Mapping transcriptomic vector fields of single cells

Xiaojie Qiu; Yan Zhang; Jorge D Martin-Rufino; Chen Weng; Shayan Hosseinzadeh; Dian Yang; Angela N Pogson; Marco Y Hein; Kyung Hoi Joseph Min; Li Wang; Emanuelle I Grody; Matthew J Shurtleff; Ruoshi Yuan; Song Xu; Yian Ma; Joseph M Replogle; Eric S Lander; Spyros Darmanis; Ivet Bahar; Vijay G Sankaran; Jianhua Xing; Jonathan S Weissman

doi:10.1016/j.cell.2021.12.045

Mapping transcriptomic vector fields of single cells

Cell. 2022 Feb 17;185(4):690-711.e45. doi: 10.1016/j.cell.2021.12.045. Epub 2022 Feb 1.

Authors

Xiaojie Qiu¹, Yan Zhang², Jorge D Martin-Rufino³, Chen Weng⁴, Shayan Hosseinzadeh⁵, Dian Yang⁶, Angela N Pogson⁶, Marco Y Hein⁷, Kyung Hoi Joseph Min⁸, Li Wang⁹, Emanuelle I Grody¹⁰, Matthew J Shurtleff¹¹, Ruoshi Yuan¹², Song Xu¹³, Yian Ma¹⁴, Joseph M Replogle¹⁵, Eric S Lander¹⁶, Spyros Darmanis¹⁷, Ivet Bahar², Vijay G Sankaran³, Jianhua Xing¹⁸, Jonathan S Weissman¹⁹

Affiliations

¹ Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: [email protected].
² 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.
³ 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.
⁴ 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.
⁵ Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
⁶ Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA.
⁸ 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.
⁹ Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA.
¹⁰ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹¹ Lycia Therapeutics, South San Francisco, San Francisco, CA, USA.
¹² California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA.
¹³ Microsoft, Redmond, WA, USA.
¹⁴ Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, USA.
¹⁵ 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.
¹⁶ 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.
¹⁷ Genentech Inc., South San Francisco, CA, USA.
¹⁸ 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: [email protected].
¹⁹ 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: [email protected].

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.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Female
Gene Expression Regulation
HL-60 Cells
Hematopoiesis / genetics
Hematopoietic Stem Cells / metabolism
Humans
Kinetics
Models, Biological
RNA, Messenger / metabolism
Single-Cell Analysis*
Staining and Labeling
Transcriptome / genetics*

Substances

RNA, Messenger

Abstract

Publication types

MeSH terms

Substances

Grants and funding