Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease
Abstract
:1. Introduction
2. Single-Cell Sequencing and Common Applications
3. Single-Cell Technology Approaches to the Study of Neurodegenerative, Metabolic and Multifactorial Diseases
4. Chromatin Immunoprecipitation (Chip) as a General Method to Study Protein–DNA Interactions
5. DamID, Chec-seq and Chic-seq Enzyme-Tethering Strategies Applied to the Study of Chromatin Profiling
6. Emerging Strategies for Efficient Genome-Wide Chromatin Profiling: CUT&RUN and CUT&TAG
7. Epigenetics Approaches to Explore the Architecture of Centromeric Chromatin
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Waddington, C.H. The Epigenotype. Int. J. Epidemiol. 2012, 41, 10–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dupont, C.; Armant, D.; Brenner, C. Epigenetics: Definition, mechanisms and clinical perspective. Semin. Reprod. Med. 2009, 27, 351–357. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bird, A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002, 16, 6–21. [Google Scholar] [CrossRef] [Green Version]
- Imhof, A. Epigenetic regulators and histone modification. Brief. Funct. Genom. Proteom. 2006, 5, 222–227. [Google Scholar] [CrossRef]
- Wei, J.-W.; Huang, K.; Yang, C.; Kang, C.-S. Non-coding RNAs as regulators in epigenetics. Oncol. Rep. 2016, 37, 3–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kouzarides, T. Chromatin modifications and their function. Cell 2007, 128, 693–705. [Google Scholar] [CrossRef] [Green Version]
- Allis, C.D.; Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 2016, 17, 487–500. [Google Scholar] [CrossRef]
- Agirre, E.; Oldfield, A.J.; Bellora, N.; Segelle, A.; Luco, R.F. Splicing-associated chromatin signatures: A combinatorial and position-dependent role for histone marks in splicing definition. Nat. Commun. 2021, 12, 682. [Google Scholar] [CrossRef]
- Holliday, R.; Pugh, E.J. DNA modification mechanisms and gene activity during development. Science 1975, 187, 226–232. [Google Scholar] [CrossRef]
- Fischle, W.; Tseng, B.S.; Dormann, H.L.; Ueberheide, B.; Garcia, B.A.; Shabanowitz, J.; Hunt, D.F.; Funabiki, H.; Allis, C.D. Regulation of HP1–chromatin binding by histone H3 methylation and phosphorylation. Nature 2005, 438, 1116–1122. [Google Scholar] [CrossRef]
- Vakoc, C.R.; Mandat, S.A.; Olenchock, B.A.; Blobel, G.A. Histone H3 lysine 9 methylation and HP1gamma are associated with transcription elongation through mammalian chromatin. Mol. Cell 2005, 19, 381–391. [Google Scholar] [CrossRef]
- Di Cerbo, V.; Schneider, R. Cancers with wrong HATs: The impact of acetylation. Brief. Funct. Genom. 2013, 12, 231–243. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mattick, J.S.; Makunin, I.V. Non-coding RNA. Hum. Mol. Genet. 2006, 15, R17–R29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, H.; Chi, Z.; Jin, H.; Yang, W. MicroRNA miR-188-5p as a mediator of long non-coding RNA MALAT1 regulates cell proliferation and apoptosis in multiple myeloma. Bioengineered 2021, 12, 1611–1626. [Google Scholar] [CrossRef]
- Ladd-Acosta, C. Epigenetic signatures as biomarkers of expose. Curr. Environ. Health Rep. 2015, 2, 117–125. [Google Scholar] [CrossRef] [Green Version]
- Andersen, A.M.; Dogan, M.V.; Beach, S.R.; Philibert, R.A. Current and future prospects for epigenetic biomarkers of substance use disorders. Genes 2015, 6, 991–1022. [Google Scholar] [CrossRef]
- Schmidl, C.; Delacher, M.; Huehn, J.; Feuerer, M. Epigenetic mechanisms regulating T-cell responses. J. Allergy Clin. Immunol. 2018, 142, 728–743. [Google Scholar] [CrossRef] [Green Version]
- Leygo, C.; Williams, M.; Jin, H.C.; Chan, M.; Chu, W.K.; Grusch, M.; Cheng, Y.Y. DNA Methylation as a noninvasive epigenetic biomarker for the detection of cancer. Dis. Markers 2017, 2017, 3726595. [Google Scholar] [CrossRef]
- Fahrner, A.J.; Bjornsson, H.T. Mendelian disorders of the epigenetic machinery: Postnatal malleability and therapeutic prospects. Hum. Mol. Genet. 2019, 28, R254–R264. [Google Scholar] [CrossRef]
- Berson, A.; Nativio, R.; Berger, S.L.; Bonini, N.M. Epigenetic regulation in neurodegenerative diseases. Trends Neurosci. 2018, 41, 587–598. [Google Scholar] [CrossRef] [PubMed]
- Izzo, L.T.; Affronti, H.C.; Wellen, K.E. The bidirectional relationship between cancer epigenetics and metabolism. Annu. Rev. Cancer Biol. 2021, 5, 235–257. [Google Scholar] [CrossRef] [PubMed]
- Fujimura, A.; Pei, H.; Zhang, H.; Sladitschek, H.L.; Chang, L. Editorial: The role of epigenetic modifictions in cancer progression. Front Oncol. 2021, 10, 617178. [Google Scholar] [CrossRef]
- Lim, Z.F.; Ma, P.C. Emerging insights of tumor heterogeneity and drug resistance mechanisms in lung cancer targeted therapy. J. Hematol. Oncol. 2019, 12, 134. [Google Scholar] [CrossRef] [Green Version]
- Bolhaqueiro, A.C.F.; Ponsioen, B.; Bakker, B.; Klaasen, S.J.; Kucukkose, E.; van Jaarsveld, R.H.; Vivié, J.; Verlaan-Klink, I.; Hami, N.; Spierings, D.C.J.; et al. Ongoing chromosomal instability and karyotype evolution in human colorectal cancer organoids. Nat. Genet. 2019, 51, 824–834. [Google Scholar] [CrossRef]
- Sun, X.; Jiao, W.; Helgason, C.D.; Gout, P.W.; Wang, Y.; Qu, S.; Clermont, P.-L. Elevated expression of the centromere protein-A(CENP-A)-encoding gene as a prognostic and predictive biomarker in human cancers. Int. J. Cancer 2016, 139, 899–907. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Yang, L.; Shi, J.; Lu, Y.; Chen, X.; Yang, Z. The oncogenic role of CENP-A in hepatocellular carcinoma development: Evidence from bioinformatic analysis. Biomed. Res. Int. 2020, 2020, 3040839. [Google Scholar]
- Xu, Y.; Liang, C.; Cai, X.; Zhang, M.; Yu, W.; Shao, Q. High centromere protein-a (CENP-A) expression correlates with progression and prognosis in gastric cancer. Onco. Targets Ther. 2020, 13, 13237–13246. [Google Scholar] [CrossRef]
- Shrestha, R.L.; Rossi, A.; Wangsa, D.; Hogan, A.K.; Zaldana, K.S.; Suva, E.; Chung, Y.J.; Sanders, C.L.; Difilippantonio, S.; Karpova, T.S.; et al. CENP-A overexpression promotes aneuploidy with karyotypic heterogeneity. J. Cell Biol. 2021, 220, e202007195. [Google Scholar] [CrossRef]
- Schwartzman, O.; Tanay, A. Single-cell epigenomics: Techniques and emerging applications. Nat. Rev. Genet. 2015, 16, 716–726. [Google Scholar] [CrossRef]
- Kelsey, G.; Stegle, O.; Reik, W. Single-cell epigenomics: Recording the past and predicting the future. Science 2017, 358, 69–75. [Google Scholar] [CrossRef] [Green Version]
- Kashima, Y.; Sakamoto, Y.; Kaneko, K.; Seki, M.; Suzuki, Y.; Suzuki, A. Single-cell sequencing techniques from individual to multiomics analyses. Exp. Mol. Med. 2020, 52, 1419–1427. [Google Scholar] [CrossRef] [PubMed]
- Lo, P.-K.; Yao, Y.; Zhou, Q. Single-Cell RNA-seq reveals obesity-induced alterations in the Brca1-mutated mammary gland microenvironment. Cancers 2020, 12, 2235. [Google Scholar] [CrossRef]
- Policastro, A.R.; Zentner, E.G. Enzymatic methods for genome-wide profiling of protein binding sites. Brief. Funct. Genom. 2017, 17, 138–145. [Google Scholar] [CrossRef]
- Klein, D.C.; Hainer, S.J. Genomic methods in profiling DNA accessibility and factor localization. Chromosom. Res. 2020, 28, 69–85. [Google Scholar] [CrossRef] [Green Version]
- Ley, T.J.; Mardis, E.R.; Ding, L.; Fulton, B.; McLellan, M.D.; Chen, K.; Dooling, D.; Dunford-Shore, B.H.; McGrath, S.; Hickenbotham, M.; et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 2008, 456, 66–72. [Google Scholar] [CrossRef] [Green Version]
- Tang, X.; Huang, Y.; Lei, J.; Luo, H.; Zhu, X.; Tang, X.; Huang, Y.; Lei, J.; Luo, H.; Zhu, X. The single-cell sequencing: New developments and medical applications. Cell Biosci. 2019, 9, 53. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Maitra, M.; Nagy, C.; Turecki, G. Sequencing the human brain at single-cell resolution. Curr. Behav. Neurosci. Rep. 2019, 6, 197–208. [Google Scholar] [CrossRef]
- Marcum, A.J. The cancer epigenome: A review. J. Biotechnol. Biomed. 2019, 2, 067–083. [Google Scholar] [CrossRef]
- Raj, T.; Li, Y.I.; Wong, G.; Humphrey, J.; Wang, M.; Ramdhani, S.; Wang, Y.-C.; Ng, B.; Gupta, I.; Haroutunian, V.; et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 2018, 50, 1584–1592. [Google Scholar] [CrossRef]
- Wang, Y.; Eng, D.G.; Kaverina, N.V.; Loretz, C.J.; Koirala, A.; Akilesh, S.; Pippin, J.W.; Shankland, S.J. Global transcriptomic changes occur in aged mouse podocytes. Kidney Int. 2020, 98, 1160–1173. [Google Scholar] [CrossRef]
- Zaina, S.; Heyn, H.; Carmona, F.J.; Varol, N.; Sayols, S.; Condom, E.; Ramírez-Ruz, J.; Gomez, A.; Goncalves, I.; Moran, S.; et al. DNA Methylation Map of Human Atherosclerosis. Circ. Cardiovasc. Genet. 2014, 7, 692–700. [Google Scholar] [CrossRef] [Green Version]
- Khyzha, N.; Alizada, A.; Wilson, M.D.; Fish, J.E. Epigenetics of atherosclerosis: Emerging mechanisms and methods. Trends Mol. Med. 2017, 23, 332–347. [Google Scholar] [CrossRef]
- Mazzone, R.; Zwergel, C.; Artico, M.; Taurone, S.; Ralli, M.; Greco, A.; Mai, A. The emerging role of epigenetics in human autoimmune disorders. Clin. Epigenetics 2019, 11, 34. [Google Scholar] [CrossRef] [Green Version]
- Mathys, H.; Davila-Velderrain, J.; Peng, Z.; Gao, F.; Mohammadi, S.; Young, J.Z.; Menon, M.; He, L.; Abdurrob, F.; Jiang, X.; et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 2019, 570, 332–337. [Google Scholar] [CrossRef]
- Fetahu, I.S.; Ma, D.; Rabidou, K.; Argueta, C.; Smith, M.; Liu, H.; Wu, F.; Shi, Y.G. Epigenetic signatures of methylated DNA cytosine in Alzheimer’s disease. Sci. Adv. 2019, 5, eaaw2880. [Google Scholar] [CrossRef] [Green Version]
- Sharma, R.; Sahota, P.; Thakkar, M.M. Chronic alcohol exposure reduces acetylated histones in the sleep-wake regulatory brain regions to cause insomnia during withdrawal. Neuropharmacology 2020, 180, 108332. [Google Scholar] [CrossRef] [PubMed]
- Yokoyama, J.S.; Wang, Y.; Schork, A.J.; Thompson, W.K.; Karch, C.; Cruchaga, C.; McEvoy, L.K.; Witoelar, A.; Chen, C.-H.; Holland, D.; et al. Association between genetic traits for immune-mediated diseases and alzheimer disease. JAMA Neurol. 2016, 73, 691–697. [Google Scholar] [CrossRef] [PubMed]
- Zusso, M.; Barbierato, M.; Facci, L.; Skaper, S.D.; Giusti, P. Neuroepigenetics and Alzheimer’s disease: An update. J. Alzheimers Dis. 2018, 64, 671–688. [Google Scholar] [CrossRef]
- Calderón-Garcidueñas, L.; Herrera-Soto, A.; Jury, N.; Maher, B.A.; González-Maciel, A.; Reynoso-Robles, R.; Ruiz-Rudolph, P.; van Zundert, B.; Varela-Nallar, L. Reduced repressive epigenetic marks, increased DNA damage and Alzheimer’s disease hallmarks in the brain of humans and mice exposed to particulate urban air pollution. Environ Res 2020, 183, 109–226. [Google Scholar] [CrossRef] [PubMed]
- Chang, C.Y.; Ting, H.C.; Liu, C.A.; Su, H.L.; Chiou, T.W.; Lin, S.Z.; Harn, H.J.; Ho, T.J. Induced pluripotent stem cell (iPSC)-based neurodegenerative disease models for phenotype recapitulation and drug screening. Molecules 2020, 25, 2000. [Google Scholar] [CrossRef]
- Cristancho, A.G.; Marsh, E.D. Epigenetics modifiers: Potential hub for understanding and treating neurodevelopmental disorders from hypoxic injury. J. Neurodev. Disord. 2020, 12, 37. [Google Scholar] [CrossRef]
- Perera, B.; Faulk, C.; Svoboda, L.K.; Goodrich, J.M.; Dolinoy, D.C. The role of environmental exposures and the epigenome in health and disease. Environ. Mol. Mutagen. 2020, 61, 176–192. [Google Scholar] [CrossRef] [Green Version]
- Evers, T.M.J.; Hochane, M.; Tans, S.J.; Heeren, R.M.A.; Semrau, S.; Nemes, P.; Mashaghi, A. Deciphering metabolic heterogeneity by single-cell analysis. Anal. Chem. 2019, 91, 13314–13323. [Google Scholar] [CrossRef]
- Li, W.V.; Li, J.J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun 2018, 9, 997. [Google Scholar] [CrossRef] [Green Version]
- Guo, F.; Li, L.; Li, J.; Wu, X.; Hu, B.; Zhu, P.; Wen, L.; Tang, F. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 2017, 27, 967–988. [Google Scholar] [CrossRef]
- Chen, L.; Zheng, S. BCseq: Accurate single cell RNA-seq quantification with bias correction. Nucleic Acids Res. 2018, 46, e82. [Google Scholar] [CrossRef] [PubMed]
- Su, X.; Shi, Y.; Zou, X.; Lu, Z.-N.; Xie, G.; Yang, J.Y.H.; Wu, C.-C.; Cui, X.-F.; He, K.-Y.; Luo, Q.; et al. Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development. BMC Genom. 2017, 18, 946. [Google Scholar] [CrossRef] [PubMed]
- Hochane, M.; Berg, P.R.V.D.; Fan, X.; Bérenger-Currias, N.; Adegeest, E.; Bialecka, M.; Nieveen, M.; Menschaart, M.; Lopes, S.M.C.D.S.; Semrau, S. Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development. PLoS Biol. 2019, 17, e3000152. [Google Scholar] [CrossRef] [Green Version]
- Park, J.; Shrestha, R.; Qiu, C.; Kondo, A.; Huang, S.; Werth, M.; Li, M.; Barasch, J.; Suszták, K. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 2018, 360, 758–763. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pan, M.; Zhu, C.; Ju, J.; Xu, Y.; Luo, S.; Sun, S.; Ou, X. Single-cell transcriptome analysis reveals that maternal obesity affects DNA repair, histone methylation, and autophagy level in mouse embryos. J. Cell. Physiol. 2021, 236, 4944–4953. [Google Scholar] [CrossRef]
- Ruebel, M.L.; Cotter, M.; Sims, C.R.; Moutos, D.M.; Badger, T.M.; Cleves, M.A.; Shankar, K.; Andres, A. Obesity Modulates Inflammation and lipid metabolism oocyte gene expression: A single-cell transcriptome perspective. J. Clin. Endocrinol. Metab. 2017, 102, 2029–2038. [Google Scholar] [CrossRef] [Green Version]
- Allocca, M.; Zola, S.; Bellosta, P. The Fruit Fly, Drosophila melanogaster: Modeling of Human Diseases (Part II); IntechOpen: London, UK, 2018; pp. 131–156. [Google Scholar]
- Ariss, M.M.; Islam, A.B.M.M.K.; Critcher, M.; Zappia, M.P.; Frolov, M.V. Single cell RNA-sequencing identifies a metabolic aspect of apoptosis in Rbf mutant. Nat. Commun. 2018, 9, 5024. [Google Scholar] [CrossRef] [PubMed]
- Fu, Y.; Huang, X.; Zhang, P.; van de Leemput, J.; Han, Z. Single-cell RNA sequencing identifies novel cell types in Drosophila blood. J. Genet. Genom. 2020, 47, 175–186. [Google Scholar] [CrossRef]
- Young, R.A. Control of the Embryonic Stem Cell State. Cell 2011, 144, 940–954. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Orlando, V. Mapping chromosomal proteins in vivo by formaldehyde-crosslinked-chromatin immunoprecipitation. Trends Biochem. Sci. 2000, 25, 99–104. [Google Scholar] [CrossRef]
- Orlando, V.; Paro, R. Mapping polycomb-repressed domains in the bithorax complex using in vivo formaldehyde cross-linked chromatin. Cell 1993, 75, 1187–1198. [Google Scholar] [CrossRef]
- Ren, B.; Robert, F.; Wyrick, J.J.; Aparicio, O.; Jennings, E.G.; Simon, I.; Zeitlinger, J.; Schreiber, J.; Hannett, N.; Kanin, E.; et al. Genome-wide location and function of DNA binding proteins. Science 2000, 290, 2306–2309. [Google Scholar] [CrossRef]
- Johnson, D.S.; Mortazavi, A.; Myers, R.M.; Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 2007, 316, 1497–1502. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Albert, I.; Mavrich, T.; Tomsho, L.P.; Qi, J.; Zanton, S.J.; Schuster, S.C.; Pugh, B.F. Translational and rotational settings of H2A.Z nucleosomes across the Saccharomyces cerevisiae genome. Nature 2007, 446, 572–576. [Google Scholar] [CrossRef]
- Voet, D.; Voet, J.G. Biochemistry, 2nd ed.; John Wiley & Sons Inc.: New York, NY, USA, 1995. [Google Scholar]
- Wong, E.; Wei, C.-L. ChIP’ing the mammalian genome: Technical advances and insights into functional elements. Genome Med. 2009, 1, 89. [Google Scholar] [CrossRef] [Green Version]
- Barski, A.; Cuddapah, S.; Cui, K.; Roh, T.-Y.; Schones, D.E.; Wang, Z.; Wei, G.; Chepelev, I.; Zhao, K. High-Resolution Profiling of Histone Methylations in the Human Genome. Cell 2007, 129, 823–837. [Google Scholar] [CrossRef] [Green Version]
- Mikkelsen, T.S.; Ku, M.; Jaffe, D.B.; Isaac, B.; Lieberman, E.; Giannoukos, G.; Alvarez, P.; Brockman, W.; Kim, T.K.; Koche, R.P.; et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 2007, 448, 553–560. [Google Scholar] [CrossRef]
- Hui, P. Next generation sequencing: Chemistry, technology and applications. Top. Curr. Chem. 2012, 336, 1–18. [Google Scholar] [CrossRef]
- Baranello, L.; Kouzine, F.; Sanford, S.; Levens, D. ChIP bias as a function of cross-linking time. Chromosome Res. 2016, 24, 175–181. [Google Scholar] [CrossRef] [Green Version]
- Poorey, K.; Viswanathan, R.; Carver, M.N.; Karpova, T.S.; Cirimotich, S.M.; McNally, J.G.; Bekiranov, S.; Auble, D.T. Measuring chromatin interaction dynamics on the second time scale at single-copy genes. Science 2013, 342, 369–372. [Google Scholar] [CrossRef] [Green Version]
- Kidder, B.L.; Hu, G.; Zhao, K. ChIP-Seq: Technical considerations for obtaining high-quality data. Nat. Immunol. 2011, 12, 918–922. [Google Scholar] [CrossRef]
- Rhee, H.S.; Pugh, B.F. Genome-wide structure and organization of eukaryotic pre-initiation complexes. Nature 2012, 483, 295–301. [Google Scholar] [CrossRef] [Green Version]
- Adli, M.; Bernstein, B.E. Whole-genome chromatin profiling from limited numbers of cells using nano-ChIP-seq. Nat. Protoc. 2011, 6, 1656–1668. [Google Scholar] [CrossRef] [Green Version]
- Shankaranarayanan, P.; Mendoza-Parra, M.-A.; Van Gool, W.; Trindade, L.M.; Gronemeyer, H. Single-tube linear DNA amplification for genome-wide studies using a few thousand cells. Nat. Protoc. 2012, 7, 328–339. [Google Scholar] [CrossRef]
- Brind’Amour, J.; Liu, S.; Hudson, M.; Chen, C.; Karimi, M.M.; Lorincz, M.C. An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations. Nat. Commun. 2015, 6, 6033. [Google Scholar] [CrossRef] [Green Version]
- Lara-Astiaso, D.; Weiner, A.; Vivas, E.L.; Zaretsky, I.; Jaitin, D.A.; David, E.; Keren-Shaul, H.; Mildner, A.; Winter, D.; Jung, S.; et al. Chromatin state dynamics during blood formation. Science 2014, 345, 943–949. [Google Scholar] [CrossRef] [Green Version]
- Fullwood, M.; Han, Y.; Wei, C.; Ruan, X.; Ruan, Y. Chromatin interaction analysis using paired-end tag sequencing. Curr. Protoc. Mol. Biol. 2010, 89, 21.15.1–21.15.25. [Google Scholar] [CrossRef]
- Rotem, A.; Ram, O.; Shoresh, N.; Sperling, R.A.; Goren, A.; Weitz, D.A.; Bernstein, B.E. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 2015, 33, 1165–1172. [Google Scholar] [CrossRef] [PubMed]
- Boyle, A.; Guinney, J.; Crawford, G.E.; Furey, T.S. F-Seq: A feature density estimator for high-throughput sequence tags. Bioinformatics 2008, 24, 2537–2538. [Google Scholar] [CrossRef] [PubMed]
- Song, L.; Crawford, G.E. DNase-seq: A high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. 2010, 2010, 5384. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Giresi, P.G.; Kim, J.; McDaniell, R.M.; Iyer, V.R.; Lieb, J.D. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 2007, 17, 877–885. [Google Scholar] [CrossRef] [Green Version]
- Buenrostro, J.D.; Giresi, P.G.; Zaba, L.C.; Chang, H.Y.; Greenleaf, W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 2013, 10, 1213–1218. [Google Scholar] [CrossRef] [PubMed]
- He, H.H.; Meyer, C.A.; Hu, S.S.; Chen, M.-W.; Zang, C.; Liu, Y.; Rao, P.K.; Fei, T.; Xu, H.; Long, H.; et al. Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification. Nat. Methods 2014, 11, 73–78. [Google Scholar] [CrossRef] [Green Version]
- Buenrostro, J.D.; Wu, B.; Chang, H.Y.; Greenleaf, W.Y. ATAC-seq: A method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 2015, 5, 21.29.1–21.29.9. [Google Scholar] [CrossRef] [PubMed]
- Van Steensel, B.; Henikoff, S. Identification of in vivo DNA targets of chromatin proteins using tethered Dam methyltransferase. Nat. Biotechnol. 2000, 18, 424–428. [Google Scholar] [CrossRef]
- Schmid, M.; Durussel, T.; Laemmli, U.K. ChIC and ChEC: Genomic mapping of chromatin proteins. Mol. Cell 2004, 16, 147–157. [Google Scholar] [CrossRef] [PubMed]
- Kind, J.; Pagie, L.; De Vries, S.S.; Nahidiazar, L.; Dey, S.S.; Bienko, M.; Zhan, Y.; Lajoie, B.; De Graaf, C.A.; Amendola, M.; et al. Genome-wide maps of nuclear lamina interactions in single human cells. Cell 2015, 163, 134–147. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Aughey, G.; Cheetham, S.W.; Southall, T.D. DamID as a versatile tool for understanding gene regulation. Development 2019, 146, dev173666. [Google Scholar] [CrossRef] [Green Version]
- Germann, S.; Juul-Jensen, T.; Letarnec, B.; Gaudin, V. DamID, a new tool for studying plant chromatin profiling in vivo, and its use to identify putative LHP1 target loci. Plant J. 2006, 48, 153–163. [Google Scholar] [CrossRef]
- Schuster, E.; McElwee, J.J.; Tullet, J.M.A.; Doonan, R.; Matthijssens, F.; Reece-Hoyes, J.S.; Hope, I.A.; Vanfleteren, J.R.; Thornton, J.M.; Gems, D. DamID in C. elegans reveals longevity-associated target of DAF-16/FoxO. Mol. Syst. Biol. 2010, 6, 399. [Google Scholar] [CrossRef]
- Tosti, L.; Ashmore, J.; Tan, B.S.N.; Carbone, B.; Mistri, T.; Wilson, V.; Tomlinson, S.R.; Kaji, K. Mapping transcription factor occupancy using minimal numbers of cells in vitro and in vivo. Genome Res. 2018, 28, 592–605. [Google Scholar] [CrossRef] [Green Version]
- Vogel, M.J.; Guelen, L.; de Wit, E.; Hupkes, D.P.; Lodén, M.; Talhout, W.; Feenstra, M.; Abbas, B.; Classen, A.-K.; Van Steensel, B. Human heterochromatin proteins form large domains containing KRAB-ZNF genes. Genome Res. 2006, 16, 1493–1504. [Google Scholar] [CrossRef] [Green Version]
- Skene, P.J.; Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 2017, 6, e21856. [Google Scholar] [CrossRef]
- Zentner, G.E.; Kasinathan, S.; Xin, B.; Rohs, R.; Henikoff, S. ChEC-seq kinetics discriminates transcription factor binding sites by DNA sequence and shape in vivo. Nat. Commun. 2015, 6, 8733. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Grünberg, S.; Henikoff, S.; Hahn, S.; Zentner, G.E. Mediator binding to UASs is broadly uncoupled from transcription and cooperative with TFIID recruitment to promoters. EMBO J. 2016, 35, 2435–2446. [Google Scholar] [CrossRef]
- Grünberg, S.; Zentner, G.E. Genome-wide characterization of Mediator recruitment, function, and regulation. Transcription 2017, 8, 169–174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Donczew, R.; Warfield, L.; Pacheco, D.; Erijman, A.; Hahn, S. Two roles for the yeast transcription coactivator SAGA and a set of genes redundantly regulated by TFIID and SAGA. eLife 2020, 9, 50109. [Google Scholar] [CrossRef] [PubMed]
- Tebbji, F.; Khemiri, I.; Sellam, A. High-resolution genome-wide occupancy in candida spp. Using ChEC-seq. mSphere 2020, 5, e00646-20. [Google Scholar] [CrossRef]
- Ku, W.L.; Nakamura, K.; Gao, W.; Cui, K.; Hu, G.; Tang, Q.; Ni, B.; Zhao, K. Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nat. Methods 2019, 16, 323–325. [Google Scholar] [CrossRef] [PubMed]
- Skene, P.J.; Henikoff, J.G.; Henikoff, S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nat. Protoc. 2018, 13, 1006–1019. [Google Scholar] [CrossRef]
- Kaya-Okur, H.S.; Wu, S.J.; Codomo, C.A.; Pledger, E.S.; Bryson, T.D.; Henikoff, J.G.; Ahmad, K.; Henikoff, S. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 2019, 10, 1930. [Google Scholar] [CrossRef] [Green Version]
- Kaya-Okur, H.S.; Janssens, D.H.; Henikoff, J.G.; Ahmad, K.; Henikoff, S. Efficient low-cost chromatin profiling with CUT&Tag. Nat. Protoc. 2020, 15, 3264–3283. [Google Scholar] [CrossRef]
- Henikoff, S.; Henikoff, J.G.; Kaya-Okur, H.S.; Ahmad, K. Efficient chromatin accessibility mapping in situ by nucleosome-tethered tagmentation. eLife 2020, 9, 963274. [Google Scholar] [CrossRef]
- Solomon, M.J.; Varshavsky, A. Formaldehyde-mediated DNA-protein crosslinking: A probe for in vivo chromatin structures. Proc. Natl. Acad. Sci. USA 1985, 82, 6470–6474. [Google Scholar] [CrossRef] [Green Version]
- Zheng, X.-Y.; Gehring, M. Low-input chromatin profiling in Arabidopsis endosperm using CUT&RUN. Plant Reprod. 2019, 32, 63–75. [Google Scholar] [CrossRef] [Green Version]
- Miura, M.; Chen, H. CUT&RUN detects distinct DNA footprints of RNA polymerase II near the transcription start sites. Chromosom. Res. 2020, 28, 381–393. [Google Scholar] [CrossRef]
- Hainer, S.J.; Fazzio, T.G. High-Resolution Chromatin Profiling Using CUT&RUN. Curr. Protoc. Mol. Biol. 2019, 126, e85. [Google Scholar] [CrossRef]
- Janssens, D.H.; Wu, S.; Sarthy, J.F.; Meers, M.P.; Myers, C.H.; Olson, J.M.; Ahmad, K.; Henikoff, S. Automated in situ chromatin profiling efficiently resolves cell types and gene regulatory programs. Epigenetics Chromatin 2018, 11, 74. [Google Scholar] [CrossRef]
- Wang, K.; Wang, H.; Li, C.; Yin, Z.; Xiao, R.; Li, Q.; Xiang, Y.; Wang, W.; Huang, J.; Chen, L.; et al. Genomic profiling of native R loops with a DNA-RNA hybrid recognition sensor. Sci. Adv. 2021, 7, eabe3516. [Google Scholar] [CrossRef]
- Yu, S.; Guo, J.; Sun, Z.; Lin, C.; Tao, H.; Zhang, Q.; Cui, Y.; Zuo, H.; Lin, Y.; Chen, S.; et al. BMP2-dependent gene regulatory network analysis reveals Klf4 as a novel transcription factor of osteoblast differentiation. Cell Death Dis. 2021, 12, 197. [Google Scholar] [CrossRef]
- Bartosovic, M.; Kabbe, M.; Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 2021, 39, 825–835. [Google Scholar] [CrossRef]
- Tao, X.; Feng, S.; Zhao, T.; Guan, X. Efficient chromatin profiling of H3K4me3 modification in cotton using CUT&Tag. Plant Methods 2020, 16, 120. [Google Scholar] [CrossRef]
- Ouyang, W.; Zhang, X.; Peng, Y.; Zhang, Q.; Cao, Z.; Li, G.; Li, X. Rapis and low-input profiling of histone marks in plants using nucleus CUT&TAG. Front Plant Sci. 2021, 12, 634679. [Google Scholar] [PubMed]
- Wu, S.J.; Furlan, S.N.; Mihalas, A.B.; Kaya-Okur, H.S.; Feroze, A.H.; Emerson, S.N.; Zheng, Y.; Carson, K.; Cimino, P.J.; Keene, C.D.; et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat. Biotechnol. 2021, 39, 819–824. [Google Scholar] [CrossRef] [PubMed]
- Vig, B.K.; Sternes, K.L.; Paweletz, N. Centromere structure and function in neoplasia. Cancer Genet. Cytogenet. 1989, 43, 151–178. [Google Scholar] [CrossRef]
- Saha, A.K.; Mourad, M.; Kaplan, M.H.; Chefetz, I.; Malek, S.N.; Buckanovich, R.; Markovitz, D.M.; Contreras-Galindo, R. The genomic landscape of centromeres in cancers. Sci. Rep. 2019, 9, 11259. [Google Scholar] [CrossRef] [Green Version]
- Dobie, K.W.; Hari, K.L.; Maggert, A.K.; Karpen, G.H. Centromere proteins and chromosome inheritance: A complex affair. Curr. Opin. Genet. Dev. 1999, 9, 206–217. [Google Scholar] [CrossRef]
- Balzano, E.; Pelliccia, F.; Giunta, S. Genome (in)stability at tandem repeats. Semin. Cell Dev. Biol. 2021, 113, 97–112. [Google Scholar] [CrossRef]
- Balzano, E.; Giunta, S. Centromeres under Pressure: Evolutionary innovation in conflict with conserved function. Genes 2020, 11, 912. [Google Scholar] [CrossRef] [PubMed]
- Cheng, Z.; Dong, F.; Langdon, T.; Ouyang, S.; Buell, C.R.; Gu, M.; Blattner, F.R.; Jiang, J. Functional rice centromeres are marked by a satellite repeat and a centromere-specific retrotransposon. Plant Cell 2002, 14, 1691–1704. [Google Scholar] [CrossRef] [Green Version]
- Chueh, A.C.; Northrop, E.L.; Brettingham-Moore, K.H.; Choo, K.H.A.; Wong, L.H. LINE Retrotransposon RNA is an essential structural and functional epigenetic component of a core neocentromeric chromatin. PLoS Genet. 2009, 5, e1000354. [Google Scholar] [CrossRef]
- Chang, C.-H.; Chavan, A.; Palladino, J.; Wei, X.; Martins, N.M.C.; Santinello, B.; Chen, C.-C.; Erceg, J.; Beliveau, B.J.; Wu, C.-T.; et al. Islands of retroelements are major components of Drosophila centromeres. PLoS Biol. 2019, 17, e3000241. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, Y.; Su, H.; Zhang, J.; Liu, Y.; Feng, C.; Han, F. Back-spliced RNA from retrotransposon binds to centromere and regulates centromeric chromatin loops in maize. PLoS Biol. 2020, 18, e3000582. [Google Scholar] [CrossRef] [PubMed]
- Earnshaw, W.; Rothfield, N. Identification of a family of human centromere proteins using autoimmune sera from patients with scleroderma. Chromosoma 1985, 91, 313–321. [Google Scholar] [CrossRef]
- Sullivan, K.; Hechenberger, M.; Masri, K. Human CENP-A contains a histone H3 related histone fold domain that is required for targeting to the centromere. J. Cell Biol. 1994, 127, 581–592. [Google Scholar] [CrossRef] [Green Version]
- Buchwitz, B.J.; Ahmad, K.; Moore, L.L.; Roth, M.B.; Henikoff, S. A histone-H3-like protein in C. elegans. Nature 1999, 401, 547–548. [Google Scholar] [CrossRef] [PubMed]
- Sullivan, B.A.; Karpen, G.H. Centromeric chromatin exhibits a histone modification pattern that is distinct from both euchromatin and heterochromatin. Nat. Struct. Mol. Biol. 2004, 11, 1076–1083. [Google Scholar] [CrossRef]
- Bergmann, J.H.; Rodríguez, M.G.; Martins, N.; Kimura, H.; Kelly, A.D.; Masumoto, H.; Larionov, V.; Jansen, L.; Earnshaw, W.C. Epigenetic engineering shows H3K4me2 is required for HJURP targeting and CENP-A assembly on a synthetic human kinetochore. EMBO J. 2010, 30, 328–340. [Google Scholar] [CrossRef] [Green Version]
- Ohzeki, J.-I.; Bergmann, J.H.; Kouprina, N.; Noskov, V.N.; Nakano, M.; Kimura, H.; Earnshaw, W.; Larionov, V.; Masumoto, H. Breaking the HAC Barrier: Histone H3K9 acetyl/methyl balance regulates CENP-A assembly. EMBO J. 2012, 31, 2391–2402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Martins, N.M.C.; Bergmann, J.H.; Shono, N.; Kimura, H.; Larionov, V.; Masumoto, H.; Earnshaw, W.C. Epigenetic engineering shows that a human centromere resists silencing mediated by H3K27me3/K9me3. Mol. Biol. Cell 2016, 27, 177–196. [Google Scholar] [CrossRef]
- Molina, O.; Vargiu, G.; Abad, M.A.; Zhiteneva, A.; Jeyaprakash, A.A.; Masumoto, H.; Kouprina, N.; Larionov, V.; Earnshaw, W.C. Epigenetic engineering reveals a balance between histone modifications and transcription in kinetochore maintenance. Nat. Commun. 2016, 7, 13334. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Stimpson, K.M.; Sullivan, B. Epigenomics of centromere assembly and function. Curr. Opin. Cell Biol. 2010, 22, 772–780. [Google Scholar] [CrossRef] [PubMed]
- Yu, Z.; Zhou, X.; Wang, W.; Deng, W.; Fang, J.; Hu, H.; Wang, Z.; Li, S.; Cui, L.; Shen, J.; et al. Dynamic phosphorylation of CENP-A at Ser68 orchestrates its Cell-Cycle-Dependent deposition at centromeres. Dev. Cell 2015, 32, 68–81. [Google Scholar] [CrossRef] [Green Version]
- Niikura, Y.; Kitagawa, R.; Ogi, H.; Abdulle, R.; Pagala, V.; Kitagawa, K. CENP-A K124 ubiquitylation is required for CENP-A deposition at the centromere. Dev. Cell 2015, 32, 589–603. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sathyan, K.M.; Fachinetti, D.; Foltz, D.R. α-amino trimethylation of CENP-A by NRMT is required for full recruitment of the centromere. Nat. Commun. 2017, 8, 14678. [Google Scholar] [CrossRef]
- Fukagawa, T. Critical histone post-translational modifications for centromere function and propagation. Cell Cycle 2017, 16, 1259–1265. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shrestha, R.L.; Ahn, G.S.; Staples, M.I.; Sathyan, K.M.; Karpova, T.; Foltz, D.R.; Basrai, M.A. Mislocalization of centromeric histone H3 variant CENP-A contributes to chromosomal instability (CIN) in human cells. Oncotarget 2017, 8, 46781–46800. [Google Scholar] [CrossRef] [Green Version]
- Mahlke, M.A.; Nechemia-Arbely, Y. Guarding the Genome: CENP-A-Chromatin in Health and Cancer. Genes 2020, 11, 810. [Google Scholar] [CrossRef] [PubMed]
- Gassmann, R.; Rechtsteiner, A.; Yuen, K.W.; Muroyama, A.; Egelhofer, T.; Gaydos, L.; Barron, F.; Maddox, P.; Essex, A.; Monen, J.; et al. An inverse relationship to germline transcription defines centromeric chromatin in C. elegans. Nature 2012, 484, 534–537. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Voullaire, L.E.; Slater, H.R.; Petrovic, V.; Choo, K.H. A functional marker centromere with no detectable alpha-satellite, satellite III, or CENP-B protein: Activation of a latent centromere? Am. J. Hum. Genet. 1993, 52, 1153–1163. [Google Scholar] [PubMed]
- Du Sart, D.; Cancilla, M.R.; Earle, E.; Mao, J.-I.; Saffery, R.; Tainton, K.M.; Kalitsis, P.; Martyn, J.; Barry, A.; Choo, K.H.A. A functional neo-centromere formed through activation of a latent human centromere and consisting of non-alpha-satellite DNA. Nat. Genet. 1997, 16, 144–153. [Google Scholar] [CrossRef]
- Choo, K.A. Centromere DNA dynamics: Latent centromeres and neocentromere formation. Am. J. Hum. Genet. 1997, 61, 1225–1233. [Google Scholar] [CrossRef] [Green Version]
- Amor, D.; Choo, K.H.A. Neocentromeres: Role in human disease, evolution, and centromere study. Am. J. Hum. Genet. 2002, 71, 695–714. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zeitlin, S.G.; Baker, N.M.; Chapados, B.R.; Soutoglou, E.; Wang, J.Y.J.; Berns, M.; Cleveland, D.W. Double-strand DNA breaks recruit the centromeric histone CENP-A. Proc. Natl. Acad. Sci. USA 2009, 106, 15762–15767. [Google Scholar] [CrossRef] [Green Version]
- Leo, L.; Marchetti, M.; Giunta, S.; Fanti, L. Epigenetics as an evolutionary tool for centromere flexibility. Genes 2020, 11, 809. [Google Scholar] [CrossRef]
- Shang, W.-H.; Hori, T.; Martins, N.; Toyoda, A.; Misu, S.; Monma, N.; Hiratani, I.; Maeshima, K.; Ikeo, K.; Fujiyama, A.; et al. Chromosome engineering allows the efficient isolation of vertebrate neocentromeres. Dev. Cell 2013, 24, 635–648. [Google Scholar] [CrossRef] [Green Version]
- Murillo-Pineda, M.; Valente, L.P.; Dumont, M.; Mata, J.F.; Fachinetti, D.; Jansen, L.E. Induction of spontaneous human neocentromere formation and long-term maturation. J. Cell Biol. 2021, 220, e202007210. [Google Scholar] [CrossRef]
- Carty, B.L.; Dunleavy, E.M. Truly epigenetic: A centromere finds a “neo” home. J. Cell Biol. 2021, 220, e202101027. [Google Scholar] [CrossRef]
- McClelland, E.S. Role of chromosomal instability in cancer progression. Endocr. Relat. Cancer 2017, 24, T23–T31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sansregret, L.; Vanhaesebroeck, B.; Swanton, C. Determinants and clinical implications of chromosomal instability in cancer. Nat. Rev. Clin. Oncol. 2018, 15, 139–150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Heun, P.; Erhardt, S.; Blower, M.D.; Weiss, S.; Skora, A.D.; Karpen, G.H. Mislocalization of the drosophila centromere-specific histone cid promotes formation of functional ectopic kinetochores. Dev. Cell 2006, 10, 303–315. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mendiburo, M.J.; Padeken, J.; Fülöp, S.; Schepers, A.; Heun, P. Drosophila CENH3 is sufficient for centromere formation. Science 2011, 334, 686–690. [Google Scholar] [CrossRef]
- Olszak, A.M.; van Essen, D.; Pereira, A.; Diehl, S.; Manke, T.; Maiato, H.; Saccani, S.; Heun, P. Heterochromatin boundaries are hotspots for de novo kinetochore formation. Nat. Cell Biol. 2011, 13, 799–808. [Google Scholar] [CrossRef] [PubMed]
- Giunta, S.; Funabiki, H. Integrity of the human centromere DNA repeats is protected by CENP-A, CENP-C, and CENP-T. Proc. Natl. Acad. Sci. USA 2017, 114, 1928–1933. [Google Scholar] [CrossRef] [Green Version]
- Thakur, J.; Henikoff, S. Unexpected conformational variations of the human centromeric chromatin complex. Genes Dev. 2018, 32, 20–25. [Google Scholar] [CrossRef] [Green Version]




Method | Typical Cell Input (Cells) | Advantages | Disadvantages | Reference |
---|---|---|---|---|
ChIP-seq | ≥5 × 105 |
|
| [69] |
DNAse-seq | ≥106 |
|
| [86] |
FAIRE-seq | ≥105 |
|
| [88] |
MNAse-seq | ≥106 |
|
| [89] |
ATAC-seq | ≥5 × 104 |
|
| [92] |
Dam-ID | ≥104 |
|
| [93] |
CHEC-seq | ≥5 × 107 |
|
| [85] |
CUT&RUN | ≥105 |
|
| [100,107] |
CUT&TAG | ≥5 × 105 |
|
| [108,109,110] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Leo, L.; Colonna Romano, N. Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease. Int. J. Mol. Sci. 2021, 22, 8809. https://doi.org/10.3390/ijms22168809
Leo L, Colonna Romano N. Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease. International Journal of Molecular Sciences. 2021; 22(16):8809. https://doi.org/10.3390/ijms22168809
Chicago/Turabian StyleLeo, Laura, and Nunzia Colonna Romano. 2021. "Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease" International Journal of Molecular Sciences 22, no. 16: 8809. https://doi.org/10.3390/ijms22168809
APA StyleLeo, L., & Colonna Romano, N. (2021). Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease. International Journal of Molecular Sciences, 22(16), 8809. https://doi.org/10.3390/ijms22168809