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Neuroforum

Organ der Neurowissenschaftlichen Gesellschaft

Editor-in-Chief: Wahle, Petra


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Volume 25, Issue 4

Issues

Single-cell RNA-Sequencing in Neuroscience

Dr. Simone MayerORCID iD: https://orcid.org/0000-0002-6381-2474 / Dr. Shokoufeh Khakipoor
  • Corresponding author
  • Eberhard Karls University Tübingen Hertie Institute for Clinical Brain Research Otfried-Müller-Str. 27 72076 Tübingen, Germany Tübingen Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Maxim A. Drömer
  • Corresponding author
  • Eberhard Karls University Tübingen Hertie Institute for Clinical Brain Research Otfried-Müller-Str. 27 72076 Tübingen, Germany Tübingen Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Daniel A. Cozetto
  • Corresponding author
  • Eberhard Karls University Tübingen Hertie Institute for Clinical Brain Research Otfried-Müller-Str. 27 72076 Tübingen, Germany Tübingen Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2019-11-06 | DOI: https://doi.org/10.1515/nf-2019-0021

Summary

Technical innovations in the last decade have allowed to sequence transcriptomes of single cells. Single-cell RNA-sequencing (scRNA-seq) has since then opened the window to a deeper understanding of cellular identity and is becoming a widely used method in molecular biology. In neuroscience, scRNA-seq has broad applications, for example in determining cellular diversity in different brain regions and in revealing transcriptomic variations across brain disorders. The method consists of several steps: isolation and lysis of single cells, reverse transcription of RNAs, amplification of cDNAs, and next-generation sequencing. The large datasets can subsequently be analysed using different bioinformatic tools to deduce biological meaning. Current developments aim to integrate scRNA-seq into cellular network analysis through multimodal analysis, spatial localisation and perturbation experiments, in order to understand brain physiology and pathology.

Zusammenfassung

Durch technische Innovationen wurde es in den letzten zehn Jahren möglich, das Transkriptom einzelner Zellen zu sequenzieren. Die Einzelzell-RNA-Sequenzierung (scRNA-seq) liefert ein tieferes Verständnis der zellulären Identität und ist inzwischen eine Standardmethode der Molekularbiologie geworden. In den Neurowissenschaften wird scRNA-seq beispielsweise zur Bestimmung der zellulären Diversität verschiedener Hirnregionen und zur Analyse transkriptomischer Variationen bei Hirnerkrankungen angewandt. Die folgenden Schritte sind nötig: Isolierung und Lyse einzelner Zellen, reverse Transkription von RNAs, Amplifikation von cDNAs und Next-Generation-Sequenzierung. Die Datensätze werden anschließend mit bioinformatischen Methoden analysiert, um sie zu interpretieren. Aktuelle Entwicklungen zielen darauf ab, scRNA-seq durch multimodale Analyse, räumliche Lokalisierung und Störungsexperimente in die zelluläre Netzwerkanalyse zu integrieren, um Neurophysiologie und -pathologie besser zu verstehen.

Keywords: Transcriptome; diversity; network integration; neurological disorder; bioinformatics

Schlüsselwörter: Transkriptom; Diversität; Netzwerkintegration; neurologische Erkrankungen; Bioinformatik

References

  • Alemany, A., Florescu, M., Baron, C.S., Peterson-Maduro, J., and van Oudenaarden, A. (2018). Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112.Google Scholar

  • AlJanahi, A.A., Danielsen, M., and Dunbar, C.E. (2018). An Introduction to the Analysis of Single-Cell RNA-Sequencing Data. Mol Ther Methods Clin Dev 10, 189–196.Google Scholar

  • Avey, D., Sankararaman, S., Yim, A.K.Y., Barve, R., Milbrandt, J., and Mitra, R.D. (2018). Single-Cell RNA-Seq Uncovers a Robust Transcriptional Response to Morphine by Glia. Cell Rep 24, 3619–3629 e3614.Google Scholar

  • Baker, D.J., and Petersen, R.C. (2018). Cellular senescence in brain aging and neurodegenerative diseases: evidence and perspectives. J Clin Invest 128, 1208–1216.Google Scholar

  • Boisset, J.C., Vivie, J., Grun, D., Muraro, M.J., Lyubimova, A., and van Oudenaarden, A. (2018). Mapping the physical network of cellular interactions. Nat Methods 15, 547–553.Google Scholar

  • Butler, A., Hoffman, P., Smibert, P., Papalexi, E., and Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36, 411–420.Google Scholar

  • Cadwell, C.R., Palasantza, A., Jiang, X., Berens, P., Deng, Q., Yilmaz, M., Reimer, J., Shen, S., Bethge, M., Tolias, K.F., et al. (2016). Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat Biotechnol 34, 199–203.Google Scholar

  • Cajigas, Iván J., Tushev, G., Will, Tristan J., tom Dieck, S., Fuerst, N., and Schuman, Erin M. (2012). The Local Transcriptome in the Synaptic Neuropil Revealed by Deep Sequencing and High-Resolution Imaging. Neuron 74, 453–466.Google Scholar

  • Camp, J.G., Badsha, F., Florio, M., Kanton, S., Gerber, T., Wilsch-Brauninger, M., Lewitus, E., Sykes, A., Hevers, W., Lancaster, M., et al. (2015). Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc Natl Acad Sci U S A 112, 15672–15677.Google Scholar

  • Chen, G., Ning, B., and Shi, T. (2019). Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Front Genet 10, 317.Google Scholar

  • Eng, C.L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.C., et al. (2019). Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239.Google Scholar

  • Fuzik, J., Zeisel, A., Mate, Z., Calvigioni, D., Yanagawa, Y., Szabo, G., Linnarsson, S., and Harkany, T. (2016). Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat Biotechnol 34, 175–183.Google Scholar

  • Genga, R.M.J., Kernfeld, E.M., Parsi, K.M., Parsons, T.J., Ziller, M.J., and Maehr, R. (2019). Single-Cell RNA-Sequencing-Based CRISPRi Screening Resolves Molecular Drivers of Early Human Endoderm Development. Cell Rep 27, 708–718 e710.Google Scholar

  • Gerlach, J.P., van Buggenum, J.A.G., Tanis, S.E.J., Hogeweg, M., Heuts, B.M.H., Muraro, M.J., Elze, L., Rivello, F., Rakszewska, A., van Oudenaarden, A., et al. (2019). Combined quantification of intracellular (phospho-)proteins and transcriptomics from fixed single cells. Sci Rep 9, 1469.Google Scholar

  • Goldstein, L.D., Chen, Y.J., Dunne, J., Mir, A., Hubschle, H., Guillory, J., Yuan, W., Zhang, J., Stinson, J., Jaiswal, B., et al. (2017). Massively parallel nanowell-based single-cell gene expression profiling. BMC Genomics 18, 519.Google Scholar

  • Griffiths, J.A., Scialdone, A., and Marioni, J.C. (2018). Using single-cell genomics to understand developmental processes and cell fate decisions. Mol Syst Biol 14, e8046.Google Scholar

  • Grindberg, R.V., Yee-Greenbaum, J.L., McConnell, M.J., Novotny, M., O’Shaughnessy, A.L., Lambert, G.M., Arauzo-Bravo, M.J., Lee, J., Fishman, M., Robbins, G.E., et al. (2013). RNA-sequencing from single nuclei. Proc Natl Acad Sci U S A 110, 19802–19807.Google Scholar

  • Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J.J., Hession, C., Zhang, F., and Regev, A. (2016). Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928.Google Scholar

  • Hedlund, E., and Deng, Q. (2018). Single-cell RNA sequencing: Technical advancements and biological applications. Mol Aspects Med 59, 36–46.Google Scholar

  • Hrvatin, S., Hochbaum, D.R., Nagy, M.A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A.M., et al. (2018). Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat Neurosci 21, 120–129.Google Scholar

  • Hwang, B., Lee, J.H., and Bang, D. (2018). Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 50, 96.Google Scholar

  • Jakel, S., Agirre, E., Mendanha Falcao, A., van Bruggen, D., Lee, K.W., Knuesel, I., Malhotra, D., Ffrench-Constant, C., Williams, A., and Castelo-Branco, G. (2019). Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566, 543–547.Google Scholar

  • Kadoshima, T., Sakaguchi, H., Nakano, T., Soen, M., Ando, S., Eiraku, M., and Sasai, Y. (2013). Self-organization of axial polarity, inside-out layer pattern, and species-specific progenitor dynamics in human ES cell-derived neocortex. Proc Natl Acad Sci U S A 110, 20284–20289.Google Scholar

  • Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T.K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017). A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease. Cell 169, 1276–1290 e1217.Google Scholar

  • Klein, A.M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D.A., and Kirschner, M.W. (2015). Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201.Google Scholar

  • Lacar, B., Linker, S.B., Jaeger, B.N., Krishnaswami, S.R., Barron, J.J., Kelder, M.J.E., Parylak, S. L., Paquola, A.C.M., Venepally, P., Novotny, M., et al. (2016). Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat Commun 7, 11022.Google Scholar

  • Lafzi, A., Moutinho, C., Picelli, S., and Heyn, H. (2018). Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies. Nat Protoc 13, 2742–2757.Google Scholar

  • Lake, B.B., Chen, S., Sos, B.C., Fan, J., Kaeser, G.E., Yung, Y.C., Duong, T.E., Gao, D., Chun, J., Kharchenko, P.V., et al. (2018). Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol 36, 70–80.Google Scholar

  • Lancaster, M.A., Renner, M., Martin, C.A., Wenzel, D., Bicknell, L.S., Hurles, M.E., Homfray, T., Penninger, J.M., Jackson, A.P., and Knoblich, J.A. (2013). Cerebral organoids model human brain development and microcephaly. Nature 501, 373–379.Google Scholar

  • Liu, J., Pan, N., Sun, L., Wang, M., Zhang, J., Zuo, Z., He, S., Wu, Q., and Wang, X. (2018). Functional In vivo Single-cell Transcriptome (FIST) Analysis Reveals Molecular Properties of Light-Sensitive Neurons in Mouse V1. bioRxiv.Google Scholar

  • Luecken, M.D., and Theis, F.J. (2019). Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol 15, e8746.Google Scholar

  • Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., et al. (2015). Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 161, 1202–1214.Google Scholar

  • Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J.Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019). Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337.Google Scholar

  • Mayer, C., Hafemeister, C., Bandler, R.C., Machold, R., Batista Brito, R., Jaglin, X., Allaway, K., Butler, A., Fishell, G., and Satija, R. (2018). Developmental diversification of cortical inhibitory interneurons. Nature 555, 457–462.Google Scholar

  • Mayer, S., Chen, J., Velmeshev, D., Mayer, A., Eze, U.C., Bhaduri, A., Cunha, C.E., Jung, D., Arjun, A., Li, E., et al. (2019). Multimodal Single-Cell Analysis Reveals Physiological Maturation in the Developing Human Neocortex. Neuron 102, 143–158 e147.Google Scholar

  • Nguyen, L.H., and Holmes, S. (2019). Ten quick tips for effective dimensionality reduction. PLoS Comput Biol 15, e1006907.Google Scholar

  • Nowakowski, T.J., Bhaduri, A., Pollen, A.A., Alvarado, B., Mostajo-Radji, M.A., Di Lullo, E., Haeussler, M., Sandoval-Espinosa, C., Liu, S.J., Velmeshev, D., et al. (2017). Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323.Google Scholar

  • Pasca, A.M., Sloan, S. A., Clarke, L.E., Tian, Y., Makinson, C.D., Huber, N., Kim, C.H., Park, J.Y., O’Rourke, N.A., Nguyen, K.D., et al. (2015). Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat Methods 12, 671–678.Google Scholar

  • Pfeffer, C.K., and Beltramo, R. (2017). Correlating Anatomy and Function with Gene Expression in Individual Neurons by Combining in Vivo Labeling, Patch Clamp, and Single Cell RNA-seq. Front Cell Neurosci 11, 376.Google Scholar

  • Pollen, A.A., Bhaduri, A., Andrews, M.G., Nowakowski, T.J., Meyerson, O.S., Mostajo-Radji, M.A., Di Lullo, E., Alvarado, B., Bedolli, M., Dougherty, M.L., et al. (2019). Establishing Cerebral Organoids as Models of Human-Specific Brain Evolution. Cell 176, 743–756 e717.Google Scholar

  • Pollen, A.A., Nowakowski, T.J., Shuga, J., Wang, X., Leyrat, A.A., Lui, J.H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014). Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol 32, 1053–1058.Google Scholar

  • Raj, B., Gagnon, J.A., and Schier, A.F. (2018). Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT. Nat Protoc 13, 2685–2713.Google Scholar

  • Regev, A., Teichmann, S. A., Lander, E.S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017). The Human Cell Atlas. Elife 6.Google Scholar

  • Rochefort, N.L., Jia, H., and Konnerth, A. (2008). Calcium imaging in the living brain: prospects for molecular medicine. Trends Mol Med 14, 389–399.Google Scholar

  • Salmen, F., Stahl, P.L., Mollbrink, A., Navarro, J.F., Vickovic, S., Frisen, J., and Lundeberg, J. (2018). Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections. Nat Protoc 13, 2501–2534.Google Scholar

  • Schenk, S., Bannister, S.C., Sedlazeck, F.J., Anrather, D., Minh, B.Q., Bileck, A., Hartl, M., von Haeseler, A., Gerner, C., Raible, F., et al. (2019). Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase. Elife 8.Google Scholar

  • Schirmer, L., Velmeshev, D., Holmqvist, S., Kaufmann, M., Werneburg, S., Jung, D., Vistnes, S., Stockley, J.H., Young, A., Steindel, M., et al. (2019). Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature.Google Scholar

  • Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B.B., Siddiqui, A., et al. (2009). mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6, 377–382.Google Scholar

  • van den Brink, S.C., Sage, F., Vértesy, A., Spanjaard, B., Peterson-Maduro, J., Baron, C.S., Robin, C., and van Oudenaarden, A. (2017). Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nature Method 14.Google Scholar

  • van der Maaten, L., and Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 2579–2605.Google Scholar

  • Velasco, S., Kedaigle, A.J., Simmons, S.K., Nash, A., Rocha, M., Quadrato, G., Paulsen, B., Nguyen, L., Adiconis, X., Regev, A., et al. (2019). Individual brain organoids reproducibly form cell diversity of the human cerebral cortex. Nature.Google Scholar

  • Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S., Bhaduri, A., Goyal, N., Rowitch, D.H., and Kriegstein, A.R. (2019). Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689.Google Scholar

  • Wang, X., Allen, W.E., Wright, M.A., Sylwestrak, E.L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018). Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361.Google Scholar

  • Wang, Y.J., Schug, J., Lin, J., Wang, Z., Kossenkov, A., and Kaestner, K.H. (2019). Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues. bioRxiv.Google Scholar

  • Wolf, F.A., Angerer, P., and Theis, F.J. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15.Google Scholar

  • Wu, Y.E., Pan, L., Zuo, Y., Li, X., and Hong, W. (2017). Detecting Activated Cell Populations Using Single-Cell RNA-Seq. Neuron 96, 313–329 e316.Google Scholar

  • Zeisel, A., Muñoz-Manchado, A., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015). Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142.Google Scholar

About the article

Dr. Simone Mayer

Dr. Simone Mayer is heading the research group “Molecular Brain Development” at the Hertie Institute for Clinical Brain Research at Tübingen University. During her postdoctoral research at the University of California, San Francisco she has developed a new method of multimodal single-cell analysis and has become fascinated with the possibilities to understand brain development at an unprecedented resolution when integrating single-cell transcriptomics with functional analyses.

Dr. Shokoufeh Khakipoor

Dr. Shokoufeh Khakipoor is a postdoc in Dr. Simone Mayer’s lab at the Hertie Institute for Clinical Brain Research. During her Ph.D. at the University of Freiburg, she has studied how growth factors influence H+ homeostasis in mature neural cells. Now she is enthusiastic to apply her insights from intercellular signalling to the developing brain.

Maxim A. Drömer

Maxim A. Drömer is a bachelor student in biochemistry and mathematics at Tübingen University and an intern at Hertie Institute for Clinical Brain Research. He is especially interested in understanding functional aspects of neuroscience on the molecular level and therefore prefers an interdisciplinary approach which makes use of molecular biology combined with analysis methods from data science.

Daniel A. Cozetto

Daniel A. Cozetto is a bachelor student in Biotechnology and Bioprocess Engineering at São Paulo State University and intern at Hertie Institute for Clinical Brain Research. Very curious about neuroscience and the molecular identity of the brain, he is interested in understanding the molecular mechanisms that lead to the complex structure of the brain and the development of neurodegenerative diseases.


Published Online: 2019-11-06

Published in Print: 2019-11-26


Citation Information: Neuroforum, Volume 25, Issue 4, Pages 251–258, ISSN (Online) 2363-7013, ISSN (Print) 0947-0875, DOI: https://doi.org/10.1515/nf-2019-0021.

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