Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China.
Nat Genet. 2022 Nov;54(11):1711-1720. doi: 10.1038/s41588-022-01197-7. Epub 2022 Oct 13.
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.
尽管人们已经做出了广泛的努力来生成和分析参考基因组，但对于大多数物种来说，仍然缺乏用于预测基因调控和细胞命运决定的遗传模型。在这里，我们生成了斑马鱼、果蝇和蚯蚓的全身体单细胞转录组图谱。然后，我们整合了来自八个代表性后生动物物种的细胞图谱，以研究跨进化的基因调控。使用这些统一构建的跨物种图谱，我们开发了一种基于深度学习的策略 Nvwa，用于在单细胞水平预测基因表达和识别调控序列。我们系统地比较了细胞类型特异性转录因子，以揭示脊椎动物和无脊椎动物中保守的遗传调控。我们的工作提供了一个有价值的资源，并为研究不同生物系统中的调控语法提供了一种新策略。
