Phenotypic feature quantification of patient derived 3D cancer spheroids in fluorescence microscopy image

Patients' responses to a drug differ at the cellular level. Here, we present an image-based cell phenotypic feature quantification method for predicting the responses of patient-derived glioblastoma cells to a particular drug. We used high-content imaging to understand the features of patient-derived cancer cells. A 3D spheroid culture formation resembles the in vivo environment more closely than 2D adherent cultures do, and it allows for the observation of cellular aggregate characteristics. However, cell analysis at the individual level is more challenging. In this paper, we demonstrate image-based phenotypic screening of the nuclei of patient-derived cancer cells. We first stitched the images of each well of the 384-well plate with the same state. We then used intensity information to detect the colonies. The nuclear intensity and morphological characteristics were used for the segmentation of individual nuclei. Next, we calculated the position of each nucleus that is appeal of the spatial pattern of cells in the well environment. Finally, we compared the results obtained using 3D spheroid culture cells with those obtained using 2D adherent culture cells from the same patient being treated with the same drugs. This technique could be applied for image-based phenotypic screening of cells to determine the patient's response to the drug.