Single-Cell Proteomics Using Mass Spectrometry

TL;DR

Single-cell proteomics using mass spectrometry is rapidly advancing, enabling detailed analysis of individual cell proteomes, overcoming technical challenges with innovative methods, and promising deeper biological insights.

Contribution

This review summarizes recent technological and computational advances in single-cell proteomics, highlighting integrated strategies to improve coverage, sensitivity, and reproducibility.

Findings

Enhanced proteome coverage and throughput

Improved data normalization and imputation methods

Emerging workflows integrating microfluidics and automation

Abstract

Single-cell proteomics (SCP) is transforming our understanding of biological complexity by shifting from bulk proteomics, where signals are averaged over thousands of cells, to the proteome analysis of individual cells. This granular perspective reveals distinct cell states, population heterogeneity, and the underpinnings of disease pathogenesis that bulk approaches may obscure. However, SCP demands exceptional sensitivity, precise cell handling, and robust data processing to overcome the inherent challenges of analyzing picogram-level protein samples without amplification. Recent innovations in sample preparation, separations, data acquisition strategies, and specialized mass spectrometry instrumentation have substantially improved proteome coverage and throughput. Approaches that integrate complementary omics, streamline multi-step sample processing, and automate workflows through microfluidics and specialized platforms promise to further push SCP boundaries. Advances in computational methods, especially for data normalization and imputation, address the pervasive issue of missing values, enabling more reliable downstream biological interpretations. Despite these strides, higher throughput, reproducibility, and consensus best practices remain pressing needs in the field. This mini review summarizes the latest progress in SCP technology and software solutions, highlighting how closer integration of analytical, computational, and experimental strategies will facilitate deeper and broader coverage of single-cell proteomes.