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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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Successful Estimation of Beating Signals on Cardiomyocytes Cultured in Different ConditionsExample of cardiomyocytes with (A) monolayer (cells from Cellular Dynamics), (B) cardio-sphere (cells from Gladstone Institute), (C) single cell (cells from Axiogenesis), corresponding to Movies S1, S2, and S3. In (C), there are five distinct beating signals corresponding to the five regions marked in the upper picture. Clustered regions correspond to regions that do not beat in synchrony.
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fig2: Successful Estimation of Beating Signals on Cardiomyocytes Cultured in Different ConditionsExample of cardiomyocytes with (A) monolayer (cells from Cellular Dynamics), (B) cardio-sphere (cells from Gladstone Institute), (C) single cell (cells from Axiogenesis), corresponding to Movies S1, S2, and S3. In (C), there are five distinct beating signals corresponding to the five regions marked in the upper picture. Clustered regions correspond to regions that do not beat in synchrony.

Mentions: Once the absolute correlation signal is quantified, we perform outlier rejection. Outliers can consist of noisy signals that occur due to moving debris or bubbles in the media. To keep the arrhythmia signals but reject the outliers, we make the assumption that the beats in a signal should have similar shapes. A Gaussian mixture model is fitted to each beating signal, and the fitting error serves as a metric to detect and reject signals that do not represent beating of cardiomyocytes. Finally, the algorithm merges the valid blocks that have similar beating patterns (Maddah and Loewke, 2014), providing the user with a distinct set of signals per data set. To demonstrate the versatility of the algorithm, Figure 2 shows a set of examples for different plating densities and successful extraction of the beating signal. Figures 2A and 2B are examples of monolayer and tissue-like cultures respectively, where the beating blocks have been identified successfully and clustered together as the cells beat in synchrony. Figure 2C shows an example of single-cell plating density where cells have not formed a syncytium and thus beat asynchronously. In this scenario, the algorithm processes each block, excludes the non-beating blocks, and clusters the blocks with similar beating profile together, assigning a distinct label to each cluster. In this example, there are five beating signals corresponding to the five regions marked in the upper picture of Figure 2C. Clustered regions correspond to regions that do not 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)

Successful Estimation of Beating Signals on Cardiomyocytes Cultured in Different ConditionsExample of cardiomyocytes with (A) monolayer (cells from Cellular Dynamics), (B) cardio-sphere (cells from Gladstone Institute), (C) single cell (cells from Axiogenesis), corresponding to Movies S1, S2, and S3. In (C), there are five distinct beating signals corresponding to the five regions marked in the upper picture. Clustered regions correspond to regions that do not beat in synchrony.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

fig2: Successful Estimation of Beating Signals on Cardiomyocytes Cultured in Different ConditionsExample of cardiomyocytes with (A) monolayer (cells from Cellular Dynamics), (B) cardio-sphere (cells from Gladstone Institute), (C) single cell (cells from Axiogenesis), corresponding to Movies S1, S2, and S3. In (C), there are five distinct beating signals corresponding to the five regions marked in the upper picture. Clustered regions correspond to regions that do not beat in synchrony.
Mentions: Once the absolute correlation signal is quantified, we perform outlier rejection. Outliers can consist of noisy signals that occur due to moving debris or bubbles in the media. To keep the arrhythmia signals but reject the outliers, we make the assumption that the beats in a signal should have similar shapes. A Gaussian mixture model is fitted to each beating signal, and the fitting error serves as a metric to detect and reject signals that do not represent beating of cardiomyocytes. Finally, the algorithm merges the valid blocks that have similar beating patterns (Maddah and Loewke, 2014), providing the user with a distinct set of signals per data set. To demonstrate the versatility of the algorithm, Figure 2 shows a set of examples for different plating densities and successful extraction of the beating signal. Figures 2A and 2B are examples of monolayer and tissue-like cultures respectively, where the beating blocks have been identified successfully and clustered together as the cells beat in synchrony. Figure 2C shows an example of single-cell plating density where cells have not formed a syncytium and thus beat asynchronously. In this scenario, the algorithm processes each block, excludes the non-beating blocks, and clusters the blocks with similar beating profile together, assigning a distinct label to each cluster. In this example, there are five beating signals corresponding to the five regions marked in the upper picture of Figure 2C. Clustered regions correspond to regions that do not 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