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Single-Cell RNA-Sequencing of the Brain Cuevas‑Diaz Duran et al. Clin Trans Med (2017) 6:20 DOI 10.1186/s40169-017-0150-9 REVIEW Open Access Single‑cell RNA‑sequencing of the brain Raquel Cuevas‑Diaz Duran1,2†, Haichao Wei1,2† and Jia Qian Wu1,2* Abstract Single-cell RNA-sequencing (scRNA-seq) is revolutionizing our understanding of the genomic, transcriptomic and epigenomic landscapes of cells within organs. The mammalian brain is composed of a complex network of millions to billions of diverse cells with either highly specialized functions or support functions. With scRNA-seq it is possible to comprehensively dissect the cellular heterogeneity of brain cells, and elucidate their specifc functions and state. In this review, we describe the current experimental methods used for scRNA-seq. We also review bioinformatic tools and algorithms for data analyses and discuss critical challenges. Additionally, we summarized recent mouse brain scRNA-seq studies and systematically compared their main experimental approaches, computational tools imple‑ mented, and important fndings. scRNA-seq has allowed researchers to identify diverse cell subpopulations within many brain regions, pinpointing gene signatures and novel cell markers, as well as addressing functional diferences. Due to the complexity of the brain, a great deal of work remains to be accomplished. Defning specifc brain cell types and functions is critical for understanding brain function as a whole in development, health, and diseases. Keywords: Single-cell RNA-sequencing, Brain, Heterogeneity, Bioinformatic analyses Introduction and the generation of cDNA libraries from low amounts Single-cells are the fundamental units of unicellular and of RNA. Trough scRNA-seq researchers are able to multicellular organisms. Every single-cell in an organ- determine expression profles in single-cell resolution. ism is unique in its transcriptome, epigenome, and its Since the introduction of scRNA-seq [7], the number of local microenvironment. Even genetically identical single-cell experiments has greatly increased. scRNA-seq cells display stochastic gene expression due to random has demonstrated to be a powerful tool to identify and fuctuations in the mechanisms driving and regulating classify cell subpopulations [8], characterize rare or small transcription and translation [1, 2]. Te underlying heter- subpopulations [9], and trace cells along dynamic cellular ogeneity within cells is a fundamental property of cellular stages, such as during diferentiation [10]. systems for homeostasis and development [3]. Diferent Te mammalian brain is a complex tissue that con- cell types specialize in the execution of specifc tasks [4]. tains a large number of specialized cells with diferences Next-generation sequencing technologies, such as in morphology, connectivity, and functions [11–13]. RNA-sequencing, have become a standard for querying Brain cells have been classifed by location, morphology, gene expression [5, 6]. However, gene expression levels electrophysiological characteristics, target specifcity, obtained through such ensemble-based approaches yield molecular markers and gene expression patterns [14–17]. expression values averaged across large populations of Single-cell analysis is critical for studying the brain since input cells, masking cellular heterogeneity. Recent experi- small diferences in a seemingly homogeneous popula- mental advances have allowed the isolation of single-cells tion may explain issues relating cells to learning, memory, and other cognitive functions [18]. scRNA-seq makes it possible to understand the heterogeneity and the regu- *Correspondence: [email protected] latory networks within brain cells at the transcriptome † Raquel Cuevas-Diaz Duran and Haichao Wei contributed equally to the level. work 1 The Vivian L. Smith Department of Neurosurgery, McGovern Medical Te general framework of a scRNA-seq experiment School, The University of Texas Health Science Center at Houston, consists of: single-cell isolation, cell lysis, mRNA cap- Houston, TX 77030, USA turing, mRNA reverse transcription into cDNA, cDNA Full list of author information is available at the end of the article © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Cuevas‑Diaz Duran et al. Clin Trans Med (2017) 6:20 Page 2 of 14 amplifcation, library preparation, and sequencing [19]. a thermoplastic polymer coating is placed on the tissue Herein, we will review recent research in brain cells with over a glass slide. Te polymer is melted and then the scRNA-seq. In the frst two sections, we will discuss the polymer-cell composition is removed from the tissue. advances and limitations of the methods for single-cell Although specifc cells in a tissue are captured, there are isolation and library generation. Section three will sum- some limitations. Contrary to FACS and MACS, LCM is marize the analysis methods of scRNA-seq data. Sub- a low-throughput technology. Additionally, LCM relies sequently, we will discuss recent and relevant fndings heavily on cell identifcation. LCM needs an expert derived from scRNA-seq of brain cells. Finally, we will pathologist or cytologist, limiting its extensive applica- highlight future applications and challenges of scRNA- tion. However, the main advantages of LCM are that it seq in brain. allows researchers to study single-cells within their niche or microenvironment and preserves their spatial loca- Single‑cell isolation protocols tion. A cell’s niche is relevant when studying cells with Te frst important step in scRNA-seq is to isolate sin- functional diversity linked to spatial location such as gle-cells from tissues keeping their expression patterns brain cells. as accurate as possible. Several technologies have been used, such as: FACS (Fluorescence-activated cell sorting), cDNA amplifcation and sequencing library MACS (Magnetic-activated cell sorting), LCM (Laser construction capture microdissection), manual cell picking and micro- A single-cell can only supply very limited starting mate- fuidics. Depending on the nature of samples, diferent rial (about 0.1 pg of mRNA in each cell), so amplifca- methods may be more suitable for single-cell isolation tion methods are needed to produce high fdelity, high in distinct samples. In this section, we will discuss some coverage and reliable data [28]. Some of the common methods used for isolating brain cells. reverse transcription and amplifcation methods used Fluorescence-activated cell sorting and MACS are include: SMART-seq/SMART-seq2 (switching mecha- widely used methods to isolate single-cells. FACS can nism at the 5′ end of the RNA transcript) [9, 29], STRT- purify single-cells based on cell size, granularity and fuo- seq (single-cell tagged reverse transcription sequencing) rescence. Surface markers are diferent in individual cells, [30], CEL-seq (cell expression by linear amplifcation so FACS can isolate specifc cells stained with difer- and sequencing) [31], PMA (Phi29 DNA polymerase- ent fuorescently-tagged monoclonal antibodies [20]. In based mRNA transcriptome amplifcation) [32], SMA brain cell research, cells have been labelled with diferent (semi-random primed PCR-based mRNA transcrip- markers. For example, Tasic et al. [21] used combinations tome amplifcation procedure) [32], and Quartz-seq of Snap25, Slc17a7, and Gad1 to fnd subpopulations in [33]. Researchers studying brain scRNA-seq typically use the primary visual cortex as listed in Table 1 and depicted SMART-seq, SMART-seq2, and STRT-seq as outlined in in Fig. 1. Similarly, Llorens-Bobadilla et al. [22] labelled Table 1. cells with GLAST/Prom1 and PSA-NCAM to dissect SMART-seq is a reverse transcription and amplifca- populations in the subventricular zone. Although FACS tion method based on template-switching [9]. First strand is a highly efcient method to isolate single-cells, it has cDNAs are created by an oligo(dT)-containing primer, its limitations: not all cell types have their own specifc and a few untemplated poly(C) nucleotides are added gene markers [23], and the binding of fuorescently- as overhang at the end of cDNA molecules. Te second tagged monoclonal antibodies to cells might alter their strand is synthesized by an oligonucleotide primer which function [24]. One major disadvantage of FACS is its can hybridize to the poly(C) overhang, generating full low cell throughput rate. Even high-speed sorters will length cDNA products. Te purifed PCR products can yield a few thousand cells per second [25]. Since many then be used for constructing cDNA libraries. SMART- experiments require large number of cells, sorting runs seq2 is an updated version of SMART-seq [29]. It can may take long times posing quality issues to sorted cells. signifcantly improve cDNA yield. In SMART-seq2 pro- MACS is another method used to isolate single-cells [26]. tocol, similar to SMART-seq, the frst strand is synthe- Te cells are isolated by biodegradable iro based nanobe- sized with 2–5 untemplated nucleotides added at the end ads bound with specifc cell surface antibodies. Although of cDNA molecules. Ten TSO (template-switching oli- MACS can produce high yield single-cells and is widely gonucleotides) with two riboguanosines and a modifed used,
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