Data-independent Acquisition Mass Spectrometry Research Articles

Proteomic changes of botulinum neurotoxin injection on muscle growth in children with spastic cerebral palsy.

The study aims to explore the proteomic profile and specific target proteins associated with muscle growth in response to botulinum neurotoxin A (BoNT-A) treatment, in order to improve spasticity management in children with cerebral palsy (CP). A total of 54 participants provided 60 plasma samples for proteomic analysis. Among them, six children were sampled before and after receiving their first BoNT-A injection. In addition, 48 unrelated children were enrolled, among whom one group had never received BoNT-A injections and another group was sampled after their first BoNT-A injection. Differentially expressed proteins were identified using the data-independent acquisition (DIA) mass spectrometry approach. Gene Ontology (GO), protein-protein interaction network, and Kyoto Encyclopedia of Genes and Genome analysis were conducted to explore the function and relationship among differentially expressed proteins. The expression levels of target proteins were verified by quantitative real-time PCR and western blotting. Analysis identified significant differential expression of 90 proteins across two time points, including 48 upregulated and 42 downregulated proteins. The upregulated thioredoxin, α-actinin-1, and aggrecan, and the downregulated integrin beta-1 may affect the growth of muscles affected by spasticity 3months after BoNT-A injection. This effect is potentially mediated through the activation or inhibition of PI3K-Akt, focal adhesion, and regulation of actin cytoskeleton signaling pathways. BoNT-A injection could lead to a disruption of protein levels and signaling pathways, a condition subsequently associated with muscle growth. This finding might aid clinicians in optimizing the management of spasticity in children with CP.

Characterization of the proteome of stable and unstable carotid atherosclerotic plaques using data-independent acquisition mass spectrometry

BackgroundCurrently, noninvasive imaging techniques and circulating biomarkers are still insufficient to accurately assess carotid plaque stability, and an in-depth understanding of the molecular mechanisms that contribute to plaque instability is still lacking.MethodsWe established a clinical study cohort containing 182 patients with carotid artery stenosis. After screening, 39 stable and 49 unstable plaques were included in the discovery group, and quantitative proteomics analysis based on data independent acquisition was performed for these plaque samples. Additionally, 35 plaques were included in the validation group to validate the proteomics results by immunohistochemistry analysis.ResultsA total of 397 differentially expressed proteins were identified in stable and unstable plaques. These proteins are primarily involved in ferroptosis and lipid metabolism-related functions and pathways. Plaque validation results showed that ferroptosis- and lipid metabolism-related proteins had different expression trends in stable plaques versus unstable fibrous cap regions and lipid core regions. Ferroptosis- and lipid metabolism-related mechanisms in plaque stability were discussed.ConclusionsOur results may provide a valuable strategy for revealing the mechanisms affecting plaque stability and will facilitate the discovery of specific biomarkers to broaden the therapeutic scope.

Regulation of urea cycle by reversible high-stoichiometry lysine succinylation.

The post-translational modification lysine succinylation is implicated in the regulation of various metabolic pathways. However, its biological relevance remains uncertain due to methodological difficulties in determining high-impact succinylation sites. Here, using stable isotope labelling and data-independent acquisition mass spectrometry, we quantified lysine succinylation stoichiometries in mouse livers. Despite the low overall stoichiometry of lysine succinylation, several high-stoichiometry sites were identified, especially upon deletion of the desuccinylase SIRT5. In particular, multiple high-stoichiometry lysine sites identified in argininosuccinate synthase (ASS1), a key enzyme in the urea cycle, are regulated by SIRT5. Mutation of the high-stoichiometry lysine in ASS1 to succinyl-mimetic glutamic acid significantly decreased its enzymatic activity. Metabolomics profiling confirms that SIRT5 deficiency decreases urea cycle activity in liver. Importantly, SIRT5 deficiency compromises ammonia tolerance, which can be reversed by the overexpression of wild-type, but not succinyl-mimetic, ASS1. Therefore, lysine succinylation is functionally important in ammonia metabolism.

Integrating single-cell and spatial transcriptomes reveals COL4A1/2 facilitates the spatial organisation of stromal cells differentiation in breast phyllodes tumours.

Breast phyllodes tumours (PTs) are a unique type of fibroepithelial neoplasms with metastatic potential and recurrence tendency. However, the precise nature of heterogeneity in breast PTs remains poorly understood. This study aimed to elucidate the cell subpopulations composition and spatial structure and investigate diagnostic markers in the pathogenesis of PTs. We applied single-cell RNA sequencing and spatial transcriptomes on tumours and adjacent normal tissues for integration analysis. Immunofluorescence experiments were conducted to verify the tissue distribution of cells. Tumour cells from patients with PTs were cultured to validate the function of genes. To validate the heterogeneity, the epithelial and stromal components of tumour tissues were separated using laser capture microdissection, and microproteomics data were obtained using data-independent acquisition mass spectrometry. The diagnostic value of genes was assessed using immunohistochemistry staining. Tumour stromal cells harboured seven subpopulations. Among them, a population of widely distributed cancer-associated fibroblast-like stroma cells exhibited strong communications with epithelial progenitors which underwent a mesenchymal transition. We identified two stromal subpopulations sharing epithelial progenitors and mesenchymal markers. They were inferred to further differentiate into transcriptionally active stromal subpopulations continuously expressing COL4A1/2. The binding of COL4A1/2 with ITGA1/B1 facilitated a growth pattern from the stroma towards the surrounding glands. Furthermore, we found consistent transcriptional changes between intratumoural heterogeneity and inter-patient heterogeneity by performing microproteomics studies on 30 samples from 11 PTs. The immunohistochemical assessment of 97 independent cohorts identified that COL4A1/2 and CSRP1 could aid in accurate diagnosis and grading. Our study demonstrates that COL4A1/2 shapes the spatial structure of stromal cell differentiation and has important clinical implications for accurate diagnosis of breast PTs.

Proteomic Analysis Identifies GSN as a Noninvasive Circulating Serum Biomarker for Predicting Early Recurrence of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is susceptible to early recurrence, but it lacks effective predictive biomarkers. In this study, we retrospectively selected 179 individuals as a discovery cohort (126 HCC patients and 53 liver cirrhosis (LC) patients) for screening candidate serum biomarkers of early recurrence based on data independent acquisition-mass spectrometry strategy. And then, the candidate biomarkers were validated in an additional independent cohort with 192 individuals (142 HCC patients and 50 LC patients) using parallel reaction monitoring targeted quantitative techniques (PXD047852). Eventually, we validated that gelsolin (GSN) concentrations were significantly lower in HCC than in LC (p < 0.0001), patients with low GSN concentrations had a poor prognosis (p < 0.0001), and GSN concentrations were significantly lower in early recurrence HCC than in late recurrence HCC (p < 0.0001). These trends were also observed in alpha-fetoprotein (AFP)-negative HCC patients. The area under the curve of machine-learning-based predictive model (GSN and microvascular invasion) for predicting early recurrence risk reached 0.803 (95% confidence interval (CI): 0.786-0.820) and maintained the same efficacy in AFP-negative patients. In conclusion, GSN is a novel serum biomarker for early recurrence of HCC. The model could provide timely warning to HCC patients at high risk of recurrence.

Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry

The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.

Longitudinal plasma proteomic analysis identifies biomarkers and combinational targets for anti-PD1-resistant cancer patients

The response rate of anti-PD1 therapy is limited, and the influence of anti-PD1 therapy on cancer patients is unclear. To address these challenges, we conducted a longitudinal analysis of plasma proteomic changes with anti-PD1 therapy in non-small cell lung cancer (NSCLC), alveolar soft part sarcoma (ASPS), and lymphoma patients. We included 339 plasma samples before and after anti-PD1 therapy from 193 patients with NSCLC, ASPS, or lymphoma. The plasma proteins were detected using data-independent acquisition-mass spectrometry and customable antibody microarrays. Differential proteomic characteristics in responders (R) and non-responders (NR) before and after anti-PD1 therapy were elucidated. A total of 1019 proteins were detected using our in-depth proteomics platform and distributed across 10–12 orders of abundance. By comparing the differential plasma proteome expression between R and NR groups, 50, 206, and 268 proteins were identified in NSCLC, ASPS, and lymphoma patients, respectively. Th17, IL-17, and JAK-STAT signal pathways were identified upregulated in NR group, while cellular senescence and transcriptional misregulation pathways were activated in R group. Longitudinal proteomics analysis revealed the IL-17 signaling pathway was downregulated after treatment. Consistently, many proteins were identified as potential combinatorial therapeutic targets (e.g., IL-17A and CD22). Five noninvasive biomarkers (FLT4, SFTPB, GNPTG, F5, and IL-17A) were further validated in an independent lymphoma cohort (n = 39), and another three noninvasive biomarkers (KIT, CCL3, and TNFSF1) were validated in NSCLC cohort (n = 76). Our results provide molecular insights into the anti-PD1 therapy in cancer patients and identify new therapeutic strategies for anti-PD1-resistant patients.

Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy.

To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers. A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry. Among 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively. Tear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR. Chinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177 . Date of registration: 2021/12/12.

Plasma proteome analysis and validation of patients with community-acquired pneumonia: A cohort study.

This study aimed to investigate the diagnostic potential of plasma biomarkers of community-acquired pneumonia (CAP) and their severity grading. Plasma proteomes from cohort I (n=32) with CAP were analyzed by data-independent acquisition mass spectrometry (MS). MetaboAnalyst 5.0 was used to statistically evaluate significant differences in proteins from different samples, and demographic and clinical data were recorded for all enrolled patients. Cohort II (n=80) was used to validate candidate biomarkers. Plasma protein levels were determined using quantitative enzyme-linked immunosorbent assay (ELISA). Correlations were assessed using Pearson's correlation coefficient. A receiver operating characteristic curve was used to verify the association between the variables, CAP diagnosis, and prognosis. 121 differentially expressed proteins (DEPs) were obtained between CAP and controls. These DEPs were mainly aggregated in pathways of phagosome(hsa04145) and complement and coagulation cascades (hsa04610). No significant differential proteins were detected in bacterial, viral, and mixed infection groups. The plasma levels of fetuin-A, alpha-1-antichymotrypsin (AACT), α1-acid glycoprotein (A1AG), and S100A8/S100A9 heterodimers detected by ELISA were consistent with those of MS. AACT, A1AG, S100A8/S100A9 heterodimer, and fetuin-A can potentially be used as diagnostic predictors, and fetuin-A and AACT are potential predictors of SCAP. Plasma protein profiling can successfully identify potential biomarkers for CAP diagnosis and disease severity assessment. These biomarkers should be further studied for their clinical application.

Identification ATP5F1D as a Biomarker Linked to Diagnosis, Prognosis, and Immune Infiltration in Endometrial Cancer Based on Data-Independent Acquisition (DIA) Analysis.

In developed countries, endometrial cancer (EC) is the most prevalent gynecological cancer. ATP5F1D is a subunit of ATP synthase, as well as an important component of the mitochondrial electron transport chain (ETC). ETC plays a compelling role in carcinogenesis. To date, little is known about the role of ATP5F1D in EC. We undertook data-independent acquisition mass spectrometry (DIA-MS) of 20 EC patients, comprising 10 high-grade and 10 low-grade cancer tissues. Biological functions of differentially expressed genes (DEGs) were analyzed by GO and KEGG. The expression level, clinicopathological features, diagnostic potency, prognostic value, RNA modifications, immune characteristics, and therapy response of ATP5F1D were investigated. In total, 77 DEGs were acquired by DIA analysis, which were closely related to regulating immune response and metabolic pathways. Among the five genes (NDUFB8, SLC26A2, RAF1, ATP5F1D, and GSTM5) involving in reactive oxygen species pathway, ATP5F1D showed the most significant differential expression (2.903-fold change). We found ATP5F1D had a high diagnostic value and was associated with a favorable prognosis in EC patients. After analyzing the RNA modifications of ATP5F1D, revealing a negative regulation between them. Additionally, ATP5F1D was closely related to tumor immune infiltration. Our results suggested T-cell dysfunction and TAM-M2 polarization might be the important mechanisms of ATP5F1D to facilitate tumor immune escape. Noticeably, EC patients with ATP5F1D-high expression had better immune treatment responses and were more sensitive to chemotherapy drugs. ATP5F1D can be used as a biomarker for diagnosis, prognosis, and immune infiltration of EC, and offers a crucial reference for personalized treatment of EC patients.

Screening of novel biomarkers for acute kidney transplant rejection using DIA-MS based proteomics.

Kidney transplantation is the preferred treatment for patients with end-stage renal disease. However, acute rejection poses a threat to the graft long-term survival. The aim of this study was to identify novel biomarkers to detect acute kidney transplant rejection. The serum proteomic profiling of kidney transplant patients with T cell-mediated acute rejection (TCMR) and stable allograft function (STA) was analyzed using data-independent acquisition mass spectrometry (DIA-MS). The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA). A total of 131 DEPs were identified between STA and TCMR patients, 114 DEPs were identified between mild and severe TCMR patients. The verification results showed that remarkable higher concentrations of serum amyloid A protein 1 (SAA1) and insulin like growth factor binding protein 2 (IGFBP2), and lower fetuin-A (AHSG) concentration were found in TCMR patients when compared with STA patients. We also found higher SAA1 concentration in severe TCMR group when compared with mild TCMR group. The receiver operating characteristics (ROC) analysis further confirmed that combination of SAA1, AHSG, and IGFBP2 had excellent performance in the acute rejection diagnosis. Our data demonstrated that serum SAA1, AHSG, and IGFBP2 could be effective biomarkers for diagnosing acute rejection after kidney transplantation. DIA-MS has great potential in biomarker screening of kidney transplantation.

In-depth metaproteomics analysis of tongue coating for gastric cancer: a multicenter diagnostic research study

BackgroundOur previous study revealed marked differences in tongue images between individuals with gastric cancer and those without gastric cancer. However, the biological mechanism of tongue images as a disease indicator remains unclear. Tongue coating, a major factor in tongue appearance, is the visible layer on the tongue dorsum that provides a vital environment for oral microorganisms. While oral microorganisms are associated with gastric and intestinal diseases, the comprehensive function profiles of oral microbiota remain incompletely understood. Metaproteomics has unique strength in revealing functional profiles of microbiota that aid in comprehending the mechanism behind specific tongue coating formation and its role as an indicator of gastric cancer.MethodsWe employed pressure cycling technology and data-independent acquisition (PCT-DIA) mass spectrometry to extract and identify tongue-coating proteins from 180 gastric cancer patients and 185 non-gastric cancer patients across 5 independent research centers in China. Additionally, we investigated the temporal stability of tongue-coating proteins based on a time-series cohort. Finally, we constructed a machine learning model using the stochastic gradient boosting algorithm to identify individuals at high risk of gastric cancer based on tongue-coating microbial proteins.ResultsWe measured 1432 human-derived proteins and 13,780 microbial proteins from 345 tongue-coating samples. The abundance of tongue-coating proteins exhibited high temporal stability within an individual. Notably, we observed the downregulation of human keratins KRT2 and KRT9 on the tongue surface, as well as the downregulation of ABC transporter COG1136 in microbiota, in gastric cancer patients. This suggests a decline in the defense capacity of the lingual mucosa. Finally, we established a machine learning model that employs 50 microbial proteins of tongue coating to identify individuals at a high risk of gastric cancer, achieving an area under the curve (AUC) of 0.91 in the independent validation cohort.ConclusionsWe characterized the alterations in tongue-coating proteins among gastric cancer patients and constructed a gastric cancer screening model based on microbial-derived tongue-coating proteins. Tongue-coating proteins are shown as a promising indicator for identifying high-risk groups for gastric cancer.1cCPG45EGUX3nZjXKAFgvfVideo

Plasma proteome profiling reveals the therapeutic effects of the PPAR pan-agonist chiglitazar on insulin sensitivity, lipid metabolism, and inflammation in type 2 diabetes

Chiglitazar is a novel peroxisome proliferator-activated receptor (PPAR) pan-agonist, which passed phase III clinical trials and was newly approved in China for use as an adjunct to diet and exercise in glycemic control in adult patients with Type 2 Diabetes (T2D). To explore the circulating protein signatures associated with the administration of chiglitazar in T2D patients, we conducted a comparative longitudinal study using plasma proteome profiling. Of the 157 T2D patients included in the study, we administered chiglitazar to a specific group, while the controls were given either placebo or sitagliptin. The plasma proteomes were profiled at baseline and 12 and 24 weeks post-treatment using data-independent acquisition mass spectrometry (DIA-MS). Our study indicated that 13 proteins were associated with chiglitazar treatment in T2D patients, including 10 up-regulated proteins (SHBG, TF, APOA2, APOD, GSN, MBL2, CFD, PGLYRP2, A2M, and APOA1) and 3 down-regulated proteins (PRG4, FETUB, and C2) after treatment, which were implicated in the regulation of insulin sensitivity, lipid metabolism, and inflammation response. Our study provides insight into the response of chiglitazar treatment from a proteome perspective and demonstrates the multi-faceted effects of chiglitazar in T2D patients, which will help the clinical application of chiglitazar and further study of its action mechanism.

Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy.

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation–enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

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

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

Reusing Peptide Spectral Reference Libraries to Discover Putative Plasma Biomarkers of Response to Cancer Chemotherapy.

Biofluids such as blood plasma are rich reservoirs of potential biomarkers for disease diagnosis, prognosis, and prediction of treatment response. However, mass spectrometry analysis of circulating plasma proteins remains challenging. The introduction of data-independent acquisition mass spectrometry (DIA-MS) is an important step toward addressing detection of less abundant plasma proteins. Numerous plasma peptide MS/MS spectral library datasets produced from extensive plasma fractionation are accessible from public archives, and these can be repurposed as spectral reference libraries to increase the depth of proteomic analysis when DIA-MS is used. Here we describe the workflow that relies on reusing the existing spectral reference libraries by populating them with locally obtained peptide MS/MS data acquired by DIA-MS. This approach was demonstrated effectively to identify putative plasma biomarkers of response to neoadjuvant chemotherapy in the setting of pancreatic ductal adenocarcinoma (PDAC) (O'Rourke et al., J Proteomics 231:103998, 2021).

Plasma prolactin axis shift from placental to pituitary origin in late prepartum mice.

The placenta secretes a prolactin (PRL)-like hormone PRL3B1 (placental lactogen II), a luteotropic hormone essential for maintaining pregnancy until labor in mice. A report from 1984 examined the secretion pattern of PRL3B1 in prepartum mice. In the current study, we found contradictory findings in the secretion pattern that invalidate the previous report. By measuring maternal plasma PRL3B1 and PRL every 4 hrs from gestational day 17 (G17), we newly discovered that maternal plasma PRL3B1 levels decrease rapidly in prepartum C57BL/6 mice. Interestingly, the onset of this decline coincided with the PRL surge at G18, demonstrating a plasma prolactin axis shift from placental to pituitary origin. We also found that maternal plasma progesterone regression precedes the onset of the PRL shift. The level of Prl3b1 mRNA was determined by RT-qPCR in the placenta and remained stable until parturition, implying that PRL3B1 peptide production or secretion was suppressed. We hypothesized that production of the PRL family, the 25 paralogous PRL proteins exclusively expressed in mice placenta, would decrease alongside PRL3B1 during this period. To investigate this hypothesis and to seek proteomic changes, we performed a shotgun proteome analysis of the placental tissue using data-independent acquisition mass spectrometry (DIA-MS). Up to 5,891 proteins were identified, including 17 PRL family members. Relative quantitative analysis between embryonic day 17 (E17) and E18 placentas showed no significant difference in the expression of PRL3B1 and most PRL family members except PRL7C1. These results suggest that PRL3B1 secretion from the placenta is suppressed at G18 (E18).

Serum proteomics identifies novel diagnostic biomarkers for asthma in preschool children.

Asthma is characterized by airway hyperresponsiveness, reversible airway obstruction, and chronic airway inflammation. It is the most common chronic disease in childhood. However, the diagnosis of childhood asthma remains challenging, and there is an urgent need to develop new diagnostic methods. To identify biomarkers of asthma in children, we adopted the Orbitrap-based data-independent acquisition (DIA) mass spectrometry proteomics method to analyze the serum proteomic signatures of children with acute asthma and convalescent children. We identified 747 proteins in 46 serum samples and 50 differentially expressed proteins (DEPs) that distinguished between asthmatic and healthy children. Next, functional enrichment analysis of the DEPs was conducted, it was indicated that the DEPs were significantly enriched in immune-related and function terms and pathways. Furthermore, we performed statistical analysis and identified MMP14, ABHD12B, PCYOX1, LTBP1, CFHR4, APOA1, IGHG4, ANG and IGFALS proteins as the diagnostic biomarker candidates. Ultimately, a promising asthma diagnostic model for preschool children based on IGFALS was built and evaluated. The area under the curve (AUC) of the IGFALS model was 0.959. In this study, the DIA proteome strategy was used and the largest number of proteins of asthmatic children serum proteomics was identified. The proteomics results showed that the DEPs play the central role of the inflammation-immune mechanism in asthma pathogenesis, suggesting that these proteins may be used in asthma diagnosis, prognosis, or therapy, and suggested biomarkers for asthma of preschool children. In conclusion, our results provide insight into the pathophysiology of asthma. We believe that the diagnostic model will facilitate clinical decision-making regarding asthma in preschool children.

Comparative profiling by data-independent acquisition mass spectrometry reveals featured plasma proteins in breast cancer: a pilot study.

Breast cancer is known to be influenced by genetic and environmental factors, and several susceptibility genes have been discovered. Still, the majority of genetic contributors remain unknown. We aimed to analyze the plasma proteome of breast cancer patients in comparison to healthy individuals to identify differences in protein expression profiles and discover novel biomarkers. This pilot study was conducted using bioresources from Seoul National University Bundang Hospital's Human Bioresource Center. Serum samples from 10 breast cancer patients and 10 healthy controls were obtained. Liquid chromatography-mass spectrometry analysis was performed to identify differentially expressed proteins. We identified 891 proteins; 805 were expressed in the breast cancer group and 882 in the control group. Gene set enrichment and differential expression analysis identified 30 upregulated and 100 downregulated proteins in breast cancer. Among these, 10 proteins were selected as potential biomarkers. Three proteins were upregulated in breast cancer patients, including cluster of differentiation 44, eukaryotic translation initiation factor 2-α kinase 3, and fibronectin 1. Seven proteins downregulated in breast cancer patients were also selected: glyceraldehyde-3-phosphate dehydrogenase, α-enolase, heat shock protein member 8, integrin-linked kinase, tissue inhibitor of metalloproteinases-1, vasodilator-stimulated phosphoprotein, and 14-3-3 protein gamma. All proteins had been previously reported to be related to tumor development and progression. The findings suggest that plasma proteome profiling can reveal potential diagnostic biomarkers for breast cancer and may contribute to early detection and personalized treatment strategies. A further validation study with a larger sample cohort of breast cancer patients is planned.