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

Abstract

Single-cell RNA sequencing (scRNA-seq) has facilitated the characterization of cell state heterogeneity and recapitulation of differentiation trajectories. However, the exclusive use of messenger RNA (mRNA) measurements comes at the risk of missing important biological information. 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 >2500 human CD34+ hematopoietic stem and progenitor cells. Through integration with scRNA-seq, we identified proteins 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 multiomics studies across biological systems.