The microbiologist's guide to metaproteomics

Abstract

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

Keywords: bioinformatics; functional dynamics; mass spectrometry; metaproteomics; microbiome.

Figure 1

Overview of metaproteomics within the multi‐meta‐omics toolbox applied to diverse microbiome research domains. This figure highlights the role of metaproteomics in identifying proteins, quantifying their abundances, detecting posttranslational modifications (PTMs), mapping protein–protein interactions (PPIs), and determining protein localizations. Metaproteomics complements other omics approaches, including metagenomics, metatranscriptomics, and metabolomics, to provide a comprehensive understanding of microbial systems. Examples of microbiome research domains include the human microbiome (oral, skin, gut, lung, and vaginal), animal microbiomes (farm, wild, and laboratory animals), environmental microbiomes (soil and ocean), and special sample sources (e.g., ancient microbiome samples).

Figure 2

Overview of key principles and workflows in metaproteomics. A typical metaproteomics workflow begins with experimental design (Section Experiment design), followed by sample collection, preservation, and preprocessing (Sections Sample collection, preservation, and storage prior to before preprocessing to Sample preprocessing). Microbial cells undergo enrichment, lysis, protein extraction, and peptide separation, processed either manually (Sections Protein sample preparation: From extraction to digestion to Separation and fractionation techniques) or automated (Section Automation) before mass spectrometry data acquisition (Section Mass spectrometry data acquisition methods). Finally, bioinformatics analysis (Section Computational analysis of metaproteomics data) performs database searches and interprets the data to reveal microbial functions and ecological insights.

Figure 3

Metaproteomic experimental designs and their comparison with metagenomics in studying microbiome dynamics. (A) Overview of common metaproteomic experimental designs. The left panel illustrates the comparison of microbial protein expression between species within a unique sample source, lacking a control. The middle panel compares microbiomes under varying conditions, such as drug treatments, using ex vivo microbiomes to assess microbial responses. The right panel shows longitudinal studies that monitor temporal changes in microbial protein expression over time. (B) Metagenomic responses to perturbations, showing shifts in taxonomic composition while assuming genome content remains relatively constant. (C) Metaproteomic responses to perturbations, showing changes in both taxonomic composition and proteome content. This approach captures microbial abundances and their functional contributions, providing deeper insights into microbiome dynamics.

Figure 4

Principle of target‐decoy analysis and false discovery rate (FDR) calculation. (Top) The experimentally obtained MS/MS spectra are matched to in silico generated spectra of the concatenated target/decoy protein sequence database. (Middle) For each obtained spectrum, the match with the highest score is retained, together with the assigned in silico digested (ISD) peptide sequence and its target or decoy label. (Bottom) The score distribution is used to select which peptide‐spectrum matches (PSMs) will be considered as true matches. The metric to control the false positives is the FDR, and is calculated as the number of decoy PSMs divided by the number of target PSMs (in the Figure depicted as area B divided by the sum of areas B and A). Figure of (schematic) target/decoy distribution adjusted from Käll et al. [253].

Figure 5

Practical example of (sub)grouping approaches. This grouping case deals with distant group members, meaning that certain proteins in the group don't share a single peptide, in this case proteins 1 and 3. Applying the rule of parsimony separates the group in this specific case. In the anti‐Occam case, protein 2 remains in a separate subgroup.

Figure 6

Calculation of the lowest common ancestor (LCA) for a tryptic peptide. In this figure, the hypothetical Peptide 1 is present in eight different proteins, which are associated with seven distinct organisms. The LCA for these organisms is identified as the hypothetical Family 1. Figure adjusted from Van Den Bosschee et al. [295].

Figure 7

Metaproteomics downstream data analysis “cheat sheet.” (A) Main domains of questions that metaproteomics downstream analysis cares about. (B) Identify desired insight levels to facilitate analysis strategy selection. (C) Proper choice of data pre‐processing workflow. (D) Selection of data analysis method set.