Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020

Novel serum protein biomarker panel for early diagnosis of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Late presentation of disease at the time of diagnosis is one of the major reasons for dismal prognostic outcomes for PDAC patients. Currently, there is a lack of clinical biomarkers, which can be used to diagnose PDAC patients at an early resectable stage. This study performed proteomic mass spectrometry to identify novel blood-based biomarkers for early diagnosis of PDAC. Serum specimens from 88 PDAC patients and 88 healthy controls (60 discovery cohort and 28 validation cohort) were analyzed using data independent acquisition high resolution mass spectrometry to identify candidate biomarker proteins. A total of 249 proteins were identified and quantified by the mass spectrometric analysis. Six proteins were markedly (>1.5 fold) and significantly (p < .05; q < 0.1) increased in PDAC patients compared to healthy controls in discovery cohort. Notably, four of these six proteins were significantly upregulated in an independent validation cohort. The top three upregulated proteins (i.e., Polymeric Immunoglobulin Receptor [PIGR], von Willebrand Factor [vWF], and Fibrinogen) were validated using enzyme linked immunosorbent assay, which led to selection of PIGR and vWF as a diagnostic biomarker panel for PDAC. The panel showed high ability to diagnose early stage (stage I and II) PDAC patients (area under the curve [AUC]: 0.8926), which was further improved after the addition of clinically used prognostic biomarker (Ca 19-9) to the panel (AUC: 0.9798). In conclusion, a novel serum protein biomarker panel for early diagnosis of PDAC was identified.

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Comparative salivary proteomics analysis of children with and without early childhood caries using the DIA approach: A pilot study

OBJECTIVE

To screen differentially expressed proteins (DEPs) in the saliva of Early childhood caries (ECC) with different degrees of severity.

METHODS

The proteomic profiles of salivary of children with ECC of varying severity by data independent acquisition data independent acquisition (DIA) technique. A total of 12 preschool children aged 3-5 years were included in this study.

RESULTS

In this study, a total of 15,083 peptides and 1944 proteins were quantified; The results of DEPs screening showed that 34 DEPs were identified between the group H and the group LC, including 18 up-regulated proteins and 16 down-regulated proteins; 34 DEPs were screened between the group H and the group HC, including 17 up-regulated proteins and 17 down-regulated proteins; 42 DEPs were screened between the group LC and the group HC, including 18 up-regulated proteins and 24 down-regulated proteins. Among these DEPs, we screened several key proteins that may play a role in ECC, such as MK, histone H4, TGFβ3, ZG16B, MUC20, and SMR-3B.

CONCLUSION

Salivary proteins, as important host factors of caries, are differentially expressed between the saliva of ECC children and healthy children. Specific DEPs are expected to become potential biomarkers for the diagnosis of ECC.

Aqueous humor proteomics analyzed by bioinformatics and machine learning in PDR cases versus controls

BACKGROUND

To comprehend the complexities of pathophysiological mechanisms and molecular events that contribute to proliferative diabetic retinopathy (PDR) and evaluate the diagnostic value of aqueous humor (AH) in monitoring the onset of PDR.

METHODS

A cohort containing 16 PDR and 10 cataract patients and another validation cohort containing 8 PDR and 4 cataract patients were studied. AH was collected and subjected to proteomics analyses. Bioinformatics analysis and a machine learning-based pipeline called inference of biomolecular combinations with minimal bias were used to explore the functional relevance, hub proteins, and biomarkers.

RESULTS

Deep profiling of AH proteomes revealed several insights. First, the combination of SIAE, SEMA7A, GNS, and IGKV3D-15 and the combination of ATP6AP1, SPARCL1, and SERPINA7 could serve as surrogate protein biomarkers for monitoring PDR progression. Second, ALB, FN1, ACTB, SERPINA1, C3, and VTN acted as hub proteins in the profiling of AH proteomes. SERPINA1 was the protein with the highest correlation coefficient not only for BCVA but also for the duration of diabetes. Third, "Complement and coagulation cascades" was an important pathway for PDR development.

CONCLUSIONS

AH proteomics provides stable and accurate biomarkers for early warning and diagnosis of PDR. This study provides a deep understanding of the molecular mechanisms of PDR and a rich resource for optimizing PDR management.

Two-Dimensional Fourier Transform Ion Cyclotron Resonance Mass Spectrometry of N-Linked Glycopeptides

Glycosylation is a common modification across living organisms and plays a central role in understanding biological systems and disease. Our ability to probe the gylcome has grown exponentially in the past several decades. However, further improvements to the analytical toolbox available to researchers would allow for increased capabilities to probe structure and function of biological systems and to improve disease treatment. This article applies the developing technique of two-dimensional Fourier transform ion cyclotron resonance mass spectrometry to a glycoproteomic workflow for the standard glycoproteins coral tree lectin (CTL) and bovine ribonuclease B (BRB) to demonstrate its feasibility as a tool for glycoproteomic workflows. 2D infrared multiphoton dissociation and electron capture dissociation spectra of CTL reveal comparable structural information to their 1D counterparts, confirming the site of glycosylation and monosaccharide composition of the glycan. Spectra collected in 2D of BRB reveal correlation lines of fragment ion scans and vertical precursor ion scans for data collected using infrared multiphoton dissociation and diagonal cleavage lines for data collected by electron capture dissociation. The use of similar techniques for glycoproteomic analysis may prove valuable in instances where chromatographic separation is undesirable or quadrupole isolation is insufficient.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Two-Dimensional Fourier Transform Ion Cyclotron Resonance Mass Spectrometry by Matrix-Assisted Laser Desorption Ionization

Two-dimensional Fourier transform ion cyclotron resonance (2D FTICR) mass spectrometry is a developing form of data-independent acquisition that allows for the simultaneous fragmentation and correlation of fragment ions to their precursors across a range of m/z values. The modern usage of 2D FTICR is performed using electrospray ionization (ESI) as the dried droplet preparation for matrix-assisted laser desorption ionization (MALDI) does not produce a consistent packet of ions over a number of scans. This work uses pneumatic spray techniques from mass spectrometry imaging to create a homogeneous surface for use with MALDI as an ionization source for 2D FTICR. A mixture of peptides and matrix was deposited onto a glass slide using an HTX pneumatic sprayer. MALDI was then used to ionize the peptide mixture for use with a standard 2D FTICR pulse sequence. The generated 2D spectrum reveals comparable structural information to spectra collected in a 1D experiment. Artifacts observed in the collected 2D MALDI spectra do not significantly differ from those expected from 2D ESI spectra.

Pesticide residues in animal-derived food: Current state and perspectives

Pesticides are widely used in the cultivation and breeding of agricultural products all over the world. However, their direct use or indirect pollution in animal breeding may lead to residual accumulation, migration, and metabolism in animal-derived foods, posing potential health risks to humans through the food chain. Therefore, it is necessary to detect pesticide residues in animal-derived food using simple, reliable, and sensitive methods. This review summarizes sample extraction and clean-up methods, as well as the instrumental determination technologies such as chromatography and chromatography-mass spectrometry for residual analysis in animal-derived foods, including meat, eggs and milk. Additionally, we perspectives on the future of this field. This information aims to assist relevant researchers in this area, contribute to the development of ideas and novel technical methods for residual detection, metabolic research and risk assessment of pesticides in animal-derived food.

Selecting potential biomarkers of plasma proteins in mares with endometritis

BACKGROUND

Endometritis is a common condition in mares that causes significant economic loss. Lacking obvious clinical signs, the clinical diagnosis of endometritis in mares relies on case-by-case clinical examinations, which can be particularly inefficient in large-scale farms. Therefore, the identification of potential biomarkers can serve as a non-invasive and efficient screening technique for endometritis in mares.

OBJECTIVES

To compare the blood proteome between fertile mares and mares with endometritis to identify biomarkers potentially associated with the development of endometritis and validate their predictive potential.

STUDY DESIGN

Observational and experimental study.

METHODS

Differentially expressed proteins were identified via Data Independent Acquisition (DIA) proteomic profiling in a screening cohort composed of eight healthy mares and eight mares with endometritis. Subsequently, enzyme-linked immunosorbent assay was employed that included a validation cohort of 40 healthy mares and 40 mares with endometritis to verify the accuracy and sensitivity of the identified proteins, thereby establishing a diagnostic threshold.

RESULTS

In the screening cohort, 12 proteins were significantly differentially expressed between endometritis mares and healthy controls (p < 0.05, outside the 1/1.2 to 1.2-fold). In the validation experiment, all six screened proteins were assessed with area under the curve (AUC) >0.8.

MAIN LIMITATIONS

The samples displayed certain levels of individual heterogeneity, and the number of samples analysed was limited. Additionally, the identified biomarkers were primarily associated with generalised inflammation, which potentially limited their specificity for endometritis.

CONCLUSION

Levels of plasma proteins are sensitive indicators of equine endometritis and potential tools for endometritis screening. In plasma, fetuin B, von Willebrand factor, vitamin K-dependent protein C, insulin-like growth factor binding protein 3, interleukin 1 receptor accessory protein, and type II cell cytoskeleton showed great predictive ability, with fetuin B being the best predictor (AUC = 0.93, 95% CI: 0.89-0.98), which performs better when combined with all six detected proteins (AUC = 1, 95% CI: 0.99-1.00).

TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics

Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the last decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using Escherichia coli K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS.

I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

SWATH-MS as a strategy for CHO host cell protein identification and quantification supporting the characterization of mAb purification platforms

Host cell proteins (HCPs) are process-related impurities expressed by the host cells during biotherapeutics' manufacturing, such as monoclonal antibodies (mAbs). Some challenging HCPs evade clearance during the downstream processing and can be co-purified with the molecule of interest, which may impact product stability, efficacy, and safety. Therefore, HCP content is a critical quality attribute to monitor and quantify across the bioprocess. Here we explored a mass spectrometry (MS)-based proteomics tool, the sequential window acquisition of all theoretical fragment-ion spectra (SWATH) strategy, as an orthogonal method to traditional ELISA. The SWATH workflow was applied for high-throughput individual HCP identification and quantification, supporting characterization of a mAb purification platform. The design space of HCP clearance of two polishing resins was evaluated through a design of experiment study. Absolute quantification of high-risk HCPs was achieved (reaching 1.8 and 4.2 ppm limits of quantification, for HCP A and B respectively) using HCP-specific synthetic heavy labeled peptide calibration curves. Profiling of other HCPs was also possible using an average calibration curve (using labeled peptides from different HCPs). The SWATH approach is a powerful tool for HCP assessment during bioprocess development enabling simultaneous monitoring and quantification of different individual HCPs and improving process understanding of their clearance.

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.

Uncovering PROTAC Sensitivity and Efficacy by Multidimensional Proteome Profiling: A Case for STAT3

Proteolysis-targeting chimera (PROTAC) is a powerful technology that can effectively trigger the degradation of target proteins. The intricate interplay among various factors leads to a heterogeneous drug response, bringing about significant challenges in comprehending drug mechanisms. Our study applied data-independent acquisition-based mass spectrometry to multidimensional proteome profiling of PROTAC (DIA-MPP) to uncover the efficacy and sensitivity of the PROTAC compound. We profiled the signal transducer and activator of transcription 3 (STAT3) PROTAC degrader in six leukemia and lymphoma cell lines under multiple conditions, demonstrating the pharmacodynamic properties and downstream biological responses. Through comparison between sensitive and insensitive cell lines, we revealed that STAT1 can be regarded as a biomarker for STAT3 PROTAC degrader, which was validated in cells, patient-derived organoids, and mouse models. These results set an example for a comprehensive description of the multidimensional PROTAC pharmacodynamic response and PROTAC drug sensitivity biomarker exploration.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Blood and urine multi-omics analysis of the impact of e-vaping, smoking, and cessation: from exposome to molecular responses

Cigarette smoking is a major preventable cause of morbidity and mortality. While quitting smoking is the best option, switching from cigarettes to non-combustible alternatives (NCAs) such as e-vapor products is a viable harm reduction approach for smokers who would otherwise continue to smoke. A key challenge for the clinical assessment of NCAs is that self-reported product use can be unreliable, compromising the proper evaluation of their risk reduction potential. In this cross-sectional study of 205 healthy volunteers, we combined comprehensive exposure characterization with in-depth multi-omics profiling to compare effects across four study groups: cigarette smokers (CS), e-vapor users (EV), former smokers (FS), and never smokers (NS). Multi-omics analyses included metabolomics, transcriptomics, DNA methylomics, proteomics, and lipidomics. Comparison of the molecular effects between CS and NS recapitulated several previous observations, such as increased inflammatory markers in CS. Generally, FS and EV demonstrated intermediate molecular effects between the NS and CS groups. Stratification of the FS and EV by combustion exposure markers suggested that this position on the spectrum between CS and NS was partially driven by non-compliance/dual use. Overall, this study highlights the importance of in-depth exposure characterization before biological effect characterization for any NCA assessment study.

Mass Spectral Library for DNA Adductomics

Endogenous electrophiles, ionizing and non-ionizing radiation, and hazardous chemicals present in the environment and diet can damage DNA by forming covalent adducts. DNA adducts can form in critical cancer driver genes and, if not repaired, may induce mutations during cell division, potentially leading to the onset of cancer. The detection and quantification of specific DNA adducts are some of the first steps in studying their role in carcinogenesis, the physiological conditions that lead to their production, and the risk assessment of exposure to specific genotoxic chemicals. Hundreds of different DNA adducts have been reported in the literature, and there is a critical need to establish a DNA adduct mass spectral database to facilitate the detection of previously observed DNA adducts and characterize newly discovered DNA adducts. We have collected synthetic DNA adduct standards from the research community, acquired MSn (n = 2, 3) fragmentation spectra using Orbitrap and Quadrupole-Time-of-Flight (Q-TOF) MS instrumentation, processed the spectral data and incorporated it into the MassBank of North America (MoNA) database, and created a DNA adduct portal Web site (https://sites.google.com/umn.edu/dnaadductportal) to serve as a central location for the DNA adduct mass spectra and metadata, including the spectral database downloadable in different formats. This spectral library should prove to be a valuable resource for the DNA adductomics community, accelerating research and improving our understanding of the role of DNA adducts in disease.

The 15-min (Sub)Cellular Proteome

Single-cell mass spectrometry (MS) opens a proteomic window onto the inner workings of cells. Here, we report the discovery characterization of the subcellular proteome of single, identified embryonic cells in record speed and molecular coverage. We integrated subcellular capillary microsampling, fast capillary electrophoresis (CE), high-efficiency nano-flow electrospray ionization, and orbitrap tandem MS. In proof-of-principle tests, we found shorter separation times to hinder proteome detection using DDA, but not DIA. Within a 15-min effective separation window, CE data-independent acquisition (DIA) was able to identify 1,161 proteins from single HeLa-cell-equivalent (∼200 pg) proteome digests vs. 401 proteins by the reference data-dependent acquisition (DDA) on the same platform. The approach measured 1,242 proteins from subcellular niches in an identified cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. CE-MS with DIA enables fast, sensitive, and deep profiling of the (sub)cellular proteome, expanding the bioanalytical toolbox of cell biology.

Authorship Contributions

P.N. and B.S. designed the study. L.R.P. collected the X. laevis cell aspirates. B.S. prepared and measured the samples. B.S. and P.N. analyzed the data and interpreted the results. P.N. and B.S. wrote the manuscript. All the authors commented on the manuscript.

Deciphering non-canonical ubiquitin signaling: biology and methodology

Ubiquitination is a dynamic post-translational modification that regulates virtually all cellular processes by modulating function, localization, interactions and turnover of thousands of substrates. Canonical ubiquitination involves the enzymatic cascade of E1, E2 and E3 enzymes that conjugate ubiquitin to lysine residues giving rise to monomeric ubiquitination and polymeric ubiquitination. Emerging research has established expansion of the ubiquitin code by non-canonical ubiquitination of N-termini and cysteine, serine and threonine residues. Generic methods for identifying ubiquitin substrates using mass spectrometry based proteomics often overlook non-canonical ubiquitinated substrates, suggesting that numerous undiscovered substrates of this modification exist. Moreover, there is a knowledge gap between in vitro studies and comprehensive understanding of the functional consequence of non-canonical ubiquitination in vivo. Here, we discuss the current knowledge about non-lysine ubiquitination, strategies to map the ubiquitinome and their applicability for studying non-canonical ubiquitination substrates and sites. Furthermore, we elucidate the available chemical biology toolbox and elaborate on missing links required to further unravel this less explored subsection of the ubiquitin system.

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.

Data-Independent Acquisition Proteomics and N-Terminomics Methods Reveal Alterations in Mitochondrial Function and Metabolism in Ischemic-Reperfused Hearts

Myocardial ischemia-reperfusion (IR) (stunning) injury triggers changes in the proteome and degradome of the heart. Here, we utilize quantitative proteomics and comprehensive degradomics to investigate the molecular mechanisms of IR injury in isolated rat hearts. The control group underwent aerobic perfusion, while the IR injury group underwent 20 min of ischemia and 30 min of reperfusion to induce a stunning injury. As MMP-2 activation has been shown to contribute to myocardial injury, hearts also underwent IR injury with ARP-100, an MMP-2-preferring inhibitor, to dissect the contribution of MMP-2 to IR injury. Using data-independent acquisition (DIA) and mass spectroscopy, we quantified 4468 proteins in ventricular extracts, whereby 447 proteins showed significant alterations among the three groups. We then used subtiligase-mediated N-terminomic labeling to identify more than a hundred specific cleavage sites. Among these protease substrates, 15 were identified following IR injury. We identified alterations in numerous proteins involved in mitochondrial function and metabolism following IR injury. Our findings provide valuable insights into the biochemical mechanisms of myocardial IR injury, suggesting alterations in reactive oxygen/nitrogen species handling and generation, fatty acid metabolism, mitochondrial function and metabolism, and cardiomyocyte contraction.

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.

Differences in aqueous humor protein profiles in patients with proliferative diabetic retinopathy before and after aflibercept treatment

PURPOSE

To investigate the changes in aqueous humor (AH) protein profiles before and after intravitreal aflibercept (IVA) treatment in patients with proliferative diabetic retinopathy (PDR).

METHODS

5 PDR patients provided 10 samples of AH before and after IVA treatment (pre-group vs. post-group). Proteins were identified using liquid chromatography-tandem mass spectrometry. Then, bioinformatics was employed to investigate the functional significance of differentially expressed proteins (DEPs) and hub proteins.

RESULTS

A total of 16 DEPs were identified, consisting of 8 downregulated proteins and 8 upregulated proteins. Bioinformatics analysis indicated that the most significantly enriched biological process was "blood coagulation, intrinsic pathway." The most significantly enriched signaling pathway was "complement and coagulation cascades." HBB, HPX, VEGFA, and CA1 were identified as hub proteins for IVA treatment.

CONCLUSIONS

Together with the downregulation of the intravitreal vascular endothelial growth factor level, IVA may also change the AH protein composition in PDR patients, with DEPs involved in the blood coagulation, intrinsic pathway, complement, and coagulation cascades. IVA treatment may protect against PDR by regulating HBB, HPX, VEGFA, and CA1 expression.

HGF secreted by hUC-MSCs mitigates neuronal apoptosis to repair the injured spinal cord via phosphorylation of Akt/FoxO3a pathway

Spinal cord injury (SCI) can cause severe and permanent neurological damage, and neuronal apoptosis could inhibit functional recovery of damaged spinal cord greatly. Human umbilical cord mesenchymal stem cells (hUC-MSCs) have great potential to repair SCI because of a series of advantages, including inhibition of neuronal apoptosis and multiple differentiation. The former may play an important role. However, the detailed regulatory mechanism associated with the inhibition of neuronal apoptosis after hUC-MSCs administration has not been elucidated. In this study, proteomics analysis of precious human cerebrospinal fluid (CSF) samples collected from SCI subjects receiving hUC-MSCs delivery indicated that hepatocyte growth factor (HGF) is largely involved in SCI repair. Furthermore, overexpression of HGF derived from hUC-MSCs could decrease reactive oxygen species to prevent neuron apoptosis to the maximum, and thus lead to significant recovery of spinal cord dysfunction. Moreover, HGF could promote phosphorylation of Akt/FoxO3a pathway to decrease reactive oxygen species to reduce neuron apoptosis. For the first time, our research revealed that HGF secreted by hUC-MSCs inhibits neuron apoptosis by phosphorylation of Akt/FoxO3a to repair SCI. This study provides important clues associated with drug selection for the effective treatment of SCI in humans.

A crustacean neuropeptide spectral library for data-independent acquisition (DIA) mass spectrometry applications

Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method of choice to achieve sensitive and accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and Cancer borealis as a model of choice for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Student's t-test, P value < 0.05) in the number of identifications achieved from the TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications was found in the library search compared to the library-free search. This work highlights the necessity for the use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.

Proteomics-based mass spectrometry profiling of SARS-CoV-2 infection from human nasopharyngeal samples

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the on-going global pandemic of coronavirus disease 2019 (COVID-19) that continues to pose a significant threat to public health worldwide. SARS-CoV-2 encodes four structural proteins namely membrane, nucleocapsid, spike, and envelope proteins that play essential roles in viral entry, fusion, and attachment to the host cell. Extensively glycosylated spike protein efficiently binds to the host angiotensin-converting enzyme 2 initiating viral entry and pathogenesis. Reverse transcriptase polymerase chain reaction on nasopharyngeal swab is the preferred method of sample collection and viral detection because it is a rapid, specific, and high-throughput technique. Alternate strategies such as proteomics and glycoproteomics-based mass spectrometry enable a more detailed and holistic view of the viral proteins and host-pathogen interactions and help in detection of potential disease markers. In this review, we highlight the use of mass spectrometry methods to profile the SARS-CoV-2 proteome from clinical nasopharyngeal swab samples. We also highlight the necessity for a comprehensive glycoproteomics mapping of SARS-CoV-2 from biological complex matrices to identify potential COVID-19 markers.

Mass spectrometry-based proteomics approaches to interrogate skeletal muscle adaptations to exercise

Acute exercise and chronic exercise training elicit beneficial whole-body changes in physiology that ultimately depend on profound alterations to the dynamics of tissue-specific proteins. Since the work accomplished during exercise owes predominantly to skeletal muscle, it has received the majority of interest from exercise scientists that attempt to unravel adaptive mechanisms accounting for salutary metabolic effects and performance improvements that arise from training. Contemporary scientists are also beginning to use mass spectrometry-based proteomics, which is emerging as a powerful approach to interrogate the muscle protein signature in a more comprehensive manner. Collectively, these technologies facilitate the analysis of skeletal muscle protein dynamics from several viewpoints, including changes to intracellular proteins (expression proteomics), secreted proteins (secretomics), post-translational modifications as well as fiber-, cell-, and organelle-specific changes. This review aims to highlight recent literature that has leveraged new workflows and advances in mass spectrometry-based proteomics to further our understanding of training-related changes in skeletal muscle. We call attention to untapped areas in skeletal muscle proteomics research relating to exercise training and metabolism, as well as basic points of contention when applying mass spectrometry-based analyses, particularly in the study of human biology. We further encourage researchers to couple the hypothesis-generating and descriptive nature of omics data with functional analyses that propel our understanding of the complex adaptive responses in skeletal muscle that occur with acute and chronic exercise.

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.

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Here, we describe a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 1002 individuals. Machine learning model determined as P4 panel (HABP2, CD163, AFP and PIVKA-II) clearly distinguish HCC from liver cirrhosis (LC, AUC 0.979, sensitivity 0.925, specificity 0.915) and healthy individuals (HC, AUC 0.992, sensitivity 0.975, specificity 1.000) in an independent validation cohort, outperforming existing clinical prediction strategies. Furthermore, the P4 panel can accurately predict LC to HCC conversion (AUC 0.890, sensitivity 0.909, specificity 0.877) with predicting HCC at a median of 11.4 months prior to imaging in prospective external validation cohorts (No.: Keshen 2018_005_02 and NCT03588442). These results suggest that proteomics-driven serum biomarker discovery provides a valuable reference for the liquid biopsy, and has great potential to improve early diagnosis of HCC.

Protocol to characterize basement membranes during kidney development using mass spectrometry-based label-free quantitative proteomics

Basement membranes are specialized extracellular matrices formed by highly insoluble structural proteins and extracellular matrix (ECM)-bound components that provide structural and signaling support to tissues and are dynamic during development. Here, we present a mass spectrometry-based label-free quantitative proteomics protocol to investigate basement membranes and define their composition using samples from human kidney organoids and mouse fetal kidneys. This protocol facilitates the study of basement membrane and other ECM components during development to improve our understanding of matrix regulation and function. For complete details on the use and execution of this protocol, please refer to Morais et al.1.

Protocol for quantitative proteomic analysis of heterogeneous adipose tissue-residing progenitor subpopulations in mice

Recent studies have revealed cellular heterogeneity of mesenchymal stromal cells and immune cells in adipose tissue and emphasized the need for quantitative analysis of small numbers of functionally distinct cells using state-of-the-art "omics" technologies. Here, we present an optimized protocol for precise protein quantification from minute amounts of samples. We describe steps for isolation of mouse adipose progenitor cells, proteomics sample preparation, mass spectrometry measurement, and computational analysis. This protocol can be adapted to other samples with limited amounts. For complete details on the use and execution of this protocol, please refer to Shan et al. (2022).1.

Small proteins in bacteria - Big challenges in prediction and identification

Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

Potential mechanism of the AgNCs-hydrogel in promoting the regeneration of diabetic infectious wounds

Diabetic infectious wound treatment is challenging due to insistent wound infections. To treat such complicated pathological diabetic infectious wounds, multifunctional materials need to be developed, and their mechanisms need to be understood. Here, we developed a material termed AgNCs-hydrogel, which is a multifunctional DNA hydrogel used as dressings by integrating it with antibacterial silver nanoclusters. The AgNCs-hydrogel was applied to promote the regeneration of diabetic infectious wounds in mice because it exhibited superior antibacterial activity and effective ROS-scavenging properties. Based on skin proteomics, we explored the potential mechanism of the AgNCs-hydrogel in treating mouse skin wounds. We found that the AgNCs-hydrogel can regulate some key proteins located primarily in the extracellular exosomes, involved in the negative regulation of the apoptotic process, and perform ATP binding to accelerate diabetic infected wound closure. Therefore, this study provided a multifunctional AgNCs-hydrogel and revealed its potential mechanism in promoting the regeneration of diabetic infectious wounds.

Top-Down Proteoform Analysis by 2D MS with Quadrupolar Detection

Two-dimensional mass spectrometry (2D MS) is a multiplexed tandem mass spectrometry method that does not rely on ion isolation to correlate the precursor and fragment ions. On a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS), 2D MS instead uses the modulation of precursor ion radii inside the ICR cell before fragmentation and yields 2D mass spectra that show the fragmentation patterns of all the analytes. In this study, we perform 2D MS for the first time with quadrupolar detection in a dynamically harmonized ICR cell. We discuss the advantages of quadrupolar detection in 2D MS and how we adapted existing data processing techniques for accurate frequency-to-mass conversion. We apply 2D MS with quadrupolar detection to the top-down analysis of covalently labeled ubiquitin with ECD fragmentation, and we develop a workflow for label-free relative quantification of biomolecule isoforms in 2D MS.

Inclusion of Porous Graphitic Carbon Chromatography Yields Greater Protein Identification and Compartment and Process Coverage and Enables More Reflective Protein-Level Label-Free Quantitation

The ubiquity of mass spectrometry-based bottom-up proteomic analyses as a component of biological investigation mandates the validation of methodologies that increase acquisition efficiency, improve sample coverage, and enhance profiling depth. Chromatographic separation is often ignored as an area of potential improvement, with most analyses relying on traditional reversed-phase liquid chromatography (RPLC); this consistent reliance on a single chromatographic paradigm fundamentally limits our view of the observable proteome. Herein, we build upon early reports and validate porous graphitic carbon chromatography (PGC) as a facile means to substantially enhance proteomic coverage without changes to sample preparation, instrument configuration, or acquisition methods. Analysis of offline fractionated cell line digests using both separations revealed an increase in peptide and protein identifications by 43% and 24%, respectively. Increased identifications provided more comprehensive coverage of cellular components and biological processes independent of protein abundance, highlighting the substantial quantity of proteomic information that may go undetected in standard analyses. We further utilize these data to reveal that label-free quantitative analyses using RPLC separations alone may not be reflective of actual protein constituency. Together, these data highlight the value and comprehension offered through PGC-MS proteomic analyses. RAW proteomic data have been uploaded to the MassIVE repository with the primary accession code MSV000091495.

Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches

Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.

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.

Small proteins in Gram-positive bacteria

Small proteins comprising less than 100 amino acids have been often ignored in bacterial genome annotations. About 10 years ago, focused efforts started to investigate whole peptidomes, which resulted in the discovery of a multitude of small proteins, but only a number of them have been characterized in detail. Generally, small proteins can be either membrane or cytosolic proteins. The latter interact with larger proteins, RNA or even metal ions. Here, we summarize our current knowledge on small proteins from Gram-positive bacteria with a special emphasis on the model organism Bacillus subtilis. Our examples include membrane-bound toxins of type I toxin-antitoxin systems, proteins that block the assembly of higher order structures, regulate sporulation or modulate the RNA degradosome. We do not consider antimicrobial peptides. Furthermore, we present methods for the identification and investigation of small proteins.

Cysteine Counting via Isotopic Chemical Labeling for Intact Mass Proteoform Identifications in Tissue

Top-down proteomics, the tandem mass spectrometric analysis of intact proteoforms, is the dominant method for proteoform characterization in complex mixtures. While this strategy produces detailed molecular information, it also requires extensive instrument time per mass spectrum obtained and thus compromises the depth of proteoform coverage that is accessible on liquid chromatography time scales. Such a top-down analysis is necessary for making original proteoform identifications, but once a proteoform has been confidently identified, the extensive characterization it provides may no longer be required for a subsequent identification of the same proteoform. We present a strategy to identify proteoforms in tissue samples on the basis of the combination of an intact mass determination with a measured count of the number of cysteine residues present in each proteoform. We developed and characterized a cysteine tagging chemistry suitable for the efficient and specific labeling of cysteine residues within intact proteoforms and for providing a count of the cysteine amino acids present. On simple protein mixtures, the tagging chemistry yields greater than 98% labeling of all cysteine residues, with a labeling specificity of greater than 95%. Similar results are observed on more complex samples. In a proof-of-principle study, proteoforms present in a human prostate tumor biopsy were characterized. Observed proteoforms, each characterized by an intact mass and a cysteine count, were grouped into proteoform families (groups of proteoforms originating from the same gene). We observed 2190 unique experimental proteoforms, 703 of which were grouped into 275 proteoform families.

Targeting neoadjuvant chemotherapy-induced metabolic reprogramming in pancreatic cancer promotes anti-tumor immunity and chemo-response

The molecular dynamics of pancreatic ductal adenocarcinoma (PDAC) under chemotherapy remain incompletely understood. The widespread use of neoadjuvant chemotherapy (NAC) provides a unique opportunity to investigate PDAC samples post-chemotherapy. Leveraging a cohort from Fudan University Shanghai Cancer Center, encompassing PDAC samples with and without exposure to neoadjuvant albumin-bound paclitaxel and gemcitabine (AG), we have compiled data from single-cell and spatial transcriptomes, proteomes, bulk transcriptomes, and metabolomes, deepening our comprehension of the molecular changes in PDACs in response to chemotherapy. Metabolic flux analysis reveals that NAC induces a reprogramming of PDAC metabolic patterns and enhances immunogenicity. Notably, NAC leads to the downregulation of glycolysis and the upregulation of CD36. Tissue microarray analysis demonstrates that high CD36 expression is linked to poorer survival in patients receiving postoperative AG. Targeting CD36 synergistically improves the PDAC response to AG both in vitro and in vivo, including patient-derived preclinical models.

A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).

Mass spectrometry as a lens into molecular human nutrition and health

Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Proteomic Profiling and Tumor Microenvironment Characterization Reveal Molecular and Immunological Hallmarks of Left-Sided and Right-Sided Colon Cancer Tumorigenesis

Left-sided and right-sided colon cancer (LSCC and RSCC) display different biological and clinical characteristics. However, the differences in their tumorigenesis and tumor microenvironment remain unclear. In this study, we profiled the proteomic landscapes of LSCC and RSCC with data-independent acquisition mass spectrometry (DIA-MS) using fresh tumor and adjacent normal tissues from 24 patients. A total of 7403 proteingroups were primarily identified with DIA-MS. After quality control, 7212 proteingroups were used for further analysis. Through comparing the difference in proteomic profiles between LSCC and RSCC samples, 2556 commonly and 1982 region-type-specific regulated proteingroups were characterized. During the development of LSCC and RSCC, metabolic, growth, cell division, cell adhesion, and migration pathways were found to be significantly dysregulated (P < 0.05), which was further confirmed by transcriptome data from TCGA. Compared to RSCC, most parts of the immune-related signatures, immune cell infiltration scores, and overall immune scores of LSCC were higher. The systematic elucidation of proteomic and transcriptomic profiles in this work improves our understanding of tumorigenesis and immune microenvironment characteristics of LSCC and RSCC.

Mouse Model of Fragile X Syndrome Analyzed by Quantitative Proteomics: A Comparison of Methods

Multiple methods for quantitative proteomics are available for proteome profiling. It is unclear which methods are most useful in situations involving deep proteome profiling and the detection of subtle distortions in the proteome. Here, we compared the performance of seven different strategies in the analysis of a mouse model of Fragile X Syndrome, involving the knockout of the fmr1 gene that is the leading cause of autism spectrum disorder. Focusing on the cerebellum, we show that data-independent acquisition (DIA) and the tandem mass tag (TMT)-based real-time search method (RTS) generated the most informative profiles, generating 334 and 329 significantly altered proteins, respectively, although the latter still suffered from ratio compression. Label-free methods such as BoxCar and a conventional data-dependent acquisition were too noisy to generate a reliable profile, while TMT methods that do not invoke RTS showed a suppressed dynamic range. The TMT method using the TMTpro reagents together with complementary ion quantification (ProC) overcomes ratio compression, but current limitations in ion detection reduce sensitivity. Overall, both DIA and RTS uncovered known regulators of the syndrome and detected alterations in calcium signaling pathways that are consistent with calcium deregulation recently observed in imaging studies. Data are available via ProteomeXchange with the identifier PXD039885.