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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. The molecular networking strategy could organize the relationship between compounds as a network based on their tandem mass spectra similarity, which can be used to improve compound annotation in metabolomics analysis. 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. [ABSTRACT FROM AUTHOR]

Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations. [ABSTRACT FROM AUTHOR]

Background: Despite extensive research, the identification of effective biomarkers for early prediction of preterm birth (PTB) continues to be a challenging endeavor. This study aims to identify amniotic fluid (AF) protein biomarkers useful for the early diagnosis of PTB. Methods: We initially identified the protein expression profiles in the AF of women with PTB (n = 22) and full-term birth (FTB, n = 22), from the First People's Hospital of Yunnan Province who underwent amniocentesis from November 2019 to February 2020, using mass spectrometry employing the data-independent acquisition (DIA) technique, and then analyzed differentially expressed proteins (DEPs). Subsequently, the least absolute shrinkage and selection operator (LASSO) and random forest analysis were employed to further screen the key proteins for PTB biomarker identification. The receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analyses (DCA) were utilized to assess the discrimination and calibration of the key biomarkers. Results: A total of 25 DEPs were identified between the PTB and FTB groups, comprising 13 up-regulated and 12 down-regulated proteins. Three key protein biomarkers for early PTB diagnosis were identified: IL1RL1 (interleukin-1 receptor-like 1), APOE (apolipoprotein E), and NECTIN4 (nectin cell adhesion molecule 4). The results of the ROC analysis showed that the area under the curve (AUC) of the three proteins combined as a biomarker for early diagnosis of PTB was 0.913 (95% CI: 0.823-1.000), with a sensitivity of 0.864 and a specificity of 0.955, both superior to those of the individual biomarkers. Bootstrap internal validation revealed a concordance index (C-index) of 0.878, with a sensitivity of 0.812 and a specificity of 0.773, indicating the robust predictive performance of these biomarkers. Conclusions: We identified three previously unexplored yet potentially useful protein biomarkers in AF for early PTB diagnosis: IL1RL1, APOE, and NECTIN4. [ABSTRACT FROM AUTHOR]

Protein tyrosine phosphorylation plays a critical role in initiating upstream cellular signaling transduction. However, the challenge in biological samples is the variability in relative concentrations (0.1%) of site‐specific tyrosine phosphorylation on proteins. To navigate these fluctuations and accurately quantify the absolute levels of tyrosine phosphosites among different samples, we reported a hybrid data‐independent acquisition‐parallel reaction monitoring (DIA‐PRM) MS technique for the robust identification and quantification of the phosphoproteome, the establishment of a comprehensive library of tyrosine phosphosites, and the specific assessment of changes in tyrosine phosphorylation. In our model study on non‐small cell lung cancer cells, our PRM strategy accomplished by a spiked‐in synthetic heavy phosphopeptide demonstrated reliable targeted quantification of the pY1197 on EGFR, revealing levels of 2.5, 4.9, and 5.3 fmol in pervanadate (PV)‐treated cells at 0, 15, and 30 min, respectively. Additionally, DIA‐extensive phosphoproteomic analysis provided 2765 tyrosine phosphosites within 14,961 global phosphosites corresponding to 1536 phosphoproteins, contributing to the phospho‐library establishment and relative quantification of phosphorylation level, especially in the PV‐treated time‐dependent increase of ErbB signaling pathway. This hybrid DIA‐PRM approach will advance the application of precise measurement of changes in multiple phosphotyrosine residues and enhance our understanding of phosphoproteomic dynamics in drug‐resistant cascades. [ABSTRACT FROM AUTHOR]

Abstract Background Despite extensive research, the identification of effective biomarkers for early prediction of preterm birth (PTB) continues to be a challenging endeavor. This study aims to identify amniotic fluid (AF) protein biomarkers useful for the early diagnosis of PTB. Methods We initially identified the protein expression profiles in the AF of women with PTB (n = 22) and full-term birth (FTB, n = 22), from the First People’s Hospital of Yunnan Province who underwent amniocentesis from November 2019 to February 2020, using mass spectrometry employing the data-independent acquisition (DIA) technique, and then analyzed differentially expressed proteins (DEPs). Subsequently, the least absolute shrinkage and selection operator (LASSO) and random forest analysis were employed to further screen the key proteins for PTB biomarker identification. The receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analyses (DCA) were utilized to assess the discrimination and calibration of the key biomarkers. Results A total of 25 DEPs were identified between the PTB and FTB groups, comprising 13 up-regulated and 12 down-regulated proteins. Three key protein biomarkers for early PTB diagnosis were identified: IL1RL1 (interleukin-1 receptor-like 1), APOE (apolipoprotein E), and NECTIN4 (nectin cell adhesion molecule 4). The results of the ROC analysis showed that the area under the curve (AUC) of the three proteins combined as a biomarker for early diagnosis of PTB was 0.913 (95% CI: 0.823-1.000), with a sensitivity of 0.864 and a specificity of 0.955, both superior to those of the individual biomarkers. Bootstrap internal validation revealed a concordance index (C-index) of 0.878, with a sensitivity of 0.812 and a specificity of 0.773, indicating the robust predictive performance of these biomarkers. Conclusions We identified three previously unexplored yet potentially useful protein biomarkers in AF for early PTB diagnosis: IL1RL1, APOE, and NECTIN4.

A capillary zone electrophoresis (CZE) system was coupled to an Orbitrap mass spectrometer operating in a data-independent acquisition (DIA) mode for in-depth proteomics analysis. The performance of this CZE-DIA-MS system was systemically evaluated and optimized under different operating conditions. The performance of the fully optimized CZE-DIA-MS system was subsequently compared to the one by using the same CZE-MS system operating in a data-dependent acquisition (DDA) mode. The experimental results show that the numbers of identified peptides and proteins acquired in the DIA mode are much higher than the ones acquired in the DDA mode, especially with the small sample loading amount. Specifically, the numbers of identified peptides and proteins acquired in the DIA mode are 1.8-fold and 2-fold higher than the ones acquired in the DDA mode by using 12.5 ng Hela digests. The proteins identified in the DIA mode also cover almost all the proteins identified in the DDA mode. In addition, a potential cancer biomarker protein, carbohydrate antigen 125, undetected in the DDA mode, can be easily identified in the DIA mode even with 12.5 ng Hela digests. The performance of the CZE-DIA-MS system for in-depth proteomics analysis with a limited sample amount has been fully demonstrated for the first time through this study. [ABSTRACT FROM AUTHOR]

Abstract Purpose Behçet’s disease-associated uveitis (BDU) is a severe, recurrent inflammatory condition affecting the eye and is part of a systemic vasculitis with unknown etiology, making biomarker discovery essential for disease management. In this study, we intend to investigate potential urinary biomarkers to monitor the disease activity of BDU. Methods Firstly, label-free data-dependent acquisition (DDA) and tandem mass tag (TMT)-labeled quantitative proteomics methods were used to profile the proteomes of urine from active and quiescent BDU patients, respectively. For further exploration, the remaining fifty urine samples were analyzed by a data-independent acquisition (DIA) quantitative proteomics method. Results Twenty-nine and 21 differential proteins were identified in the same urine from BDU patients by label-free DDA and TMT-labeled analyses, respectively. Seventy-nine differentially expressed proteins (DEPs) were significantly changed in other active BDU urine samples compared to those in quiescent BDU urine samples by IDA analysis. Gene Ontology (GO) and protein-protein interaction (PPI) analyses revealed that the DEPs were associated with multiple functions, including the immune and neutrophil activation responses. Finally, seven proteins were identified as candidate biomarkers for BDU monitoring and recurrence prediction, namely, CD38, KCRB, DPP4, FUCA2, MTPN, S100A8 and S100A9. Conclusions Our results showed that urine can be a good source of biomarkers for BDU. These dysregulated proteins provide potential urinary biomarkers for BDU activity monitoring and provide valuable clues for the analysis of the pathogenic mechanisms of BDU.

Purpose: Behçet's disease-associated uveitis (BDU) is a severe, recurrent inflammatory condition affecting the eye and is part of a systemic vasculitis with unknown etiology, making biomarker discovery essential for disease management. In this study, we intend to investigate potential urinary biomarkers to monitor the disease activity of BDU. Methods: Firstly, label-free data-dependent acquisition (DDA) and tandem mass tag (TMT)-labeled quantitative proteomics methods were used to profile the proteomes of urine from active and quiescent BDU patients, respectively. For further exploration, the remaining fifty urine samples were analyzed by a data-independent acquisition (DIA) quantitative proteomics method. Results: Twenty-nine and 21 differential proteins were identified in the same urine from BDU patients by label-free DDA and TMT-labeled analyses, respectively. Seventy-nine differentially expressed proteins (DEPs) were significantly changed in other active BDU urine samples compared to those in quiescent BDU urine samples by IDA analysis. Gene Ontology (GO) and protein-protein interaction (PPI) analyses revealed that the DEPs were associated with multiple functions, including the immune and neutrophil activation responses. Finally, seven proteins were identified as candidate biomarkers for BDU monitoring and recurrence prediction, namely, CD38, KCRB, DPP4, FUCA2, MTPN, S100A8 and S100A9. Conclusions: Our results showed that urine can be a good source of biomarkers for BDU. These dysregulated proteins provide potential urinary biomarkers for BDU activity monitoring and provide valuable clues for the analysis of the pathogenic mechanisms of BDU. [ABSTRACT FROM AUTHOR]

Introduction: Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe–microbe and host–microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data. Areas covered: This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed. Expert opinion: Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, often diagnosed at stages that dis‐qualify for surgical resection. Neoadjuvant therapies offer potential tumor regression and improved resectability. Although features of the tumor biology (e.g., molecular markers) may guide adjuvant therapy, biological alterations after neoadjuvant therapy remain largely unexplored. We performed mass spectrometry to characterize the proteomes of 67 PDAC resection specimens of patients who received either neoadjuvant chemo (NCT) or chemo‐radiation (NCRT) therapy. We employed data‐independent acquisition (DIA), yielding a proteome coverage in excess of 3500 proteins. Moreover, we successfully integrated two publicly available proteome datasets of treatment‐naïve PDAC to unravel proteome alterations in response to neoadjuvant therapy, highlighting the feasibility of this approach. We found highly distinguishable proteome profiles. Treatment‐naïve PDAC was characterized by enrichment of immunoglobulins, complement and extracellular matrix (ECM) proteins. Post‐NCT and post‐NCRT PDAC presented high abundance of ribosomal and metabolic proteins as compared to treatment‐naïve PDAC. Further analyses on patient survival and protein expression identified treatment‐specific prognostic candidates. We present the first proteomic characterization of the residual PDAC mass after NCT and NCRT, and potential protein candidate markers associated with overall survival. We conclude that residual PDAC exhibits fundamentally different proteome profiles as compared to treatment‐naïve PDAC, influenced by the type of neoadjuvant treatment. These findings may impact adjuvant or targeted therapy options. [ABSTRACT FROM AUTHOR]

Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in proteomics core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in proteomics laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of 4 exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analyzed by focusing on 2 datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings, and skill levels.

Fragment correlation mass spectrometry correlates ion pairs generated from the same fragmentation pathway, achieved by covariance mapping of tandem mass spectra generated with an unmodified linear ion trap without preseparation. We enable the identification of different precursors at different charge states in a complex mixture from a large isolation window, empowering an analytical approach for data-independent acquisition. The method resolves and matches isobaric fragments, internal ions, and disulfide bond fragments. We suggest that this method represents a major advance for analyzing structures of biopolymers in mixtures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

Liquid chromatography coupled with high-resolution mass spectrometry data-independent acquisition (LC-HRMS/DIA), including MSE, enable comprehensive metabolomics analyses though they pose challenges for data processing with automatic annotation and molecular networking (MN) implementation. This motivated the present proposal, in which we introduce DIA-IntOpenStream, a new integrated workflow combining open-source software to streamline MSE data handling. It provides 'in-house' custom database construction, allows the conversion of raw MSE data to a universal format (.mzML) and leverages open software (MZmine 3 and MS-DIAL) all advantages for confident annotation and effective MN data interpretation. This pipeline significantly enhances the accessibility, reliability and reproducibility of complex MSE/DIA studies, overcoming previous limitations of proprietary software and non-universal MS data formats that restricted integrative analysis. We demonstrate the utility of DIA-IntOpenStream with two independent datasets: dataset 1 consists of new data from 60 plant extracts from the Ocotea genus; dataset 2 is a publicly available actinobacterial extract spiked with authentic standard for detailed comparative analysis with existing methods. This user-friendly pipeline enables broader adoption of cutting-edge MS tools and provides value to the scientific community. Overall, it holds promise for speeding up metabolite discoveries toward a more collaborative and open environment for research. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate dynamic urinary proteome changes of electroacupuncture (EP) on cerebral ischemia‑reperfusion (CI/R) injured rats and to explore the therapeutic biological mechanisms of EP. Methods: First, changed urinary proteins were found in EP stimulation in healthy rats. Then, we used a CI/R injury rat model induced by Pulsinelli's four‐vessel occlusion (4‐VO) method to explore the function of EP on urinary proteome in CI/R injury. Urine samples were collected for proteome analysis by liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) and bioinformatics analysis. Results: In total, 384 proteins were identified, among which 47 proteins (23 upregulated, 24 downregulated) were differentially expressed with 0.6‐log FC and p <.05. Gene ontology analysis revealed that the cell redox homeostasis, acute‐phase response, response to lipopolysaccharide, and cellular response to glucocorticoid stimulus were significantly enriched. The partially biologically connected differential proteins were found by the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis in the EP group. With the CI/R rat model, 80 proteins (27 upregulated, 53 downregulated) were significantly changed in the CI/R rats compared to the controls. Among these differentially expressed proteins (DEPs), 23 proteins (17 upregulated, six downregulated) showed significant changes after EP treatment (0.6‐log FC change, p <.05). The main related biological processes were aging, immune response, acute‐phase response, liver regeneration, protein catabolic process, and response to oxidative stress. Many metabolic pathways were enriched by KEGG analysis. Conclusion: Our results indicate that the EP could alleviate cerebral damage induced by ischemia‑reperfusion through an anti‐inflammatory and metabolism regulation mechanism. The urinary proteome might reflect the pathophysiological changes in EP pretreatment in the treatment and prevention of CI/R injury. [ABSTRACT FROM AUTHOR]

Simple Summary: Fragments of pathogens, such as viruses and bacteria as well as cancer cells, are presented to the immune system via the major histocompatibility complex (MHC). Immunopeptidomics is a mass spectrometry-based technique that allows us to identify which peptide fragments of pathogens or aberrant mutated proteins are presented and potentially recognized by the immune system. Recent technological developments in mass spectrometry as well as sample preparation, peptide separation, and data analysis have substantially propelled the field of immunopeptidomics further and often initially were developed or incentivized for the measurement of single-cell proteomics. This perspective describes how developments in single-cell proteomics have benefitted immunopeptidomics and how further implementations could still boost sensitivity, and it will explore future directions and trends in immunopeptidomics. Immunopeptidomics, as the analysis of antigen peptides being presented to the immune system via major histocompatibility complexes (MHC), is being seen as an imperative tool for identifying epitopes for vaccine development to treat cancer and viral and bacterial infections as well as parasites. The field has made tremendous strides over the last 25 years but currently still faces challenges in sensitivity and throughput for widespread applications in personalized medicine and large vaccine development studies. Cutting-edge technological advancements in sample preparation, liquid chromatography as well as mass spectrometry, and data analysis, however, are currently transforming the field. This perspective showcases how the advent of single-cell proteomics has accelerated this transformation of immunopeptidomics in recent years and will pave the way for even more sensitive and higher-throughput immunopeptidomics analyses. [ABSTRACT FROM AUTHOR]

Chengyu Yang,1,2,&ast; Li Yang,3,4,&ast; Lei Yang,5 Shuiming Li,5 Ling Ye,1,6 Jinfeng Ye,5 Chengshui Chen,3,4,7 Yiming Zeng,8 Mengchan Zhu,1 Xiaoping Lin,8 Qing Peng,9 Yun Wang,5,10 Meiling Jin1,6 1Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, Huadong Hospital, Fudan University, Shanghai, 200040, People’s Republic of China; 3Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, People’s Republic of China; 4Key Laboratory of Interventional Pulmonology of Zhejiang Province, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, People’s Republic of China; 5Longhua Innovation Institute for Biotechnology, Shenzhen University, Shenzhen, Guangdong, 518055, People’s Republic of China; 6Department of Allergy, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China; 7Department of Pulmonary and Critical Care Medicine, the Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Zhejiang, 324000, People’s Republic of China; 8Department of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital of Fujian Medical University, Respiratory Medicine Center of Fujian Province, Quanzhou, Fujian, 362000, People’s Republic of China; 9Department of Pulmonary and Critical Care Medicine, Minhang Hospital, Fudan University, Shanghai, 201199, People’s Republic of China; 10Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, Shantou, Guangdong, 515041, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Meiling Jin, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China, Email jin.meiling@zs-hospital.sh.cn Yun Wang, Longhua Innovation Institute for Biotechnology, Shenzhen University, Shenzhen, Guangdong, 518055, People’s Republic of China, Email yunw@szu.edu.cnBackground: Frequent exacerbation (FE) and infrequent exacerbation (IE) are two phenotypes of chronic obstructive pulmonary disease (COPD), of which FE is associated with a higher incidence of exacerbation and a serious threat to human health. Because the pathogenesis mechanisms of FE are unclear, this study aims to identify FE-related proteins in the plasma via proteomics for use as predictive, diagnostic, and therapeutic biomarkers of COPD.Methods: A cross-sectional study was conducted in which plasma protein profiles were analyzed in COPD patients at stable stage, and differentially expressed proteins (DEPs) were screened out between the FE and IE patients. FE-related DEPs were identified using data-independent acquisition-based proteomics and bioinformatics analyses. In addition, FE-related candidates were verified by enzyme-linked immunosorbent assay.Results: In this study, 47 DEPs were screened out between the FE and IE groups, including 20 upregulated and 27 downregulated proteins. Key biological functions (eg, neutrophil degranulation, extracellular exosome, protein homodimerization activity) and signaling pathways (eg, arginine and proline metabolism) were enriched in association with the FE phenotype. Receiver operating characteristic (ROC) analysis of the 11 combined DEPs revealed an area under the curve of 0.985 (p < 0.05) for discriminating FE from IE. Moreover, correlation and ROC curve analyses indicated that creatine kinase, M-type (CKM) and fat storage-inducing transmembrane protein 1 (FITM1) might be clinically significant in patients with the FE phenotype. In addition, plasma expression levels of CKM and FITM1 were validated to be significantly decreased in the FE group compared with the IE group (CKM: p < 0.01; FITM1: p < 0.05).Conclusion: In this study, novel insights into COPD pathogenesis were provided by investigating and comparing plasma protein profiles between the FE and IE patients. CKM, FITM1, and a combinative biomarker panel may serve as useful tools for assisting in the precision diagnosis and effective treatment of the FE phenotype of COPD.Keywords: chronic obstructive pulmonary disease, frequent exacerbation, data-independent acquisition, proteomics, bioinformatics analysis

High‐grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5‐year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced‐stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high‐quality ovary‐specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan‐human spectral library (DPHL), this spectral library provides 10% more ovary‐specific and 3% more ovary‐enriched proteins. This library was then applied to analyze data‐independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six‐protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log‐rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary‐specific spectral library for targeted proteome analysis, and propose a six‐protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT‐IDS treatment.

Sinan Wu,1– 4,&ast; Ke Huang,1– 4,&ast; Chenli Chang,1– 5 Xu Chu,1– 4,6 Kun Zhang,7 Baicun Li,1– 4 Ting Yang1– 4 1National Center for Respiratory Medicine, Beijing, People’s Republic of China; 2National Clinical Research Center for Respiratory Diseases, Beijing, People’s Republic of China; 3Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 4Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, People’s Republic of China; 5China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 6Department of Pulmonary and Critical Care Medicine The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, People’s Republic of China; 7Biotree-Shanghai, Focus Dream Park, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ting Yang; Baicun Li, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, No. 2, East Yinghua Road, Chaoyang District, Beijing, 100029, People’s Republic of China, Tel +86 10-84206273, Email zryyyangting@163.com; lbc19890303@126.comPurpose: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with high morbidity and mortality rates. This study used proteomic profiling of serum to identify the differentially expressed proteins in COPD patients compared with healthy controls, to expand the knowledge of COPD pathogenesis and to ascertain potential new targets for diagnosis and treatment of COPD.Methods: Serum samples were collected from 56 participants (COPD group n = 28; Healthy Control group n = 28). A data-independent acquisition quantitative proteomics approach was used to identify differentially expressed proteins (DEPs) between the two groups. Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment, and protein–protein interaction analyses of DEPs were conducted to identify their relevant biological processes, cellular components, and related pathways. We used a parallel reaction monitoring (PRM)-based targeted quantitative proteomics approach to validate those findings.Results: Of 8484 peptides identified by searching the UniProtKB/Swiss-Prot knowledgebase, 867 proteins were quantifiable, of which 20 were upregulated and 35 were downregulated in the COPD group. GO functional annotation indicated that the subcellular localization of most DEPs was extracellular. The top three molecular functions of the DEPs were signaling receptor binding, antigen binding, and immunoglobulin receptor binding. The most relevant biological process was immune response. The transforming growth factor-β signaling pathway, Staphylococcus aureus infection, and hematopoietic cell lineage were the top three pathways identified in the KEGG pathway functional enrichment. Our PRM analyses confirmed the identification of 11 DEPs identified in our data-independent acquisition analyses, 8 DEPs were upregulated and 3 DEPs were downregulated.Conclusion: This study using data-independent acquisition analyses with PRM confirmation of findings identified 11 DEPs in the serum of patients with COPD. These DEPs are potential diagnostic or prognostic biomarkers or may be future targets for the treatment of COPD.Keywords: chronic obstructive pulmonary disease, proteomics, differentially expressed proteins, data-independent acquisition, parallel reaction monitoring

ABSTRACT: Yak milk is rich in essential milk proteins of nutritional and therapeutic value. In this study, whey proteins of milk from 3 yak breeds (Gannan, GN; Huanhu, HH; Maiwa, MW) in China were comprehensively identified and compared using a data-independent acquisition quantitative proteomics approach. A total of 632 proteins were identified in yak milk whey samples, in which immune-related proteins were abundant. Compared with other milks, more proteins were involved in oxidation-reduction process and with ATP binding. In addition, we identified 96, 155, and 164 differentially expressed proteins (DEP) for GN versus HH, GN versus MW, and HH versus MW, respectively. “Phagosome” and “complement and coagulation cascades” were the most significant pathways for DEP of GN versus HH and GN or HH versus MW yak milk based on the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Protein-protein interaction network analysis showed that DEP for the 3 comparisons had significant biological interactions but were associated with different functions. The results provide useful information on yak milk from different breeds in China, and elucidate the biological functions of yak milk proteins.

Monitoring of host cell proteins (HCPs) during the manufacturing of monoclonal antibodies (mAb) has become a critical requirement to provide effective and safe drug products. Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities. However, this technique has several limitations and does, among others, not enable the precise identification of proteins. In this context, mass spectrometry (MS) became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs. However, in order to be routinely implemented in biopharmaceutical companies, liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification. Here, we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard, the HCP Profiler solution, with a spectral library-based data-independent acquisition (DIA) method and strict data validation criteria. The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical data-dependent acquisition on a series of samples produced at various stages of the manufacturing process. While we also explored spectral library-free DIA interpretation, the spectral library-based approach still showed highest accuracy and reproducibility (coefficients of variation

Abstract Background This study aimed to investigate dynamic urinary proteome changes of electroacupuncture (EP) on cerebral ischemia‑reperfusion (CI/R) injured rats and to explore the therapeutic biological mechanisms of EP. Methods First, changed urinary proteins were found in EP stimulation in healthy rats. Then, we used a CI/R injury rat model induced by Pulsinelli's four‐vessel occlusion (4‐VO) method to explore the function of EP on urinary proteome in CI/R injury. Urine samples were collected for proteome analysis by liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) and bioinformatics analysis. Results In total, 384 proteins were identified, among which 47 proteins (23 upregulated, 24 downregulated) were differentially expressed with 0.6‐log FC and p

Introduction: Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host. Methods: We present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice. Results and discussion: We show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields. [ABSTRACT FROM AUTHOR]

Accurate estimation of the postmortem interval (PMI) is crucial in forensic medico-legal investigations to understand case circumstances (e.g. narrowing down list of missing persons or include/exclude suspects). Due to the complex decomposition chemistry, estimation of PMI remains challenging and currently often relies on the subjective visual assessment of gross morphological/taphonomic changes of a body during decomposition or entomological data. The aim of the current study was to investigate the human decomposition process up to 3 months after death and propose novel time-dependent biomarkers (peptide ratios) for the estimation of decomposition time. An untargeted liquid chromatography tandem mass spectrometry–based bottom-up proteomics workflow (ion mobility separated) was utilized to analyse skeletal muscle, collected repeatedly from nine body donors decomposing in an open eucalypt woodland environment in Australia. Additionally, general analytical considerations for large-scale proteomics studies for PMI determination are raised and discussed. Multiple peptide ratios (human origin) were successfully proposed (subgroups < 200 accumulated degree days (ADD), < 655 ADD and < 1535 ADD) as a first step towards generalised, objective biochemical estimation of decomposition time. Furthermore, peptide ratios for donor-specific intrinsic factors (sex and body mass) were found. Search of peptide data against a bacterial database did not yield any results most likely due to the low abundance of bacterial proteins within the collected human biopsy samples. For comprehensive time-dependent modelling, increased donor number would be necessary along with targeted confirmation of proposed peptides. Overall, the presented results provide valuable information that aid in the understanding and estimation of the human decomposition processes. [ABSTRACT FROM AUTHOR]

Ion mobility spectrometry‐mass spectrometry (IMS‐MS) is experiencing rapid growth in proteomic studies, driven by its enhancements in dynamic range and throughput, increasing the quantitation precision, and the depth of proteome coverage. The core principle of ion mobility spectrometry is to separate ions in an inert gas under the influence of an electric field based on differences in drift time. This minireview provides an introduction to IMS operation modes and a description of advantages and limitations is presented. Moreover, the principles of trapped IMS‐MS (TIMS‐MS), including parallel accumulation‐serial fragmentation are discussed. Finally, emerging applications linked to TIMS focusing on sample throughput (in clinical proteomics) and sensitivity (single‐cell proteomics) are reviewed, and the possibilities of intact protein analysis are discussed. [ABSTRACT FROM AUTHOR]

The methodology of data-independent acquisition (DIA) within mass spectrometry (MS) was developed into a method of choice for quantitative proteomics, to capture the depth and dynamics of biological systems, and to perform large-scale protein quantification. DIA provides deep quantitative proteome coverage with high sensitivity, high quantitative accuracy, and excellent acquisition-to-acquisition reproducibility. DIA workflows benefited from the latest advancements in MS instrumentation, acquisition/isolation schemes, and computational algorithms, which have further improved data quality and sample throughput. This powerful DIA-MS scan type selects all precursor ions contained in pre-determined isolation windows, and systematically fragments all precursor ions from each window by tandem mass spectrometry, subsequently covering the entire precursor ion m / z range. Comprehensive proteolytic peptide identification and label-free quantification are achieved post-acquisition using spectral library-based or library-free approaches. To celebrate the > 10 years of success of this quantitative DIA workflow, we interviewed some of the scientific leaders who have provided crucial improvements to DIA, to the quantification accuracy and proteome depth achieved, and who have explored DIA applications across a wide range of biology. We discuss acquisition strategies that improve specificity using different isolation schemes, and that reduce complexity by combining DIA with sophisticated chromatography or ion mobility separation. Significant leaps forward were achieved by evolving data processing strategies, such as library-free processing, and machine learning to interrogate data more deeply. Finally, we highlight some of the diverse biological applications that use DIA-MS methods, including large-scale quantitative proteomics, post-translational modification studies, single-cell analysis, food science, forensics, and small molecule analysis. Data-independent acquisition (DIA) has developed into an important method for quantitative proteomics, to capture the depth and dynamics of biological systems, and to perform large-scale protein quantification. To celebrate the > 10 years of DIA, we interviewed some of the scientific leaders who have provided crucial improvements to the DIA workflow. [ABSTRACT FROM AUTHOR]

High‐grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5‐year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced‐stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high‐quality ovary‐specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan‐human spectral library (DPHL), this spectral library provides 10% more ovary‐specific and 3% more ovary‐enriched proteins. This library was then applied to analyze data‐independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six‐protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log‐rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary‐specific spectral library for targeted proteome analysis, and propose a six‐protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT‐IDS treatment. [ABSTRACT FROM AUTHOR]

Gonadotropin‐releasing hormone (GnRH) and its synthetic analogues are considered banned substances by the racing industry. GnRH is used as a pharmaceutical to regulate the female oestrous cycle, but the hormone is also thought to increase the production of testosterone in male animals. Using liquid chromatography in conjunction with high‐resolution mass spectrometry (LC–HRMS) and data‐independent acquisition (DIA), a method is presented for the detection of intact and truncated peptides of GnRH and its analogues down to the low picogram level in equine urine. The study of the catabolism of GnRH and analogues in plasma, combined with the analysis of urine from administration studies, reveals a common pattern of peptide catabolites that can be used to guide the design of MS‐based screens for this class of drugs. This culminated in the successful detection of the peptide in two out‐of‐competition canine urine samples. [ABSTRACT FROM AUTHOR]

Cellular senescence is a complex stress response that induces an essentially permanent cell cycle arrest and a complex secretory phenotype termed the senescence-associated secretory phenotype (SASP), which drives numerous aging pathologies. Characterization of the SASP can provide insights into aging and disease mechanisms, aging biomarker candidates, and targets for counteracting the deleterious effects of senescent cells. Here we describe a mass spectrometry (MS)-compatible protocol to (1) generate senescent cells using different stimuli, (2) collect conditioned medium containing proteins secreted by senescent cells (i.e., SASP), and (3) prepare the SASP for quantitative proteomic analysis using data-independent acquisition (DIA) MS. © 2021 The Authors. Basic Protocol 1: Generating ionizing radiation-induced senescent and control cells Alternate Protocol 1: Generating doxorubicin-induced senescent and control cells Alternate Protocol 2: Generating oncogenic RAS-induced senescent and control cells Alternate Protocol 3: Generating mitochondrial dysfunction-induced senescent and control cells Alternate Protocol 4: Generating atazanavir/ritonavir-induced senescent and control cells Support Protocol: A multiple-assay approach to confirm the phenotype of senescent cells Basic Protocol 2: Generating conditioned medium from senescent cells cultured in low serum and quiescent control cells Alternate Protocol 5: Generating conditioned medium from senescent cells cultured in complete medium and quiescent control cells Basic Protocol 3: Quantitative proteomic analysis of the SASP.

IntroductionMetaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.MethodsWe present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.Results and discussionWe show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

Proteomics is the scientific discipline that deals with the protein composition of cells, tissues, and organisms and their patterns of change. It is considered an invaluable tool for tackling many challenges faced in medicine and biology. Among the available approaches, label‐free quantitative proteomics techniques are extensively used to study malignant tumors due to their low cost, simple operation, and short cycle time. Therefore, it provides a novel approach to explore the pathogenesis, diagnostic markers, and targeted drugs of malignant tumors. Here, we summarize the research progress and potential of label‐free quantitative proteomics and discuss the application of such techniques in the research on malignancies. [ABSTRACT FROM AUTHOR]

Targeted mass spectrometry is extensively used for the quantitative measurement of various molecules present in complex matrices. It is certainly one of the most important analytical duties in a mass spectrometry laboratory. Systematic development of selected‐reaction monitoring (SRM), multiple‐reaction monitoring (MRM) and parallel‐reaction monitoring (PRM) methods for targeted mass spectrometry‐based analysis was performed without considering future opportunities. The advancement of hardware and software technologies has resulted in greater resolution, accuracy, speed and depth. For sure, SRM, MRM or PRM acquisitions can quantify molecules very accurately at trace levels. However, they do not provide datasets allowing future data mining. Obviously, we cannot truly quantify something that we do not know is there. However, using non‐targeted data acquisition for target analysis, we can generate a MS1 and MS2 digital libraries of each sample, providing future proof datasets. This is instrumental for data mining following new questions potentially arising in time permitting new and deeper processing and interpretation. This perspective article provides thoughts on why we believe it is time to question the status quo in targeted mass spectrometry. [ABSTRACT FROM AUTHOR]

Yak milk is rich in essential milk proteins of nutritional and therapeutic value. In this study, whey proteins of milk from 3 yak breeds (Gannan, GN; Huanhu, HH; Maiwa, MW) in China were comprehensively identified and compared using a data-independent acquisition quantitative proteomics approach. A total of 632 proteins were identified in yak milk whey samples, in which immune-related proteins were abundant. Compared with other milks, more proteins were involved in oxidation-reduction process and with ATP binding. In addition, we identified 96, 155, and 164 differentially expressed proteins (DEP) for GN versus HH, GN versus MW, and HH versus MW, respectively. "Phagosome" and "complement and coagulation cascades" were the most significant pathways for DEP of GN versus HH and GN or HH versus MW yak milk based on the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Protein-protein interaction network analysis showed that DEP for the 3 comparisons had significant biological interactions but were associated with different functions. The results provide useful information on yak milk from different breeds in China, and elucidate the biological functions of yak milk proteins. [ABSTRACT FROM AUTHOR]

Background: Clozapine is an atypical antipsychotic drug used to treat treatment-resistant schizophrenia. Its side effects, including liver enzyme abnormalities, experienced by many patients preclude its more common use as a first-line therapy for schizophrenia. Toxicoproteomic approaches have been demonstrated to effectively guide the identification of toxicological mechanisms. Methods: To further our understanding of the molecular effects of clozapine, we performed a data-independent acquisition (DIA)-based quantitative proteomics investigation of clozapine-treated human liver spheroid cultures. Results: In total, we quantified 4479 proteins across the five treatment groups (vehicle; 15 µM, 30 µM, and 60 µM clozapine; and 10 ng/mL TNFα + IL-1β). Clozapine (60 µM) treatment yielded 36 differentially expressed proteins (FDR < 0.05). Gene-set enrichment analysis indicated perturbation of several gene sets, including interferon gamma signaling (e.g. interferon gamma receptor 1) and prominent autophagy-related processes (e.g. upregulation of sequestosome-1 (SQSTM1), MAP1LC3B/LC3B2, GABARAPL2, and nuclear receptor coactivator 4). The effects of clozapine on autophagy were confirmed by targeted mass spectrometry and western blotting using conventional SQSTM1 and LC3B markers. Conclusions: Combined with prior literature, our work suggests a broad contribution of autophagy to both the therapeutic and side effects of clozapine. Overall, this study demonstrates how proteomics can contribute to the elucidation of physiological and toxicological mechanisms of drugs. [ABSTRACT FROM AUTHOR]

Although data-independent acquisition (DIA) has the ability to identify and quantify all peptides in a sample, highly complex mixed mass spectra present difficulties for accurate peptide and protein identification. Additionally, the correspondence between the precursor and its fragments is broken, making it challenging to perform peptide identification directly using conventional DDA search engines. In this paper, we propose a cosine-similarity-based deconvolution method: CorrDIA. This is achieved by reconstructing the correspondence between precursor and fragment ions based on the consistency of extracted ion chromatograms (XICs). A deisotope peak cluster operation is added and centered on the MS/MS spectrum to improve the accuracy of spectrum interpretation and increase the number of identified peptides. The resulting MS/MS spectra can be identified using any data-dependent acquisition (DDA) sequencing software. The experimental results demonstrate that the number of peptide results increased by 12 percent and 21 percent respectively, and the repetition rate decreased by 12 percent. This reduces mass spectra complexity and difficulties in mass spectra analysis without the need for any mass spectra libraries. [ABSTRACT FROM AUTHOR]

Monitoring of host cell proteins (HCPs) during the manufacturing of monoclonal antibodies (mAb) has become a critical requirement to provide effective and safe drug products. Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities. However, this technique has several limitations and does, among others, not enable the precise identification of proteins. In this context, mass spectrometry (MS) became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs. However, in order to be routinely implemented in biopharmaceutical companies, liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification. Here, we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard, the HCP Profiler solution, with a spectral library-based data-independent acquisition (DIA) method and strict data validation criteria. The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical data-dependent acquisition on a series of samples produced at various stages of the manufacturing process. While we also explored spectral library-free DIA interpretation, the spectral library-based approach still showed highest accuracy and reproducibility (coefficients of variation < 10%) with a sensitivity down to the sub-ng/mg mAb level. Thus, this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control. [Display omitted] • HCP Profiler standard enables accurate quantification of HCPs. • DIA improves coverage and quantification of HCPs. • Strict data validation for a reliable monitoring of protein impurities. • MS-based method as quality control tool to overcome ELISA issues. [ABSTRACT FROM AUTHOR]

ABSTRACT: Casein micelles (CM) play an important role in milk secretion, stability, and processing. The composition and content of milk proteins are affected by physiological factors, which have been widely investigated. However, the variation in CM proteins in goat milk throughout the lactation cycle has yet to be fully clarified. In the current study, milk samples were collected at d 1, 3, 30, 90, 150, and 240 of lactation from 15 dairy goats. The size of CM was determined using laser light scattering, and CM proteins were separated, digested, and identified using data-independent acquisition (DIA) and data-dependent acquisition (DDA)-based proteomics approaches. According to clustering and principal component analysis, protein profiles identified using DIA were similar to those identified using the DDA approach. Significant differences in the abundance of 115 proteins during the lactation cycle were identified using the DIA approach. Developmental changes in the CM proteome corresponding to lactation stages were revealed: levels of lecithin cholesterol acyltransferase, folate receptor α, and prominin 2 increased from 1 to 240 d, whereas levels of growth/differentiation factor 8, peptidoglycan-recognition protein, and 45 kDa calcium-binding protein decreased in the same period. In addition, lipoprotein lipase, glycoprotein IIIb, and α-lactalbumin levels increased from 1 to 90 d and then decreased to 240 d, which is consistent with the change in CM size. Protein–protein interaction analysis showed that fibronectin, albumin, and apolipoprotein E interacted more with other proteins at the central node. These findings indicate that changes in the CM proteome during lactation could be related to requirements of newborn development, as well as mammary gland development, and may thus contribute to elucidating the physical and chemical properties of CM.

ABSTRACT: The composition and content of goat milk proteins are affected by many factors and have been extensively studied. However, variation in whey protein composition in goat milk throughout the lactation cycle has not been clarified. In the current study, 15 dairy goats were selected, and milk samples were collected at 1, 3, 30, 90, 150, and 240 d after delivery. Whey proteins were separated and digested and then identified using data-independent acquisition (DIA) and data-dependent acquisition proteomics approaches. Protein profiles identified using DIA were consistent with those of the data-dependent acquisition proteomics approach according to clustering and principal component analyses. Significant differences in the abundance of 238 proteins around the lactation cycle were identified using the DIA approach. Developmental changes of the whey proteome corresponding to lactation stage were revealed: plasminogen, α-2-macroglobulin, and fibronectin levels decreased from d 1 to 240, whereas polymeric immunoglobulin receptor, nucleobindin 2, fatty acid-binding protein 3, and lactoperoxidase increased from d 1 to 240. Protein-protein interaction analysis showed that fibronectin with a higher degree of connectivity is a central node. The findings are of great significance to better understanding the potential role of specific proteins and the mechanism of protein biosynthesis or intercellular transport in the mammary glands related to the physiological changes of dairy goats.

Abstract Background Sarcopenia refers to the progressive loss of skeletal muscle mass and muscle function, which seriously threatens the quality of life of the older adults. Therefore, early diagnosis is urgently needed. This study aimed to explore the changes of serum protein profiles in sarcopenia patients through a cross-sectional study, and to provide the reference for clinical diagnosis. Methods This study was a cross-sectional study carried out in the Tianjin institute of physical education teaching experiment training center from December 2019 to December 2020. Ten older adults were recruited, including 5 sarcopenia and 5 healthy older adults. After a detailed diagnostic evaluation, blood samples were collected to prepare serum for proteomic analysis using the HPLC System Easy nLC method. The differentially expressed proteins (DEPs) were screened by the limma package of R software (version 4.1.0). Results A total of 114 DEPs were identified between the patients and healthy older adults, including 48 up-regulated proteins and 66 down-regulated proteins. The functional enrichment analysis showed that the 114 DEPs were significantly enriched in 153 GO terms, which mainly involved in low-density lipoprotein particle remodeling, and negative regulation of immune response,etc. The PPI network further suggested that the cholesteryl ester transfer protein and Apolipoprotein A2 could serve as biomarkers to facilitate diagnosis of sarcopenia. Conclusions This study provided a serum proteomic profile of sarcopenia patients, and identified two proteins with diagnostic value, which might help to improve the diagnostic accuracy of sarcopenia.

Lipidomics coverage improvement is essential for functional lipid and pathway construction. A powerful approach to discovering organism lipidome is to combine various data acquisitions, such as full scan mass spectrometry (full MS), data-dependent acquisition (DDA), and data-independent acquisition (DIA). Caenorhabditis elegans (C. elegans) is a useful model for discovering toxic-induced metabolism, high-throughput drug screening, and a variety of human disease pathways. To determine the lipidome of C. elegans and investigate lipid disruption from the molecular level to the system biology level, we used integrative data acquisition. The methyl-tert-butyl ether method was used to extract L4 stage C. elegans after exposure to triclosan (TCS), perfluorooctanoic acid, and nanopolystyrene (nPS). Full MS, DDA, and DIA integrations were performed to comprehensively profile the C. elegans lipidome by Q-Exactive Plus MS. All annotated lipids were then analyzed using lipid ontology and pathway analysis. We annotated up to 940 lipids from 20 lipid classes involved in various functions and pathways. The biological investigations revealed that when C. elegans were exposed to nPS, lipid droplets were disrupted, whereas plasma membrane-functionalized lipids were likely to be changed in the TCS treatment group. The nPS treatment caused a significant disruption in lipid storage. Triacylglycerol, glycerophospholipid, and ether class lipids were those primarily hindered by toxicants. Finally, toxicant exposure frequently involved numerous lipid-related pathways, including the phosphoinositide 3-kinase/protein kinase B pathway. In conclusion, an integrative data acquisition strategy was used to characterize the C. elegans lipidome, providing valuable biological insights into hypothesis generation and validation.

Background and aim: Moxibustion is widely used in China and other East Asian countries to manage the symptom of ankylosing spondylitis (AS). This study investigated the effects of moxibustion intervention on protein expression through proteomics analysis in AS mice. Experimental procedure: Proteoglycan-induced spondylitis (PGISp) was established in Balb/c mice. PGISp mice were intervened with daily moxibustion at ST36, BL23, and DU4 for four weeks. Various biochemical (including pro-inflammatory cytokines and bone metabolism indexes) and histopathological parameters were determined. The effects of moxibustion on protein changes in AS mice were analyzed using data-independent acquisition-mass spectrometry (DIA-MS). The target proteins were then confirmed by Western blot analysis. Results: Moxibustion significantly decreased pro-inflammatory cytokine expression including IL-1β, TNF-α, IL-17, and IL-6, reduced the mRNA expression of RANKL, RANK, ALP, and OCN, and improved the histopathological examination in AS mice. DIA-MS proteomic technique has identified 25 candidate proteins involved in the mechanisms of moxibustion for AS mice, most of which are mainly associated with the regulation of Wnt/β-catenin. Integrated pathway analysis revealed that glycine, serine and threonine metabolism together with lipid metabolism were the most important canonical pathways involved in the anti-AS effect of moxibustion. In line with the multi-omic data, the levels of BPGM, APOC2, APOE, and GPD1 modified in the AS mice, intervened with moxibustion as confirmed by Western blot. In particular, APOE may play a key role in linking the lipid metabolism and the Wnt/β-catenin pathway of new bone formation. Conclusion: In conclusion, moxibustion may reduce pro-inflammatory cytokines and improve bone erosion for AS mice. The regulation of APOE by moxibustion may have a potential inhibitory effect on the Wnt/β-catenin pathway in AS mice. However, due to the lack of silencing or overexpression of key molecules of the signal pathway, whether the beneficial and positive effect of moxibustion involved in the regulation of Wnt/β-catenin signaling pathway by APOE or other aspects, needed to be explored in further study.

Currently, the bottom-up approach, in which proteins are digested by enzymes such as trypsin prior to mass spectrometry, is the mainstream approach in mass spectrometer-based proteomics. In this approach, the enzymatic digestion process strongly affects the reproducibility of protein identification and quantification. Here, we quantitatively evaluated the enzymatic digestion of proteins under various conditions by quantitative proteomics using data-independent acquisition and found that proteins precipitated with acetone after solubilization with SDS were fully digestible without re-solubilization. This result implies that organic solvent treatment makes cells amenable to trypsin digestion. Direct trypsin digestion of methanol-fixed cells achieved the same digestion efficiency and quantitative reproducibility as the conventional method. Furthermore, this method was found to be equally applicable to mouse liver samples. The establishment of this method indicates that the sample preparation process in bottom-up proteomics can be simplified while maintaining high digestion efficiency and is expected to become a general method for sample preparation in bottom-up proteomics in the future. [ABSTRACT FROM AUTHOR]

Objective: Ischemic stroke (IS) is a common disease that causes severe and long-term neurological disability in people worldwide. Although rehabilitation is indispensable to promote neurological recovery in ischemic stroke, it is limited to providing a timely and efficient reference for developing and adjusting treatment strategies because neurological assessment after stroke treatment is mostly performed using scales and imaging. Therefore, there is an urgent need to find biomarkers that can help us evaluate and optimize the treatment plan. Methods: We used data-independent acquisition (DIA) technology to screen differentially expressed proteins (DEPs) before and after ischemic stroke rehabilitation treatment, and then performed Gene Ontology (GO) and pathway enrichment analysis of DEPs using bioinformatics tools such as KEGG pathway and Reactome. In addition, the protein-protein interaction (PPI) network and modularity analysis of DEPs were integrated to identify the hub proteins (genes) and hub signaling pathways for neurological recovery in ischemic stroke. PRM-targeted proteomics was also used to validate some of the screened proteins of interest. Results: Analyzing the serum protein expression profiles before and after rehabilitation, we identified 22 DEPs that were upregulated and downregulated each. Through GO and pathway enrichment analysis and subsequent PPI network analysis constructed using STRING data and subsequent Cytoscape MCODE analysis, we identified that complement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammatory and immune pathways are the major pathways involved in the improvement of neurological function after stroke rehabilitation. Conclusion: Complement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammation and immunity pathways are not only key pathways in the pathogenesis of ischemic stroke but also the main pathways of action of rehabilitation therapy. In addition, IGHA1, LRG1, IGHV3-64D, and CP are upregulated in patients with ischemic stroke and downregulated after rehabilitation, which may be used as biomarkers to monitor neurological impairment and recovery after stroke. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the detoxification and homoeostasis functions of salivary glands, and to look for possible target proteins of new drugs for tick prevention and treatment. Different concentrations of chlorpyrifos to deal with the semi-engorged female Haemaphysalis longicornis, through the DIA quantitative proteomics method to quantitatively determine of all the proteins in the salivary glands, and through a variety of the depth of the bioinformatics methods for protein analysis and function mining, explain the molecular mechanism of detoxification function of salivary glands. Quantitative information from 5 normal groups and 8 different treatment groups showed that a total of 3152 proteins were identified. The expression levels of these proteins changed with the chlorpyrifos different concentration and treatment times. However, after the overall analysis, it was found that after the action of chlorpyrifos, the proteins involved in detoxification, such as glutathione S-transferase and glutamine synthetase, the heat shock proteins involved in stress, and the acid sphingomyelinase involved in maintaining the body's homoeostasis had corresponding changes. We found and explained the functions of detoxification, stress, and homoeostasis of salivary glands. The regulatory strategies of proteins that played a key role in these functions were explored. [ABSTRACT FROM AUTHOR]

Untargeted lipidomics applied to biological samples typically involves the coupling of separation methods to high-resolution mass spectrometry (HRMS). Getting an exhaustive coverage of the lipidome with a high confidence in structure identification is still highly challenging due to the wide concentration range of lipids in complex matrices and the presence of numerous isobaric and isomeric species. The development of innovative separation methods and HRMS(/MS) acquisition workflows helped improving the situation but issues still remain regarding confident structure characterization. To overcome these issues, thoroughly optimized MS/MS acquisition methods are needed. For this purpose, different methodologies have been developed to enable MS and MS/MS acquisition in parallel. Those methodologies, derived from the proteomics, are referred to Data Dependent Acquisition (DDA) and Data Independent Acquisition (DIA). In this context, this perspective paper presents the latest developments of DDA- and DIA-based lipidomic workflows and lists available bioinformatic tools for the analysis of resulting spectral data. [ABSTRACT FROM AUTHOR]