DOCSLIB.ORG

Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming Resource Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming Graphical Abstract Authors Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, ..., Rudolf Jaenisch, Aviv Regev, Eric S. Lander Correspondence [email protected] (J.S.), [email protected] (A.R.), [email protected] (E.S.L.) In Brief Application of a new analytical approach to examine developmental trajectories of single cells offers insight into how paracrine interactions shape reprogramming. Highlights d Optimal transport analysis recovers trajectories from 315,000 scRNA-seq profiles d Induced pluripotent stem cell reprogramming produces diverse developmental programs d Regulatory analysis identifies a series of TFs predictive of specific cell fates d Transcription factor Obox6 and cytokine GDF9 increase reprogramming efficiency Schiebinger et al., 2019, Cell 176, 928–943 February 7, 2019 ª 2019 Elsevier Inc. https://doi.org/10.1016/j.cell.2019.01.006 Resource Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming Geoffrey Schiebinger,1,11,16 Jian Shu,1,2,16,* Marcin Tabaka,1,16 Brian Cleary,1,3,16 Vidya Subramanian,1 Aryeh Solomon,1,17 Joshua Gould,1 Siyan Liu,1,15 Stacie Lin,1,6 Peter Berube,1 Lia Lee,1 Jenny Chen,1,4 Justin Brumbaugh,5,7,8,9,10 Philippe Rigollet,11,12 Konrad Hochedlinger,7,8,9,13 Rudolf Jaenisch,2,3 Aviv Regev,1,6,13,* and Eric S. Lander1,6,14,18,* 1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA 3Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA 4Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA 5Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA 6Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 7Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA 8Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA 9Harvard Stem Cell Institute, Cambridge, MA 02138, USA 10Harvard Medical School, Boston, MA 02115, USA 11MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 12Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 13Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA 14Department of Systems Biology Harvard Medical School, Boston, MA 02125, USA 15Biochemistry Program, Wellesley College, Wellesley, MA 02481, USA 16These authors contributed equally 17Present address: Weizmann Institute of Science, Rehovot, Israel 18Lead Contact *Correspondence: [email protected] (J.S.), [email protected] (A.R.), [email protected] (E.S.L.) https://doi.org/10.1016/j.cell.2019.01.006 SUMMARY ramming and provides a framework applicable to diverse temporal processes in biology. Understanding the molecular programs that guide differentiation during development is a major chal- lenge. Here, we introduce Waddington-OT, an INTRODUCTION approach for studying developmental time courses to infer ancestor-descendant fates and model Waddington introduced two metaphors that shaped biological thinking about cellular differentiation: first, trains moving along the regulatory programs that underlie them. We branching railroad tracks and, later, marbles rolling through a apply the method to reconstruct the landscape developmental landscape (Waddington, 1936, 1957). Studying of reprogramming from 315,000 single-cell RNA the actual landscapes, fates, and trajectories associated with sequencing (scRNA-seq) profiles, collected at cellular differentiation and de-differentiation—in development, half-day intervals across 18 days. The results physiological responses, and reprogramming—requires us to reveal a wider range of developmental programs answer questions such as: What classes of cells are present at than previously characterized. Cells gradually each stage? What was their origin at earlier stages? What are adopt either a terminal stromal state or a mesen- their likely fates at later stages? What regulatory programs con- chymal-to-epithelial transition state. The latter trol their dynamics? gives rise to populations related to pluripotent, Approaches based on bulk analysis of cell populations are not extra-embryonic, and neural cells, with each well suited to address these questions, because they do not pro- vide general solutions to two challenges: discovering cell classes harboring multiple finer subpopulations. The anal- in a population and tracing the development of each class. ysis predicts transcription factors and paracrine The first challenge has been largely solved by the advent of signals that affect fates and experiments vali- single-cell RNA sequencing (scRNA-seq) (Tanay and Regev, date that the TF Obox6 and the cytokine GDF9 2017). The second remains a work-in-progress. Because enhance reprogramming efficiency. Our approach scRNA-seq destroys cells in the course of recording their pro- sheds light on the process and outcome of reprog- files, one cannot follow expression of the same cell and its direct 928 Cell 176, 928–943, February 7, 2019 ª 2019 Elsevier Inc. descendants across time. While various approaches can record scape of differentiation trajectories and intermediate states information about cell lineage, they currently provide only very that give rise to these diverse fates, we describe a gradual tran- limited information about a cell’s state at earlier time points (Kes- sition to either stroma-like cells or a mesenchymal-to-epithelial ter and van Oudenaarden, 2018). transition (MET) state. Trajectories emerge from the MET state Comprehensive studies of cell trajectories thus rely heavily on to iPSCs, extraembryonic cells, and neural cells. Based on the computational approaches to connect discrete ‘‘snapshots’’ into trajectories, we infer TFs predictive of various fates and suggest continuous ‘‘movies.’’ Pioneering work to infer trajectories (Sae- paracrine interactions between the stromal cells and other cell lens et al., 2018) has shed light on various biological systems, types. We experimentally showed that two top predictions including whole-organism development (Farrell et al., 2018; indeed enhance reprogramming efficiency. Wagner et al., 2018), but many important challenges remain. First, with few exceptions, most methods do not explicitly RESULTS leverage temporal information (Table S6). Historically, most were designed to extract information about stationary pro- Reconstruction of Probabilistic Trajectories by Optimal cesses, such as adult stem cell differentiation, in which all stages Transport exist simultaneously. However, time courses are becoming Our goal is to learn the relationship between ancestor cells at one commonplace. Second, many methods model trajectories in time point and descendant cells at another time point: given that terms of graph theory, which imposes strong constraints on a cell has a specific expression profile at one time point, where the model, such as one-dimensional trajectories (‘‘edges’’) and will its descendants likely be at a later time point and where zero-dimensional branch points (‘‘nodes’’). Thus, gradual diver- are its likely ancestors at an earlier time point? We model a gence of fates is not captured well by these models. Third, few differentiating population of cells as a time-varying probability methods account for cellular growth and death during develop- distribution (i.e., stochastic process) on a high-dimensional ment (Table S6). expression space. By sampling this probability distribution Pt Here, we describe a conceptual framework, implemented in a at various time points t, we wish to infer how the differentiation method called Waddington-OT, that aims to capture the notion process evolves over time (Figure 1A). From a large number of that cells at any time are drawn from a probability distribution cells at a given time point (Figure 1B), we can approximate the in gene-expression space, and each cell has a distribution of distribution at that time point, but, because different cells are both probable origins and probable fates (Figure 1). It uses sampled independently at different time points, we lose the joint scRNA-seq data collected across a time course to infer how distribution of expression between pairs of time points, called these probability distributions evolve over time, by using the temporal coupling. Absent any constraint on cellular transitions, mathematical approach of optimal transport (OT). we cannot infer the temporal coupling, but if we assume that We apply this framework to the challenge of understanding cells move short distances over short time periods, then we cellular reprogramming following transient overexpression of a can infer the temporal coupling by using the mathematical tech- set of transcription factors (TFs) (Takahashi and Yamanaka, nique of optimal transport (Figure 1A; Methods S1). 2016). We aim to address questions such as: What classes of Optimal transport was originally developed to redistribute cells arise in reprogramming? What are the developmental paths earth for the purpose of building fortifications with minimal that lead to reprogramming and to any alternative fates? Which work (Monge, 1781) and soon applied by Napoleon in Egypt. cell intrinsic factors and cell-cell interactions drive progress
Load more

Recommended publications
  • The Transcription Factor Pou3f1 Promotes Neural Fate Commitment
    RESEARCH ARTICLE elifesciences.org The transcription factor Pou3f1 promotes neural fate commitment via activation of neural lineage genes and inhibition of external signaling pathways Qingqing Zhu1,2†, Lu Song1†, Guangdun Peng1, Na Sun3, Jun Chen1, Ting Zhang1, Nengyin Sheng1, Wei Tang1, Cheng Qian1, Yunbo Qiao1, Ke Tang4, Jing-Dong Jackie Han3, Jinsong Li1, Naihe Jing1* 1State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; 2Department of Neurosurgery, West China Hospital, Sichuan University, Sichuan, China; 3Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; 4Institute of Life Science, Nanchang University, Nanchang, Jiangxi, China Abstract The neural fate commitment of pluripotent stem cells requires the repression of extrinsic inhibitory signals and the activation of intrinsic positive transcription factors. However, how these two events are integrated to ensure appropriate neural conversion remains unclear. In this study, we showed that Pou3f1 is essential for the neural differentiation of mouse embryonic stem cells (ESCs), specifically during the transition from epiblast stem cells (EpiSCs) to neural progenitor cells (NPCs). Chimeric analysis showed that Pou3f1 knockdown leads to a markedly decreased *For correspondence: njing@ incorporation of ESCs in the neuroectoderm. By contrast, Pou3f1-overexpressing ESC derivatives sibcb.ac.cn preferentially contribute to the neuroectoderm. Genome-wide ChIP-seq and RNA-seq analyses †These authors contributed indicated that Pou3f1 is an upstream activator of neural lineage genes, and also is a repressor of equally to this work BMP and Wnt signaling.
    [Show full text]
  • Using Web-Based Annotation Tools for Bioinformatic Analyses of Proteomics Data
    Using web-based annotation tools for bioinformatic analyses of proteomics data Kasper Lage, PhD Massachusetts General Hospital Harvard Medical School the Broad Institute Overview of this session 1) Biological networks 2) Annotating genetic and proteomic data using biological networks 3) Tissue-specific networks with disease resolution 4) Emerging resources at the Broad Institute What is a biological network? Jeong et al., Nature 2001 1) Gene expression correlations 2) Protein-protein interactions 3) Co-mentioning in text D 4) Synthetic lethality 5) TF binding 6) Pathway database mining 7) Epigenetic data 8) All of the above Building a human protein-protein interaction network - InWeb Email [email protected] if you want to use the data. Lage, Karlberg et al., Nat. Biotech., 2007 Lage, Hansen et al. PNAS, 2008 Lage et al., Mol Syst Biol, 2010 Rossin et al., PLoS Genetics, 2011 Lage et al., PNAS, 2012 Social human networks D are a good model for understanding biological networks Social networks People are represented by “nodes”, work related interactions by “edges” daily weekly monthly Social networks People that work together are close to each other in the network daily weekly monthly Social networks People that work together are close to each other in the network daily weekly monthly Social networks People that work together are close to each other in the network daily weekly monthly Lone< wolf Social networks People that work together are close to each other in the network Do loci connect more than a random expectation? Quantify
    [Show full text]
  • Senior Counsel's Office
    Senior Counsel’s Office In its fifth year, fiscal year 2004, the Senior Counsel’s Office is MIT’s central in‐house counsel’s office, providing comprehensive legal services and counseling on MIT matters in all areas of concern to the academy and the administration (except intellectual property law). The office represents MIT’s president, provost, executive vice president, and chancellor, as well as the deans, vice presidents, the departments, laboratories, and research centers, and administrative offices. We also arrange for and manage outside legal services when needed and oversee all litigation. Our office includes MIT’s senior counsel Jamie Lewis Keith, contracts counsel Margaret Brill, and litigation and risk management counsel Mark DiVincenzo, as well as a law clerk, Kathryn Johnson. (In FY2004 our office also included a part‐time environmental counsel. In FY2005 the office will include an associate counsel.) We are problem solvers and strategic‐thinking partners for our clients. We strive to be enablers, helping MIT’s senior officers, faculty, and staff to accomplish their mission‐critical objectives and to make informed decisions. Our office also brings the lessons learned from litigation, other disputes, and major transactions back to the MIT community through counseling and education. These lessons support management and faculty initiatives to minimize and avoid those reputational, financial, operational, and legal risks that can be appropriately managed, while still achieving MIT’s core mission. The Senior Counsel’s Office advises
    [Show full text]
  • HBS Annual Report 2008
    2 0 0 8 h a r v a r d u n i v harvard university e r s i t y f i financial report n a n c i a l r e p o r t fiscal year 2008 001215 We want all students who might dream of a Harvard “ education to know that it is a realistic and affordable option . Education is fundamental to the future of individuals and the nation, and we are determined to do our part to restore its place as an engine of opportunity, rather than a source An upperclass student in Kirkland House sketches during her free time. of financial stress…This is a huge investment for Harvard, but there is no more important commitment we could make. Excellence and opportunity must go hand in hand. ” —President Drew Gilpin Faust announcing the Middle Income Financial Aid Initiative, December 10, 2007 2 message from the president 3 financial highlights s t 8 annual report of the harvard n e t management company n o 15 report of independent auditors c f financial statements o 16 e l b 20 notes to financial statements a t Message from the President I am pleased to present Harvard University’s financial report for fiscal 2008. Under very challenging market conditions, we achieved endowment returns of 8.6%, raising the endowment to $36.9 billion. Income from the endowment contributed approximately one-third of the University’s operating budget, while also supporting substantial capital outlays. In addition, our alumni and friends contributed $690.1 million during fiscal 2008, the second highest level of fundraising receipts in the University’s history.
    [Show full text]
  • CURRICULUM VITAE – June 13, 2017
    Chris Cotsapas PhD CURRICULUM VITAE – June 13, 2017 Education ARCS Imperial College, London UK (Biochemistry) 2000 BSc Imperial College, London UK (Biochemistry) 2000 PhD University of New South Wales, Sydney, Australia (Biochemistry & Molecular Genetics) 2007 Career/Academic Appointments: 2005-2007 Research Associate, Center for Human Genetics Research, MGH (Boston, MA) 2007-2010 Research Fellow, Center for Human Genetics Research, MGH (Boston, MA) 2005-present Research Affiliate, Broad Institute of MIT and Harvard (Cambridge, MA) 2010-present Visiting Faculty, Analytical/Translational Genetics Unit, MGH (Boston, MA) 2010-2016 Assistant Professor, Dept. of Neurology, Yale School of Medicine (New Haven, CT) 2011-2016 Assistant Professor, Dept. of Genetics, Yale School of Medicine (New Haven, CT) 2011-present Research Affiliate, Stanley Center for Psychiatric Research (Boston, MA) 2016-present Associate Professor, Dept. of Neurology, Yale School of Medicine (New Haven, CT) 2016-present Associate Professor, Dept. of Genetics, Yale School of Medicine (New Haven, CT) Funding Record Current Grants Agency: NIH/NIAID I.D.# R01 AI122220 Title: “Genomics of NFkB-mediated gene regulation in multiple sclerosis” P.I.: Chris Cotsapas PhD Percent effort: 12% Total costs for project period: $1,827,500 Project period: 09/01/2015 – 08/31/2020 Agency: European Commission (Horizon 2020 program) I.D.#: 733161 Title: “MultipleMS: Multiple manifestations of genetic and non-genetic factors in multiple sclerosis disentangled with a multi-omics approach to accelerate
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
    www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1.
    [Show full text]
  • Functional Genomics Atlas of Synovial Fibroblasts Defining Rheumatoid Arthritis
    medRxiv preprint doi: https://doi.org/10.1101/2020.12.16.20248230; this version posted December 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability Xiangyu Ge1*, Mojca Frank-Bertoncelj2*, Kerstin Klein2, Amanda Mcgovern1, Tadeja Kuret2,3, Miranda Houtman2, Blaž Burja2,3, Raphael Micheroli2, Miriam Marks4, Andrew Filer5,6, Christopher D. Buckley5,6,7, Gisela Orozco1, Oliver Distler2, Andrew P Morris1, Paul Martin1, Stephen Eyre1* & Caroline Ospelt2*,# 1Versus Arthritis Centre for Genetics and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK 2Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland 3Department of Rheumatology, University Medical Centre, Ljubljana, Slovenia 4Schulthess Klinik, Zurich, Switzerland 5Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK 6NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, UK 7Kennedy Institute of Rheumatology, University of Oxford Roosevelt Drive Headington Oxford UK *These authors contributed equally #corresponding author: [email protected] NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2020.12.16.20248230; this version posted December 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
    [Show full text]
  • Boston Tech Hub Faculty Working Group Annual Report: 2019-2020
    BOSTON TECH HUB FACULTY WORKING GROUP Annual Report 2019–2020 Technology and Public Purpose Project Belfer Center for Science and International Affairs Harvard Kennedy School 79 JFK Street Cambridge, MA 02138 www.belfercenter.org/TAPP Harvard John A. Paulson School of Engineering and Applied Sciences 29 Oxford St., Cambridge, MA 02138 www.seas.harvard.edu Statements and views expressed in this report are solely those of the authors and do not imply endorsement by Harvard University, Harvard Kennedy School, Harvard Paulson School, or the Belfer Center for Science and International Affairs. Design and Layout by Andrew Facini Copyright 2020, President and Fellows of Harvard College Printed in the United States of America BOSTON TECH HUB FACULTY WORKING GROUP Annual Report 2019-2020 Table of Contents Foreword ........................................................................................................................1 FWG Members and Guests .........................................................................................5 Introduction ................................................................................................................ 13 Summary ..................................................................................................................... 14 FWG Session Briefs: Fall 2019 ................................................................................19 FWG Session Briefs: Spring 2020 ..........................................................................31 Carol Rose, Executive Director
    [Show full text]
  • Angela N. Koehler
    Angela N. Koehler Broad Institute of MIT and Harvard Office: 617-714-7364 Chemical Biology Program Fax: 617-714-8943 7 Cambridge Center Email: [email protected] Cambridge, MA 02142 Website: www.broadinstitute.org/node/2445 Education Ph.D. Chemistry, Harvard University, 2003 B.A. Biochemistry and Molecular Biology, Reed College, 1997 Research Experience Investigator, Chemical Biology Program, Broad Institute 2009-present Project & Center Manager, Broad NCI Cancer Target Discovery and Development (CTD2) Center 2010-2012 Institute Fellow, Chemical Biology Program, Broad Institute 2003-2009 Director, Ligand Discovery, NCI Initiative for Chemical Genetics (ICG) at Harvard 2003-2009 Graduate Student, Department of Chemistry and Chemical Biology, Harvard University 1998-2003 Laboratory of Professor Stuart L. Schreiber Thesis: Small molecule microarrays: A high-throughput tool for discovering protein-small molecule interactions Researcher, Department of Chemistry, California Institute of Technology 1997-1998 Laboratory of Professor Barbara Imperiali Project: Biochemical reconstitution of the oligosaccharyltransferase (OST) complex Undergraduate Researcher, Department of Chemistry, Reed College 1995-1997 Laboratory of Professor Arthur Glasfeld Co-mentored by Professor Richard G. Brennan at Oregon Health Sciences University Thesis: Biochemical and structural characterization of the tRNA-modifying enzyme QueA from Escherichia coli Teaching Experience Research Advisor to postdoctoral fellows, undergraduates, high school students, and staff scientists 2003-present Broad Institute of MIT and Harvard Instructor, Biochemical Sciences Research (BS91r) 2004-2006, 2010-2011 Harvard University Associated Instructor, Experimental Research in the Life Sciences (LS100r) 2009 Harvard University Associated Instructor, Experimental Molecular and Cellular Biology (MCB100) 2004-2006 Harvard University Teaching Fellow, Chemical Biology (Chem 170) 1999, 2000 Harvard University, Professor David R.
    [Show full text]
  • BIOGRAPHICAL SKETCH NAME: Arlene H. Sharpe Era COMMONS
    OMB No. 0925-0001 and 0925-0002 (Rev. 12/2020 Approved Through 02/28/2023) BIOGRAPHICAL SKETCH Provide the following information for the Senior/key personnel and other significant contributors. Follow this format for each person. DO NOT EXCEED FIVE PAGES. NAME: Arlene H. Sharpe eRA COMMONS USER NAME (credential, e.g., agency login): asharpe POSITION TITLE: George Fabyan Professor of Comparative Pathology EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.) DEGREE Completion (if Date FIELD OF STUDY INSTITUTION AND LOCATION applicable) MM/YYYY Radcliffe College, Cambridge, MA AB 05/1975 Biochemistry Harvard University, Cambridge, MA PhD 05/1981 Microbiology Harvard Medical School, Boston, MA MD 05/1982 Medicine A. Personal Statement My laboratory investigates T cell costimulation and its immunoregulatory functions. My laboratory focUses on the roles of T cell costimUlatory and coinhibitory pathways in regUlating immUne responses needed for the indUction and maintenance of T cell tolerance and effective antimicrobial and antitumor immunity. My laboratory has been at the forefront of this field for two decades. We have discovered T cell costimulatory pathways, and elucidated their functions, inclUding the functions of B7-1 and B7-2, CTLA-4, ICOS, PD-1 and PD-1 ligands. We are also involved in stUdies aimed at translating fUndamental Understanding of T cell costimUlation into new therapies for autoimmune diseases, viral infections, and cancer. Understanding how costimulatory pathways regulate tissUe inflammation, tolerance and anti-tumor immUnity is an important aspect of my cUrrent research.
    [Show full text]
  • Transitions in Cell Potency During Early Mouse Development Are Driven by Notch
    bioRxiv preprint doi: https://doi.org/10.1101/451922; this version posted October 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Menchero et al. Transitions in cell potency during early mouse development are driven by Notch Sergio Menchero1, Antonio Lopez-Izquierdo1, Isabel Rollan1, Julio Sainz de Aja1, Ϯ, Maria Jose Andreu1, Minjung Kang2, Javier Adan1, Rui Benedito3, Teresa Rayon1, †, Anna-Katerina Hadjantonakis2 and Miguel Manzanares1,* 1 Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Melchor Fernández Almagro 3, 28029 Madrid, Spain. 2 Developmental Biology Program, Sloan Kettering Institute, New York NY 10065, USA. 3 Molecular Genetics of Angiogenesis Group, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Melchor Fernández Almagro 3, 28029 Madrid, Spain. Ϯ Current address: Children's Hospital, Stem Cell Program, 300 Longwood Ave, Boston MA 02115, USA. † Current address: The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK. * Corresponding author ([email protected]) 1 bioRxiv preprint doi: https://doi.org/10.1101/451922; this version posted October 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Menchero et al. Abstract The Notch signalling pathway plays fundamental roles in diverse developmental processes in metazoans, where it is important in driving cell fate and directing differentiation of various cell types.
    [Show full text]