Experimental design for single-cell RNA sequencing

Jeanette Baran-Gale; Tamir Chandra; Kristina Kirschner

doi:10.1093/bfgp/elx035

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

GigaScience ◽

10.1093/gigascience/giab062 ◽

2021 ◽

Vol 10 (9) ◽

Cited By ~ 1

Author(s):

Lukas M Weber ◽

Ariel A Hippen ◽

Peter F Hickey ◽

Kristofer C Berrett ◽

Jason Gertz ◽

...

Keyword(s):

Genetic Variation ◽

Experimental Design ◽

Single Cell ◽

Rna Sequencing ◽

In Silico ◽

Cost Savings ◽

Cancer Tissue ◽

Library Preparation ◽

Natural Genetic Variation ◽

Single Cell Rna Sequencing

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.

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Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies

Nature Protocols ◽

10.1038/s41596-018-0073-y ◽

2018 ◽

Vol 13 (12) ◽

pp. 2742-2757 ◽

Cited By ~ 38

Author(s):

Atefeh Lafzi ◽

Catia Moutinho ◽

Simone Picelli ◽

Holger Heyn

Keyword(s):

Experimental Design ◽

Single Cell ◽

Rna Sequencing ◽

Single Cell Rna Sequencing ◽

Sequencing Studies

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

10.1101/2020.11.06.371963 ◽

2020 ◽

Author(s):

Lukas M. Weber ◽

Ariel A. Hippen ◽

Peter F. Hickey ◽

Kristofer C. Berrett ◽

Jason Gertz ◽

...

Keyword(s):

Genetic Variation ◽

Experimental Design ◽

Single Cell ◽

Rna Sequencing ◽

In Silico ◽

Cost Savings ◽

Cancer Tissue ◽

Library Preparation ◽

Natural Genetic Variation ◽

Single Cell Rna Sequencing

AbstractPooling 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 natural genetic variation. Here, we performed in silico benchmark evaluations by combining 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, confirming that genetic demultiplexing tools can be effectively deployed on cancer tissue using a pooled experimental design. We demonstrate that 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.

Download Full-text