SPROUTS_DB: An Implemented Database of Contaminants for Extracellular Vesicle Proteomics Studies

Current proteomics techniques allow rapid identification and quantification of proteins within any given biological source. However, LC-MS/MS proteomics is vulnerable to laboratory and sample-associated contaminants. Therefore, accurate identification and annotation of such contaminants is crucial for development of reliable databases, especially for secretome and extracellular vesicle studies. When working in ex vivo/in vitro settings, proteins from fetal bovine serum (FBS) interfere with the proteome analysis. To address this issue, we designed SPROUTS_DB, Serum Protein Repository Of Unwanted Target(ed) Sequences DataBase, a dedicated resource to catalog serum-derived contaminants. Starting from media with EV-depleted FBS, we simulated cell growth conditions-without cells-followed by ultracentrifugation. LC-MS/MS analysis resulted in the identification of a novel set of 1288 contaminant proteins. SPROUTS_DB contains primarily soluble proteins linked to the Extracellular Region and Space, in line with the nature of the starting sample. In contrast, few membrane-associated proteins were found, confirming minimal vesicle contamination from the use of EV-depleted FBS. Finally, we demonstrated that SPROUTS_DB outperforms existing contaminants' databases, ensuring that only peptide spectra relevant to the examined sample are retained as true positive data. It is, to our knowledge, the most up-to-date resource for proteomic analysis of secretomes and EV-containing samples.

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Publication Details

Journal
PROTEOMICS
Published
2026-04-01
DOI
https://doi.org/10.1002/pmic.70128
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.00

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article

SPROUTS_DB: An Implemented Database of Contaminants for Extracellular Vesicle Proteomics Studies

Loredana Leggio, Emanuele Alpi, Antonella Di Francesco, Laura Civiero et al.
PROTEOMICS
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article

SPROUTS_DB: An Implemented Database of Contaminants for Extracellular Vesicle Proteomics Studies

Loredana Leggio, Emanuele Alpi, Antonella Di Francesco, Laura Civiero, Rosaria Saletti, Silvia Vivarelli, Greta Paternò, Vincenzo Cunsolo, Maria Gaetana Giovanna Pittalà, Elena Giusto, Nunzio Iraci
article en

Abstract

Current proteomics techniques allow rapid identification and quantification of proteins within any given biological source. However, LC-MS/MS proteomics is vulnerable to laboratory and sample-associated contaminants. Therefore, accurate identification and annotation of such contaminants is crucial for development of reliable databases, especially for secretome and extracellular vesicle studies. When working in ex vivo/in vitro settings, proteins from fetal bovine serum (FBS) interfere with the proteome analysis. To address this issue, we designed SPROUTS_DB, Serum Protein Repository Of Unwanted Target(ed) Sequences DataBase, a dedicated resource to catalog serum-derived contaminants. Starting from media with EV-depleted FBS, we simulated cell growth conditions-without cells-followed by ultracentrifugation. LC-MS/MS analysis resulted in the identification of a novel set of 1288 contaminant proteins. SPROUTS_DB contains primarily soluble proteins linked to the Extracellular Region and Space, in line with the nature of the starting sample. In contrast, few membrane-associated proteins were found, confirming minimal vesicle contamination from the use of EV-depleted FBS. Finally, we demonstrated that SPROUTS_DB outperforms existing contaminants' databases, ensuring that only peptide spectra relevant to the examined sample are retained as true positive data. It is, to our knowledge, the most up-to-date resource for proteomic analysis of secretomes and EV-containing samples.

PROTEOMICS
University of Messina (IT), Babraham Institute (GB), University of Padua (IT), University of Catania (IT), Babraham Bioscience Technologies (United Kingdom) (GB), IRCCS San Camillo Hospital (IT)
Università di Catania
Openalex Percentile: Top 13%
Extracellular vesicles in disease
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