Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.

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...

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Autores principales:	Bill Zhao, Kehan Zhang, Christopher S Chen, Emma Lejeune
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/27c7a29ccffb4b8580e6d30fbdc13217
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id	oai:doaj.org-article:27c7a29ccffb4b8580e6d30fbdc13217
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spelling	oai:doaj.org-article:27c7a29ccffb4b8580e6d30fbdc132172021-11-25T05:40:32ZSarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.1553-734X1553-735810.1371/journal.pcbi.1009443https://doaj.org/article/27c7a29ccffb4b8580e6d30fbdc132172021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009443https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A 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.Bill ZhaoKehan ZhangChristopher S ChenEmma LejeunePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 10, p e1009443 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Bill Zhao Kehan Zhang Christopher S Chen Emma Lejeune Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.
description	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.
format	article
author	Bill Zhao Kehan Zhang Christopher S Chen Emma Lejeune
author_facet	Bill Zhao Kehan Zhang Christopher S Chen Emma Lejeune
author_sort	Bill Zhao
title	Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.
title_short	Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.
title_full	Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.
title_fullStr	Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.
title_full_unstemmed	Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.
title_sort	sarc-graph: automated segmentation, tracking, and analysis of sarcomeres in hipsc-derived cardiomyocytes.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/27c7a29ccffb4b8580e6d30fbdc13217
work_keys_str_mv	AT billzhao sarcgraphautomatedsegmentationtrackingandanalysisofsarcomeresinhipscderivedcardiomyocytes AT kehanzhang sarcgraphautomatedsegmentationtrackingandanalysisofsarcomeresinhipscderivedcardiomyocytes AT christopherschen sarcgraphautomatedsegmentationtrackingandanalysisofsarcomeresinhipscderivedcardiomyocytes AT emmalejeune sarcgraphautomatedsegmentationtrackingandanalysisofsarcomeresinhipscderivedcardiomyocytes
_version_	1718414507422777344

Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.

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