Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity

Specht, Harrison ORCID: 0000-0003-3151-6803, Emmott, Edward ORCID: 0000-0002-3239-8178, Petelski, Aleksandra ORCID: 0000-0001-9843-8876, Huffman, Gray, Perlman, David, Serra, Marco, Kharchenko, Peter, Koller, Antonius and Slavov, Nikolai ORCID: 0000-0003-2035-1820
(2019) Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity.

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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. <h4>Abstract Figure</h4>

Item Type: Article
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 14 Mar 2022 16:33
Last Modified: 18 Jan 2023 21:10
DOI: 10.1101/665307
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