TY - JOUR
T1 - Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design
AU - Weber, Lukas M.
AU - Hippen, Ariel A.
AU - Hickey, Peter F.
AU - Berrett, Kristofer C.
AU - Gertz, Jason
AU - Doherty, Jennifer Anne
AU - Greene, Casey S.
AU - Hicks, Stephanie C.
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Oxford University Press GigaScience.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - 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.
AB - 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.
KW - benchmarking
KW - cancer
KW - computational methods
KW - genetic demultiplexing
KW - high-grade serous ovarian cancer
KW - lung adenocarcinoma
KW - simulations
KW - single-cell RNA sequencing
KW - tumor mutational burden
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U2 - 10.1093/gigascience/giab062
DO - 10.1093/gigascience/giab062
M3 - Article
C2 - 34553212
AN - SCOPUS:85116796682
SN - 2047-217X
VL - 10
JO - GigaScience
JF - GigaScience
IS - 9
M1 - giab062
ER -