Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Clinical and Translational Medicine
Published in: Clinical and Translational Medicine 1/2017

Open Access 01-12-2017 | Review

Single-cell RNA-sequencing of the brain

Authors: Raquel Cuevas-Diaz Duran, Haichao Wei, Jia Qian Wu

Published in: Clinical and Translational Medicine | Issue 1/2017

Login to get access

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 specific 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 implemented, and important findings. 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 differences. Due to the complexity of the brain, a great deal of work remains to be accomplished. Defining specific brain cell types and functions is critical for understanding brain function as a whole in development, health, and diseases.
Literature
1.
2.
3.
4.
Arendt D (2008) The evolution of cell types in animals: emerging principles from molecular studies. Nat Rev Genet 9(11):868–882PubMedCrossRef
5.
Schuster SC (2008) Next-generation sequencing transforms today’s biology. Nat Methods 5(1):16–18PubMedCrossRef
6.
7.
Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6(5):377–382PubMedCrossRef
8.
Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G, Jureus A et al (2015) Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347(6226):1138–1142PubMedCrossRef
9.
Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR et al (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30(8):777–782PubMedPubMedCentralCrossRef
10.
Tang F, Barbacioru C, Bao S, Lee C, Nordman E, Wang X et al (2010) Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6(5):468–478PubMedPubMedCentralCrossRef
11.
Nelson SB, Sugino K, Hempel CM (2006) The problem of neuronal cell types: a physiological genomics approach. Trends Neurosci 29(6):339–345PubMedCrossRef
12.
13.
14.
Molyneaux BJ, Arlotta P, Menezes JR, Macklis JD (2007) Neuronal subtype specification in the cerebral cortex. Nat Rev Neurosci 8(6):427–437PubMedCrossRef
15.
DeFelipe J, Lopez-Cruz PL, Benavides-Piccione R, Bielza C, Larranaga P, Anderson S et al (2013) New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat Rev Neurosci 14(3):202–216PubMedPubMedCentralCrossRef
16.
Gelman DM, Marin O (2010) Generation of interneuron diversity in the mouse cerebral cortex. Eur J Neurosci. 31(12):2136–2141PubMedCrossRef
17.
Diez-Roux G, Banfi S, Sultan M, Geffers L, Anand S, Rozado D et al (2011) A high-resolution anatomical atlas of the transcriptome in the mouse embryo. PLoS Biol 9(1):e1000582PubMedPubMedCentralCrossRef
18.
19.
Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA (2015) The technology and biology of single-cell RNA sequencing. Mol Cell 58(4):610–620PubMedCrossRef
20.
Basu S, Campbell HM, Dittel BN, Ray A (2010) Purification of specific cell population by fluorescence activated cell sorting (FACS). J Vis Exp. 41:e1546
21.
Tasic B, Menon V, Nguyen TN, Kim TK, Jarsky T, Yao Z et al (2016) Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat Neurosci 19(2):335–346PubMedPubMedCentralCrossRef
22.
Llorens-Bobadilla E, Zhao S, Baser A, Saiz-Castro G, Zwadlo K, Martin-Villalba A (2015) Single-cell transcriptomics reveals a population of dormant neural stem cells that become activated upon brain injury. Cell Stem Cell 17(3):329–340PubMedCrossRef
23.
Ponten F, Gry M, Fagerberg L, Lundberg E, Asplund A, Berglund L et al (2009) A global view of protein expression in human cells, tissues, and organs. Mol Syst Biol. 5:337PubMedPubMedCentralCrossRef
24.
Orfao A, Ruiz-Arguelles A (1996) General concepts about cell sorting techniques. Clin Biochem 29(1):5–9PubMedCrossRef
25.
Arnold LW, Lannigan J (2010) Practical issues in high-speed cell sorting. Current protocols in cytometry. Chapter 1: Unit 1. pp 1–30
26.
Almeida M, Garcia-Montero AC, Orfao A (2014) Cell purification: a new challenge for biobanks. Pathobiol J Immunopathol Mol Cell Biol. 81(5–6):261–275
27.
Espina V, Milia J, Wu G, Cowherd S, Liotta LA (2006) Laser capture microdissection. Methods Mol Biol 319:213–229PubMedCrossRef
28.
Liu N, Liu L, Pan X (2014) Single-cell analysis of the transcriptome and its application in the characterization of stem cells and early embryos. Cell Mol Life Sci CMLS. 71(14):2707–2715PubMedCrossRef
29.
Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10(11):1096–1098PubMedCrossRef
30.
Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P et al (2011) Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21(7):1160–1167PubMedPubMedCentralCrossRef
31.
Hashimshony T, Wagner F, Sher N, Yanai I (2012) CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2(3):666–673PubMedCrossRef
32.
Pan X, Durrett RE, Zhu H, Tanaka Y, Li Y, Zi X et al (2013) Two methods for full-length RNA sequencing for low quantities of cells and single cells. Proc Natl Acad Sci USA 110(2):594–599PubMedCrossRef
33.
Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T et al (2013) Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol 14(4):R31PubMedPubMedCentralCrossRef
34.
Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, Sandberg R (2014) Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 9(1):171–181PubMedCrossRef
35.
Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M et al (2014) Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods 11(2):163–166PubMedCrossRef
36.
Kivioja T, Vaharautio A, Karlsson K, Bonke M, Enge M, Linnarsson S et al (2011) Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods 9(1):72–74PubMedCrossRef
37.
Grun D, Kester L, van Oudenaarden A (2014) Validation of noise models for single-cell transcriptomics. Nat Methods 11(6):637–640PubMedCrossRef
38.
Streets AM, Huang Y (2014) How deep is enough in single-cell RNA-seq? Nat Biotechnol 32(10):1005–1006PubMedCrossRef
39.
Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH et al (2014) Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol 32(10):1053–1058PubMedPubMedCentralCrossRef
40.
Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B, Rothenberg ME et al (2014) Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods 11(1):41–46PubMedCrossRef
41.
Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M et al (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161(5):1202–1214PubMedPubMedCentralCrossRef
42.
Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V et al (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5):1187–1201PubMedPubMedCentralCrossRef
43.
Brennecke P, Anders S, Kim JK, Kolodziejczyk AA, Zhang X, Proserpio V et al (2013) Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods 10(11):1093–1095PubMedCrossRef
44.
DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C et al (2012) RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28(11):1530–1532PubMedPubMedCentralCrossRef
45.
Wang L, Wang S, Li W (2012) RSeQC: quality control of RNA-seq experiments. Bioinformatics 28(16):2184–2185PubMedCrossRef
46.
Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I et al (2014) Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343(6172):776–779PubMedPubMedCentralCrossRef
47.
Davis MP, van Dongen S, Abreu-Goodger C, Bartonicek N, Enright AJ (2013) Kraken: a set of tools for quality control and analysis of high-throughput sequence data. Methods 63(1):41–49PubMedPubMedCentralCrossRef
48.
Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC et al (2016) Classification of low quality cells from single-cell RNA-seq data. Genome Biol 17:29PubMedPubMedCentralCrossRef
49.
Anders S, Pyl PT, Huber W (2015) HTSeq–a python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169PubMedCrossRef
50.
Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform 12:323CrossRef
51.
Zhang J, Kuo CC, Chen L (2015) WemIQ: an accurate and robust isoform quantification method for RNA-seq data. Bioinformatics 31(6):878–885PubMedCrossRef
52.
Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923–930PubMedCrossRef
53.
Ghosh S, Chan CK (2016) Analysis of RNA-Seq data using TopHat and Cufflinks. Methods Mol Biol 1374:339–361PubMedCrossRef
54.
Pollier J, Rombauts S, Goossens A (2013) Analysis of RNA-Seq data with TopHat and Cufflinks for genome-wide expression analysis of jasmonate-treated plants and plant cultures. Methods Mol Biol 1011:305–315PubMedCrossRef
55.
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578PubMedPubMedCentralCrossRef
56.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21PubMedCrossRef
57.
Dulken BW, Leeman DS, Boutet SC, Hebestreit K, Brunet A (2017) Single-cell transcriptomic analysis defines heterogeneity and transcriptional dynamics in the adult neural stem cell lineage. Cell Rep. 18(3):777–790PubMedPubMedCentralCrossRef
58.
Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M et al (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res 21(9):1543–1551PubMedPubMedCentralCrossRef
59.
Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D et al (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510(7505):363–369PubMedPubMedCentral
60.
Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH et al (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509(7500):371–375PubMedPubMedCentralCrossRef
61.
Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M et al (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32(4):381–386PubMedPubMedCentralCrossRef
62.
Deng Q, Ramskold D, Reinius B, Sandberg R (2014) Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343(6167):193–196PubMedCrossRef
63.
64.
McCarthy DJ, Chen Y, Smyth GK (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 40(10):4288–4297PubMedPubMedCentralCrossRef
65.
Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ et al (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol 33(2):155–160PubMedCrossRef
66.
67.
Dueck H, Khaladkar M, Kim TK, Spaethling JM, Francis C, Suresh S et al (2015) Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation. Genome Biol 16:122PubMedPubMedCentralCrossRef
68.
Mahata B, Zhang X, Kolodziejczyk AA, Proserpio V, Haim-Vilmovsky L, Taylor AE et al (2014) Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Reports. 7(4):1130–1142PubMedPubMedCentralCrossRef
69.
Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16(3):133–145PubMedCrossRef
70.
71.
Lun AT, Bach K, Marioni JC (2016) Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 17:75PubMedCrossRef
72.
73.
Shin J, Berg DA, Zhu Y, Shin JY, Song J, Bonaguidi MA et al (2015) Single-cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17(3):360–372PubMedCrossRef
74.
Gokce O, Stanley GM, Treutlein B, Neff NF, Camp JG, Malenka RC et al (2016) Cellular taxonomy of the mouse striatum as revealed by single-cell RNA-Seq. Cell Rep. 16(4):1126–1137PubMedPubMedCentralCrossRef
75.
Marques S, Zeisel A, Codeluppi S, van Bruggen D, Mendanha Falcao A, Xiao L et al (2016) Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352(6291):1326–1329PubMedPubMedCentralCrossRef
76.
Grun D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N et al (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525(7568):251–255PubMedCrossRef
77.
Rotem A, Ram O, Shoresh N, Sperling RA, Goren A, Weitz DA et al (2015) Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol 33(11):1165–1172PubMedPubMedCentralCrossRef
78.
LvdMG Hinton (2008) Visualizing data using t-SNE. J Mach Learn Res. 9:2579–2605
79.
Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R et al (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498(7453):236–240PubMedPubMedCentralCrossRef
80.
Cann GM, Gulzar ZG, Cooper S, Li R, Luo S, Tat M et al (2012) mRNA-Seq of single prostate cancer circulating tumor cells reveals recapitulation of gene expression and pathways found in prostate cancer. PLoS ONE 7(11):e49144PubMedPubMedCentralCrossRef
81.
82.
Bendall SC, Davis KL, el Amir AD, Tadmor MD, Simonds EF, Chen TJ et al (2014) Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157(3):714–725PubMedPubMedCentralCrossRef
83.
Marco E, Karp RL, Guo G, Robson P, Hart AH, Trippa L et al (2014) Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci USA 111(52):E5643–E5650PubMedPubMedCentralCrossRef
84.
85.
Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner H et al (2017) Reversed graph embedding resolves complex single-cell developmental trajectories. BioRxiv. doi:10.​1101/​110668
86.
Rodriguez A, Laio A (2014) Machine learning. Clustering by fast search and find of density peaks. Science 344(6191):1492–1496PubMedCrossRef
87.
Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D et al (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34(2):166–176PubMedCrossRef
88.
Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 4:1128CrossRef
89.
Xue Z, Huang K, Cai C, Cai L, Jiang CY, Feng Y et al (2013) Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500(7464):593–597PubMedPubMedCentralCrossRef
90.
Ocone A, Haghverdi L, Mueller NS, Theis FJ (2015) Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics 31(12):i89–i96PubMedPubMedCentralCrossRef
91.
Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B, Warner F et al (2005) Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc Natl Acad Sci USA 102(21):7426–7431PubMedPubMedCentralCrossRef
92.
Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA et al (2012) An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489(7416):391–399PubMedPubMedCentralCrossRef
93.
Zhang Y, Chen K, Sloan SA, Bennett ML, Scholze AR, O’Keeffe S et al (2014) An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci Off J Soc Neurosci. 34(36):11929–11947CrossRef
94.
Yan Q, Weyn-Vanhentenryck SM, Wu J, Sloan SA, Zhang Y, Chen K et al (2015) Systematic discovery of regulated and conserved alternative exons in the mammalian brain reveals NMD modulating chromatin regulators. Proc Natl Acad Sci USA 112(11):3445–3450PubMedPubMedCentralCrossRef
95.
Fuzik J, Zeisel A, Mate Z, Calvigioni D, Yanagawa Y, Szabo G et al (2016) Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat Biotechnol 34(2):175–183PubMedCrossRef
96.
97.
98.
99.
100.
Bartlett PF, Berninger B (2012) Introduction to the special issue on neural stem cells: regulation and function. Dev Neurobiol. 72(7):953–954PubMedCrossRef
101.
Lugert S, Basak O, Knuckles P, Haussler U, Fabel K, Gotz M et al (2010) Quiescent and active hippocampal neural stem cells with distinct morphologies respond selectively to physiological and pathological stimuli and aging. Cell Stem Cell 6(5):445–456PubMedCrossRef
102.
103.
Benner EJ, Luciano D, Jo R, Abdi K, Paez-Gonzalez P, Sheng H et al (2013) Protective astrogenesis from the SVZ niche after injury is controlled by Notch modulator Thbs4. Nature 497(7449):369–373PubMedPubMedCentralCrossRef
104.
Luo Y, Coskun V, Liang A, Yu J, Cheng L, Ge W et al (2015) Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells. Cell 161(5):1175–1186PubMedPubMedCentralCrossRef
105.
Kessaris N, Fogarty M, Iannarelli P, Grist M, Wegner M, Richardson WD (2006) Competing waves of oligodendrocytes in the forebrain and postnatal elimination of an embryonic lineage. Nat Neurosci 9(2):173–179PubMedCrossRef
106.
Tomassy GS, Berger DR, Chen HH, Kasthuri N, Hayworth KJ, Vercelli A et al (2014) Distinct profiles of myelin distribution along single axons of pyramidal neurons in the neocortex. Science 344(6181):319–324PubMedPubMedCentralCrossRef
107.
Bechler ME, Byrne L, Ffrench-Constant C (2015) CNS myelin sheath lengths are an intrinsic property of oligodendrocytes. Curr Biol CB. 25(18):2411–2416PubMedCrossRef
108.
Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R et al (2016) Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352(6293):1586–1590PubMedPubMedCentralCrossRef
109.
Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM et al (2015) A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci USA 112(23):7285–7290PubMedPubMedCentralCrossRef
110.
Johnsson P, Lipovich L, Grander D, Morris KV (2014) Evolutionary conservation of long non-coding RNAs; sequence, structure, function. Biochem Biophys Acta 1840(3):1063–1071PubMedCrossRef
111.
Ponjavic J, Ponting CP, Lunter G (2007) Functionality or transcriptional noise? Evidence for selection within long noncoding RNAs. Genome Res 17(5):556–565PubMedPubMedCentralCrossRef
112.
Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H et al (2012) The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res 22(9):1775–1789PubMedPubMedCentralCrossRef
113.
Pang KC, Frith MC, Mattick JS (2006) Rapid evolution of noncoding RNAs: lack of conservation does not mean lack of function. Trends Genet. 22(1):1–5PubMedCrossRef
114.
Ramos AD, Diaz A, Nellore A, Delgado RN, Park KY, Gonzales-Roybal G et al (2013) Integration of genome-wide approaches identifies lncRNAs of adult neural stem cells and their progeny in vivo. Cell Stem Cell 12(5):616–628PubMedPubMedCentralCrossRef
115.
Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A et al (2011) Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev 25(18):1915–1927PubMedPubMedCentralCrossRef
116.
Lipovich L, Dachet F, Cai J, Bagla S, Balan K, Jia H et al (2012) Activity-dependent human brain coding/noncoding gene regulatory networks. Genetics 192(3):1133–1148PubMedPubMedCentralCrossRef
117.
van de Vondervoort II, Gordebeke PM, Khoshab N, Tiesinga PH, Buitelaar JK, Kozicz T et al (2013) Long non-coding RNAs in neurodevelopmental disorders. Front Mol Neurosci. 6:53PubMedPubMedCentral
118.
Necsulea A, Soumillon M, Warnefors M, Liechti A, Daish T, Zeller U et al (2014) The evolution of lncRNA repertoires and expression patterns in tetrapods. Nature 505(7485):635–640PubMedCrossRef
119.
Liu SJ, Nowakowski TJ, Pollen AA, Lui JH, Horlbeck MA, Attenello FJ et al (2016) Single-cell analysis of long non-coding RNAs in the developing human neocortex. Genome Biol 17:67PubMedPubMedCentralCrossRef
120.
Dong X, Chen K, Cuevas-Diaz Duran R, You Y, Sloan SA, Zhang Y et al (2015) Comprehensive identification of long non-coding RNAs in purified cell types from the brain reveals functional LncRNA in OPC fate determination. PLoS Genet 11(12):e1005669PubMedPubMedCentralCrossRef
121.
Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL et al (2015) Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348(6237):910–914PubMedPubMedCentralCrossRef
122.
Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP et al (2015) Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523(7561):486–490PubMedPubMedCentralCrossRef
123.
Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W et al (2013) Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502(7469):59–64PubMedCrossRef
124.
Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J et al (2014) Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods 11(8):817–820PubMedPubMedCentralCrossRef
125.
126.
Burns JC, Kelly MC, Hoa M, Morell RJ, Kelley MW (2015) Single-cell RNA-Seq resolves cellular complexity in sensory organs from the neonatal inner ear. Nat Commun. 6:8557PubMedPubMedCentralCrossRef
Metadata
Title
Single-cell RNA-sequencing of the brain
Authors
Raquel Cuevas-Diaz Duran
Haichao Wei
Jia Qian Wu
Publication date
01-12-2017
Publisher
Springer Berlin Heidelberg
Published in
Clinical and Translational Medicine / Issue 1/2017
Electronic ISSN: 2001-1326
DOI
https://doi.org/10.1186/s40169-017-0150-9

Other articles of this Issue 1/2017

Clinical and Translational Medicine 1/2017 Go to the issue

Research

Profiling of cardio-metabolic risk factors and medication utilisation among Type II diabetes patients in Ghana: a prospective cohort study

Research

Survivin a radiogenetic promoter for glioblastoma viral gene therapy independently from CArG motifs

Review

Significance of long chain polyunsaturated fatty acids in human health

Review

Can glypican-3 be a disease-specific biomarker?

Research

Post-zygotic genomic changes in glutamate and dopamine pathway genes may explain discordance of monozygotic twins for schizophrenia

Perspective

Translational research in pediatric pulmonary disease, 2017