Talk:Single cell epigenomics
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This article is or was the subject of a Wiki Education Foundation-supported course assignment. Further details are available on the course page. Student editor(s): Ds17b.
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Wiki Education Foundation-supported course assignment
edit This article was the subject of a Wiki Education Foundation-supported course assignment, between 27 August 2019 and 6 December 2019. Further details are available on the course page. Student editor(s): Cseibert832.
Above undated message substituted from Template:Dashboard.wikiedu.org assignment by PrimeBOT (talk) 03:35, 18 January 2022 (UTC)
Image description: Too long?
editIs the image description on the right too long? I had never seen one like that before. MX (✉ • ✎) 01:29, 9 September 2017 (UTC)
- Maybe? I actually copy/pasted it from the article it was posted in (since the text was CC-BY). If you feel moved to summarise it, please do! -Kieran (talk) 00:25, 29 September 2017 (UTC)
- OK, OK, I added some more information to the figure to make it clearer, and vastly reduced the legend. -Kieran (talk) 20:35, 5 October 2017 (UTC)
Student edit
edit- I am a student in the Advanced Molecular biology PCB5595 class with Dr. Hank Bass. Please review the following suggested edits. These are changes that I would like to add to the respective sections.Ds17b (talk) 18:06, 7 December 2017 (UTC)
Single cell epigenomics is the study of epigenomics (the complete set of epigenetic modifications on the genetic material of a cell) in individual cells by single cell sequencing[1][2][3]. Until recently, bulk cell population measurements were the source for our understanding of epigenetic modifications and correlations. Now, single-cell methods provide understanding of the relationship between epigenetic modifications and gene expression, comparable between cells in an organism[2][4]. Since 2013, methods have been created including whole-genome single-cell bisulfite sequencing to measure DNA methylation, whole-genome ChIP-sequencing to measure histone modifications, whole-genome ATAC-seq to measure chromatin accessibility and chromosome conformation capture[citation needed].
Single-cell DNA methylome sequencing
editTreatment with bisulfite has drawbacks as it causes nicks and creates abasic sites in the DNA. Using an alternate method, a study by Lorthongpanich et al. combined methylation sensitive restriction enzymes (MSRE) and qPCR to detect failure in DNA-methylation maintenance in mice pre-implantation embryos[5][6].
Single-cell ATAC-seq
editTn5-transposase cuts and tags accessible chromatin with sequencing adaptors, in a process known as transposition. These regions can then be sequenced into libraries[7][8][9]. In single cell ATAC-seq, cells are first individually captured and assayed under optimized conditions before transposition and library preparation. With primers specific to single cells, these libraries can be amplified for genome wide mapping by high-throughput sequencing. Chromatin accessibility maps can then be generated for individual cells.[8][10]
Single-cell chromatin conformation capture
editAll 3C methods start with a similar set of steps, performed on a sample of cells.[11] First, the cell genomes are cross-linked, which introduces bonds that "freeze" interactions between genomic loci.[11] The genome is then cut into fragments. Next, random ligation with T4 DNA ligase is performed with ligating ends marked with a biotinylated nucleotide, enabling streptavidin mediated enrichment of ligated junctions[11][12][13] Lastly, the fragments are sequenced to determine their proximity to each other (fragments are more likely to be ligated to nearby fragments.) [11]
In single-cell 3C, the ligation step takes place within intact cells, following which, individual cells are isolated under the microscope. With indexed primers, libraries are prepared from single cells, and quantified by qPCR[13]. Lastly, using a high-throughput sequencing (Hi-C)[2][14]pipeline, involving the processing of paired/single end reads, and assessing reproducibility and quality measures, contact read maps are generated[13][15].
Reference
edit- ^ Schwartzman, Omer; Tanay, Amos (13 October 2015). "Single-cell epigenomics: techniques and emerging applications". Nature Reviews Genetics. 16 (12): 716–726. doi:10.1038/nrg3980.
- ^ a b c Clark, Stephen J.; Lee, Heather J.; Smallwood, Sébastien A.; Kelsey, Gavin; Reik, Wolf (18 April 2016). "Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity". Genome Biology. 17 (1). doi:10.1186/s13059-016-0944-x.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Hyun, Byung-Ryool; McElwee, John L.; Soloway, Paul D. (January 2015). "Single molecule and single cell epigenomics". Methods. 72: 41–50. doi:10.1016/j.ymeth.2014.08.015.
- ^ Linnarsson, Sten; Teichmann, Sarah A. (2016-05-10). "Single-cell genomics: coming of age". Genome Biology. 17: 97. doi:10.1186/s13059-016-0960-x. ISSN 1474-760X.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Farlik, Matthias; Sheffield, Nathan C.; Nuzzo, Angelo; Datlinger, Paul; Schönegger, Andreas; Klughammer, Johanna; Bock, Christoph (2015-03-03). "Single-Cell DNA Methylome Sequencing and Bioinformatic Inference of Epigenomic Cell-State Dynamics". Cell Reports. 10 (8): 1386–1397. doi:10.1016/j.celrep.2015.02.001. ISSN 2211-1247.
- ^ Lorthongpanich, Chanchao; Cheow, Lih Feng; Balu, Sathish; Quake, Stephen R.; Knowles, Barbara B.; Burkholder, William F.; Solter, Davor; Messerschmidt, Daniel M. (2013-09-06). "Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos". Science (New York, N.Y.). 341 (6150): 1110–1112. doi:10.1126/science.1240617. ISSN 1095-9203. PMID 24009393.
- ^ Pott, Sebastian; Lieb, Jason D. (21 August 2015). "Single-cell ATAC-seq: strength in numbers". Genome Biology. 16 (1). doi:10.1186/s13059-015-0737-7.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ a b Corces, M Ryan; Trevino, Alexandro E; Hamilton, Emily G; Greenside, Peyton G; Sinnott-Armstrong, Nicholas A; Vesuna, Sam; Satpathy, Ansuman T; Rubin, Adam J; Montine, Kathleen S (2017/10). "An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues". Nature Methods. 14 (10): 959–962. doi:10.1038/nmeth.4396. ISSN 1548-7105.
{{cite journal}}: Check date values in:|date=(help) - ^ Buenrostro, Jason; Wu, Beijing; Chang, Howard; Greenleaf, William (2015-01-05). "ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide". Current protocols in molecular biology / edited by Frederick M. Ausubel ... [et al.] 109: 21.29.1–21.29.9. doi:10.1002/0471142727.mb2129s109. ISSN 1934-3639. PMC 4374986. PMID 25559105.
{{cite journal}}: CS1 maint: PMC format (link) - ^ Buenrostro, Jason D.; Wu, Beijing; Litzenburger, Ulrike M.; Ruff, Dave; Gonzales, Michael L.; Snyder, Michael P.; Chang, Howard Y.; Greenleaf, William J. (2015/07). "Single-cell chromatin accessibility reveals principles of regulatory variation". Nature. 523 (7561): 486–490. doi:10.1038/nature14590. ISSN 1476-4687.
{{cite journal}}: Check date values in:|date=(help) - ^ a b c d de Wit, E.; de Laat, W. (3 January 2012). "A decade of 3C technologies: insights into nuclear organization". Genes & Development. 26 (1): 11–24. doi:10.1101/gad.179804.111.
- ^ Belton, Jon-Matthew; McCord, Rachel Patton; Gibcus, Johan; Naumova, Natalia; Zhan, Ye; Dekker, Job (2012-11). "Hi-C: A comprehensive technique to capture the conformation of genomes". Methods (San Diego, Calif.). 58 (3). doi:10.1016/j.ymeth.2012.05.001. ISSN 1046-2023. PMC 3874846. PMID 22652625.
{{cite journal}}: Check date values in:|date=(help)CS1 maint: PMC format (link) - ^ a b c Nagano, Takashi; Wingett, Steven W.; Fraser, Peter (2017). Functional Genomics. Methods in Molecular Biology. Humana Press, New York, NY. pp. 79–97. doi:10.1007/978-1-4939-7231-9_6. ISBN 9781493972302.
- ^ Sekelja, Monika; Paulsen, Jonas; Collas, Philippe (7 April 2016). "4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?". Genome Biology. 17 (1). doi:10.1186/s13059-016-0923-2.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Servant, Nicolas; Varoquaux, Nelle; Lajoie, Bryan R.; Viara, Eric; Chen, Chong-Jian; Vert, Jean-Philippe; Heard, Edith; Dekker, Job; Barillot, Emmanuel (2015-12-01). "HiC-Pro: an optimized and flexible pipeline for Hi-C data processing". Genome Biology. 16: 259. doi:10.1186/s13059-015-0831-x. ISSN 1474-760X.
{{cite journal}}: CS1 maint: unflagged free DOI (link)