Skip to main content

Transcriptomic Analysis of Human Naïve and Primed Pluripotent Stem Cells

  • Protocol
  • First Online:
Human Naïve Pluripotent Stem Cells

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2416))

Abstract

Over the last decade, RNA-Sequencing (RNA-Seq) has revolutionized the field of transcriptomics due to its sheer advantage over previous technologies for studying gene expression. Even the domain of stem cell bioinformatics has benefited from these advancements. It has helped look deeper into how the process of pluripotency is maintained by stem cells and how it may be exploited for application in regenerative medicine. However, as it is still an evolving technology, there is no single accepted protocol for RNA-Seq data analysis. From a wide array of tools and/or algorithms available for the purpose, researchers tend to develop a pipeline that is best suited for their sample, experimental design, and computational power. In this tutorial, we describe a pipeline based on open-source tools to analyze RNA-Seq data from naïve and primed state human pluripotent stem cell samples. Precisely, we show how RNA-Seq data can be downloaded from databases, processed, and used to identify differentially expressed genes and construct a co-expression network. Further, we also show how the list of interesting genes obtained from differential expression testing or co-expression network be analyzed to gain biological insights.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adewumi O, Aflatoonian B, Ahrlund-Richter L et al (2007) Characterization of human embryonic stem cell lines by the international stem cell initiative. Nat Biotechnol 25:803–816. https://doi.org/10.1038/nbt1318

    Article  CAS  PubMed  Google Scholar 

  2. Smith KP, Luong MX, Stein GS (2009) Pluripotency: toward a gold standard for human ES and iPS cells. J Cell Physiol 220:21–29. https://doi.org/10.1002/jcp.21681

    Article  CAS  PubMed  Google Scholar 

  3. Nestor MW, Noggle SA (2013) Standardization of human stem cell pluripotency using bioinformatics. Stem Cell Res Ther 4:37. https://doi.org/10.1186/scrt185

    Article  PubMed  PubMed Central  Google Scholar 

  4. Nichols J, Smith A (2009) Naive and primed pluripotent states. Cell Stem Cell 4:487–492. https://doi.org/10.1016/j.stem.2009.05.015

    Article  CAS  PubMed  Google Scholar 

  5. Kumari D (2016) States of pluripotency: Naïve and primed pluripotent stem cells. In: Tomizawa M (ed) Pluripotent stem cells - from the bench to the clinic. InTech, London. ISBN: 978-953-51-2472-6

    Google Scholar 

  6. Gafni O, Weinberger L, Mansour AA et al (2013) Derivation of novel human ground state naive pluripotent stem cells. Nature 504:282–286. https://doi.org/10.1038/nature12745

    Article  CAS  PubMed  Google Scholar 

  7. Young RA (2011) Control of the embryonic stem cell state. Cell 144:940–954. https://doi.org/10.1016/j.cell.2011.01.032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Weinberger L, Ayyash M, Novershtern N, Hanna JH (2016) Dynamic stem cell states: naive to primed pluripotency in rodents and humans. Nat Rev Mol Cell Biol 17:155–169. https://doi.org/10.1038/nrm.2015.28

    Article  CAS  PubMed  Google Scholar 

  9. Smith A (2017) Formative pluripotency: the executive phase in a developmental continuum. Development 144:365–373. https://doi.org/10.1242/dev.142679

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Dundes CE, Loh KM (2020) Bridging naïve and primed pluripotency. Nat Cell Biol 22:513–515. https://doi.org/10.1038/s41556-020-0509-9

    Article  CAS  PubMed  Google Scholar 

  11. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63. https://doi.org/10.1038/nrg2484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. https://doi.org/10.1186/s13059-016-0881-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Andrews S (2010) Babraham bioinformatics—FastQC A quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 2 Sep 2019

  14. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. https://doi.org/10.1093/bioinformatics/btu170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360. https://doi.org/10.1038/nmeth.3317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930. https://doi.org/10.1093/bioinformatics/btt656

    Article  CAS  PubMed  Google Scholar 

  17. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mi H, Muruganujan A, Casagrande JT, Thomas PD (2013) Large-scale gene function analysis with PANTHER classification system. Nat Protoc 8:1551–1566. https://doi.org/10.1038/nprot.2013.092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ghosh A, Som A (2021) Decoding molecular markers and transcriptional circuitry of naive and primed states of human pluripotency.Stem Cell Res 53:102334. https://doi.org/10.1016/j.scr.2021.102334

  21. Zhao S, Zhang B (2015) A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification. BMC Genomics 16:97. https://doi.org/10.1186/s12864-015-1308-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dillies M-A, Rau A, Aubert J et al (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14:671–683. https://doi.org/10.1093/bib/bbs046

    Article  CAS  PubMed  Google Scholar 

  23. Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-Coexpression network for global discovery of conserved genetic modules. Science 302:249–255. https://doi.org/10.1126/science.1087447

    Article  CAS  PubMed  Google Scholar 

  24. Ballouz S, Verleyen W, Gillis J (2015) Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics 31:2123–2130. https://doi.org/10.1093/bioinformatics/btv118

    Article  CAS  PubMed  Google Scholar 

  25. Contreras-López O, Moyano TC, Soto DC, Gutiérrez RA (2018) Step-by-step construction of gene co-expression networks from high-throughput Arabidopsis RNA sequencing data. Methods Mol Biol 1761:275–301. https://doi.org/10.1007/978-1-4939-7747-5_21

    Article  CAS  PubMed  Google Scholar 

  26. Singh R, Som A (2020) Role of network biology in cancer research. In: Katara P (ed) Recent trends in ‘Computational Omics: concepts and methodology. Nova Science Publishers, New York. ISBN: 978-1-53617-941-5

    Google Scholar 

  27. van Dam S, Võsa U, van der Graaf A et al (2018) Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform 19:575–592. https://doi.org/10.1093/bib/bbw139

    Article  CAS  PubMed  Google Scholar 

  28. Ghosh A, Som A (2020) RNA-Seq analysis reveals pluripotency-associated genes and their interaction networks in human embryonic stem cells. Comput Biol Chem 85:107239. https://doi.org/10.1016/j.compbiolchem.2020.107239

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the Department of Biotechnology (DBT), Government of India (Grant No. BT/PR12842/BID/7/521/2015).

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Ghosh, A., Som, A. (2022). Transcriptomic Analysis of Human Naïve and Primed Pluripotent Stem Cells. In: Rugg-Gunn, P. (eds) Human Naïve Pluripotent Stem Cells. Methods in Molecular Biology, vol 2416. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1908-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1908-7_14

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1907-0

  • Online ISBN: 978-1-0716-1908-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics