SarcNet: A Novel AI-based Framework to Automatically Analyze and Score Sarcomere Organizations in Fluorescently Tagged hiPSC-CMs

TL;DR

SarcNet is a deep learning framework that automatically assesses sarcomere organization in hiPSC-derived cardiomyocytes, enabling high-throughput, accurate, and consistent analysis crucial for cardiac research and drug screening.

Contribution

The paper introduces SarcNet, a novel deep learning model that improves sarcomere organization scoring accuracy and efficiency over traditional methods.

Findings

Achieves a Spearman correlation of 0.831 with expert scores.

Outperforms the previous state-of-the-art Linear Regression approach.

Demonstrates consistent increase in sarcomere organization during differentiation.

Abstract

Quantifying sarcomere structure organization in human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is crucial for understanding cardiac disease pathology, improving drug screening, and advancing regenerative medicine. Traditional methods, such as manual annotation and Fourier transform analysis, are labor-intensive, error-prone, and lack high-throughput capabilities. In this study, we present a novel deep learning-based framework that leverages cell images and integrates cell features to automatically evaluate the sarcomere structure of hiPSC-CMs from the onset of differentiation. This framework overcomes the limitations of traditional methods through automated, high-throughput analysis, providing consistent, reliable results while accurately detecting complex sarcomere patterns across diverse samples. The proposed framework contains the SarcNet, a linear layers-added ResNet-18 module, to output a continuous score ranging from one to five that captures the level of sarcomere structure organization. It is trained and validated on an open-source dataset of hiPSC-CMs images with the endogenously GFP-tagged alpha-actinin-2 structure developed by the Allen Institute for Cell Science (AICS). SarcNet achieves a Spearman correlation of 0.831 with expert evaluations, demonstrating superior performance and an improvement of 0.075 over the current state-of-the-art approach, which uses Linear Regression. Our results also show a consistent pattern of increasing organization from day 18 to day 32 of differentiation, aligning with expert evaluations. By integrating the quantitative features calculated directly from the images with the visual features learned during the deep learning model, our framework offers a more comprehensive and accurate assessment, thereby enhancing the further utility of hiPSC-CMs in medical research and therapy development.