Chapter One - Introduction to Sequencing the Brain Transcriptome

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Abstract

High-throughput next-generation sequencing is now entering its second decade. However, it was not until 2008 that the first report of sequencing the brain transcriptome appeared (Mortazavi, Williams, Mccue, Schaeffer, & Wold, 2008). These authors compared short-read RNA-Seq data for mouse whole brain with microarray results for the same sample and noted both the advantages and disadvantages of the RNA-Seq approach. While RNA-Seq provided exon level resolution, the majority of the reads were provided by a small proportion of highly expressed genes and the data analysis was exceedingly complex. Over the past 6 years, there have been substantial improvements in both RNA-Seq technology and data analysis. This volume contains 11 chapters that detail various aspects of sequencing the brain transcriptome. Some of the chapters are very methods driven, while others focus on the use of RNA-Seq to study such diverse areas as development, schizophrenia, and drug abuse. This chapter briefly reviews the transition from microarrays to RNA-Seq as the preferred method for analyzing the brain transcriptome. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, and can be used to extract genotype information, e.g., nonsynonymous coding single nucleotide polymorphisms. RNA-Seq embraces the complexity of the brain transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain–behavior–disease relationships is substantial.

Introduction

Next-generation sequencing (NGS) refers to a variety of related technologies, often termed massively parallel sequencing. The first NGS platform (Roche 454) was introduced in 2004. Subsequently, other platforms were released by several manufacturers: Illumina (Solexa), Helicos, Pacific Biosciences, and Life Technologies (ABI). Although the instruments differ in the underlying chemistry and technical approach, the platforms are similar in their capability of producing very large numbers of simultaneous reads relative to traditional methods. Thus, it is now possible to sequence whole genomes, exomes, and transcriptomes for a reasonable cost and effort. The technology of transcriptome sequencing, also known as RNA-Seq, has matured to the point that it is reasonable to propose substituting RNA-Seq for microarray-based assessments of global gene expression. Of particular importance to our laboratories are the advantages RNA-Seq has over microarray platforms when analyzing complex rodent crosses, e.g., heterogeneous stocks (HSs). However, the same argument can be made when analyzing any outbred population, including humans. Of particular relevance to the brain transcriptome are the advantages RNA-Seq has over microarrays in analyzing alternative splicing. This chapter provides a starting point for understanding the emergence of RNA-Seq and emphasizes transcriptome/behavior relationships.

Section snippets

From Microarrays to RNA-Seq

Cirelli and Tononi (1999) were among the first to report genome-wide brain gene expression profiling associated with a behavioral phenotype; both mRNA differential display and cDNA arrays were used to examine the effects of sleep deprivation on rat prefrontal cortex gene expression. Sandberg et al. (2000) used Affymetrix microarrays to detect differences in brain gene expression between two inbred mouse strains (C57BL/6J [B6] and 129SvEv [129; now 129S6/SvEvTac]). Importantly, these authors

NGS Platforms

There are several excellent reviews of the various NGS platforms (e.g., Mardis, 2008, Mardis, 2011, Martin and Wang, 2011, Metzker, 2010, Ozsolak and Milos, 2011, Rothberg et al., 2011). Understanding in some depth how the platforms work is critical to understanding where errors develop and are propagated from sample preparation to alignment to data analysis. The differences in platforms will not be discussed here. We simply note that for RNA-Seq experiments, the majority have used the Illumina

RNA-Seq Overview

The first and perhaps the most important step of an RNA-Seq experiment is the same as that for a microarray experiment, the isolation of high-quality RNA. Although both RNA-Seq and microarrays can be used on fragmented RNA such as that found in formalin-fixed-paraffin-embedded samples, the biases present in such samples for genome-wide sequencing are difficult to assess. RNA quality is routinely examined on the Agilent BioAnalyzer or a similar instrument; an RNA integrity number (RIN) of ≥ 8 is

RNA-Seq and Data Analysis

Before commenting on the analysis of RNA-Seq data, it is useful to recount the analysis controversies that arose with the introduction of microarrays. In 1999, Nature Genetics devoted an entire issue (volume 21—January) to microarrays. Cautionary concerns were raised around issues of data analysis (Lander, 1999). Microarray experiments, at the time, were generally expensive, limiting sample sizes. Small sample sizes and thousands of independent observations per sample were seen as a

Sequencing the Brain Transcriptome

PubMed lists 2702 RNA-Seq publications (6/1/14) with the first appearing in June 2008 (Nagalakshmi et al., 2008); the number has steadily increased from 11 in 2008, to 34 in 2009, to 127 in 2010, to 339 in 2011, to 639 in 2012, and to 1123 in 2013. Of these publications, 162 are also coded as “RNA-Seq and Brain” (~ 6% of total). However, this number most certainly represents a low estimate of the number of publications where RNA-Seq is used to assess the brain transcriptome or brain surrogates

Conclusions

Historically, the main arguments against using RNA-Seq (as opposed to using microarrays) have been cost and difficulties with data analysis. Over the past 6 years, technical improvements have and will continue to reduce costs; if the primary goal is gene-wide summarization, transcriptome samples can now be multiplexed and sequenced at adequate depth for less than $200/sample (not including the cost of library preparation). RNA-Seq data analysis remains substantially more complex than a

Acknowledgments

This study was supported in part by grants MH 51372, AA 11034, AA 13484, and a grant from the Veterans Affairs Research Service. The authors want to thank Dr. Kristin Demarest and Kris Thomason for editorial assistance in preparing the review.

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