Prior to the advent of high-throughput methods to measure the entire transcriptome, such as microarrays or next-generation sequencing, studies of gene expression and function in the brain were restricted to a relatively small number of genes (Luo and Geschwind, 2001
; Zhang et al., 2002
). Recently, whole transcriptome sequencing (RNA-Seq) has enabled the measurement of abundance of tens of thousands of RNA species in a given biological sample (Wang et al., 2009
; Ozsolak and Milos, 2011
). This new generation of high-throughput sequencing technology has delivered on its promise of sequencing DNA, cDNA, and RNA at unprecedented speed and accuracy, thereby providing an increasingly wide-ranging array of data sets that provide insight into biological and disease diversity (Schuster, 2008
). Single-cell analysis represents one of the novel areas of application for high-throughput sequencing, which is particularly important for the study of tissues that have a high degree of intrinsic variation, such as the brain. To date, this technology has achieved success in tumor profiling to study somatic DNA mutations in clonal sub-populations: for example, Navin et al. (2011)
applied single-nucleus DNA sequencing to investigate tumor population structure and evolution in human breast cancer cases. Single-cell RNA sequencing also can be used to study differences of individual cells with identical genomes, for example, in a pool of neurons from a single brain.
There is a strong rationale to perform single-neuron RNA expression analysis. The mammalian brain consists of billions of neurons that each typically exhibits over 100,000 macroconnections (Bota et al., 2003
; Luo et al., 2008
). Individual neurons can be characterized into distinct cell types based on morphology, electrophysiological characteristics, connections, and expressed molecular diversity. Such cell-specific information is diluted when pooling groups of neurons. Gene expression differences occurring in rare cell types may go undetected, because they contribute to only a small fraction of total tissue RNA. Moreover, gene expression may be regulated in opposing directions in different cell types, thereby appearing static in composite data. Cell type-specific transcriptomics will provide a more panoramic view of gene expression, and ultimately networks, rather than from a viewpoint dominated by the effects of single genes. Moreover, correlating gene expression data of individual neurons with single-cell phenotypic data has the potential to refine cell type definitions.
Transcriptome analyses of single neurons have been reported using mostly microarrays. Earlier pioneering studies of single-cell microarray analysis used a universal PCR amplification procedure, or two rounds of T7 RNA polymerase amplification. For example, Esumi et al. (2008)
developed method for single-cell microarray analysis and applied them to gene-expression profiling of GABAergic neuron progenitors. Iscove et al. (2002)
demonstrated that the representation is faithfully preserved in global cDNA amplified exponentially from sub-picogram quantities of mRNA. Kamme et al. (2003)
examined expression profiling of CA1 neurons in the rat hippocampus using a combination of laser capture, T7 RNA amplification, and cDNA microarray analysis. Single GABAergic neuron progenitors from mouse neocortex were isolated by dissociation and aspiration of green fluorescent protein (GFP)-positive cells, then processed by Super SMART PCR and T7 RNA polymerase amplification. Similarly, Gustincich et al. (2004)
combined SMART PCR and T7 RNA polymerase amplification to interrogate the transcriptome in single dopaminergic neurons of the retina in mice. Tietjen et al. (2003)
also used similar techniques in single cells from dissociated tissues or collected from intact slices using laser capture. These studies demonstrated that microarray-based analysis of gene expression can be performed on single neuronal cells isolated from defined areas of the brain.
Compared with microarray methods, sequence-based transcriptome profiling has major advantages, such as extended linear range of detection, accuracy, binary low noise reading, and independence from a reference genome. Next-generation sequencing techniques have been applied recently to single-cell transcriptome analysis. For example, Tang et al. (2009)
used single-cell transcriptome analysis based on RNA-Seq and applied the methods to analyze the developmental program of embryonic stem cells (Tang et al., 2010a
). Recently, Eberwine and Bartfai (2011)
have examined the single-cell transcriptome in hypothalamic warm sensitive neurons that control core body temperature and fever response. These authors assayed cDNA libraries from single neurons using Affymetrix gene expression arrays, and confirmed the frequency of specific cDNAs by Illumina sequencing. The importance of single-cell transcriptome analysis has been increasingly recognized recently, especially for tracing cell lineages and in diagnostic applications (Stahlberg et al., 2011
; Tang et al., 2011
). The feasibility and reproducibility of RNA-Seq in single neurons, however, has not been systematically studied, though a protocol to assay single neurons by qPCR was recently published (Citri et al., 2012
). Furthermore, experimental variability related to extremely low levels of RNA, and bioinformatics analysis of the RNA-Seq data, can be technically demanding. Our study differs from previous studies in that we developed protocols specifically for assaying neurons, and our method can be directly coupled with electrophysiology studies on individual neurons, thus enabling the correlation of single-cell cellular phenotypes with single-cell expression phenotypes. Below we describe the experimental strategy and application that was designed to test the practicality of single-neuron RNA-Seq using equipment readily available in a typical neurophysiology laboratory. The study demonstrates the ability to generate RNA-Seq data with reasonable reproducibility from individual, electrophysiologically characterized neurons.