Abstracts

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Rationale: Somatic variants are a major cause of human disease, including tumors and focal epilepsies, but can be challenging to study due to their mosaicism in bulk tissue biopsies. Coupling single-cell genotype and transcriptomic data has potential to provide insight into the role somatic variants play in disease etiology, by determining what cell types are affected or how their gene expression is altered. Here we asked whether commonly used single-cell 3’ or 5’ RNA-sequencing assays can be used to derive single-cell genotype data for a priori known variants that are located near to either end of a transcript. To that end, we compared performance of 10x Genomics Chromium NextGEM Single-Cell 3’ and 5’ gene gene expression kits. We then quantified our ability to detect germline variants in single-cell datasets depending on distance from the transcript end. Finally, we demonstrated the ability to elucidate affected cell types in a patient with a RHEB somatic variant causing an epilepsy-associated cortical malformation.

Methods: We established a workflow to compare performance between single-cell 3’ and 5’ gene expression kits and evaluate detection of known germline and somatic variants in the resulting datasets. For these analyses, we obtained frozen resected brain tissue from three pediatric focal epilepsy patients. Resected brain tissue from patients was split for exome sequencing and nuclei isolation. Exome sequencing data was used for somatic variant calling via the Mutect2 pipeline, where patient blood samples were used as the matched normal sample. Patient samples that underwent nuclei isolation were sorted, libraries were constructed using both GEX kits, resulting libraries were sequenced, run through the cell ranger pipeline, and analyzed using Seurat. We evaluated GEX kit differences by nuclei quality, gene detection, cell type distribution, and differential expression between cell populations. Then we used Vartrix to overlay variant information within our GEX data. We evaluated number of variant cells detected in each dataset and cell type enrichment of the variant.

Results: Our results show that the 3’ and 5’GEX kits have comparable performance, which will allow for future kit selection to be made based on targeted variant location. We also demonstrated that native 3’ or 5’ scRNA-sequencing data can be used to genotype single-cells for somatic variants that are expressed within proximity to a transcript end. Analysis of the RHEB variant revealed that more genotyped cells were detected in the 5’ datasets which is consistent with the variant’s typical 5' end location. We also determined that the RHEB variant is enriched in neurons, oligodendrocytes, and astrocytes. As these cells originate from common neural progenitor cells, these findings suggests the RHEB variant arose during corticogenesis.

Conclusions: Here, we provided a much needed comparison of the same patient sample between available scRNA-sequencing approaches and introduced a method to detect genetic variants in native 10x chromium gene expression datasets. This approach allows researchers to gather insights on genetic variant information from scRNA-Seq datasets without the need for additional sequencing methods.

Funding: Not applicable