Semi-automated computational assessment of cancer organoid viability using rapid live-cell microscopy

Buehler, J. D.; Bird, C. E.; Savani, M. R.; Gattie, L. C.; Hicks, W. H.; Levitt, M. M.; El Shami, M.; Hatanpaa, K. J.; Richardson, T. E.; McBrayer, S. K.; Abdullah, K. G.

doi:10.1101/2021.12.07.471003

bioRxiv · 10.1101/2021.12.07.471003

Semi-automated computational assessment of cancer organoid viability using rapid live-cell microscopy

Buehler, J. D.·Bird, C. E.·Savani, M. R.·Gattie, L. C.·Hicks, W. H.·Levitt, M. M.·El Shami, M.·Hatanpaa, K. J.·Richardson, T. E.·McBrayer, S. K.·Abdullah, K. G.

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Abstract

The creation of patient-derived cancer organoids represents a key advance in preclinical modeling and has recently been applied to a variety of human solid tumor types. However, conventional methods used to assess cellular viability in tissue specimens are poorly suited for the evaluation of cancer organoids because they are time-intensive and involve tissue destruction. To address this issue, we established a suite of 3-dimensional patient-derived glioma organoids, treated them with chemoradiotherapy, stained organoids with non-toxic cell dyes, and imaged them using a rapid laser scanning confocal microscopy method termed "Apex Imaging". We then developed and tested a fragmentation algorithm to quantify heterogeneity in the topography of the organoids as a potential surrogate marker of viability. This algorithm, SSDquant, provides a 3-dimensional visual representation of the organoid surface and a numerical measurement of the sum-squared distance (SSD) from the derived mass center of the organoid. We tested whether SSD scores correlate with traditional immunohistochemistry-derived cell viability markers (cellularity and cleaved caspase 3 expression) and observed statistically significant associations between them using linear regression analysis. Our work describes a quantitative, non-invasive approach for the serial measurement of patient-derived cancer organoid viability, thus opening new avenues for the application of these models to studies of cancer biology and therapy.

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Buehler, J. D., Bird, C. E., Savani, M. R., Gattie, L. C., Hicks, W. H., Levitt, M. M., El Shami, M., Hatanpaa, K. J., Richardson, T. E., McBrayer, S. K., Abdullah, K. G.. 2021-12-09. Semi-automated computational assessment of cancer organoid viability using rapid live-cell microscopy. https://doi.org/10.1101/2021.12.07.471003

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