Glycosylation Profiling of Tumor Marker in Plasma Using Bead-Based Immunoassay

Characterizing M-protein light chain glycosylation via mass spectrometry

OBJECTIVES

Monoclonal gammopathy of undetermined significance (MGUS) patients with M-proteins containing n-glycosylated light chains (GLC) have an increased risk for progression to symptomatic plasma cell disorders (PCD). Large-scale research involving the determination of glycan specific moieties is understudied due to the lack of clinically viable methods. This report documents a proof-of-concept glycan characterization method for patients with M-protein GLCs.

DESIGN AND METHODS

Twenty-three previously characterized MGUS patients with glycosylated light chains identified by MASS-FIX were used for this study. Glycosylated light chains were enriched from patient serum using light chain (LC) specific Sepharose nanobody beads (NB), followed by glycan digestion via PNGase F. Glycan moieties were derivatized on-target using Girard's reagent T for MALDI-TOF analysis and confirmed with top-down GLC LC-ESI-Q-TOF-MS analysis.

RESULTS

Intact GLC LC-ESI-Q-TOF-MS and cleaved glycan MALDI-TOF MS analysis had 100% agreement for the top three intensity glycans between spectra and 88 percent agreement for all reported glycan moieties. GLC moieties among patients were similar with fucosylation being the only notable difference. Additionally, doubly glycosylated light chains were observed in two patients.

CONCLUSIONS

The MALDI-TOF method provides the tools to characterize and evaluate GLCs in a clinical setting as it is adaptable to our clinical MASS-Fix assay, relatively cheap, and accurate in glycan moiety assignments as confirmed by top-down GLC LC-ESI-Q-TOF-MS.

In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine

Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.