scholarly journals Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

2017 ◽  
Vol 36 (1) ◽  
pp. 89-94 ◽  
Author(s):  
Hyun Min Kang ◽  
Meena Subramaniam ◽  
Sasha Targ ◽  
Michelle Nguyen ◽  
Lenka Maliskova ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Natural Genetic Variation ◽  
Single Cell Rna Sequencing

scholarly journals Author Correction: Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

2020 ◽  
Vol 38 (11) ◽  
pp. 1356-1356
Author(s):  
Hyun Min Kang ◽  
Meena Subramaniam ◽  
Sasha Targ ◽  
Michelle Nguyen ◽  
Lenka Maliskova ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Natural Genetic Variation ◽  
Single Cell Rna Sequencing

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

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
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.

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

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.

41-OR: Single Cell RNA-Sequencing Reveals the Developmental Heterogeneity of Brown Adipocytes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 41-OR
Author(s):  
FARNAZ SHAMSI ◽  
MARY PIPER ◽  
LI-LUN HO ◽  
TIAN LIAN HUANG ◽  
YU-HUA TSENG
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Brown Adipocytes ◽  
Single Cell Rna Sequencing
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