Cited by 57Single-cell and spatial transcriptomicsGenomics and Chromatin DynamicsGene Regulatory Network Analysis

Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types

Li, Jiaqi, Wang, Jingjing, Zhang, Peijing, Wang, Renying, Mei, Yuqing, Sun, Zhongyi, Fei, Lijiang, Jiang, Mengmeng +21 more·2022·Nature Genetics

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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Crossref · 10.1038/s41588-022-01197-7

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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”

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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
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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