Aug 2026· Nature Communications· Vol 17· 0 citations· 47 references
Medicine
TL;DR
DIA- and Slice-PASEF show strong quantitative reproducibility, reduced ratio compression, and consistent species-abundance scaling, while functional profiling reveals expanded annotation depth, and the two highest-scoring methods, DIA- and Slice-PASEF, capture concordant host and microbial responses.
Abstract
Metaproteomics measures functional expression in complex microbial communities, but extreme sample complexity and dynamic range challenge acquisition strategies. Trapped ion mobility spectrometry with parallel accumulation–serial fragmentation (PASEF) has expanded into multiple acquisition modes, yet systematic evaluations in high-complexity metaproteomes remain limited. Here, we benchmark five PASEF modes—DDA-, DIA-, Slice-, Synchro-, and midia-PASEF—using a complex fecal peptide background spiked with defined bacterial references. Across three gradients and input levels, 540 LC–MS acquisitions are analyzed under matched conditions. Based on data-derived performance scores, DIA-based strategies outperform DDA-PASEF in peptide and protein coverage, particularly for low-abundance microbial features. DIA- and Slice-PASEF show strong quantitative reproducibility, reduced ratio compression, and consistent species-abundance scaling, while functional profiling reveals expanded annotation depth. When tested in a murine colonic injury model, the two highest-scoring methods, DIA- and Slice-PASEF, capture concordant host and microbial responses. Metaproteomics uniquely reveals microbes’ and host cells’ function, therefore the ecosystem’s health. Decoding this complex dialogue requires optimal methods. Here, the authors benchmark five PASEF strategies and show which approaches best improve sensitivity, reproducibility, and functional insights.
Chemoproteomics aims to achieve precise and comprehensive quantification of protein-small molecule interactions, yet methodological comparisons across quantitative proteomics workflows remain scarce. Here, we systematically benchmark tandem mass tag data-dependent acquisition (TMT-DDA) on Orbitrap Exploris/Eclipse inst...
Ursula M. Glocker, Michael Steidel, Henrik M. Hammarén et al.· Proteomics· 0 citations
A sample-aware diaPASEF method optimization strategy for a timsTOF platform that demonstrates the most substantial impact on low-abundance proteins and provides an opportunity for a more precise and comprehensive analysis of the biological functions of biomarkers.
J. Rijal, Imane Charmarke Askar, Aurélie Hirschler et al.· Analytical Chemistry· 0 citations
This work provides a framework for extracting microbial signals from host-dominated human metatranscriptomes, enabling the reuse of existing transcriptomic datasets for microbiome-related analyses, including but not limited to microbial translocation studies.
Antonino Colajanni, R. Uricaru, S. Darko et al.· Briefings in Bioinformatics· 0 citations
This study addresses current limitations in the application of metaproteomics to rumen microbiome research by developing a streamlined and scalable sample preparation workflow that enables more efficient processing of larger sample sets.
A. L. Pedersen, L. Dayon, Michael Affolter et al.· Journal of Proteomics· 0 citations
Protal combines a newly developed alignment algorithm, machine-learning-based classification and conserved bacterial marker genes to profile species represented in the standardized and regularly updated GTDB taxonomy, making strain-resolved profiling of thousands of metagenomes feasible on commodity hardware.
Joachim Fritscher, A. Duncan, F. Hildebrand· bioRxiv· 0 citations
This broad evaluation demonstrated that Metax resolved bacterial and viral signatures of peri-implantitis in oral microbiomes and revealed signals suggestive of reagent-borne contaminants and reference misassemblies in plasma-cell-free DNA.
Zhi-Luo Deng, Nasim Safaei, A. McHardy· Cell· 0 citations
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