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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.