Quantitative characterization of variability in cortical neurons between healthy human induced pluripotent stem cell lines
When studying developmental neurological diseases such as autism spectrum disorder, human brain tissue samples are only available postmortem. The breakthrough advances allowing reprogramming of human blood or skin cells into pluripotent stem cells (iPSCs), which can be differentiated into numerous cell types including neurons and astrocytes, allow for human cell models of neurodevelopmental disorders. How different control iPSCs behave under different culturing conditions has not been compared in parallel. Here, we characterize 11 healthy control human iPSC lines differentiated into cortical neurons, 2 commercial and 9 in house lines using a combination qPCR and immunofluorescence (IF). We first tested if cell lines could be distinguished based on IF in microscopy images using a Random Forest classifier. We find that cell lines can be clearly distinguished. We next used unsupervised machine learning to cluster neurons into groups. We measure a panel of genes using qPCR across 11 cell lines from NPC and 4 weeks cortical neurons derived from iPSC grown in two types of media. We further characterize the cell lines by quantifying the proportion of cells expressing neuronal precursor and cortical neuron markers using an automated image analysis macro.