# Beyond the Granuloma: New Insights into Cardiac Sarcoidosis Using Spatial Proteomics

## TL;DR

Researchers used advanced protein analysis to better understand cardiac sarcoidosis and developed a model to detect the disease even when it's not visible under a microscope.

## Contribution

The study introduces a predictive model for cardiac sarcoidosis detection using spatial proteomics in archival human tissue.

## Key findings

- A high-plex spatial protein analysis revealed immunologic, fibrotic, and metabolic changes in cardiac sarcoidosis.
- A predictive model was developed to distinguish sarcoidosis tissue from control tissue, even without visible inflammation.
- The findings suggest potential for improving clinical diagnosis of cardiac sarcoidosis.

## Abstract

Cardiac sarcoidosis is poorly understood, challenging to diagnose, and portends a poor prognosis. A lack of animal models necessitates the use of residual human samples to study sarcoidosis, which in turn necessitates the use of analytical tools compatible with archival, fixed tissue. We employed high-plex spatial protein analysis within a large cohort of archival human cardiac sarcoidosis and control tissue samples, studying the immunologic, fibrotic, and metabolic landscape of sarcoidosis at different stages of disease, in different cardiac tissue compartments, and in tissue regions with and without overt inflammation. Utilizing a small set of differentially expressed protein biomarkers, we also report the development of a predictive model capable of accurately discriminating between control cardiac tissue and sarcoidosis tissue, even when no histologic evidence of sarcoidosis is present. This finding has major translational implications, with the potential to markedly improve the diagnostic yield of clinical biopsies obtained from suspected sarcoidosis patients.