This work optimized LC–MS/MS acquisition parameters for both DDA and DIA using a model microbiome, demonstrating how DIA enables increased sample throughput without compromising quantitative performance and establishing a scalable and cost-effective pipeline for metaproteomics of complex microbial communities.
Abstract
The functional complexity inherent in microbiomes complicates analytical approaches aimed at defining phenotype. As proteins are the functional effectors of microbiome phenotypes, improving the performance of mass spectrometry-based metaproteomics is critical to achieving the functional characterization of these systems. Data-independent acquisition (DIA) improves protein coverage and reduces data missingness when compared to data-dependent acquisition (DDA) in metaproteomics. However, the application of DIA to complex microbial systems remains constrained by analytical throughput and computational scalability. Here, we optimized LC–MS/MS acquisition parameters for both DDA and DIA using a model microbiome, demonstrating how DIA enables increased sample throughput without compromising quantitative performance. In addition, we demonstrated a computationally efficient, library-free DIA workflow that overcomes reliance on empirical spectral libraries. Our analytical and computational innovations establish a scalable and cost-effective pipeline for metaproteomics of complex microbial communities.
This review presents a practical, workflow-oriented guide to microbiome data analysis, from raw DNA sequence processing to statistical interpretation and biological insight, and highlights emerging technologies, including machine learning methods that are beginning to reshape the field.
Jenna Poelzer, D. Wishart· Frontiers in Microbiology· 0 citations
Metaproteomics can provide direct functional insights into complex microbial communities, yet its application in rumen research remains limited due to labor-intensive and low-throughput sample preparation workflows before the MS analysis. This work aimed to develop and characterize a streamlined, high throughput metaproteomic workflow optimized for rumen samples. Key steps, including microbial cell extraction, cell lysis, protein digestion, and LC-MS/MS acquisition, were systematically assessed and optimized to reduce hands-on time while maintaining deep proteome coverage. The optimized workflow integrates a minimized cell extraction protocol using 0.5 g starting material and in-solution tryptic digestion. Application of the final workflow to 72 samples from in vitro fermentation revealed that biological variability between inocula dominated technical variability, which remained moderate (median CV of 21-24% across batches). Overall, the optimized workflow supports robust taxonomic and functional characterization of the rumen microbiome with improved scalability. These advances provide a foundation for applying metaproteomics to larger experimental designs, including nutritional trials and cohort studies, thereby enabling broader functional interrogation of rumen microbial ecosystems. SIGNIFICANCE: This study addresses current limitations in the application of metaproteomics to rumen microbiome research by developing a streamlined and scalable sample preparation workflow. By optimizing key steps and reducing sample input while maintaining reproducibility and proteome coverage, this work enables more efficient processing of larger sample sets. These advances support the broader use of metaproteomics in rumen studies and facilitate functional investigations relevant to animal nutrition and sustainable livestock production.
A. L. Pedersen, L. Dayon, Michael Affolter et al.· Journal of Proteomics· 0 citations
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.
Feng Xian, Goran Mitulović, Ranjith Kumar Ravi Kumar et al.· Nature Communications· 0 citations
The human gut microbiome is a complex and constantly evolving community of trillions of microorganisms that are crucial to various aspects of health and disease. It impacts digestion, metabolism, immune function, neurological processes, and vulnerability to illnesses. Recent technological advancements in biology and engineering have transformed microbiome research, allowing for more detailed analysis of microbial composition, functions, and interactions with the host. This review offers a thorough overview of both current and emerging methods for studying the gut microbiome, including sample collection techniques, culture-based approaches like culturomics and microfluidics, as well as culture-independent methods such as 16S rRNA sequencing, shotgun metagenomics, and the integration of multi-omics approaches like metabolomics, proteomics, and transcriptomics. It also discusses innovative tools including single-cell genomics, spatial transcriptomics, and microbiome-on-a-chip platforms, which hold promise for revealing host-microbe interactions at unprecedented levels of detail. The review underscores the importance of combining biological insights with engineering innovations particularly microfluidics and organ-on-a-chip models to recreate gut environments that mimic physiological conditions. Additionally, it explores the potential of artificial intelligence and machine learning in analyzing data and developing predictive models for personalized microbiome-based diagnostics and therapies. Acknowledging challenges such as microbial diversity, environmental sensitivity, and technical hurdles, this review aims to guide researchers in choosing optimal tools to study the gut microbiota, deepen mechanistic understanding, and translate findings into clinical applications that enhance human health.
Divya Kaki, Uday Kore, Anusha Talari et al.· Journal of Microbiological M...· 0 citations
Application to a human sample from a patient with type 2 Diabetes Mellitus recovered a dysbiotic signature consistent with the literature, including reduced Firmicutes abundance, elevated Bacteroidetes and Proteobacteria, and a predominance of clinical associations within metabolic and gastrointestinal categories.
Rodrigo Lima Andrade, Tayná da Silva Fiúza, J. Kroll et al.· bioRxiv· 0 citations
FEDKEA, an enzyme annotation tool leveraging protein language models, and a user-friendly, FEDKEA-based metagenomic pipeline, MEnzMap, which encompasses the entire analysis workflow—from raw data quality control to function prediction and downstream analyses are designed.
Lei Zheng, Bowen Li, Siqi Xu et al.· Science Advances· 0 citations
