ArticleResearch square2025
High-Throughput Label-free Single-Cell Proteomics Enabled by Multicolumn NanoLC with a 5-min Cycle Time.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
10 authors.
Funding
Abstract
Mass spectrometry (MS)-based single-cell proteomics (SCP) enables proteome-wide analysis at single-cell resolution, offering insights into cellular heterogeneity, biological processes, and disease mechanisms. However, conventional nanoLC-MS workflows are constrained by long gradients and low throughput, limiting their application to large cohorts. Here, we present a multicolumn nanoLC-MS platform that achieves 5-minute separation windows with 100% duty cycles at ~100 nL/min, enabling the analysis of up to 288 single cells per day with minimal additional hardware. The system provides stable peptide separation, negligible carryover, and robust retention-time reproducibility across 2,000 consecutive injections. Over the course of this study, we successfully analyzed more than 4,000 samples at nearly 288 samples per day (SPD) throughput. The platform identified ~4,400 proteins per 250 pg digest injection and an average of ~3,200 proteins per single HeLa cell with maxima exceeding 4,300, which is comparable to state-of-the-art longer-gradient workflows. Quantitative benchmarking with mixed-species standards confirmed accurate measurements across ~6,000 proteins. The workflow distinguished proteome profiles across hundreds of single cells, and profiling of RAW264.7 macrophages revealed LPS-induced markers and macrophage activation pathways. Together, these results establish a robust and scalable platform for high-throughput SCP, demonstrating the feasibility of thousands of single-cell analyses within a single study while maintaining deep proteome coverage and biological interpretability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.