Rapid advancements in transcriptomics have enabled the quantification of individual transcripts for thousands of genes in millions of single cells. By coupling a machine learning inference framework with biophysical models describing the RNA life cycle, we can explore the dynamics driving RNA production, processing and degradation across cell types.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). This paper uses quantification of unspliced and spliced mRNA molecules to trace cell lineages.
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309 (2006). This article provides experimental evidence of transcriptional bursting in mammalian cells.
Gorin, G., Vastola, J. J. & Pachter, L. Studying stochastic systems biology of the cell with single-cell genomics data. Cell Syst. 14, 822–843 (2023). A paper outlining how to model stochastic systems of single-cell omics data.
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018). This method article presents a variational autoencoder for single-cell RNA sequencing data.
Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 598, 103–110 (2021). A paper that presents and analyzes multiple single-cell transcriptomic datasets from the mouse primary motor cortex.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Carilli, M., Gorin, G., Choi, Y., Chari, T. & Pachter, L. Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data. Nat. Methods https://doi.org/10.1038/s41592-024-02365-9 (2024).
Rights and permissions
About this article
Cite this article
Studying RNA dynamics from single-cell RNA sequencing snapshots. Nat Methods 21, 1418–1419 (2024). https://doi.org/10.1038/s41592-024-02366-8
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41592-024-02366-8
