Deep mRNA sequencing for in vivo functional analysis of cardiac transcriptional regulators: application to Galphaq.

Scot J Matkovich
Scot J Matkovich
Center for Pharmacogenomics
Yan Zhang
Yan Zhang
Key Laboratory of Food Nutrition and Safety
Gerald W Dorn
Gerald W Dorn
Center for Pharmacogenomics
Palo Alto | United States

Circ Res 2010 May 1;106(9):1459-67. Epub 2010 Apr 1.

Department of Medicine, Center for Pharmacogenomics, Washington University School of Medicine, St Louis, Mo 63110, USA.

Rationale: Transcriptional profiling can detect subclinical heart disease and provide insight into disease etiology and functional status. Current microarray-based methods are expensive and subject to artifact.

Objective: To develop RNA sequencing methodologies using next generation massively parallel platforms for high throughput comprehensive analysis of individual mouse cardiac transcriptomes. To compare the results of sequencing- and array-based transcriptional profiling in the well-characterized Galphaq transgenic mouse hypertrophy/cardiomyopathy model.

Methods And Results: The techniques for preparation of individually bar-coded mouse heart RNA libraries for Illumina Genome Analyzer II resequencing are described. RNA sequencing showed that 234 high-abundance transcripts (>60 copies/cell) comprised 55% of total cardiac mRNA. Parallel transcriptional profiling of Galphaq transgenic and nontransgenic hearts by Illumina RNA sequencing and Affymetrix Mouse Gene 1.0 ST arrays revealed superior dynamic range for mRNA expression and enhanced specificity for reporting low-abundance transcripts by RNA sequencing. Differential mRNA expression in Galphaq and nontransgenic hearts correlated well between microarrays and RNA sequencing for highly abundant transcripts. RNA sequencing was superior to arrays for accurately quantifying lower-abundance genes, which represented the majority of the regulated genes in the Galphaq transgenic model.

Conclusions: RNA sequencing is rapid, accurate, and sensitive for identifying both abundant and rare cardiac transcripts, and has significant advantages in time- and cost-efficiencies over microarray analysis.
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May 2010
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