Abstract
A better fundamental understanding of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) has the potential to advance applications ranging from drug discovery to cardiac repair. Automated quantitative analysis of beating hiPSC-CMs is an important and fast developing component of the hiPSC-CM research pipeline. Here we introduce "Sarc-Graph," a computational framework to segment, track, and analyze sarcomeres in fluorescently tagged hiPSC-CMs. Our framework includes functions to segment z-discs and sarcomeres, track z-discs and sarcomeres in beating cells, and perform automated spatiotemporal analysis and data visualization. In addition to reporting good performance for sarcomere segmentation and tracking with little to no parameter tuning and a short runtime, we introduce two novel analysis approaches. First, we construct spatial graphs where z-discs correspond to nodes and sarcomeres correspond to edges. This makes measuring the network distance between each sarcomere (i.e., the number of connecting sarcomeres separating each sarcomere pair) straightforward. Second, we treat tracked and segmented components as fiducial markers and use them to compute the approximate deformation gradient of the entire tracked population. This represents a new quantitative descriptor of hiPSC-CM function. We showcase and validate our approach with both synthetic and experimental movies of beating hiPSC-CMs. By publishing Sarc-Graph, we aim to make automated quantitative analysis of hiPSC-CM behavior more accessible to the broader research community.
Highlights
Quantitative analysis of movies of beating cardiomyocytes is a compelling approach for connecting cell morphology to dynamic cell function [1, 2]
We focus on defining scalar metrics from Favg that are directly comparable to average sarcomere shortening (~s, savg) [10], and the Orientational Order Parameter (OOP) [11, 14, 40]
In S2 Text, we demonstrate the feasibility of constructing a machine learning phenotype classification model using Sarc-Graph derived metrics with the data published with Pasqualini et al 2015 [6]
Summary
Quantitative analysis of movies of beating cardiomyocytes is a compelling approach for connecting cell morphology to dynamic cell function [1, 2]. Connecting structure and function is a crucial step towards a better fundamental understanding of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) [3, 4]. In stark contrast to sarcomere chains in mature cardiomyocytes which have a highly ordered regular structure [5], sarcomere chains in hiPSC-CMs are typically immature and disordered [6]. This irregular structure, combined with large variation between cells, makes developing tools for quantitative analysis both more difficult and more pressing [7]. We aim to make automated quantitative analysis of hiPSC-CM contractile behavior more accessible to the broader research community
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