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Imaging Mass Spectrometry for Characterization of Atrial Fibrillation Subtypes

Oliver Klein, Thorsten Hanke, Grit Nebrich, Junfeng Yan, Benedikt Schubert, Patrick Giavalisco, Frank Noack, Herbert Thiele, Salah A. Mohamed*

*Corresponding author for this work

Abstract

Purpose: Atrial fibrillation (AF) is a cardiac arrhythmia characterized by a rapid and irregular heart rhythm. AF types, paroxysmal (PX), persistent (PE), and long-lasting persistent (LSP), require differences in clinical management. Unfortunately, a significant proportion of AF patients are clinically misclassified. Therefore, the aim of this study is to prove that MALDI-Imaging (IMS) is valuable as a diagnostic aid in AF subtypes’ assessment. Experimental design: Patients are clinically classified according to the guidelines of the European Society of Cardiology. FFPE tissue specimens from PE, PX, and LSP subtypes are analyzed by MALDI-IMS and evaluated by multi-statistical testing. Proteins are subsequently identified using LC-MS/MS and findings are confirmed by immunohistochemistry and through the determination of potential fibrosis via histopathology. Result: Determined that characteristic peptide signatures and peptide values facilitate to distinguish between PE, PX, and LSP arterial fibrillation subtypes. In particular, peptide values from alpha 1 type I collagen (CO1A1) are identified that are significantly higher in LSP and PE tissues but not in PX myocardial AF tissue. These findings are confirmed by immunohistochemistry and through the determination of potential fibrosis via histopathology. Conclusion and relevance: These results represent an improvement in AF risk stratification by using MALDI-IMS as a promising approach for AF tissue assessment.

Original languageEnglish
Article number1700155
JournalProteomics - Clinical Applications
Volume12
Issue number6
ISSN1862-8346
DOIs
Publication statusPublished - 11.2018

Funding

We thank Angelika Krajewski for providing excellent technical assistance in the sample preparation. We thank Dr. Boltze for his revision of this manuscript and his input. This work was supported by grants from the BCRT and BSRT through funding by the German Federal Ministry of Education and Research (BMBF). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported by Medtronic GmbH, Meerbusch, Germany.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

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