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scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors

Authors: YU, Lei;

scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors

Abstract

Single-cell multi-omics can provide a unique perspective on tumor cellular heterogeneity. Most previous single-cell-whole-genome-RNA-sequencing (scWGS-RNA-seq) methods require physical separation of DNA and RNA, which makes these methods labor-intensive and technically demanding, time-consuming, or requiring special devices. And they are not applicable to frozen samples that cannot generate intact single-cell suspensions. We have developed scONE-seq, a versatile method for simultaneous transcriptome and genome profiling of single cells or single nuclei. Compared with existing methods, scONE-seq is a one-tube reaction that eliminates loss due to DNA and RNA separation, and thus is highly scalable and compatible with frozen biobanked tissue. We benchmarked scONE-seq against existing methods using fresh and biobanked samples and demonstrated superior performance, applying it to a 2-year-frozen astrocytoma sample and identifying a unique transcriptionally normal-like tumor clone. scONE-seq makes it possible to perform single-cell multi-omics interrogation with ease on the vast quantities of biobanked tissue. We also have a Git-hub repository : https://github.com/0YuLei0/scONE-seq-data-processing

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Keywords

Single cell multiomics, Cancer

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Cancer Research