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Mapping Vector Field of Single Cells

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Dynamo: Mapping Vector Field of Single Cells

Inclusive model of expression dynamics with metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and potential landscape mapping.

Installation - Ten minutes to dynamo - Tutorials - API - Citation - Theory

Dynamo

Understanding how gene expression in single cells progress over time is vital for revealing the mechanisms governing cell fate transitions. RNA velocity, which infers immediate changes in gene expression by comparing levels of new (unspliced) versus mature (spliced) transcripts (La Manno et al. 2018), represents an important advance to these efforts. A key question remaining is whether it is possible to predict the most probable cell state backward or forward over arbitrary time-scales. To this end, we introduce an inclusive model (termed Dynamo) capable of predicting cell states over extended time periods, that incorporates promoter state switching, transcription, splicing, translation and RNA/protein degradation by taking advantage of scRNA-seq and the co-assay of transcriptome and proteome. We also implement scSLAM-seq by extending SLAM-seq to plate-based scRNA-seq (Hendriks et al. 2018; Erhard et al. 2019; Cao, Zhou, et al. 2019) and augment the model by explicitly incorporating the metabolic labelling of nascent RNA. We show that through careful design of labelling experiments and an efficient mathematical framework, the entire kinetic behavior of a cell from this model can be robustly and accurately inferred. Aided by the improved framework, we show that it is possible to analytically reconstruct the transcriptomic vector field from sparse and noisy vector samples generated by single cell experiments. The analytically reconstructed vector further enables global mapping of potential landscapes that reflects the relative stability of a given cell state, and the minimal transition time and most probable paths between any cell states in the state space This work thus foreshadows the possibility of predicting long-term trajectories of cells during a dynamic process instead of short time velocity estimates. Our methods are implemented as an open source tool, dynamo.

Discussion

Please use github issue tracker to report coding related issues of dynamo. For community discussion of novel usage cases, analysis tips and biological interpretations of dynamo, please join our public slack workspace: dynamo-discussion (Only a working email address is required from the slack side).

Contribution

If you want to contribute to the development of dynamo, please check out CONTRIBUTION instruction: Contribution

Acknowledgement

We would like to sincerely thank the developers of velocyto (La Manno Gioele and others), scanpy (Alex Wolf and others) and svelo (Volker Bergen and others) on their amazing tools which demonstrate the best practice of scientific programming in Python. Dynamo takes various technical inspiration from those packages. It also provides full compatibilities with them. Velocity estimations from either velocyto or scvelo can both be used as input in dynamo to learn the functional form of vector field and then to predict the cell fate over extended time period as well as to map global cell state potential.

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