Expression Analysis of Genes Regulated by Thyroid Hormone in Neural Cells

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1801)

Abstract

The actions of thyroid hormones on brain development and function are due primarily to regulation of gene expression. Identification of direct transcriptional responses requires cell culture approaches given the difficulty of in vivo studies. Here, we describe the use of primary cells in culture obtained from embryonic mouse cerebral cortex, to identify the set of genes regulated directly and indirectly by T3 using RNA-Seq.

Key words

Cerebral cortex Cycloheximide Neurons Primary culture RNA-Seq Thyroid hormones 

Notes

Acknowledgments

Supported by grants SAF2014-54919-R from the Plan Estatal de Investigación Científica y Técnica y de Innovación, Spain and by the Center for Research on Rare Dieseases (Ciberer) under the frame of E-Rare-2, the ERA-Net for Research on rare Diseases. The contribution of Drs Pilar Gil-Ibañez and Mónica M. Belinchón is gratefully acknowledged.

References

  1. 1.
    Chatonnet F, Flamant F, Morte B (2015) A temporary compendium of thyroid hormone target genes in brain. Biochim Biophys Acta 1849(2):122–129.  https://doi.org/10.1016/j.bbagrm.2014.05.023CrossRefPubMedGoogle Scholar
  2. 2.
    Gil-Ibanez P, Bernal J, Morte B (2014) Thyroid hormone regulation of gene expression in primary cerebrocortical cells: role of thyroid hormone receptor subtypes and interactions with retinoic acid and glucocorticoids. PLoS One 9(3):e91692.  https://doi.org/10.1371/journal.pone.0091692CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Gil-Ibanez P, Garcia-Garcia F, Dopazo J, Bernal J, Morte B (2015) Global Transcriptome analysis of primary Cerebrocortical cells: identification of genes regulated by Triiodothyronine in specific cell types. Cereb Cortex 27(1):706–717.  https://doi.org/10.1093/cercor/bhv273CrossRefGoogle Scholar
  4. 4.
    Samuels HH, Stanley F, Casanova J (1979) Depletion of L-3,5,3′-triiodothyronine and L-thyroxine in euthyroid calf serum for use in cell culture studies of the action of thyroid hormone. Endocrinology 105(1):80–85.  https://doi.org/10.1210/endo-105-1-80CrossRefPubMedGoogle Scholar
  5. 5.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25.  https://doi.org/10.1186/gb-2009-10-3-r25CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105–1111.  https://doi.org/10.1093/bioinformatics/btp120CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Garcia-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Gotz S, Tarazona S, Dopazo J, Meyer TF, Conesa A (2012) Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28(20):2678–2679.  https://doi.org/10.1093/bioinformatics/bts503CrossRefPubMedGoogle Scholar
  8. 8.
    Anders S, Pyl PT, Huber W (2015) HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169.  https://doi.org/10.1093/bioinformatics/btu638CrossRefPubMedGoogle Scholar
  9. 9.
    Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A (2011) Differential expression in RNA-seq: a matter of depth. Genome Res 21(12):2213–2223.  https://doi.org/10.1101/gr.124321.111CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11(3):R25.  https://doi.org/10.1186/gb-2010-11-3-r25CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Zhang Y, Sloan SA, Clarke LE, Caneda C, Plaza CA, Blumenthal PD, Vogel H, Steinberg GK, Edwards MS, Li G, Duncan JA 3rd, Cheshier SH, Shuer LM, Chang EF, Grant GA, Gephart MG, Barres BA (2016) Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89(1):37–53.  https://doi.org/10.1016/j.neuron.2015.11.013CrossRefPubMedGoogle Scholar
  12. 12.
    Zhang Y, Chen K, Sloan SA, Bennett ML, Scholze AR, O'Keeffe S, Phatnani HP, Guarnieri P, Caneda C, Ruderisch N, Deng S, Liddelow SA, Zhang C, Daneman R, Maniatis T, Barres BA, Wu JQ (2014) An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci 34(36):11929–11947.  https://doi.org/10.1523/JNEUROSCI.1860-14.2014CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G, Jureus A, Marques S, Munguba H, He L, Betsholtz C, Rolny C, Castelo-Branco G, Hjerling-Leffler J, Linnarsson S (2015) Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347(6226):1138–1142.  https://doi.org/10.1126/science.aaa1934CrossRefPubMedGoogle Scholar
  14. 14.
    Garber M, Grabherr MG, Guttman M, Trapnell C (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 8(6):469–477.  https://doi.org/10.1038/nmeth.1613CrossRefPubMedGoogle Scholar
  15. 15.
    Goecks J, Nekrutenko A, Taylor J, Galaxy T (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11(8):R86.  https://doi.org/10.1186/gb-2010-11-8-r86CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57(1):289–300Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto de Investigaciones Biomedicas, Consejo Superior de Investigaciones Científicas (CSIC)Universidad Autónoma de Madrid (UAM)MadridSpain
  2. 2.Center for Biomedical Research on Rare Diseases (CIBERER)Instituto de Salud Carlos IIIMadridSpain

Personalised recommendations