Cell Ranger was used to demultiplex raw base call files to FASTQ files, align reads to the reference genome and transcriptome with the default alignment parameters, demultiplex human and mouse cells, and generate the count matrices for the human cells. Seurat (v.3.1) was then applied to the human scRNA-seq data for further preprocessing. The scRNA-seq data of all 409b2 cells from iPSCs to NGN2-iN was integrated with cluster similarity spectrum (CSS) (He et al., 2020). The scRNA-seq data of NGN2-iN cells at day 35 of samples from the 409b2 and Sc102a1 human iPSC lines was integrated with Seurat. Generation of UMAP embeddings, clustering, and pseudotime analysis was done on the integrated spaces. Marker genes of different NGN2-iN populations were identified as genes with BH corrected p < 0.01 and expression fold change >1.2. The benchmark of NGN2-iN populations was done by comparisons with the adolescent mouse nervous system atlas (Zeisel et al., 2018), the developing mouse brain (La Manno et al., 2020), and the developing mouse retina (Clark et al., 2019). The NGN2-iN scRNA-seq data with varied Dox treatment durations was processed similarly and integrated with CSS. Details of the computational analysis are described in the supplemental experimental procedures.
