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Updated: May 8, 2026

Non-Laser Capture Microscopy Approach for the Microdissection of Discrete Mouse Brain Regions for Total RNA Isolation and Downstream Next-Generation Sequencing and Gene Expression Profiling

Non-Laser Capture Microscopy Approach for the Microdissection of Discrete Mouse Brain Regions for Total RNA Isolation and Downstream Next-Generation Sequencing and Gene Expression Profiling

Published on: November 13, 2011

Whole transcriptome RNA-Seq allelic expression in human brain.

Ryan M Smith¹, Amy Webb, Audrey C Papp

¹Department of Pharmacology, Program in Pharmacogenomics; College of Medicine, The Ohio State University Wexner Medical Center, 5184A Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. Ryan.Smith2@osumc.edu

|August 24, 2013

Summary

This summary is machine-generated.

Accurate allelic RNA expression measurement using RNA sequencing (RNA-Seq) requires careful bias correction. This study identifies key factors and improves RNA-Seq accuracy for detecting regulatory variants and RNA editing across human brain regions.

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Area of Science:

Genomics
Neuroscience
Molecular Biology

Background:

Allelic RNA expression ratios reveal regulatory variants, RNA editing, and copy number variations.
Whole transcriptome RNA sequencing (RNA-Seq) is a powerful tool for identifying allelic expression imbalance (AEI).
Numerous factors can bias allelic RNA ratio measurements in RNA-Seq.

Purpose of the Study:

Compare RNA-Seq allelic ratios with a sensitive SNaPshot method across human brain regions.
Identify factors that influence reliable allelic ratio measurements.
Survey RNA editing variability across brain regions and individuals.

Main Methods:

Compared RNA-Seq allelic ratios with SNaPshot measurements in nine human brain regions.
Applied alternative alignment strategies and bias correction methods to RNA-Seq data.
Surveyed transcriptome-wide AEI and RNA editing in human brain tissues.

Main Results:

Standard RNA-Seq alignment methods showed poor correlation with SNaPshot, but bias correction significantly improved accuracy.
Identified genes with AEI across all brain regions and numerous genes with region-specific AEI.
Found frequent regulatory variants affecting gene expression in the human brain and consistent RNA editing levels across regions and individuals.

Conclusions:

Identified critical factors for accurate RNA-Seq allelic ratio measurement, enabling transcriptome-wide screening.
Discovered tens to hundreds of genes with frequent functional variants affecting RNA expression in the human brain.
Concluded that RNA editing is tightly regulated to maintain optimal levels for biological function.