Detection and Quantitation of Small Proteins Using Mass Spectrometry

Small proteins or microproteins, despite long being ignored, have important roles in the regulation of larger protein complexes, metabolic pathways, and gene expression. However, these proteins remain under-represented in proteomics studies because of low detection efficiency in traditional mass spectrometry (MS) workflows. Their inherent characteristics often lead to depletion during sample preparation and a low detection efficiency in LC-MS. To improve detection and quantitation, we took advantage of the large set of documented small proteins in Escherichia coli and systematically compared and optimized different sample preparation and LC-MS approaches. We evaluated different top-down and bottom-up approaches, including data-dependent acquisition, data-independent acquisition, and parallel reaction monitoring. Our data highlight the benefit of top-down proteomics for identifying new small proteins and bottom-up preparation coupled with parallel reaction monitoring acquisition for quantitation of protein levels in different growth conditions. Our systematic comparison can serve as a guideline for MS detection of small proteins in all organisms.

Authors

Institutions

Publication Details

Journal
Molecular & Cellular Proteomics
Published
2025-08-15
DOI
https://doi.org/10.1016/j.mcpro.2025.101052
Citations
2
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.51

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detection and Quantitation of Small Proteins Using Mass Spectrometry

Pedro Franco, Gisela Storz, Julian D. Langer, Rilee Zeinert et al.
2 citations
Molecular & Cellular Proteomics
Advanced Proteomics Techniques and Applications
1.51
article

Detection and Quantitation of Small Proteins Using Mass Spectrometry

Pedro Franco, Gisela Storz, Julian D. Langer, Rilee Zeinert, Jakob Meier‐Credo
article en
2 citations

Abstract

Small proteins or microproteins, despite long being ignored, have important roles in the regulation of larger protein complexes, metabolic pathways, and gene expression. However, these proteins remain under-represented in proteomics studies because of low detection efficiency in traditional mass spectrometry (MS) workflows. Their inherent characteristics often lead to depletion during sample preparation and a low detection efficiency in LC-MS. To improve detection and quantitation, we took advantage of the large set of documented small proteins in Escherichia coli and systematically compared and optimized different sample preparation and LC-MS approaches. We evaluated different top-down and bottom-up approaches, including data-dependent acquisition, data-independent acquisition, and parallel reaction monitoring. Our data highlight the benefit of top-down proteomics for identifying new small proteins and bottom-up preparation coupled with parallel reaction monitoring acquisition for quantitation of protein levels in different growth conditions. Our systematic comparison can serve as a guideline for MS detection of small proteins in all organisms.

Molecular & Cellular ProteomicsVol. 24(9)
National Institutes of Health (US), Max Planck Institute of Biophysics (DE), Eunice Kennedy Shriver National Institute of Child Health and Human Development (US)
U.S. Department of Health and Human Services, Deutsche Forschungsgemeinschaft, Max-Planck-Gesellschaft, National Institutes of Health, National Institute of General Medical Sciences, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Openalex Percentile: Top 17%
Advanced Proteomics Techniques and Applications
1.51
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.