High-Throughput Single-Cell Proteomics Enabled by Integrating nPOP Workflow with Quantitative Hyperplexing.

Authors

Kunpei Cai, Qing Zeng, Chuanxi Huang, Jing Yang, Fuchu He, Yun Yang

Published in

Analytical chemistry. Jul 17, 2026. Epub Jul 17, 2026.

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 nanoproteomic sample preparation (nPOP) workflow with an IBT16-TMTpro 16 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 3000 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 2000 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-TMTpro 16 quantitative hyperplexing workflow. Across four human cell lines (293T, HeLa, A549, and LM3), our quantitative hyperplexing strategy achieved over 95% labeling efficiency and consistently identified 1400 to 2000 protein groups from single cells. In comparison to conventional multiplexing methods, our hyperplexing strategy not only enhanced proteome depth but also achieved ultrahigh-throughput (∼2000 single cells per day when using Orbitrap Astral Zoom). Overall, our label-free and quantitative hyperplexing workflows provide an efficient and scalable platform for large-scale scProteomics studies and clinical applications.

PMID:
42469589
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.

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