Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome.

Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n = 21) compared to the age- and sex-matched controls (n  = 20) using data-independent acquisition mass spectrometry. Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.

Exploring Infantile Epileptic Spasm Syndrome: A Proteomic Analysis of Plasma Using the Data-Independent Acquisition Approach.

This study aimed to identify characteristic proteins in infantile epileptic spasm syndrome (IESS) patients' plasma, offering insights into potential early diagnostic biomarkers and its underlying causes. Plasma samples were gathered from 60 patients with IESS and 40 healthy controls. Data-independent acquisition proteomic analysis was utilized to identify differentially expressed proteins (DEPs). These DEPs underwent functional annotation through Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Gene set enrichment analysis (GSEA) was employed for both GO (GSEA-GO) and KEGG (GSEA-KEGG) analyses to examine the gene expression profiles. Receiver operating characteristic (ROC) curves assessed biomarkers' discriminatory capacity. A total of 124 DEPs were identified in IESS patients' plasma, mainly linked to pathways, encompassing chemokines, cytokines, and oxidative detoxification. GSEA-GO and GSEA-KEGG analyses indicated significant enrichment of genes associated with cell migration, focal adhesion, and phagosome pathways. ROC curve analysis demonstrated that the combination of PRSS1 and ACTB, PRSS3, ACTB, and PRSS1 alone exhibited AUC values exceeding 0.7. This study elucidated the significant contribution of cytokines, chemokines, oxidative detoxification, and phagosomes to the IESS pathogenesis. The combination of PRSS1 and ACTB holds promise as biomarkers for the early diagnosis of IESS.

An automated workflow based on data independent acquisition for practical and high-throughput personalized assay development and minimal residual disease monitoring in multiple myeloma patients.

OBJECTIVES: Minimal residual disease (MRD) status in multiple myeloma (MM) is an important prognostic biomarker. Personalized blood-based targeted mass spectrometry detecting M-proteins (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to MRD-assessment in bone marrow. However, MS-MRD still comprises of manual steps that hamper upscaling of MS-MRD testing. Here, we introduce a proof-of-concept for a novel workflow using data independent acquisition-parallel accumulation and serial fragmentation (dia-PASEF) and automated data processing. METHODS: Using automated data processing of dia-PASEF measurements, we developed a workflow that identified unique targets from MM patient sera and personalized protein sequence databases. We generated patient-specific libraries linked to dia-PASEF methods and subsequently quantitated and reported M-protein concentrations in MM patient follow-up samples. Assay performance of parallel reaction monitoring (prm)-PASEF and dia-PASEF workflows were compared and we tested mixing patient intake sera for multiplexed target selection. RESULTS: No significant differences were observed in lowest detectable concentration, linearity, and slope coefficient when comparing prm-PASEF and dia-PASEF measurements of serial dilutions of patient sera. To improve assay development times, we tested multiplexing patient intake sera for target selection which resulted in the selection of identical clonotypic peptides for both simplex and multiplex dia-PASEF. Furthermore, assay development times improved up to 25× when measuring multiplexed samples for peptide selection compared to simplex. CONCLUSIONS: Dia-PASEF technology combined with automated data processing and multiplexed target selection facilitated the development of a faster MS-MRD workflow which benefits upscaling and is an important step towards the clinical implementation of MS-MRD.

Comprehensive Micro-SPE-Based Bottom-Up Proteomic Workflow for Sensitive Analysis of Limited Samples.

Clinical and biological samples are often scarce and precious (e.g., rare cell isolates, microneedle tissue biopsies, small-volume liquid biopsies, and even single cells or organelles). Typical large-scale proteomic methods, where significantly higher protein amounts are analyzed, are not directly transferable to the analysis of limited samples due to their incompatibility with pg-, ng-, and low-µg-level protein sample amounts. Here, we report the on-microsolid-phase extraction tip (OmSET)-based sample preparation workflow for sensitive analysis of limited biological samples to address this challenge. The developed platform was successfully tested for the analysis of 100-10,000 typical mammalian cells and is scalable to allow for lower and larger protein amounts and more samples to be analyzed (i.e., higher throughput of analysis).

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix.

INTRODUCTION: Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis. OBJECTIVE: This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. METHODOLOGY: The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation. RESULTS: The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis. CONCLUSION: The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Enhanced Proteomic Coverage in Tissue Microenvironment by Immune Cell Subtype Library-Assisted DIA-MS.

Immune cells that infiltrate the tumor microenvironment (TME) play crucial roles in shaping cancer development and influencing clinical outcomes and therapeutic responses. However, obtaining a comprehensive proteomic snapshot of tumor-infiltrating immunity in clinical specimens is often hindered by small sample amounts and a low proportion of immune infiltrating cells in the TME. To enable in-depth and highly sensitive profiling of microscale tissues, we established an immune cell-enriched library-assisted strategy for data-independent acquisition mass spectrometry (DIA-MS). Firstly, six immune cell subtype-specific spectral libraries were established from sorted cluster of differentiation markers, CD8+, CD4+ T lymphocytes, B lymphocytes, natural killer cells, dendritic cells, and macrophages in murine mesenteric lymph nodes (MLNs), covering 7815 protein groups with surface markers and immune cell-enriched proteins. The feasibility of microscale immune proteomic profiling was demonstrated on 1 µg tissue protein from the tumor of murine colorectal cancer (CRC) models using single-shot DIA; the immune cell-enriched library increased coverage to quantify 7419 proteins compared to directDIA analysis (6978 proteins). The enhancement enabled the mapping of 841 immune function-related proteins and exclusive identification of many low-abundance immune proteins, such as CD1D1, and CD244, demonstrating high sensitivity for immune landscape profiling. This approach was used to characterize the MLNs in CRC models, aiming to elucidate the mechanism underlying their involvement in cancer development within the TME. Even with a low percentage of immune cell infiltration (0.25-3%) in the tumor, our results illuminate downregulation in the adaptive immune signaling pathways (such as C-type lectin receptor signaling, and chemokine signaling), T cell receptor signaling, and Th1/Th2/Th17 cell differentiation, suggesting an immunosuppressive status in MLNs of CRC model. The DIA approach using the immune cell-enriched libraries showcased deep coverage and high sensitivity that can facilitate illumination of the immune proteomic landscape for microscale samples.

Proteomic identification of exosomes derived from psoriasis cells using data-independent acquisition mass spectrometry.

Psoriasis is renowned for its chronic nature and complex pathophysiology, with exosomes playing a crucial regulatory role within it. However, the proteomic composition of exosomes extracted from psoriasis cells remains largely unexplored. This study aimed to analyze the proteomic makeup of exosomes derived from psoriasis-model keratinocytes and compare it with that of normal controls, with the goal of identifying specific proteins that could aid in understanding the disease's pathology and potentially serve as biomarkers or therapeutic targets. The normal cultured keratinocyte line HaCaT served as the control group, while a concentration of 10 ng/mL of TNF-α was utilized to stimulate HaCaT cells and induce the formation of psoriasis model cells for the test group. Exosomes were extracted and prepared from the culture supernatant using the magnetic bead method, and their identity was confirmed through transmission electron microscopy, nanoparticle tracking analysis, and Western blotting. Data-independent acquisition (DIA) mass spectrometry was employed to detect the protein composition of exosomes, followed by GO, KEGG, Reactome, and PPI analyses. The analysis revealed a total of 2796 proteins within the exosomes, with 131 showing significant differential expression between the test and control groups. Notably, this study identified the proteins ADO, CBX1, and MIF within the exosomes derived from psoriasis model cells for the first time, highlighting their potential roles in angiogenesis, epigenetic regulation, and inflammatory responses in psoriasis. Several differentially expressed proteins identified in the KEGG enrichment analysis were implicated in immune infiltration pathways, keratinocyte-regulating pathways, angiogenesis pathways, and inflammation pathways. The identification of unique proteins within exosomes derived from psoriasis-model cells offers novel insights into the molecular mechanisms underlying psoriasis. These findings pave the way for further research into the biological functions of these exosomal proteins and their potential utility in diagnosing and treating psoriasis.

Umami Altering Salivary Proteome: A Study across a Sensitivity Spectrum on Subjects.

This study delved into the relationship between umami taste sensitivity (UTS) and variations in the salivary proteome among 12 healthy nonsmokers utilizing 4D data-independent acquisition-based proteomics. By assessing UTS through monosodium l-glutamate (MSG) detection thresholds, we discovered notable differences: individuals with high UTS detected umami at significantly lower MSG concentrations (0.20 ± 0.12 mM) compared to their low UTS counterparts (2.51 ± 1.21 mM). Both groups showed an upregulation of the S100A1 protein under MSG stimulation, indicating a potent biochemical response to umami stimuli. The high UTS group exhibited enhanced metabolic pathways including those for amino acid, lipid, and organic acid biosynthesis, essential for maintaining taste receptor functionality and enhancing signal transduction. This group also demonstrated increased activity in cytochrome P450 enzymes and ribonucleoprotein complexes, suggesting a readiness to manage metabolic challenges and optimize umami perception. In contrast, the low UTS group showed adaptive mechanisms, possibly through modulation of receptor availability and function, with an upregulation of structural and ribosomal proteins that may support taste receptor production and turnover. These findings suggest that varying biological mechanisms underpin differences in umami perception, which could significantly influence dietary preferences and nutritional outcomes, highlighting the intricate interplay of genetic, physiological, and metabolic factors in taste sensitivity.

Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient.

Thermal proteome profiling (TPP) is a powerful tool for drug target deconvolution. Recently, data-independent acquisition mass spectrometry (DIA-MS) approaches have demonstrated significant improvements to depth and missingness in proteome data, but traditional TPP (a.k.a. CEllular Thermal Shift Assay "CETSA") workflows typically employ multiplexing reagents reliant on data-dependent acquisition (DDA). Herein, we introduce a new experimental design for the Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA). We highlight the proteome coverage and sensitivity achieved by using multiple overlapping thermal gradients alongside DIA-MS, which maximizes efficiencies in PISA sample concatenation and safeguards against missing protein targets that exist at high melting temperatures. We demonstrate our extended PISA-DIA design has superior proteome coverage as compared to using tandem-mass tags (TMT) necessitating DDA-MS analysis. Importantly, we demonstrate our PISA-DIA approach has the quantitative and statistical rigor using A-1331852, a specific inhibitor of BCL-xL. Due to the high melt temperature of this protein target, we utilized our extended multiple gradient PISA-DIA workflow to identify BCL-xL. We assert our novel overlapping gradient PISA-DIA-MS approach is ideal for unbiased drug target deconvolution, spanning a large temperature range whilst minimizing target dropout between gradients, increasing the likelihood of resolving the protein targets of novel compounds.

Proteo-transcriptomic profiles reveal key regulatory pathways and functions of LDHA in the ovulation of domestic chickens (Gallus gallus).

BACKGROUND: In poultry, the smooth transition of follicles from the preovulatory-to-postovulatory phase impacts egg production in hens and can benefit the poultry industry. However, the regulatory mechanism underlying follicular ovulation in avians is a complex biological process that remains unclear. RESULTS: Critical biochemical events involved in ovulation in domestic chickens (Gallus gallus) were evaluated by transcriptomics, proteomics, and in vitro assays. Comparative transcriptome analyses of the largest preovulatory follicle (F1) and postovulatory follicle (POF1) in continuous laying (CL) and intermittent laying (IL) chickens indicated the greatest difference between CL_F1 and IL_F1, with 950 differentially expressed genes (DEGs), and the smallest difference between CL_POF1 and IL_POF1, with 14 DEGs. Additionally, data-independent acquisition proteomics revealed 252 differentially abundant proteins between CL_F1 and IL_F1. Perivitelline membrane synthesis, steroid biosynthesis, lysosomes, and oxidative phosphorylation were identified as pivotal pathways contributing to ovulation regulation. In particular, the regulation of zona pellucida sperm-binding protein 3, plasminogen activator, cathepsin A, and lactate dehydrogenase A (LDHA) was shown to be essential for ovulation. Furthermore, the inhibition of LDHA decreased cell viability and promoted apoptosis of ovarian follicles in vitro. CONCLUSIONS: This study reveals several important biochemical events involved in the process of ovulation, as well as crucial role of LDHA. These findings improve our understanding of ovulation and its regulatory mechanisms in avian species.

Comparative proteomics analysis of samples from hepatic cystic echinococcosis patients using data-independent acquisition approach.

Cystic echinococcosis is a zoonotic disease resulting from infection caused by the larval stage of Echinococcus granulosus. This study aimed to assess the specific proteins that are potential candidates for the development of a vaccine against E. granulosus. The data-independent acquisition approach was employed to identify differentially expressed proteins (DEPs) in E. granulosus samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to identify several noteworthy proteins. Results: The DEPs in E. granulosus samples were identified (245 pericystic wall vs. parasite-free yellowish granuloma (PYG, 1725 PY vs. PYG, 2274 PN vs. PYG). Further examination of these distinct proteins revealed their predominant enrichment in metabolic pathways, amyotrophic lateral sclerosis, and neurodegeneration-associated pathways. Notably, among these DEPs, SH3BGRL, MST1, TAGLN2, FABP5, UBE2V2, and RARRES2 exhibited significantly higher expression levels in the PYG group compared with the PY group (P < 0.05). The findings may contribute to the understanding of the pathological mechanisms underlying echinococcosis, providing valuable insights into the development of more effective diagnostic tools, treatment modalities, and preventive strategies. SIGNIFICANCE: CE is a major public health hazard in the western regions of China, Central Asia, South America, the Mediterranean countries, and eastern Africa. Echinococcus granulosus is responsible for zoonotic disease through infection Our analysis focuses on the proteins in various samples by data-dependent acquisition (DIA) for proteomic analysis. The importance of this research is to develop new strategies and targets to protect against E. granulosus infections in humans.

Generation of a Deep Mouse Brain Spectral Library for Transmembrane Proteome Profiling in Mental Disease Models.

Transmembrane (TM) proteins constitute over 30% of the mammalian proteome and play essential roles in mediating cell-cell communication, synaptic transmission, and plasticity in the central nervous system. Many of these proteins, especially the G protein-coupled receptors (GPCRs), are validated or candidate drug targets for therapeutic development for mental diseases, yet their expression profiles are underrepresented in most global proteomic studies. Herein, we establish a brain TM protein-enriched spectral library based on 136 data-dependent acquisition runs acquired from various brain regions of both naïve mice and mental disease models. This spectral library comprises 3043 TM proteins including 171 GPCRs, 231 ion channels, and 598 transporters. Leveraging this library, we analyzed the data-independent acquisition data from different brain regions of two mouse models exhibiting depression- or anxiety-like behaviors. By integrating multiple informatics workflows and library sources, our study significantly expanded the mental stress-perturbed TM proteome landscape, from which a new GPCR regulator of depression was verified by in vivo pharmacological testing. In summary, we provide a high-quality mouse brain TM protein spectral library to largely increase the TM proteome coverage in specific brain regions, which would catalyze the discovery of new potential drug targets for the treatment of mental disorders.

[Deciphering cellular processes responding to lethality of 17ß-estradiol by quantitative phosphoproteomics].

17ß-Estradiol (E2), an important endocrine hormone in the mammalian body, participates in the regulation of the physiological functions of the reproductive system, mammary glands, bone, and cardiovascular system, among others. Paradoxically, despite the physiological actions of endogenous E2 (0.2-1.0 nmol/L), numerous clinical and experimental studies have demonstrated that high-dose E2 treatment can cause tumor regression and exert pro-apoptotic actions in multiple cell types; however, the underlying mechanism remains undescribed. In particular, little information of the cellular processes responding to the lethality of E2 is available. In the present study, we attempted to characterize the cellular processes responding to high-dose (µmol/L) E2 treatment using quantitative phosphoproteomics to obtain a better understanding of the regulatory mechanism of E2-induced cell death. First, the cell phenotype induced by high-dose E2 was determined by performing Cell Counting Kit-8 assay (CCK8), cell cytotoxicity analysis by trypan blue staining, and microscopic imaging on HeLa cells treated with 1-10 µmol/L E2 or dimethyl sulfoxide (DMSO) for 1-3 d. E2 inhibited cell proliferation and induced cell death in a dose- and time-dependent manner. Compared with the DMSO-treated HeLa cells, the cells treated with 5 µmol/L E2 for 2 d demonstrated >74% growth inhibition and approximately 50% cell death. Thus, these cells were used for quantitative phosphoproteomic analysis. Next, a solid-phase extraction (SPE)-based immobilized titanium ion affinity chromatography (Ti4+-IMAC) phosphopeptide-enrichment method coupled with data-independent acquisition (DIA)-based quantitative proteomics was employed for the in-depth screening of high-dose E2-regulated phosphorylation sites to investigate the intracellular processes responding to high-dose E2 treatment. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) identified over 10000 phosphorylation sites regulated by E2 and DMSO in HeLa cells. In comparison with the DMSO-treated cells, the cells treated with 5 µmol/L E2 showed 537 upregulated phosphorylation sites and 387 downregulated phosphorylation sites, with a threshold of p<0.01 and |log2(fold change)|≥1. A total of 924 phosphorylation sites on 599 proteins were significantly regulated by high-dose E2, and these sites were subjected to enrichment analysis. In addition, 453 differently regulated phosphorylation sites on 325 proteins were identified only in the E2- or DMSO-treated cell samples. These phosphorylation sites may be phosphorylated or dephosphorylated in response to high-dose E2 stimulation and were subjected to parallel enrichment analyses. Taken together, 1218 phosphorylation sites on 741 proteins were significantly regulated by high-dose E2 treatment. The functional phosphoproteins in these two groups were then analyzed using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) to determine the biological processes in which they participate and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. Consistent with the cell-phenotype data, cell cycle-related proteins were highly enriched in the two groups of E2-regulated phosphoproteins (p<0.05), indicating that high-dose E2 treatment can regulate cell proliferation. In addition, E2-regulated phosphoproteins were highly enriched in the cellular processes of ribosome biogenesis, nucleocytoplasmic transport, and messenger ribonucleic acid (mRNA) processing/splicing (p<0.05), indicating that the activation of these processes may contribute to high-dose E2-induced cell death. These results further confirm that high-dose E2 treatment inhibits protein translation and induces cell death. Furthermore, the significant upregulation of multiple phosphorylation sites associated with epidermal growth factor receptor (EGFR) and mitogen-activated protein kinases (MAPKs) MAPK1, MAPK4, and MAPK14 by high-dose E2 indicates that the EGFR and MAPK signaling pathways are likely involved in the regulation of E2-induced cell death. These phosphorylation sites likely play vital roles in E2-induced cell death in HeLa cells. Overall, our phosphoproteomic data could be a valuable resource for uncovering the regulatory mechanisms of E2 in the micromolar range.

Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA.

BACKGROUND: Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. METHODS: Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. RESULTS: We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. CONCLUSIONS: Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows.

Data-independent acquisition (DIA) has become a well-established method for MS-based proteomics. However, the list of options to analyze this type of data is quite extensive, and the use of spectral libraries has become an important factor in DIA data analysis. More specifically the use of in silico predicted libraries is gaining more interest. By working with a differential spike-in of human standard proteins (UPS2) in a constant yeast tryptic digest background, we evaluated the sensitivity, precision, and accuracy of the use of in silico predicted libraries in data DIA data analysis workflows compared to more established workflows. Three commonly used DIA software tools, DIA-NN, EncyclopeDIA, and Spectronaut, were each tested in spectral library mode and spectral library-free mode. In spectral library mode, we used independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to classical data-dependent acquisition (DDA)-based spectral libraries. In total, we benchmarked 12 computational workflows for DIA. Our comparison showed that DIA-NN reached the highest sensitivity while maintaining a good compromise on the reproducibility and accuracy levels in either library-free mode or using in silico predicted libraries pointing to a general benefit in using in silico predicted libraries.

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

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

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities.

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

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.

Quantitative proteomic analysis to identify potential biomarkers linked to quality traits of beef tripe from different sources.

In this work, the 4D data-independent acquisition (DIA) quantitative strategy was used for differential proteomic analysis of four beef tripe samples from different sources to explore the associations between differentially expressed proteins (DEPs) and meat quality traits. A total of 68 shared DEPs were identified in all comparison groups, which were mainly involved in phosphorylation signaling pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway, and glucuronic acid pathway. In the correlation analysis between DEPs and quality traits of beef tripe, it was found that 21 proteins were significantly associated with the quality traits in beef tripe, which could be considered as the potential biomarkers of beef tripe quality. This study has successfully uncovered the protein composition of beef tripe for the very first time, which helps to understand the key proteins and biological processes associated with the quality traits of beef tripe from different sources and improve the quality control of beef tripe.