  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.
