Multiple Reaction Monitoring Analysis Research Articles

Screening biomarkers for autism spectrum disorder using plasma proteomics combined with machine learning methods

Background and aimsAutism spectrum disorder (ASD) is a common neurodevelopmental disorder in children. Early intervention is effective. Investigation of novel blood biomarkers of ASD facilitates early detection and intervention. Materials and MethodsSequential window acquisition of all theoretical spectra-mass spectrometry (SWATH-MS)-based proteomics technology and 30 DSM-V defined ASD cases versus age- and sex-matched controls were initially evaluated, and candidate biomarkers were screened using machine learning methods. Candidate biomarkers were validated by targeted proteomics multiple reaction monitoring (MRM) analysis using an independent group of 30 ASD cases vs. controls. ResultsFifty-one differentially expressed proteins (DEPs) were identified by SWATH analysis. They were associated with the immune response, complements and coagulation cascade pathways, and apolipoprotein-related metabolic pathways. Machine learning analysis screened 10 proteins as biomarker combinations (TFRC, PPBP, APCS, ALDH1A1, CD5L, SPARC, FGG, SHBG, S100A9, and PF4V1). In the MRM analysis, four proteins (PPBP, APCS, FGG, and PF4V1) were significantly different between the groups, and their combination as a screening indicator showed high potential (AUC = 0.8087, 95 % confidence interval 0.6904–0.9252, p < 0.0001). ConclusionsOur study provides data that suggests that a few plasma proteins have potential use in screening for ASD.

Contactin proteins in cerebrospinal fluid show different alterations in dementias.

The proteins contactin (CNTN) 1-6 are synaptic proteins for which there is evidence that they are dysregulated in neurodegenerative dementias. Less is known about CNTN changes and differences in cerebrospinal fluid (CSF) of dementias, which can provide important information about alterations of the CNTN network and be of value for differential diagnosis. We developed a mass spectrometry-based multiple reaction monitoring (MRM) method to simultaneously determine all six CNTNs in CSF samples using stable isotope-labeled standard peptides. The analytical performance of the method was evaluated for peptide stability, dilution linearity and precision. CNTNs were measured in 82 CSF samples from patients with Alzheimer's disease (AD, n = 19), behavioural variant frontotemporal dementia (bvFTD, n = 18), Parkinson's disease dementia/dementia with Lewy bodies (PDD/DLB, n = 18) and non-neurodegenerative controls (n = 27) and compared with core AD biomarkers. The MRM analysis revealed down-regulation of CNTN2 (fold change (FC) = 0.77), CNTN4 (FC = 0.75) and CNTN5 (FC = 0.67) in bvFTD and CNTN3 (FC = 0.72), CNTN4 (FC = 0.75) and CNTN5 (FC = 0.73) in PDD/DLB compared to AD. CNTN levels strongly correlated with each other in controls (r = 0.73), bvFTD (r = 0.86) and PDD/DLB (r = 0.70), but the correlation was significantly lower in AD (r = 0.41). CNTNs in AD did not show correlation even with core AD biomarkers. Combined use of CNTN1-6 levels increased diagnostic performance of AD core biomarkers. Our data show CNTNs differentially altered in dementias and indicate CNTN homeostasis being selectively dysregulated in AD. The combined use of CNTNs with AD core biomarkers might help to improve differential diagnosis.

A broad-spectrum LC-MS/MS method for screening and quantification of 100 analytes in clinical and autopsy blood samples

Liquid chromatography coupled with mass spectrometry (LC-MS) has been tremendously used for screening purposes in forensic toxicology, because of their great adaptability and reasonable time/resource consumption. Herein, a fully validated method based on liquid-liquid extraction (LLE) in human whole blood, by a multiple reaction monitoring (MRM) analysis through LC-MS/MS, is described. The proposed method simultaneously detects 100 analytes (plus three deuterated internal standard compounds) belonging to many different classes, including drugs of abuse, prescription and over-the-counter drugs commonly involved in poisoning and medical malpractice cases in our territory, as well as certain new psychoactive substances (NPS) and toxic substances potentially associated with adverse effects. The optimised LLE employs one extraction step of 200 μL blood using 0.1 M HCl methyl-tert-butyl-ether (MTBE) (acidified with concentrated HCl) proved to be suitable for the extraction of basic and neutral substances; as a reconstitution solvent a mixture of 88:12v/v, 0.1 % formic acid in 10 mM aqueous ammonium acetate, pH 3.5: 0.1 % formic acid in acetonitrile was used, yielding satisfactory recoveries for all analytes. The method was sensitive, showing low LOD/ LOQ for all substances ranging from 0.01 to 5/ 0.05–20 ng/mL, respectively. Linearity ranged between 0.05–500 ng/mL (R2 = 0.9811–0.9995), and the inter- and intra-day precisions ranged between 3–15 % and 7–18 %, respectively. Accuracy was evaluated in terms of percentage recovery, lying within acceptable range. The matrix effect expressed as ion suppression/enhancement of each analyte was in the range ±25 % for all analytes. Post-preparative stability of analytes was higher than 85 %, while no carryover between runs was observed. The developed method has been successfully applied in routine toxicological analyses for the analysis of biological samples from clinical and autopsy cases.

Systematic Proteome Profiling of Maternal Plasma for Development of Preeclampsia Biomarkers

Preeclampsia (PE) is a hypertensive disorder of pregnancy with various clinical symptoms. However, traditional markers for the disease including high blood pressure and proteinuria are poor indicators of the related adverse outcomes. Here, we performed systematic proteome profiling of plasma samples obtained from pregnant women with PE to identify clinically effective diagnostic biomarkers. Proteome profiling was performed using TMT-based liquid chromatography-mass spectrometry (LC-MS/MS) followed by subsequent verification by multiple reaction monitoring (MRM) analysis on normal and PE maternal plasma samples. Functional annotations of differentially expressed proteins (DEPs) in PE were predicted using bioinformatic tools. The diagnostic accuracies of the biomarkers for PE were estimated according to the area under the receiver-operating characteristics curve (AUC). A total of 1307 proteins were identified, and 870 proteins of them were quantified from plasma samples. Significant differences were evident in 138 DEPs, including 71 upregulated DEPs and 67 downregulated DEPs in the PE group, compared with those in the control group. Upregulated proteins were significantly associated with biological processes including platelet degranulation, proteolysis, lipoprotein metabolism, and cholesterol efflux. Biological processes including blood coagulation and acute-phase response were enriched for down-regulated proteins. Of these, 40 proteins were subsequently validated in an independent cohort of 26 PE patients and 29 healthy controls. APOM, LCN2, and QSOX1 showed high diagnostic accuracies for PE detection (AUC >0.9 and p<0.001, for all) as validated by MRM and ELISA. Our data demonstrate that three plasma biomarkers, identified by systematic proteomic profiling, present a possibility for the assessment of PE, independent of the clinical characteristics of pregnant women.

Changes in neuroinflammatory biomarkers correlate with disease severity and neuroimaging alterations in patients with COVID-19 neurological complications

COVID-19 induces acute and persistent neurological symptoms in mild and severe cases. Proposed concomitant mechanisms include direct viral infection and strain, coagulopathy, hypoxia, and neuroinflammation. However, underlying molecular alterations associated with multiple neurological outcomes in both mild and severe cases are majorly unexplored. To illuminate possible mechanisms leading to COVID-19 neurological disease, we retrospectively investigated in detail a cohort of 35 COVID-19 mild and severe hospitalized patients presenting neurological alterations subject to clinically indicated cerebrospinal fluid (CSF) sampling. Clinical and neurological investigation, brain imaging, viral sequencing, and cerebrospinal CSF analyses were carried out. We found that COVID-19 patients presented heterogeneous neurological symptoms dissociated from lung burden. Nasal swab viral sequencing revealed a dominant strain at the time of the study, and we could not detect traces of SARS-CoV-2's spike protein in patients' CSF by multiple reaction monitoring analysis. Patients presented ubiquitous systemic hyper-inflammation and broad alterations in CSF proteomics related to inflammation, innate immunity, and hemostasis, irrespective of COVID-19 severity or neuroimaging alterations. Elevated CSF interleukin-6 (IL6) correlated with disease severity (sex-, age-, and comorbidity-adjusted mean Severe 24.5 pg/ml, 95% confidence interval (CI) 9.62–62.23 vs. Mild 3.91 pg/mL CI 1.5–10.3 patients, p = 0.019). CSF tumor necrosis factor-alpha (TNFα) and IL6 levels were higher in patients presenting pronounced neuroimaging alterations compared to those who did not (sex-, age-, and comorbidity-adjusted mean TNFα Pronounced 3.4, CI 2.4–4.4 vs. Non-Pronounced 2.0, CI 1.4–2.5, p = 0.022; IL6 Pronounced 33.11, CI 8.89–123.31 vs Non-Pronounced 6.22, CI 2.9–13.34, p = 0.046). Collectively, our findings put neuroinflammation as a possible driver of COVID-19 acute neurological disease in mild and severe cases.

Proteomic analysis of plasma proteins from patients with cardiac rupture after acute myocardial infarction using TMT-based quantitative proteomics approach

BackgroundCardiac rupture (CR) is a rare but catastrophic mechanical complication of acute myocardial infarction (AMI) that seriously threatens human health. However, the reliable biomarkers for clinical diagnosis and the underlying signaling pathways insights of CR has yet to be elucidated.MethodsIn the present study, a quantitative approach with tandem mass tag (TMT) labeling and liquid chromatography–tandem mass spectrometry was used to characterize the differential protein expression profiles of patients with CR. Plasma samples were collected from patients with CR (n = 37), patients with AMI (n = 47), and healthy controls (n = 47). Candidate proteins were selected for validation by multiple reaction monitoring (MRM) and enzyme-linked immunosorbent assay (ELISA).ResultsIn total, 1208 proteins were quantified and 958 differentially expressed proteins (DEPs) were identified. The difference in the expression levels of the DEPs was more noticeable between the CR and Con groups than between the AMI and Con groups. Bioinformatics analysis showed most of the DEPs to be involved in numerous crucial biological processes and signaling pathways, such as RNA transport, ribosome, proteasome, and protein processing in the endoplasmic reticulum, as well as necroptosis and leukocyte transendothelial migration, which might play essential roles in the complex pathological processes associated with CR. MRM analysis confirmed the accuracy of the proteomic analysis results. Four proteins i.e., C-reactive protein (CRP), heat shock protein beta-1 (HSPB1), vinculin (VINC) and growth/differentiation factor 15 (GDF15), were further validated via ELISA. By receiver operating characteristic (ROC) analysis, combinations of these four proteins distinguished CR patients from AMI patients with a high area under the curve (AUC) value (0.895, 95% CI, 0.802–0.988, p < 0.001).ConclusionsOur study highlights the value of comprehensive proteomic characterization for identifying plasma proteome changes in patients with CR. This pilot study could serve as a valid foundation and initiation point for elucidation of the mechanisms of CR, which might aid in identifying effective diagnostic biomarkers in the future.

Proteomic analysis of serum small extracellular vesicles identifies diagnostic biomarkers for neuroblastoma.

Neuroblastoma (NB) primarily arises in children who are <10 years of age, and originates from developing sympathetic nervous system, which results in tumors in adrenal glands and/or sympathetic ganglia. The diagnosis of NB involves a combination of laboratory and imaging tests, and biopsies. Small extracellular vesicles (sEVs) have gained attention as potential biomarkers for various types of tumors. Here, we performed proteomic analysis of serum sEVs and identified potential biomarkers for NB. Label-free proteomics of serum sEVs were performed in the discovery phase. A bulk RNA-seq dataset of NB tissues was used to analyze the association between genes encoding sEVs proteins and prognosis. Potential biomarkers were validated via multiple reaction monitoring (MRM) or western blot analysis in the validation phase. A public single-cell RNA-seq (scRNA-seq) dataset was integrated to analyze the tissue origin of sEVs harboring biomarkers. A total of 104 differentially expressed proteins were identified in NB patients with label-free proteomics, and 26 potential biomarkers were validated with MRM analysis. Seven proteins BSG, HSP90AB1, SLC44A1, CHGA, ATP6V0A1, ITGAL and SELL showed the strong ability to distinguish NB patients from healthy controls and non-NB patients as well. Integrated analysis of scRNA-seq and sEVs proteomics revealed that these sEVs-derived biomarkers originated from different cell populations in tumor tissues. sEVs-based biomarkers may aid the molecular diagnosis of NB, representing an innovative strategy to improve NB detection and management.

Determination of Four Chemical Components in Qishen Yiqi Dripping Pills by UPLC-MS/MS Method

Background In this study, the ultrahigh performance liquid chromatography mass spectrometry/mass spectrometry (UPLC-MS/MS) method was established to test the contents of protocatechuic acid (C7H6O4), salvianolic acid B (C36H30O16), rosmarinic acid (C19H14O3), and cryptotanshinone (C19H20O3) in Qishen Yiqi dripping pills. Materials and Methods Methanol was chosen as the solvent according to a certain material–liquid ratio as a solution, and the ultrasonic extraction method was used to prepare the sample solution. UPLC-MS/MS was used to complete a quantitative analysis of the contents of Qishen Yiqi dripping pills. Then, the chromatographic conditions were shown as follows. The stationary phase was Agilent ZORBAX SB-C18 (2.1 mm × 100 mm, 1.8 µm), and the mobile phase was acetonitrile–0.1% formic acid water. The column temperature was 40℃, the injection volume was 3 µL, and the flow rate was 0.2 mL⋅min–1. Finally, the determination method was built to test four chemical constituents under the multiple reaction monitoring (MRM) analysis mode. Results The retention times of protocatechuic acid, salvianolic acid B, rosmarinic acid, and cryptotanshinone were 1.26, 1.32, 1.35, and 6.76 min, respectively. In addition, the concentration ranges of four compounds were 0.25–2.0 µg⋅mL−1, 10.0–50.0 µg⋅mL−1, 5.0–50.0 µg⋅mL−1, and 1.0–10.0 µg⋅mL−1, respectively. And the standard curve showed a good linear relationship in a reasonable range ( r2 ≥ 0.9950). The average recovery rate was from 96% to 124%. The contents of protocatechuic acid, salvianolic acid B, rosmarinic acid, and cryptotanshinone were 0.12%–0.022%, 17%–8.4%, and 6.5%–8.6% in seven batches of Qishen Yiqi dripping pills, which were in the range of 23%–31%. The instrument ran stably, and the method had good accuracy and repeatability (relative standard deviation [RSD] ≤ 3%). Additionally, the test result was stable within 10 h. Conclusion The contents of the four constituents were different among different batches. The quantitative method was established to determine the four chemical components in Qishen Yiqi dripping pills, which could provide a reference for quality evaluation in vivo.

High-Throughput Comprehensive Quantitative LC-MS/MS Analysis of Common Drugs and Metabolites (62 Compounds) in Human Urine.

In recent years a multitude of LC-MS/MS assays have been widely reported in commercial and clinical literature demonstrating the simultaneous analyses of dozens of drugs of abuse in human samples. The utility of such assays is meant to supplant the indirect detection based on the classical spectral library approach. Direct and simultaneous analysis via LC-MS/MS technology is made possible by fast acquisition rates in multiple reaction monitoring, as well as sensitivity and high selectivity of the technology for each individual analyte in a complex mixture. Hence, unlike immunoassays, which are not well-suited for the analyses of mixtures, and which may also be prone to false positives from potential interferences, quantitative LC-MS/MS analyses are feasible for complex patient mixtures of drugs of abuse. We hereby present a robust clinical LC-MS/MS assay for the simultaneous and semi-quantitative analysis of up to 62 drugs of abuse in human urine, representing major classes that include opiates, benzodiazepines, amphetamines, etc. The assay utilizes dilute and shoot, whereby the sample is diluted ten times in internal standard reagent and thereafter submitted to the LC-MS instrument, i.e., reversed-phase liquid chromatography coupled to the electrospray ionization multiple reaction monitoring analysis, via the TSQ Endura triple-quadrupole instrument. The assay employs stable isotope-labeled internal standards with a linear response in the 30-300ng/mL range, effectively semi-quantitative, since this analytical range is well within typical immunoassay cutoffs for most drugs.

Food allergen detection by mass spectrometry: From common to novel protein ingredients.

Food allergens are molecules, mainly proteins, that trigger immune responses in susceptible individuals upon consumption even when they would otherwise be harmless. Symptoms of a food allergy can range from mild to acute; this last effect is a severe and potentially life-threatening reaction. The European Union (EU) has identified 14 common food allergens, but new allergens are likely to emerge with constantly changing food habits. Mass spectrometry (MS) is a promising alternative to traditional antibody-based assays for quantifying multiple allergenic proteins in complex matrices with high sensitivity and selectivity. Here, the main allergenic proteins and the advantages and drawbacks of some MS acquisition protocols, such as multiple reaction monitoring (MRM) and data-dependent analysis (DDA) for identifying and quantifying common allergenic proteins in processed foodstuffs are summarized. Sections dedicated to novel foods like microalgae and insects as new sources of allergenic proteins are included, emphasizing the significance of establishing stable marker peptides and validated methods using database searches. The discussion involves the in-silico digestion of allergenic proteins, providing insights into their potential impact on immunogenicity. Finally, case studies focussing on microalgae highlight the value of MS as an effective analytical tool for ensuring regulatory compliance throughout the food control chain.

Serum Protein Profiling Reveals a Decrease in Apolipoprotein A-IV During a Clinical Depressive Mood State.

Depressive mood is a major psychiatric symptom that causes serious disturbances in daily life. Unlike physical symptoms, psychiatric symptoms are more difficult to evaluate objectively. Therefore, we aimed to discover biomarkers that reflect changes in serum protein metabolism during a clinical depressive mood. Serum protein profiling was conducted in participants who were not experiencing a current depressive episode (healthy individuals and patients in remission). Serum proteins were identified and quantified using liquid chromatography-tandem mass spectrometry. Differentially expressed proteins with a p-value <0.05 were selected, and candidate biomarkers were verified using multiple reaction monitoring analysis for absolute quantification. Apolipoprotein A-IV levels were lower in the group with a current episode of depression than in the remission and healthy control groups. Further, fibronectin levels were also lower in the group with a current episode of depression than in the healthy control group but not in the remission group. We found that apolipoprotein A-IV-mediated inflammation is involved in clinical depressive moods, possibly by inducing neurological changes in the brain. Therefore, apolipoprotein A-IV and fibronectin levels may be explored as potentially novel biomarkers for detecting a current episode of depression.

Metabolomic characterization benefits the identification of acute lung injury in patients with type A acute aortic dissection.

Introduction: Acute aortic dissection (AAD) often leads to the development of acute lung injury (ALI). However, the early detection and diagnosis of AAD in patients with ALI pose significant challenges. The objective of this study is to investigate distinct metabolic alterations in the plasma samples of AAD patients with ALI, AAD patients without ALI, and healthy individuals. Method: Between September 2019 and September 2022, we retrospectively collected data from 228 AAD patients who were diagnosed with ALI through post-surgery chest X-ray and PaO2/FiO2 assessments. Univariate analysis was employed to identify pre-surgery risk factors for ALI. Additionally, we conducted high-throughput target metabolic analysis on 90 plasma samples, comprising 30 samples from AAD patients with ALI, 30 from patients with AAD only, and 30 from healthy controls. After LC-MS spectral processing and metabolite quantification, the recursive feature elimination with cross-validation (RFECV) analysis based on the random forest was used to select the optimal metabolites as a diagnostic panel for the detection of AAD patients with ALI. The support vector machines (SVM) machine learning model was further applied to validate the diagnostic accuracy of the established biomarker panel. Results: In the univariate analysis, preoperative β-HB and TNF-α exhibited a significant association with lung injury (OR = 0.906, 95% CI 0.852-0.965, p = 0.002; OR = 1.007, 95% CI 1.003-1.011, p < 0.0001). The multiple-reaction monitoring analysis of 417 common metabolites identified significant changes in 145 metabolites (fold change >1.2 or <0.833, p < 0.05) across the three groups. Multivariate statistical analysis revealed notable differences between AAD patients and healthy controls. When compared with the non-ALI group, AAD patients with ALI displayed remarkable upregulation in 19 metabolites and downregulation in 4 metabolites. Particularly, combining citric acid and glucuronic acid as a biomarker panel improved the classification performance for distinguishing between the ALI and non-ALI groups. Discussion: Differentially expressed metabolites in the ALI group were primarily involved in amino acids biosynthesis, carbohydrate metabolism (TCA cycle), arginine and proline metabolism, and glucagon signaling pathway. These findings demonstrate a great potential of the targeted metabolomic approach for screening, routine surveillance, and diagnosis of pulmonary injury in patients with AAD.

Validation of the Ultra-Performance Liquid Chromatography with Tandem Mass Spectrometry Method for Simultaneous Analysis of Eighteen Compounds in the Traditional Herbal Prescription, Sanjoin-Tang

Sanjoin-tang (SJIT) is an ancient oriental medicine prescription listed in the Jinguiyaolue that is mainly used for the treatment of primary insomnia. This study was conducted to develop and validate an ultra-performance liquid chromatography with tandem mass spectrometry (UPLC–MS/MS) simultaneous analysis method for the quality control of SJIT using 18 target compounds. The 18 analytes were separated on an Acquity UPLC BEH C18 column maintained at 45 °C using a mobile phase composed of distilled water and acetonitrile. The MS system was used to simultaneously detect all analytes using the multiple reaction monitoring (MRM) method of Xevo TQ-XS coupled with an electrospray ionization source. The concentrations of the 18 analytes investigated in the SJIT samples ranged from below the limit of detection to 9.553 mg/g. In conclusion, the validated UPLC–MS/MS MRM analysis method can be used to obtain basic data to establish chemical-nonclinical linkage efficacy and for the clinical research and quality evaluation of SJIT.

A retrospective screening method for carbamate toxicant exposure based on butyrylcholinesterase adducts in human plasma with ultra-high performance liquid chromatography–tandem mass spectrometry

Carbamate pesticides are extensively used in agriculture for their inhibition to acetylcholinesterase and damages to the insects’ neural systems. Because of their toxicity, human poisoning incidents caused by carbamate pesticide exposure have occurred from time to time. What’s more, some lethally toxic carbamate toxicants known as carbamate nerve agents (CMNAs) have been supplemented in Schedule 1 of the Annex on Chemicals in the Chemical Weapons Convention (CWC) by Organisation of the Prohibition of Chemical Weapons (OPCW) from 2020. And some other carbamates, like physostigmine, have been used in clinical treatment as anticholinergic drugs and their misuse may also cause damages to the body. Similar to organophosphorus toxicants, carbamate toxicants would react with butyrylcholinesterase (BChE) in plasma when entering the human body, resulting in the BChE adducts, based on which the exposure of carbamate toxicants could be detected retrospectively. In this study, methylcarbamyl nonapeptide and dimethylcarbamyl nonapeptide from pepsin digestion of BChE adducts were identified with ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS) in product ion scan mode. Carbofuran was chosen as the target to establish the detection method of carbamate toxicant exposure based on methylcarbamyl nonapeptide digested from methylcarbamyl BChE. Procainamide-gel affinity purification, pepsin digestion and UHPLC–MS/MS analysis in multiple reaction monitoring (MRM) mode were applied. Under the optimized conditions of sample preparation and UHPLC–MS/MS MRM analysis, the limits of detection (LODs) reached 10.0 ng/mL of plasma exposed to carbofuran with satisfactory specificity. The quantitation approach was established with d3-carbofuran-exposed plasma as the internal standard (IS) and the linearity range was 30.0–1.00 × 103 nmol/L (R2 >0.998) with the accuracy of 95.6%–107% and precision of ≤9% relative standard deviation (RSD). The applicability was also evaluated by N,N-dimethyl-carbamates with the LODs of 30.0 nmol/L for pirimicarb-exposed plasma based on dimethylcarbamyl nonapeptide. Because most of carbamate toxicants has methylcarbamyl or dimethylcarbamyl groups, this approach could be applied on the retrospective screening of carbamate toxicant exposure including CMNAs, carbamate pesticides or carbamate drugs. This study could provide an effective means in the fields of CWC verification, toxicological mechanism investigation and down-selection of potential treatment options.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies.

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

The use of data independent acquisition based proteomic analysis and machine learning to reveal potential biomarkers for autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurological developmental disorder in children, and is associated with social isolation and restricted interests. The etiology of this disorder is still unknown. There is neither any confirmed laboratory test nor any effective therapeutic strategy to diagnose or cure it. We performed data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis of plasma from children with ASD and controls. The result showed that 45 differentially expressed proteins (DEPs) were identified between autistic subjects and controls. Among these, only one DEP was down-regulated in ASD; other DEPs were up-regulated in ASD children's plasma. These proteins are found associated with complement and coagulation cascades, vitamin digestion and absorption, cholesterol metabolism, platelet degranulation, selenium micronutrient network, extracellular matrix organization and inflammatory pathway, which have been reported to be related to ASD. After MRM verification, five key proteins in complement pathway (PLG, SERPINC1, and A2M) and inflammatory pathway (CD5L, ATRN, SERPINC1, and A2M) were confirmed to be significantly up-regulated in ASD group. Through the screening of machine learning model and MRM verification, we found that two proteins (biotinidase and carbonic anhydrase 1) can be used as early diagnostic markers of ASD (AUC = 0.8, p = 0.0001). SignificanceASD is the fastest growing neurodevelopmental disorder in the world and has become a major public health problem worldwide. Its prevalence has been steadily increasing, with a global prevalence rate of 1%. Early diagnosis and intervention can achieve better prognosis. In this study, data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis was applied to analyze the plasma proteome of ASD patients (31 (±5) months old), and 378 proteins were quantified. 45 differentially expressed proteins (DEPs) were identified between the ASD group and the control group. They mainly were associated with platelet degranulation, ECM proteoglycar, complement and coagulation cascades, selenium micronutrient network, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), cholesterol metabolism, vitamin metabolism, and inflammatory pathway. Through the integrated machine learning methods and the MRM verification of independent samples, it is considered that biotinidase and carbon anhydrase 1 have the potential to become biomarkers for the early diagnosis of ASD. These results complement proteomics database of the ASD patients, broaden our understanding of ASD, and provide a panel of biomarkers for the early diagnosis of ASD.

Proteomic profiling of urinary small extracellular vesicles in children with pneumonia: a pilot study.

Small extracellular vesicles (sEV) play a crucial role in immune responses to viral infection. However, the composition of sEV derived from children with viral pneumonia remains ill defined. First, we performed mass spectrometry-based label-free proteomic analysis of urinary sEV in 7 children with viral pneumonia, 4 children with Mycoplasma pneumoniae pneumonia and 20 healthy children. Then a total of 33 proteins were selected to validate by multiple reaction monitoring analysis in an independent cohort of 20 healthy children and 29 children with pneumonia. In the discovery phase, a total of 1621 proteins were identified, while 260 proteins have differential expression in children with viral pneumonia compared to healthy children. Biological pathways primarily associated with neutrophil degranulation, carbohydrate metabolism and endocytosis were enriched in children with viral pneumonia. Finally, the abundance of eight proteins was verified to be significantly higher in children with viral pneumonia than in healthy children. This pilot study with proteomic profiles of urinary sEV provided insights to the host response to viral pathogen exposure and potential diagnostic biomarkers for children with viral pneumonia, and served as the basis for understanding the fundamental biology of infection. There were significant differences in the proteomic features of urinary sEV between children with viral pneumonia and those with Mycoplasma pneumoniae pneumonia. Many viral infection-related proteins were identified in urinary sEV and overrepresented in children with viral pneumonia, which facilitates our understanding of the fundamental biology of viral infection. A total of eight proteins (ANPEP, ASAH1, COL11A1, EHD4, HEXB, LGALS3BP, SERPINA1 and SERPING1) were verified as potential biomarkers for the diagnosis of viral pneumonia in children.

Simultaneous Quantification of Apolipoprotein C-III O-Glycoforms by Protein-MRM.

Apolipoprotein C-III (APOC-III) regulates triglyceride levels, associated with a risk of cardiovascular disease. One gene generates several proteoforms, each with a different molecular mass and a unique function. Unlike peptide multiple reaction monitoring (MRM), protein-MRM without digestion is required to analyze clinically relevant individual proteoforms. We developed a protein-MRM method without digestion to individually quantify APOC-III proteoforms in human serum. We optimized the protein-MRM method following 60% acetonitrile extraction with C18 filtration. Bovine serum and myoglobin served as supporting cushions and the internal standard during sample preparation, respectively. Furthermore, we evaluated the LOD, lower limit of quantification, linearity, accuracy, and precision. Good correlation compared with turbidimetric immunoassay (TIA) and peptide-MRM was observed using 30 clinical sera. Individual APOC-III O-glycoforms were identified by top-down proteomics and simultaneously quantified using the protein-MRM method. The sum abundance of APOC-III proteoforms was significantly correlated with TIA and peptide-MRM. Our protein-MRM method provides an affordable and rapid quantification of potential disease-specific proteoforms. Precise quantification of each proteoform allows investigators to identify novel biological roles potentially related to cardiovascular disease or novel biomarkers. We expect our protein-oriented method to be more clinically useful than antibody-based immunoassays and peptide-oriented MRM analysis, especially for quantification of a biomarker proteoform with certain post-translational modifications.

Qualitative Profiling and Quantitative Analysis of Major Constituents in Jinmu-tang by UHPLC-Q-Orbitrap-MS and UPLC-TQ-MS/MS.

Jinmu-tang (JMT) is a traditional herbal medicine consisting of five herbal medicines: Poria cocos Wolf, Paeonia lactiflora Pallas, Zingiber officinale Roscoe, Atractylodes japonica Koidzumi, and Aconitum carmichaeli Debeaux. In this study, the JMT components were profiled using UHPLC-Q-Orbitrap-MS, and 23 compounds were identified and characterized. In addition, UPLC-TQ-MS/MS analysis was performed in the positive and negative ion modes of an electrospray ionization source for the simultaneous quantification of the identified compounds. The multiple reaction monitoring (MRM) method was established to increase the sensitivity of the quantitative analysis, and the method was verified through linearity, recovery, and precision. All analytes showed good linearity (R2 ≤ 0.9990). Moreover, the recovery and the relative standard deviation of precision were 86.19-114.62% and 0.20-8.00%, respectively. Using the established MRM analysis method, paeoniflorin was found to be the most abundant compound in JMT. In conclusion, these results provide information on the constituents of JMT and can be applied to quality control and evaluation.

Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection.

BackgroundThe majority of those infected by ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) during the UK first wave (starting March 2020) did not require hospitalisation. Most had a short-lived mild or asymptomatic infection, while others had symptoms that persisted for weeks or months. We hypothesized that the plasma proteome at the time of first infection would reflect differences in the inflammatory response that linked to symptom severity and duration.MethodsWe performed a nested longitudinal case-control study and targeted analysis of the plasma proteome of 156 healthcare workers (HCW) with and without lab confirmed SARS-CoV-2 infection. Targeted proteomic multiple-reaction monitoring analysis of 91 pre-selected proteins was undertaken in uninfected healthcare workers at baseline, and in infected healthcare workers serially, from 1 week prior to 6 weeks after their first confirmed SARS-CoV-2 infection. Symptom severity and antibody responses were also tracked. Questionnaires at 6 and 12 months collected data on persistent symptoms.FindingsWithin this cohort (median age 39 years, interquartile range 30–47 years), 54 healthcare workers (44% male) had PCR or antibody confirmed infection, with the remaining 102 (38% male) serving as uninfected controls. Following the first confirmed SARS-CoV-2 infection, perturbation of the plasma proteome persisted for up to 6 weeks, tracking symptom severity and antibody responses. Differentially abundant proteins were mostly coordinated around lipid, atherosclerosis and cholesterol metabolism pathways, complement and coagulation cascades, autophagy, and lysosomal function. The proteomic profile at the time of seroconversion associated with persistent symptoms out to 12 months. Data are available via ProteomeXchange with identifier PXD036590.InterpretationOur findings show that non-severe SARS-CoV-2 infection perturbs the plasma proteome for at least 6 weeks. The plasma proteomic signature at the time of seroconversion has the potential to identify which individuals are more likely to suffer from persistent symptoms related to SARS-CoV-2 infection.Funding informationThe COVIDsortium is supported by funding donated by individuals, charitable Trusts, and corporations including Goldman Sachs, Citadel and Citadel Securities, The Guy Foundation, GW Pharmaceuticals, Kusuma Trust, and Jagclif Charitable Trust, and enabled by Barts Charity with support from University College London Hospitals (UCLH) Charity. This work was additionally supported by the Translational Mass Spectrometry Research Group and the Biomedical Research Center (BRC) at Great Ormond Street Hospital.