SINGLE-CELL sequencing workflow to study cellular composition and cell type specific expression profiles of human Cerebral Organoids

Abstract

Human cerebral organoid culture is a technology with immense potential in the areas of developmental neurobiology and neurodegeneration for example to study cell types, mechanisms involved, to discover of new biomarkers, to propose specific therapeutic strategies or to study the effects of compound-induced toxicity. Single-cell RNA sequencing (scRNA-seq) is a promising technology that will help to define the identity of the cerebral organoids and to understand cellular composition and cell type specific expression profiles. Standardization of workflows to do the scRNA-seq analysis is an important means to improve the use of this technology. We present the workflow and results of the scRNA-seq performed for cerebral organoids generated from the AND-2 cell line of human embryonic stem cells (hESCs). Dissociated cerebral organoid samples were loaded on the 10X Chromium and single cell libraries were prepared according to 10X Genomics standard procedures and sequenced on the Novaseq sequencer (Illumina).The data were checked and aligned to the GRCh38 human reference genome with CellRanger v6.0.2 and analyzed with Seurat v4.0. After quality filtering and data normalization with the SCTransform function, we performed Principal component analysis (PCA) using the highly variable genes, built a Shared Nearest Neighbor (SNN) graph using the Louvain method. To visualize data, Uniform Manifold Approximation and Projection (UMAP) dimensional reduction was performed. The identities of the cell clusters were assigned using the expression of genes specific of each cell type. We annotate in the AND2 cerebral organoids clusters for intermediate progenitor cells, astrocytes, oligodendrocyte precursor cells, excitatory neurons, inhibitory neurons, and mesodermal cells. We find also some cells in these organoids with expression of endothelial and microglial gene markers. Enrichment analysis of the highly variable differentially expressed genes (DEGs) was utilized to characterize the assigned cell types with Gene Ontology (GO), PanglaoDB and Cellmarker databases.