2017
DOI: 10.1038/s41598-017-10094-7
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Optical-flow based non-invasive analysis of cardiomyocyte contractility

Abstract: Characterization of cardiomyocyte beat patterns is needed for quality control of cells intended for surgical injection as well as to establish phenotypes in disease modeling or toxicity studies. Optical-flow based analysis of videomicroscopic recordings offer a manipulation-free and efficient characterization of contractile cycles, an important characteristics of cardiomyocyte phenotype. We demonstrate that by appropriate computational analysis of optical flow data one can identify distinct contractile centers… Show more

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Cited by 50 publications
(40 citation statements)
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“…SV1), as well as increases in other parameters of sheet contractility (for extended data see Supporting Information Fig. S3) [44,45]. Although some of these methods may be sensitive to the rate of beating, we found no significant difference between the spontaneous beat frequency in hPSC-CMs from pIC-treated and -untreated CPCs (Fig.…”
Section: Results Pic Priming Of Cpcs Accelerates Hpsc-cm Maturationmentioning
confidence: 69%
“…SV1), as well as increases in other parameters of sheet contractility (for extended data see Supporting Information Fig. S3) [44,45]. Although some of these methods may be sensitive to the rate of beating, we found no significant difference between the spontaneous beat frequency in hPSC-CMs from pIC-treated and -untreated CPCs (Fig.…”
Section: Results Pic Priming Of Cpcs Accelerates Hpsc-cm Maturationmentioning
confidence: 69%
“…The optical flow-based method does not distinguish between active and passive (elastic response of the adjacent cell layer) contractility. To identify contractile centers, we estimated the convergence maps of the displacement field as its negative divergence from optical flow data d(t,x) as previously described (Czirok et al, 2017).…”
Section: Convergence Analysismentioning
confidence: 99%
“…To characterize tissue deformation, we first apply our non-invasive, optical flowbased method described in (Czirok et al, 2017) for each image of the recording.…”
Section: Optical Flow-based Analysis Of Local Tissue Rotationmentioning
confidence: 99%
“…To quantify the tissue deformation induced by the ferromagnetic aggregates, we modified our image analysis tools used to characterize cardiomyocyte beating activity (Czirok et al, 2017). We compared a sequence of images to a common reference frame by PIV analysis, yielding a displacement field (u).…”
Section: Tissue Deformation Forced By External Fieldsmentioning
confidence: 99%