Evidence map›Paper›PMID 42918804›Full record

ArticleNature communications2026

High-throughput label-free single-cell proteomics enabled by multicolumn NanoLC.

Chao Wang, Hsien-Jung L Lin, Siqi Huang, Garrett D Haynie, Kenneth S Triggs, Ella E Norton, R J S Miercort, Yen-Jou Chang, David V Hansen, Thy Truong and 2 more

Abstract read
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Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Chao Wang *Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.
Hsien-Jung L Lin *Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.ORCID http://orcid.org/0000-0003-4704-3692
Siqi HuangDepartment of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.
Garrett D HaynieDepartment of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.ORCID http://orcid.org/0009-0004-4547-5330
Kenneth S TriggsDepartment of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.ORCID http://orcid.org/0009-0001-5091-7058
Ella E NortonDepartment of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.
R J S MiercortDepartment of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.
Yen-Jou ChangDepartment of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.
David V HansenDepartment of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.
Thy TruongMicrOmics Technologies, LLC, Spanish Fork, UT, USA.
Xiaofeng XieMicrOmics Technologies, LLC, Spanish Fork, UT, USA.
Ryan T KellyDepartment of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA. ryan.kelly@byu.edu.ORCID http://orcid.org/0000-0002-3339-4443

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 small 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. The system provides stable peptide separation, negligible carryover, and robust retention-time reproducibility across nearly 1200 consecutive injections. We successfully analyze more than 4000 samples at nearly 288 samples per day (SPD) throughput. The platform identifies ~5000 proteins per 250 pg digest injection and an average of ~4000 proteins per single HeLa cell with maxima exceeding 5100, which is comparable to state-of-the-art longer-gradient workflows. Quantitative benchmarking with mixed-species standards confirm the quantitative accuracy is largely maintained during extended acquisition. Applying the workflow to 783 LPS-treated RAW264.7 macrophages reveals substantial cellular heterogeneity under a uniform inflammatory stimulus. Together, these results establish a robust and scalable platform for high-throughput SCP, enabling thousands of single-cell analyses within a single study while maintaining deep proteome coverage and biological interpretability.

Indexed as

Liquid Chromatography-Mass SpectrometryNanotechnologyProteomeProteomicsSingle-Cell AnalysisAnimalsHeLa CellsHigh-Throughput Screening AssaysHumansLipopolysaccharidesMacrophagesMass SpectrometryMiceRAW 264.7 CellsReproducibility of ResultsLipopolysaccharidesProteome

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Registered trials

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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.