Regardless of the differentiation method used, it is imperative that the regional/cellular specificity of a neuronal differentiation protocol is properly analyzed. Few cellular markers are generally not sufficient for this purpose, and a better way to address this question is to perform global transcriptome analysis exploiting rich and well curated databases of developmental transcriptome of the human brain, like Brainspan 156, by classifying samples against it. A very simple classification algorithm, based on correlation analysis, showed that the organoid’s transcriptome resembled best human brain development at 8 to 10 post-conceptional weeks, with weaker correspondence to later stages of fetal development 50 suggesting that the in-vitro developmental timeline mimics early in-vivo brain development. A more sophisticated machine learning based algorithm, CoNTExT 157, was later developed, which identified strong conservation of transcriptomics network signatures between primary human neural progenitor cells and developing human fetal brain, but highlighted differences between these primary human neural progenitor cells and hiPSC-derived neural progenitors from multiple laboratories. However, CoNTExT had not yet been applied to transcriptome data derived from organoids, which may be different from neural progenitors dissociated and grown in 2D culture.
