Automatic motion estimation with applicationsto hiPSC-CMs

TL;DR

This paper introduces a Python-based software framework for accurate, efficient motion analysis of hiPSC-CMs from microscopy images, enabling better characterization of cardiac tissue responses to drugs and disease.

Contribution

A unified, validated software framework for motion estimation in hiPSC-CMs, facilitating analysis of cardiac tissue dynamics from microscopy data.

Findings

Validated with synthetic test cases

Extracted displacements and velocities in hiPSC-CM microtissues

Quantified effects of an inotropic compound

Abstract

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Changes to motion patterns in these cells are one of the important features to be characterized to understand how an introduced drug or disease may alter the human heart beat. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently lacking. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.