relative growth measurements for nonessential gene deletions derived from eight independent approaches: colony size measurements (this study), competitive growth analyzed by barcode hybridization10, flow cytometry4 or gene expression profiles33, gene-expression microarrays36, liquid growth profiling34, 35 and a spot assay on solid growth medium (phenotypic array)37. () Distribution of single-mutant fitness measures reported by the studies described in reporting fitness or relative growth rate. () Correlation of double-mutant fitness measures obtained from two independent replicates of a representative genome-wide SGA screen. Red line, y = x. Inset, distribution of correlations between double-mutant fitness measures obtained from 211 genome-wide SGA screens conducted in duplicate. * Figure 3: Evaluation of quantitative genetic interactions. () Scatter plots of genetic interactions derived from 211 genome-wide SGA screens conducted in duplicate. Pearson correlation coefficients were computed after applying a lenient (P < 0.05) or intermediate (SGA score absolute value, |ε| > 0.08; P < 0.05) confidence threshold on the SGA score. () Scatter plots of genetic interaction measures between reciprocally tested gene pairs. Pearson correlation coefficients were computed after applying a lenient (P < 0.05) or intermediate (|ε| > 0.08, P < 0.05) confidence threshold on the SGA score. () A scatter plot illustrating the overlap between genetic interaction scores for 239 unique gene pairs extracted from a large-scale SGA dataset7 and a small-scale, high-resolution liquid growth profiling study5. Data in were adapted from reference 7. * Figure 4: Evaluation of functional information derived from genetic interactions. () Plots of precision versus recall (number of true positives (TP)) for negative and positive genetic interactions, as determined by the SGA score or the S score. An SGA score without normalization methods applied is also plotted. True positive interactions were defined as those involving both genes annotated to the same GO gold standard set of terms12. Precision and recall were calculated as described previously12. FP, false positive. () Plots of precision versus recall (number of TP) for genetic interaction profile similarities computed using the SGA score or the S score. An SGA score without normalization methods applied is also plotted. Pearson correlation was used to compute profile similarity for every pair of array mutant strains across profiles consisting of interactions with the 1,712 query mutant strains. True positive pairs were those for which both genes were annotated to the same GO gold standard set of terms (GO annotation)12 or pairs encoding physically intera! cting proteins (physical interaction standard). Precision and recall were calculated as described previously12. * Figure 5: Analysis of genetic interactions within and between protein complexes. () For 92 complexes enriched for negative and/or positive genetic interactions that we assembled, nonessential genes are represented as circles and essential genes as diamonds. Complexes connected by purely positive or negative genetic interactions are indicated by yellow and blue, respectively; gray denotes complexes connected by a mixture of positive and negative genetic interactions. () Degree analysis of the complex-complex genetic interaction network in which node color reflects the prevalence of positive (yellow) or negative (blue) genetic interactions in a complex. Gray nodes denote complexes for which too few gene pairs were screened to assess within-complex interactions. Node size indicates the number of proteins associated with the complex. Positive and negative degree are the number of positive and negative genetic interactions, respectively, for a given complex. Inset, number of between-complex interactions was measured for complexes connected by purely negative ! and purely positive genetic interactions. Error bars, s.e.m. (n = 37, positive and n = 25, negative; *P < 0.002 by a rank-sum test). * Figure 6: Cross-complex genetic suppression network revealed by quantitative genetic interaction analysis. () A network illustrating suppression interactions between protein complexes (nodes). Edges indicate positive SGA interactions classified as suppression; arrows point to the complex whose fitness defect was suppressed. () Colony size–derived single- and double-mutant fitness plotted for the indicated strains (top). Error bars for single mutants, s.e.m. derived from bootstrapping (n = 800); error bars for double mutant, s.d. (n = 4). Liquid growth profiling5 of the same strains (bottom). () Schematic showing activation of the Rim101 pathway in response to alkaline stress. () Growth of the indicated strains on glucose and galactose, with plated strains indicated (left). WT, wild type. (,) Serial dilution growth assays of the indicated yeast strains at the indicated temperatures () and after exposure to UV light. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Anastasia Baryshnikova & * Michael Costanzo Affiliations * Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada. * Anastasia Baryshnikova, * Michael Costanzo, * Huiming Ding, * Judice Koh, * Kiana Toufighi, * Ji-Young Youn, * Bryan-Joseph San Luis, * Gary D Bader, * Brenda Andrews & * Charles Boone * Department of Molecular Genetics, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada. * Anastasia Baryshnikova, * Ji-Young Youn, * Gary D Bader, * Brenda Andrews & * Charles Boone * Department of Computer Science and Engineering, University of Minnesota–Twin Cities, Minneapolis, Minnesota, USA. * Yungil Kim, * Sunayan Bandyopadhyay & * Chad L Myers * Department of Electrical and Computer Engineering, University of Minnesota–Twin Cities, Minneapolis, Minnesota, USA. * Yungil Kim * Department of Biochemistry, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada. * Jiongwen Ou & * Grant W Brown * The Jackson Laboratory, Bar Harbor, Maine, USA. * Matthew Hibbs * Department of Biology, Santa Clara University, Santa Clara, California, USA. * David Hess * Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Canada. * Anne-Claude Gingras * Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, New Jersey, USA. * Olga G Troyanskaya Contributions M.C., C.L.M., C.B. and B.A. conceived and coordinated the project. C.L.M. and A.B. designed and implemented the algorithm. A.B., C.L.M., Y.K., J.K. and S.B. performed statistical analysis. J.-Y.Y., B.-J.S.L., J.O., G.W.B. and M.C. validated experiments. H.D. and K.T. analyzed and processed images. M.H., D.H., G.D.B. and O.G.T. provided statistical insight. A.-C.G. provided biological insight. M.C., C.L.M., C.B. and A.B. prepared the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Brenda Andrews (brenda.andrews@utoronto.ca) or * Charles Boone (charlie.boone@utoronto.ca) or * Chad L Myers (cmyers@cs.umn.edu) Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Data 1 (516K) Single-mutant fitness standard. * Supplementary Data 2 (208K) Protein complex standard. * Supplementary Data 3 (180K) List of complex-complex pairs enriched for positive genetic interactions. Zip files * Supplementary Software (180K) Matlab source code for the SGA score algorithm. PDF files * Supplementary Text and Figures (10M) Supplementary Figures 1–11, Supplementary Tables 1–3 and Supplementary Notes 1–6 Additional data
• Erratum: From promising to practical: tools to study networks of neurons
  - Nat Meth 7(12):1025 (2010)
  Nature Methods | Erratum Erratum: From promising to practical: tools to study networks of neurons * Monya Baker Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Page:1025Year published:(2010)DOI:doi:10.1038/nmeth1210-1025bPublished online29 November 2010 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Methods7, 877–883 (2010); published online 28 October 2010; corrected after print 10 November 2010 In the version of this article initially published, a scale bar showing 1 mm was mislabeled on page 879 as showing 10 mm. The error has been corrected in the HTML and PDF versions of the article. Additional data
• Erratum: A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans
  - Nat Meth 7(12):1025 (2010)
  Nature Methods | Erratum Erratum: A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans * Marko Jovanovic Search for this author in: * NPG journals * PubMed * Google Scholar * Lukas Reiter Search for this author in: * NPG journals * PubMed * Google Scholar * Paola Picotti Search for this author in: * NPG journals * PubMed * Google Scholar * Vinzenz Lange Search for this author in: * NPG journals * PubMed * Google Scholar * Erica Bogan Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin A Hurschler Search for this author in: * NPG journals * PubMed * Google Scholar * Cherie Blenkiron Search for this author in: * NPG journals * PubMed * Google Scholar * Nicolas J Lehrbach Search for this author in: * NPG journals * PubMed * Google Scholar * Xavier C Ding Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel Weiss Search for this author in: * NPG journals * PubMed * Google Scholar * Sabine P Schrimpf Search for this author in: * NPG journals * PubMed * Google Scholar * Eric A Miska Search for this author in: * NPG journals * PubMed * Google Scholar * Helge Groβhans Search for this author in: * NPG journals * PubMed * Google Scholar * Ruedi Aebersold Search for this author in: * NPG journals * PubMed * Google Scholar * Michael O Hengartner Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature MethodsVolume: 7,Page:1025Year published:(2010)DOI:doi:10.1038/nmeth1210-1025aPublished online29 November 2010 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Methods7, 837–842 (2010); published online 12 September 2010; corrected after print 9 November 2010 In the version of this article initially published, the reported P values were incorrectly written and an incorrect wording change was inadvertently made to the Figure 1 legend. The errors have been corrected in the HTML and PDF versions of the article. Additional data