Abstract
Regenerative potential in adult stem cells is closely associated with the establishment of—and exit from—a temporary state of quiescence. Emerging evidence not only provides a rationale for the link between lineage determination programs and cell cycle regulation but also highlights the understanding of quiescence as an actively maintained cellular program, encompassing networks and mechanisms beyond mitotic inactivity or metabolic restriction. Interrogating the quiescent genome and transcriptome using deep-sequencing technologies offers an unprecedented view of the global mechanisms governing this reversibly arrested cellular state and its importance for cell identity. While many efforts have identified and isolated pure target stem cell populations from a variety of adult tissues, there is a growing appreciation that their isolation from the stem cell niche in vivo leads to activation and loss of hallmarks of quiescence. Thus, in vitro models that recapitulate the dynamic reversibly arrested stem cell state in culture and lend themselves to comparison with the activated or differentiated state are useful templates for genome-wide analysis of the quiescence network.
In this chapter, we describe the methods that can be adopted for whole genome epigenomic and transcriptomic analysis of cells derived from one such established culture model where mouse myoblasts are triggered to enter or exit quiescence as homogeneous populations. The ability to synchronize myoblasts in G0 permits insights into the genome in “deep quiescence.” The culture methods for generating large populations of quiescent myoblasts in either 2D or 3D culture formats are described in detail in a previous chapter in this series (Arora et al. Methods Mol Biol 1556:283–302, 2017). Among the attractive features of this model are that genes isolated from quiescent myoblasts in culture mark satellite cells in vivo (Sachidanandan et al., J Cell Sci 115:2701–2712, 2002) providing a validation of its approximation of the molecular state of true stem cells. Here, we provide our working protocols for ChIP-seq and RNA-seq analysis, focusing on those experimental elements that require standardization for optimal analysis of chromatin and RNA from quiescent myoblasts, and permitting useful and revealing comparisons with proliferating myoblasts or differentiated myotubes.
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References
Cavallucci V, Fidaleo M, Pani G (2016) Neural stem cells and nutrients: poised between quiescence and exhaustion. Trends Endocrinol Metab 27:756–769. doi:10.1016/j.tem.2016.06.007
Daignan-Fornier B, Sagot I (2011) Proliferation/quiescence: when to start? Where to stop? What to stock? Cell Div 6:20. doi:10.1186/1747-1028-6-20
Cheung TH, Rando TA (2013) Molecular regulation of stem cell quiescence. Nat Rev Mol Cell Biol 14:329–340. doi:10.1038/nrm3591
Rumman M, Dhawan J, Kassem M (2015) Concise review: quiescence in adult stem cells: biological significance and relevance to tissue regeneration. Stem Cells Dayt Ohio 33:2903–2912. doi:10.1002/stem.2056
Coller HA, Sang L, Roberts JM (2006) A new description of cellular quiescence. PLoS Biol. doi:10.1371/journal.pbio.0040083
Yao G (2014) Modelling mammalian cellular quiescence. Interf Focus. doi:10.1098/rsfs.2013.0074
García-Prat L, Martínez-Vicente M, Perdiguero E et al (2016) Autophagy maintains stemness by preventing senescence. Nature 529:37–42. doi:10.1038/nature16187
Sousa-Victor P, Gutarra S, García-Prat L et al (2014) Geriatric muscle stem cells switch reversible quiescence into senescence. Nature 506:316–321. doi:10.1038/nature13013
Yanagida M (2009) Cellular quiescence: are controlling genes conserved? Trends Cell Biol 19:705–715. doi:10.1016/j.tcb.2009.09.006
Gray JV, Petsko GA, Johnston GC et al (2004) “Sleeping Beauty”: quiescence in Saccharomyces cerevisiae. Microbiol Mol Biol Rev 68:187–206. doi:10.1128/MMBR.68.2.187-206.2004
Dhawan J, Laxman S (2015) Decoding the stem cell quiescence cycle--lessons from yeast for regenerative biology. J Cell Sci 128:4467–4474. doi:10.1242/jcs.177758
Rodgers JT, King KY, Brett JO et al (2014) mTORC1 controls the adaptive transition of quiescent stem cells from G0 to G(Alert). Nature 510:393–396. doi:10.1038/nature13255
Moore KA, Lemischka IR (2006) Stem cells and their niches. Science 311:1880–1885. doi:10.1126/science.1110542
Wagers AJ, Weissman IL (2004) Plasticity of adult stem cells. Cell 116:639–648. doi:10.1016/S0092-8674(04)00208-9
Yu H, Fang D, Kumar SM et al (2006) Isolation of a novel population of multipotent adult stem cells from human hair follicles. Am J Pathol 168:1879–1888. doi:10.2353/ajpath.2006.051170
Freter R, Osawa M, Nishikawa S-I (2010) Adult stem cells exhibit global suppression of RNA polymerase II serine-2 phosphorylation. Stem Cells 28:1571–1580. doi:10.1002/stem.476
Venezia TA, Merchant AA, Ramos CA et al (2004) Molecular signatures of proliferation and quiescence in hematopoietic stem cells. PLoS Biol 2:e301. doi:10.1371/journal.pbio.0020301
Dhawan J, Rando TA (2005) Stem cells in postnatal myogenesis: molecular mechanisms of satellite cell quiescence, activation and replenishment. Trends Cell Biol 15:666–673. doi:10.1016/j.tcb.2005.10.007
Mauro A (1961) Satellite cell of skeletal muscle fibers. J Biophys Biochem Cytol 9:493–495
Anderson J, Pilipowicz O (2002) Activation of muscle satellite cells in single-fiber cultures. Nitric Oxide Biol Chem 7:36–41
Zammit PS, Relaix F, Nagata Y et al (2006) Pax7 and myogenic progression in skeletal muscle satellite cells. J Cell Sci 119:1824–1832. doi:10.1242/jcs.02908
Moyle LA, Zammit PS (2014) Isolation, culture and immunostaining of skeletal muscle fibres to study myogenic progression in satellite cells. Methods Mol Biol Clifton N J 1210:63–78. doi:10.1007/978-1-4939-1435-7_6
Gromova A, Tierney M, Sacco A (2015) FACS-based satellite cell isolation from mouse hind limb muscles. Bio Protoc 5:e1558. doi:10.21769/BioProtoc.1558
Danoviz ME, Yablonka-Reuveni Z (2012) Skeletal muscle satellite cells: background and methods for isolation and analysis in a primary culture system. Methods Mol Biol Clifton N J 798:21–52. doi:10.1007/978-1-61779-343-1_2
Lagha M, Sato T, Regnault B et al (2010) Transcriptome analyses based on genetic screens for Pax3 myogenic targets in the mouse embryo. BMC Genomics 11:696. doi:10.1186/1471-2164-11-696
Sambasivan R, Yao R, Kissenpfennig A et al (2011) Pax7-expressing satellite cells are indispensable for adult skeletal muscle regeneration. Development 138:3647–3656. doi:10.1242/dev.067587
Liu L, Cheung TH, Charville GW, Rando TA (2015) Isolation of skeletal muscle stem cells by fluorescence-activated cell sorting. Nat Protoc 10:1612–1624. doi:10.1038/nprot.2015.110
Arora R, Rumman M, Venugopal N, et al (2017) Mimicking muscle stem cell quiescence in culture: methods for synchronization in reversible arrest. Methods Mol Biol 1556:283–302. doi: 10.1007/978-1-4939-6771-1_15
Srivastava S, Mishra RK, Dhawan J (2010) Regulation of cellular chromatin state: insights from quiescence and differentiation. Organogenesis 6:37–47
Hui Ng H, Bird A (2000) Histone deacetylases: silencers for hire. Trends Biochem Sci 25:121–126. doi:10.1016/S0968-0004(00)01551-6
Kaeser MD, Emerson BM (2006) Remodeling plans for cellular specialization: unique styles for every room. Curr Opin Genet Dev 16:508–512. doi:10.1016/j.gde.2006.08.001
Turner BM (2007) Defining an epigenetic code. Nat Cell Biol 9:2–6. doi:10.1038/ncb0107-2
Buermans HPJ, den Dunnen JT (2014) Next generation sequencing technology: advances and applications. Biochim Biophys Acta 1842:1932–1941. doi:10.1016/j.bbadis.2014.06.015
Darzynkiewicz Z, Evenson D, Staiano-Coico L et al (1979) Relationship between RNA content and progression of lymphocytes through S phase of cell cycle. Proc Natl Acad Sci U S A 76:358–362
Habets PEMH, Franco D, Ruijter JM et al (1999) RNA content differs in slow and fast muscle fibers: implications for interpretation of changes in muscle gene expression. J Histochem Cytochem 47:995–1004. doi:10.1177/002215549904700803
Darzynkiewicz Z, Evenson DP, Staiano-Coico L et al (1979) Correlation between cell cycle duration and RNA content. J Cell Physiol 100:425–438. doi:10.1002/jcp.1041000306
Darzynkiewicz Z (1987) Cytochemical probes of cycling and quiescent cells applicable to flow cytometry. Springer, New York, NY
Poot M, Rizk-Rabin M, Hoehn H, Pavlovitch JH (1990) Cell size and RNA content correlate with cell differentiation and proliferative capacity of rat keratinocytes. J Cell Physiol 143:279–286. doi:10.1002/jcp.1041430211
Schmidt EE, Schibler U (1995) Cell size regulation, a mechanism that controls cellular RNA accumulation: consequences on regulation of the ubiquitous transcription factors Oct1 and NF-Y and the liver-enriched transcription factor DBP. J Cell Biol 128:467–483
Elliott SG, McLaughlin CS (1978) Rate of macromolecular synthesis through the cell cycle of the yeast Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 75:4384–4388
Killander D, Zetterberg A (1965) Quantitative cytochemical studies on interphase growth. i. determination of DNA, RNA and mass content of age determined mouse fibroblasts in vitro and of intercellular variation in generation time. Exp Cell Res 38:272–284
Marguerat S, Bähler J (2012) Coordinating genome expression with cell size. Trends Genet 28:560–565. doi:10.1016/j.tig.2012.07.003
Benecke BJ, Ben-Ze’ev A, Penman S (1980) The regulation of RNA metabolism in suspended and reattached anchorage-dependent 3T6 fibroblasts. J Cell Physiol 103:247–254. doi:10.1002/jcp.1041030209
Lovén J, Orlando DA, Sigova AA et al (2012) Revisiting global gene expression analysis. Cell 151:476–482. doi:10.1016/j.cell.2012.10.012
Kouzine F, Wojtowicz D, Yamane A et al (2013) Global regulation of promoter melting in naive lymphocytes. Cell 153:988–999. doi:10.1016/j.cell.2013.04.033
Sachidanandan C, Sambasivan R, Dhawan J (2002) Tristetraprolin and LPS-inducible CXC chemokine are rapidly induced in presumptive satellite cells in response to skeletal muscle injury. J Cell Sci 115:2701–2712
Blau HM, Chiu CP, Webster C (1983) Cytoplasmic activation of human nuclear genes in stable heterocaryons. Cell 32:1171–1180
Yaffe D, Saxel O (1977) Serial passaging and differentiation of myogenic cells isolated from dystrophic mouse muscle. Nature 270:725–727
Milasincic DJ, Dhawan J, Farmer SR (1996) Anchorage-dependent control of muscle-specific gene expression in C2C12 mouse myoblasts. In Vitro Cell Dev Biol Anim 32:90–99
Sellathurai J, Cheedipudi S, Dhawan J, Schrøder HD (2013) A novel in vitro model for studying quiescence and activation of primary isolated human myoblasts. PLoS One 8:e64067. doi:10.1371/journal.pone.0064067
Cheedipudi S, Puri D, Saleh A et al (2015) A fine balance: epigenetic control of cellular quiescence by the tumor suppressor PRDM2/RIZ at a bivalent domain in the cyclin a gene. Nucleic Acids Res 43:6236–6256. doi:10.1093/nar/gkv567
Fukada S, Uezumi A, Ikemoto M et al (2007) Molecular signature of quiescent satellite cells in adult skeletal muscle. Stem Cells 25:2448–2459. doi:10.1634/stemcells.2007-0019
Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169. doi:10.1093/bioinformatics/btu638
Anders S, McCarthy DJ, Chen Y et al (2013) Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc 8:1765–1786. doi:10.1038/nprot.2013.099
Cui P, Lin Q, Ding F et al (2010) A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing. Genomics 96:259–265. doi:10.1016/j.ygeno.2010.07.010
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:R25. doi:10.1186/gb-2009-10-3-r25
Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:1105–1111. doi:10.1093/bioinformatics/btp120
Trapnell C, Roberts A, Goff L et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7:562–578. doi:10.1038/nprot.2012.016
Kim D, Pertea G, Trapnell C et al (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14:R36. doi:10.1186/gb-2013-14-4-r36
Goff L, Trapnell C, Kelley D (2013) cummeRbund: analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. R package version 2.16.0
Acknowledgments
We gratefully acknowledge Divya Tej Sowpati for help with the bioinformatics analysis pipeline and development of custom visualization tools. HG was supported by doctoral fellowships from CSIR. The Mishra and Dhawan labs are supported by core funds from the Council of Scientific and Industrial Research to CCMB, and a collaborative grant from the Dept. of Biotechnology Indo-Australia Biotechnology Fund. JD also acknowledges funding from and the Dept. of Biotechnology Indo-Danish Strategic Fund, Indo-French Center for the Promotion of Advanced Research and from the Dept. of Biotechnology Institute for Stem Cell Biology and Regenerative Medicine.
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Srivastava, S., Gala, H.P., Mishra, R.K., Dhawan, J. (2018). Distinguishing States of Arrest: Genome-Wide Descriptions of Cellular Quiescence Using ChIP-Seq and RNA-Seq Analysis. In: Lacorazza, H. (eds) Cellular Quiescence. Methods in Molecular Biology, vol 1686. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7371-2_16
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DOI: https://doi.org/10.1007/978-1-4939-7371-2_16
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