Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography–tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data showed that DIA-based LC-MS/MS related consensus QC metric is more sensitive than DDA-based QC in detecting MS status changes. We then optimized 15 DIA-QC metrics, and invited to manually assess the quality of 2638 DIA files generated by 21 mass spectrometers based on each metric. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2059 DIA files, and predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 523). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC

Japanese encephalitis virus is a leading cause of neurological infection in the Asia-Pacific region with no means of detection in more remote areas. We aimed to test the hypothesis of a Japanese encephalitis (JE) protein signature in human cerebrospinal fluid (CSF) that could be harnessed in a rapid diagnostic test (RDT), contribute to understanding the host response and predict outcome during infection. Liquid chromatography and tandem mass spectrometry (LC–MS/MS), using extensive offline fractionation and tandem mass tag labeling (TMT), enabled comparison of the deep CSF proteome in JE vs other confirmed neurological infections (non-JE). Verification was performed using data-independent acquisition (DIA) LC–MS/MS. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature. This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy. Ultimately, validation in a larger group of patients and different locations could help refine the list to 2–3 proteins for an RDT. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789.

Direct infusion shotgun proteome analysis (DISPA) is a new paradigm for expedited mass spectrometry-based proteomics, but the original data analysis workflow was onerous. Here, we introduce CsoDIAq, a user-friendly software package for the identification and quantification of peptides and proteins from DISPA data. In addition to establishing a complete and automated analysis workflow with a graphical user interface, CsoDIAq introduces algorithmic concepts to spectrum-spectrum matching to improve peptide identification speed and sensitivity. These include spectra pooling to reduce search time complexity and a new spectrum–spectrum match score called match count and cosine, which improves target discrimination in a target-decoy analysis. Fragment mass tolerance correction also increased the number of peptide identifications. Finally, we adapt CsoDIAq to standard LC–MS DIA and show that it outperforms other spectrum–spectrum matching software.

We developed a simple protein extraction method for dried blood spots (DBS) that potentially meets the throughput required for newborn screening (NBS) and optimizes nontargeted proteomic analysis in combination with liquid chromatography coupled mass spectrometry in the data-independent-acquisition mode (DIA–LC–MS/MS). The developed pipeline, termed Non-targeted Analysis of Non-specifically DBS-Absorbed proteins (NANDA), successfully addressed the following three challenges: (1) processing of 96 3.2 mm DBS punches in parallel using low-cost iron powders with a robotic system, (2) identifying more than 5,000 proteins using DIA–LC–MS/MS, and (3) improving DIA–LC–MS/MS throughput to 45 samples/day with minimal compromise in protein coverage depth. The results imply that this pipeline can open new venues for conducting NBS using nontargeted quantitative proteome profiling, which has been a missing modality in NBS.

The data includes outputs from FragPipe, Maxquant, DIA-NN and Spectronaut

• A library file (*.lbm2) which was converted the RT in the original database to solvent B% values in the LC gradient and then used the time at the B% value in the fast LC gradient (8.6 min) for the lipid RT.
• A raw data file (*.wiff/ .wiff.scan/ .wiff2/ .timeseries.data) of lipid extraction from NIST Srm1950 human plasma using both LC-DDA-MS and LC-DIA-MS.

To reproduce the tutorial results:

1. Add the LBM2 file to the MS-DIAL folder. (NOTE: Exclude the original LBM2 format. The program accepts only one LBM/LBM2 file in the MS-DIAL folder.)
2. Follow the tutorial instructions: Proceed through the tutorial steps to analyze the data and achieve the same results.

Sample information:

• Methyl-tert-butyl ether (MTBE) lipid extraction from NIST Srm1950 human plasma

Experiment parameter:

• A Nexera X2 UPLC system (Shimadzu) was used as the LC system. Lipids were separated using an Imtakt UK-C18 MF column (50 mm × 2 mm:3 μm) (Imtakt). The MS detection of lipids was performed using a QTOF-MS (ZenoTOF 7600; SCIEX).
• See below for the details of "Hybrid MS" and dataset: K. Tokiyoshi et al. Anal Chem, 96, 991-996. 2024

In this study, we investigated the benefits of adding high-field asymmetric ion mobility spectrometry (FAIMS) separation prior to data dependent acquisition (DDA) and gas phase fractionation (GPF) prior to data independent acquisition (DIA) LC-MS/MS analysis. Native digestion followed by LC-MS/MS with FAIMS allowed the identification of 221 HCPs among which 158 were reliably quantified for a global amount of 880 ng/mg of NIST mAb Reference Material. Our methods have also been applied to commercial DPs and demonstrate their ability to dig deeper into the HCP landscape with the identification of 60 and 67 HCPs, and accurate quantification of 29 and 31 of these impurities in nivolumab and trastuzumab respectively, with sensitivity down to the sub-ng/mg of mAb level.

Ketosis is a common metabolic disease in dairy cows during early lactation. However, information about the metabolomic and proteomic profiles associated with the incidence and progression of ketosis is still limited. In this study, an integrated metabolomics and proteomics approach was performed on blood serum sampled from cows diagnosed with clinical ketosis (case, ≥ 2.60 mmol/L plasma β-hydroxybutyrate; BHBA) and healthy controls (control, < 1.0 mmol/L BHBA). Samples were taken 2 weeks before parturition and 2 weeks after parturition from 19 animals (nine cases, 10 controls). All serum samples (n = 38) were subjected to Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomic analysis, and 20 samples underwent Data-Independent Acquisition (DIA) LC-MS based proteomic analysis. A total of 97 metabolites and 540 proteins were successfully identified, and multivariate analysis revealed significant differences in both metabolomic and proteomic profiles between cases and controls. We investigated clinical ketosis-associated metabolomic and proteomic changes using statistical analyses. Correlation analysis of statistically significant metabolites and proteins showed 78 strong correlations (correlation coefficient, R ≥ 0.7) between 38 metabolites and 25 proteins, which were then mapped to pathways using IMPaLA. Results showed that ketosis altered a wide range of metabolic pathways, such as metabolism, metabolism of proteins, gene expression and post-translational protein modification, vitamin metabolism, signaling, and disease related pathways. Findings presented here are relevant for identifying molecular targets for ketosis and biomarkers for ketosis detection during the transition period.

IntroductionAnalysis of residual host cell proteins in adeno-associated virus (AAV) preparations is challenging due to low availability and high complexity of samples. One strategy to address these challenges is through development of improved liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods with greater sensitivity and reduced sample requirement.MethodsIn this work, we compare the performance of four sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS) methods for identification and quantitation of residual HCPs in rAAV2, -5, -8, and -9 preparations produced with human embryonic kidney 293 (HEK293) cells and purified using immunoaffinity chromatography. Key SWATH-MS parameters including spectral library construction (data dependent vs. in silico), data processing software (DIA-NN vs. Skyline), and mass spectrometer instrument (Sciex TripleTOF 6600 vs. Sciex ZenoTOF 7600) were assessed. Method attributes including sample requirement and processing time, and method outputs including protein and precursor identifications, host cell protein quantitation comparisons across methods, and quantitation coefficients of variance (CV) were considered to help establish a SWATH-MS workflow well-suited for rAAV HCP analytics.ResultsA 78% increase in HCP identifications, 80% reduction in sample requirement, and 70% reduction in instrument runtime was achieved with an in silico spectral library, data processing in DIA-NN, and data collection with the Sciex ZenoTOF 7600 instrument (DIA-NN-7600 method) compared to a previously established method using a DDA-derived spectral library, data processing in Skyline, and data collection with the Sciex TripleTOF 6600 instrument (Skyline-DDA-6600 method). Additionally, the DIA-NN-7600 method shows median HCP quantitation CV below 10% for triplicate data acquisitions, and comparable quantitation to other methods for a panel of highly abundant residual HCPs previously identified in rAAV downstream processing.DiscussionThis work highlights a SWATH-MS method with data collection and processing specifically tailored for rAAV residual HCP analysis.

To examine the different mass spectrometry approaches to monitoring kinases after enrichment with desthiobiotinylating probes for activity-based protein profiling (ABPP), two experiments were performed with H1993 lung cancer cells. First, cell lysates were pre-treated with DMSO vehicle, dasatinib, or erlotinib prior to addition of the ATP probe for ABPP to compare the differences in kinase labeling associated with examples of kinase inhibitors that vary in target selectivity. Then, to examine changes in cellular signaling, H1993 cells were treated with vehicle controls, BEX-235 (PI3K inhibitor), or Crizotinib. LC-MS/MS using data dependent acquisition, data-independent acquisition (pSMART), parallel reaction monitoring, and selected reaction monitoring (or multiple reaction monitoring) mass spectrometry were used to detect and relatively quantify the desthiobiotinylated kinase peptides.

The breast cancer cell line MCF-7 was treated with doxorubicin. Protein expression differences between treated and non-treated cells were semiquantitatively determined using the precursor acquisition independent from ion count (PAcIFIC) mass spectrometric method. The acquired proteomic data sets were searched for regulated Reactome pathways and Gene Ontology annotation terms using a new algorithm (SetRank). Using this approach, we described significantly changed pathways (p-value of 0.05 or less) already known to be influenced by chemotherapeutic drugs, such as chromatin organization, DNA binding, embryo development, condensed chromosome, sequence-specific DNA binding, response to oxidative stress and response to toxin. Additionally, we found pathways such as central nervous system neuron differentiation, neuron projection membrane and SNAP receptor activity explaining side-effects of doxorubicin chemotherapy, characterized as ‘chemo brain’, on a molecular level.

BackgroundChronic coronary syndrome (CCS) is a newly proposed concept and is hallmarked by more long-term major adverse cardiovascular events (MACEs), calling for accurate prognostic biomarkers for initial risk stratification.MethodsData-independent acquisition liquid chromatography tandem mass spectrometry (DIA LC-MS/MS) quantitative proteomics was performed on 38 patients with CCS; 19 in the CCS events group and 19 in the non-events group as the controls. We also developed a machine-learning-based pipeline to identify proteins as potential biomarkers and validated the target proteins by enzyme-linked immunosorbent assay in an independent prospective cohort.ResultsFifty-seven differentially expressed proteins were identified by quantitative proteomics and three final biomarkers were preliminarily selected from the machine-learning-based pipeline. Further validation with the prospective cohort showed that endothelial protein C receptor (EPCR) and cholesteryl ester transfer protein (CETP) levels at admission were significantly higher in the CCS events group than they were in the non-events group, whereas the carboxypeptidase B2 (CPB2) level was similar in the two groups. In the Cox survival analysis, EPCR and CETP were independent risk factors for MACEs. We constructed a new prognostic model by combining the Framingham coronary heart disease (CHD) risk model with EPCR and CETP levels. This new model significantly improved the C-statistics for MACE prediction compared with that of the Framingham CHD risk model alone.ConclusionPlasma proteomics was used to find biomarkers of predicting MACEs in patients with CCS. EPCR and CETP were identified as promising prognostic biomarkers for CCS.

Urine provides a diverse source of information related to health status and is ideal for clinical proteomics because of its ease of collection. To date, there is no standard operating procedure for reproducible and robust urine sample processing for mass spectrometry-based clinical proteomics. To address this need, a novel workflow was developed based on an on-bead protein capture, clean up, and digestion without the requirement from pre-processing steps such as precipitation. The workflow was applied to an acute kidney injury (AKI) pilot study. Urine from clinical samples and a pooled sample were subjected to automated sample preparation in a KingFisher™ Flex magnetic handling station using a novel urine-HILIC (uHLC) approach based on MagReSyn® HILIC microspheres. For benchmarking, the pooled sample was also prepared using a published protocol based on an on-membrane (OM) protein capture and digestion workflow. Peptides were analysed by LCMS in DIA mode using a Dionex Ultimate 3000 UPLC coupled to a Sciex 5600 mass spectrometer. Data was searched in Spectronaut™ 17. Following statistical analysis, candidate protein markers were filtered at ≥ 2-fold change in abundance, ≥ 2 uniques peptides and ≤ 1% false discovery rate. Both workflows showed similar peptide and protein identifications in the pooled sample. The uHLC workflow was easier to set up and complete, having less hands-on time than the OM method. With fewer manuel processiing steps, a lower peptide and protein CV was observed in the uHLC technical replicates. Analysis of clinical samples revealed many significant, differentially abundant kidney injury-associated urinary proteins. The pilot data derived using this novel workflow provides information on the urinary proteome of patients with AKI. Further exploration in a larger cohort using this novel high-throughput method is warranted.

A rat model of skeletal muscle mechanical compression injury was established with varying degrees of injury severity, one control group, and two compression groups (Mild Injury and Severe Injury Group). LC-MS/MS-4D-DIA quantitative proteomics technology was employed to detect the plasma protein expression profiles of rats in different injury groups.

Telehealth, accessing healthcare and wellness remotely, should be a cost-effective and efficient way for individuals to receive care. The convenience of having a reliable remote collection device for blood tests will facilitate access to precision medicine and healthcare. Herein, we tested a 60-biomarker health surveillance panel (HSP), containing 35 FDA/LDT assays and covering at least 14 pathological states, on 8 healthy individuals’ ability to collect their own capillary blood from a lancet finger prick and directly compared it to the traditional phlebotomist venous blood and plasma collection methods. All samples were spiked with 114 stable-isotope-labeled (SIL) HSP peptides and quantitatively analyzed by liquid chromatography-multiple reaction monitoring-mass spectrometry (LC/MRM-MS) scheduled method targeting 466 transitions from 114 HSP peptides and by a discovery data-independent acquisition mass spectrometry (DIA-MS) method. The average peak area ratio (PAR) of the HSP quantifier peptide transitions from all 8 volunteers’ capillary blood (n = 48), venous blood (n = 48), and matched plasma (n = 24) was 90% similarity. Discovery DIA-MS analysis of the same samples using a plasma spectral library and a pan-human spectral library identified 1121 and 4661 total proteins, respectively. In addition, at least 122 FDA-approved biomarkers were identified. DIA-MS analysis reproducibly quantitated (

Anal squamous cell carcinoma (ASCC) is an infrequent tumor. Since 70s, treatment of stages II-III consists on a combination of 5-fluorouracil (5FU), mitomycin C (MMC), and radiotherapy. The aim of this study is the identification of biomarkers that allow personalized treatment and improvement of therapeutic outcomes. Forty-six tumor paraffin samples from ASCC patients were analyzed by whole-exome sequencing. Single nucleotide polymorphisms and copy number variants (CNVs) were identified and their relation to disease-free survival (DFS) was studied using BRB Array Tool and Kaplan-Meier analyses. Obtained findings were validated in an independent retrospective cohort of 101 ASCC patients with stages I-III from eleven hospitals within the Multidisciplinary Spanish Digestive Cancer Group (GEMCAD) using qPCR Copy Number Assays. GEMCAD validation cohort was also analyzed using mass spectrometry proteomics to assess the biological features of these tumors.

Acute postsurgerical pain and it's management represent a major clinical challenge. In particular, severe and prolonged pain impairs immediate recovery and might lead to long-term consequences like chronic pain, opioid abuse, and reduced quality of life. Rodent incision models greatly contributed to the understanding of cutaneous wound repair, but the underlying mechanisms' knowledge remains incomplete, yet translational data are urgently needed to develop novel treatment options and preventive measures. We combined extensive sensory phenotyping with unbiased quantitative proteomics in human and mouse skin after an incision to assess interspecies protein signatures to overcome this gap. Additionally, stratification of human volunteers based on the hyperalgesic area after surgery in correlation with the corresponding proteomic fingerprint revealed novel insides into human-specific mechanisms.Unbiased skin proteome analysis of both species unveiled comparable protein signatures after incision, notably for inflammatory activity and actin polymerization. Upon incision, we could discern 50 commonly regulated proteins, amounting to 61% of all regulated human and 10% regulated mouse proteins. Notably, the direction of regulation upon incision, i.e. up-or downregulated, was conserved in humans and mice to a great extent. Integrative analysis of pain-related phenotyping with quantitative mass spectrometry identified hitherto unknown skin protein signatures, providing a tool for stratification on the protein level. Protein-protein interaction (PPI)-networks differed between volunteers with small incision-related hyperalgesic areas (termed"low responder") versus those with large areas ("high responder"). In particular, PPIs of volunteers exhibiting a large hyperalgesic area were characterized by a predominant dysregulated proteolytic environment associated with persistent inflammation reponse. In contrast, PPI- networks of low responders point to anti-inflammatory processes. Here, we present a framework for specific-specific alterations in human and mice upon incision, highlighting similarities and differences on a protein network level. Moreover, the detection of developing hyperalgesia and pain after surgery in volunteers and it's correlation with corresponding molecular fingerprints indicates the need for tailored treatments to prevent chronification processes in humans after surgery.

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

In Dendritic cells (DC), the MHC II eluted immunopeptidome reflects the antigenic composition of the microenvironment. Proteins are transported and processed into peptides in endosomal MHC II compartments through autophagy or phagocytosis; extracellular peptides can also directly bind MHC II proteins at the cell surface. Altogether, these mechanisms allow DC to sample both the intra and extracellular environment. With an increase in mass spectrometry sensitivity and accuracy, we can now finally tackle important questions on the nature and plasticity of the MHC-II immunopeptidome in health and disease. Presented epitopes, neoepitopes, and PTM-modified epitopes can be quantitatively and qualitatively analyzed to provide a comprehensive picture of DC role in immunosurveillance. To determine whether the redox metabolic conditions induce an altered spectrum of presented peptides, we eluted immunoaffinity-purified I-Ab from conventional dendritic cells isolated from control B6 or obese Ob/Ob mice, and analyzed MHC-II-associated peptides by LC/MS/MS using combined data-dependent (DDA) and data-independent acquisition (DIA) approaches. We analyzed the DIA data by employing a reference spectral library consisting of all peptides identified by database matching in the pool of spectra from combined DDA dataset, thus allowing a direct label-free quantitation of relative abundances between the two sample categories. The quantitative analysis of the I-Ab-eluted immunopeptidomes pinpoint important differences in peptide presentation and epitope selection in obese mice.