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

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.

尽管学界已在参考基因组的生成与分析方面开展了大量研究工作，但绝大多数物种仍缺乏可用于预测基因调控与细胞命运决定的遗传模型。本研究生成了斑马鱼、果蝇与蚯蚓的全身单细胞转录组图谱。随后整合了8种代表性后生动物的细胞图谱，以研究跨演化历程的基因调控机制。依托这些统一构建的跨物种细胞图谱，本研究开发了一种基于深度学习的策略Nvwa，可在单细胞水平预测基因表达并识别调控序列。本研究系统性比较了细胞类型特异性转录因子，以揭示脊椎动物与无脊椎动物中保守的遗传调控机制。本研究不仅提供了极具价值的科研资源，更为解析多样生物系统中的调控语法提供了全新策略。