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Chemical proteomics has advanced small molecule ligand discovery by providing insights into protein-ligand binding mechanism and enabling medicinal chemistry optimization of protein selectivity on a global scale. Mass spectrometry is the predominant analytical method for chemoproteomics, and various approaches have been deployed to investigate and target a rapidly growing number of protein classes and biological systems. Two methods, intact mass analysis (IMA) and top-down proteomics (TDMS), have gained interest in recent years due to advancements in high resolution mass spectrometry instrumentation. Both methods apply mass spectrometry analysis at the proteoform level, as opposed to the peptide level of bottom-up proteomics (BUMS), thus addressing some of the challenges of protein inference and incomplete information on modification stoichiometry. This Review covers recent research progress utilizing MS-based proteomics methods, discussing in detail the capabilities and opportunities for improvement of each method. Further, heightened attention is given to IMA and TDMS, highlighting these methods' strengths and considerations when utilized in chemoproteomic studies. Finally, we discuss the capabilities of native mass spectrometry (nMS) and ion mobility mass spectrometry (IM-MS) and how these methods can be used in chemoproteomics research to complement existing approaches to further advance the field of functional proteomics., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ku-Lung Hsu reports a relationship with Hyku Biosciences that includes: board membership, consulting or advisory, and equity or stocks. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography-mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research., Competing Interests: Competing interests: The authors declare no competing interests. Ethics: This study was approved by the Ethics Committee of the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences (#047-2019) with an exemption of informed consent and was performed according to the Declaration of Helsinki Principles., (© 2025. The Author(s).)

Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrate that DIA-based LC-MS/MS-related consensus QC metrics exhibit higher sensitivity compared to DDA-based QC metrics in detecting changes in LC-MS status. We then prioritize 15 metrics and invite 21 experts to manually assess the quality of 2754 DIA files based on those metrics. We develop an AI model for DIA-based QC using 2110 training files. It achieves AUCs of 0.91 (LC) and 0.97 (MS) in the first validation dataset (n = 528), and 0.78 (LC) and 0.94 (MS) in an independent validation dataset (n = 116). Finally, we develop an offline software called iDIA-QC for convenient adoption of this methodology., Competing Interests: Competing interests: T.G. and Y. Zhu are shareholders of Westlake Omics Biotechnology Co., Ltd. Three patents related to iDIA-QC technologies have been filed. Two have been granted, with the numbers CN 114858958 B and CN 116106464 B, while the third is currently pending, with the application number CN 202210783026.2. Y.L., Z.N. and Y.L. are employees of Westlake Omics Inc. H.F. and M.C. are employees of Shanghai Luming Biological Technology Inc. Q.F. and J.T. are employees of Shanghai Applied Protein Technology co. ltd. C.C., X.L., X.L. and F.K. were employees of SCIEX China during this project. Z.G., Y. Q. and T.T. are employees of Thermo Fisher Scientific China while R.W., X.D., L.M. and M.W. are employees of Bruker Daltonics China. The remaining authors declare no competing interests., (© 2024. The Author(s).)

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures tissue heterogeneity, precluding proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single microscale tissue voxels and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single voxels dissected by LCM to the tube cap and SOP voxel processing in the same collection cap. This method enables reproducible, label-free quantification of approximately 900 and 4600 proteins for single voxels at 20 µm × 20 µm × 10 µm (~1 cell region) and 200 µm × 200 µm × 10 µm (~100 cell region) from fresh frozen human spleen tissue, respectively. It can reveal spatially resolved protein signatures and region-specific signaling pathways. Furthermore, wcSOP-MS is demonstrated to be broadly applicable for OCT-embedded and FFPE human archived tissues as well as for small-scale 2D proteome mapping of tissues at high spatial resolutions. wcSOP-MS may pave the way for routine robust single voxel proteomics and spatial proteomics., Competing Interests: Competing interests: The authors declare no competing interest., (© 2025. The Author(s).)

Here we show that when using a mix of 274 light synthetic peptide standards (NAT) as surrogates for 270 human plasma proteins, as well as stable isotope-labelled standards (SIS) as normalizers (both from MRM Proteomics Inc.) for targeted quantitative analysis by liquid chromatography multiple reaction monitoring mass spectrometry (LC/MRM-MS), the Seer Proteograph™ platform allowed for the enrichment and absolute quantitation of up to an additional 62 targets (median) compared to two standard proteomic workflows without enrichment, representing an increase of 44%. The nanoparticle-based fractionation workflow resulted in improved reproducibility compared to a traditional proteomic workflow with no fractionation (median 8.3% vs. 13.1% CV). As expected, the protein concentrations in nanoparticle coronas were higher and had more compressed dynamic range in comparison to the concentrations determined either by a 3-hour Trypsin/LysC or overnight tryptic digestion methods. As the nanoparticle-based fractionation technology gains popularity, additional steps such as establishing technique-specific protein reference ranges across plasma samples and comparisons to well-established protein quantitation methods like enzyme-linked immunosorbent assay (ELISA) and LC/MRM-MS may be explored to enable absolute quantification of plasma proteins based on nanoparticle-based fractionation data.

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

jPOST (https://jpostdb.org/) comprises jPOSTrepo (https://repository.jpostdb.org/) (over 2000 projects), a repository for proteome mass spectrometry data, the reanalysis of raw proteome data based on a standardised protocol using UniScore, and jPOSTdb (https://globe.jpostdb.org/) (over 600 datasets), a database that integrates the reanalysed data. The jPOST reanalysis protocol rescores MS/MS spectra using a new scale, UniScore, to evaluate the extent to which the spectral peaks correspond to the amino acid sequences identified by search engines. However, the metadata registered in the repository database is insufficient for conducting the reanalysis. To address this issue, the Japanese Proteomics Society launched a data journal, the Journal of Proteome Data and Methods (JPDM), which accepts data descriptor articles detailing metadata that can be reanalysed. Within jPOST, raw proteome data is reanalysed based on the metadata described in the JPDM data descriptor articles, utilising UniScore. The reanalysed data is deposited in jPOSTdb, and a link to the JPDM articles is added to jPOSTrepo. These reanalysis accelerations within the jPOST environment will promote FAIR data principles and open science., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Advanced liquid chromatogram-mass spectrometry (LC-MS) and automated large-scale data processing have made MS-based quantitative analysis increasingly useful for research in fields such as biology, medicine, food safety, and beyond. This is because MS-based quantitative analysis can accurately and sensitively analyze thousands of proteins and peptides in a single experiment. However, the precision, coverage, complexity, and resilience of conventional quantification methods vary as a result of the modifications to the analytic environment and the physicochemical characteristics of analytes. Therefore, specially designed approaches are necessary for sample preparation. Dozens of methods have been developed and adapted for these needs based on stable isotopic labeling or isobaric tagging, each with distinct characteristics. In this review, we will summarize the leading strategies and techniques used thus far for MS-based protein quantification as well as analyze the advantages and shortcomings of different approaches. Additionally, we provide an overview of protein metrology development.

Background: Tubular injury triggered by hyperglycemia is an important pathological characteristic in diabetic nephropathy (DN). Accumulated advanced glycation end products and their precursor methylglyoxal (MGO), contribute to the development of DN. Carnosine has been shown to prevent the development of DN but the underlying mechanism still needs to be studied in depth. In this study, we explored the potential proteins influenced by MGO and carnosine in tubule epithelial cells., Methods and Results: HK-2 cells were treated with MGO, carnosine, or a combination. Differentially expressed proteins (DEPs) between different groups were identified by isobaric tag for relative and absolute quantitation-based mass spectrometry. In the comparison between MGO and control, 29 DEPs were found to be associated with antioxidation and RNA methylation. In the comparison between carnosine and control, 10 DEPs were associated with ubiquitin protein ligase activity and RNA metabolism. In the comparison between MGO + carnosine and MGO, carnosine-induced DEPs in the presence of MGO were mainly related to RNA splicing and mRNA processing. MGO effects on OSTC expression was inversely correlated with that of carnosine. Some DEPs (OSTC, PRDX5, NEDD4L, NOP2, TRMT6, and GEMIN2) were validated by Western blotting. Additional experiments showed the 28 kD particle of Smith antigen was also influenced by MGO and carnosine., Conclusions: Carnosine can influence RNA processing and spliceosome-related proteins, and change MGO's effect on HK-2 cells. This study helps to understand the mechanism by which MGO contributes to the development of DN and promotes further identification of carnosine downstream proteins as therapeutic targets for DN., Competing Interests: Declarations. Ethical approval: It is not applicable. Consent to participate: It is not applicable. Consent to publish: It is not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Proteomics has revolutionized the field of cancer biology because the use of a large number of in vivo (SILAC), in vitro (iTRAQ, ICAT, TMT, stable-isotope Dimethyl, and 18 O) labeling techniques or label-free methods (spectral counting or peak intensities) coupled with mass spectrometry enables us to profile and identify dysregulated proteins in diseases such as cancer. These proteome and genome studies have led to many challenges, such as the lack of consistency or correlation between copy numbers, RNA, and protein-level data. This review covers solely mass spectrometry-based approaches used for cancer biomarker discovery. It also touches on the emerging role of oncoproteogenomics or proteogenomics in cancer biomarker discovery and how this new area is attracting the integration of genomics and proteomics areas to address some of the important questions to help impinge on the biology and pathophysiology of different malignancies to make these mass spectrometry-based studies more realistic and relevant to clinical settings., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Posttranslational modifications (PTMs) of proteins regulate several biological processes, and investigating their diversity is crucial for understanding the mechanisms of cell regulation. Glycosylation is one of the most complex posttranslational modifications that control fundamental cellular processes such as protein folding, protein trafficking, host-pathogen interactions, cell adhesion, and cytokine receptor signaling networks. N-linked glycosylation denotes the attachment of glycans (oligosaccharides) to a nitrogen atom of asparagine (N) residues in the consensus motif Asn-X-Ser/Thr (NXS/T), where X is any amino acid except proline. Therefore, mutations in this posttranslational modification (i.e., N-glycosylation) site cause many human genetic diseases, including cancer. In the past decade, high-throughput quantitative proteome profiling tools have significantly renewed our interest in discovering novel cancer diagnostic or prognostic biomarkers through the simultaneous examination of the enormous amount of high-quality data of thousands of proteins and genes in complex biological systems. In this chapter, we describe how aberrant N-linked glycopeptides could be selectively identified as novel single tumor markers through the use of mass spectrometry (MS)-based proteomics, also known as Solid-phase extraction of N-glycopeptides (SPEG), and reasonable hypotheses that have the potential capacity to revolutionize biomarker discovery and bring those markers to the clinic as early as possible., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Single-cell proteomics (SCP) has advanced significantly in recent years, with new tools specifically designed for the preparation and analysis of single cells now commercially available to researchers. The field is sufficiently mature to be broadly accessible to any lab capable of isolating single cells and performing bulk-scale proteomic analyses. In this review, we highlight recent work in the SCP field that has significantly lowered the barrier to entry, thus providing a practical guide for those who are newly entering the SCP field. We outline the fundamental principles and report multiple paths to accomplish the key steps of a successful SCP experiment including sample preparation, separation, and mass spectrometry data acquisition and analysis. We recommend that researchers start with a label-free SCP workflow, as achieving high-quality and quantitatively accurate results is more straightforward than label-based multiplexed strategies. By leveraging these accessible means, researchers can confidently perform SCP experiments and make meaningful discoveries at the single-cell level., (© 2024 Wiley‐VCH GmbH.)

Microtubules, dynamic polymers assembled from α, β-tubulin dimers, contribute to myriad cellular processes. This is largely attributed to microtubule-associated proteins (MAPs). How MAPs selectively bind microtubules to carry out various functions is not known. The "Tubulin Code" theory proposes that posttranslational modifications (PTMs) of microtubules serve as signs that can be read by specific MAPs, thereby conferring specific functional properties to the microtubules. In support of this hypothesis, "reader" MAPs have been identified for various tubulin PTMs, but, until recently, no systematic screening had been performed to identify readers in an unbiased manner. We addressed this by developing a reader identification pipeline that uses quantitative mass spectrometry to interrogate the microtubule proteome of cells programmed to express specific PTMs. This pipeline can be used to identify readers for any tubulin PTM from various cell types as long as the writer enzymes are known. We also provide an alternative, complementary approach to obtain modified microtubules using a generic writer enzyme in vitro., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Protein inference is an often neglected though crucial step in most proteomic experiments. In the bottom-up proteomic approach, the actual molecules of interest, the proteins, are digested into peptides before measurement on a mass spectrometer. This approach introduces a loss of information: The actual proteins must be inferred based on the identified peptides. While this might seem trivial, there are certain problems, one of the biggest being the presence of peptides that are shared among proteins. These amino acid sequences can, based on the database used for identification, belong to more than one protein. If such peptides are identified in a sample, it cannot be said which proteins actually were in the sample, but only an estimate on the most probable proteins or protein groups can be given based on a predefined inference strategy.Here we describe the effect of the chosen database for peptide identification on the number of shared peptides. Afterward, the mainly used protein inference methods will be sketched, and the necessity of stringent false discovery rate on peptide and protein level is discussed. Finally, we explain how the tool "PIA or protein inference algorithms" can be used together with the workflow environment KNIME and OpenMS to perform protein inference in a common proteomic experiment., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Biological fluids are proteinaceous liquids or suspensions released through different body orifices or through penetration of the skin. These fluids are the result of multiple tissues and cell types and contain extensive, highly complex, and dynamic protein populations that reflect both the transcriptional program of the originating cells and a record of the individual's health status. Body fluids are readily accessible to clinicians and researchers, and as such proteomic analyses are an important component of clinical studies, fertility studies, oral health studies, and forensic investigations. Current mass spectrometry (MS) datasets have a dynamic range of up to six orders of magnitude and are as diverse as the originating tissue types. Mass spectrometry has the potential to provide information across a wide range of applications, including basic research into human biology and pathology, biochemical analysis of protein function, biomarker discovery and detection, as well as forensic investigations wherein investigators interpret a protein profile to identify the body site origin of a biological fluid. The method below describes a specimen processing workflow that is flexible in terms of biological fluid type, sample state (e.g., a dried sample extracted from evidence or neat fluid), and level of degradation. The method described here is compatible with both high sensitivity shotgun liquid chromatography-mass spectrometry LC/MS analysis and targeted (qualitative or quantitative) MS-based analysis of biomarker candidates., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Protein phosphorylation plays a crucial role in regulating disease phenotypes and serves as a key target for drug development. Mapping nanoscale-to-single-cell samples can unravel the heterogeneity of cellular signaling events. However, it remains a formidable analytical challenge due to the low detectability, abundance, and stoichiometry of phosphorylation sites. Here, we present a Chip-DIA strategy, integrating a microfluidic-based phosphoproteomic chip (iPhosChip) with data-independent acquisition mass spectrometry (DIA-MS) for ultrasensitive nanoscale-to-single-cell phosphoproteomic profiling. The iPhosChip operates as an all-in-one station that accommodates both quantifiable cell capture/imaging and the entire phosphoproteomic workflow in a highly streamlined and multiplexed manner. Coupled with a sample size-comparable library-based DIA-MS strategy, Chip-DIA achieved ultra-high sensitivity, detecting 1076±158 to 15869±1898 phosphopeptides from 10±0 to 1013±4 cells, and revealed the first single-cell phosphoproteomic landscape comprising druggable sites and basal phosphorylation-mediated networks in lung cancer. Notably, the sensitivity and coverage enabled the illumination of heterogeneous cytoskeleton remodeling and cytokeratin signatures in patient-derived cells resistant to third-generation EGFR therapy, stratifying mixed-lineage adenocarcinoma-squamous cell carcinoma subtypes, and identifying alternative targeted therapy for late-stage patients. With flexibility in module design and functionalization, Chip-DIA can be adapted to other PTM-omics to explore dysregulated PTM landscapes, thereby guiding therapeutic strategies toward precision oncology., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included., (© 2024 The Author(s). The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Protein biomarker discovery in human biological fluids has greatly developed over the past two decades thanks to technological advances allowing deeper proteome coverage and higher sample throughput, among others. While blood samples are most commonly investigated due to their moderate ease of collection and high information content, other biological fluids such as cerebrospinal fluid (CSF) and urine are highly relevant for specific pathologies, such as brain and urologic diseases, respectively. Independently of the biofluid of interest, platforms that can robustly handle a large number of samples are essential in the discovery phase of a clinical study.We have previously described a scalable automated proteomic pipeline (ASAP 2 ) for the sample preparation of hundreds to thousands of blood plasma, serum, and CSF samples before liquid chromatography (LC)-mass spectrometry (MS) analysis. Here, we describe how the workflow was further adapted to milk and urine samples, with small modifications at the beginning of the workflow. For blood and CSF samples, an optional immuno-affinity depletion step for abundant-protein removal constitutes the first step of the workflow. In the analysis of milk, a defatting step is incorporated before the samples are further processed with ASAP 2 , while acetone precipitation is used for the analysis of urine samples. The main sample preparation steps then remain identical for all sample types, are automated on a liquid handling workstation, and include reduction of disulfide bridges, alkylation of free thiols, protein digestion, isobaric labeling of peptides, sample pooling, and purifications. The workflow is completed by LC-MS analysis of the samples and subsequent data processing., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Mass spectrometry (MS) enables the high-throughput characterization of thousands of proteins in one single run. Nowadays, advances in the field have allowed for deep analysis of low cell numbers, even reaching the single-cell level. However, standardized protocols are still needed in this regard, especially oriented for non-proteomics specialists. Here we detail a reproducible, easy-to-implement workflow for cell-limited sample preparation, from cell storage to peptide labelling, for further TMT-based relative quantitative MS-based analysis., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology., (© 2024 The Author(s). PROTEOMICS published by Wiley‐VCH GmbH.)

Cellular fate is regulated by intricate signal transduction mediated by posttranslational protein modifications like phosphorylation to transmit information. As other cancer types, lymphomas frequently show dysregulation of signaling pathways that contribute to malignant transformation and tumor progression. For example, in diffuse large B-cell lymphoma the B-cell antigen receptor was identified as an oncogenic driver mediating cellular growth and survival signals. Thus, the elucidation of these complex signaling networks is crucial to gain insight into the mechanisms underlying tumorigenesis and to identify target proteins for innovative therapeutic approaches.Here, we describe a mass spectrometry-based phosphoproteomic approach for the global analysis of intracellular signaling events and their dynamics. The workflow combines phosphopeptide enrichment and fractionation with liquid chromatography-coupled mass spectrometry for the amino acid site-specific identification and quantification of thousands of phosphorylation events. Such global signaling analyses have great potential for the elucidation of oncogenic pathomechanisms, diagnostic biomarkers, and drug targets., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The ability to bring spatial resolution to omics studies enables a deeper understanding of cell populations and interactions in biological tissues. In the case of proteomics, single-cell and spatial approaches have been particularly challenging, due to limitations in sensitivity and throughput relative to other omics fields. Recent developments at the level of sample handling, chromatography, and mass spectrometry have set the stage for proteomics to be established in these new disciplines., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer characterized by a lack of targeted treatment options. Intratumoral heterogeneity significantly drives disease progression and complicates therapeutic responses, necessitating advanced analytical approaches to understand its underlying biology. This review aims to explore the advancements in single-cell proteomics and their application in uncovering cellular diversity in TNBC. It highlights innovations in sample preparation, mass spectrometry-based techniques, and the potential for integrating proteomics into multi-omics platforms., Methods: The review discusses the combination of improved sample preparation methods and cutting-edge mass spectrometry techniques in single-cell proteomics. It emphasizes the challenges associated with protein analysis, such as the inability to amplify proteins akin to transcripts, and examines strategies to overcome these limitations., Results: Single-cell proteomics provides a direct link to phenotype and cell behavior, complementing transcriptomic approaches and offering new insights into the mechanisms driving TNBC. The integration of advanced techniques has enabled deeper exploration of cellular heterogeneity and disease mechanisms., Conclusion: Despite the challenges, single-cell proteomics holds immense potential to evolve into a high-throughput and scalable multi-omics platform. Addressing existing hurdles will enable deeper biological insights, ultimately enhancing the diagnosis and treatment of TNBC., (© 2024 The Author(s). PROTEOMICS ‐ Clinical Applications published by Wiley‐VCH GmbH.)

Bacteria are unicellular organisms with the ability to exist in the harshest of climate and cope with sub-optimal fluctuating environmental conditions. They accomplish this by modification of their internal cellular environment. When external conditions are varied, change in the cell is triggered at the transcriptional level, which usually leads to proteolysis and rewiring of the proteome. Changes in cellular homeostasis, modifications in proteome, and dynamics of such survival mechanisms can be studied using various scientific techniques. Our focus in this chapter would be on comparative proteomics of bacteria under stress conditions using approaches like 2D electrophoresis accompanied by N-terminal sequencing and recently, mass spectrometry. More than 170 such studies on bacteria have been accomplished till to date and involve analysis of whole cells as well as that of cellular fractions, i.e., outer membrane, inner membrane, cell envelope, cytoplasm, thylakoid, lipid bodies, etc. Similar studies conducted on gram-negative and gram-positive model organism, i.e., Escherichia coli and Bacillus subtilis, respectively, have been summarized. Vital information, hypothesis about conservation of stress-specific proteome, and conclusions are also presented in the light of research conducted over the last decades., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Low-input proteomics, also referred to as micro- or nanoproteomics, has become increasingly popular as it allows one to elucidate molecular processes in rare biological materials. A major prerequisite for the analytics of minute protein amounts, e.g., derived from low cell numbers, down to single cells, is the availability of efficient sample preparation methods. Digital microfluidics (DMF), a technology allowing the handling and manipulation of low liquid volumes, has recently been shown to be a powerful and versatile tool to address the challenges in low-input proteomics. Here, an overview is provided on recent advances in proteomics sample preparation using DMF. In particular, the capability of DMF to isolate proteomes from cells and small model organisms, and to perform all necessary chemical sample preparation steps, such as protein denaturation and proteolytic digestion on-chip, are highlighted. Additionally, major prerequisites to making these steps compatible with follow-up analytical methods such as liquid chromatography-mass spectrometry will be discussed., (© 2024 The Author(s). Small Methods published by Wiley‐VCH GmbH.)

Mass spectrometry-based investigation of the heterogeneous glycoproteome from complex biological specimens is a robust approach to mapping the structure, function, and dynamics of the glycome and proteome. Sampling whole wet tissues often provides a large amount of starting material; however, there is a reasonable variability in tissue handling prior to downstream processing steps, and it is difficult to capture all the different biomolecules from a specific region. The on-slide tissue digestion approach, outlined in this protocol chapter, is a simple and cost-effective method that allows comprehensive mapping of the glycoproteome from a single spot of tissue of 1 mm or greater diameter. It provides a selection of target areas on tissue slides appropriate for tissue volumes of 10 nL or greater, corresponding to a 1 μL droplet of enzyme solution applied to a 1-mm diameter target on a 10-μm-thick tissue slice. Sequential enzymatic digestions and desalting of the biomolecules without any prior derivatization from the surface of fresh frozen or formalin-fixed paraffin-embedded tissue slides enable the simultaneous identification of glycosaminoglycan disaccharides such as hyaluronan, chondroitin sulfate and heparan sulfate, asparagine or N-linked glycans, and intact (glyco)peptides using liquid chromatography-tandem mass spectrometry. The in-depth information obtained from this method including the disaccharide compositions, glycan structures, peptide abundances, and site-specific glycan occupancies provides a detailed profiling of a single spot of tissue which has the potential to be disseminated to biomedical laboratories., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The biochemical makeup of any organism provides insight into key factors regarding its biological functions. These factors can be explored using proteomics, which allows us to obtain a snapshot of the protein content and abundance in an organism, cell type or sub-cellular compartment. Here, we describe proteomic methodologies that can be used to dissect the biochemical mechanism of phytopathogenicity in oomycetes. These methodologies include protein extraction, purification, subsequent processing, mass spectrometry analysis, and qualitative and quantitative data processing of oomycete proteomes for comparative studies. Additionally, the use of mass spectra to assist in gene validation and modelling in unfinished oomycete genomes is also described., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Insects are known invertebrate species with economic, ecological, pathological, and medicinal value, as well as closely associated with human populations. Entomophagy and entomotherapy are future promising prospects largely attributable to the abundant availability, high protein content, and climatic sustainability of insects. In particular, the insect brain is an important system with a secluded, compact, and protective exoskeleton. It is immunologically privileged and capable of producing a robust immune response against pathogens. It is also a source of materials that initiate key activity throughout the body. Proteomic interrogation of Periplaneta americana enables understanding the role of this insect in the fields of food and pharmacology. Proximate analyses of P. americana highlights its richness in proteins. Here we perform a simple proteomic analysis to study the brain proteome of P. americana. The processes applied during the study include gel-based isolation and separation of proteins, followed by NanoLC-MS (Orbitrap) analyses and bioinformatic interrogation of the data. The results demonstrated that this insect proteome comprises antimicrobial proteins, allergens, and proteins required for metabolic processes., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Proteogenomics enables the confirmation and refinement of gene models, the detection of new ones, and the proposition of alternative transcripts using support at the protein level. Such evidence is usually generated using mass spectrometry and subsequent result mapping to various sequence databases. This workflow entails several problems: (1) To speed up the analysis, only a small set of expected proteins is searched; (2) database search tools generally do not provide mapping to the genome; and (3) upon new releases of the sequence databases, expensive rerunning of all results would need to be performed. Therefore, fast and accurate peptide mapping is needed as part of proteogenomic pipelines. Unfortunately, some available tools have technical shortcomings. Thus, a set of test cases was developed to allow tool developers to test their implementations comprehensively. The need for comprehensive testing is exemplified by PGx and PGM, two published tools that could only solve a subset of test cases. Lelantos passed all test cases. A set of comprehensive test cases has been developed to overcome these issues. Many unpublished peptide mapping tools are part of proteogenomic workflows, and such tools would also benefit from comprehensive testing. Finally, peptide mapping may also be crucial for proteomics because sequence databases change over time. In response, peptide remapping should be performed to ensure that peptides identifying a protein are proteotypic in a larger sequence context., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The nuclear envelope (NE) defines the eukaryotic cell and functions in a myriad of fundamental cellular processes including but not limited to signal transduction, lipid metabolism, chromatin organization, and nucleocytoplasmic transportation. Although the general structure of the NE is well-conserved across eukaryotic kingdoms, its composition and functions vary substantially between species and remain largely unknown in plants. In this chapter, we describe a proximity-labeling-based proteomic approach to profile novel NE components in the model organism Arabidopsis. This method is generally suitable for the identification of protein components in subcellular compartments or protein complexes that are poorly accessible to traditional mass spectrometry approaches and can be easily applied to other plant species. In addition to giving a step-by-step detailed description of the proximity labeling proteomics procedure in plant samples, we also provide guidelines on the appropriate use of controls and statistical analysis to achieve a highly specific selection of probed candidates., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Mass spectrometry-based proteomics is widely applied to human blood serum or plasma in the search of biomarkers for various diseases. However, the enormous complexity and dynamic range of protein concentrations in these samples render a significant analytical challenge, particularly for detecting low-abundance candidate biomarkers. As a result, strategies for enriching low-abundance proteins and improving their identification in serum or plasma proteomics are commonly used. Here, we describe an immunodepletion technique that is routinely used in our lab for removing high-abundance proteins from human serum/plasma., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Proximity labeling-based proteomics (proximity proteomics) has emerged as a popular and versatile approach to illuminate the molecular interactions between viruses and their hosts. In this approach, a proximity labeling enzyme tag is fused to a bait protein and labels neighboring proteins with a chemical handle such as biotin, allowing for downstream affinity purification. Compared to another widely used technique, affinity purification coupled mass spectrometry, proximity proteomics enables the detection of low affinity or transient interactors that might have important functions in the viral life cycle. Further, proximity proteomics can identify interactors of a labile bait protein, of which affinity purification is technically challenging. Here, we describe a proximity proteomic protocol to identify cellular interactors of the Ebola virus polymerase. A similar strategy is readily applicable to elucidate the virus-host interactions for Marburg virus., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Protein phosphorylation introduces post-genomic diversity to proteins, which plays a crucial role in various cellular activities. Elucidation of system-wide signaling cascades requires high-performance tools for precise identification and quantification of dynamics of site-specific phosphorylation events. Recent advances in phosphoproteomic technologies have enabled the comprehensive mapping of the dynamic phosphoproteomic landscape, which has opened new avenues for exploring cell type-specific functional networks underlying cellular functions and clinical phenotypes. Here, we provide an overview of the basics and challenges of phosphoproteomics, as well as the technological evolution and current state-of-the-art global and quantitative phosphoproteomics methodologies. With a specific focus on highly sensitive platforms, we summarize recent trends and innovations in miniaturized sample preparation strategies for micro-to-nanoscale and single-cell profiling, data-independent acquisition mass spectrometry (DIA-MS) for enhanced coverage, and quantitative phosphoproteomic pipelines for deep mapping of cell and disease biology. Each aspect of phosphoproteomic analysis presents unique challenges and opportunities for improvement and innovation. We specifically highlight evolving phosphoproteomic technologies that enable deep profiling from low-input samples. Finally, we discuss the persistent challenges in phosphoproteomic technologies, including the feasibility of nanoscale and single-cell phosphoproteomics, as well as future outlooks for biomedical applications., (© 2024 The Author(s). Proteomics published by Wiley‐VCH GmbH.)

As an interferon-stimulating factor protein, STING plays a role in the response and downstream liaison in antiviral natural immunity. Upon viral invasion, the immediate response of STING protein leads to a series of changes in downstream proteins, which ultimately leads to an antiviral immune response in the form of proinflammatory cytokines and type I interferons, thus triggering an innate immune response, an adaptive immune response in vivo, and long-term protection of the host. In the field of antiviral natural immunity, it is particularly important to rigorously and sequentially probe the dynamic changes in the antiviral natural immunity connector protein STING caused by the entire anti-inflammatory and anti-pathway mechanism and the differences in upstream and downstream proteins. Traditionally, proteomics technology has been validated by detecting proteins in a 2D platform, for which it is difficult to sensitively identify changes in the nature and abundance of target proteins. With the development of mass spectrometry (MS) technology, MS-based proteomics has made important contributions to characterizing the dynamic changes in the natural immune proteome induced by viral infections. MS analytical techniques have several advantages, such as high throughput, rapidity, sensitivity, accuracy, and automation. The most common techniques for detecting complex proteomes are liquid chromatography (LC) and mass spectrometry (MS). LC-MS (Liquid Chromatography-Mass Spectrometry), which combines the physical separation capability of LC and the mass analysis capability of MS, is a powerful technique mainly used for analyzing the proteome of cells, tissues, and body fluids. To explore the combination of traditional proteomics techniques such as Western blotting, Co-IP (co-Immunoprecipitation), and the latest LC-MS methods to probe the anti-inflammatory pathway and the differential changes in upstream and downstream proteins induced by the antiviral natural immune junction protein STING., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Yeast serves as a functional alternative and sustainable protein source in aquaculture. This protocol outlines feeding strategy and intestinal proteome analysis of zebrafish (Danio rerio), using S-trap for digestion, iTRAQ, and mass spectrometry for protein quantification. Additionally, it details the analysis of chemical components in feed and functional assessments via Kyoto Encyclopedia of Genes and Genomes (KEGG), Eukaryotic Orthologous Group (KOG), and Gene Ontology (GO). For complete details on the use and execution of this protocol, please refer to Purushothaman et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data-independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA-MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation based on 8- or 11-point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies., (© 2024 Wiley-VCH GmbH.)

Proteins congregate into complexes to perform diverse cellular functions. Protein complexes are remodeled by protein-coding mutations or cellular signaling changes, driving phenotypic outcomes in health and disease. We present an affinity purification-mass spectrometry (AP-MS) proteomics protocol to express affinity-tagged "bait" proteins in mammalian cells, identify and quantify purified protein interactors, and visualize differential protein-protein interaction networks between pairwise conditions. Our protocol possesses general applicability to various cell types and biological areas. For complete details on the use and execution of this protocol, please refer to Bouhaddou et al. 1 ., Competing Interests: Declaration of interests The Krogan Laboratory has received research support from Vir Biotechnology, F. Hoffmann-La Roche, and Rezo Therapeutics. N.J.K. has a financially compensated consulting agreement with Maze Therapeutics. N.J.K. is the President and is on the Board of Directors of Rezo Therapeutics, and he is a shareholder in Tenaya Therapeutics, Maze Therapeutics, Rezo Therapeutics, and Interline Therapeutics. M.B. is a scientific advisor for GEn1E LifeSciences., (Copyright © 2024. Published by Elsevier Inc.)

Deep mass spectrometry-based proteomic profiling of rare cell populations has been constrained by sample input requirements. Here, we present a protocol for droplet-based one-pot preparation for proteomic samples (DROPPS), an accessible low-input platform that generates high-fidelity proteomic profiles of 100-2,500 cells. We describe steps for depositing cellular material, cell lysis, and digesting proteins in the same microliter-droplet well. We anticipate DROPPS will accelerate biology-driven proteomic research for a multitude of rare cell populations. For complete details on the use and execution of this protocol, please refer to Waas et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Recent advances in mass spectrometry (MS)-based methods have significantly expanded the capabilities for quantitative glycoproteomics, enabling highly sensitive and accurate quantitation of glycosylation at intact glycopeptide level. These developments have provided valuable insights into the roles of glycoproteins in various biological processes and diseases. In this short review, we summarize pertinent studies on quantitative techniques and tools for site-specific glycoproteomic analysis published over the past decade. We also highlight state-of-the-art MS-based software that facilitate multi-dimension quantification of the glycoproteome, targeted quantification of specific glycopeptides, and the analysis of glycopeptide isomers. Additionally, we discuss the potential applications of these technologies in clinical biomarker discovery and the functional characterization of glycoproteins in health and disease. The review concludes with a discussion of current challenges and future perspectives in the field, emphasizing the need for more precise, high-throughput and efficient methods to further advance quantitative glycoproteomics and its applications., (© 2024 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.)

Mass-spectrometry-based proteomics frequently utilizes label-free quantification strategies due to their cost-effectiveness, methodological simplicity, and capability to identify large numbers of proteins within a single analytical run. Despite these advantages, the prevalence of missing values (MV), which can impact up to 50% of the data matrix, poses a significant challenge by reducing the accuracy, reproducibility, and interpretability of the results. Consequently, effective handling of missing values is crucial for reliable quantitative analysis in proteomic studies. This study systematically evaluated the performance of selected imputation methods for addressing missing values in proteomic dataset. Two protein identification algorithms, FragPipe and MaxQuant, were employed to generate datasets, enabling an assessment of their influence on im-putation efficacy. Ten imputation methods, representing three methodological categories-single-value (LOD, ND, SampMin), local-similarity (kNN, LLS, RF), and global-similarity approaches (LSA, BPCA, PPCA, SVD)-were analyzed. The study also investigated the impact of data logarithmization on imputation performance. The evaluation process was conducted in two stages. First, performance metrics including normalized root mean square error (NRMSE) and the area under the receiver operating characteristic (ROC) curve (AUC) were applied to datasets with artificially introduced missing values. The datasets were designed to mimic varying MV rates (10%, 25%, 50%) and proportions of values missing not at random (MNAR) (0%, 20%, 40%, 80%, 100%). This step enabled the assessment of data characteristics on the relative effectiveness of the imputation methods. Second, the imputation strategies were applied to real proteomic datasets containing natural missing values, focusing on the true-positive (TP) classification of proteins to evaluate their practical utility. The findings highlight that local-similarity-based methods, particularly random forest (RF) and local least-squares (LLS), consistently exhibit robust performance across varying MV scenarios. Furthermore, data logarithmization significantly enhances the effectiveness of global-similarity methods, suggesting it as a beneficial preprocessing step prior to imputation. The study underscores the importance of tailoring imputation strategies to the specific characteristics of the data to maximize the reliability of label-free quantitative proteomics. Interestingly, while the choice of protein identification algorithm (FragPipe vs. MaxQuant) had minimal influence on the overall imputation error, differences in the number of proteins classified as true positives revealed more nuanced effects, emphasizing the interplay between imputation strategies and downstream analysis outcomes. These findings provide a comprehensive framework for improving the accuracy and reproducibility of proteomic analyses through an informed selection of imputation approaches.

In modern biomedical research, cultivable cell lines are an indispensable tool, and the selection of cell lines that exhibit specific functional profiles is often critical to success. Cellular functional pathways have evolved through the selection of protein intra- and intermolecular interactions collectively referred to as the interactome. In the present work, quantitative in vivo protein cross-linking and mass spectrometry were used to probe large-scale protein interactome differences among three commonly employed human cell lines, namely, HEK293, MCF-7, and HeLa cells. These data illustrated highly reproducible quantitative interactome levels with R values larger than 0.8 for all biological replicates. Proteome abundance levels were also measured using data-independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteome information allowed the visualization of cell type-specific interactome changes mediated by proteome level adaptations and independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the largest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study system-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight into cellular functional landscapes.2 values larger than 0.8 for all biological replicates. Proteome abundance levels were also measured using data-independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteome information allowed the visualization of cell type-specific interactome changes mediated by proteome level adaptations and independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the largest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study system-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight into cellular functional landscapes.

The coefficient of variation (CV) is a measure that is frequently used to assess data dispersion for mass spectrometry-based proteomics. In the current era of burgeoning technical developments, there has been an increased focus on using CVs to measure the quantitative precision of new methods. Thus, it has also become important to define a set of guidelines on how to calculate and report the CVs. This perspective shows the effects that the CV equation, data normalization as well as software parameters, can have on data dispersion and CVs, highlighting the importance of reporting all these variables within the methods section. It also proposes a set of recommendations to calculate and report CVs for technical studies, where the main objective is to benchmark technical developments with a focus on precision. To assist in this process, a novel R package to calculate CVs (proteomicsCV) is also included.

Targeted mass spectrometry (MS) methods are powerful tools for the selective and sensitive analysis of peptides identified in global discovery experiments. Selected reaction monitoring (SRM) is the most widely accepted clinical MS method due to its reliability and performance. However, SRM and parallel reaction monitoring (PRM) are limited in throughput and are typically used for assays with around 100 targets or fewer. Here we introduce a new MS platform featuring a quadrupole mass filter, collision cell, and linear ion trap architecture, capable of targeting 5000-8000 peptides per hour. This high multiplexing capability is facilitated by acquisition rates of 70-100 Hz and real-time chromatogram alignment. We present a Skyline external software tool for building targeted methods based on data-independent acquisition chromatogram libraries or unscheduled analysis of heavy labeled standards. Our platform demonstrates ∼10× lower limits of quantitation (LOQs) than traditional SRM on a triple quadrupole instrument for highly multiplexed assays, due to parallel product ion accumulation. Finally, we explore how analytical figures of merit vary with method duration and the number of analytes, providing insights into optimizing assay performance.

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. Here, we introduce nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells via RNA sequencing and mass spectrometry-based proteomics, respectively. Benchmarking of nanoSPLITS demonstrates high measurement precision with deep proteomic and transcriptomic profiling of single-cells. We apply nanoSPLITS to cyclin-dependent kinase 1 inhibited cells and found phospho-signaling events could be quantified alongside global protein and mRNA measurements, providing insights into cell cycle regulation. We extend nanoSPLITS to primary cells isolated from human pancreatic islets, introducing an efficient approach for facile identification of unknown cell types and their protein markers by mapping transcriptomic data to existing large-scale single-cell RNA sequencing reference databases. Accordingly, we establish nanoSPLITS as a multiomic technology incorporating global proteomics and anticipate the approach will be critical to furthering our understanding of biological systems., Competing Interests: Competing interests: J.C.B., J.W.B, and A.S. are employees of Scienion/Cellenion. Y.Z. is an employee of Genentech Inc. and shareholder of Roche Group. Battelle Memorial Institute has submitted a U.S. patent application for the design of nanoSPLITS devices and the associated operation methods (Application number: 17/954,834, filed 09/28/2022; Inventors: Y.Z., J.M.F., L.M.M., and L.P.T.; Status of application: Pending). Other authors declare no other competing interests., (© 2024. Battelle Memorial Institute, Scienion US Inc. and Cellenion SASU 2024.)

Ubiquitylation is a structurally and functionally diverse post-translational modification that involves the covalent attachment of the small protein ubiquitin to other protein substrates. Trypsin-based proteomics is the most common approach for globally identifying ubiquitylation sites. However, we estimate that such methods are unable to detect ∼40% of ubiquitylation sites in the human proteome, i.e. , "the dark ubiquitylome", including many important for human health and disease. In this meta-analysis of three large ubiquitylomic data sets, we performed a series of bioinformatic analyses to assess experimental features that could aid in uniquely identifying site-specific ubiquitylation events. Spectral predictions from Prosit were compared to experimental spectra of tryptic ubiquitylated peptides, revealing previously uncharacterized fragmentation of the diGly scar. Analysis of the LysC-derived ubiquitylated peptides reveals systematic, multidimensional peptide fragmentation, including diagnostic b-ions from fragmentation of the LysC ubiquitin scar. Comprehensively, these findings provide diagnostic spectral signatures of modification events that could be applied to new analysis methods for nontryptic ubiquitylomics.

Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radical protein footprinting (HRPF). Traditional methods like Excel's Power Pivot (PP) are cumbersome and time-intensive, posing challenges for large-scale analyses. coADAPTr simplifies analysis by mimicking the functions used in the previous quantitation platform using PowerPivot in Microsoft Excel but with fewer steps, offering proteome-wide insights with enhanced graphical interpretations. Several features have been added to improve the fidelity and throughput compared to those of PowerPivot. These include filters to remove any duplicate data and the use of the arithmetic mean rather than the geometric mean for quantitation of the extent of modification. Validation studies confirm coADAPTr's accuracy and efficiency while processing data up to 200 times faster than conventional methods. Its open-source design and user-friendly interface make it accessible for researchers exploring intricate biological phenomena via HRPF and other covalent labeling MS methods. coADAPTr marks a significant leap in structural proteomics, providing a versatile and efficient platform for data interpretation. Its potential to transform the field lies in its seamless handling of proteome-wide data analyses, empowering researchers with a robust tool for deciphering complex structural biology data.

Background: Immunoglobulin (Ig) measurements in the clinical laboratory have been traditionally performed by nephelometry, turbidimetry, electrophoresis, and ELISA assays. Mass spectrometry (MS) measurements have the potential to provide deeper insights on the nature of these markers., Content: Different approaches-top-down, middle-down, or bottom-up-have been described for measuring specific Igs for endogenous monoclonal immunoglobulins (M-proteins) and exogenous therapeutic monoclonal antibody therapies (t-mAbs). Challenges arise in distinguishing the Ig of interest from the polyclonal Ig background. MS is emerging as a practical method to provide quantitative analysis and information about structural and clonal features that are not easily determined by current clinical laboratory methods. This review discusses clinically implemented examples, including isotyping and quantification of M-proteins and quantitation of t-mAbs within the polyclonal Ig background, as examples of how MS can enhance our detection and characterization of Igs., Summary: This review of current clinically available MS proteomic tests for Igs highlights both analytical and nonanalytical challenges for implementation. Given the new insight into Igs from these methods, it is hoped that vendors, laboratorians, healthcare providers, and payment systems can work to overcome these challenges and advance the care of patients., (© Association for Diagnostics & Laboratory Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Proteomic profiling of small extracellular vesicles (sEV) is a powerful tool for discovering biomarkers of various diseases. This process most often assisted by mass spectrometry (MS) usually lacks standardization and recognition of challenges which may lead to unreliable results. General recommendations for sEV MS analyses have been briefly given in the MISEV2023 guidelines. The present work goes into detail for every step of sEV protein profiling with an overview of factors influencing such analyses. This includes reporting and defining the sEV source and vesicle isolation, protein solubilization and digestion, 'offline' and 'online' sample complexity reduction, the analysis type itself, and subsequent data analysis. Every stage in this process affects the others, which could result in different outcomes. Although characterization and comparisons of different sEV isolation methods are known and accessible and MS-based profiling details are provided for cell or tissue samples, no consensus work has been ever published to describe the whole process of sEV proteomic analysis. Reliable results can be obtained from sEV profiling provided that the analysis is well planned, prepared for, and backed by pilot studies or appropriate research., (© 2024 The Author(s). Journal of Extracellular Vesicles published by Wiley Periodicals LLC on behalf of International Society for Extracellular Vesicles.)

Targeted proteomics methods have been greatly improved and refined over the last decade and are becoming increasingly the method of choice in protein and peptide quantitative assays. Despite the tremendous progress, targeted proteomics assays still suffer from inadequate sensitivity for lower abundant proteins and throughput, especially in complex biological samples. These attributes are essential for establishing targeted proteomics methods at the forefront of clinical use. Here, we report an assay utilizing the SureQuant internal standard-triggered targeted method on a latest generation mass spectrometer coupled with an EvoSep One liquid chromatography platform, which displays high sensitivity and a high throughput of 100 samples per day. We demonstrate the robustness of this method by quantifying proteins spanning six orders of magnitude in human wound fluid exudates, a biological fluid that exhibits sample complexity and composition similar to plasma. Among the targets quantified were low-abundance proteins such at tumor necrosis factor A and interleukin 1-β, highlighting the value of this method in the quantification of trace amounts of invaluable biomarkers that were until recently hardly accessible by targeted proteomics methods. Taken together, this method extends the toolkit of targeted proteomics assays and will help to drive forward mass spectrometry-based proteomics biomarker quantification., Competing Interests: Conflicts of Interest H. S. is a full-time employee of HARTMANN. The other authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

There has been a rapid increase in the number of individuals utilizing mass spectrometry-based proteomics to study complex biological systems and questions since the start of the 2000's. Building off the advancements in ionization and liquid chromatography scientists continued to push towards technology that would enable in-depth analysis of biological specimen. Donald F Hunt and the Hunt laboratory were major contributors to this effort with their work on improving upon existing Fourier Transform MS, development of electron transfer dissociation, and continued work on ion-ion reactions to improve intact protein analysis. Collaboration with other instrumentation laboratories and instrument companies led to the sharing of technology and eventual commercialization providing greater access. Additionally, the Hunt laboratory spread the gospel of MS-based proteomics through collaborations that lasted decades with other scientists who were experts in immunology, cellular signaling, epigenetics, and other fascinating fields. This article attempts to highlight the many contributions of Don and the Hunt laboratory to peptide and protein identification since the year 2000., Competing Interests: Conflicts of interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)