= Emerging. More than 5 years before clinical availability. (16.60%, 2024)
= Expected to be clinically available in 1 to 4 years. (37.02%, 2024)
= Clinically available now. (46.38%, 2024)
MSACL 2024 : Storey

MSACL 2024 Abstract

Self-Classified Topic Area(s): Proteomics > Cases of Unmet Clinical Needs > Precision Medicine

Podium Presentation in Steinbeck 2 on Thursday at 10:30 (Chair: Carrie Adler / Kwasi Mawuenyega)

Classifying Membranous Nephropathy Using Mass Spectrometry

Aaron J Storey (1), Samar Hassen (1), Christian Herzog (2), John M Arthur (2), Rick D Edmondson (2), Tiffany N Caza (1), Chris P Larsen (1)
(1) Arkana Laboratories, Little Rock, AR (2) University of Arkansas for Medical Sciences, Little Rock, AR

Aaron Storey, PhD (Presenter)
Arkana Laboratories

Presenter Bio: I am interested in applying mass spectrometry techniques in clinical settings to improve the practice of medicine. My academic career has provided me with a broad knowledge base, spanning neuroscience, genetics, proteomics, statistics, and data science. My skill set combines the application of cutting-edge proteomics techniques to generate large data sets containing high quality information, and the use of programming languages and data analysis techniques to turn this information into actionable insights. Currently, I am working on translating a mass spectrometry-based assay for typing membranous nephropathy into clinical practice at Arkana Laboratories.

Abstract

INTRODUCTION: Membranous nephropathy (MN) is a major cause of nephrotic syndrome in adults and leads to kidney failure in one-third of affected patients. MN is caused by the development of autoantibodies against specific antigens, leading to the formation of bulky immune complexes that deposit in glomeruli and result in proteinuria and impaired kidney function. Identification of a specific MN antigen type can lead to development of serologic tests for disease monitoring, allowing for non-invasive diagnostics. Therefore, typing the autoantigen for each MN case can improve diagnosis by reducing the number of invasive repeat biopsies needed to monitor disease progression.

Historically, clinical laboratories have utilized immunohistochemistry or immunofluorescence techniques for autoantigen detection. However, there are now more than 20 antigens that could potentially cause MN, and there is often insufficient biopsy material to test for all causative antigens by immunostaining. Thus, there is a growing need for diagnostic assays for the detection of autoantigens in each specific autoimmune disorder. Here, we report our efforts in developing an MS assay to detect MN antigens for use in a CLIA laboratory.

METHODS: The study utilized residual biopsy material from core biopsies of frozen tissue. Glomerular immune complexes were enriched by Protein G immunoprecipitation. Protein digestion was performed by filter-aided sample preparation (FASP). Peptides were separated on a 150 x 0.075 mm column packed with CSH 2.5 ┬Ám (Waters). Peptides were eluted using a 60 minutes gradient from 98:2 to 65:35 buffer A:B ratio. Eluted peptides were ionized by electrospray followed by either DIA mass spectrometry on an Orbitrap Exploris 480 or MRM on a TSQ Altis.

RESULTS: We sought to compare standard immunostaining techniques against a DIA-MS workflow for typing MN cases. In order to allow sufficient sample number (N) for machine learning methods, we processed kidney biopsies from PLA2R (n=100), THSD7A (n=63), EXT2 (n=56), and NELL1 (n=59) membranous nephropathy cases, along with 142 samples that were negative for these proteins via immunostaining. Immune deposits were enriched by Protein G-conjugated microspheres and processed by filter-aided sample preparation (FASP). Samples were analyzed by DIA on an Exploris 480 using a staggered-window acquisition method and with empirically corrected libraries prepared by gas-phase fractionation. DIA data was searched using ScaffoldDIA, and protein quantitative values exported into tabular format. DIA data is available for download at PRIDE using accession number PXD035853. Using classifier algorithms from the Python package Scikit-learn, we trained and tested different machine learning algorithms for predicting MN type from the MS data. We found that immunostaining and MS results were >95% concordant for typing PLA2R, THSD7A, NELL1, and EXT. Discordant results were attributable to artifacts in laboratory processing, and revealed the importance of quality assurance checks that precede classification.

CONCLUSION: From this study, we conclude that typing membranous nephropathy by MS analysis of captured immune deposits has sufficient sensitivity, specificity, and throughput for implementation in a CLIA laboratory. Ongoing work is focused on translating the workflow from DIA-MS on an Exploris 480 to a targeted assay on a Thermo TSQ Altis, as well as including all additional known MN antigens. The status and experience of ongoing clinical implementation efforts will be discussed.


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