Unbiased identification of cell identity in dense mixed neural cultures

Abstract

AbstractInduced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine screening settings. To facilitate the validation of iPSC-derived cell culture composition, we have implemented an imaging assay based on cell painting and convolutional neural networks to recognize neural cell types in dense and mixed cultures with high fidelity. We have benchmarked our approach using pure and mixed cultures of neuroblastoma and astrocytoma cell lines and attained a classification accuracy above 96%. Through iterative data erosion we found that inputs containing the nuclear region of interest and its close environment, allow achieving equally high classification accuracy as inputs containing the whole cell for semi-confluent cultures and preserved its accuracy even in very dense cultures. We then applied the nucleocentric cell profiling approach to evaluate the differentiation status of iPSC-derived neural cultures, by determining the ratio of postmitotic neurons and neural progenitors. We found that the cell-based prediction significantly outperformed an approach in which the culture time point was used as classification criterion (96%vs.86%, resp.). Thus, nucleocentric morphological single cell profiling provides a means to quantify cell composition in complex mixed neural cultures and holds promise for use in quality control of iPSC-derived neural cell culture models.