Neat plasma proteomics: getting the best out of the worst.
DIA-NN
Extracellular vesicles (EVs)
Neat plasma
Plasma proteomics
diaPASEF
Journal
Clinical proteomics
ISSN: 1542-6416
Titre abrégé: Clin Proteomics
Pays: England
ID NLM: 101184586
Informations de publication
Date de publication:
12 Mar 2024
12 Mar 2024
Historique:
received:
11
01
2024
accepted:
29
02
2024
medline:
13
3
2024
pubmed:
13
3
2024
entrez:
13
3
2024
Statut:
epublish
Résumé
Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.
Identifiants
pubmed: 38475715
doi: 10.1186/s12014-024-09477-6
pii: 10.1186/s12014-024-09477-6
doi:
Types de publication
Journal Article
Langues
eng
Pagination
22Informations de copyright
© 2024. The Author(s).
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