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 instruments against label-free data-independent acquisition parallel accumulation-serial fragmentation (LFQ-diaPASEF) on a timsTOF Pro 2 mass spectrometer when applied for 2-dimensional thermal proteome profiling (2D-TPP) and Kinobeads-based chemoproteomics. Our findings demonstrate that TMT-DDA provided more confident detection of compound-induced thermal shifts despite having lower proteome coverage in high-complexity 2D-TPP datasets. In contrast, LFQ-diaPASEF excelled in low-complexity affinity enrichment experiments, achieving up to 63% broader total proteome coverage and comparable quantitative accuracy, even with ultra-short gradients. Additionally, we show that normalization strategies in DIA require careful selection to avoid artifacts when transforming IC50s into apparent dissociation constants (Kd apps). These findings emphasize that the trade-offs inherent to each platform critically impact chemoproteomics experiments and that instrument-method pairings should be selected based on experimental complexity, quantitative precision, data completeness needs, and throughput demands.
Ursula M. Glocker, Michael Steidel, Henrik M. Hammarén et al.· Proteomics· 0 citations
Recent instrumental and computational innovations in mass-spectrometry-based proteomics offer new promise in biomarker discovery, thanks to unprecedented proteome coverage and depth. Data-independent acquisition (DIA) methods are very promising in this context as they allow improved proteome coverage, reduced missing value rates, and enhanced quantification precision. However, DIA methods also suffer from their own challenges, such as increased data complexity, cycle times, and background noise. In this work, we propose a sample-aware diaPASEF method optimization strategy for a timsTOF platform. Thorough method optimizations have first been conducted on standard HeLa lysates. Then, a ground-truth calibrated sample series, consisting of a range of UPS amounts spiked into a complex Arabidopsis background, was used to mimic differential analyses under controlled conditions. These benchmark experiments demonstrate clear benefits of using narrowPASEF for differential protein discovery. Finally, our strategy was applied to real use case biological samples to conduct a differential analysis of purified mouse astrocyte cells across two different conditions. narrowPASEF improved the proteome depth by 13%, considering proteins quantified with a coefficient of variation (CV) of <20%, and led to a 68% (435 vs 729) increase in differentially expressed proteins. These results provide an opportunity for a more precise and comprehensive analysis of the biological functions of biomarkers, offering a more profound understanding of the disease mechanisms. The benefits of our sample-aware narrowPASEF strategy demonstrated the most substantial impact on low-abundance proteins. Overall, these results show promise for more valuable and robust biomarker discoveries in the future.
J. Rijal, Imane Charmarke Askar, Aurélie Hirschler et al.· Analytical Chemistry· 0 citations
Abstract Human RNA sequencing (RNA-seq) data originally generated for human transcriptome profiling are overwhelmingly dominated by host sequences, yet they often contain a small fraction of non-human reads that can be exploited for microbial detection. When such datasets are repurposed for secondary microbiome-oriented analyses, extracting and accurately classifying this weak microbial signal becomes technically challenging, and no ready-to-use pipeline currently exists. In this study, we evaluate computational strategies for filtering host reads and classifying microbial transcripts in host-dominated RNA sequencing data. We compare assembly-based approaches similar to those used in a previous study focusing on microbial translocation with state-of-the-art assembly-free methods, and assess their respective strengths and limitations using simulated datasets reflecting low microbial abundance. Our results show that assembly-based methods yield accurate taxonomic predictions but struggle at low read depth, whereas assembly-free methods are more robust in sparse settings at the cost of reduced precision. To leverage the complementarity of both approaches, we propose a hybrid pipeline that integrates assembly-based and assembly-free classification. On simulated data, this hybrid strategy improves microbial classification performance compared with either approach alone. Application to a real human metatranscriptomic dataset analyzed in a microbial translocation context illustrates the broader microbial signal captured by the hybrid approach, despite intrinsic challenges related to the absence of reliable ground truth and the risk of host read misclassification. Our 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
Large-scale metagenomic studies increasingly require taxonomic profiles that are sensitive, precise, strain-resolved and computationally tractable. Existing profilers typically trade taxonomic breadth, sensitivity, precision and speed against one another, limiting their utility for high-resolution microbiome analyses. Here we present protal — profiling through alignment — an ultra-fast alignment-based method for species-and strain-resolved profiling of metagenomes. 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. Protal reliably profiles all 143,614 bacterial and archaeal species in GTDB r226 and achieved higher precision on CAMI2 benchmarks than all tested contemporary profilers, including MetaPhlAn 4, mOTUs4, sylph and Kraken2+Bracken (mean species-level precision 98.3% versus 97.5% for the next-best profiler, sylph). In custom benchmarks, protal showed particularly strong gains for rare species represented by a single reference genome and for highly complex communities containing 10,000 species (F1-score 9% and 14% higher than the second best profiler, respectively). At the strain level, protal reconstructs intraspecific phylogenetic relationships among detected bacteria with similar accuracy as StrainPhlAn 4; because protal produces precise alignments, the phylogenies can be de novo produced without reliance on reference strain collections. Unlike dedicated strain-profiling workflows, however, protal performs strain analysis concurrently with species-level profiling, making it up to 40-fold faster without requiring additional steps. Together, these features make strain-resolved profiling of thousands of metagenomes feasible on commodity hardware. The software, databases and tutorials are available at https://github.com/4less/protal and http://protal.earlham.ac.uk.
Joachim Fritscher, A. Duncan, F. Hildebrand· bioRxiv· 0 citations
Taxonomic profiling is fundamental to microbiome research, yet achieving high species-level accuracy remains challenging for complex communities that span bacteria, viruses, eukaryotes, and archaea, and these limitations are exacerbated in low-biomass, host-dominated samples. We introduce Metax, a cross-domain taxonomic profiler that integrates coverage-based probabilistic modeling with an expectation-maximization framework to distinguish true microbial signals from artifacts. Across >600 samples from host-associated, environmental, wastewater, and low-biomass clinical settings, including benchmarks with limited reference representation, Metax improved profiling accuracy, achieving on average 55% higher F1 scores and 45% lower Bray-Curtis dissimilarity than other methods. Moreover, 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. By leveraging genome-wide coverage evidence, Metax enables robust cross-domain profiling across diverse sample types and sequencing depths, including settings where reference databases are highly incomplete.
Zhi-Luo Deng, Nasim Safaei, A. Mchardy· Cell· 0 citations
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