IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0069734.html
   My bibliography  Save this article

Heterogeneity of Astrocytes: From Development to Injury – Single Cell Gene Expression

Author

Listed:
  • Vendula Rusnakova
  • Pavel Honsa
  • David Dzamba
  • Anders Ståhlberg
  • Mikael Kubista
  • Miroslava Anderova

Abstract

Astrocytes perform control and regulatory functions in the central nervous system; heterogeneity among them is still a matter of debate due to limited knowledge of their gene expression profiles and functional diversity. To unravel astrocyte heterogeneity during postnatal development and after focal cerebral ischemia, we employed single-cell gene expression profiling in acutely isolated cortical GFAP/EGFP-positive cells. Using a microfluidic qPCR platform, we profiled 47 genes encoding glial markers and ion channels/transporters/receptors participating in maintaining K+ and glutamate homeostasis per cell. Self-organizing maps and principal component analyses revealed three subpopulations within 10–50 days of postnatal development (P10–P50). The first subpopulation, mainly immature glia from P10, was characterized by high transcriptional activity of all studied genes, including polydendrocytic markers. The second subpopulation (mostly from P20) was characterized by low gene transcript levels, while the third subpopulation encompassed mature astrocytes (mainly from P30, P50). Within 14 days after ischemia (D3, D7, D14), additional astrocytic subpopulations were identified: resting glia (mostly from P50 and D3), transcriptionally active early reactive glia (mainly from D7) and permanent reactive glia (solely from D14). Following focal cerebral ischemia, reactive astrocytes underwent pronounced changes in the expression of aquaporins, nonspecific cationic and potassium channels, glutamate receptors and reactive astrocyte markers.

Suggested Citation

  • Vendula Rusnakova & Pavel Honsa & David Dzamba & Anders Ståhlberg & Mikael Kubista & Miroslava Anderova, 2013. "Heterogeneity of Astrocytes: From Development to Injury – Single Cell Gene Expression," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-15, August.
    • Handle: RePEc:plo:pone00:0069734
    DOI: 10.1371/journal.pone.0069734
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0069734
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0069734&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0069734?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Paola Bezzi & Giorgio Carmignoto & Lucia Pasti & Sabino Vesce & Daniela Rossi & Barbara Lodi Rizzini & Tullio Pozzan & Andrea Volterra, 1998. "Prostaglandins stimulate calcium-dependent glutamate release in astrocytes," Nature, Nature, vol. 391(6664), pages 281-285, January.
    2. Arjun Raj & Charles S Peskin & Daniel Tranchina & Diana Y Vargas & Sanjay Tyagi, 2006. "Stochastic mRNA Synthesis in Mammalian Cells," PLOS Biology, Public Library of Science, vol. 4(10), pages 1-13, September.
    3. Woo-Ping Ge & Atsushi Miyawaki & Fred H. Gage & Yuh Nung Jan & Lily Yeh Jan, 2012. "Local generation of glia is a major astrocyte source in postnatal cortex," Nature, Nature, vol. 484(7394), pages 376-380, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammad Soltani & Cesar A Vargas-Garcia & Duarte Antunes & Abhyudai Singh, 2016. "Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-23, August.
    2. Amy L. Hughes & Aleksander T. Szczurek & Jessica R. Kelley & Anna Lastuvkova & Anne H. Turberfield & Emilia Dimitrova & Neil P. Blackledge & Robert J. Klose, 2023. "A CpG island-encoded mechanism protects genes from premature transcription termination," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    3. Alistair N Boettiger & Peter L Ralph & Steven N Evans, 2011. "Transcriptional Regulation: Effects of Promoter Proximal Pausing on Speed, Synchrony and Reliability," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-14, May.
    4. Matthieu Wyart & David Botstein & Ned S Wingreen, 2010. "Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-14, November.
    5. Qiwen Sun & Zhaohang Cai & Chunjuan Zhu, 2022. "A Novel Dynamical Regulation of mRNA Distribution by Cross-Talking Pathways," Mathematics, MDPI, vol. 10(9), pages 1-14, May.
    6. Stuart Aitken & Marie-Cécile Robert & Ross D Alexander & Igor Goryanin & Edouard Bertrand & Jean D Beggs, 2010. "Processivity and Coupling in Messenger RNA Transcription," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-12, January.
    7. Singh, Abhyudai & Vahdat, Zahra & Xu, Zikai, 2019. "Time-triggered stochastic hybrid systems with two timer-dependent resets," OSF Preprints u8fzg, Center for Open Science.
    8. Muir Morrison & Manuel Razo-Mejia & Rob Phillips, 2021. "Reconciling kinetic and thermodynamic models of bacterial transcription," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-30, January.
    9. Elijah Roberts & Andrew Magis & Julio O Ortiz & Wolfgang Baumeister & Zaida Luthey-Schulten, 2011. "Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-21, March.
    10. Ross D. Jones & Yili Qian & Katherine Ilia & Benjamin Wang & Michael T. Laub & Domitilla Del Vecchio & Ron Weiss, 2022. "Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    11. Xinyu Hu & Bob van Sluijs & Óscar García-Blay & Yury Stepanov & Koen Rietrae & Wilhelm T. S. Huck & Maike M. K. Hansen, 2024. "ARTseq-FISH reveals position-dependent differences in gene expression of micropatterned mESCs," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    12. Song, Yi & Xu, Wei & Wei, Wei & Niu, Lizhi, 2023. "Dynamical transition of phenotypic states in breast cancer system with Lévy noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    13. Marc S Sherman & Barak A Cohen, 2014. "A Computational Framework for Analyzing Stochasticity in Gene Expression," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-13, May.
    14. Jingyao Wang & Shihe Zhang & Hongfang Lu & Heng Xu, 2022. "Differential regulation of alternative promoters emerges from unified kinetics of enhancer-promoter interaction," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    15. Anissa Guillemin & Ronan Duchesne & Fabien Crauste & Sandrine Gonin-Giraud & Olivier Gandrillon, 2019. "Drugs modulating stochastic gene expression affect the erythroid differentiation process," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-19, November.
    16. Rajesh Ramaswamy & Ivo F Sbalzarini & Nélido González-Segredo, 2011. "Noise-Induced Modulation of the Relaxation Kinetics around a Non-Equilibrium Steady State of Non-Linear Chemical Reaction Networks," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
    17. Anton J M Larsson & Christoph Ziegenhain & Michael Hagemann-Jensen & Björn Reinius & Tina Jacob & Tim Dalessandri & Gert-Jan Hendriks & Maria Kasper & Rickard Sandberg, 2021. "Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-16, March.
    18. Chen, Aimin & Tian, Tianhai & Chen, Yiren & Zhou, Tianshou, 2022. "Stochastic analysis of a complex gene-expression model," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    19. Zachary R Fox & Brian Munsky, 2019. "The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-23, January.
    20. Abhyudai Singh & Mohammad Soltani, 2013. "Quantifying Intrinsic and Extrinsic Variability in Stochastic Gene Expression Models," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0069734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.