title: GSM7675737: FS, scRNAseq; Homo sapiens; RNA-Seq
library_construction_protocol: DIV70 forebrain organoids were incubated for 30 min at 37 °C in Earle's Balanced Salt Solution (EBSS)/Albumin-ovomucoid inhibitor/papain with gentle shaking and mechanical dissociation. The pellet was resuspended and incubated with (EBSS)/Albumin-ovomucoid inhibitor/DNAse for 2 min followed by incubation with ovomucoid for 2 min. The cells were washed and resuspended with DMEM/F12. After determination of high cell viability (>90%), the single cell suspension was sequenced on the 10x Genomics platform at the Columbia Genome Center to recover ~5000 sequenced cells per sample Library was performed according to the manufacter's instructions (single cell 3' v2 protocol, 10x Genomics). Briefly, The Cell Ranger (5.0.1) pipeline (10x Genomics) was used to align and aggregate all reads from scRNA-seq to the GRCh38 human reference genome with default settings and produce a balanced combined “filtered feature bc matrix” for all samples. The data was imported into the SCANPY software (Version 1.7.1), where genotype information was added. Quality control was conducted to ensure all samples analyzed contained highly consistent quality. Cells expressing a minimum of 200 genes were kept, and counts were normalized for each cell by the total expression, multiplied by 106 and log-transformed. LIGER (Version 2.0.1) software was used to integrate the different samples and variation in the cells' transcriptional profile was visualized by uniform manifold approximation and projection for dimension reduction (UMAP) function in SCANPY. To annotate the cell types in brain organoids, we downloaded the gene-cell count matrix and cell type annotation files of the single-cell RNAseq analysis of human embryonic prefrontal cortex tissue from the cell browser(https://cells.ucsc.edu/). We then utilized this dataset to train the SingleR software (Version 1.0.6 to infer the cell types in our dataset. Differentially expressed genes (DEGs) were determined using model-based analysis of single-cell transcriptomics (MAST, Version 1.12.0) algorithm implemented in Seurat (Version 4.2).