# SarcGraph for High-Throughput Regional Analysis of Sarcomere Organization and Contractile Function in 2D Cardiac Muscle Bundles

## TL;DR

A new tool called SarcGraph improves the analysis of heart muscle cell contractions in lab-grown tissues, enabling faster and more accurate studies.

## Contribution

Adaptations to SarcGraph enable automated high-throughput analysis of sarcomere organization and contractile function in 2D cardiac muscle bundles.

## Key findings

- SarcGraph's modifications include frame-by-frame detection, segmentation, and Gaussian Process Regression for denoising.
- 130 example movies are provided through Harvard Dataverse for high-throughput analysis.
- The tool enables automated contractile phase detection in human induced pluripotent stem cell-derived cardiomyocytes.

## Abstract

Timelapse images of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide rich information on cell structure and contractile function. The two-dimensional cardiac muscle bundle (2DMB) platform standardizes tissue geometry, enabling physiologic, uniaxial contractions on elastomeric substrates. However, larger sarcomere displacements in 2DMBs challenge existing tracking pipelines. We present adaptations to SarcGraph, an open-source Python package for sarcomere detection and tracking, enabling automated analysis of high-frame-rate 2DMB videos. Key modifications include frame-by-frame detection with automated segmentation, Gaussian Process Regression for denoising, and automatic contractile phase detection. We provide 130 example movies through Harvard Dataverse, enabling high-throughput analysis and advancing hiPSC-CM research.