PT  - JOURNAL ARTICLE
AU  - De Beuckeleer, Sarah
AU  - Van De Looverbosch, Tim
AU  - Van Den Daele, Johanna
AU  - Ponsaerts, Peter
AU  - De Vos, Winnok H.
TI  - Unbiased identification of cell identity in dense mixed neural cultures
AID  - 10.1101/2024.01.06.574474
DP  - 2024 Jan 01
TA  - bioRxiv
PG  - 2024.01.06.574474
4099  - http://biorxiv.org/content/early/2024/10/29/2024.01.06.574474.short
4100  - http://biorxiv.org/content/early/2024/10/29/2024.01.06.574474.full
AB  - Induced 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 preclinical 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 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 prediction accuracy even in very dense cultures. We then applied this regionally restricted 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 time in culture was used as classification criterion (96% vs. 86%, resp.). In mixed iPSC-derived neuronal cultures, microglia could be unequivocally discriminated from neurons, regardless of their reactivity state. A tiered strategy, allowed for discriminating microglial cell states as well, albeit with lower accuracy. Thus, 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 cell culture models.Competing Interest StatementThe authors have declared no competing interest.BrdUBromodeoxyuridineCNNconvolutional neural networkCPcell paintingDAPI4’,6- diamidino-2-fenylindoolDIVdays in vitroEdU5-ethynyl-2’-deoxyuridineERendoplasmic reticulumGrad-CAMGradient-weighted Class Activation MappingIFimmunofluorescenceiPSCinduced pluripotent stem cellNPCneural progenitor cellRFrandom forestROIregion of interestTUBB3beta-III-tubulinUMAPUniform Manifold Approximation and Projection