Background

Cardiovascular disease is an umbrella term for morbidities that eventually lead to heart failure, the leading cause of death worldwide. Early detection and individualized treatment addressing the underlying cause of CVD will improve outcomes and prevent complications. However, the sensitivity of current diagnostic and prognostic measures may limit patient stratification and options for personalized therapy.

Previous studies have shown that cellular morphological features in cancer cells have prognostic and diagnostic value. Furthermore, research has shown that cardiomyocytes display a high degree of morphological plasticity and respond to cardiovascular challenges by changing size and shape, thereby validating morphology as an important indicator of disease state. However, cardiomyocyte cellular heterogeneity limits the use of phenotypic image analysis to a few stand-alone parameters such as cell size and sarcomere organization. Therefore, an integrated approach looking at changes in cellular morphology in high content by analyzing numerous morphological features per single cell would be a valuable tool for translational medicine and basic research.

Technology Overview

The inventors have created an algorithm for single cell morphology analysis in heterogenous cardiomyocyte populations called C-MORE. The ready to use work flow includes image acquisition and analysis by C-MORE of over 1000 morphological features in response to liquid biopsy or experimental treatment. Employing the Cell- Profiler pipeline, data is organized, quality controlled, and preprocessed for further analysis on a single cell or population level. To demonstrate the predictive power of C-MORE for cardiovascular diseases the inventors have shown C-MORE functionality in the following context:

• Pharmacological screening for inhibitors of pathological hypertrophy.
• Diagnostic and Prognostic value in TAVR and ATTR disease states by liquid biopsy
• Detection of pathological phenotypes caused by mutations of clinical relevance

Currently, the inventors are further tuning the algorithm to create neural networks with the goal of establishing a comprehensive patient stratification tool to streamline cardiovascular clinical diagnosis.

Stage of Development

Training on CVD patient data and neural network construction

Further Details

• Furkel, Jennifer, et al. “C-MORE: A high-content single-cell morphology recognition methodology for liquid biopsies toward personalized cardiovascular medicine.” Cell Rep Med 2021 Nov 3
• Hein, Selina, et al. “Impaired in vitro growth response of plasma-treated cardiomyocytes predicts poor outcome in patients with transthyretin amyloidosis.” Clin Res Cardiol 2021 Jan 22

Benefits

• Ready-to-use workflow
• Powerful automated analysis of high content morphological changes
• High-throughput
• Easy adaptable to other heterogenous cell types

Applications

• High-throughput pharmacological drug screening
• Patient Stratification (Diagnosis and Prognosis) by liquid biopsy
• Genotype-Phenotype morphological association
• Single cell morphological analysis of any primary heterogenous cells

Opportunity

Out‑licensing or collaboration