a-plasma-sample-preparation-for-mass-spectrometry-using-an-automated

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves ...

a-surgical-model-for-graft-brown-adipose-tissue-on-heart-surface

A Surgical Model for Graft of Brown Adipose Tissue on the Heart Surface in Mice

A mouse model for investigating the paracrine effects of browning epicardial adipose tissue (EAT) on coronary arteries and the myocardium should be ...

Cedars Sinai Medical Center

The Second Xiangya Hospital of Central South University

Many avenues have been proposed for a seamless transition between biomarker discovery data and selected reaction monitoring (SRM) assays for biomarker validation. Unfortunately, studies with the abundant urinary protein uromodulin have shown that these methods do not converge on a consistent set of surrogate peptides for targeted mass spectrometry. As an alternative, we present an empirical peptide selection work flow for robust protein quantification.

An Empirical Approach to Signature Peptide Choice for Selected Reaction Monitoring: Quantification of Uromodulin in Urine.

Multiple reaction monitoring (MRM), sometimes referred to as selective reaction monitoring (SRM), is a mass spectrometry method that can target selective peptides for the detection and quantitation of a protein. Compared to traditional ELISA, MRM assays have a number of advantages including ease in multiplexing several proteins in the same assay and independence from the necessity for high-quality, expensive, and at times unreliable antibodies. Furthermore, MRM assays can be developed to quantify multiple proteoforms of a single protein allowing the quantification of allelic expression of a particular sequence polymorphism, protein isoform, as well as determining site occupancy of posttranslational modification(s). In this chapter, we describe our workflow for target peptide selection, assay optimization, and acquisition multiplexing. Our workflow is presented using the example of constrained MRM assays developed for the serum protein ApoL1 in its various proteoforms to highlight the specific technical considerations necessary for the difficult task of quantifying peptide targets based on highly specific amino acid sequences by MRM. 

Multiple and Selective Reaction Monitoring Using Triple Quadrupole Mass Spectrometer: Preclinical Large Cohort Analysis.

Sample preparation for protein quantification by mass spectrometry requires multiple processing steps including denaturation, reduction, alkylation, protease digestion, and peptide cleanup. Scaling these procedures for the analysis of numerous complex biological samples can be tedious and time-consuming, as there are many liquid transfer steps and timed reactions where technical variations can be introduced and propagated. We established an automated sample preparation workflow with a total processing time for 96 samples of 5 h, including a 2 h incubation with trypsin. Peptide cleanup is accomplished by online diversion during the LC/MS/MS analysis. In a selected reaction monitoring (SRM) assay targeting 6 plasma biomarkers and spiked β-galactosidase, mean intraday and interday cyclic voltammograms (CVs) for 5 serum and 5 plasma samples over 5 days were <20%. In a highly multiplexed SRM assay targeting more than 70 proteins, 90% of the transitions from 6 plasma samples repeated on 3 separate days had total CVs below 20%. Similar results were obtained when the workflow was transferred to a second site: 93% of peptides had CVs below 20%. An automated trypsin digestion workflow yields uniformly processed samples in less than 5 h. Reproducible quantification of peptides was observed across replicates, days, instruments, and laboratory sites, demonstrating the broad applicability of this approach.

Highly Reproducible Automated Proteomics Sample Preparation Workflow for Quantitative Mass Spectrometry.

Since 1984, each year, more women than men die of ischemic heart disease (IHD) and heart failure (HF), yet more men are diagnosed. Because biomarker assessment is often the first diagnostic employed in such patients, understanding biomarker differences in men vs. women may improve female morbidity and mortality rates.Some key examples of cardiac biomarker utility based on sex include contemporary use of "unisex" troponin reference intervals under-diagnosing myocardial necrosis in women; greater use of hsCRP in the setting of acute coronary syndrome (ACS) could lead to better stratification in women; and greater use of BNP with sex-specific thresholds in ACS could also lead to more timely risk stratification in women.Accurate diagnosis, appropriate risk management, and monitoring are key in the prevention and treatment of cardiovascular diseases; however, the assessment tools used must also be useful or at least assessed for utility in both sexes. In other words, going forward, we need to evaluate sex-specific reference intervals or cutoffs for laboratory tests used to assess cardiovascular disease to help close the diagnostic gap between men and women.

Sex differences in ischemic heart disease and heart failure biomarkers.

We describe the protocol, design, and methodology of the Prediction, Risk, and Evaluation of Major Adverse Cardiac Events (PRE-MACE) study as a multicomponent remote patient monitoring in cardiology. Using biosensor, biomarkers, and patient-reported outcomes in participants with stable ischemic heart disease, the PRE-MACE study is designed to measure cross-sectional correlations and establish the ability of remote monitoring to predict major adverse cardiovascular event (MACE) biomarkers and incident MACE at baseline and 12-month follow-up. It will further assess the adherence and cost-effectiveness of remote monitoring and blood sampling over the initial months. Despite medication and lifestyle changes, patients with cardiovascular disease can experience MACE due to undertreatment, poor adherence, or failure to recognize clinical or biochemical changes that presage MACE. Identifying patients using remote monitoring to detect MACE forerunners has potential to improve outcomes, avoid MACE, and reduce resource utilization. Data collection will include: (1) continuous remote monitoring using wearable biosensors; (2) biomarker measurements using plasma and at-home micro-sampling blood collection; and (3) patient-reported outcomes to monitor perceived stress, anxiety, depression, and health-related quality of life. Two hundred participants will be followed for 90 days with a subset ( = 80) monitored for 180 days. All participants will be followed up for MACE at 12 months.The PRE-MACE study will utilize remote monitoring with biosensors, biomarkers, and patient-reported outcomes to identify intermediate biomarkers of MACE in patients with stable ischemic heart disease. If shown to be effective, this intervention can be utilized between health visits to predict MACE and reduce financial impact of MACE.

A protocol integrating remote patient monitoring patient reported outcomes and cardiovascular biomarkers.

Although there is abundant evidence of vascular perturbation from studies of peripheral blood in systemic sclerosis (SSc), there are few data about the ability to use biomarkers of vascular injury and growth factors to predict vascular outcomes and response to therapy. We sought to explore the association between candidate vascular biomarkers and digital ulcerations (DU) in a clinical trial context.

Vascular biomarkers and digital ulcerations in systemic sclerosis: results from a randomized controlled trial of oral treprostinil (DISTOL-1).

Plasma is one of the most important and common matrices for clinical chemistry and proteomic analyses. Data-independent acquisition (DIA) mass spectrometry has enabled the simultaneous quantitative analysis of hundreds of proteins in plasma samples in support population and disease studies. Depletion of the highest abundant proteins is a common tool to increase plasma proteome coverage, but this strategy can result in the nonspecific depletion of protein subsets with which proteins targeted for depletion interact, adversely affecting their analysis. Our work using an antibody-based depletion column revealed significant complementarity not only in the identification of the proteins derived from depleted and undepleted plasma, but importantly also in the extent to which different proteins can be reproducibly quantified in each fraction. We systematically defined four major quantitative parameters of increasing stringency in both the depleted plasma fraction and in undepleted plasma for 757 observed plasma proteins: Linearity cutoff  > 0.8; lower limit of quantification (LLOQ); measurement range; limit of detection (LOD). We applied the results of our study to build a web-based tool, PlasmaPilot, that can serve as a protocol decision tree to determine whether the analysis of a specific protein warrants IgY14 mediated depletion.

A Dual Workflow to Improve the Proteomic Coverage in Plasma Using Data-Independent Acquisition-MS.

Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised 'convex analysis of mixtures' (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired abdominal aorta (AA) and left anterior descending coronary artery (LAD) specimens (N = 198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N = 62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of "pure" fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) assay quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N = 46) or free from apparent CAD (control, N = 40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (confidence interval 0.968-0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogeneous tissue types.

ArticleTotal : 2	Year
Publication title Cited by 26	2020
Publication title	2025

Article	Year
An Empirical Approach to Signature Peptide Choice for Selected Reaction Monitoring: Quantification of Uromodulin in Urine. Clinical chemistry\| PubMed ID: 26589548	2016
Multiple and Selective Reaction Monitoring Using Triple Quadrupole Mass Spectrometer: Preclinical Large Cohort Analysis. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 26867749	2016
Highly Reproducible Automated Proteomics Sample Preparation Workflow for Quantitative Mass Spectrometry. Journal of proteome research\| PubMed ID: 29083196	2018
Sex differences in ischemic heart disease and heart failure biomarkers. Biology of sex differences\| PubMed ID: 30223899	2018
A protocol integrating remote patient monitoring patient reported outcomes and cardiovascular biomarkers. NPJ digital medicine\| PubMed ID: 31508495	2019
Vascular biomarkers and digital ulcerations in systemic sclerosis: results from a randomized controlled trial of oral treprostinil (DISTOL-1). Clinical rheumatology\| PubMed ID: 31858338	2020
A Dual Workflow to Improve the Proteomic Coverage in Plasma Using Data-Independent Acquisition-MS. Journal of proteome research\| PubMed ID: 32176508	2020
Identification of Putative Early Atherosclerosis Biomarkers by Unsupervised Deconvolution of Heterogeneous Vascular Proteomes. Journal of proteome research\| PubMed ID: 32202800	2020

Qin Fu

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