Tutorials

Affinity purification coupled with mass spectrometry (AP–MS) and proximity-dependent biotinylation identification (BioID) methods have made substantial contributions to interaction proteomics studies. Whereas AP−MS results in the identification of proteins that are in a stable complex, BioID labels and identifies proteins that are in close proximity to the bait, resulting in overlapping yet distinct protein identifications. Integration of AP–MS and BioID data has been shown to comprehensively characterize a protein’s molecular context, but interactome analysis using both methods in parallel is still labor and resource intense with respect to cell line generation and protein purification. Therefore, we developed the Multiple Approaches Combined (MAC)-tag workflow, which allows for both AP–MS and BioID analysis with a single construct and with almost identical protein purification and mass spectrometry (MS) identification procedures. We have applied the MAC-tag workflow to a selection of subcellular markers to provide a global view of the cellular protein interactome landscape. This localization database is accessible via our online platform (http://proteomics.fi) to predict the cellular localization of a protein of interest (POI) depending on its identified interactors. In this protocol, we present the detailed three-stage procedure for the MAC-tag workflow: (1) cell line generation for the MAC-tagged POI; (2) parallel AP–MS and BioID protein purification followed by MS analysis; and (3) protein interaction data analysis, data filtration and visualization with our localization visualization platform. The entire procedure can be completed within 25 d.

Recent tools addressed to accelerate the different steps of the sample treatment for protein identification in modern workflows are reviewed and critically commented in this manuscript. Heating, microspin columns, ultrasonic energy, high pressure, infrared energy, microwave energy, alternating electric fields and microreactors are outlined as useful tools that can be used to accelerate all or some of the following steps for in-gel or in-liquid based approaches for protein identification: (i) protein dissolution/denaturation, (ii) protein reduction, (iii) protein alkylation and (iv) protein digestion. The advantages and drawbacks, along with the main differences among the different tools are also commented. Future prospects for hyphenation of methods are also discussed. Researchers are informed also in this work regarding the main problems to be found when implementing any of the above mentioned methods.

Data-independent acquisition (DIA) is a powerful mass spectrometric technique to perform both protein identification and quantification of complex protein samples. Setting up DIA methods on Orbitrap analyzers requires a thorough overview of the actions the Orbitrap mass spectrometers carry out. This Tutorial is written with the intention to give an overview of the important parameters to consider as well as which measurements to carry out to get the most out of your DIA method when setting it up on an Orbitrap mass analyzer. Instead of giving the optimal DIA settings, all steps in the construction and optimization of the DIA method are shown and discussed in a way that allows tailored DIA methods. They key steps are building the spectral library after sample fractionation, deciding upon the number of data points per chromatographic peak, determining the scan times of each mass spectrometric step, constructing various DIA methods using these data, and evaluating their performance. This proposed DIA method development strategy was tested on digested lysates from Pseudomonas aeruginosa and compared with conventional DDA analysis to put the DIA results into perspective.

Significant technical advancements in phosphopeptide enrichment have enabled the identification of thousands of p-peptides (mono and multiply phosphorylated) in a single experiment. However, it is still not possible to enrich all p-peptide species in a single step. A range of new techniques and materials has been developed, with the potential to provide a step-change in phosphopeptide enrichment. The first half of this review contains a tutorial for new potential phosphoproteomic researchers; discussing the key steps of a typical phosphoproteomic experiment used to investigate canonical phosphorylation sites (serine, threonine and tyrosine). The latter half then show-cases the latest developments in p-peptide enrichment including: i) Strategies to mitigate non-specific binding in immobilized metal ion affinity chromatography and metal oxide affinity chromatography protocols; ii) Techniques to separate multiply phosphorylated peptides from monophosphorylated peptides (including canonical from non-canonical phosphorylated peptides), or to simultaneously co-enrich other post-translational modifications; iii) New hybrid materials and methods directed towards enhanced selectivity and efficiency of metal-based enrichment; iv) Novel materials that hold promise for enhanced phosphotyrosine enrichment. A combination of well-understood techniques and materials is much more effective than any technique in isolation; but the field of phosphoproteomics currently requires benchmarking of novel materials against current methodologies to fully evaluate their utility in peptide based proteoform analysis.

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography–mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Effectively dealing with batch effects will be the next frontier in large-scale biological data analysis, particularly involving the integration of different data sets.

Given how batch-effect correction exaggerates cross-validation outcomes, cross-validation is becoming considered a less authoritative form of evaluation.

Batch effect-resistant methods will become important in the future, alongside existing batch effect-correction methods.

Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, “proBatch”, containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

Mass spectrometry based metaproteomics is a relatively new field of research that provides the ability to characterize the functionality of microbiota. Recently, we were the first to demonstrate the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the conventionally used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the previous method still required additional DDA data on the samples to assist the DIA analysis. Here, we introduce, for the first time, a DIA metaproteomics approach that does not require any DDA data, but instead replaces a spectral library generated from DDA data with a pseudospectral library generated directly from the metaproteomics DIA samples. We demonstrate that using the new DIA-only approach, we can achieve higher peptide yields than with the DDA-assisted approach, while the amount of required mass spectrometry data is reduced to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as open-source software package DIAtools 2.0, which is freely available from DockerHub.

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.

Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

The human gut microbiota, a complex, dynamic and biodiverse community, has been increasingly shown to influence many aspects of health and disease. Metaproteomic analysis has proven to be a powerful approach to study the functionality of the microbiota. However, the processing and analyses of metaproteomic mass spectrometry data remains a daunting task in metaproteomics data analysis. We developed iMetaLab, a web based platform to provide a user-friendly and comprehensive data analysis pipeline with a focus on lowering the technical barrier for metaproteomics data analysis.

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.

A popular artificial-intelligence method provides a powerful tool for surveying and classifying biological data. But for the uninitiated, the technology poses significant difficulties.