Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition

SPE-CZE-MS Quantifies Zeptomole Amounts of Phosphorylated Peptides

Capillary zone electrophoresis (CZE) is gaining attention in the field of single-cell proteomics for its ultralow-flow and high-resolution separation abilities. Even more sample-limited yet rich in biological information are phosphoproteomics experiments, as the phosphoproteome composes only a fraction of the whole cellular proteome. Rapid analysis, high sensitivity, and maximization of sample utilization are paramount for single-cell analysis. Some challenges of coupling CZE analysis with mass spectrometry analysis (MS) of complex mixtures include 1. sensitivity due to volume loading limitations of CZE and 2. incompatibility of MS duty cycles with electropherographic time scales. Here, we address these two challenges as applied to single-cell-equivalent phosphoproteomics experiments by interfacing a microchip-based CZE device integrated with a solid-phase-extraction (SPE) bed with the Orbitrap Astral mass spectrometer. Using 225 phosphorylated peptide standards and phosphorylated peptide-enriched mouse brain tissue, we investigate microchip-based SPE-CZE functionality, quantitative performance, and complementarity to nano-LC-MS (nLC-MS) analysis. We highlight unique SPE-CZE separation mechanisms that can empower fit-for-purpose applications in single-cell-equivalent phosphoproteomics.

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

Proteomics of colorectal tumors identifies the role of CAVIN1 in tumor relapse

Colorectal cancer molecular signatures derived from omics data can be employed to stratify CRC patients and aid decisions about therapies or evaluate prognostic outcome. However, molecular biomarkers for identification of patients at increased risk of disease relapse are currently lacking. Here, we present a comprehensive multi-omics analysis of a Danish colorectal cancer tumor cohort composed of 412 biopsies from tumors of 371 patients diagnosed at TNM stage II or III. From mass spectrometry-based patient proteome profiles, we classified the tumors into four molecular subtypes, including a mesenchymal-like subtype. As the mesenchymal-rich tumors are known to represent the most invasive and metastatic phenotype, we focused on the protein signature defining this subtype to evaluate their potential as relapse risk markers. Among signature-specific proteins, we followed-up Caveolae-Associated Protein-1 (CAVIN1) and demonstrated its role in tumor progression in a 3D in vitro model of colorectal cancer. Compared to previous omics analyses of CRC, our multi-omics classification provided deeper insights into EMT in cancer cells with stronger correlations with risk of relapse.

Unifying the analysis of bottom-up proteomics data with CHIMERYS

Proteomic workflows generate vastly complex peptide mixtures that are analyzed by liquid chromatography-tandem mass spectrometry, creating thousands of spectra, most of which are chimeric and contain fragment ions from more than one peptide. Because of differences in data acquisition strategies such as data-dependent, data-independent or parallel reaction monitoring, separate software packages employing different analysis concepts are used for peptide identification and quantification, even though the underlying information is principally the same. Here, we introduce CHIMERYS, a spectrum-centric search algorithm designed for the deconvolution of chimeric spectra that unifies proteomic data analysis. Using accurate predictions of peptide retention time, fragment ion intensities and applying regularized linear regression, it explains as much fragment ion intensity as possible with as few peptides as possible. Together with rigorous false discovery rate control, CHIMERYS accurately identifies and quantifies multiple peptides per tandem mass spectrum in data-dependent, data-independent or parallel reaction monitoring experiments.

Simple In-Cell Processing Enables Deep Proteome Analysis of Low-Input Caenorhabditis elegans

Caenorhabditis elegans is a widely used genetic model organism; however, the worm cuticle complicates extraction of intracellular proteins, a prerequisite for typical bottom-up proteomics. Conventional physical disruption procedures are not only time-consuming but can also cause significant sample loss, making it difficult to perform proteomics with low-input samples. Here, for the first time, we present an on-filter in-cell (OFIC) processing approach that can digest C. elegans proteins directly in the cells of the organism after methanol fixation. With OFIC processing and single-shot LC-MS analysis, we identified over 9400 proteins from a sample of only 200 worms, the largest C. elegans proteome reported to date that did not require fractionation or enrichment. We systematically evaluated the performance of the OFIC approach by comparing it to conventional lysis-based methods. Our data suggest superior performance of OFIC processing for C. elegans proteome identification and quantitation. We further evaluated the OFIC approach with even lower-input samples, including single worms. Then, we used this method to determine how the proteome is impacted by loss of superoxide dismutase sod-1, the ortholog of human SOD1, a gene associated with amyotrophic lateral sclerosis. Analysis of 8800 proteins from only 50 worms as the initial input showed that loss of sod-1 affects the abundance of proteins required for stress response, ribosome biogenesis, and metabolism. In conclusion, our streamlined OFIC approach, which can be broadly applied to other systems, minimizes sample loss while offering the simplest workflow reported to date for C. elegans proteomics.

Deep Visual Proteomics maps proteotoxicity in a genetic liver disease

Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood1-3. We use spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudotime across fibrosis stages. We achieve proteome depth of up to 4,300 proteins from one-third of a single cell in formalin-fixed, paraffin-embedded tissue. This dataset reveals a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show α1-antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with artificial intelligence-guided image-based phenotyping across several disease stages, revealing a late-stage hepatocyte phenotype characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10 (also known as TRAIL) amounts. This phenotype may represent a critical disease progression stage. Our study offers new insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.

Advances in Tandem Mass Spectrometry Imaging for Next-Generation Spatial Metabolomics

Spatial metabolomics based on mass spectrometry imaging (MSI) is a promising approach for fundamental biological research and disease biomarker discovery. It simultaneously reveals the spatial distributions of hundreds of metabolites across tissue sections. While previous MSI experiments predominantly rely on high-resolution mass analysis for metabolite annotation, the high specificity in resolving molecular structures is essential to distinguish isomers or isobars to obtain ultimate identities of the metabolites. This is also critical for correlating their biological functions with spatial distribution patterns. Tandem mass spectrometry (MS/MS) is effectively used to obtain molecular structural information and has been integrated into MSI for spatial mapping of structurally distinct biomolecules, though typically with low coverage. The main technical challenge in achieving high-coverage, high-structure-resolving spatial mapping of biomolecules lies in the limited amount of sample available from each tissue pixel in conventional MS/MS analysis, which restricts the number of MS/MS scans that can be conducted on the metabolite precursors of interest. In this Perspective, we highlight recent developments in advanced MS/MS imaging strategies aimed at achieving high-coverage spatial metabolomics.

Single cell spatial proteomics maps human liver zonation patterns and their vulnerability to fibrosis

Understanding protein distribution patterns across tissue architecture is crucial for deciphering organ function in health and disease. Here, we applied single-cell Deep Visual Proteomics to perform spatially-resolved proteome analysis of individual cells in native tissue. We combined this with a novel strategic cell selection pipeline and a continuous protein gradient mapping framework to investigate larger clinical cohorts. We generated a comprehensive spatial map of the human hepatic proteome by analyzing hundreds of individual hepatocytes from 18 individuals. Among more than 2,500 proteins per cell about half exhibited zonated expression patterns. Cross-species comparison with mouse data revealed conserved metabolic functions and human-specific features of liver zonation. Analysis of fibrotic samples demonstrated widespread disruption of protein zonation, with pericentral proteins being particularly susceptible. Our study provides a comprehensive resource of human liver organization while establishing a broadly applicable framework for spatial proteomics analyses along tissue gradients.

Chemoproteomic Profiling of C. albicans for Characterization of Antifungal Kinase Inhibitors

Candida albicans is a major cause of systemic candidiasis, a severe fungal infection with a ∼40% mortality rate. Yck2, a casein kinase 1 (CK1) in C. albicans, is targeted by antifungal inhibitors YK-I-02 (YK) and MN-I-157 (MN). Using multiplexed inhibitor beads and mass spectrometry (MIB/MS), the selectivity of these inhibitors was determined across the fungal kinome. The MIB matrix captured 89% of C. albicans protein kinases, revealing that YK and MN selectively engage three CK1 homologues (Yck2, Yck22, and Hrr25) and a human p38α homologue (Hog1). Chemoproteomics using a custom MN-kinobead confirmed the remarkable fungal kinome selectivity. To identify new Yck2 inhibitors with selectivity over Hog1, 13 human CK1 inhibitors were screened, leading to the discovery of a new chemotype with antifungal activity. These findings highlight the utility of MIB/MS in profiling nonhuman kinomes and developing selective fungal kinase inhibitors as antimicrobial agents.

Nanopore sensing of protein and peptide conformation for point-of-care applications

The global population's aging and growth will likely result in an increase in chronic aging-related diseases. Early diagnosis could improve the medical care and quality of life. Many diseases are linked to misfolding or conformational changes in biomarker peptides and proteins, which affect their function and binding properties. Current clinical methods struggle to detect and quantify these changes. Therefore, there is a need for sensitive conformational sensors that can detect low-concentration analytes in biofluids. Nanopore electrical detection has shown potential in sensing subtle protein and peptide conformation changes. This technique can detect single molecules label-free while distinguishing shape or physicochemical property changes. Its proven sensitivity makes nanopore sensing technology promising for ultra-sensitive, personalized point-of-care devices. We focus on the capability of nanopore sensing for detecting and quantifying conformational modifications and enantiomers in biomarker proteins and peptides and discuss this technology as a solution to future societal health challenges.

Formaldehyde Fixation Helps Preserve the Proteome State during Single-Cell Proteomics Sample Processing and Analysis

Mass spectrometry-based single-cell proteomics (SCP) is gaining momentum but remains limited to a few laboratories due to the high costs and specialized expertise required. The ability to send samples to specialized core facilities would benefit nonspecialist laboratories and popularize SCP for biological applications. However, no methods have been tested in SCP to "freeze" the proteome state while maintaining cell integrity for transfer between laboratories or prolonged sorting using fluorescence-activated cell sorting (FACS). This study evaluates whether short-term formaldehyde (FA) fixation can maintain the cell states. We demonstrate that short-term FA fixation does not substantially affect protein recovery, even without heating and strong detergents, and maintains analytical depth compared with classical workflows. Fixation also preserves drug-induced specific perturbations of the protein abundance during cell sorting and sample preparation for SCP analysis. Our findings suggest that FA fixation can facilitate SCP by enabling sample shipping and prolonged sorting, potentially democratizing access to SCP technology and expanding its application in biological research, thereby accelerating discoveries in cell biology and personalized medicine.

Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Infrared-Tandem Mass Tags

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS3 approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.

Caffeic acid and chlorogenic acid mediate the ADPN-AMPK-PPARα pathway to improve fatty liver and production performance in laying hens

BACKGROUND

Caffeic acid (CA) and its derivative, chlorogenic acid (CGA), have shown promise in preventing and alleviating fatty liver disease. CA, compared to CGA, has much lower production costs and higher bioavailability, making it a potentially superior feed additive. However, the efficacy, mechanistic differences, and comparative impacts of CA and CGA on fatty liver disease in laying hens remain unclear. This study aimed to evaluate and compare the effects of CA and CGA on production performance, egg quality, and fatty liver disease in laying hens.

RESULTS

A total of 1,440 61-week-old Hyline Brown laying hens were randomly divided into 8 groups and fed diets supplemented with basal diet, 25, 50, 100 and 200 mg/kg of CA, and 100, 200 and 400 mg/kg of CGA (CON, CA25, CA50, CA100, CA200, CGA100, CGA200 and CGA400, respectively) for 12 weeks. Both CA and CGA improved production performance and egg quality, while reducing markers of hepatic damage and lipid accumulation. CA and CGA significantly decreased TG, TC, and LDL-C levels and increased T-SOD activity. Transcriptomic and proteomic analyses revealed that CA and CGA reduced hepatic lipid accumulation through downregulation of lipid biosynthesis-related genes (ACLY, ACACA, FASN, and SCD1) and enhanced lipid transport and oxidation genes (FABPs, CD36, CPT1A, ACOX1, and SCP2). Of note, low-dose CA25 exhibited equivalent efficacy to the higher dose CGA100 group in alleviating fatty liver conditions. Mechanistically, CA and CGA alleviated lipid accumulation via activation of the ADPN-AMPK-PPARα signaling pathway.

CONCLUSIONS

This study demonstrates that dietary CA and CGA effectively improve laying performance, egg quality, and hepatic lipid metabolism in laying hens, with CA potentially being more economical and efficient. Transcriptomic and proteomic evidence highlight shared mechanisms between CA25 and CGA100. These findings provide a foundation for CA and CGA as therapeutic agents for fatty liver disease and related metabolic diseases in hens, and also offer insights into the targeted modification of CGA (including the isomer of CGA) into CA, thereby providing novel strategies for the efficient utilization of CGA.

Toward Real-Time Proteomics: Blood to Biomarker Quantitation in under One Hour

Multistep multihour tryptic proteolysis has limited the utility of bottom-up proteomics for cases that require immediate quantitative information. The power of proteomics to quantify biomarkers of health status cannot practically assist in clinical care if the dynamics of disease outpaces the turnaround of analysis. The recently available hyperthermoacidic archaeal (HTA) protease "Krakatoa" digests samples in a single 5 to 30 min step at pH 3 and >80 °C in conditions that disrupt most cells and tissues, denature proteins, and block disulfide reformation thereby dramatically expediting and simplifying sample preparation. The combination of quick single-step proteolysis with high-throughput dual-trapping single analytical column (DTSC) liquid chromatography-mass spectrometry (LC-MS) returns actionable data in less than 1 h from collection of unprocessed biofluid. The systematic evaluation of this methodology finds that over 160 proteins are quantified in less than 1 h from 1 μL of whole blood. Furthermore, labile Angiotensin I and II bioactive peptides along with a panel of protein species can be measured at 8 min intervals with a 20 min initial lag using targeted MS. With these methods, we analyzed serum and plasma from 53 individuals and quantified Angiotensin I and II and over 150 proteins including at least 46 that were not detected with trypsin. We discuss some of the implications of real-time proteomics including the immediate potential to advance several clinical and research applications.

A review of advances in in vitro RNA preparation by ssRNAP

In vitro transcription (IVT) based on single-subunit RNA polymerase (ssRNAP) has enhanced the widespread application of RNA drugs in the biomedical field, showcasing unprecedented potential for disease prevention and treatment. While the classical enzyme T7 RNA polymerase (T7 RNAP) has driven significant progress in RNA production, several challenges persist. These challenges include the selectivity of the initiation nucleotide, low incorporation efficiency of modified nucleotides, limited processivity on certain templates, heterogeneity at the 3' end of RNA products, and high level of double-stranded RNA (dsRNA) byproducts. No review has systematically addressed the efforts to overcome these challenges. To fill this gap, we reviewed recent advances in engineering T7 RNAP variants and the discovery of novel ssRNAPs aimed at addressing the shortcomings of T7 RNAP. We also discussed the underlying mechanisms of ssRNAP-mediated byproduct formation, strategies to mitigate dsRNA production using modified nucleotides, and for the first time to sorted out the application of artificial intelligence in IVT. Overall, this review summarizes the advances in RNA synthesis via IVT and provides potential strategies for improving RNA products. We believe that ssRNAPs with more excellent performance will be on the stage of RNA synthesis in the near future to meet the growing demands of both scientific research and pharmaceutical industry.

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Sympathetic nociceptive afferent signaling drives the chronic structural and functional autonomic remodeling after myocardial infarction

After myocardial infarction (MI), pathological autonomic remodeling, including vagal dysfunction and sympathoexcitation, occurs and predisposes to ventricular arrhythmias (VT/VF). The underlying factors that drive this remodeling, including the observed neuroinflammation and glial activation, remain unknown. We hypothesized that sympathetic nociceptive afferents underlie this remodeling post-MI. Epidural resiniferatoxin (RTX, to ablate sympathetic cardiac afferent neurons) vs. saline was administered in pigs prior to MI and autonomic and electrophysiological effects assessed four to six weeks post-infarction. Acute effects of afferent ablation after chronic MI were also assessed in a separate group of animals. Baroreflex sensitivity and vagal tone, as measured by parasympathetic neuronal activity and cardiac nociceptive responses, were improved in infarcted animals which received epidural RTX prior to MI. These animals also demonstrated reduced spinal cord inflammation and glial activation, downregulation of circulating stress and inflammatory pathways, and stabilization of electrophysiological parameters, with reduced VT/VF-inducibility. Epidural RTX after chronic MI also acutely restored vagal function and decreased VT/VF. These data suggest that cardiac spinal nociceptive afferents directly contribute to VT/VF susceptibility and MI-induced autonomic remodeling, including oxidative stress, inflammation, glial activation, and reduced vagal function, providing novel insights into the causal role of these afferents in driving sympathovagal imbalance after MI.

Single-Cell Proteomics Using Mass Spectrometry

Single-cell proteomics (SCP) is transforming our understanding of biological complexity by shifting from bulk proteomics, where signals are averaged over thousands of cells, to the proteome analysis of individual cells. This granular perspective reveals distinct cell states, population heterogeneity, and the underpinnings of disease pathogenesis that bulk approaches may obscure. However, SCP demands exceptional sensitivity, precise cell handling, and robust data processing to overcome the inherent challenges of analyzing picogram-level protein samples without amplification. Recent innovations in sample preparation, separations, data acquisition strategies, and specialized mass spectrometry instrumentation have substantially improved proteome coverage and throughput. Approaches that integrate complementary omics, streamline multi-step sample processing, and automate workflows through microfluidics and specialized platforms promise to further push SCP boundaries. Advances in computational methods, especially for data normalization and imputation, address the pervasive issue of missing values, enabling more reliable downstream biological interpretations. Despite these strides, higher throughput, reproducibility, and consensus best practices remain pressing needs in the field. This mini review summarizes the latest progress in SCP technology and software solutions, highlighting how closer integration of analytical, computational, and experimental strategies will facilitate deeper and broader coverage of single-cell proteomes.

Sympathetic nociceptive afferent signaling drives the chronic structural and functional autonomic remodeling after myocardial infarction

After myocardial infarction (MI), pathological autonomic remodeling, including vagal dysfunction and sympathoexcitation, occurs and predisposes to ventricular arrhythmias (VT/VF). The underlying factors that drive this remodeling, including the observed neuroinflammation and glial activation, remain unknown. We hypothesized that sympathetic nociceptive afferents underlie this remodeling post-MI. Epidural resiniferatoxin (RTX, to ablate sympathetic cardiac afferent neurons) vs. saline was administered in pigs prior to MI and autonomic and electrophysiological effects assessed four to six weeks post-infarction. Acute effects of afferent ablation after chronic MI were also assessed in a separate group of animals. Baroreflex sensitivity and vagal tone, as measured by parasympathetic neuronal activity and cardiac nociceptive responses, were improved in infarcted animals which received epidural RTX prior to MI. These animals also demonstrated reduced spinal cord inflammation and glial activation, downregulation of circulating stress and inflammatory pathways, and stabilization of electrophysiological parameters, with reduced VT/VF-inducibility. Epidural RTX after chronic MI also acutely restored vagal function and decreased VT/VF. These data suggest that cardiac spinal nociceptive afferents directly contribute to VT/VF susceptibility and MI-induced autonomic remodeling, including oxidative stress, inflammation, glial activation, and reduced vagal function, providing novel insights into the causal role of these afferents in driving sympathovagal imbalance after MI.

Global analysis of protein turnover dynamics in single cells

Single-cell proteomics (SCPs) has advanced significantly, yet it remains largely unidimensional, focusing primarily on protein abundances. In this study, we employed a pulsed stable isotope labeling by amino acids in cell culture (pSILAC) approach to simultaneously analyze protein abundance and turnover in single cells (SC-pSILAC). Using a state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from ∼4,000 proteins in single HeLa cells recapitulating known biology. We performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSCs) encompassing 6 sampling times over 2 months and analyzed >1,000 cells. Protein turnover dynamics highlighted differentiation-specific co-regulation of protein complexes with core histone turnover, discriminating dividing and non-dividing cells. Lastly, correlating cell diameter with the abundance of individual proteins showed that histones and some cell-cycle proteins do not scale with cell size. The SC-pSILAC method provides a multidimensional view of protein dynamics in single-cell biology.

In-depth plasma N-glycoproteome profiling using narrow-window data-independent acquisition on the Orbitrap Astral mass spectrometer

Recently, a conceptually new mass analyzer was introduced by pairing a quadrupole Orbitrap mass spectrometer with an asymmetric track lossless (Astral™) analyzer. This system provides >200 Hz MS/MS scanning speed, high resolving power, sensitivity, and mass accuracy. Due to its speed, the instrument allows for a narrow-window data-independent acquisition (nDIA) strategy, representing a new technical milestone in peptide-centric proteomics. However, this new system may also be applied to other complex and clinically important proteomes, such as the human plasma N-glycoproteome. Here, we evaluate the Orbitrap Astral mass spectrometer for the in-depth analysis of the plasma N-glycoproteome and pioneer a dedicated nDIA workflow, termed "nGlycoDIA", on glycopeptide enriched and crude plasma. This strategy leads to the cumulative identification of over 3000 unique glycoPSMs derived from 181 glycoproteins in just 40 minutes and covers a dynamic range of 7 orders of magnitude for a glycopeptide enriched plasma sample. Notably, we detect several glycosylated cytokines that have reported plasma concentrations in the ng/L range. Furthermore, shortening the gradient to 10 min still allows for the detection of almost 1850 (95% CI [1840-1860]) unique glycoPSMs, indicating that high-throughput in-depth clinical plasma glycoproteomics may be within reach.

Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics

Mass spectrometry-based proteomics is about 35 years old, and recent progress appears to be speeding up across all subfields. In this review, we focus on advances over the last two years in select areas within bottom-up proteomics, including approaches to high-throughput experiments, data analysis using machine learning, drug discovery, glycoproteomics, extracellular vesicle proteomics, and structural proteomics.

Proteomics reveals genetic mechanisms of cold resistance in Hezuo pig liver tissue

Cold stress poses a significant challenge to pig farming in northern China, leading to reduced productivity and, in severe cases, even mortality. However, the mechanisms underlying cold resistance in pigs are not well understood. To explore the genetic mechanism of cold resistance in pigs under low-temperature conditions, the cold-tolerant Hezuo pig was selected as a model. DIA proteomics analysis was performed on liver tissues from Hezuo pigs after 24 h of exposure to low-temperature treatments. The results showed that approximately 149 differential abundance proteins (DAPs) were detected (95 up-regulated and 54 down-regulated). GO analysis showed that these DAPs were mainly associated with lipid metabolism, vesicle fusion, and membrane function. KEGG analysis showed that these DAPs were primarily enriched in lipid metabolism-related pathways such as cholesterol metabolism and vitamin digestion and absorption. Comprehensive analysis identified APOA4, APOA2, SREBF2, ATP23, STX2, USO1, ETFA, RAB11FIP1, ETNPPL, and SGMS1 as potential key proteins involved in cold resistance mechanisms. The mRNA expression of the genes for two key candidate proteins (APOA4 and SREBF2), which are involved in lipid metabolism, was analyzed using qRT-PCR, revealing a significant up-regulation after low-temperature treatment. These findings provide significant insights into the mechanisms of cold resistance in animals and may serve as candidate markers for further studies on cold tolerance. SIGNIFICANCE: Cold resistance is one of the key traits in pigs and involves multiple complex coordinated regulatory mechanisms. However, its genetic mechanisms are not completely understood. In this study, a DIA proteomics approach was used to identify proteins and pathways associated with cold resistance in the liver of low-temperature-treated Hezuo pigs. These findings offer novel candidate proteins and key pathways for investigating the molecular mechanisms of cold resistance in Hezuo pigs, providing a base for further elucidating the mechanisms of cold tolerance in pigs.

A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis

The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. The MSAID Platform offers a fully automated, managed proteomics data pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw data to biological insights. Backed by the cloud-native search algorithm CHIMERYS, as well as scalable cloud compute instances and data lakes, the platform facilitates efficient processing of large data sets, automation of processing via the command line, systematic result storage, analysis, and visualization. The data lake supports elastically growing storage and unified query capabilities, facilitating large-scale analyses and efficient reuse of previously processed data, such as aggregating longitudinally acquired studies. Users interact with the platform via a web interface, CLI client, or API, providing flexible, automated access. Readily available tools for accessing result data include browser-based interrogation and one-click visualizations for statistical analysis. The platform streamlines research processes, making advanced and automated proteomic workflows accessible to a broader range of scientists. The MSAID Platform is globally available via https://platform.msaid.io.

Modified Decision Tree with Custom Splitting Logic Improves Generalization across Multiple Brains' Proteomic Data Sets of Alzheimer's Disease

Many factors negatively affect a generalization of the findings in discovery proteomics. They include differentiation between patient cohorts, a variety of experimental conditions, etc. We presented a machine-learning-based workflow for proteomics data analysis, aiming at improving generalizability across multiple data sets. In particular, we customized the decision tree model by introducing a new parameter, min_groups_leaf, which regulates the presence of the samples from each data set inside the model's leaves. Further, we analyzed a trend for the feature importance's curve as a function of the novel parameter for feature selection to a list of proteins with significantly improved generalization. The developed workflow was tested using five proteomic data sets obtained for post-mortem human brain samples of Alzheimer's disease. The data sets consisted of 535 LC-MS/MS acquisition files. The results were obtained for two different pipelines of data processing: (1) MS1-only processing based on DirectMS1 search engine and (2) a standard MS/MS-based one. Using the developed workflow, we found seven proteins with expression patterns that were unique for asymptomatic Alzheimer patients. Two of them, Serotransferrin TRFE and DNA repair nuclease APEX1, may be potentially important for explaining the lack of dementia in patients with the presence of neuritic plaques and neurofibrillary tangles.

In-Depth Proteomic Analysis of Tissue Interstitial Fluid Reveals Biomarker Candidates Related to Varying Differentiation Statuses in Gastric Adenocarcinoma

The proteomic heterogeneity of gastric adenocarcinoma (GC) has been extensively investigated at the bulk tissue level, which can only provide an average molecular state. In this study, we collected an in-depth quantitative proteomic dataset of tissues and interstitial fluids (ISFs) from both poorly and non-poorly differentiated GC and presented a comprehensive analysis from several perspectives. Comparison of proteomes between ISFs and tissues revealed that ISF exhibited higher abundances of proteins associated with blood microparticles, protein-lipid complexes, immunoglobulin complexes, and high-density lipoprotein particles. Also, consistent and inconsistent protein abundance changes between them were revealed by a correlation analysis. Interestingly, a more pronounced difference between tumors and normal adjacent tissues was found at the ISF level, which accurately reflected tissue properties compared to those of bulk tissue. Two ISF-derived biomarker candidates, calsyntenin-1 (CLSTN1) and prosaposin (PSAP), were identified by distinguishing patients with different differentiation statuses and were further validated in serum samples. Additionally, the silencing of CLSTN1 and PSAP was demonstrated to suppress cell proliferation, migration, and invasion in poorly differentiated gastric cancer cell lines. In summary, the ISF proteome offers a new perspective on tumor biology. This study provides a valuable resource that significantly enhances the understanding of GC and may ultimately benefit clinical practice.

Middle-throughput LC-MS-based Platelet Proteomics with Minute Sample Amounts Using Semiautomated Positive Pressure FASP in 384-Well Format

Comprehensive characterization of platelets requires various functional assays and analytical techniques, including omics disciplines, each demanding a separate aliquot of the given sample. Consequently, sample material for each assay is often highly limited, necessitating the downscaling of methods to work with just a few micrograms of platelet protein.Here, we present a novel sample preparation platform for proteomics analysis using only 3 μg of purified platelet protein, corresponding to 2 × 106 platelets, which can be obtained from approximately 2 to 8 μL of blood from a healthy individual (1.5 × 105-4.5 × 105 platelets/μL) or approximately 100 μL of blood from a patient with severe thrombocytopenia (<2 × 104 platelets/µL).Using this platform, we detected a significant fraction of key players in the platelet activation cascade and, most importantly, identified 36 clinically relevant platelet disease markers even with a non-state-of-the art instrument. This makes LC-MS-based proteomics a highly attractive alternative to conventional assays, which often require milliliters of blood. Our platform transitions from our previously established 96-well proteomics workflow (PF96), which has been successfully employed in numerous platelet proteomics studies, into the 384-well format. This transition is accompanied by (1) a more than two-fold increase in sensitivity, (2) improved reproducibility, (3) a four-fold increase in throughput, allowing 1,536 samples to be processed per lab worker per week, and (4) reduced sample preparation costs.Thus, LC-MS-based platelet proteomics offers a compelling alternative to immunoaffinity assays (which depend on antibody availability and quality), as well as to genomic assays (which can only reveal genotypes). In summary, in conjunction with recent advances in LC-MS instrumentation, our platform represents a highly valuable tool for rapid phenotyping of platelets in research with extraordinary potential for future employment in companion or routine diagnostics.

Deciphering functional tumor-immune crosstalk through highly multiplexed imaging and deep visual proteomics

Deciphering the intricate tumor-immune interactions within the microenvironment is crucial for advancing cancer immunotherapy. Here, we introduce mipDVP, an advanced approach integrating highly multiplexed imaging, single-cell laser microdissection, and sensitive mass spectrometry to spatially profile the proteomes of distinct cell populations in a human colorectal and tonsil cancer with high sensitivity. In a colorectal tumor-a representative cold tumor-we uncovered spatial compartmentalization of an immunosuppressive macrophage barrier that potentially impedes T cell infiltration. Spatial proteomic analysis revealed distinct functional states of T cells in different tumor compartments. In a tonsil cancer sample-a hot tumor-we identified significant proteomic heterogeneity among cells influenced by proximity to cytotoxic T cell subtypes. T cells in the tumor parenchyma exhibit metabolic adaptations to hypoxic regions. Our spatially resolved, highly multiplexed strategy deciphers the complex cellular interplay within the tumor microenvironment, offering valuable insights for identifying immunotherapy targets and predictive signatures.

diaPASEF Analysis for HLA-I Peptides Enables Quantification of Common Cancer Neoantigens

Human leukocyte antigen class I (HLA-I) molecules present short peptide sequences from endogenous or foreign proteins to cytotoxic T cells. The low abundance of HLA-I peptides poses significant technical challenges for their identification and accurate quantification. While mass spectrometry is currently a method of choice for direct system-wide identification of cellular immunopeptidomes, there is still a need for enhanced sensitivity in detecting and quantifying tumor-specific epitopes. As gas phase separation in data-dependent MS data acquisition increased HLA-I peptide detection by up to 50%, here, we aimed to evaluate the performance of data-independent acquisition (DIA) in combination with parallel accumulation serial fragmentation ion mobility (diaPASEF) for high-sensitivity identification of HLA presented peptides. Our streamlined diaPASEF workflow enabled identification of 11,412 unique peptides from 12.5 million A375 cells and 3426 8-11mers from as low as 500,000 cells with high reproducibility. By taking advantage of HLA binder-specific in silico predicted spectral libraries, we were able to further increase the number of identified HLA-I peptides. We applied SILAC-DIA to a mixture of labeled HLA-I peptides, calculated heavy-to-light ratios for 7742 peptides across five conditions and demonstrated that diaPASEF achieves high quantitative accuracy up to 5-fold dilution. Finally, we identified and quantified shared neoantigens in a monoallelic C1R cell line model. By spiking in heavy synthetic peptides, we verified the identification of the peptide sequences and calculated relative abundances for 13 neoantigens. Taken together, diaPASEF analysis workflows for HLA-I peptides can increase the peptidome coverage for lower sample amounts. The sensitivity and quantitative precision provided by DIA can enable the detection and quantification of less abundant peptide species such as neoantigens across samples from the same background.

Enhanced sensitivity and scalability with a Chip-Tip workflow enables deep single-cell proteomics

Single-cell proteomics (SCP) promises to revolutionize biomedicine by providing an unparalleled view of the proteome in individual cells. Here, we present a high-sensitivity SCP workflow named Chip-Tip, identifying >5,000 proteins in individual HeLa cells. It also facilitated direct detection of post-translational modifications in single cells, making the need for specific post-translational modification-enrichment unnecessary. Our study demonstrates the feasibility of processing up to 120 label-free SCP samples per day. An optimized tissue dissociation buffer enabled effective single-cell disaggregation of drug-treated cancer cell spheroids, refining overall SCP analysis. Analyzing nondirected human-induced pluripotent stem cell differentiation, we consistently quantified stem cell markers OCT4 and SOX2 in human-induced pluripotent stem cells and lineage markers such as GATA4 (endoderm), HAND1 (mesoderm) and MAP2 (ectoderm) in different embryoid body cells. Our workflow sets a benchmark in SCP for sensitivity and throughput, with broad applications in basic biology and biomedicine for identification of cell type-specific markers and therapeutic targets.

Plasma proteome variation and its genetic determinants in children and adolescents

Our current understanding of the determinants of plasma proteome variation during pediatric development remains incomplete. Here, we show that genetic variants, age, sex and body mass index significantly influence this variation. Using a streamlined and highly quantitative mass spectrometry-based proteomics workflow, we analyzed plasma from 2,147 children and adolescents, identifying 1,216 proteins after quality control. Notably, the levels of 70% of these were associated with at least one of the aforementioned factors, with protein levels also being predictive. Quantitative trait loci (QTLs) regulated at least one-third of the proteins; between a few percent and up to 30-fold. Together with excellent replication in an additional 1,000 children and 558 adults, this reveals substantial genetic effects on plasma protein levels, persisting from childhood into adulthood. Through Mendelian randomization and colocalization analyses, we identified 41 causal genes for 33 cardiometabolic traits, emphasizing the value of protein QTLs in drug target identification and disease understanding.

A comprehensive view of the molecular features within the serum and serum EV of Alzheimer's disease

Conventional Alzheimer's disease research mainly focuses on cerebrospinal fluid, which requires an invasive sampling procedure. This method carries inherent risks for patients and could potentially lower patient compliance. EVs (Extracellular Vesicles) and blood are two emerging noninvasive mediators reflecting the pathological changes of Alzheimer's disease. Integrating the two serum proteomic information based on DIA (Data Independent Acquisition) is conducive to the comparison of serological research strategies, mining pathological information of AD, and evaluating the potential of EVs and blood in the diagnosis of AD. We generated a combined proteomic data resource of 39 serum samples derived from patients with AD and from age-matched controls (AMC) and identified 639 PGs (protein groups) in serum samples and 714 PGs in serum EV samples. The differentially expressed protein groups identified in both serum and serum EV provide a reflective profile of the pathological characteristics associated with AD. The combined strategy performed well, identifying 40 potential diagnostic markers with AUC values above 0.85, including two molecular diagnostic models that achieved an effectiveness score of 0.991.

Differential Expression of ARG1 and MRC2 in Retinal Müller Glial Cells During Autoimmune Uveitis

Retinal Müller glial cells (RMG) play a crucial role in retinal neuroinflammation, including autoimmune uveitis. Increasing evidence supports their function as active modulators of immune responses and potential atypical antigen-presenting cells (APCs). To further investigate this hypothesis, we conducted a differential proteome analysis of primary equine RMG from healthy controls and horses with equine recurrent uveitis (ERU), a spontaneous model of autoimmune uveitis. This analysis identified 310 proteins with differential abundance. Among these, the Major Histocompatibility Complex (MHC) class II and the enzyme Arginase 1 (ARG1) were significantly enriched in RMG from uveitis-affected horses, whereas Mannose Receptor C-type 2 (MRC2) and its interactor Thrombospondin 1 (THBS1) were more abundant in healthy RMG. The detection of MHC class II in equine RMG, consistent with previous studies, validates the robustness of our approach. Furthermore, the identification of ARG1 and MRC2, together with THBS1, provides new insights into the immunomodulatory and antigen-presenting properties of RMG. Immunohistochemical analyses confirmed the proteomic findings and revealed the spatial distribution of ARG1 and MRC2. ARG1 and MRC2 are thus markers for RMG in the neuroinflammatory or physiological milieu and highlight potential differences in the immune function of RMG, particularly in antigen presentation.

Automated High-Throughput Affinity Capture-Mass Spectrometry Platform with Data-Independent Acquisition

Affinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small-molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process have limited its use in a high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small-molecule target enrichment in the 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis. The streamlined process significantly reduced both the overall and hands-on time needed for sample preparation. Additionally, we developed a data-independent acquisition-mass spectrometry (DIA-MS) method to establish an efficient label-free quantitative chemical proteomic kinome profiling workflow. DIA-MS yielded a coverage of ∼380 kinases, a > 60% increase compared to using a data-dependent acquisition (DDA)-MS method, and provided reproducible target profiling of the kinase inhibitor dasatinib. We further showcased the applicability of this AC-MS workflow for assessing the selectivity of two clinical-stage CDK9 inhibitors against ∼250 probe-enriched kinases. Our study here provides a roadmap for efficient target engagement and selectivity profiling in native cell or tissue lysates using AC-MS.

Phosphoproteomics for studying signaling pathways evoked by hormones of the renin-angiotensin system: A source of untapped potential

The Renin-Angiotensin System (RAS) is a complex neuroendocrine system consisting of a single precursor protein, angiotensinogen (AGT), which is processed into various peptide hormones, including the angiotensins [Ang I, Ang II, Ang III, Ang IV, Ang-(1-9), Ang-(1-7), Ang-(1-5), etc] and Alamandine-related peptides [Ang A, Alamandine, Ala-(1-5)], through intricate enzymatic pathways. Functionally, the RAS is divided into two axes with opposing effects: the classical axis, primarily consisting of Ang II acting through the AT1 receptor (AT1R), and in contrast the protective axis, which includes the receptors Mas, AT2R and MrgD and their respective ligands. A key area of RAS research is to gain a better understanding how signaling cascades elicited by these receptors lead to either "classical" or "protective" effects, as imbalances between the two axes can contribute to disease. On the other hand, therapeutic benefits can be achieved by selectively activating protective receptors and their associated signaling pathways. Traditionally, robust "hypothesis-driven" methods like Western blotting have built a solid knowledge foundation on RAS signaling. In this review, we introduce untargeted mass spectrometry-based phosphoproteomics, a "hypothesis-generating approach", to explore RAS signaling pathways. This technology enables the unbiased discovery of phosphorylation events, offering insights into previously unknown signaling mechanisms. We review the existing studies which used phosphoproteomics to study RAS signaling and discuss potential future applications of phosphoproteomics in RAS research including advantages and limitations. Ultimately, phosphoproteomics represents a so far underused tool for deepening our understanding of RAS signaling and unveiling novel therapeutic targets.

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

Filter-Aided Extracellular Vesicle Enrichment (FAEVEr) for Proteomics

Extracellular vesicles (EVs), membrane-delimited nanovesicles that are secreted by cells into the extracellular environment, are gaining substantial interest due to their involvement in cellular homeostasis and their contribution to disease pathology. The latter in particular has led to an exponential increase in interest in EVs as they are considered to be circulating packages containing potential biomarkers and are also a possible biological means to deliver drugs in a cell-specific manner. However, several challenges hamper straightforward proteome analysis of EVs as they are generally low abundant and reside in complex biological matrices. These matrices typically contain abundant proteins at concentrations that vastly exceed the concentrations of proteins found in the EV proteome. Therefore, extensive EV isolation and purification protocols are imperative and many have been developed, including (density) ultracentrifugation, size-exclusion, and precipitation methods. Here, we describe filter-aided extracellular vesicle enrichment (FAEVEr) as an approach based on 300 kDa molecular weight cutoff filtration that allows the processing of multiple samples in parallel within a reasonable time frame and at moderate cost. We demonstrate that FAEVEr is capable of quantitatively retaining EV particles on filters, while allowing extensive washing with the mild detergent Tween-20 to remove interfering non-EV proteins. The retained particles are directly lysed on the filter for a complete recovery of the EV protein cargo toward proteome analysis. Here, we validate and optimize FAEVEr on recombinant EV material and apply it on conditioned medium as well as on complex bovine serum, human plasma, and urine. Our results indicate that EVs isolated from MCF7 cells cultured with or without serum have a drastic different proteome because of nutrient deprivation.

Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics

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.

Global cellular proteo-lipidomic profiling of diverse lysosomal storage disease mutants using nMOST

Lysosomal storage diseases (LSDs) comprise ~50 monogenic disorders marked by the buildup of cellular material in lysosomes, yet systematic global molecular phenotyping of proteins and lipids is lacking. We present a nanoflow-based multiomic single-shot technology (nMOST) workflow that quantifies HeLa cell proteomes and lipidomes from over two dozen LSD mutants. Global cross-correlation analysis between lipids and proteins identified autophagy defects, notably the accumulation of ferritinophagy substrates and receptors, especially in NPC1-/- and NPC2-/- mutants, where lysosomes accumulate cholesterol. Autophagic and endocytic cargo delivery failures correlated with elevated lysophosphatidylcholine species and multilamellar structures visualized by cryo-electron tomography. Loss of mitochondrial cristae, MICOS complex components, and OXPHOS components rich in iron-sulfur cluster proteins in NPC2-/- cells was largely alleviated when iron was provided through the transferrin system. This study reveals how lysosomal dysfunction affects mitochondrial homeostasis and underscores nMOST as a valuable discovery tool for identifying molecular phenotypes across LSDs.

Restructuring a Complex Genetic Function on Episomal Vectors in Escherichia coli

Genetic functions have evolved over long timescales and can be encoded by multiple genes dispersed in different locations in genomes, and although contemporary molecular biology enables control over single genes, more complex genetic functions remain challenging. Here, we study the restructuring and mobilization of a complex genetic function encoded by 10 genes, originally expressed from four operons and two loci on the Escherichia coli genome. We observe subtle phenotypic differences and reduced fitness when expressed from episomal DNA and demonstrate that mutations in the transcriptional machinery are necessary for successful implementation in different bacteria. The work provides new approaches for advanced genome editing and constitutes a first step toward modularization and genome-level engineering of complex genetic functions.

An accessible workflow for high-sensitivity proteomics using parallel accumulation-serial fragmentation (PASEF)

Deep and accurate proteome analysis is crucial for understanding cellular processes and disease mechanisms; however, it is challenging to implement in routine settings. In this protocol, we combine a robust chromatographic platform with a high-performance mass spectrometric setup to enable routine yet in-depth proteome coverage for a broad community. This entails tip-based sample preparation and pre-formed gradients (Evosep One) combined with a trapped ion mobility time-of-flight mass spectrometer (timsTOF, Bruker). The timsTOF enables parallel accumulation-serial fragmentation (PASEF), in which ions are accumulated and separated by their ion mobility, maximizing ion usage and simplifying spectra. Combined with data-independent acquisition (DIA), it offers high peak sampling rates and near-complete ion coverage. Here, we explain how to balance quantitative accuracy, specificity, proteome coverage and sensitivity by choosing the best PASEF and DIA method parameters. The protocol describes how to set up the liquid chromatography-mass spectrometry system and enables PASEF method generation and evaluation for varied samples by using the py_diAID tool to optimally position isolation windows in the mass-to-charge and ion mobility space. Biological projects (e.g., triplicate proteome analysis in two conditions) can be performed in 3 d with ~3 h of hands-on time and minimal marginal cost. This results in reproducible quantification of 7,000 proteins in a human cancer cell line in quadruplicate 21-min injections and 29,000 phosphosites for phospho-enriched quadruplicates. Synchro-PASEF, a highly efficient, specific and novel scan mode, can be analyzed by Spectronaut or AlphaDIA, resulting in superior quantitative reproducibility because of its high sampling efficiency.

A multi-tissue longitudinal proteomics study to evaluate the suitability of post-mortem samples for pathophysiological research

Recent developments in mass spectrometry-based proteomics have established it as a robust tool for system-wide analyses essential for pathophysiological research. While post-mortem samples are a critical source for these studies, our understanding of how body decomposition influences the proteome remains limited. Here, we have revisited published data and conducted a clinically relevant time-course experiment in mice, revealing organ-specific proteome regulation after death, with only a fraction of these changes linked to protein autolysis. The liver and spleen exhibit significant proteomic alterations within hours post-mortem, whereas the heart displays only modest changes. Additionally, subcellular compartmentalization leads to an unexpected surge in proteome alterations at the earliest post-mortem interval (PMI). Additionally, we have conducted a comprehensive analysis of semi-tryptic peptides, revealing distinct consensus motifs for different organs, indicating organ-specific post-mortem protease activity. In conclusion, our findings emphasize the critical importance of considering PMI effects when designing proteomics studies, as these effects may significantly overshadow the impacts of diseases. Preferably, the samples should be taken in the operation room, especially for studies including subcellular compartmentalization or trans-organ comparison. In single-organ studies, the planning should involve careful control of PMI.

Chemoproteomic Profiling of C. albicans for Characterization of Anti-fungal Kinase Inhibitors

Candida albicans is a growing health concern as the leading causal agent of systemic candidiasis, a life-threatening fungal infection with a mortality rate of ~40% despite best available therapy. Yck2, a fungal casein kinase 1 (CK1) family member, is the cellular target of inhibitors YK-I-02 (YK) and MN-I-157 (MN). Here, multiplexed inhibitor beads paired with mass spectrometry (MIB/MS) employing ATP-competitive kinase inhibitors were used to define the selectivity of these Yck2 inhibitors across the global C. albicans proteome. The MIB matrix captured 89% of the known and predicted C. albicans protein kinases present in cell lysate. In MIB/MS competition assays, YK and MN demonstrated exquisite selectivity across the C. albicans fungal kinome with target engagement of only three CK1 homologs (Yck2, Yck22, and Hrr25) and a homolog of human p38α (Hog1). Additional chemoproteomics using a custom MN-kinobead identified only one additional C. albicans protein, confirming its remarkable fungal proteome-wide selectivity. To identify new Yck2 inhibitors with selectivity over Hog1, thirteen human CK1 kinase inhibitors were profiled for fungal kinase-binding activity using MIB/MS competition assays and in-cell NanoBRET target engagement assays. A new chemotype of family-selective Yck2 inhibitors with antifungal activity was identified. Together, these findings expand the application of MIB/MS proteomic profiling for non-human kinomes and demonstrate its utility in the discovery and development of selective inhibitors of fungal kinases with potential antimicrobial activity.

Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics

Identifying pharmacological probes for human proteins represents a key opportunity to accelerate the discovery of new therapeutics. High-content screening approaches to expand the ligandable proteome offer the potential to expedite the discovery of novel chemical probes to study protein function. Screening libraries of reactive fragments by chemoproteomics offers a compelling approach to ligand discovery, however, optimising sample throughput, proteomic depth, and data reproducibility remains a key challenge. We report a versatile, label-free quantification proteomics platform for competitive profiling of cysteine-reactive fragments against the native proteome. This high-throughput platform combines SP4 plate-based sample preparation with rapid chromatographic gradients. Data-independent acquisition performed on a Bruker timsTOF Pro 2 consistently identified ~23,000 cysteine sites per run, with a total of ~32,000 cysteine sites profiled in HEK293T and Jurkat lysate. Crucially, this depth in cysteinome coverage is met with high data completeness, enabling robust identification of liganded proteins. In this study, 80 reactive fragments were screened in two cell lines identifying >400 ligand-protein interactions. Hits were validated through concentration-response experiments and the platform was utilised for hit expansion and live cell experiments. This label-free platform represents a significant step forward in high-throughput proteomics to evaluate ligandability of cysteines across the human proteome.

Data acquisition approaches for single cell proteomics

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.

Spatial Proteomics towards cellular Resolution

INTRODUCTION

Spatial biology is an emerging interdisciplinary field facilitating biological discoveries through the use of spatial omics technologies. Recent advancements in spatial transcriptomics, spatial genomics (e.g. genetic mutations and epigenetic marks), multiplexed immunofluorescence, and spatial metabolomics/lipidomics have enabled high-resolution spatial profiling of gene expression, genetic variation, protein expression, and metabolites/lipids profiles in tissue. These developments contribute to a deeper understanding of the spatial organization within tissue microenvironments at the molecular level.

AREAS COVERED

This report provides an overview of the untargeted, bottom-up mass spectrometry (MS)-based spatial proteomics workflow. It highlights recent progress in tissue dissection, sample processing, bioinformatics, and liquid chromatography (LC)-MS technologies that are advancing spatial proteomics toward cellular resolution.

EXPERT OPINION

The field of untargeted MS-based spatial proteomics is rapidly evolving and holds great promise. To fully realize the potential of spatial proteomics, it is critical to advance data analysis and develop automated and intelligent tissue dissection at the cellular or subcellular level, along with high-throughput LC-MS analyses of thousands of samples. Achieving these goals will necessitate significant advancements in tissue dissection technologies, LC-MS instrumentation, and computational tools.

SPE-CZE-MS quantifies zeptomole concentrations of phosphorylated peptides

Capillary zone electrophoresis (CZE) is gaining attention in the field of single-cell proteomics for its ultra-low-flow and high-resolution separation abilities. Even more sample-limited yet rich in biological information are phosphoproteomics experiments, as the phosphoproteome composes only a fraction of the whole cellular proteome. Rapid analysis, high sensitivity, and maximization of sample utilization are paramount for single-cell analysis. Some challenges of coupling CZE analysis with mass spectrometry analysis (MS) of complex mixtures include 1. sensitivity due to volume loading limitations of CZE and 2. incompatibility of MS duty cycles with electropherographic timescales. Here, we address these two challenges as applied to single-cell equivalent phosphoproteomics experiments by interfacing a microchip-based CZE device integrated with a solid-phase-extraction (SPE) bed with the Orbitrap Astral mass spectrometer. Using 225 phosphorylated peptide standards and phosphorylated peptide-enriched mouse brain tissue, we investigate microchip-based SPE-CZE functionality, quantitative performance, and complementarity to nano-LC-MS (nLC-MS) analysis. We highlight unique SPE-CZE separation mechanisms that can empower fit-for-purpose applications in single-cell-equivalent phosphoproteomics.

Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics

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.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.