Single-Cell proteomics using inCapS to dissect cellular states through protein correlation analysis

Protein and mRNA levels within cells fluctuate to maintain cellular homeostasis, leading to proteome differences among individual cells. However, it is unknown to what extent variation in protein expression at the single cell level is a random or coordinated process, by what principles it is guided, and which proteins are involved. Therefore, here we employed a novel single-cell proteomics approach to investigate protein co-regulation in three breast cancer cell lines. This revealed distinct modules of proteins whose expression show strong co- or anti-regulation, either in all cell lines or in a cell-line specific manner, suggesting that co-regulation of specific proteins define cellular states within heterogeneous populations.

To understand protein co-regulation, we developed a robust novel proteomic workflow called inCapS (in-capillary sample preparation) for single-cell proteome analysis. This method integrates miniaturized sample preparation, low flow-rate chromatography, and state-of-the-art mass spectrometry using a timsTOF ultra. Utilizing inCapS, we achieved extensive proteome coverage of approximately 3,000 proteins in a label-free manner across 178 samples, examining proteome differences in three breast cancer cell lines: MB468, T47D, and MCF7.

We evaluated interdependencies of protein expression for the pairwise combinations of all proteins in the single-cell proteomic data sets of each cell line (926,841 combinations). This revealed distinct modules of proteins whose expression showed a strong positive correlation, such as CFL1, ATP5F1A, and CCT6A that each correlated with the expression of >140 other proteins operating in the same complex or pathway. In addition, >1000 protein pairs correlated negatively, indicating mutual exclusivity in their expression. For instance, across all three cell lines, FLI1 was consistently the most negatively correlated with 83 other proteins, including the Nop56p-associated pre-rRNA complex. Furthermore, in a GSEA analysis, SETD7 and SNRNP70 were identified as key transcription factors associated with proteins that are anti-correlated across all three cell lines. These results suggest the co-existence of cells with distinct proteome compositions, representing different cellular states. Both positively and negatively regulated protein modules only partially overlapped between the three cell lines, suggesting cell-line specific proteome regulation in individual cells. Altogether, these findings potentially underlie the distinct biological properties of the respective cell lines.

Collectively, our single-cell proteome analysis revealed coordinated protein covariation as a novel dimension of proteome regulation afforded by single-cell proteomics, and it showed the existence and properties of cellular states as a defining principle of heterogeneity within cellular populations.