Mapping transcriptomic vector fields of single cells
Name
nihms-1802444.pdf
Description
Accepted version
Size
4.51 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
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
Date Issued
2022
Journal
Cell
Publisher
Elsevier BV
Citation
Qiu, Xiaojie, Zhang, Yan, Martin-Rufino, Jorge D, Weng, Chen, Hosseinzadeh, Shayan et al. 2022. "Mapping transcriptomic vector fields of single cells." Cell, 185 (4).
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.CELL.2021.12.045
