AI and single-cell genomics
New software predicts cell fate

Date:
August 3, 2020
Source:
Helmholtz Zentrum Mu"nchen - German Research Center for
Environmental Health
Summary:
The study of cellular dynamics is crucial to understand how cells
develop and how diseases progress. Scientist have now created
'scVelo' - a machine learning method and open source software to
estimate the dynamics of gene activity in single cells. This allows
biologists to robustly predict the future state of individual cells.



FULL STORY ========================================================================== Traditional single-cell sequencing methods help to reveal insights
about cellular differences and functions -- but they do this with
static snapshots only rather than time-lapse films. This limitation
makes it difficult to draw conclusions about the dynamics of cell
development and gene activity. The recently introduced method "RNA
velocity" aims to reconstruct the developmental trajectory of a cell
on a computational basis (leveraging ratios of unspliced and spliced transcripts). This method, however, is applicable to steady-state
populations only. Researchers were therefore looking for ways to extend
the concept of RNA velocity to dynamic populations which are of crucial importance to understand cell development and disease response.


========================================================================== Single-cell velocity Researchers from the Institute of Computational
Biology at Helmholtz Zentrum Mu"nchen and the Department of Mathematics
at TUM developed "scVelo" (single- cell velocity). The method estimates
RNA velocity with an AI-based model by solving the full gene-wise transcriptional dynamics. This allows them to generalize the concept of
RNA velocity to a wide variety of biological systems including dynamic populations.

"We have used scVelo to reveal cell development in the endocrine
pancreas, in the hippocampus, and to study dynamic processes in lung regeneration -- and this is just the beginning," says Volker Bergen,
main creator of scVelo and first author of the corresponding study in
Nature Biotechnology.

With scVelo researchers can estimate reaction rates of RNA transcription, splicing and degradation without the need of any experimental data. These
rates can help to better understand the cell identity and phenotypic heterogeneity.

Their introduction of a latent time reconstructs the unknown developmental
time to position the cells along the trajectory of the underlying
biological process. That is particularly useful to better understand
cellular decision making. Moreover, scVelo reveals regulatory changes
and putative driver genes therein. This helps to understand not only
how but also why cells are developing the way they do.

Empowering personalized treatments AI-based tools like scVelo give rise
to personalized treatments. Going from static snapshots to full dynamics
allows researchers to move from descriptive towards predictive models. In
the future, this might help to better understand disease progression
such as tumor formation, or to unravel cell signaling in response to
cancer treatment.

"scVelo has been downloaded almost 60,000 times since its release last
year. It has become a stepping-stone tooltowards the kinetic foundation
for single-cell transcriptomics," adds Prof. Fabian Theis, who conceived
the study and serves as Director at the Institute for Computational
Biology at Helmholtz Zentrums Mu"nchen and Chair for Mathematical Modeling
of Biological Systems at TUM.


========================================================================== Story Source: Materials provided by Helmholtz_Zentrum_Mu"nchen_-_German_Research_Center_for
Environmental_Health. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Volker Bergen, Marius Lange, Stefan Peidli, F. Alexander Wolf,
Fabian J.

Theis. Generalizing RNA velocity to transient cell states
through dynamical modeling. Nature Biotechnology, 2020; DOI:
10.1038/s41587-020- 0591-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200803140009.htm

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