SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells

SIDR:同时从单细胞中分离和并行测序基因组 DNA 和总 RNA

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作者:Kyung Yeon Han, Kyu-Tae Kim, Je-Gun Joung, Dae-Soon Son, Yeon Jeong Kim, Areum Jo, Hyo-Jeong Jeon, Hui-Sung Moon, Chang Eun Yoo, Woosung Chung, Hye Hyeon Eum, Sangmin Kim, Hong Kwan Kim, Jeong Eon Lee, Myung-Ju Ahn, Hae-Ock Lee, Donghyun Park, Woong-Yang Park

Abstract

Simultaneous sequencing of the genome and transcriptome at the single-cell level is a powerful tool for characterizing genomic and transcriptomic variation and revealing correlative relationships. However, it remains technically challenging to analyze both the genome and transcriptome in the same cell. Here, we report a novel method for simultaneous isolation of genomic DNA and total RNA (SIDR) from single cells, achieving high recovery rates with minimal cross-contamination, as is crucial for accurate description and integration of the single-cell genome and transcriptome. For reliable and efficient separation of genomic DNA and total RNA from single cells, the method uses hypotonic lysis to preserve nuclear lamina integrity and subsequently captures the cell lysate using antibody-conjugated magnetic microbeads. Evaluating the performance of this method using real-time PCR demonstrated that it efficiently recovered genomic DNA and total RNA. Thorough data quality assessments showed that DNA and RNA simultaneously fractionated by the SIDR method were suitable for genome and transcriptome sequencing analysis at the single-cell level. The integration of single-cell genome and transcriptome sequencing by SIDR (SIDR-seq) showed that genetic alterations, such as copy-number and single-nucleotide variations, were more accurately captured by single-cell SIDR-seq compared with conventional single-cell RNA-seq, although copy-number variations positively correlated with the corresponding gene expression levels. These results suggest that SIDR-seq is potentially a powerful tool to reveal genetic heterogeneity and phenotypic information inferred from gene expression patterns at the single-cell level.

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