Correlated protein modules reflecting functional coordination of interacting proteins are uncovered by single-cell proteomics

Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than single-cell transcriptomics. However, most work to date focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlations between the translational levels of any pair of proteins in a single mammalian cell. To do so, we chose label-free instead of isobaric labeling quantification for single-cell mass spectrometry in order to minimize measurement noise. In measuring pairwise correlations among ∼1,000 proteins in steady-state K562 cells, we found multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functional interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Although widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations to bulk samples, which would mask the coordination of gene expression in steady-state condition. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs. Significance Statement In a mammalian cell, thousands of proteins are expressed, barely continuously, but at fluctuating levels over time. Yet many proteins need to work together in order to perform a specific biological function. By conducting accurate mass spectrometry-based proteomics of single cells, we discovered multiple protein groups, each exhibiting positive pairwise correlations under steady-state condition. Through such observations, it can be inferred that certain proteins are synchronized to collectively carry out specific biological functions, such as protein synthesis. Our results demonstrate that single-cell proteomics can be used to probe protein-protein interactions and study how proteins work together in a system-wide approach.