There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

m6A is one of the most common, abundant, and conserved post-transcriptional modifications that regulate broad biological processes in the human body through m6A regulators. m6A regulators are figuratively classified according to their functions: writers, erasers, and readers, which can methylate RNAs, demethylate RNAs, and recognize RNA m6A sites, consequently affecting RNA fate. Tumors are an essential class of diseases that threaten human health, and as the study of m6A modification in tumors continues to advance, more and more relevant studies are emerging. In this review, we overview the recent studies of m6A in various types of tumors to demonstrate the role of m6A modification in providing a vision in diagnosing and treating tumors.

The time-resolved impact of monensin on the active rumen microbiome was studied in a rumen-simulating technique (Rusitec) with metaproteomic and metabolomic approaches. Monensin treatment caused a decreased fibre degradation potential that was observed by the reduced abundance of proteins assigned to fibrolytic bacteria and glycoside hydrolases, sugar transporters and carbohydrate metabolism. Decreased proteolytic activities resulted in reduced amounts of ammonium as well as branched-chain fatty acids. The family Prevotellaceae exhibited increased resilience in the presence of monensin, with a switch of the metabolism from acetate to succinate production. Prevotella species harbour a membrane-bound electron transfer complex, which drives the reduction of fumarate to succinate, which is the substrate for propionate production in the rumen habitat. Besides the increased succinate production, a concomitant depletion of methane concentration was observed upon monensin exposure. Our study demonstrates that Prevotella sp. shifts its metabolism successfully in response to monensin exposure and Prevotellaceae represents the key bacterial family stabilizing the rumen microbiota during exposure to monensin.

Matrix Assisted Laser Desorption/Ionization Time-of-flight (MALDI-ToF) MS is a popular method to analyze glycans released from proteins, cell lines, and tissue samples. Chemical modification of glycans (derivatization) can enhance ionization, enable semi-quantitation, and assist in linkage identification. However, the mass changes incurred by novel and more recently developed derivatizations are not accommodated by most spectral assignment programs, necessitating manual assignment which increases both the difficultly and the likelihood of error. AssignMALDI is a software tool designed to create glycan databases with customized derivatizations (labels) and automatically assign glycan masses in MALDI-TOF spectra using the new database. It can also average peak intensities across multiple spectra and prepare publication-ready assignment tables. To make it easy to use with different platforms, all input files and most output files are in text format. An interactive display enables users to inspect and edit peak assignments prior to producing charts and tables for publication. The program is freely available through GitHUB and Python-savvy users can add or adjust features as needed.

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased "hypothesis-free" analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.

Human plasma is a rich source of biomedical information and biomarkers. However, the enormous dynamic range of plasma proteins limits its accessibility to mass spectrometric (MS) analysis. Here, we show that enrichment of extracellular vesicles (EVs) by ultracentrifugation increases plasma proteome depth by an order of magnitude. With this approach, more than two thousand proteins are routinely and reproducibly quantified by label-free quantification and data independent acquisition (DIA) in single-shot liquid chromatography tandem mass spectrometry runs of less than one hour. We present an optimized plasma proteomics workflow that enables high-throughput with very short chromatographic gradients analyzing hundred samples per day with deep proteome coverage, especially when including a study-specific spectral library generated by repeated injection and gas-phase fractionation of pooled samples. Finally, we test the workflow on clinical biobank samples from malignant melanoma patients in immunotherapy to demonstrate the improved proteome coverage supporting the potential for future biomarker discovery.

Membrane lipids play important roles in the regulation of cell fate, including the execution of ferroptosis. Ferroptosis is a non-apoptotic cell death mechanism defined by iron-dependent membrane lipid peroxidation. Phospholipids containing polyunsaturated fatty acids (PUFAs) are highly vulnerable to peroxidation and are essential for ferroptosis execution. By contrast, the incorporation of less oxidizable monounsaturated fatty acids (MUFAs) in membrane phospholipids protects cells from ferroptosis. The enzymes and pathways that govern PUFA and MUFA metabolism therefore play a critical role in determining cellular sensitivity to ferroptosis. Here, we review three lipid metabolic processes-fatty acid biosynthesis, ether lipid biosynthesis, and phospholipid remodeling-that can govern ferroptosis sensitivity by regulating the balance of PUFAs and MUFAs in membrane phospholipids.

In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.

Arguably, 2020 was the year of high-accuracy protein structure predictions, with AlphaFold 2.0 achieving previously unseen accuracy in the Critical Assessment of Protein Structure Prediction (CASP). In 2021, DeepMind and EMBL-EBI developed the AlphaFold Protein Structure Database to make an unprecedented number of reliable protein structure predictions easily accessible to the broad scientific community. We provide a brief overview and describe the latest developments in the AlphaFold database. We highlight how the fields of data services, bioinformatics, structural biology, and drug discovery are directly affected by the influx of protein structure data. We also show examples of cutting-edge research that took advantage of the AlphaFold database. It is apparent that connections between various fields through protein structures are now possible, but the amount of data poses new challenges. Finally, we give an outlook regarding the future direction of the database, both in terms of data sets and new functionalities.