The ability to predict gene-expression landscapes at single-cell resolution has long been a challenge in the field of genomics. We mapped whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila, and earthworm using Microwell-seq. We propose the first sequence-based model, Nvwa, that can predict gene expression at single-cell resolution directly from genomic sequences.
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
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Eraslan, G., Avsec, Z., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019). This review article describes the computational and modeling methods used in genomics.
Zhou, J. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat .Genet. 50, 1171–1179 (2018). This paper reports the ExPecto model for predicting variant effects on gene expression.
Avsec, Z. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021). This paper reports the Enformer model for gene-expression prediction through learning long-range information.
Arendt, D. et al. The origin and evolution of cell types. Nat. Rev. Genet. 17, 744–757 (2016). This review article proposes a new evolutionary definition of cell types and their regulatory mechanisms.
Wang, J. et al. Tracing cell-type evolution by cross-species comparison of cell atlases. Cell Rep. 34, 108803 (2021). This paper reports the conservation and divergence of cell types using single-cell transcriptomic landscapes.
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: Li, J. et al. Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types. Nat. Genet. https://doi.org/10.1038/s41588-022-01197-7 (2022).
Rights and permissions
About this article
Cite this article
Deciphering single-cell transcriptional programs across species. Nat Genet 54, 1595–1596 (2022). https://doi.org/10.1038/s41588-022-01198-6
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-022-01198-6
