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A non-invasive platform for functional characterization of stem-cell-derived cardiomyocytes with applications in cardiotoxicity testing.

Maddah M, Heidmann JD, Mandegar MA, Walker CD, Bolouki S, Conklin BR, Loewke KE - Stem Cell Reports (2015)

Bottom Line: We present a non-invasive method to characterize the function of pluripotent stem-cell-derived cardiomyocytes based on video microscopy and image analysis.The platform, called Pulse, generates automated measurements of beating frequency, beat duration, amplitude, and beat-to-beat variation based on motion analysis of phase-contrast images captured at a fast frame rate.Using Pulse, we demonstrate recapitulation of drug effects in stem-cell-derived cardiomyocytes without the use of exogenous labels and show that our platform can be used for high-throughput cardiotoxicity drug screening and studying physiologically relevant phenotypes.

View Article: PubMed Central - PubMed

Affiliation: Cellogy, Inc., Palo Alto, CA 94301, USA. Electronic address: mmaddah@alum.mit.edu.

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Processing Steps of Pulse Video Analysis(A) Block-wise segmentation of the image sequence; background blocks are excluded from further processing.(B) A motion signal is estimated for each block, which can include noisy signals from moving debris.(C) Signal processing is performed on each signal to identify peaks and model the shape of the beats, and to measure parameters such as frequency and duration. If the beats in a signal are not similar, the signal is identified as outlier and is rejected.(D) Beating signals with similar pattern are automatically clustered together.(E) A relative correlation signal is estimated per block.(F) From the relative correlation signal, the resting state is identified and is used a reference to measure the absolute correlation signal, from which the quantitative parameters are automatically measured.
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fig1: Processing Steps of Pulse Video Analysis(A) Block-wise segmentation of the image sequence; background blocks are excluded from further processing.(B) A motion signal is estimated for each block, which can include noisy signals from moving debris.(C) Signal processing is performed on each signal to identify peaks and model the shape of the beats, and to measure parameters such as frequency and duration. If the beats in a signal are not similar, the signal is identified as outlier and is rejected.(D) Beating signals with similar pattern are automatically clustered together.(E) A relative correlation signal is estimated per block.(F) From the relative correlation signal, the resting state is identified and is used a reference to measure the absolute correlation signal, from which the quantitative parameters are automatically measured.

Mentions: The Pulse video analysis software extracts and quantifies beating signals from the video of cardiomyocytes. Our method enables automated extraction of quantitative parameters that are of interest in clinical studies from cultures with different cell densities and with either regular or irregular beating patterns. Specifically, it captures and quantifies the biomechanical beating of cardiomyocytes by performing motion analysis on the image sequence to capture changes in the image intensity due to cardiomyocyte contraction and relaxation. The design of our algorithm is guided by the fact that it should be possible to work on different tissue types without the need for any parameter tuning. This is why we avoid the use of specific cell segmentation algorithms and apply a more data-driven approach. Figures 1A–1D diagrams the steps of the Pulse algorithm, which includes (1) block-wise segmentation of the image sequence, (2) extraction of the beating signal for each block, (3) quantification of the beating signals, (4) outlier rejection, and (5) clustering of the beating signals into a set of unique signals, each representing a region of the culture where cardiomyocytes beat in synchrony.


A non-invasive platform for functional characterization of stem-cell-derived cardiomyocytes with applications in cardiotoxicity testing.

Maddah M, Heidmann JD, Mandegar MA, Walker CD, Bolouki S, Conklin BR, Loewke KE - Stem Cell Reports (2015)

Processing Steps of Pulse Video Analysis(A) Block-wise segmentation of the image sequence; background blocks are excluded from further processing.(B) A motion signal is estimated for each block, which can include noisy signals from moving debris.(C) Signal processing is performed on each signal to identify peaks and model the shape of the beats, and to measure parameters such as frequency and duration. If the beats in a signal are not similar, the signal is identified as outlier and is rejected.(D) Beating signals with similar pattern are automatically clustered together.(E) A relative correlation signal is estimated per block.(F) From the relative correlation signal, the resting state is identified and is used a reference to measure the absolute correlation signal, from which the quantitative parameters are automatically measured.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4400609&req=5

fig1: Processing Steps of Pulse Video Analysis(A) Block-wise segmentation of the image sequence; background blocks are excluded from further processing.(B) A motion signal is estimated for each block, which can include noisy signals from moving debris.(C) Signal processing is performed on each signal to identify peaks and model the shape of the beats, and to measure parameters such as frequency and duration. If the beats in a signal are not similar, the signal is identified as outlier and is rejected.(D) Beating signals with similar pattern are automatically clustered together.(E) A relative correlation signal is estimated per block.(F) From the relative correlation signal, the resting state is identified and is used a reference to measure the absolute correlation signal, from which the quantitative parameters are automatically measured.
Mentions: The Pulse video analysis software extracts and quantifies beating signals from the video of cardiomyocytes. Our method enables automated extraction of quantitative parameters that are of interest in clinical studies from cultures with different cell densities and with either regular or irregular beating patterns. Specifically, it captures and quantifies the biomechanical beating of cardiomyocytes by performing motion analysis on the image sequence to capture changes in the image intensity due to cardiomyocyte contraction and relaxation. The design of our algorithm is guided by the fact that it should be possible to work on different tissue types without the need for any parameter tuning. This is why we avoid the use of specific cell segmentation algorithms and apply a more data-driven approach. Figures 1A–1D diagrams the steps of the Pulse algorithm, which includes (1) block-wise segmentation of the image sequence, (2) extraction of the beating signal for each block, (3) quantification of the beating signals, (4) outlier rejection, and (5) clustering of the beating signals into a set of unique signals, each representing a region of the culture where cardiomyocytes beat in synchrony.

Bottom Line: We present a non-invasive method to characterize the function of pluripotent stem-cell-derived cardiomyocytes based on video microscopy and image analysis.The platform, called Pulse, generates automated measurements of beating frequency, beat duration, amplitude, and beat-to-beat variation based on motion analysis of phase-contrast images captured at a fast frame rate.Using Pulse, we demonstrate recapitulation of drug effects in stem-cell-derived cardiomyocytes without the use of exogenous labels and show that our platform can be used for high-throughput cardiotoxicity drug screening and studying physiologically relevant phenotypes.

View Article: PubMed Central - PubMed

Affiliation: Cellogy, Inc., Palo Alto, CA 94301, USA. Electronic address: mmaddah@alum.mit.edu.

Show MeSH
Related in: MedlinePlus