Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Proteomics of tumor and serum samples from isocitrate dehydrogenase-wildtype glioblastoma patients: is the detoxification of reactive oxygen species associated with shorter survival?

Proteomics has been little used for the identification of novel prognostic and/or therapeutic markers in isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GB). In this study, we analyzed 50 tumor and 30 serum samples from short- and long-term survivors of IDH-wildtype GB (STS and LTS, respectively) by data-independent acquisition mass spectrometry (DIA-MS)-based proteomics, with the aim of identifying such markers. DIA-MS identified 5422 and 826 normalized proteins in tumor and serum samples, respectively, with only three tumor proteins and 26 serum proteins displaying significant differential expression between the STS and LTS groups. These dysregulated proteins were principally associated with the detoxification of reactive oxygen species (ROS). In particular, GB patients in the STS group had high serum levels of malate dehydrogenase 1 (MDH1) and ribonuclease inhibitor 1 (RNH1) and low tumor levels of fatty acid-binding protein 7 (FABP7), which may have enabled them to maintain low ROS levels, counteracting the effects of the first-line treatment with radiotherapy plus concomitant and adjuvant temozolomide. A blood score built on the levels of MDH1 and RNH1 expression was found to be an independent prognostic factor for survival based on the serum proteome data for a cohort of 96 IDH-wildtype GB patients. This study highlights the utility of circulating MDH1 and RNH1 biomarkers for determining the prognosis of patients with IDH-wildtype GB. Furthermore, the pathways driven by these biomarkers, and the tumor FABP7 pathway, may constitute promising therapeutic targets for blocking ROS detoxification to overcome resistance to chemoradiotherapy in potential GB STS.

Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis

The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.

Bone Proteomics Method Optimization for Forensic Investigations

The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC-MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications.

Paradigm shift required for translational research on the brain

Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.

MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization

With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Proteomics of prostate cancer serum and plasma using low and high throughput approaches

Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.

Data-Independent Acquisition and Label-Free Quantification for Quantitative Proteomics Analysis of Human Cerebrospinal Fluid

This article presents a practical guide to mass spectrometry-based data-independent acquisition and label-free quantification for proteomics analysis applied to cerebrospinal fluid, offering a robust and scalable approach to probing the proteomic composition of the central nervous system. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Cerebrospinal fluid sample collection and preparation for mass spectrometry analysis Basic Protocol 2: Mass spectrometry sample analysis with data-independent acquisition Support Protocol: Data-dependent mass spectrometry and spectral library construction Basic Protocol 3: Analysis of mass spectrometry data.

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

High-Throughput Extracellular Matrix Proteomics of Human Lungs Enabled by Photocleavable Surfactant and diaPASEF

The extracellular matrix (ECM) is a complex assembly of proteins that provide interstitial scaffolding and elastic recoil for human lungs. The pulmonary extracellular matrix is increasingly recognized as an independent bioactive entity, by creating biochemical and mechanical signals that influence disease pathogenesis, making it an attractive therapeutic target. However, the pulmonary ECM proteome ("matrisome") remains challenging to analyze by mass spectrometry due to its inherent biophysical properties and relatively low abundance. Here, we introduce a strategy designed for rapid and efficient characterization of the human pulmonary ECM using the photocleavable surfactant Azo. We coupled this approach with trapped ion mobility MS with diaPASEF to maximize the depth of matrisome coverage. Using this strategy, we identify nearly 400 unique matrisome proteins with excellent reproducibility that are known to be important in lung biology, including key core matrisome proteins.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Comprehensive Matrisome Profiling of Human Adipose Tissue for Soft Tissue Reconstruction

For effective translation of research from tissue engineering and regenerative medicine domains, the cell-instructive extracellular matrix (ECM) of specific tissues must be accurately realized. As adipose tissue is gaining traction as a biomaterial for soft tissue reconstruction, with highly variable clinical outcomes obtained, a quantitative investigation of the adipose tissue matrisome is overdue. In this study, the human adipose tissue matrisome is profiled using quantitative sequential windowed acquisition of all theoretical fragment ion spectra - mass spectrometry (SWATH-MS) proteomics across a cohort of 13 fat-grafting patients, to provide characterization of ECM proteins within the tissue, and to understand human population variation. There are considerable differences in the expression of matrisome proteins across the patient cohort, with age and lipoaspirate collection technique contributing to the greatest variation across the core matrisome. A high abundance of basement membrane proteins (collagen IV and heparan sulfate proteoglycan) is detected, as well as fibrillar collagens I and II, reflecting the hierarchical structure of the tissue. This study provides a comprehensive proteomic evaluation of the adipose tissue matrisome and contributes to an enhanced understanding of the influence of the matrisome in adipose-related pathologies by providing a healthy reference cohort and details an experimental pipeline that can be further exploited for future biomaterial development.

Data-independent acquisition proteomics of cerebrospinal fluid implicates endoplasmic reticulum and inflammatory mechanisms in amyotrophic lateral sclerosis

While unbiased proteomics of human cerebrospinal fluid (CSF) has been used successfully to identify biomarkers of amyotrophic lateral sclerosis (ALS), high-abundance proteins mask the presence of lower abundance proteins that may have diagnostic and prognostic value. However, developments in mass spectrometry (MS) proteomic data acquisition methods offer improved protein depth. In this study, MS with library-free data-independent acquisition (DIA) was used to compare the CSF proteome of people with ALS (n = 40), healthy (n = 15) and disease (n = 8) controls. Quantified protein groups were subsequently correlated with clinical variables. Univariate analysis identified 7 proteins, all significantly upregulated in ALS versus healthy controls, and 9 with altered abundance in ALS versus disease controls (FDR < 0.1). Elevated chitotriosidase-1 (CHIT1) was common to both comparisons and was proportional to ALS disability progression rate (Pearson r = 0.41, FDR-adjusted p = 0.035) but not overall survival. Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1; upregulated in ALS versus healthy controls) was proportional to disability progression rate (Pearson r = 0.53, FDR-adjusted p = 0.003) and survival (Kaplan Meier log-rank p = 0.013) but not independently in multivariate proportional hazards models. Weighted correlation network analysis was used to identify functionally relevant modules of proteins. One module, enriched for inflammatory functions, was associated with age at symptom onset (Pearson r = 0.58, FDR-adjusted p = 0.005) and survival (Hazard Ratio = 1.78, FDR = 0.065), and a second module, enriched for endoplasmic reticulum proteins, was negatively correlated with disability progression rate (r = -0.42, FDR-adjusted p = 0.109). DIA acquisition methodology therefore strengthened the biomarker candidacy of CHIT1 and UCHL1 in ALS, while additionally highlighted inflammatory and endoplasmic reticulum proteins as novel sources of prognostic biomarkers.

Comparison of high-resolution mass spectrometry acquisition methods for the simultaneous quantification and identification of per- and polyfluoroalkyl substances (PFAS)

Simultaneous identification and quantification of per- and polyfluoroalkyl substances (PFAS) were evaluated for three quadrupole time-of-flight mass spectrometry (QTOF) acquisition methods. The acquisition methods investigated were MS-Only, all ion fragmentation (All-Ions), and automated tandem mass spectrometry (Auto-MS/MS). Target analytes were the 25 PFAS of US EPA Method 533 and the acquisition methods were evaluated by analyte response, limit of quantification (LOQ), accuracy, precision, and target-suspect screening identification limit (IL). PFAS LOQs were consistent across acquisition methods, with individual PFAS LOQs within an order of magnitude. The mean and range for MS-Only, All-Ions, and Auto-MS/MS are 1.3 (0.34-5.1), 2.1 (0.49-5.1), and 1.5 (0.20-5.1) pg on column. For fast data processing and tentative identification with lower confidence, MS-Only is recommended; however, this can lead to false-positives. Where high-confidence identification, structural characterisation, and quantification are desired, Auto-MS/MS is recommended; however, cycle time should be considered where many compounds are anticipated to be present. For comprehensive screening workflows and sample archiving, All-Ions is recommended, facilitating both quantification and retrospective analysis. This study validated HRMS acquisition approaches for quantification (based upon precursor data) and exploration of identification workflows for a range of PFAS compounds.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

DIAgui: a Shiny application to process the output from DIA-NN

Summary

DIAgui is an R package to simplify the processing of the report file from the DIA-NN software thanks to a Shiny application. It returns the quantification of either the precursors, the peptides, the proteins, or the genes thanks to the MaxLFQ algorithm. In addition, the latest version provides the Top3 and iBAQ quantification and the number of peptides used for the quantification. In the end, DIAgui produces ready-to-interpret files from the results of DIA mass spectrometry analysis and provides visualization and statistical tools that can be used in a user-friendly way.

Availability and implementation

Code and documentation are available on GitHub at https://github.com/marseille-proteomique/DIAgui. The package is written in R and also uses C++ code. A vignette shows its use in an R command line workflow.

Exploring immunoregulatory properties of a phenolic-enriched maple syrup extract through integrated proteomics and in vitro assays

Our laboratory has established a comprehensive program to investigate the phytochemical composition and nutritional/medicinal properties of phenolic-enriched maple syrup extract (MSX). Previous studies support MSX's therapeutic potential in diverse disease models, primarily through its anti-inflammatory effects. We recently demonstrated MSX's ability to regulate inflammatory signaling pathways and modulate inflammatory markers and proteins in a lipopolysaccharide (LPS)-induced peritonitis mouse model. However, MSX's immunoregulatory properties remain unknown. Herein, we investigated MSX's immunoregulatory properties for the first time using an integrated approach, combining data-dependent acquisition (DDA) and data-independent acquisition (DIA) strategies in a proteomic analysis of spleen tissue collected from the aforementioned peritonitis mouse model. Additionally, we conducted immune cell activation assays using macrophages and T lymphocytes. The DIA analysis unveiled a distinctive expression pattern involving three proteins-Krt83, Thoc2, and Vps16-which were present in both the control and MSX-treated groups but absent in the LPS-induced model group. Furthermore, proteins Ppih and Dpp9 exhibited significant reductions in the MSX-treated group. Ingenuity pathway analysis indicated that MSX may modulate several critical signaling pathways, exerting a suppressive effect on immune responses in various cell types involved in both innate and adaptive immunity. Our in vitro cell assays supported findings from the proteomics, revealing that MSX significantly reduced the levels of interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α) in LPS-stimulated human macrophage cells, as well as the levels of IL-2 in anti-CD3/anti-CD28-induced Jurkat T cells. Taken together, our investigations provide evidence that MSX exerts immune regulatory effects that impact both innate and adaptive immunity, which adds to the data supporting MSX's development as a functional food.

Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins

Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.

Measurement and utilization of the proteomic reactivity by mass spectrometry

Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.

Data-Independent Acquisition Peptidomics

In recent years, data-independent acquisition (DIA) has emerged as a powerful analysis method in biological mass spectrometry (MS). Compared to the previously predominant data-dependent acquisition (DDA), it offers a way to achieve greater reproducibility, sensitivity, and dynamic range in MS measurements. To make DIA accessible to non-expert users, a multifunctional, automated high-throughput pipeline DIAproteomics was implemented in the computational workflow framework "Nextflow" ( https://nextflow.io ). This allows high-throughput processing of proteomics and peptidomics DIA datasets on diverse computing infrastructures. This chapter provides a short summary and usage protocol guide for the most important modes of operation of this pipeline regarding the analysis of peptidomics datasets using the command line. In brief, DIAproteomics is a wrapper around the OpenSwathWorkflow and relies on either existing or ad-hoc generated spectral libraries from matching DDA runs. The OpenSwathWorkflow extracts chromatograms from the DIA runs and performs chromatographic peak-picking. Further downstream of the pipeline, these peaks are scored, aligned, and statistically evaluated for qualitative and quantitative differences across conditions depending on the user's interest. DIAproteomics is open-source and available under a permissive license. We encourage the scientific community to use or modify the pipeline to meet their specific requirements.

Label-Free Quantitation of Endogenous Peptides

Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.

Mass spectrometry in cerebrospinal fluid uncovers association of glycolysis biomarkers with Alzheimer's disease in a large clinical sample

Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disorder with contributions from multiple pathophysiological pathways. One of the long-recognized and important features of AD is disrupted cerebral glucose metabolism, but the underlying molecular basis remains unclear. In this study, unbiased mass spectrometry was used to survey CSF from a large clinical cohort, comparing patients who are either cognitively unimpaired (CU; n = 68), suffering from mild-cognitive impairment or dementia from AD (MCI-AD, n = 95; DEM-AD, n = 72), or other causes (MCI-other, n = 77; DEM-other, n = 23), or Normal Pressure Hydrocephalus (NPH, n = 57). The results revealed changes related to altered glucose metabolism. In particular, two glycolytic enzymes, pyruvate kinase (PKM) and aldolase A (ALDOA), were found to be upregulated in CSF from patients with AD compared to those with other neurological conditions. Increases in full-length PKM and ALDOA levels in CSF were confirmed with immunoblotting. Levels of these enzymes furthermore correlated negatively with CSF glucose in matching CSF samples. PKM levels were also found to be increased in AD in publicly available brain-tissue data. These results indicate that ALDOA and PKM may act as technically-robust potential biomarkers of glucose metabolism dysregulation in AD.

Label-free quantitative proteomics analysis for type 2 diabetes mellitus early diagnostic marker discovery using data-independent acquisition mass spectrometry (DIA-MS)

Type-2 diabetes mellitus (T2DM) therapy requires early diagnosis and complication avoidance. Unfortunately, current diagnostic markers do not meet these needs. Data-independent acquisition mass spectrometry (DIA-MS) offers a solution for clinical diagnosis, providing reliable and precise sample quantification. This study utilized DIA-MS to investigate proteomic differential expression in the serum of recently diagnosed T2DM patients. The study conducted a comparative protein expression analysis between healthy and recently diagnosed T2DM groups (discovery cohort). A candidate protein was then validated using enzyme-linked immune assay (ELISA) on serum samples collected from T2DM patients (n = 87) and healthy control (n = 60) (validation cohort). A total of 1074 proteins were identified, and 90 were significantly dysregulated between the two groups, including 32 newly associated with T2DM. Among these proteins, the expression of S100 calcium-binding protein A6 (S100A6) was validated by ELISA. It showed a significant increase in T2DM samples compared to the control group. It was evaluated as a biomarker using the receiver operating characteristic (ROC) curve, consistent with the DIA-MS results. Novel proteins are reported to be involved in the development and progression of T2DM. Further studies are required to investigate the differential expression of candidate marker proteins in a larger population of T2DM patients.

Advances in minimal residual disease monitoring in multiple myeloma

Multiple myeloma (MM) is characterized by the clonal expansion of plasma cells and the excretion of a monoclonal immunoglobulin (M-protein), or fragments thereof. This biomarker plays a key role in the diagnosis and monitoring of MM. Although there is currently no cure for MM, novel treatment modalities such as bispecific antibodies and CAR T-cell therapies have led to substantial improvement in survival. With the introduction of several classes of effective drugs, an increasing percentage of patients achieve a complete response. This poses new challenges to traditional electrophoretic and immunochemical M-protein diagnostics because these methods lack sensitivity to monitor minimal residual disease (MRD). In 2016, the International Myeloma Working Group (IMWG) expanded their disease response criteria with bone marrow-based MRD assessment using flow cytometry or next-generation sequencing in combination with imaging-based disease monitoring of extramedullary disease. MRD status is an important independent prognostic marker and its potential as a surrogate endpoint for progression-free survival is currently being studied. In addition, numerous clinical trials are investigating the added clinical value of MRD-guided therapy decisions in individual patients. Because of these novel clinical applications, repeated MRD evaluation is becoming common practice in clinical trials as well as in the management of patients outside clinical trials. In response to this, novel mass spectrometric methods that have been developed for blood-based MRD monitoring represent attractive minimally invasive alternatives to bone marrow-based MRD evaluation. This paves the way for dynamic MRD monitoring to allow the detection of early disease relapse, which may prove to be a crucial factor in facilitating future clinical implementation of MRD-guided therapy. This review provides an overview of state-of-the-art of MRD monitoring, describes new developments and applications of blood-based MRD monitoring, and suggests future directions for its successful integration into the clinical management of MM patients.

Clinical Proteomics for Solid Organ Tissues

The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Recent applications of quantitative mass spectrometry in biopharmaceutical process development and manufacturing

Biopharmaceutical products have seen rapid growth over the past few decades and continue to dominate the global pharmaceutical market. Aligning with the quality by design (QbD) framework and realization, recent advances in liquid chromatography-mass spectrometry (LC-MS) instrumentation and related techniques have enhanced biopharmaceutical characterization capabilities and have supported an increased development of biopharmaceutical products. Beyond its routine qualitative characterization, the quantitative feature of LC-MS has unique applications in biopharmaceutical process development and manufacturing. This review describes the recent applications and implications of the advancement of quantitative MS methods in biopharmaceutical process development, and characterization of biopharmaceutical product, product-related variants, and process-related impurities. We also provide insights on the emerging applications of quantitative MS in the lifecycle of biopharmaceutical product development including quality control in the Good Manufacturing Practice (GMP) environment and process analytical technology (PAT) practices during process development and manufacturing. Through collaboration with instrument and software vendors and regulatory agencies, we envision broader adoption of phase-appropriate quantitative MS-based methods for the analysis of biopharmaceutical products, which in turn has the potential to enable manufacture of higher quality products for patients.

CRISPR/Cas9-mediated knockout of the ubiquitin variant UbKEKS reveals a role in regulating nucleolar structures and composition

Ubiquitination is a post-translational modification responsible for one of the most complex multilayered communication and regulation systems in the cell. Over the past decades, new ubiquitin variants and ubiquitin-like proteins arose to further enrich this mechanism. Recently discovered ubiquitin variant UbKEKS can specifically target several proteins and yet, functional consequences of this new modification remain unknown. Depletion of UbKEKS induces accumulation of lamin A in the nucleoli, highlighting the need for deeper investigations about protein composition and functions regulation of this highly dynamic and membrane-less compartment. Using data-independent acquisition mass spectrometry and microscopy, we show that despite not impacting protein stability, UbKEKS is required to maintain a normal nucleolar organization. The absence of UbKEKS increases nucleoli's size and accentuate their circularity while disrupting dense fibrillar component and fibrillar centre structures. Moreover, depletion of UbKEKS leads to distinct changes in nucleolar composition. Lack of UbKEKS favours nucleolar sequestration of known apoptotic regulators such as IFI16 or p14ARF, resulting in an increase of apoptosis observed by flow cytometry and real-time monitoring. Overall, these results identify the first cellular functions of the UbKEKS variant and lay the foundation stone to establish UbKEKS as a new universal layer of regulation in the ubiquitination system.

Ultra-high-performance liquid chromatography-tandem high-resolution elevated mass spectrometry profiling of anti-methicillin-resistant Staphylococcus aureus metabolites from the endophytic bacteria collected from the weeds of a previous dumpsite

The culturable endophytic bacteria from the weeds Cleome rutidosperma of the family Cleomaceae and Digitaria sanguinalis of the family Poaceae obtained from a previous dumpsite in Pampanga, Philippines have been assessed for their anti-methicillin-resistant Staphylococcus aureus (MRSA) activity, and the analytes with such activity should be identified. However, due to the limited amounts collected from the isolation process, 1.8 mg yield of compound 1 from the endophyte of C. rutidosperma and 1.2 mg of a mixture from the endophyte of D. sanguinalis were selected for LC-MSE analysis. The production of compounds from the culturable endophytic bacteria Pseudomonas aeruginosa- determined by gene-sequencing, an untargeted and data-independent analysis (DIA) by ultra-high performance liquid chromatography-high resolution-elevated energy mass spectrometry (UHPLC-HR-MSE) technique was employed to profile the metabolites present in the two high-performance liquid chromatography (HPLC) fractions. The analytes present from P. aeruginosa detected by UHPLC-HR-MSE isolated from C. rutidosperma was phenazine-1-carboxylic acid (1), and for D. sanguinalis were chamigrenal (2), dialkyl resorcinol (3), and a pyoverdine elicitor (4). This study proves that UHPLC-HR-MSE could identify the anti-MRSA constituents in P. aeruginosa from commensal weeds C. rutidosperma and D. sanguinalis. The UHPLC-HR-MSE could help strengthen metabolomics antibacterial research and its related applications from a future perspective. Application of metabolomics research using UHPLC-HR-MSE could enhance the rehabilitation of dumpsites by the microbial community present.

Comparative analysis of the molecular response characteristics in Platycodon grandiflorus irradiated with heavy ion beams and X-rays

The response of plants to radiation is an essential topic in both space plant cultivation and mutation breeding by radiation. In this study, heavy ion beams (HIB) generated by the ground accelerator and X-rays (XR) were used as models of high linear energy transfer (LET) and low LET radiation to study the molecular response mechanism of Platycodon grandiflorus (P. grandiflorus) seedlings after irradiation. The gene and protein expression profiles of P. grandiflorus after 15 Gy HIB and 20 Gy XR radiation were analyzed by transcriptome and proteome. The results showed that the number of differentially expressed genes (DEGs) induced by HIB radiation was less than that of XR group, but HIB radiation induced more differentially expressed proteins (DEPs). Both HIB and XR radiation activated genes of RNA silencing, double-strand break repair and cell catabolic process. DNA replication and cell cycle related genes were down-regulated. The genes of cell wall and external encapsulating structure were up-regulated after HIB radiation. The gene expression of protein folding and glucan biosynthesis increased after XR radiation. Protein enrichment analysis indicated that HIB radiation resulted in differential protein enriched in photosynthesis and secondary metabolite biosynthesis pathways, while XR radiation induced differential protein of glyoxylate and dicarboxylate metabolism and carbon metabolism. After HIB and XR radiation, the genes of antioxidant system and terpenoid and polyketide metabolic pathways presented different expression patterns. HIB radiation led to the enrichment of non-homologous end-joining pathway. The results will contribute to understanding the biological effects of plants under space radiation.

Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus

OBJECTIVE

Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients.

METHOD

Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile.

RESULTS

In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617).

CONCLUSIONS

Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.

The salivary proteome in relation to oral mucositis in autologous hematopoietic stem cell transplantation recipients: a labelled and label-free proteomics approach

BACKGROUND

Oral mucositis is a frequently seen complication in the first weeks after hematopoietic stem cell transplantation recipients which can severely affects patients quality of life. In this study, a labelled and label-free proteomics approach were used to identify differences between the salivary proteomes of autologous hematopoietic stem cell transplantation (ASCT) recipients developing ulcerative oral mucositis (ULC-OM; WHO score ≥ 2) or not (NON-OM).

METHODS

In the TMT-labelled analysis we pooled saliva samples from 5 ULC-OM patients at each of 5 timepoints: baseline, 1, 2, 3 weeks and 3 months after ASCT and compared these with pooled samples from 5 NON-OM patients. For the label-free analysis we analyzed saliva samples from 9 ULC-OM and 10 NON-OM patients at 6 different timepoints (including 12 months after ASCT) with Data-Independent Acquisition (DIA). As spectral library, all samples were grouped (ULC-OM vs NON-OM) and analyzed with Data Dependent Analysis (DDA). PCA plots and a volcano plot were generated in RStudio and differently regulated proteins were analyzed using GO analysis with g:Profiler.

RESULTS

A different clustering of ULC-OM pools was found at baseline, weeks 2 and 3 after ASCT with TMT-labelled analysis. Using label-free analysis, week 1-3 samples clustered distinctly from the other timepoints. Unique and up-regulated proteins in the NON-OM group (DDA analysis) were involved in immune system-related processes, while those proteins in the ULC-OM group were intracellular proteins indicating cell lysis.

CONCLUSIONS

The salivary proteome in ASCT recipients has a tissue protective or tissue-damage signature, that corresponded with the absence or presence of ulcerative oral mucositis, respectively.

TRIAL REGISTRATION

The study is registered in the national trial register (NTR5760; automatically added to the International Clinical Trial Registry Platform).

Mass Spectrometry in Cerebrospinal Fluid Uncovers Association of Glycolysis Biomarkers with Alzheimer's Disease in a Large Clinical Sample

Background

Alzheimer's disease (AD) is a complex heterogenous neurodegenerative disorder, characterized by multiple pathophysiologies, including disruptions in brain metabolism. Defining markers for patient stratification across these pathophysiologies is an important step towards personalized treatment of AD. Efficient brain glucose metabolism is essential to sustain neuronal activity, but hypometabolism is consistently observed in AD. The molecular changes underlying these observations remain unclear. Recent studies have indicated dysregulation of several glycolysis markers in AD cerebrospinal fluid and tissue.

Methods

In this study, unbiased mass spectrometry was used to perform a deep proteomic survey of cerebrospinal fluid (CSF) from a large-scale clinically complex cohort to uncover changes related to impaired glucose metabolism.

Results

Two glycolytic enzymes, Pyruvate kinase (PKM) and Aldolase A (ALDOA) were found to be specifically upregulated in AD CSF compared to other non-AD groups. Presence of full-length protein of these enzymes in CSF was confirmed through immunoblotting. Levels of tryptic peptides of these enzymes correlated significantly with CSF glucose and CSF lactate in matching CSF samples.

Conclusions

The results presented here indicate a general dysregulation of glucose metabolism in the brain in AD. We highlight two markers ALDOA and PKM that may act as potential functionally-relevant biomarkers of glucose metabolism dysregulation in AD.

The protein and miRNA profile of plasma extracellular vesicles (EVs) can distinguish feline mammary adenocarcinoma patients from healthy feline controls

Feline mammary adenocarcinomas (FMA) are aggressive tumours with metastatic capability and limited treatment options. This study aims to investigate whether miRNAs associated with FMA tumours are secreted in extracellular vesicles (EVs) and whether they can potentially be used as a cancer biomarker in EVs from feline plasma. Tumours and matched tumour free margins from 10 felines with FMA were selected. Following a detailed literature search, RT-qPCR analyses of 90 miRNAs identified 8 miRNAs of interest for further investigation. Tumour tissue, margins and plasma were subsequently collected from a further 10 felines with FMA. EVs were isolated from the plasma. RT-qPCR expression analyses of the 8 miRNAs of interest were carried out in tumour tissue, margins, FMA EVs and control EVs. Additionally, proteomic analysis of both control and FMA plasma derived EVs was undertaken. RT-qPCR revealed significantly increased miR-20a and miR-15b in tumours compared to margins. A significant decrease in miR-15b and miR-20a was detected in EVs from FMAs compared to healthy feline EVs. The proteomic content of EVs distinguished FMAs from controls, with the protein targets of miR-20a and miR-15b also displaying lower levels in the EVs from patients with FMA. This study has demonstrated that miRNAs are readily detectable in both the tissue and plasma derived EVs from patients with FMA. These miRNAs and their protein targets are a detectable panel of markers in circulating plasma EVs that may inform future diagnostic tests for FMA in a non-invasive manner. Moreover, the clinical relevance of miR-20a and miR-15b warrants further investigation.

Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer

Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.

Proteomic profiling of cutaneous melanoma explains the aggressiveness of distant organ metastasis

Despite recent developments in managing metastatic melanomas, patients' overall survival remains low. Therefore, the current study aims to understand better the proteome-wide changes associated with melanoma metastasis that will assist with identifying targeted therapies. The latest development in mass spectrometry-based proteomics, together with extensive bioinformatics analysis, was used to investigate the molecular changes in 60 formalin-fixed and paraffin-embedded samples of primary and lymph nodes (LN) and distant organ metastatic melanomas. A total of 4631 proteins were identified, of which 72 and 453 were significantly changed between the LN and distant organ metastatic melanomas compared to the primary lesions (adj. p-value <0.05). An increase in proteins such as SLC9A3R1, CD20 and GRB2 and a decrease in CST6, SERPINB5 and ARG1 were associated with regional LN metastasis. By contrast, increased metastatic activities in distant organ metastatic melanomas were related to higher levels of CEACAM1, MC1R, AKT1 and MMP3-9 and decreased levels of CDKN2A, SDC1 and SDC4 proteins. Furthermore, machine learning analysis classified the lesions with up to 92% accuracy based on their metastatic status. The findings from this study provide up to date proteome-level information about the progression of melanomas to regional LN and distant organs, leading to the identification of protein signatures with potential for clinical translation.

Proteomic analysis reveals CAAP1 negatively correlates with platinum resistance in ovarian cancer

The present study sought to investigate the correlation between CAAP1 and platinum resistance in ovarian cancer and to preliminarily explore the potential biological function of CAAP1. Proteomic analysis was used to analyze differentially expressed proteins in platinum-sensitive and -resistant tissue samples of ovarian cancer. The Kaplan-Meier plotter was used for prognostic analysis. Immunohistochemistry assay and chi-square test were employed to explore the relationship between CAAP1 and platinum resistance in tissue samples. Lentivirus transfection, immunoprecipitation-mass spectrometry, and bioinformatics analysis were used to determine the potential biological function of CAAP1. Based on results, the expression level of CAAP1 was significantly higher in platinum-sensitive tissues compared to that in resistant tissues. Chi-square test demonstrated that there is a negative correlation between high expression of CAAP1 and platinum resistance. Overexpression of CAAP1 increased cis‑platinum sensitivity of the A2780/DDP cell line likely via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. In summary, there is a negative correlation between high expression of CAAP1 and platinum resistance. CAAP1 might be a potential biomarker for platinum resistance in ovarian cancer. SIGNIFICANCE: Platinum resistance is a key factor affecting the survival of ovarian cancer patients. Understanding the mechanisms of platinum resistance is highly important for ovarian cancer management. Here, we performed the DIA- and DDA-based proteomics to analyze differentially expressed proteins in tissue and cell samples of ovarian cancer. We found that the protein identified as CAAP1, which was first reported to be involved in the regulation of apoptosis, may be negatively correlates with platinum resistance in ovarian cancer. In addition, we also found that CAAP1 enhanced the sensitivity of platinum-resistant cells to cis‑platinum via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. Our data would be useful to reveal novel molecular mechanisms of platinum resistance in ovarian cancer.

Temporal Proteomic and Lipidomic Profiles of Cerulein-Induced Acute Pancreatitis Reveal Novel Insights for Metabolic Alterations in the Disease Pathogenesis

The pathophysiological mechanisms of acute pancreatitis (AP) are complex and have remained a mystery to date, but metabolism is gradually recognized as an important driver of AP onset and development. We used a cerulein-induced AP mouse model to conduct liquid chromatography-mass spectrometry (LC-MS/MS)-based time-course proteomics and lipidomics in order to better understand the underlying metabolic alterations linked with AP. Results showed that a series of significant changes in proteins over time with a boost in expression were enriched in lipase activity, lipoprotein, and lipids absorption and transport regulation. Furthermore, 16 proteins associated with lipid metabolism and signaling pathways together with the whole lipid species changing profile led to the vital identification of changing law in glycerides, phosphoglycerides, and free fatty acids. In addition to lipid metabolism and regulation-associated proteins, several digestive enzymes and adaptive anti-trypsin, stress response, and energy metabolism-related proteins showed an increment in abundance. Notably, central carbon and branched chain amino acid metabolism were enhanced during 0-24 h from the first cerulein stimulation. Taken together, this integrated proteomics and lipidomics revealed a novel metabolic insight into metabolites transforming rules that might be relevant to their function and drug targets investigation. (Created with Biorender.com.).

HA15 inhibits binding immunoglobulin protein and enhances the efficacy of radiation therapy in esophageal squamous cell carcinoma

Proteomic profiling is a promising approach to identify novel predictors of radiation response. The present study aimed to identify potential biomarkers of radiation response by serum proteomics in esophageal squamous cell carcinoma (ESCC) patients and find efficacious therapeutic drugs to enhance the efficacy of radiation therapy (RT). Serum binding immunoglobulin protein (BIP) was identified and validated as a treatment response predictor in ESCC patients treated with RT. Novel BIP inhibitor HA15 showed antitumor activity in ESCC cells by viability assay. Tumor cell colony formation and apoptosis assay revealed targeting BIP was associated with significant improvements of radiation sensitivity. Further analyses revealed that HA15 enhanced radiation-induced endoplasmic reticulum (ER) stress and immunogenic cell death (ICD) in ESCC. Clinical data indicated that high expression of BIP was associated with poor survival in patients of ESCC. In conclusion, proteomics analysis suggested BIP was a promising predictor of radiation response in locally advanced ESCC. The BIP inhibitor HA15 acted as an ER stress inducer and ICD stimulator; RT combined with HA15 was effective in suppressing the growth of ESCC in vitro and in vivo. Pretreatment BIP was an essential prognostic biomarker in locally advanced ESCC patients treated with RT.

Data-independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation-associated carcinomas

Early events associated with chronic inflammation and cancer involve significant remodeling of the extracellular matrix (ECM), which greatly affects its composition and functional properties. Using lung squamous cell carcinoma (LSCC), a chronic inflammation-associated cancer (CIAC), we optimized a robust proteomic pipeline to discover potential biomarker signatures and protein changes specifically in the stroma. We combined ECM enrichment from fresh human tissues, data-independent acquisition (DIA) strategies, and stringent statistical processing to analyze "Tumor" and matched adjacent histologically normal ("Matched Normal") tissues from patients with LSCC. Overall, 1802 protein groups were quantified with at least two unique peptides, and 56% of those proteins were annotated as "extracellular." Confirming dramatic ECM remodeling during CIAC progression, 529 proteins were significantly altered in the "Tumor" compared to "Matched Normal" tissues. The signature was typified by a coordinated loss of basement membrane proteins and small leucine-rich proteins. The dramatic increase in the stromal levels of SERPINH1/heat shock protein 47, that was discovered using our ECM proteomic pipeline, was validated by immunohistochemistry (IHC) of "Tumor" and "Matched Normal" tissues, obtained from an independent cohort of LSCC patients. This integrated workflow provided novel insights into ECM remodeling during CIAC progression, and identified potential biomarker signatures and future therapeutic targets.

Development of a spectral library for the discovery of altered genomic events in Mycobacterium avium associated with virulence using mass spectrometry-based proteogenomic analysis

Mycobacterium avium is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. M. avium has ∼4,500 predicted protein-coding regions on average, which can help discover several variants at the proteome level. Many of them are potentially associated with virulence; thus, identifying such proteins can be a helpful feature in developing panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of M. avium with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using i) a protein database for Mycobacterium tuberculosis, ii) a M. avium genome six-frame translated database, and iii) a variant protein database of M. avium. A search of mass spectrometry data against M. avium protein database resulted in identifying 2,954 proteins. Further, proteogenomic analyses aided in identifying 1,301 novel peptide sequences and correcting translation start sites for 15 proteins. Ultimately, we created a spectral library of M. avium proteins, including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the M. avium proteome. Thus, we present an M. avium spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.

Omics-based approaches for the systematic profiling of mitochondrial biology

Mitochondria are essential for cellular functions such as metabolism and apoptosis. They dynamically adapt to the changing environmental demands by adjusting their protein, nucleic acid, metabolite, and lipid contents. In addition, the mitochondrial components are modulated on different levels in response to changes, including abundance, activity, and interaction. A wide range of omics-based approaches has been developed to be able to explore mitochondrial adaptation and how mitochondrial function is compromised in disease contexts. Here, we provide an overview of the omics methods that allow us to systematically investigate the different aspects of mitochondrial biology. In addition, we show examples of how these methods have provided new biological insights. The emerging use of these toolboxes provides a more comprehensive understanding of the processes underlying mitochondrial function.

Bringing synapses into focus: Recent advances in synaptic imaging and mass-spectrometry for studying synaptopathy

Synapses are integral for healthy brain function and are becoming increasingly recognized as key structures in the early stages of brain disease. Understanding the pathological processes driving synaptic dysfunction will unlock new therapeutic opportunities for some of the most devastating diseases of our time. To achieve this we need a solid repertoire of imaging and molecular tools to interrogate synaptic biology at greater resolution. Synapses have historically been examined in small numbers, using highly technical imaging modalities, or in bulk, using crude molecular approaches. However, recent advances in imaging techniques are allowing us to analyze large numbers of synapses, at single-synapse resolution. Furthermore, multiplexing is now achievable with some of these approaches, meaning we can examine multiple proteins at individual synapses in intact tissue. New molecular techniques now allow accurate quantification of proteins from isolated synapses. The development of increasingly sensitive mass-spectrometry equipment means we can now scan the synaptic molecular landscape almost in totality and see how this changes in disease. As we embrace these new technical developments, synapses will be viewed with clearer focus, and the field of synaptopathy will become richer with insightful and high-quality data. Here, we will discuss some of the ways in which synaptic interrogation is being facilitated by methodological advances, focusing on imaging, and mass spectrometry.

10 years of extracellular matrix proteomics: Accomplishments, challenges, and future perspectives

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural scaffold of multicellular organisms. In addition to its structural role, the ECM conveys signals orchestrating cellular phenotypes. Alterations of ECM composition, abundance, structure, or mechanics, have been linked to diseases and disorders affecting all physiological systems, including fibrosis and cancer. Deciphering the protein composition of the ECM and how it changes in pathophysiological contexts is thus the first step toward understanding the roles of the ECM in health and disease and toward the development of therapeutic strategies to correct disease-causing ECM alterations. Potentially, the ECM also represents a vast, yet untapped reservoir of disease biomarkers. ECM proteins are characterized by unique biochemical properties that have hindered their study: they are large, heavily and uniquely post-translationally modified, and highly insoluble. Overcoming these challenges, we and others have devised mass-spectrometry-based proteomic approaches to define the ECM composition, or "matrisome", of tissues. This review provides a historical overview of ECM proteomics research and presents the latest advances that now allow the profiling of the ECM of healthy and diseased tissues. The second part highlights recent examples illustrating how ECM proteomics has emerged as a powerful discovery pipeline to identify prognostic cancer biomarkers. The third part discusses remaining challenges limiting our ability to translate findings to clinical application and proposes approaches to overcome them. Last, the review introduces readers to resources available to facilitate the interpretation of ECM proteomics datasets. The ECM was once thought to be impenetrable. MS-based proteomics has proven to be a powerful tool to decode the ECM. In light of the progress made over the past decade, there are reasons to believe that the in-depth exploration of the matrisome is within reach and that we may soon witness the first translational application of ECM proteomics.

Proteomic and Bioinformatic Tools to Identify Potential Hub Proteins in the Audiogenic Seizure-Prone Hamster GASH/Sal

The GASH/Sal (Genetic Audiogenic Seizure Hamster, Salamanca) is a model of audiogenic seizures with the epileptogenic focus localized in the inferior colliculus (IC). The sound-induced seizures exhibit a short latency (7–9 s), which implies innate protein disturbances in the IC as a basis for seizure susceptibility and generation. Here, we aim to study the protein profile in the GASH/Sal IC in comparison to controls. Protein samples from the IC were processed for enzymatic digestion and then analyzed by mass spectrometry in Data-Independent Acquisition mode. After identifying the proteins using the UniProt database, we selected those with differential expression and performed ontological analyses, as well as gene-protein interaction studies using bioinformatics tools. We identified 5254 proteins; among them, 184 were differentially expressed proteins (DEPs), with 126 upregulated and 58 downregulated proteins, and 10 of the DEPs directly related to epilepsy. Moreover, 12 and 7 proteins were uniquely found in the GASH/Sal or the control. The results indicated a protein profile alteration in the epileptogenic nucleus that might underlie the inborn occurring audiogenic seizures in the GASH/Sal model. In summary, this study supports the use of bioinformatics methods in proteomics to delve into the relationship between molecular-level protein mechanisms and the pathobiology of rodent models of audiogenic seizures.

Antigen discovery for the development of cancer immunotherapy

Central to successful cancer immunotherapy is effective T cell antitumor immunity. Multiple targeted immunotherapies engineered to invigorate T cell-driven antitumor immunity rely on identifying the repertoire of T cell antigens expressed on the tumor cell surface. Mass spectrometry-based survey of such antigens ("immunopeptidomics") combined with other omics platforms and computational algorithms has been instrumental in identifying and quantifying tumor-derived T cell antigens. In this review, we discuss the types of tumor antigens that have emerged for targeted cancer immunotherapy and the immunopeptidomics methods that are central in MHC peptide identification and quantification. We provide an overview of the strength and limitations of mass spectrometry-driven approaches and how they have been integrated with other technologies to discover targetable T cell antigens for cancer immunotherapy. We highlight some of the emerging cancer immunotherapies that successfully capitalized on immunopeptidomics, their challenges, and mass spectrometry-based strategies that can support their development.

Automated proteomic sample preparation: The key component for high throughput and quantitative mass spectrometry analysis

Sample preparation for mass spectrometry-based proteomics has many tedious and time-consuming steps that can introduce analytical errors. In particular, the steps around the proteolytic digestion of protein samples are prone to inconsistency. One route for reliable sample processing is the development and optimization of a workflow utilizing an automated liquid handling workstation. Diligent assessment of the sample type, protocol design, reagents, and incubation conditions can significantly improve the speed and consistency of preparation. When combining robust liquid chromatography-mass spectrometry with either discovery or targeted methods, automated sample preparation facilitates increased throughput and reproducible quantitation of biomarker candidates. These improvements in analysis are also essential to process the large patient cohorts necessary to validate a candidate biomarker for potential clinical use. This article reviews the steps in the workflow, optimization strategies, and known applications in clinical, pharmaceutical, and research fields that demonstrate the broad utility for improved automation of sample preparation in the proteomic field.