Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry

IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.

Serum proteome reveals distinctive molecular features of H7N9- and SARS-CoV-2-infected patients

The coronavirus disease 2019 (COVID-19) pandemic has reminded us of human infections with the H7N9 virus and has raised questions related to the clinical and molecular pathophysiological diversity between the two diseases. Here, we performed a proteomic approach on sera samples from patients with H7N9-virus or SARS-CoV-2-virus infection and healthy controls. Compared to SARS-CoV-2, H7N9-virus infection caused elevated neutrophil concentrations, T cell exhaustion, and increased cytokine/interleukin secretion. Cell-type deconvolution and temporal analysis revealed that T cells and neutrophils could regulate the core immunological trajectory and influence the prognosis of patients with severe H7N9-virus infection. Elevated tissue-enhanced proteins combined with alterations of clinical biochemical indexes suggested that H7N9 infection induced more severe inflammatory organ injury and dysfunction in the liver and intestine. Further mechanical analysis revealed that the high concentration of neutrophils might impact the intestinal enterocyte cells through cytokine-receptor interaction, leading to intestinal damage in patients with H7N9-virus infection.

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

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

Osteoarthritis Year in Review 2024: Molecular biomarkers of osteoarthritis

OBJECTIVE

To provide a comprehensive and insightful summary of studies on molecular biomarkers at the gene, protein, and metabolite levels across different sample types and joints affected by osteoarthritis (OA).

METHODS

A literature search using the PubMed database for publications on OA biomarkers published between April 1, 2023 and April 30, 2024 was performed. Publications were then screened, examined at length, and summarized in a narrative review.

RESULTS

Out of the 364 papers initially identified, 44 publications met inclusion criteria, were relevant to OA, and were further examined for data extraction and discussion. These studies included 1 genomic analysis, 22 on protein markers, 6 on metabolite markers, 9 on inflammatory mediators, and 6 integrating multiple molecular levels.

CONCLUSIONS

Significant advancements have been made in identifying molecular biomarkers for OA, encompassing various joints, sample types, and molecular levels. Despite this progress, gaps remain, particularly in the need for validation, larger sample sizes, the integration of more clinical data, and consideration of covariates. For early detection and improved treatment of OA, continued efforts in biomarker identification are needed. This effort should seek to identify effective biomarkers that advance early detection, support prevention, evaluate interventions, and improve patient outcomes.

Proteomic Characterization of Transfusable Blood Components: Fresh Frozen Plasma, Cryoprecipitate, and Derived Extracellular Vesicles via Data-Independent Mass Spectrometry

Extracellular vesicles (EVs) are a heterogeneous collection of particles that play a crucial role in cell-to-cell communication, primarily due to their ability to transport molecules, such as proteins. Thus, profiling EV-associated proteins offers insight into their biological effects. EVs can be isolated from various biological fluids, including donor blood components such as cryoprecipitate and fresh frozen plasma (FFP). In this study, we conducted a proteomic analysis of five single donor units of cryoprecipitate, FFP, and EVs derived from these blood components using a quantitative mass spectrometry approach. EVs were successfully isolated from both cryoprecipitate and FFP based on community guidelines. We identified and quantified approximately 360 proteins across all sample groups. Principal component analysis and heatmaps revealed that both cryoprecipitate and FFP are similar. Similarly, EVs derived from cryoprecipitate and FFP are comparable. However, they differ between the originating fluids and their derived EVs. Using the R-package MS-DAP, differentially expressed proteins (DEPs) were identified. The DEPs for all comparisons, when submitted for gene enrichment analysis, are involved in the complement and coagulation pathways. The protein profile generated from this study will have important clinical implications in increasing our knowledge of the proteins that are associated with EVs derived from blood components.

Impact of Nanomedicine in Women's Metastatic Breast Cancer

Metastatic breast cancer is responsible for 90% of mortalities among women suffering from various types of breast cancers. Traditional cancer treatments such as chemotherapy and radiation therapy can cause significant side effects and may not be effective in many cases. However, recent advances in nanomedicine have shown great promise in the treatment of metastatic breast cancer. For example, nanomedicine demonstrated robust capacity in detection of metastatic cancers at early stages (i.e., before the metastatic cells leave the initial tumor site), which gives clinicians a timely option to change their treatment process (for example, instead of endocrine therapy they may use chemotherapy). Here recent advances in nanomedicine technology in the identification and treatment of metastatic breast cancers are reviewed.

Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules

Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals.

Proteogenomic Analysis Identifies a Causal Association between Plasma Apolipoprotein B Levels and Liver Cancer Risk

Liver oncogenesis is accompanied by discernible protein changes in the bloodstream. By employing plasma proteomic profiling, we can delve into the molecular mechanisms of liver cancer and pinpoint potential biomarkers. In this nested case-control study, we applied liquid chromatography-tandem mass spectrometry for proteome profiling in baseline plasma samples. Differential protein expression was determined and was subjected to functional enrichment, network, and Mendelian randomization (MR) analyses. We identified 193 proteins with notable differential levels between the groups. Of these proteins, MR analysis offered a compelling negative association between apolipoprotein B (APOB) and liver cancer. This association was further corroborated in the UK Biobank cohort: genetically predicted APOB levels were associated with a 31% (95% CI 19-42%) decreased risk of liver cancer; and phenotypic analysis indicated an 11% (95% CI 8-14%) decreased liver cancer risk for every 0.1 g/L increase of circulating APOB levels. Multivariable MR analysis suggested that the hepatic fat content might fully mediate the APOB-liver cancer connection. In summary, we identified some plasma proteins, particularly APOB, as potential biomarkers of liver cancer. Our findings underscore the intricate link between lipid metabolism and liver cancer, offering hints for targeted prophylactic strategies and early detection.

6-Plex Tandem Phosphorus Tags (TPT) for Accurate Quantitative Proteomics

Isobaric chemical labeling is a widely used strategy for high-throughput quantitative proteomics based on mass spectrometry. However, commercially available reagents have high costs in applications as well as the sensitivity limitations for detection of the trace protein samples. Previously, we developed a 2-plex isobaric labeling strategy based on phosphorus chemistry for ultrasensitive proteome quantification with high accuracy. In this work, 6-plex tandem phosphorus tags (TPT) were developed with 3-fold increase in the multiplexing quantitative capacity compared to the 2-plex isobaric phosphorus reagents introduced previously. High isotope enrichment of 18O labeling was incorporated into the phosphoryl group with three exchangeable oxygen atoms by using commercially available H218O. The combinational incorporations of 18O atom in reporter ions and balance group set up the low-cost foundation for development of multiplex TPT reagents. The novel 6-plex TPT reagents could produce phosphoramidate as unique reporter ions with approximately 1 Da mass difference and thus enable 6-plex quantitative analysis in high-resolution ESI-MS/MS analysis. Using HeLa cell tryptic peptides, we concluded that 6-plex TPT reagents could facilitate large-scale accurate quantitative proteomics with very high labeling efficiency.

High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response

Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.

Exploring affinity chromatography in proteomics: A comprehensive review

Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.

IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.

Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.

Deep proteomic analysis of obstetric antiphospholipid syndrome by DIA-MS of extracellular vesicle enriched fractions

Proteins in the plasma/serum mirror an individual's physiology. Circulating extracellular vesicles (EVs) proteins constitute a large portion of the plasma/serum proteome. Thus, deep and unbiased proteomic analysis of circulating plasma/serum extracellular vesicles holds promise for discovering disease biomarkers as well as revealing disease mechanisms. We established a workflow for simple, deep, and reproducible proteome analysis of both serum large and small EVs enriched fractions by ultracentrifugation plus 4D-data-independent acquisition mass spectrometry (4D-DIA-MS). In our cohort study of obstetric antiphospholipid syndrome (OAPS), 4270 and 3328 proteins were identified from large and small EVs enriched fractions respectively. Both of them revealed known or new pathways related to OAPS. Increased levels of von Willebrand factor (VWF) and insulin receptor (INSR) were identified as candidate biomarkers, which shed light on hypercoagulability and abnormal insulin signaling in disease progression. Our workflow will significantly promote our understanding of plasma/serum-based disease mechanisms and generate new biomarkers.

Differential Expression of Fibrinogen Alpha and Its Potential Involvement in Osteoarthritis Pathogenesis

The deterioration of cartilage tissue and other joint components composed of synovial tissue is a defining characteristic of osteoarthritis (OA) disease. Because of the lack of understanding of the underlying cause and important molecular pathways, there are currently no effective diagnostic or treatment methods for OA. The purpose of the study is to find a specific protein biomarker with high sensitivity and specificity in order to understand the pathophysiology of the disease and the underlying molecular pathways. We examined plasma samples of matched age and sex from OA patients (n = 150) and healthy controls (HC) (n = 70) to find proteins that were differentially expressed and validated by western blotting, enzyme-linked immunosorbent assay (ELISA), immunohistochemistry, and immunofluorescence. The results of western blotting demonstrated that the expression level of the fibrinogen alpha (FGA) protein was higher in plasma samples of osteoarthritis (OAPL) (p = 0.0343), and the ROC (receiver operating characteristic curve) curve supported the high sensitivity (95.22%) and specificity (74%) of FGA in OA plasma compared to healthy controls. FGA protein was detected to be deposited in the synovial tissue of OA patients (p = 0.0073). By activating the Toll-like receptor (TLR-4) receptor pathway in PBMCs (p = 0.04) and synovial tissue, FGA protein may be involved in the molecular mechanism of OA pathogenesis. Our findings collectively suggested that FGA, which is significantly expressed in OA plasma, synovial tissue, and PBMCs and is connected to the disease's advancement through the TLR-4 receptor, may serve as a diagnostic or disease-evolving tool for OA.

Clinical characterization and proteomic profiling of lean nonalcoholic fatty liver disease

Introduction

Obesity has been historically associated with nonalcoholic fatty liver disease (NAFLD), but it can also occur in lean individuals. However, limited data is available on this special group. To investigate the clinical and proteomic characteristics of lean subjects with NAFLD, and to identify potential clinical variables and plasma proteins for diagnosing NAFLD in lean individuals, we collected clinical data from a large cohort of 2,236 subjects.

Methods

Diagnosis of NAFLD relied on detecting pronounced hepatic steatosis through abdominal ultrasonography. Participants were categorized into four groups based on body mass index: overweight NAFLD, overweight control, lean NAFLD, and lean control. Plasma proteomic profiling was performed on samples from 20 subjects in each group. The lean NAFLD group was compared to both lean healthy and obese NAFLD groups across all data.

Results and discussion

The results indicated that the lean NAFLD group exhibited intermediate metabolic profiles, falling between those of the lean healthy and overweight NAFLD groups. Proteomic profiling of plasma in lean subjects with or without NAFLD revealed 45 statistically significant changes in proteins, of which 37 showed high diagnostic value (AUC > 0.7) for lean NAFLD. These potential biomarkers primarily involved lipid metabolism, the immune and complement systems, and platelet degranulation. Furthermore, AFM, GSN, CFH, HGFAC, MMP2, and MMP9 have been previously associated with NAFLD or NAFLD-related factors such as liver damage, insulin resistance, metabolic syndromes, and extracellular homeostasis. Overall, lean individuals with NAFLD exhibit distinct clinical profiles compared to overweight individuals with NAFLD. Despite having worse metabolic profiles than their healthy counterparts, lean NAFLD patients generally experience milder systemic metabolic disturbances compared to obese NAFLD patients. Additionally, the plasma proteomic profile is significantly altered in lean NAFLD, highlighting the potential of differentially expressed proteins as valuable biomarkers or therapeutic targets for diagnosing and treating NAFLD in this population.

Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.

Elucidating regulatory processes of intense physical activity by multi-omics analysis

BACKGROUND

Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations, such as firefighting, law enforcement, military, and sports. A better understanding of such processes can ultimately help improve human performance and prevent illnesses in the work environment.

METHODS

To study regulatory processes in intense physical activity simulating real-life conditions, we performed a multi-omics analysis of three biofluids (blood plasma, urine, and saliva) collected from 11 wildland firefighters before and after a 45 min, intense exercise regimen. Omics profiles post- versus pre-exercise were compared by Student's t-test followed by pathway analysis and comparison between the different omics modalities.

RESULTS

Our multi-omics analysis identified and quantified 3835 proteins, 730 lipids and 182 metabolites combining the 3 different types of samples. The blood plasma analysis revealed signatures of tissue damage and acute repair response accompanied by enhanced carbon metabolism to meet energy demands. The urine analysis showed a strong, concomitant regulation of 6 out of 8 identified proteins from the renin-angiotensin system supporting increased excretion of catabolites, reabsorption of nutrients and maintenance of fluid balance. In saliva, we observed a decrease in 3 pro-inflammatory cytokines and an increase in 8 antimicrobial peptides. A systematic literature review identified 6 papers that support an altered susceptibility to respiratory infection.

CONCLUSION

This study shows simultaneous regulatory signatures in biofluids indicative of homeostatic maintenance during intense physical activity with possible effects on increased infection susceptibility, suggesting that caution against respiratory diseases could benefit workers on highly physical demanding jobs.

Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies

Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.

The proteome of extracellular vesicles of the lung fluke Paragonimus kellicotti produced in vitro and in the lung cyst

Paragonimiasis is a zoonotic, food-borne trematode infection that affects 21 million people globally. Trematodes interact with their hosts via extracellular vesicles (EV) that carry protein and RNA cargo. We analyzed EV in excretory-secretory products (ESP) released by Paragonimus kellicotti adult worms cultured in vitro (EV ESP) and EV isolated from lung cyst fluid (EV CFP) recovered from infected gerbils. The majority of EV were approximately 30-50 nm in diameter. We identified 548 P. kellicotti-derived proteins in EV ESP by mass spectrometry and 8 proteins in EV CFP of which 7 were also present in EV ESP. No parasite-derived proteins were reliably detected in EV isolated from plasma samples. A cysteine protease (MK050848, CP-6) was the most abundant protein found in EV CFP in all technical and biological replicates. Immunolocalization of CP-6 showed strong labeling in the tegument of P. kellicotti and in the adjacent cyst and lung tissue that contained worm eggs. It is likely that CP-6 present in EV is involved in parasite-host interactions. These results provide new insights into interactions between Paragonimus and their mammalian hosts, and they provide potential clues for development of novel diagnostic tools and treatments.

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders

Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in silico deep learning-based methods to discover new enzymatic reaction links between metabolites and proteins to further expand the landscape of existing metabolite-protein interactome. Computational approaches to predict the enzymatic reaction link by metabolite-protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE)-based framework to predict MPI in genome-scale heterogeneous enzymatic reaction networks across ten organisms. By incorporating molecular features of metabolites and proteins as well as neighboring information in the MPI networks, our MPI-VGAE predictor achieved the best predictive performance compared to other machine learning methods. Moreover, when applying the MPI-VGAE framework to reconstruct hundreds of metabolic pathways, functional enzymatic reaction networks and a metabolite-metabolite interaction network, our method showed the most robust performance among all scenarios. To the best of our knowledge, this is the first MPI predictor by VGAE for enzymatic reaction link prediction. Furthermore, we implemented the MPI-VGAE framework to reconstruct the disease-specific MPI network based on the disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of novel enzymatic reaction links were identified. We further validated and explored the interactions of these enzymatic reactions using molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and facilitate the study of the disrupted metabolisms in diseases.

Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity

Type 1 diabetes (T1D) results from autoimmune destruction of β cells. Insufficient availability of biomarkers represents a significant gap in understanding the disease cause and progression. We conduct blinded, two-phase case-control plasma proteomics on the TEDDY study to identify biomarkers predictive of T1D development. Untargeted proteomics of 2,252 samples from 184 individuals identify 376 regulated proteins, showing alteration of complement, inflammatory signaling, and metabolic proteins even prior to autoimmunity onset. Extracellular matrix and antigen presentation proteins are differentially regulated in individuals who progress to T1D vs. those that remain in autoimmunity. Targeted proteomics measurements of 167 proteins in 6,426 samples from 990 individuals validate 83 biomarkers. A machine learning analysis predicts if individuals would remain in autoimmunity or develop T1D 6 months before autoantibody appearance, with areas under receiver operating characteristic curves of 0.871 and 0.918, respectively. Our study identifies and validates biomarkers, highlighting pathways affected during T1D development.

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).

Depletion of abundant plasma proteins for extracellular vesicle proteome characterization: benefits and pitfalls

Blood extracellular vesicles (EVs) play essential roles in cell-cell communication and their molecular cargo is a promising source of disease biomarkers. However, proteomic characterization of plasma-derived EVs is challenged by the presence of highly abundant plasma proteins, which limits the detection of less abundant proteins, and by the low number of EVs in biological fluids. The aim of this study was to investigate if the removal of abundant plasma proteins prior to EV isolation could improve plasma-derived EV characterization by LC-MS/MS and expand the proteome coverage. Plasma depletion was performed using a single-use spin column and EVs were isolated from only 100 µL of non-depleted and depleted plasma by size exclusion chromatography. Afterwards, EVs were characterized by nanoparticle tracking analysis and mass spectrometry-based proteomics using a data-independent acquisition approach. Depleted plasma-derived EVs had higher particle concentrations and particle-to-protein ratios. Depletion did increase the protein coverage with a higher number of identifications in EVs from depleted plasma (474 proteins) than from non-depleted (386 proteins). However, EVs derived from non-depleted plasma carried a slightly higher number of common EV markers. Overall, our findings suggest that plasma depletion prior to EV isolation by size exclusion chromatography provides higher yield and protein coverage, but slightly lower identification of EV markers. This study also showed the possibility to characterize the proteome of EVs derived from small plasma volumes, encouraging the clinical feasibility of the discovery of EV biomarkers.

Data-independent acquisition-based mass spectrometry(DIA-MS) for quantitative analysis of patients with chronic hepatitis B

Chronic hepatitis B is a significant public health problem and complex pathologic process, and unraveling the underlying mechanisms and pathophysiology is of great significance. Data independent acquisition mass spectrometry (DIA-MS) is a label-free quantitative proteomics method that has been successfully applied to the study of a wide range of diseases. The aim of this study was to apply DIA-MS for proteomic analysis of patients with chronic hepatitis B. We performed comprehensive proteomics analysis of protein expression in serum samples from HBV patients and healthy controls by using DIA-MS. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein network analysis were performed on differentially expressed proteins and were further combined with literature analysis. We successfully identified a total of 3786 serum proteins with a high quantitative performance from serum samples in this study. We identified 310 differentially expressed proteins (DEPs) (fold change > 1.5 and P value < 0.05 as the criteria for a significant difference) between HBV and healthy samples. A total of 242 upregulated proteins and 68 downregulated proteins were among the DEPs. Some protein expression levels were significantly elevated or decreased in patients with chronic hepatitis B, indicating a relation to chronic liver disease, which should be further investigated.

Comprehensive and deep profiling of the plasma proteome with protein corona on zeolite NaY

Proteomic characterization of plasma is critical for the development of novel pharmacodynamic biomarkers. However, the vast dynamic range renders the profiling of proteomes extremely challenging. Here, we synthesized zeolite NaY and developed a simple and rapid method to achieve comprehensive and deep profiling of the plasma proteome using the plasma protein corona formed on zeolite NaY. Specifically, zeolite NaY and plasma were co-incubated to form plasma protein corona on zeolite NaY (NaY-PPC), followed by conventional protein identification using liquid chromatography-tandem mass spectrometry. NaY was able to significantly enhance the detection of low-abundance plasma proteins, minimizing the "masking" effect caused by high-abundance proteins. The relative abundance of middle- and low-abundance proteins increased substantially from 2.54% to 54.41%, and the top 20 high-abundance proteins decreased from 83.63% to 25.77%. Notably, our method can quantify approximately 4000 plasma proteins with sensitivity up to pg/mL, compared to only about 600 proteins identified from untreated plasma samples. A pilot study based on plasma samples from 30 lung adenocarcinoma patients and 15 healthy subjects demonstrated that our method could successfully distinguish between healthy and disease states. In summary, this work provides an advantageous tool for the exploration of plasma proteomics and its translational applications.

Protective Alzheimer's disease-associated APP A673T variant predominantly decreases sAPPβ levels in cerebrospinal fluid and 2D/3D cell culture models

The rare A673T variant was the first variant found within the amyloid precursor protein (APP) gene conferring protection against Alzheimer's disease (AD). Thereafter, different studies have discovered that the carriers of the APP A673T variant show reduced levels of amyloid beta (Aβ) in the plasma and better cognitive performance at high age. Here, we analyzed cerebrospinal fluid (CSF) and plasma of APP A673T carriers and control individuals using a mass spectrometry-based proteomics approach to identify differentially regulated targets in an unbiased manner. Furthermore, the APP A673T variant was introduced into 2D and 3D neuronal cell culture models together with the pathogenic APP Swedish and London mutations. Consequently, we now report for the first time the protective effects of the APP A673T variant against AD-related alterations in the CSF, plasma, and brain biopsy samples from the frontal cortex. The CSF levels of soluble APPβ (sAPPβ) and Aβ42 were significantly decreased on average 9-26% among three APP A673T carriers as compared to three well-matched controls not carrying the protective variant. Consistent with these CSF findings, immunohistochemical assessment of cortical biopsy samples from the same APP A673T carriers did not reveal Aβ, phospho-tau, or p62 pathologies. We identified differentially regulated targets involved in protein phosphorylation, inflammation, and mitochondrial function in the CSF and plasma samples of APP A673T carriers. Some of the identified targets showed inverse levels in AD brain tissue with respect to increased AD-associated neurofibrillary pathology. In 2D and 3D neuronal cell culture models expressing APP with the Swedish and London mutations, the introduction of the APP A673T variant resulted in lower sAPPβ levels. Concomitantly, the levels of sAPPα were increased, while decreased levels of CTFβ and Aβ42 were detected in some of these models. Our findings emphasize the important role of APP-derived peptides in the pathogenesis of AD and demonstrate the effectiveness of the protective APP A673T variant to shift APP processing towards the non-amyloidogenic pathway in vitro even in the presence of two pathogenic mutations.

The protein corona from nanomedicine to environmental science

The protein corona spontaneously develops and evolves on the surface of nanoscale materials when they are exposed to biological environments, altering their physiochemical properties and affecting their subsequent interactions with biosystems. In this Review, we provide an overview of the current state of protein corona research in nanomedicine. We next discuss remaining challenges in the research methodology and characterization of the protein corona that slow the development of nanoparticle therapeutics and diagnostics, and we address how artificial intelligence can advance protein corona research as a complement to experimental research efforts. We then review emerging opportunities provided by the protein corona to address major issues in healthcare and environmental sciences. This Review details how mechanistic insights into nanoparticle protein corona formation can broadly address unmet clinical and environmental needs, as well as enhance the safety and efficacy of nanobiotechnology products.

Genome-scale enzymatic reaction prediction by variational graph autoencoders

Background

Enzymatic reaction networks are crucial to explore the mechanistic function of metabolites and proteins in biological systems and understanding the etiology of diseases and potential target for drug discovery. The increasing number of metabolic reactions allows the development of deep learning-based methods to discover new enzymatic reactions, which will expand the landscape of existing enzymatic reaction networks to investigate the disrupted metabolisms in diseases.

Results

In this study, we propose the MPI-VGAE framework to predict metabolite-protein interactions (MPI) in a genome-scale heterogeneous enzymatic reaction network across ten organisms with thousands of enzymatic reactions. We improved the Variational Graph Autoencoders (VGAE) model to incorporate both molecular features of metabolites and proteins as well as neighboring features to achieve the best predictive performance of MPI. The MPI-VGAE framework showed robust performance in the reconstruction of hundreds of metabolic pathways and five functional enzymatic reaction networks. The MPI-VGAE framework was also applied to a homogenous metabolic reaction network and achieved as high performance as other state-of-art methods. Furthermore, the MPI-VGAE framework could be implemented to reconstruct the disease-specific MPI network based on hundreds of disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of new potential enzymatic reactions were predicted and validated by molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and drug targets in real-world applications.

Data availability and implementation

The MPI-VGAE framework and datasets are publicly accessible on GitHub https://github.com/mmetalab/mpi-vgae .

Author Biographies

Cheng Wang received his Ph.D. in Chemistry from The Ohio State Univesity, USA. He is currently a Assistant Professor in School of Public Health at Shandong University, China. His research interests include bioinformatics, machine learning-based approach with applications to biomedical networks. Chuang Yuan is a research assistant at Shandong University. He obtained the MS degree in Biology at the University of Science and Technology of China. His research interests include biochemistry & molecular biology, cell biology, biomedicine, bioinformatics, and computational biology. Yahui Wang is a PhD student in Department of Chemistry at Washington University in St. Louis. Her research interests include biochemistry, mass spectrometry-based metabolomics, and cancer metabolism. Ranran Chen is a master graduate student in School of Public Health at University of Shandong, China. Yuying Shi is a master graduate student in School of Public Health at University of Shandong, China. Gary J. Patti is the Michael and Tana Powell Professor at Washington University in St. Louis, where he holds appointments in the Department of Chemisrty and the Department of Medicine. He is also the Senior Director of the Center for Metabolomics and Isotope Tracing at Washington University. His research interests include metabolomics, bioinformatics, high-throughput mass spectrometry, environmental health, cancer, and aging. Leyi Wei received his Ph.D. in Computer Science from Xiamen University, China. He is currently a Professor in School of Software at Shandong University, China. His research interests include machine learning and its applications to bioinformatics. Qingzhen Hou received his Ph.D. in the Centre for Integrative Bioinformatics VU (IBIVU) from Vrije Universiteit Amsterdam, the Netherlands. Since 2020, He has serveved as the head of Bioinformatics Center in National Institute of Health Data Science of China and Assistant Professor in School of Public Health, Shandong University, China. His areas of research are bioinformatics and computational biophysics.

Key points

Genome-scale heterogeneous networks of metabolite-protein interaction (MPI) based on thousands of enzymatic reactions across ten organisms were constructed semi-automatically.An enzymatic reaction prediction method called Metabolite-Protein Interaction Variational Graph Autoencoders (MPI-VGAE) was developed and optimized to achieve higher performance compared with existing machine learning methods by using both molecular features of metabolites and proteins.MPI-VGAE is broadly useful for applications involving the reconstruction of metabolic pathways, functional enzymatic reaction networks, and homogenous networks (e.g., metabolic reaction networks).By implementing MPI-VGAE to Alzheimer's disease and colorectal cancer, we obtained several novel disease-related protein-metabolite reactions with biological meanings. Moreover, we further investigated the reasonable binding details of protein-metabolite interactions using molecular docking approaches which provided useful information for disease mechanism and drug design.

Liquid Biopsies, Novel Approaches and Future Directions

Cancer is among the leading causes of death worldwide. Early diagnosis and prognosis are vital to improve patients' outcomes. The gold standard of tumor characterization leading to tumor diagnosis and prognosis is tissue biopsy. Amongst the constraints of tissue biopsy collection is the sampling frequency and the incomplete representation of the entire tumor bulk. Liquid biopsy approaches, including the analysis of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating miRNAs, and tumor-derived extracellular vesicles (EVs), as well as certain protein signatures that are released in the circulation from primary tumors and their metastatic sites, present a promising and more potent candidate for patient diagnosis and follow up monitoring. The minimally invasive nature of liquid biopsies, allowing frequent collection, can be used in the monitoring of therapy response in real time, allowing the development of novel approaches in the therapeutic management of cancer patients. In this review we will describe recent advances in the field of liquid biopsy markers focusing on their advantages and disadvantages.

Sex-Specific Silica Nanoparticle Protein Corona Compositions Exposed to Male and Female BALB/c Mice Plasmas

As various nanoparticles (NPs) are increasingly being used in nanomedicine products for more effective and less toxic therapy and diagnosis of diseases, there is a growing need to understand their biological fate in different sexes. Herein, we report a proof-of-concept result of sex-specific protein corona compositions on the surface of silica NPs as a function of their size and porosity upon incubation with plasma proteins of female and male BALB/c mice. Our results demonstrate substantial differences between male and female protein corona profiles on the surface of silica nanoparticles. By comparing protein abundances between male and female protein coronas of mesoporous silica nanoparticles and Stöber silica nanoparticles of ∼100, 50, and 100 nm in diameter, respectively, we detected 17, 4, and 4 distinct proteins, respectively, that were found at significantly different concentrations for these constructs. These initial findings demonstrate that animal sex can influence protein corona formation on silica NPs as a function of the physicochemical properties. A more thorough consideration of the role of plasma sex would enable nanomedicine community to design and develop safer and more efficient diagnostic and therapeutic nanomedicine products for both sexes.

The Small-Protein Enrichment Assay (SPEA) for Analysis of Low Abundance Peptide Hormones in Plasma

The analysis of low abundance peptide hormones such as insulin in blood plasma is difficult with unbiased mass spectrometry-based proteomics, as they are overshadowed by very abundant proteins such as albumin and IgG. The small-protein enrichment assay (SPEA) can greatly increase detection and discovery of these factors through specific enrichment, which enables fast and efficient analysis of many small-protein factors using a single untargeted LC-MS/MS acquisition. SPEA uses an alcohol-acid-based dissociation and precipitation step, prior to denaturing SEC to remove the large highly abundant plasma proteins leaving only a small-protein fraction. This is followed by an efficient sample preparation and cleanup before either data-dependent acquisition (DDA), or data-independent acquisition (DIA), LC-MS/MS analysis. Combining these workflows increases discovery of proteins, posttranslational modifications (PTMs), and cleavage sites using DDA, while DIA provides consistent analysis useful for large cohort analysis.

An Optimized Data-Independent Acquisition Strategy for Comprehensive Analysis of Human Plasma Proteome

Cartography of the plasma proteome remains technically challenging, primarily due to the abundance and dynamic range of plasma proteins and their concentrations, exceeding ten orders of magnitude, including low-abundant tissue-derived proteins in the pg/mL range. Data-independent acquisition mass spectrometry (DIA-MS) has seen advances in unbiased mass spectrometry-based proteomic analysis of the plasma proteome. Here, we describe a comprehensive proteomic workflow of human plasma from clinically relevant sample (10 μL) that includes anti-protein immunodepletion and highly sensitive sample preparation workflow, with optimized scheduled isolation DIA-MS and deep learning analysis. This approach results in over 960 proteins quantified from a single-shot analysis of broad dynamic range, across 8 orders of magnitude (8.2 ng/L to 0.67 g/L). We further compare data-dependent acquisition (DDA) MS to highlight the advantage in protein quantification and inter-sample variation. These developments have provided streamlined identification of the human plasma proteome, including low-abundant tissue-enriched proteins, and applications toward understanding the plasma proteome.

Reproducibility of double agar gel immunodiffusion test using stored serum and plasma from paracoccidioidomycosis patients

Background

Serological evaluation performed by double agar gel immunodiffusion test (DID) is used for diagnosis, evaluation of severity, management of paracoccidioidomycosis patients, and development of new clinical studies. For these reasons, the Botucatu Medical School of UNESP maintains a serum bank at the Experimental Research Unit with patient clinical data. This study aimed to evaluate the influence of the freeze-thaw cycle and different blood matrices on the titration of circulating antibodies.

Methods

The study included 207 patients with confirmed (etiology-demonstrated) or probable (serology-demonstrated) paracoccidioidomycosis, and DID was performed with culture filtrate from Paracoccidioides brasiliensis B339 as antigen. First experiment: the antibody levels were determined in serum samples from 160 patients with the chronic form and 20 with the acute/subacute form, stored at -80^oC for more than six months. Second experiment: titers of 81 samples of serum and plasma with ethylenediaminetetraacetic acid (EDTA) or heparin, from 27 patients, were compared according to matrix and effect of storage at -20^oC for up to six months. Differences of titers higher than one dilution were considered discordant.

Results

First experiment: test and retest presented concordant results in serum stored for up to three years, and discordant titers in low incidence in storage for four to six years but high incidence when stored for more than six years, including conversion from reagent test to non-reagent retest. Second experiment: serum, plasma-EDTA and plasma-heparin samples showed concordant titers, presenting direct correlation, with no interference of storage for up to six months.

Conclusions

Storage at -80^oC for up to six years has no or little influence on the serum titers determined by DID, permitting its safe use in studies depending on this parameter. The concordant titrations in different blood matrices demonstrated that the plasma can be used for immunodiffusion test in paracoccidioidomycosis, with stability for at least six months after storage at -20^oC.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Proteomics to study cancer immunity and improve treatment

Cancer survival and progression depend on the ability of tumor cells to avoid immune recognition. Advances in the understanding of cancer immunity and tumor immune escape mechanisms enabled the development of immunotherapeutic approaches. In patients with otherwise incurable metastatic cancers, immunotherapy resulted in unprecedented response rates with the potential for durable complete responses. However, primary and acquired resistance mechanisms limit the efficacy of immunotherapy. Further therapeutic advances require a deeper understanding of the interplay between immune cells and tumors. Most high-throughput studies within the past decade focused on an omics characterization at DNA and RNA level. However, proteins are the molecular effectors of genomic information; therefore, the study of proteins provides deeper understanding of cellular functions. Recent advances in mass spectrometry (MS)-based proteomics at a system-wide scale may allow translational and clinical discoveries by enabling the analysis of understudied post-translational modifications, subcellular protein localization, cell signaling, and protein-protein interactions. In this review, we discuss the potential contribution of MS-based proteomics to preclinical and clinical research findings in the context of tumor immunity and cancer immunotherapies.

High-Throughput and In-Depth Proteomic Profiling of 5 μL Plasma and Serum Using TMTpro 16-Plex

High-throughput and in-depth proteomic analysis of plasma and serum samples remains challenging due to the presence of multiple high-abundance proteins. Here, we provide a detailed protocol for proteomic analysis of serum and plasma specimens using a high-abundance protein depletion kit and TMTpro 16-plex reagents. This method requires only 5 μL serum or plasma, identifying and quantifying about 1000 proteins. A batch of 16 samples can be processed in 36 h. On average, each sample consumes about 1.5 h of mass spectrometer instrument time. Overall, our method can identify proteins across six orders of magnitude with high reproducibility (CV < 20%) using a shorter instrument time and less sample volume compared to existing methods. Thus, the method is suitable to be applied to large-scale proteomic studies.

Proteome Analysis of Whole Blood Collected by Volumetric Absorptive Microsampling

Proteomic biomarker discovery and analysis from human biofluids using liquid chromatography-mass spectrometry (LC-MS) is an area of intense biomedical research. There is a growing interest to analyze microsampled patient blood specimens as this is potentially more patient-friendly enabling at-home and bedside self-collection of small blood volumes. However, there are limited studies applying LC-MS proteomic analysis of whole blood as it is dominated by red blood cell proteins such as hemoglobin which suppresses the detection of other less abundant proteins. Volumetric absorptive microsampling (VAMS) devices overcome this issue in part by providing a trapping matrix which allows depletion of abundant blood cell proteins through washing, prior to proteolysis and LC-MS. This approach allows the analysis of proteins from erythrocytes, leukocytes, and plasma and leads to deeper proteomic coverage compared to conventional plasma proteomics, increasing the prospects to discover novel biomarker proteins.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Proteomic Insights into Cardiac Fibrosis: From Pathophysiological Mechanisms to Therapeutic Opportunities

Cardiac fibrosis is a common pathophysiologic process in nearly all forms of heart disease which refers to excessive deposition of extracellular matrix proteins by cardiac fibroblasts. Activated fibroblasts are the central cellular effectors in cardiac fibrosis, and fibrotic remodelling can cause several cardiac dysfunctions either by reducing the ejection fraction due to a stiffened myocardial matrix, or by impairing electric conductance. Recently, there is a rising focus on the proteomic studies of cardiac fibrosis for pathogenesis elucidation and potential biomarker mining. This paper summarizes the current knowledge of molecular mechanisms underlying cardiac fibrosis, discusses the potential of imaging and circulating biomarkers available to recognize different phenotypes of this lesion, reviews the currently available and potential future therapies that allow individualized management in reversing progressive fibrosis, as well as the recent progress on proteomic studies of cardiac fibrosis. Proteomic approaches using clinical specimens and animal models can provide the ability to track pathological changes and new insights into the mechanisms underlining cardiac fibrosis. Furthermore, spatial and cell-type resolved quantitative proteomic analysis may also serve as a minimally invasive method for diagnosing cardiac fibrosis and allowing for the initiation of prophylactic treatment.

A highly efficient protein corona-based proteomic analysis strategy for the discovery of pharmacodynamic biomarkers

The composition of serum is extremely complex, which complicates the discovery of new pharmacodynamic biomarkers via serum proteome for disease prediction and diagnosis. Recently, nanoparticles have been reported to efficiently reduce the proportion of high-abundance proteins and enrich low-abundance proteins in serum. Here, we synthesized a silica-coated iron oxide nanoparticle and developed a highly efficient and reproducible protein corona (PC)-based proteomic analysis strategy to improve the range of serum proteomic analysis. We identified 1,070 proteins with a median coefficient of variation of 12.56% using PC-based proteomic analysis, which was twice the number of proteins identified by direct digestion. There were also more biological processes enriched with these proteins. We applied this strategy to identify more pharmacodynamic biomarkers on collagen-induced arthritis (CIA) rat model treated with methotrexate (MTX). The bioinformatic results indicated that 485 differentially expressed proteins (DEPs) were found in CIA rats, of which 323 DEPs recovered to near normal levels after treatment with MTX. This strategy can not only help enhance our understanding of the mechanisms of disease and drug action through serum proteomics studies, but also provide more pharmacodynamic biomarkers for disease prediction, diagnosis, and treatment.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Synthesis of Surface-Functionalized Molybdenum Disulfide Nanomaterials for Efficient Adsorption and Deep Profiling of the Human Plasma Proteome by Data-Independent Acquisition

Blood is one of the most important clinical samples for protein biomarker discovery, as it provides rich physiological and pathological information and is easy to obtain with low invasiveness. However, the discovery of protein biomarkers in the blood by mass spectrometry (MS)-based proteomic strategies has been shown to be highly challenging due to the particularly large concentration range of proteins and the strong interference by the high-abundant proteins in the blood. Therefore, developing sensitive methods for low-abundant biomarker protein identification is a key issue that has received great attention. Here, we report the synthesis and characterization of surface-functionalized magnetic molybdenum disulfide (MoS₂) for the large-scale adsorption of low-abundant plasma proteins and deep profiling by MS. MoS₂ nanomaterials resulted in the coverage of more than 3400 proteins (including a single-peptide hit) in a single LC-MS analysis without peptide prefractionation using pooled plasma samples, which were five times more than those obtained by the direct analysis of the plasma proteome. A detection limit in the low ng L^-1 range was obtained, which is rare compared with previous reports.

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma

Classical pediatric Hodgkin Lymphoma (HL) is a rare malignancy. Therapeutic regimens for its management may be optimized by establishing treatment response early on. The aim of this study was to identify plasma protein biomarkers enabling the prediction of relapse in pediatric/adolescent HL patients treated under the pediatric EuroNet-PHL-C2 trial. We used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics at the time of diagnosis—before any therapy—as semiquantitative method to profile plasma proteins specifically associated with relapse in 42 children with nodular sclerosing HL. In both the exploratory and the validation cohorts, six proteins (apolipoprotein E, C4b-binding protein α chain, clusterin, fibrinogen γ chain, prothrombin, and vitronectin) were more abundant in the plasma of patients whose HL relapsed (|fold change| ≥ 1.2, p < 0.05, Student’s t-test). Predicting protein function with the Gene Ontology classification model, the proteins were included in four biological processes (p < 0.01). Using immunoblotting and Luminex assays, we validated two of these candidate biomarkers—C4b-binding protein α chain and clusterin—linked to innate immune response function (GO:0045087). This study identified C4b-binding protein α chain and clusterin as candidate early plasma biomarkers of HL relapse, and important for the purpose of shedding light on the molecular scenario associated with immune response in patients treated under the EuroNet-PHL-C2 trial.

Biomarkers in Neurodegenerative Diseases: Proteomics Spotlight on ALS and Parkinson’s Disease

Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD) are both characterized by pathogenic protein aggregates that correlate with the progressive degeneration of neurons and the loss of behavioral functions. Both diseases lack biomarkers for diagnosis and treatment efficacy. Proteomics is an unbiased quantitative tool capable of the high throughput quantitation of thousands of proteins from minimal sample volumes. We review recent proteomic studies in human tissues, plasma, cerebrospinal fluid (CSF), and exosomes in ALS and PD that identify proteins with potential utility as biomarkers. Further, we review disease-related post-translational modifications in key proteins TDP43 in ALS and α-synuclein in PD studies, which may serve as biomarkers. We compare relative and absolute quantitative proteomic approaches in key biomarker studies in ALS and PD and discuss recent technological advancements which may identify suitable biomarkers for the early-diagnosis treatment efficacy of these diseases.

A genetic model for in vivo proximity labelling of the mammalian secretome

Organ functions are highly specialized and interdependent. Secreted factors regulate organ development and mediate homeostasis through serum trafficking and inter-organ communication. Enzyme-catalysed proximity labelling enables the identification of proteins within a specific cellular compartment. Here, we report a BirA*G3 mouse strain that enables CRE-dependent promiscuous biotinylation of proteins trafficking through the endoplasmic reticulum. When broadly activated throughout the mouse, widespread labelling of proteins was observed within the secretory pathway. Streptavidin affinity purification and peptide mapping by quantitative mass spectrometry (MS) proteomics revealed organ-specific secretory profiles and serum trafficking. As expected, secretory proteomes were highly enriched for signal peptide-containing proteins, highlighting both conventional and non-conventional secretory processes, and ectodomain shedding. Lower-abundance proteins with hormone-like properties were recovered and validated using orthogonal approaches. Hepatocyte-specific activation of BirA*G3 highlighted liver-specific biotinylated secretome profiles. The BirA*G3 mouse model demonstrates enhanced labelling efficiency and tissue specificity over viral transduction approaches and will facilitate a deeper understanding of secretory protein interplay in development, and in healthy and diseased adult states.

Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R² > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Improved profiling of low molecular weight serum proteome for gastric carcinoma by data-independent acquisition

Low molecular weight proteins (LMWPs) in the bloodstream participate in various biological processes and are closely associated with disease status, whereas identification of serous LMWPs remains a great technical challenge due to the wide dynamic range of protein components. In this study, we constructed an integrated LMWP library by combining the LMWPs obtained by three enrichment methods (50% ACN, 20% ACN + 20 mM ABC, and 30 kDa) and their fractions identified by the data-dependent acquisition method. With this newly constructed library, we comprehensively profiled LMWPs in serum using data-independent acquisition and reliably achieved quantitative results for 75% serous LMWPs. When applying this strategy to quantify LMWPs in human serum samples, we could identify 405 proteins on average per sample, of which 136 proteins were with a MW less than 30 kDa and 293 proteins were with a MW less than 65 kDa. Of note, pre- and post-operative gastric carcinoma (GC) patients showed differentially expressed serous LWMPs, which was also different from the pattern of LWMP expression in healthy controls. In conclusion, our results showed that LMWPs could efficiently distinguish GC patients from healthy controls as well as between pre- and post-operative statuses, and more importantly, our newly developed LMWP profiling platform could be used to discover candidate LMWP biomarkers for disease diagnosis and status monitoring.

Comparing Efficiency of Lysis Buffer Solutions and Sample Preparation Methods for Liquid Chromatography-Mass Spectrometry Analysis of Human Cells and Plasma

The use of a proper sample processing methodology for maximum proteome coverage and high-quality quantitative data is an important choice to make before initiating a liquid chromatography–mass spectrometry (LC–MS)-based proteomics study. Popular sample processing workflows for proteomics involve in-solution proteome digestion and single-pot, solid-phase-enhanced sample preparation (SP3). We tested them on both HeLa cells and human plasma samples, using lysis buffers containing SDS, or guanidinium hydrochloride. We also studied the effect of using commercially available depletion mini spin columns before SP3, to increase proteome coverage in human plasma samples. Our results show that the SP3 protocol, using either buffer, achieves the highest number of quantified proteins in both the HeLa cells and plasma samples. Moreover, the use of depletion mini spin columns before SP3 results in a two-fold increase of quantified plasma proteins. With additional fractionation, we quantified nearly 1400 proteins, and examined lower-abundance proteins involved in neurodegenerative pathways and mitochondrial metabolism. Therefore, we recommend the use of the SP3 methodology for biological sample processing, including those after depletion of high-abundance plasma proteins.