RT Journal Article
SR Electronic
T1 Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 665307
DO 10.1101/665307
A1 Harrison Specht
A1 Edward Emmott
A1 Aleksandra A. Petelski
A1 R. Gray Huffman
A1 David H. Perlman
A1 Marco Serra
A1 Peter Kharchenko
A1 Antonius Koller
A1 Nikolai Slavov
YR 2020
UL http://biorxiv.org/content/early/2020/12/20/665307.abstract
AB Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of limitations of quantitative single-cell protein analysis. To overcome this limitation, we developed SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantified over 3,042 proteins in 1,490 single monocytes and macrophages in ten days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. This gradient correlates to the inflammatory axis of classically and alternatively activated macrophages. Parallel measurements of transcripts by 10x Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus SCoPE2 supports quantification with improved count statistics. The joint distributions of proteins and transcripts allowed exploring regulatory interactions, such as between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. Our methodology lays the foundation for quantitative single-cell analysis of proteins by mass-spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.Competing Interest StatementThe authors have declared no competing interest.