RT Journal
A1 Han, Kyung Yeon
A1 Kim, Kyu-Tae
A1 Joung, Je-Gun
A1 Son, Dae-Soon
A1 Kim, Yeon Jeong
A1 Jo, Areum
A1 Jeon, Hyo-Jeong
A1 Moon, Hui-Sung
A1 Yoo, Chang Eun
A1 Chung, Woosung
A1 Eum, Hye Hyeon
A1 Kim, Sangmin
A1 Kim, Hong Kwan
A1 Lee, Jeong Eon
A1 Ahn, Myung-Ju
A1 Lee, Hae-Ock
A1 Park, Donghyun
A1 Park, Woong-Yang
T1 SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells
JF Genome Research 
JO Genome Research 
YR 2018 
FD January 01 
VO 28 
IS 1 
SP 75 
OP 87 
DO 10.1101/gr.223263.117 
UL http://genome.cshlp.org/content/28/1/75.abstract 
AB 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.