Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 57,309 bioRxiv papers from 263,909 authors.
Most downloaded bioRxiv papers, since beginning of last month
55,912 results found. For more information, click each entry to expand.
956 downloads neuroscience
Miniaturized fluorescence microscopes (miniscopes) have been instrumental to monitor neural activity during unrestrained behavior and their open-source versions have helped to distribute them at an affordable cost. Generally, the footprint and weight of open-source miniscopes is sacrificed for added functionality. Here, we present NINscope: a light-weight, small footprint open-source miniscope that incorporates a high-sensitivity image sensor, an inertial measurement unit (IMU), and an LED driver for an external optogenetic probe. We highlight the advantages of NINscope by performing the first simultaneous cellular resolution (dual scope) recordings from cerebellum and cerebral cortex in unrestrained mice, revealing that the activity of both regions generally precede the onset of behavioral acceleration. At the same time, we demonstrate the optogenetic stimulation capabilities of NINscope and show that cerebral cortical activity can be driven strongly by cerebellar stimulation. Finally, we combine optogenetic stimulation of cortex with imaging in the dorsal striatum and replicate previous studies that show action space is encoded by neurons in this subcortical region. In combination with cross-platform control software NINscope is a versatile addition to the expanding toolbox of open-source miniscopes and will aid multi-region circuit investigations during unrestrained behavior.
952 downloads genetics
Katrina L. Grasby, Neda Jahanshad, Jodie N Painter, Lucía Colodro-Conde, Janita Bralten, Derrek P Hibar, Penelope A Lind, Fabrizio Pizzagalli, Christopher R.K. Ching, Mary AB McMahon, Natalia Shatokhina, Leo C.P. Zsembik, Ingrid Agartz, Saud Alhusaini, Marcio AA Almeida, Dag Alnæs, Inge K Amlien, Micael Andersson, Tyler Ard, Nicola J. Armstrong, Allison Ashley-Koch, Joshua R Atkins, Manon Bernard, Rachel M. Brouwer, Elizabeth EL Buimer, Robin Bülow, Christian Bürger, Dara M. Cannon, Mallar Chakravarty, Qiang Chen, Joshua W. Cheung, Baptiste Couvy-Duchesne, Anders M Dale, Shareefa Dalvie, Tânia K de Araujo, Greig I. de Zubicaray, Sonja MC de Zwarte, Anouk den Braber, Nhat Trung Doan, Katharina Dohm, Stefan Ehrlich, Hannah-Ruth Engelbrecht, Susanne Erk, Chun Chieh Fan, Iryna O. Fedko, Sonya F Foley, Judith M Ford, Masaki Fukunaga, Melanie E. Garrett, Tian Ge, Sudheer Giddaluru, Aaron L. Goldman, Melissa J Green, Nynke A. Groenewold, Dominik Grotegerd, Tiril P. Gurholt, Boris A. Gutman, Narelle K. Hansell, Mathew A Harris, Marc B Harrison, Courtney C. Haswell, Michael Hauser, Stefan Herms, Dirk J. Heslenfeld, New Fei Ho, David Hoehn, Per Hoffmann, Laurena Holleran, Martine Hoogman, Jouke-Jan Hottenga, Masashi Ikeda, Deborah Janowitz, Iris E Jansen, Tianye Jia, Christiane Jockwitz, Ryota Kanai, Sherif Karama, Dalia Kasperaviciute, Tobias Kaufmann, Sinead Kelly, Masataka Kikuchi, Marieke Klein, Michael Knapp, Annchen R Knodt, Bernd Krämer, Max Lam, Thomas M Lancaster, Phil H. Lee, Tristram A Lett, Lindsay B Lewis, Iscia Lopes-Cendes, Michelle Luciano, Fabio Macciardi, Andre F. Marquand, Samuel R Mathias, Tracy R Melzer, Yuri Milaneschi, Nazanin Mirza-Schreiber, Jose CV Moreira, Thomas W Mühleisen, Bertram Müller-Myhsok, Pablo Najt, Soichiro Nakahara, Kwangsik Nho, Loes M Olde Loohuis, Dimitri Papadopoulos Orfanos, John F Pearson, Toni L Pitcher, Benno Pütz, Yann Quidé, Anjanibhargavi Ragothaman, Faisal M. Rashid, William R Reay, Ronny Redlich, Céline S Reinbold, Jonathan Repple, Geneviève Richard, Brandalyn C Riedel, Shannon L. Risacher, Cristiane S Rocha, Nina Roth Mota, Lauren Salminen, Arvin Saremi, Andrew J. Saykin, Fenja Schlag, Lianne Schmaal, Peter R. Schofield, Rodrigo Secolin, Chin Yang Shapland, Li Shen, Jean Shin, Elena Shumskaya, Ida E Sønderby, Emma Sprooten, Lachlan T. Strike, Katherine E Tansey, Alexander Teumer, Anbupalam Thalamuthu, Sophia I. Thomopoulos, Diana Tordesillas-Gutiérrez, Jessica A. Turner, Anne Uhlmann, Costanza Ludovica Vallerga, Dennis van der Meer, Marjolein MJ van Donkelaar, Liza van Eijk, Theo G.M. van Erp, Neeltje E.M. van Haren, Daan Van Rooij, Marie-José van Tol, Jan H Veldink, Ellen Verhoef, Esther Walton, Mingyuan Wang, Yunpeng Wang, Joanna M Wardlaw, Wei Wen, Lars T. Westlye, Christopher D. Whelan, Stephanie H. Witt, Katharina Wittfeld, Christiane Wolf, Thomas Wolfers, Jing Qin Wu, Clarissa L. Yasuda, Dario Zaremba, Zuo Zhang, Alyssa H Zhu, Marcel P. Zwiers, Eric Artiges, Amelia A. Assareh, Rosa Ayesa-Arriola, Aysenil Belger, Christine L. Brandt, Gregory G Brown, Sven Cichon, Joanne E. Curran, Gareth E. Davies, Franziska Degenhardt, Michelle F Dennis, Bruno Dietsche, Srdjan Djurovic, Colin P. Doherty, Ryan Espiritu, Daniel Garijo, Yolanda Gil, Penny A Gowland, Robert C. Green, Alexander N Häusler, Walter Heindel, Beng-Choon Ho, Wolfgang U Hoffmann, Florian Holsboer, Georg Homuth, Norbert Hosten, Clifford R. Jack, MiHyun Jang, Andreas Jansen, Nathan A Kimbrel, Knut Kolskår, Sanne Koops, Axel Krug, Kelvin O. Lim, Jurjen J. Luykx, Daniel H Mathalon, Karen A. Mather, Venkata S. Mattay, Sarah Matthews, Jaqueline Mayoral Van Son, Sarah C McEwen, Ingrid Melle, Derek W Morris, Bryon A. Mueller, Matthias Nauck, Jan E. Nordvik, Markus M Nöthen, Daniel S O'Leary, Nils Opel, Marie-Laure Paillère Martinot, G B Pike, Adrian Preda, Erin B. Quinlan, Paul E Rasser, Varun Ratnakar, Simone Reppermund, Vidar M. Steen, Paul A Tooney, Fábio R Torres, Dick J. Veltman, James T Voyvodic, Robert Whelan, Tonya White, Hidenaga Yamamori, Hieab HH Adams, Joshua C Bis, Stéphanie Debette, Charles Decarli, Myriam Fornage, Vilmundur Gudnason, Edith Hofer, M. A Ikram, Lenore Launer, W T Longstreth, Oscar L. Lopez, Bernard Mazoyer, Thomas H Mosley, Gennady V Roshchupkin, Claudia L Satizabal, Reinhold Schmidt, Sudha Seshadri, Qiong Yang, The Alzheimer's Disease Neuroimaging Initiative, CHARGE Consortium, EPIGEN Consortium, IMAGEN Consortium, Mid-Atlantic MIRECC Workgroup, SYS Consortium, The Parkinson's Progression Markers Initiative, Marina KM Alvim, David Ames, Tim J Anderson, Ole A Andreassen, Alejandro Arias-Vasquez, Mark E Bastin, Bernhard T. Baune, John Blangero, Dorret I Boomsma, Henry Brodaty, Han G Brunner, Randy L. Buckner, Jan K Buitelaar, Juan R Bustillo, Wiepke Cahn, Murray J Cairns, Vince Calhoun, Vaughan j Carr, Xavier Caseras, Svenja Caspers, Gianpiero L. Cavalleri, Fernando Cendes, Aiden Corvin, Benedicto Crespo-Facorro, John C Dalrymple-Alford, Udo Dannlowski, Eco J.C. de Geus, Ian J Deary, Norman Delanty, Chantal Depondt, Sylvane Desrivières, Gary Donohoe, Thomas Espeseth, Guillén Fernández, Simon E. Fisher, Herta Flor, Andreas J. Forstner, Clyde Francks, Barbara Franke, David C Glahn, Randy L. Gollub, Hans J Grabe, Oliver Gruber, Asta K Håberg, Ahmad R Hariri, Catharina A. Hartman, Ryota Hashimoto, Andreas Heinz, Frans A Henskens, Manon H.J. Hillegers, Pieter J. Hoekstra, Avram J. Holmes, L E Hong, William D Hopkins, Hilleke E. Hulshoff Pol, Terry L Jernigan, Erik G Jönsson, René S. Kahn, Martin A Kennedy, Tilo TJ Kircher, Peter Kochunov, John BJ Kwok, Stephanie Le Hellard, Carmel M Loughland, Nicholas G Martin, Jean-Luc Martinot, Colm McDonald, Katie L. McMahon, Andreas Meyer-Lindenberg, Patricia T Michie, Rajendra A. Morey, Bryan Mowry, Lars Nyberg, Jaap Oosterlaan, Roel A. Ophoff, Christos Pantelis, Tomáŝ Paus, Zdenka Pausova, Brenda W.J.H. Penninx, Tinca JC Polderman, Danielle Posthuma, Marcella Rietschel, Joshua L. Roffman, Laura M Rowland, Perminder S. Sachdev, Philipp G Sämann, Ulrich Schall, Gunter Schumann, Rodney J. Scott, Kang Sim, Sanjay M. Sisodiya, Jordan W. Smoller, Iris E Sommer, Beate St Pourcain, Dan J. Stein, Arthur W Toga, Julian N. Trollor, Nic JA Van der Wee, Dennis van't Ent, Henry Völzke, Henrik Walter, Bernd Weber, Daniel R Weinberger, Margaret J Wright, Juan Zhou, Jason L Stein, Paul M Thompson, Sarah E Medland
The cerebral cortex underlies our complex cognitive capabilities, yet we know little about the specific genetic loci influencing human cortical structure. To identify genetic variants, including structural variants, impacting cortical structure, we conducted a genome-wide association meta-analysis of brain MRI data from 51,662 individuals. We analysed the surface area and average thickness of the whole cortex and 34 regions with known functional specialisations. We identified 255 nominally significant loci (P ≤ 5 x 10-8); 199 survived multiple testing correction (P ≤ 8.3 x 10-10; 187 surface area; 12 thickness). We found significant enrichment for loci influencing total surface area within regulatory elements active during prenatal cortical development, supporting the radial unit hypothesis. Loci impacting regional surface area cluster near genes in Wnt signalling pathways, known to influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression and ADHD. NOTE: K.L.G. and N.J. contributed to this work as co-first authors for this preprint. J.N.P., L.C.-C., J.B., D.P.H., P.A.L., F.P. contributed to this work as co-second authors for this preprint. J.L.S., P.M.T., S.E.M. contributed to this work as co-last authors for this preprint.
951 downloads immunology
Travis K Hughes, Marc H Wadsworth, Todd M Gierahn, Tran Do, David Weiss, Priscilla R. Andrade, Feiyang Ma, Bruno J. de Andrade Silva, Shuai Shao, Lam C Tsoi, Jose Ordovas-Montanes, Johann E Gudjonsson, Robert L Modlin, J Christopher Love, Alex K Shalek
The development of high-throughput single-cell RNA-sequencing (scRNA-Seq) methodologies has empowered the characterization of complex biological samples by dramatically increasing the number of constituent cells that can be examined concurrently. Nevertheless, these approaches typically recover substantially less information per-cell as compared to lower-throughput microtiter plate-based strategies. To uncover critical phenotypic differences among cells and effectively link scRNA-Seq observations to legacy datasets, reliable detection of phenotype-defining transcripts – such as transcription factors, affinity receptors, and signaling molecules – by these methods is essential. Here, we describe a substantially improved massively-parallel scRNA-Seq protocol we term Seq-Well S^3 (“Second-Strand Synthesis”) that increases the efficiency of transcript capture and gene detection by up to 10- and 5-fold, respectively, relative to previous iterations, surpassing best-in-class commercial analogs. We first characterized the performance of Seq-Well S^3 in cell lines and PBMCs, and then examined five different inflammatory skin diseases, illustrative of distinct types of inflammation, to explore the breadth of potential immune and parenchymal cell states. Our work presents an essential methodological advance as well as a valuable resource for studying the cellular and molecular features that inform human skin inflammation.
941 downloads neuroscience
Hod Dana, Yi Sun, Boaz Mohar, Brad Hulse, Jeremy P Hasseman, Getahun Tsegaye, Arthur Tsang, Allan Wong, Ronak Patel, John J Macklin, Yang Chen, Arthur Konnerth, Vivek Jayaraman, Loren L Looger, Eric R Schreiter, Karel Svoboda, Douglas S Kim
Calcium imaging with genetically encoded calcium indicators (GECIs) is routinely used to measure neural activity in intact nervous systems. GECIs are frequently used in one of two different modes: to track activity in large populations of neuronal cell bodies, or to follow dynamics in subcellular compartments such as axons, dendrites and individual synaptic compartments. Despite major advances, calcium imaging is still limited by the biophysical properties of existing GECIs, including affinity, signal-to-noise ratio, rise and decay kinetics, and dynamic range. Using structure-guided mutagenesis and neuron-based screening, we optimized the green fluorescent protein-based GECI GCaMP6 for different modes of in vivo imaging. The jGCaMP7 sensors provide improved detection of individual spikes (jGCaMP7s,f), imaging in neurites and neuropil (jGCaMP7b), and tracking large populations of neurons using 2-photon (jGCaMP7s,f) or wide-field (jGCaMP7c) imaging.
926 downloads bioinformatics
Kevin R Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel Burkhardt, William Chen, Kristina Yim, Antonia van den Elzen, Matthew J Hirn, Ronald R. Coifman, Natalia B Ivanova, Guy Wolf, Smita Krishnaswamy
With the advent of high-throughput technologies measuring high-dimensional biological data, there is a pressing need for visualization tools that reveal the structure and emergent patterns of data in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure in data by an information-geometric distance between datapoints. We perform extensive comparison between PHATE and other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data including continual progressions, branches, and clusters. We define a manifold preservation metric DEMaP to show that PHATE produces quantitatively better denoised embeddings than existing visualization methods. We show that PHATE is able to gain unique insight from a newly generated scRNA-seq dataset of human germ layer differentiation. Here, PHATE reveals a dynamic picture of the main developmental branches in unparalleled detail, including the identification of three novel subpopulations. Finally, we show that PHATE is applicable to a wide variety of datatypes including mass cytometry, single-cell RNA-sequencing, Hi-C, and gut microbiome data, where it can generate interpretable insights into the underlying systems.
925 downloads bioinformatics
Karen H Miga, Sergey Koren, Arang Rhie, Mitchell R. Vollger, Ariel Gershman, Andrey Bzikadze, Shelise Brooks, Edmund Howe, David Porubsky, Glennis A. Logsdon, Valerie A Schneider, Tamara Potapova, Jonathan Wood, William Chow, Joel Armstrong, Jeanne Fredrickson, Evgenia Pak, Kristof Tigyi, Milinn Kremitzki, Christopher Markovic, Valerie Maduro, Amalia Dutra, Gerard G Bouffard, Alexander M Chang, Nancy F Hansen, Françoisen Thibaud-Nissen, Anthony D Schmitt, Jon-Matthew Belton, Siddarth Selvaraj, Megan Y. Dennis, Daniela C Soto, Ruta Sahasrabudhe, Gulhan Kaya, Josh Quick, Nicholas J Loman, Nadine Holmes, Matthew Loose, Urvashi Surti, Rosa ana Risques, Tina A. Graves Lindsay, Robert Fulton, Ira Hall, Benedict Paten, Kerstin Howe, Winston Timp, Alice Young, James C. Mullikin, Pavel A Pevzner, Jennifer E. Gerton, Beth A Sullivan, Evan E Eichler, Adam M Phillippy
After nearly two decades of improvements, the current human reference genome (GRCh38) is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has been finished end to end, and hundreds of unresolved gaps persist ,. The remaining gaps include ribosomal rDNA arrays, large near-identical segmental duplications, and satellite DNA arrays. These regions harbor largely unexplored variation of unknown consequence, and their absence from the current reference genome can lead to experimental artifacts and hide true variants when re-sequencing additional human genomes. Here we present a de novo human genome assembly that surpasses the continuity of GRCh38 , along with the first gapless, telomere-to-telomere assembly of a human chromosome. This was enabled by high-coverage, ultra-long-read nanopore sequencing of the complete hydatidiform mole CHM13 genome, combined with complementary technologies for quality improvement and validation. Focusing our efforts on the human X chromosome , we reconstructed the ∼2.8 megabase centromeric satellite DNA array and closed all 29 remaining gaps in the current reference, including new sequence from the human pseudoautosomal regions and cancer-testis ampliconic gene families (CT-X and GAGE). This complete chromosome X, combined with the ultra-long nanopore data, also allowed us to map methylation patterns across complex tandem repeats and satellite arrays for the first time. These results demonstrate that finishing the human genome is now within reach and will enable ongoing efforts to complete the remaining human chromosomes. : #ref-1 : #ref-2 : #ref-3
921 downloads immunology
High throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets. Here, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.
919 downloads neuroscience
A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity, and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensional basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.
906 downloads biophysics
Single-molecule localization microscopy (SMLM) promises to provide truly molecular scale images of biological specimens. However, mechanical instabilities in the instrument, readout errors and sample drift constitute significant challenges and severely limit both the useable data acquisition length and the localization accuracy of single molecule emitters. Here, we developed an actively stabilized total internal fluorescence (TIRF) microscope that performs 3D real-time drift corrections and achieves a stability of ≤1 nm. Self-alignment of the emission light path and corrections of readout errors of the camera automate channel alignment and ensure localization precisions of 1-4 nm in DNA origami structures and cells for different labels. We used Feedback SMLM to measure the separation distance of signaling receptors and phosphatases in T cells. Thus, an improved SMLM enables direct distance measurements between molecules in intact cells on the scale between 1-20 nm, potentially replacing Forster resonance energy transfer (FRET) to quantify molecular interactions. In summary, by overcoming the major bottlenecks in SMLM imaging, it is possible to generate molecular images with nanometer accuracy and conduct distance measurements on the biological relevant length scales.
902 downloads developmental biology
Small RhoGTPases and Myosin-II direct cell shape changes and movements during tissue morphogenesis. Their activities are tightly regulated in space and time to specify the desired pattern of contractility that supports tissue morphogenesis. This is expected to stem from polarized surface stimuli and from polarized signaling processing inside cells. We examined this general problem in the context of cell intercalation that drives extension of the Drosophila ectoderm. In the ectoderm, G protein coupled receptors (GPCRs) and their downstream heterotrimeric G proteins (Gα and Gβγ) activate Rho1 both medial-apically, where it exhibits pulsed dynamics, and at junctions, where its activity is planar polarized (Kerridge et al., 2016; Munjal et al., 2015). However, the mechanisms responsible for polarizing Rho1 activity are unclear. In particular, it is unknown how Rho1 activity is controlled at junctions. We report a division of labor in the mechanisms of Rho1 activation in that distinct guanine exchange factors (GEFs), that serve as activators of Rho1, operate in these distinct cellular compartments. RhoGEF2 acts uniquely to activate medial-apical Rho1. Although RhoGEF2 is recruited both medial-apically and at junctions by Gα12/13-GTP, also called Concertina (Cta) in Drosophila, its activity is restricted to the medial-apical compartment. Furthermore, we characterize a novel RhoGEF, p114RhoGEF/Wireless (Wrl), and report its requirement for cell intercalation in the extending ectoderm. p114RhoGEF/Wireless activates Rho1 specifically at junctions. Strikingly it is restricted to adherens junctions and is under Gβ13F/Gγ1 control. Gβ13F/Gγ1 activates junctional Rho1 and exerts quantitative control over planar polarization of Rho1. In particular, overexpression of Gβ13F/Gγ1 leads to hyper planar polarization of Rho1 and MyoII. Finally, we found that p114RhoGEF/Wireless is absent in the mesoderm, arguing for a tissue-specific control over junctional Rho1 activity. These results shed light on the mechanisms of polarization of Rho1 activity in different cellular compartments and reveal that distinct GEFs are sensitive tuning parameters of cell contractility in remodeling epithelia.
900 downloads microbiology
John P Pribis, Libertad García-Villada, Yin Zhai, Ohad Lewin-Epstein, Anthony Wang, Jingjing Liu, Jun Xia, Qian Mei, Devon M Fitzgerald, Julia Bos, Robert Austin, Christophe Herman, David Bates, Lilach Hadany, P.J. Hastings, Susan M Rosenberg
Antibiotics can induce mutations that cause antibiotic resistance. Yet, despite their importance, mechanisms of antibiotic-promoted mutagenesis remain elusive. We report that the fluoroquinolone antibiotic ciprofloxacin (cipro) induces mutations that cause drug resistance by triggering differentiation of a mutant-generating cell subpopulation, using reactive oxygen species (ROS) to signal the sigma-S (σS) general-stress response. Cipro-generated DNA breaks activate the SOS DNA-damage response and error-prone DNA polymerases in all cells. However, mutagenesis is restricted to a cell subpopulation in which electron transfer and SOS induce ROS, which activate the σS response, allowing mutagenesis during DNA-break repair. When sorted, this small σS-response-'on' subpopulation produces most antibiotic cross-resistant mutants. An FDA-approved drug prevents σS induction specifically inhibiting antibiotic-promoted mutagenesis. Furthermore, SOS-inhibited cell division, causing multi-chromosome cells, is required for mutagenesis. The data support a model in which within-cell chromosome cooperation together with development of a 'gambler' cell subpopulation promote resistance evolution without risking most cells.
898 downloads bioinformatics
The rapid development of novel spatial transcriptomics technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational algorithms and pipelines. Here we present Giotto, a comprehensive, flexible, robust, and open-source pipeline for spatial transcriptomic data analysis and visualization. The data analysis module implements a wide range of algorithms ranging from basic tasks such as data pre-processing to innovative approaches for cell-cell interaction characterization. The data visualization module provides a user-friendly workspace that allows users to interactively visualize, explore and compare multiple layers of information. These two modules can be used iteratively for refined analysis and hypothesis development. We illustrate the functionalities of Giotto by using the recently published seqFISH+ dataset for mouse brain. Our analysis highlights the utility of Giotto for characterizing tissue spatial organization as well as for the interactive exploration of multi- layer information in spatial transcriptomic and imaging data. We find that single-cell resolution spatial information is essential for the investigation of ligand-receptor mediated cell-cell interactions. Giotto is generally applicable and can be easily integrated with external software packages for multi-omic data integration.
895 downloads microbiology
David C. Danko, Daniela Bezdan, Ebrahim Afshinnekoo, Sofia Ahsanuddin, Josue Alicea, Chandrima Bhattacharya, Malay Bhattacharyya, Ran Blekhman, Daniel J Butler, Eduardo Castro-Nallar, Ana M Canas, Aspassia D Chatziefthimiou, Kern Rei Chng, David A Coil, Denise Syndercombe Court, Robert W Crawford, Christelle Desnues, Emmanuel Dias-Neto, Daisy Donnellan, Marius Dybwad, Jonathan A. Eisen, Eran Elhaik, Danilo Ercolini, Francesca De Filippis, Alina Frolova, Alexandra B Graf, David C Green, Patrick K. H. Lee, Jochen Hecht, Mark Hernandez, Soojin Jang, Andre Kahles, Mikhail Karasikov, Kaymisha Knights, Nikos C. Kyrpides, Per Ljungdahl, Abigail Lyons, Gabriella Mason-Buck, Ken McGrath, Emmanuel F Mongodin, Harun Mustafa, Beth Mutai, Niranjan Nagarajan, Russell Y Neches, Amanda Ng, Marina Nieto-Caballero, Olga Nikolayeva, Tatyana Nikolayeva, Houtan Noushmehr, Manuela Oliveira, Stephan Ossowski, Olayinka O Osuolale, David Paez-Espino, Eileen Png, Nicolas Rascovan, Hugues Richard, Gunnar Ratsch, Jorge L Sanchez, Lynn M Schriml, Heba Shaaban, Leming Shi, Maria A Sierra, Le Huu Song, Haruo Suzuki, Dominique Thomas, Klas I Udekwu, Juan A. Ugalde, Brandon Valentine, Dimitar I Vassilev, Elena Vayndorf, Marcus H Y Leung, Ben Young, Maria M Zambrano, Jifeng Zhu, Sibo Zhu, Pawel P Labaj, Christopher E Mason
Although studies have shown that urban environments and mass-transit systems have geospatially distinct metagenomes, no study has ever systematically studied these dense, human/microbial ecosystems around the world. To address this gap in knowledge, we created a global metagenomic and antimicrobial resistance (AMR) atlas of urban mass transit systems from 58 cities, spanning 3,741 samples and 4,424 taxonomically-defined microorganisms collected for three years. The map provides annotated, geospatial data about microbial strains, functional genetics, antimicrobial resistance, and novel genetic elements, including 10,928 novel predicted viral species. Urban microbiomes often resemble human commensal microbiomes from the skin and airways but contain a consistent "core" of 61 species which are predominantly not human commensal species. These data also show that AMR density across cities varies by several orders of magnitude with many AMRs present on plasmids with cosmopolitan distributions. Conversely, samples may be accurately (91.4%) classified to their city-of-origin using a linear support vector machine over taxa. Together, these results constitute a high-resolution global metagenomic atlas, which enables the discovery of new genetic components of the built human environment, forensic application, and an essential first draft of the global AMR burden of the world's cities
893 downloads biophysics
Bacteria have evolved adaptive immune systems encoded by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and the CRISPR-associated (Cas) genes to maintain genomic integrity in the face of relentless assault from pathogens and mobile genetic elements. Type I CRISPR-Cas systems canonically target foreign DNA for degradation via the joint action of the ribonucleoprotein complex Cascade and the helicase-nuclease Cas3, but nuclease-deficient Type I systems lacking Cas3 have been repurposed for RNA-guided transposition by bacterial Tn7-like transposons. How CRISPR -and transposon-associated machineries collaborate during DNA targeting and insertion has remained elusive. Here we determined structures of a novel TniQ-Cascade complex encoded by the Vibrio cholerae Tn6677 transposon using single particle electron cryo-microscopy (cryo-EM), revealing the mechanistic basis of this functional coupling. The quality of the cryo-EM maps allowed for de novo modeling and refinement of the transposition protein TniQ, which binds to the Cascade complex as a dimer in a head-to-tail configuration, at the interface formed by Cas6 and Cas7 near the 3' end of the crRNA. The natural Cas8-Cas5 fusion protein binds the 5' crRNA handle and contacts the TniQ dimer via a flexible insertion domain. A target DNA-bound structure reveals critical interactions necessary for protospacer adjacent motif (PAM) recognition and R-loop formation. The present work lays the foundation for a structural understanding of how DNA targeting by TniQ-Cascade leads to downstream recruitment of additional transposon-associated proteins, and will guide protein engineering efforts to leverage this system for programmable DNA insertions in genome engineering applications.
892 downloads genomics
Cristopher V Van Hout, Ioanna Tachmazidou, Joshua D Backman, Joshua X Hoffman, Bin Yi, Ashutosh Pandey, Claudia Gonzaga-Jauregui, Shareef Khalid, Daren Liu, Nilanjana Banerjee, Alexander H Li, Colm O'Dushlaine, Anthony Marcketta, Jeffrey Staples, Claudia Schumann, Alicia Hawes, Evan Maxwell, Leland Barnard, Alexander Lopez, John Penn, Lukas Habegger, Andrew L Blumenfeld, Ashish Yadav, Kavita Praveen, Marcus Jones, William J Salerno, Wendy K Chung, Ida Surakka, Cristen J. Willer, Kristian Hveem, Joseph B Leader, David J Carey, David H Ledbetter, Lon Cardon, George D Yancopoulos, Aris Economides, Giovanni Coppola, Alan R Shuldiner, Suganthi Balasubramanian, Michael Cantor, Matthew R. Nelson, John C Whittaker, Jeffrey G Reid, Jonathan Marchini, John D Overton, Robert A Scott, Goncalo Abecasis, Laura M Yerges-Armstrong, Aris Baras
The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.
887 downloads bioinformatics
Taxonomic classification is a crucial step for metagenomics applications including disease diagnostics, microbiome analyses, and outbreak tracing. Yet it is unknown what deep learning architecture can capture microbial genome-wide features relevant to this task. We report DeepMicrobes (https://github.com/MicrobeLab/DeepMicrobes), a computational framework that can perform large-scale training on > 10,000 RefSeq complete microbial genomes and accurately predict the species-of-origin of whole metagenome shotgun sequencing reads. We show the advantage of DeepMicrobes over state-of-the-art tools in precisely identifying species from microbial community sequencing data. Therefore, DeepMicrobes expands the toolbox of taxonomic classification for metagenomics and enables the development of further deep learning-based bioinformatics algorithms for microbial genomic sequence analysis.
884 downloads bioinformatics
De novo genome assembly provides comprehensive, unbiased genomic information and makes it possible to gain insight into new DNA sequences not present in reference genomes. Many de novo human genomes have been published in the last few years, leveraging a combination of inexpensive short-read and single-molecule long-read technologies. As long-read DNA sequencers become more prevalent, the computational burden of generating assemblies persists as a critical factor. The most common approach to long-read assembly, using an overlap-layout-consensus (OLC) paradigm, requires all-to-all read comparisons, which quadratically scales in computational complexity with the number of reads. We assert that recently achievements in sequencing technology (i.e. with accuracy ~99% and read length ~10-15k) enables a fundamentally better strategy for OLC that is effectively linear rather than quadratic. Our genome assembly implementation, Peregrine uses sparse hierarchical minimizers (SHIMMER) to index reads thereby avoiding the need for an all-to-all read comparison step. Peregrine can assemble 30x human PacBio CCS read datasets in less than 30 CPU hours and around 100 wall-clock minutes to a high contiguity assembly (N50 > 20Mb). The continued advance of sequencing technologies coupled with the Peregrine assembler enables routine generation of human de novo assemblies. This will allow for population scale measurements of more comprehensive genomic variations -- beyond SNPs and small indels -- as well as novel applications requiring rapid access to de novo assemblies.
880 downloads genomics
Davis McCarthy, Raghd Rostom, Yuanhua Huang, Daniel J Kunz, Petr Danecek, Marc Jan Bonder, Tzachi Hagai, HipSci Consortium, Wenyi Wang, Daniel J Gaffney, Benjamin D Simons, Oliver Stegle, Sarah A Teichmann
Decoding the clonal substructures of somatic tissues sheds light on cell growth, development and differentiation in health, ageing and disease. DNA-sequencing, either using bulk or using single-cell assays, has enabled the reconstruction of clonal trees from frequency and co-occurrence patterns of somatic variants. However, approaches to systematically characterize phenotypic and functional variations between individual clones are not established. Here we present cardelino (https://github.com/PMBio/cardelino), a computational method for inferring the clone of origin of individual cells that have been assayed using single-cell RNA-seq (scRNA-seq). After validating our model using simulations, we apply cardelino to matched scRNA-seq and exome sequencing data from 32 human dermal fibroblast lines, identifying hundreds of differentially expressed genes between cells from different somatic clones. These genes are frequently enriched for cell cycle and proliferation pathways, indicating a key role for cell division genes in non-neutral somatic evolution.
876 downloads synthetic biology
In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In biology, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Learning the natural distribution of evolutionary protein sequence variation is a logical step toward predictive and generative modeling for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million sequences spanning evolutionary diversity. The resulting model maps raw sequences to representations of biological properties without labels or prior domain knowledge. The learned representation space organizes sequences at multiple levels of biological granularity from the biochemical to proteomic levels. Learning recovers information about protein structure: secondary structure and residue-residue contacts can be extracted by linear projections from learned representations. With small amounts of labeled data, the ability to identify tertiary contacts is further improved. Learning on full sequence diversity rather than individual protein families increases recoverable information about secondary structure. We show the networks generalize by adapting them to variant activity prediction from sequences only, with results that are comparable to a state-of-the-art variant predictor that uses evolutionary and structurally derived features.
874 downloads biochemistry
Stress granules are condensates of non-translating mRNAs and proteins involved in the stress response and neurodegenerative diseases. Stress granules are proposed to form in part through intermolecular RNA-RNA interactions, although the process of RNA condensation is not well understood. In vitro , we demonstrate that the minimization of surface free energy promotes the recruitment and interaction of RNAs on RNA or RNP condensate surfaces. We demonstrate that the ATPase activity of the DEAD-box RNA helicase eIF4A reduces RNA recruitment to RNA condensates in vitro and in cells, as well as limiting stress granule formation. This defines a new function for eIF4A, and potentially other RNA helicases, to limit thermodynamically favored intermolecular RNA-RNA interactions in cells, thereby allowing for proper RNP function. Highlights
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