Mapping early human blood cell differentiation using single-cell proteomics and transcriptomics

Single-cell transcriptomics (scRNA-seq) has facilitated the characterization of cell state heterogeneity and recapitulation of differentiation trajectories. However, the exclusive use of mRNA measurements comes at the risk of missing important biological information. Here we leveraged recent technological advances in single-cell proteomics by Mass Spectrometry (scp-MS) to generate an scp-MS dataset of an in vivo differentiation hierarchy encompassing over 2,500 human CD34+ hematopoietic stem and progenitor cells. Through integration with scRNA-seq, we identified proteins that are important for stem cell function, which were not indicated by their mRNA transcripts. Further, we showed that modeling translation dynamics can infer cell progression during differentiation and explain substantially more protein variation from mRNA than linear correlation. Our work offers a framework for single-cell multi-omics studies across biological systems.单细胞转录组测序（scRNA-seq）已推动细胞状态异质性的解析与分化轨迹的重构。然而，仅依赖mRNA检测会面临丢失关键生物学信息的风险。本研究依托近年来基于质谱的单细胞蛋白质组学（scp-MS）技术进展，构建了涵盖2500余例人类CD34+造血干细胞及祖细胞的体内分化层级scp-MS数据集。通过与scRNA-seq数据整合分析，我们鉴定出一批对干细胞功能至关重要的蛋白质，而此类蛋白质的mRNA转录本并未体现其功能相关性。此外，我们证实，翻译动力学建模能够推断分化过程中的细胞演进进程，且相较于线性相关性分析，该模型可从mRNA层面解释更多的蛋白质表达变异。本研究为跨生物系统的单细胞多组学研究提供了一套分析框架。