在这里，该研究团队利用单细胞蛋白质组学的最新技术进展，通过质谱（scp-MS）生成了一个包含2500多个人类CD34+造血干细胞和祖细胞的体内分化层次的scp-MS数据集。通过与scRNA-seq的整合，该研究组发现了对干细胞功能很重要的蛋白质，这些蛋白质没有被它们的mRNA转录物所指示。此外，该课题组研究人员发现建模翻译动力学可以推断分化过程中的细胞进展，并从mRNA中解释比线性相关更多的蛋白质变异。他们的工作为跨生物系统的单细胞多组学研究提供了一个框架。
据了解，单细胞转录组学（scRNA-seq）促进了细胞状态异质性的表征和分化轨迹的再现。然而，mRNA测量的专有主题有丢失重要生物信息的风险。
附：英文原文
Title: Mapping early human blood cell differentiation using single-cell proteomics and transcriptomics
Author: Benjamin Furtwngler, Nil üresin, Sabrina Richter, Mikkel Bruhn Schuster, Despoina Barmpouri, Henrietta Holze, Anne Wenzel, Kirsten Grnbk, Kim Theilgaard-Mnch, Fabian J. Theis, Erwin M. Schoof, Bo T Porse
Issue&Volume: 2025-08-21
Abstract: 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 2500 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.
DOI: adr8785
Source: https://www.science.org/doi/10.1126/science.adr8785