Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design

Abstract

Background

Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower library preparation costs and reduced unwanted technological variation, such as batch effects. Computational demultiplexing tools based on natural genetic variation between individuals provide a simple approach to demultiplex samples, which does not require complex additional experimental procedures. However, to our knowledge these tools have not been evaluated in cancer, where somatic variants, which could differ between cells from the same sample, may obscure the signal in natural genetic variation.

Results

Here, we performed in silico benchmark evaluations by combining raw sequencing reads from multiple single-cell samples in high-grade serous ovarian cancer, which has a high copy number burden, and lung adenocarcinoma, which has a high tumor mutational burden. Our results confirm that genetic demultiplexing tools can be effectively deployed on cancer tissue using a pooled experimental design, although high proportions of ambient RNA from cell debris reduce performance.

Conclusions

This strategy provides significant cost savings through pooled library preparation. To facilitate similar analyses at the experimental design phase, we provide freely accessible code and a reproducible Snakemake workflow built around the best-performing tools found in our in silico benchmark evaluations, available at https://github.com/lmweber/snp-dmx-cancer.

Now published in GigaScience doi: 10.1093/gigascience/giab062

Lukas M. Weber 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Lukas M. WeberAriel A. Hippen 2Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, PA, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Ariel A. HippenPeter F. Hickey 3Advanced Technology & Biology Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Peter F. HickeyKristofer C. Berrett 4Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, UT, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Kristofer C. BerrettJason Gertz 4Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, UT, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Jason GertzJennifer Anne Doherty 4Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, UT, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteStephanie C. Hicks 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Stephanie C. HicksFor correspondence: shicks19@jhu.edu

A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giab062 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

These peer reviews were as follows:

Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102826 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102827

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Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design

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Abstract

Background

Results

Conclusions

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