Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types
Abstract
Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.
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In 2022, Li et al. developed Nvwa, a deep-learning-based strategy, which predicts gene expression and identifies conserved regulatory programs underlying cell types at the cross-species single-cell level.
“In 2022, Li et al. developed Nvwa, a deep-learning-based strategy, which predicts gene expression and identifies conserved regulatory programs underlying cell types at the cross-species single-cell level.373”
From Inflammation and aging: signaling pathways and intervention therapies (2023) · cites this paper as [373]
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Self-citationThe evolution of gene regulation in mammalian cerebellum development
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Self-citationIdentification, characterization, and design of plant genome sequences using deep learning
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Self-citationDecoding maize meristems maintenance and differentiation: integrating single-cell and spatial omics
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Self-citationUNICORN: Towards universal cellular expression prediction with a multi-task learning framework
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Self-citationFamily single-cell atlases reveal pig pregnancy and fetal growth restriction critical cell types
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Self-citationSekwencjonowanie transkryptomu pojedynczych komórek: droga do odkrywania bioróżnorodności komórkowej
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Self-citationAlternative integrative systems in nerveless animals: Chemoconnectomics before synapses
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Self-citationDanioDecima: A DNA sequence-to-function model of zebrafish embryogenesisPreprint
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“Similarly, the Nvwa framework predicts single-cell gene expression landscapes across zebrafish, fruit fly, and earthworm using multi-species single-cell atlases [17].”
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Self-citationA Comprehensive Comparative Analysis of Sequence‐Based Deep Learning Models for Single‐Cell Genomics
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Self-citation
- Authors
- Li, J., Wang, J., Zhang, P., Wang, R., Mei, Y., Sun, Z., Fei, L., Jiang, M., Ma, L., E, W., Chen, H., Wang, X., Fu, Y., Wu, H., Liu, D., Wang, X., Li, J., Guo, Q., Liao, Y., Yu, C., Jia, D., Wu, J., He, S., Liu, H., Ma, J., Lei, K., Chen, J., Han, X., & Guo, G.
- Affiliations
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University-University of Edinburgh Institute (CN)
- Zhejiang University (CN)
- Westlake University (CN)
- Corresponding author
- Wang, Jingjing
- Han, Xiaoping
- Guo, Guoji
- ORCID
- Venue
- Nature Genetics
- Published
- 2022-10-13
- Volume
- 54
- Issue
- 11
- Pages
- 1711-1720
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W4305014070
- Cited by
- 57
- References
- 63
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 16 · 2023: 10 · 2022: 1
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (31930028, 91842301, 32000461, 31922049)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- gene expression regulation, gene regulatory evolution, cell type-specific transcription factors, zebrafish, Drosophila, earthworms
- MeSH terms
- Deep Learning, Animals, Drosophila, Gene Expression Regulation, Transcription Factors, Zebrafish, Conserved Sequence
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