ABSTRACT Posttranslational modifications (PTMs) play critical regulatory roles in cellular processes, and their dysregulation is implicated in numerous diseases and pathological conditions. Their importance to biological functions has spawned many efforts to accurately identify and quantify protein modifications using high‐throughput methods, especially liquid chromatography tandem‐mass spectrometry (LC‐MS/MS). The typically low stoichiometry of PTMs in biological samples often necessitates enrichment strategies to achieve sufficient analytical sensitivity. However, the growing number and diversity of published enrichment methods have made it challenging to evaluate key methodological differences and their respective advantages. This review examines enrichment strategies for the major PTM classes: phosphorylation, acetylation, ubiquitination, SUMOylation, methylation, glycosylation, and ADP‐ribosylation. Immunoaffinity‐based, chromatographic, and chemical derivatization approaches are compared for each class in terms of their underlying principles and practical trade‐offs. Based on published methods, the review considers enrichment specificity, coverage depth, sample input requirements, and workflow compatibility, with the aim of providing a consolidated reference to guide method selection and optimization for single‐ and multi‐PTM profiling.
ABSTRACT Spatial heterogeneity is a defining feature of tissue organization, yet conventional proteomic analysis provides limited information about the spatial origins of identified proteins. Spatially guided proteomics addresses this limitation by using tissue imaging techniques to select regions of interest (ROIs) for localized analysis by liquid chromatography‐tandem mass spectrometry (LC–MS/MS). Among the imaging modalities available for this purpose, mass spectrometry imaging (MSI) stands out for its ability to map diverse molecular species directly within tissue without predefined targets, allowing chemically distinct regions to guide tissue sampling. By bridging spatial molecular profiles with high protein coverage, MSI‐guided proteomics offers comprehensive biological insight. This review examines the workflow from MSI data acquisition through ROI definition, image registration, tissue isolation, and sample preparation to LC–MS/MS analysis. We compare common MSI modalities, discuss methods for ROI isolation, and summarize advances in nanoproteomics methods that support analysis of tissue samples at the microscopic scale. We critically evaluate applications in cancer research, infectious disease, neuroscience, and environmental science. Finally, we discuss the challenges and future directions of MSI‐guided proteomics. We conclude that ongoing developments in the MSI‐guided proteomics workflow position it as an emerging strategy for investigating localized biological processes within heterogeneous tissues.
ABSTRACT Top‐down proteomics approaches have emerged as powerful strategies for investigating the effects of proteoform modifications, such as amino acid substitutions, truncation, and post‐translational modifications (PTMs), on protein function and activity in biological systems. In addition to proteoform structure, proteoform abundance also influences cellular biochemistry by regulating signaling pathways and altering protein–protein interaction dynamics. Thus, accurate measurement of proteoform abundance is critical for examining cellular phenotypes and disease states. The application of quantitative proteomics strategies to top‐down proteomics, however, has been challenging due to difficulties in extracting and solubilizing intact proteins, the wide dynamic range in proteoform abundance, limited sensitivity for low‑abundance species, and the need for advanced separations and robust deconvolution algorithms. Recent advances in top‐down proteomics strategies, including methods to improve proteoform mass spectrometry data collection, sample preparation, and data analysis, have laid the groundwork for quantitative top‐down proteomics. Here, we provide a comprehensive review of current quantitative top‐down proteomics strategies and examine the applications, advantages, and challenges, including label‐free, metabolic labeling, chemical tag labeling, and targeted quantification strategies.
ABSTRACT Top‐down proteomics and top‐down mass spectrometry approaches provide direct characterization of proteoforms; the specific molecular forms of proteins, including post‐translational modifications, truncations, alternative splicing, and sequence variants. To date, most top‐down studies have been performed under denaturing conditions, losing critical information about protein folding and noncovalent protein‐protein and protein‐ligand interactions. However, recent efforts to apply non‐denaturing, or native, ionization to top‐down mass spectrometry and top‐down proteomics have shown substantial promise for characterizing these higher‐order and more transient interactions. This review covers progress over the last 35 years in non‐denaturing top‐down approaches and addresses the inherent strengths and challenges of the technique. We cover three major areas of applications: (1) Ligand binding and drug discovery, (2) Structural biology, and (3) Proteomics aspects and bioinformatics.
ABSTRACT Non‐targeted screening (NTS) coupled with high‐resolution mass spectrometry (HRMS) has revolutionized detection of unknown contaminants of emerging concern (CECs), including pharmaceutical transformation products, novel disinfection byproducts, emerging PFAS variants, and a wide diversity of other organic micropollutants, without reliance on reference standards. This critical review focuses on aquatic matrices (wastewater, surface water, drinking water, and related environmental waters) and examines current trends in LC‐HRMS and GC‐HRMS workflows, highlighting their complementarity for comprehensive chemical‐space coverage in complex environmental matrices. Principal advances include generic sample preparation strategies, advanced data processing tools (MZmine, patRoon, Compound Discoverer), Schymanski et al.‐based identification frameworks with recent harmonization efforts, and the integration of suspect‐enhanced non‐target approaches. Complementary LC/GC workflows, enhanced by ion mobility and multi‐dimensional separations, significantly improve feature detection and identification confidence for unknowns. The review further explores the translation of NTS data into risk‐assessment frameworks via effect‐directed and virtual EDA approaches, as well as selected applications in wastewater‐based epidemiology (WBE). Key challenges persist, including high data volume and complexity, false positives/negatives, matrix effects, semi‐quantification uncertainty, and limited inter‐laboratory reproducibility. Recent harmonization initiatives by the NORMAN network and interlaboratory studies are progressively addressing these gaps through standardized QA/QC and reporting practices. In conclusion, LC/GC‐HRMS NTS offers transformative potential for early‐risk detection and proactive regulatory decision‐making, particularly in the context of expanding EPA PFAS monitoring. Future perspectives emphasize AI‐driven automation, multi‐omics integration, real‐time monitoring platforms, and stronger linkage to policy frameworks. As harmonization and technological advances mature, NTS will shift environmental analysis from reactive targeted monitoring toward predictive, systems‐level protection of human and ecological health.
