High-throughput chemotherapeutic drug screening of tumor spheroids with individual spheroid results using image cytometry

Jordan Bell, Shilpaa Mukundan, Matthew Teryek, Bo Lin, Biju Parekkadan, Sun Yung, Suzanne Riches, and Leo Li-Ying Chan

Three-dimensional cancer models have gained popularity for in vitro studies of chemotherapeutic compounds by providing a more physiologically relevant analog of gas, nutrient, and drug diffusion throughout the tumor microenvironment. Some 3D assays are performed to study individual spheroids over time, where most of these assays rely on maintaining a single spheroid in each well of a 96-well round-bottom ultra-low attachment plate, limiting the number of spheroids in a study. Other assays may gather populationlevel data from large ensembles of spheroids grown together, but the information about individual differences amongst the spheroids is lost. Important kinetic information may also be lost for destructive endpoint assays such as MTS or MTT. Here, we describe the development of an image cytometry assay that can generate kinetic data for thousands of
breast cancer spheroids at the individual level. T47D spheroids are grown and maintained in a 24-well AggrewellTM400 plate (STEMCELL Technologies) and imaged using the Celigo® image cytometer (Nexcelom Bioscience). Each well contains more than 1000 subwells that
both aid in spheroid formation and constrain each spheroid to a specific location. Using the spheroid location data, we are able to track and monitor the growth of each spheroid over time. Furthermore, we investigate the dose-dependent effects on spheroid viability of 6 anti-cancer drugs (Doxorubicin, Everolimus, Gemcitabine, Metformin, Paclitaxel and Tamoxifen) using calcein AM and propidium iodide (PI). To validate the results, we compare dose-dependent trends in spheroid diameter with viability readings obtained from the CellTiter96® MTS assay (Promega Corporation). This work may lay a foundation for the investigation of other spheroids, organoids, or tissue samples, significantly increasing the number of spheroids analyzed per condition, improving the statistical analysis, and adding more parameters to further analyze the spheroids. These improvements may be especially helpful for spheroids grown from patient-derived or otherwise heterogeneous cell populations