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Wei-Jun Qian

Publications and source records attributed to Wei-Jun Qian.

6 recordsLinked to original sources
PubMed2026

Interlaboratory Comparison of a Glucagon and Oxyntomodulin Immuno-LC-MS/MS Assay: Implications for Diabetes Research.

BACKGROUND: The quantification of plasma glucagon and oxyntomodulin is important in the assessment of α-cell function, which is impaired in patients with diabetes. We aimed to transfer between laboratories a novel assay that uses liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the sensitive and specific measurement of these highly homologous hormones. METHODS: Simultaneous measurement of glucagon and oxyntomodulin used immunoaffinity enrichment and LC-MS/MS. Immunoenrichment used monoclonal antibodies that are available at-cost to researchers (deposited at the Developmental Studies Hybridoma Bank). Pure synthetic glucagon, characterized for purity and concentration, was used as a calibrant and is available to others. A detailed standard operating procedure was shared between 3 laboratories and the performance of the method was evaluated with samples collected from patients with and without diabetes. Method comparison was made with 2 commercially available, FDA-registered glucagon immunoassays. RESULTS: The method was linear over the normal range (1-20 pM). When measured in duplicate, the median interlaboratory imprecision (%CV) of the measurement of 40 samples was 6.3% (IQR 4.5%) and 14.4% (IQR 12.4%) for glucagon and oxyntomodulin, respectively. Method comparison with commercially available immunoassays demonstrated good (Mercodia, R = 0.92) or fair (Ansh, R = 0.70) agreement. Multivariable linear regression using LC-MS/MS glucagon and oxyntomodulin concentrations to predict immunoassay results indicated significant cross-reactivity of each immunoassay with oxyntomodulin. CONCLUSION: We have validated a sensitive and specific assay for glucagon and oxyntomodulin that can be deployed in high-complexity clinical laboratories for research or the care of patients. Commercially available glucagon immunoassays have significant interference from molecules other than glucagon.

Journal Article↗
PubMed2026

Proteomic analyses of human islets reveal potential markers of β cell dysfunction during prediabetes.

The mechanisms driving progressive β cell dysfunction in type 2 diabetes remain incompletely understood. This study aimed to identify pancreatic islet proteome changes that could predict diabetes onset. We isolated islets from individuals without diabetes undergoing partial pancreatectomy, previously characterized for glucose tolerance, insulin sensitivity, and insulin secretion, using laser capture microdissection, and analyzed them via high-performance liquid chromatography-mass spectrometry. Proteomic analysis revealed that individuals with impaired glucose tolerance (IGT) had reductions in proteins regulating glycolysis (PGK1, G3P), lipid metabolism (ACBP, ARF1), glucose transport (14-3-3B), and insulin secretion (STARD10, CAPDS) compared with normal glucose-tolerant (NGT) individuals. Additionally, IGT islets showed impaired expression of proteins involved in glucose- and incretin-stimulated insulin response (CREB1, IQGA1). Stratification by β cell glucose sensitivity (βGS) indicated that individuals with lower βGS exhibited reduced levels of insulin maturation (ERO1B) and antiapoptotic proteins (CASP8, PAK2, SKP1), along with increased SEL1L, a factor promoting endocrine precursor differentiation. These findings suggest that early defects in glucose metabolism and insulin secretion characterize IGT, while reduced βGS may trigger compensatory mechanisms, through enhanced β cell survival or neogenesis, to delay type 2 diabetes progression. Overall, proteomic alterations in prediabetic islets provide potential early predictive markers and targets for interventions aimed at preserving β cell function.

Humans↗
PubMed2024

Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-Treated Pancreatic Beta Cells.

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse posttranslational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated β-cells. We observed a swift response in the global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-κB signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple colocalized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

Proteomics↗
PubMed2024

Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.

Proteome↗
PubMed2024

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Journal Article↗
PubMed2013

The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics.

Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Acute Kidney Injury↗