High-throughput Single-cell Proteomics Enabled by Integrating nPOP-workflow with Quantitative Hyperplexing

Abstract

Single-cell proteomics (scProteomics) has emerged as a powerful approach to dissect cellular heterogeneity and dynamic molecular mechanisms at unprecedented resolution. However, achieving high proteome coverage and quantitative accuracy while maintaining high-throughput remains a major challenge. In this study, we established a high-throughput scProteomics workflow that integrates a modified nano-proteomic sample preparation (nPOP) workflow with an IBT16 and TMT16 quantitative hyperplexing strategy. Through systematic optimization of chromatographic and mass spectrometric conditions, we established a label-free workflow for high-sensitivity and high quantification accuracy. On average, more than 3,000 protein groups were identified from individual 293T and HeLa cells on timsTOF SCP. When applied to single cholangiocarcinoma (CCA) cells and matched paracancerous cells dissociated from human fresh-frozen CCA tissue, approximately 2,000 protein groups were quantified per cell, revealing distinct metabolic and translational regulation patterns consistent with previously reported molecular features of CCA subtypes. To achieve high-throughput scProteomics, we then established an nPOP-based IBT16-TMT16 quantitative hyperplexing workflow. Across four human cell lines (293T, HeLa, A549, and LM3), our quantitative hyperplexing strategy achieved over 95% labelling efficiency and consistently identified 1,400 to 2,000 protein groups from single cells with a dynamic range spanning five orders of magnitude. In comparison to conventional multiplexing methods, our hyperplexing strategy not only enhanced proteome depth but also achieved ultrahigh-throughput ([~] 2,000 single cells per day if use Orbitrap Astral Zoom or timsUltra AIP etc.). Overall, our label-free and quantitative hyperplexing workflows provide an efficient and scalable platform for large scale scProteomics studies and clinical applications.