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Mapping transcriptomic vector fields of single cells.
Qiu, Xiaojie; Zhang, Yan; Martin-Rufino, Jorge D; Weng, Chen; Hosseinzadeh, Shayan; Yang, Dian; Pogson, Angela N; Hein, Marco Y; Hoi Joseph Min, Kyung; Wang, Li; Grody, Emanuelle I; Shurtleff, Matthew J; Yuan, Ruoshi; Xu, Song; Ma, Yian; Replogle, Joseph M; Lander, Eric S; Darmanis, Spyros; Bahar, Ivet; Sankaran, Vijay G; Xing, Jianhua; Weissman, Jonathan S.
Afiliação
  • 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
  • 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; Halicioglu 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 A
  • 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.
Cell ; 185(4): 690-711.e45, 2022 02 17.
Article em En | MEDLINE | ID: mdl-35108499
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Análise de Célula Única / Transcriptoma Tipo de estudo: Prognostic_studies Limite: Female / Humans Idioma: En Revista: Cell Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Análise de Célula Única / Transcriptoma Tipo de estudo: Prognostic_studies Limite: Female / Humans Idioma: En Revista: Cell Ano de publicação: 2022 Tipo de documento: Article