Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut MicrobiotaImplementation and Computational Analysis

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of proteome research 2020-01, Vol.19 (1), p.432-436
Hauptverfasser: Aakko, Juhani, Pietilä, Sami, Suomi, Tomi, Mahmoudian, Mehrad, Toivonen, Raine, Kouvonen, Petri, Rokka, Anne, Hänninen, Arno, Elo, Laura L
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof of concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package, diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratory-assembled microbial mixtures as well as human fecal samples.
ISSN:1535-3893
1535-3907
DOI:10.1021/acs.jproteome.9b00606