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.
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References
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
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
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
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
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
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
Young RA (2011) Control of the embryonic stem cell state. Cell 144:940–954. https://doi.org/10.1016/j.cell.2011.01.032
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
Smith A (2017) Formative pluripotency: the executive phase in a developmental continuum. Development 144:365–373. https://doi.org/10.1242/dev.142679
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Acknowledgments
This work was supported by the Department of Biotechnology (DBT), Government of India (Grant No. BT/PR12842/BID/7/521/2015).
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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
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DOI: https://doi.org/10.1007/978-1-0716-1908-7_14
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