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

View ORCID ProfileLukas M. Weber, View ORCID ProfileAriel A. Hippen, View ORCID ProfilePeter F. Hickey, View ORCID ProfileKristofer C. Berrett, View ORCID ProfileJason Gertz, Jennifer Anne Doherty, View ORCID ProfileCasey S. Greene, View ORCID ProfileStephanie C. Hicks
doi: https://doi.org/10.1101/2020.11.06.371963
Lukas M. Weber
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Ariel A. Hippen
2Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, PA, USA
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Peter F. Hickey
3Advanced Technology & Biology Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
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Kristofer C. Berrett
4Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, UT, USA
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Jason Gertz
4Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, UT, USA
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Jennifer Anne Doherty
4Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, UT, USA
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Casey S. Greene
5Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, CO, USA
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Stephanie C. Hicks
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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  • For correspondence: shicks19@jhu.edu
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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, 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 19, 2021.
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Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design
Lukas M. Weber, Ariel A. Hippen, Peter F. Hickey, Kristofer C. Berrett, Jason Gertz, Jennifer Anne Doherty, Casey S. Greene, Stephanie C. Hicks
bioRxiv 2020.11.06.371963; doi: https://doi.org/10.1101/2020.11.06.371963
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Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design
Lukas M. Weber, Ariel A. Hippen, Peter F. Hickey, Kristofer C. Berrett, Jason Gertz, Jennifer Anne Doherty, Casey S. Greene, Stephanie C. Hicks
bioRxiv 2020.11.06.371963; doi: https://doi.org/10.1101/2020.11.06.371963

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