丹麦哥本哈根大学Joshua M. Brickman课题组发现，基于单细胞RNA测序的深度学习模型可用于小鼠和人类胚胎的植入前分析。这一研究成果于2024年11月14日在线发表在国际学术期刊《自然—方法学》上。

研究人员利用了一套深度学习工具来整合和分类多个数据集。这能够定义小鼠和人类胚胎的细胞类型、谱系和状态，从而最大化研究者可以从这些宝贵实验资源中获得的信息。该方法基于最近的大规模人类器官图谱的倡议，但研究人员专注于难以获得和处理的材料，涵盖了小鼠和人类的早期发育。

利用这些阶段的公开数据，研究人员测试了不同的深度学习方法，并开发了一种模型，以无偏的方式分类细胞类型，同时定义模型用于识别谱系、细胞类型和状态的基因集。

研究人员使用在体内发育上训练的模型来分类小鼠和人类发育的多能干细胞模型，展示了这一资源作为早期胚胎发生动态参考的重要性。

据介绍，单细胞转录组技术的快速发展产生了越来越多的，关于胚胎发育和体外多能干细胞衍生模型的数据集。围绕多能性和谱系特化过程的大量数据，使得在体内定义特定的细胞类型或状态变得愈加困难，也使得与体外分化过程进行比较变得复杂。

附：英文原文

Title: Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing

Author: Proks, Martin, Salehin, Nazmus, Brickman, Joshua M.

Issue&Volume: 2024-11-14

Abstract: The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process of lineage specification has meant it has become increasingly difficult to define specific cell types or states in vivo, and compare these with in vitro differentiation. Here we utilize a set of deep learning tools to integrate and classify multiple datasets. This allows the definition of both mouse and human embryo cell types, lineages and states, thereby maximizing the information one can garner from these precious experimental resources. Our approaches are built on recent initiatives for large-scale human organ atlases, but here we focus on material that is difficult to obtain and process, spanning early mouse and human development. Using publicly available data for these stages, we test different deep learning approaches and develop a model to classify cell types in an unbiased fashion at the same time as defining the set of genes used by the model to identify lineages, cell types and states. We used our models trained on in vivo development to classify pluripotent stem cell models for both mouse and human development, showcasing the importance of this resource as a dynamic reference for early embryogenesis.

DOI: 10.1038/s41592-024-02511-3

Source: https://www.nature.com/articles/s41592-024-02511-3