Multipotent neural stem cells (NSCs) are found in several isolated niches of the adult mammalian brain where they have unique potential to assist in tissue repair. Modern transcriptomics offer high-throughput methods for identifying disease or injury associated gene expression signatures in endogenous adult NSCs, but they require adaptation to accommodate the rarity of NSCs. Bulk RNA sequencing (RNAseq) of NSCs requires pooling several mice, which impedes application to labor-intensive injury models. Alternatively, single cell RNAseq can profile hundreds to thousands of cells from a single mouse and is increasingly used to study NSCs. The consequences of the low RNA input from a single NSC on downstream identification of differentially expressed genes (DEGs) remains insufficiently explored.
To clarify the role that low RNA input plays in NSC DEG identification, researchers at the Ohio State University directly compared DEGs in an oxidative stress model of cultured NSCs by bulk and single cell sequencing. While both methods yielded DEGs that were replicable, single cell sequencing using the 10X Chromium platform yielded DEGs derived from genes with higher relative transcript counts compared to non-DEGs and exhibited smaller fold changes than DEGs identified by bulk RNAseq. The loss of high fold-change DEGs in the single cell platform presents an important limitation for identifying disease-relevant genes. To facilitate identification of such genes, the researchers determined an RNA-input threshold that enables transcriptional profiling of NSCs comparable to standard bulk sequencing and used it to establish a workflow for in vivo profiling of endogenous NSCs. They then applied this workflow to identify DEGs after lateral fluid percussion injury, a labor-intensive animal model of traumatic brain injury.
RNA input leads to bias in DEG discovery
(A) Venn diagram of DEGs identified by scRNAseq and 1 ng RNAseq with CLEAR filtering after H2O2 vs. vehicle treatment. (B) qRT-PCR analysis corroborated the majority of DEGs identified by scRNAseq and 1 ng RNAseq in cultured NSCs following H2O2-induced oxidative stress. X2 contingency test (df = 1) = 2.331, p = 0.127. (C) Venn diagram of GO terms associated with DEGs identifies by scRNAseq and 1 ng RNAseq. (D) Venn diagram of DEGs identified by scRNAseq with no filtering, filtering for low feature counts, and filtering for both low and high feature counts (i.e., the default for Seurat analysis). (E) Venn diagram of DEGs identified by scRNAseq using the Wilcoxon test or DESeq2 compared with DEGs identified by 1 ng RNAseq. (F,G) DEGs were ranked by average transcript count level relative to all detected gene counts. (F) Violin plot of genes ranked by transcript count level in scRNAseq dataset. DEGs ranked significantly higher in transcript count compared to non-DEGs. ****p < 0.0001 unpaired t-test. (G) Violin plot of genes ranked by transcript count level in 1 ng RNAseq dataset. There was no significant difference in rank of transcript count between DEGs and non-DEGs. (H) Comparison of fold changes in average transcript count between treatment groups for DEGs from sc- and 1 ng- RNAseq indicated that DEGs identified by 1 ng RNAseq showed significantly larger fold change in gene expression than DEGs identified by scRNAseq.
This work joins an emerging body of evidence suggesting that single cell RNA sequencing may underestimate the diversity of pathologic DEGs. However, these data also suggest that population level transcriptomic analysis can be adapted to capture more of these DEGs with similar efficacy and diversity as standard bulk sequencing. Together, othese ur data and workflow will be useful for investigators interested in understanding and manipulating adult hippocampal NSC responses to various stimuli.