Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry

Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live <i>Xenopus laevis</i> (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

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Publication Details

Journal
Journal of Proteome Research
Published
2024-09-26
DOI
https://doi.org/10.1021/acs.jproteome.4c00491
Citations
11
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.65

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article

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry

Péter Nemes, Bo‐Wen Shen, Leena R. Pade
11 citations
Journal of Proteome Research
Advanced Proteomics Techniques and Applications
2.65
article

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry

Péter Nemes, Bo‐Wen Shen, Leena R. Pade
article en
11 citations

Abstract

Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live <i>Xenopus laevis</i> (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

Journal of Proteome ResearchVol. 24(4)
University of Maryland, College Park (US)
Arnold and Mabel Beckman Foundation, University of Maryland, National Institute of General Medical Sciences
Openalex Percentile: Top 9%
Advanced Proteomics Techniques and Applications
2.65
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