Mapping transcriptomic vector fields of single cells.
Mapping transcriptomic vector fields of single cells.
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DOI:
10.1016/j.cell.2021.12.045
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发表时间:
2022-02-17
期刊:
影响因子:
64.5
通讯作者:
Weissman, Jonathan S.
中科院分区:
文献类型:
--
作者:
Qiu, Xiaojie;Zhang, Yan;Martin-Rufino, Jorge D.;Weng, Chen;Hosseinzadeh, Shayan;Yang, Dian;Pogson, Angela N.;Hein, Marco Y.;Min, Kyung Hoi (Joseph);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.
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, that infers absolute RNA velocity, reconstructs continuous vector-field functions 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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影响因子:
8.8
作者:
Capellera-Garcia S;Pulecio J;Dhulipala K;Siva K;Rayon-Estrada V;Singbrant S;Sommarin MN;Walkley CR;Soneji S;Karlsson G;Raya Á;Sankaran VG;Flygare J
通讯作者:
Flygare J
影响因子:
24.8
作者:
Drissen, Roy;Thongjuea, Supat;Nerlov, Claus
通讯作者:
Nerlov, Claus
影响因子:
56.9
作者:
Battich, Nico;Beumer, Joep;van Oudenaarden, Alexander
通讯作者:
van Oudenaarden, Alexander
影响因子:
64.5
作者:
Dixit, Atray;Pamas, Oren;Li, Biyu;Chen, Jenny;Fulco, Charles P.;Jerby-Amon, Livnat;Marjanovic, Nemanja D.;Dionne, Danielle;Burks, Tyler;Raychowdhury, Raktima;Adamson, Britt;Norman, Thomas M.;Lander, Eric S.;Weissman, Jonathan S.;Friedman, Nir;Regev, Aviv
通讯作者:
Regev, Aviv
影响因子:
64.8
作者:
Tabula Muris Consortium
通讯作者:
Tabula Muris Consortium