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MS-based amyloidosis typing in FFPE tissue samples

ABSTRACT: Amyloidoses are characterized by the pathological deposition of non-degradable misfolded protein fibrils. Precise identification of the fibril-forming protein is crucial for prognosis and correct therapeutic intervention. Here, we present a reproduceable method for amyloid typing using relative quantification to enhance the accuracy and reliability of proteomic amyloid typing. In this study, we analyzed 62 FFPE tissue samples (+4 replicates of one tissue sample using different set-ups), using liquid chromatography-tandem mass spectrometry and employed internal normalization of iBAQ values of amyloid-related proteins relative to serum amyloid-P component (APCS) for amyloidosis typing. This method demonstrated robust performance across multiple LC-MS/MS platforms, as well as for samples with low amounts of amyloid, and achieved complete concordance with IHC typed amyloidosis cases. More importantly, it resolved several unclear amyloid cases with inconclusive staining results. Finally, we established machine learning approach (XGBoost) achieving 94% accuracy by using ~160 amyloid-related proteins as input variables.

INSTRUMENT(S): timsTOF, LTQ Velos, Orbitrap Fusion, Q Exactive Plus, Q Exactive HF

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Prof. Dr. Stephan Singer

PROVIDER: MSV000097248 | MassIVE | Thu Mar 06 15:35:00 GMT 2025

SECONDARY ACCESSION(S): PXD061551

REPOSITORIES: MassIVE

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Project description:Cardiac amyloidosis is a severe clinical condition leading to restrictive cardiomyopathy and heart failure. With increasing options for specific treatment of different cardiac amyloid diseases, correct amyloid subtyping is essential for clinical outcome. Mass spectrometry-based methods for cardiac amyloid subtyping have become important diagnostic tools but are currently used only in a few reference laboratories. Such methods typically include laser-capture microdissection to ensure the specific analysis of amyloid deposits. While this strategy effectively reduces background signals, it is a labor-intensive and costly procedure hampering the implementation of these methods into a wider group of laboratories. Here we introduce a more direct proteomics-based method for subtyping of cardiac amyloids. Endomyocardial biopsies were retrospectively analyzed from fresh frozen material of 78 patients with cardiac amyloidosis and from 12 biopsies of unused donor heart explants. Cryostat sections were directly digested with trypsin and analyzed with the nano-liquid chromatography (nLC-MS/MS), and the data were evaluated by a proteomic software. With a diagnostic threshold set to 70% for each of the four most common amyloid proteins affecting the heart (LC k, LC l, TTR and SAA), 65 of the cases (87%) could be diagnosed, and of these, 61 cases (94%) were in concordance with the original diagnoses. The specimens were also analyzed for the summed intensities of the amyloid signature proteins (ApoE, ApoA-IV and SAP). The summed intensities were significantly higher (p<0.001) for all assigned amyloid cases, whereas unassigned cases were not significantly different from controls and had lower signals than the assigned cases (p<0.001), suggesting that signature proteins can serve as in situ quality markers of cardiac amyloid deposits. We argue that this efficient workflow can be useful for disseminating proteomics for cardiac amyloid subtyping into accredited clinical laboratories.

Project description:Crespo2012 - Kinetics of Amyloid Fibril Formation This model is described in the article: A generic crystallization-like model that describes the kinetics of amyloid fibril formation. Crespo R, Rocha FA, Damas AM, Martins PM. J. Biol. Chem. 2012 Aug; 287(36): 30585-30594 Abstract: Associated with neurodegenerative disorders such as Alzheimer, Parkinson, or prion diseases, the conversion of soluble proteins into amyloid fibrils remains poorly understood. Extensive "in vitro" measurements of protein aggregation kinetics have been reported, but no consensus mechanism has emerged until now. This contribution aims at overcoming this gap by proposing a theoretically consistent crystallization-like model (CLM) that is able to describe the classic types of amyloid fibrillization kinetics identified in our literature survey. Amyloid conversion represented as a function of time is shown to follow different curve shapes, ranging from sigmoidal to hyperbolic, according to the relative importance of the nucleation and growth steps. Using the CLM, apparently unrelated data are deconvoluted into generic mechanistic information integrating the combined influence of seeding, nucleation, growth, and fibril breakage events. It is notable that this complex assembly of interdependent events is ultimately reduced to a mathematically simple model, whose two parameters can be determined by little more than visual inspection. The good fitting results obtained for all cases confirm the CLM as a good approximation to the generalized underlying principle governing amyloid fibrillization. A perspective is presented on possible applications of the CLM during the development of new targets for amyloid disease therapeutics. This model is hosted on BioModels Database and identified by: BIOMD0000000531. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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MS-based amyloidosis typing in FFPE tissue samples

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