Identifying tumor vs normal cells in scRNAseq samples

Question

[bompipi95]

Hi everyone,

I currently have the gene expression matrix for mouse scRNA-seq bladder samples in WT vs KO conditions. These samples contain a mixture of normal (immune & bladder cells) and tumor cells. I have carried out the standard quality-control, normalisation, dimensional reduction and integration steps with Seurat v4.0.6. My question here is: how can I identify the tumor vs normal cells in the scRNA-seq dataset apart from one another?

I currently have 2 ideas:

1. Obtain the marker genes for the clusters and then have experts identify which clusters correspond to normal cells and which are likely the tumors. I can also perform automated cell-type annotation (e.g. reference-based annotation) against age-matched reference mouse bladder samples, which will help to narrow down the clusters that correspond to normal tissue samples, leaving the remainder as likely candidates for tumor-cell containing clusters.

2. Identify tumor cells as cells that have CNV. Identify CNVs using tools like honeybadger & inferCNV, and based on that, probabilistically label cells as tumor / normal.

I would appreciate your suggestions on the matter, as well as references to papers that have performed similar analysis. Thank you for reading!

[benformatics]

You could also check the mitochondrial variants to potentially identify the cancer clone:

http://www.bioconductor.org/packages/release/bioc/html/mitoClone2.html

Answer 1

[Rafael Soler]

Hello,

You can do both. Check for common marker genes first, and then test cells for CNV. I would also recommend that you check out the latest tools for CNV recognition, as Identifying tumor cells at the single-cell level using machine learning