Abstract
BackgroundEarly diagnosis and typing are crucial for improving the prognosis of patients with renal amyloidosis. Currently, Untargeted proteomics based precise diagnosis and typing of amyloid deposits are crucial for guiding patient management. Although untargeted proteomics achieve ultra-high-throughput by selecting the most abundant eluting cationic peptide precursors in series for tandem MS events, it lacks in sensitivity and reproducibility, which may not be suitable for early-stage renal amyloidosis with minor damages. Here, we aimed to develop parallel reaction monitoring (PRM)-based targeted proteomics to achieve high sensitivity and specificity by determining absolute abundances and codetecting all transitions of highly repeatable peptides of preselected amyloid signature and typing proteins in identifying early-stage renal immunoglobulin-derived amyloidosis.Methods and resultsIn 10 discovery cohort cases, Congo red-stained FFPE slices were micro-dissected and analyzed by data-dependent acquisition-based untargeted proteomics for preselection of typing specific proteins and peptides. Further, a list of proteolytic peptides from amyloidogenic proteins and internal standard proteins were quantified by PRM-based targeted proteomics to validate performance for diagnosis and typing in 26 validation cohort cases. The diagnosis and typing effectiveness of PRM-based targeted proteomics in 10 early-stage renal amyloid cases was assessed via a comparison with untargeted proteomics. A peptide panel of amyloid signature proteins, immunoglobulin light chain and heave chain in PRM-based targeted proteomics showed significantly distinguishing ability and amyloid typing performance in patients. The diagnostic algorithm of targeted proteomics with a low amount of amyloid deposits in early-stage renal immunoglobulin-derived amyloidosis showed better performance than untargeted proteomics in amyloidosis typing.ConclusionsThis study demonstrates that the utility of these prioritized peptides in PRM-based targeted proteomics ensure high sensitivity and reliability for identifying early-stage renal amyloidosis. Owing to the development and clinical application of this method, rapid acceleration of the early diagnosis, and typing of renal amyloidosis is expected.
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