دورية أكاديمية
أعرض في EDS

Mapping transcriptomic vector fields of single cells.

التفاصيل البيبلوغرافية
العنوان: Mapping transcriptomic vector fields of single cells.
المؤلفون: Qiu X; Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: xqiu@wi.mit.edu., Zhang Y; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA., Martin-Rufino JD; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA., Weng C; Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA., Hosseinzadeh S; Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA., Yang D; Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA., Pogson AN; Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA., Hein MY; Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA., Hoi Joseph Min K; Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA., Wang L; Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA., Grody EI; Broad Institute of MIT and Harvard, Cambridge, MA, USA., Shurtleff MJ; Lycia Therapeutics, South San Francisco, San Francisco, CA, USA., Yuan R; California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA., Xu S; Microsoft, Redmond, WA, USA., Ma Y; Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, USA., Replogle JM; Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Medical Scientist Training Program, University of California, San Francisco, CA, USA., Lander ES; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Systems Biology Harvard Medical School, Boston, MA 02125, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA., Darmanis S; Genentech Inc., South San Francisco, CA, USA., Bahar I; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA., Sankaran VG; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA., Xing J; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA; UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA; Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: xing1@pitt.edu., Weissman JS; Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA. Electronic address: weissman@wi.mit.edu.
المصدر: Cell [Cell] 2022 Feb 17; Vol. 185 (4), pp. 690-711.e45. Date of Electronic Publication: 2022 Feb 01.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Cell Press Country of Publication: United States NLM ID: 0413066 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-4172 (Electronic) Linking ISSN: 00928674 NLM ISO Abbreviation: Cell Subsets: MEDLINE
أسماء مطبوعة: Publication: Cambridge, Ma : Cell Press
Original Publication: Cambridge, MIT Press.
مواضيع طبية MeSH: Single-Cell Analysis*, Transcriptome/*genetics, Algorithms ; Female ; Gene Expression Regulation ; HL-60 Cells ; Hematopoiesis/genetics ; Hematopoietic Stem Cells/metabolism ; Humans ; Kinetics ; Models, Biological ; RNA, Messenger/metabolism ; Staining and Labeling
مستخلص: Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.
Competing Interests: Declaration of interests E.S.L. is currently on leave from MIT and Harvard to serve as the Director of the White House Office of Science and Technology Policy. J.S.W. declares outside interest in 5 AM Venture, Amgen, Chroma Medicine, KSQ Therapeutics, Maze Therapeutics, Tenaya Therapeutics, Tessera Therapeutics, and Third Rock Ventures. V.G.S. serves as an advisor to and/or has equity in Novartis, Forma, Cellarity, Ensoma, and Branch Biosciences. All interests mentioned earlier are unrelated to this work.
(Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
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معلومات مُعتمدة: RM1 HG009490 United States HG NHGRI NIH HHS; R01 DK103794 United States DK NIDDK NIH HHS; P41 GM103712 United States GM NIGMS NIH HHS; R01 DK119232 United States DK NIDDK NIH HHS; R37 CA232209 United States CA NCI NIH HHS; United States HHMI Howard Hughes Medical Institute
فهرسة مساهمة: Keywords: RNA Jacobian; RNA metabolic labeling; cell-fate transitions; differential geometry analysis; dynamical systems theory; dynamo; hematopoiesis; in silico perturbation; least action path; vector field reconstruction
المشرفين على المادة: 0 (RNA, Messenger)
تواريخ الأحداث: Date Created: 20220202 Date Completed: 20220309 Latest Revision: 20220731
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC9332140
DOI: 10.1016/j.cell.2021.12.045
PMID: 35108499
قاعدة البيانات: MEDLINE
الوصف
تدمد:1097-4172
DOI:10.1016/j.cell.2021.12.045