Single-nulceus RNA sequencing of human-induced neuron and astrocytes treated with cell-secreted amyloid beta peptides
Description
In this work, we investigated the early effects of Aβ peptides accumulation on gene expression profiles of human-induced neurons (hiNs). Using single-cell RNA sequencing, we show that cell-secreted Aβ up-regulates the expression of several synaptic-related genes and down-regulates the expression of genes associated with metabolic stress mainly in glutamatergic neurons and to a lesser degree in GABAergic neurons and astrocytes. These neuronal alterations correlate with activation of SEMA5, EPHA and NECTIN signaling pathways, which are important regulators of synaptic plasticity. Altogether, our findings indicate that slight elevations in Aβ concentrations are sufficient to elicit transcriptional changes in human neurons that can contribute to early alterations on neural network activity. Count table showing the number of counts per nucleus in 6-week-old human-induced neurons and astrocytes treated with conditioned media from CHO cell lines overexpressing the human APP695 (CHO-APPWT) or the London mutated APP695 (CHO-APPV717L) (Guillot-Sestier et al., 2012) or non-conditioned medium (Blank/control) . Metadata file associated with count table.
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Steps to reproduce
Unique Molecular Index (UMI) Count Matrices for gene expression and for Hash Tag Oligonucleotide (HTO) libraries were generated using the CellRanger count (Feature Barcode) pipeline. Reads were aligned on the GRCh38-3.0.0 transcriptome reference (10x Genomics). Filtering for low quality cells according to the number of RNA, genes detected, and percentage of mitochondrial RNA was performed. For HTO sample, the HTO matrix was normalized using centered log-ratio (CLR) transformation and cells were assigned back to their sample of origin using HTODemux function of the Seurat R Package. Then, normalizations of the gene expression matrix for cellular sequencing depth, mitochondrial percentage and cell cycle phases using the variance stabilizing transformation (vst) based Seurat::SCTransform function were performed. To integrate the experimental replicates of the single cell experiments, the harmony R package (https://github.com/immunogenomics/harmony) was used. In order to integrate the datasets, the SCTransform normalized matrices was merged and PCA was performed using Seurat::RunPCA default parameter. The 50 principal components (dimensions) of the PCA were corrected for batch effect using harmony::RunHarmony function. Then, the 30 first batch corrected dimensions were used as input for graph-based cell clustering and visualization tool. Seurat::FindNeighbors using default parameters and Seurat::FindClusters function using the Louvain algorithm were used to cluster cells according to their batch corrected transcriptomes similarities. To visualize the cells similarities in a 2-dimension space, the Seurat::RunUMAP function using default parameter was used. Cell clusters were then annotated based on cell type specific gene expression markers.