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Spatial DNA Methylation Profiling

From EverybodyWiki Bios & Wiki



Spatial DMT Chemistry Workflow: A diagram of the chemistry workflow of Spatial DMT as described in Lee et al. 2025. Created by BioRender

Spatial DNA methylation profiling refers to technologies that combine the measurements of DNA methylation and the spatial information of cells. These experimental technologies are used to gather insight into these distinct patterns and how they can impact gene regulation in individual cells. DNA methylation is one of multiple major epigenetic factors that influence genetic expression. Traditional bulk and newer single-cell methylation assays measure methylation levels but lose important information about where cells reside, thus losing a level of analysis on outside influences on cells.

By incorporating spatial information, researchers can use spatial DNA methylation profiling to investigate the influence of epigenetic regulation across various tissue environments and how it varies between them. The technologies are still being developed and tested, but promise to enable a more comprehensive understanding of tissue biology and its influences on development and disease.

Background

DNA methylation

DNA methylation is a direct chemical modification to the DNA molecule that influences mechanisms such as cell differentiation as well as gene regulation.[1]. The amount of DNA methylation within the genome varies to mediate cell-specific processes, meaning that gene expression differs within and across tissues, despite having the same DNA sequence.[1] DNA methylation is catalysed by a family of proteins called DNA methyltransferases, which work by transferring a methyl group from an S-adenyl methionine to the fifth carbon of a cytosine to form a 5-methylcytosine (5mC).[1] DNA methylation accounts for only ~1% of nucleic acids in the human genome, with the majority occurring on cytosines preceding a guanine, creating a CpG site.[2] Methylation is found more in intergenic regions to silence transposable and viral elements that have inserted themselves into the human genome over time.[2] CpG islands are associated with promoters and transcription factors, where methylation can cause stable silencing of gene expression.[2] DNA methylation of the gene body acts differently, being associated with active transcription, not silencing.[3]Functionally, DNA methylation plays a role in:[2]

Early DNA methylation assays

DNA methylation was discovered in mammals almost as early as DNA was identified, with Rolin Hotchkiss discovering modified cytosine or 5-methylcytosine (5mC).[1] But, 5mC was reported in bacteria in 1925 by Johnson and Coghill.[4] 5mC was shown to be found across a variety of species and cell types in the 1970s, and further studies showed it lagged behind DNA replication in 1971.[5][6] Initial assays used restriction enzymes to see where DNA methylation occurred and if any changes occurred.[4] Early DNA methylation assays did not show what methylation was associated with, but they understood it was associated with a change in cell identity.[4] Once it was understood to have a function in [[]]bacteria, other types of assays were used to explore the methylation content using mass spectrometry.[7] A culmination of these assays and studies into DNA methylation concluded that 5mC plays a role in gene expression. In the 1990s, bisulfite sequencing was invented, allowing for bases in nucleic acids to be detected, which led to microarrays being created in the 2000s to have large-scale, high-throughput studies of specific CpG sites.[8]

Bulk DNA Methylation sequencing

Bulk DNA methylation sequencing is similar to bulk single-cell sequencing, taking an average of measurements. In this, measurements are of the level of 5mC across a population of cells, showing averages of methylation, to understand the current epigenetic regulation and gene expression in a specific tissue. Typically, bulk DNA methylation analysis uses bisulfite conversion or nanopore sequencing technologies to measure methylation.[9] Some of these analyses have been used to identify abnormal methylation patterns associated with cancer.[9]

Single-Cell Methylation sequencing

In recent years, many fields have begun utilizing single-cell technologies, ranging from single-cell sequencing to single-cell ATAC-seq. Following this technology boom, single-cell methylation studies have been produced; some of these are Q-RRBS, snmC-seq, and scMspJI.[10] These kits and assays are not as powerful - meaning rapid, cost-effective, and simultaneous - as Bulk DNA methylation or other standard methylation analyses, but have started to be used to analyse and profile rare cells and disease states, to capture the underlying differences in rarer cell types that are missed in bulk analyses that average methylation across a population of cells.[10][11]

Scientific principles

Preservation of spatial information in analyses

Tissues are organised into structured microenvironments within the body. They are composed of diverse cell types, extracellular components, and signalling gradients. Certain elements of spatial information influence DNA methylation, like cell lineage, environmental exposures, cell-cell interactions, and disease microenvironments.[12] Conventional assays cannot determine the ways epigenetic differences are influenced by physical location, which has been linked to differences in disease severity.[13] Preserving spatial information allows for better analysis of methylation states and allows linkage between physical states and methylation states.[14]

Relevance of Spatial DNA methylation

Illustration demonstrating cellular heterogeneity in tissues due to differences in DNA methylation that impacts gene expression, cellular state, and phenotype. Created in https://BioRender.com

Identifying spatial differences in DNA methylation captures the underlying biology and diversity of methylation patterns observed in the genome. For example, tumours exhibit different patterns compared to healthy cells, such as CpG islands exhibiting hypermethylation, with differences between tumours also identified at the genomic level and within the same tumour origin.[15][16] Furthermore, senescent cells have disorganized methylation compared to normal cells, environmental factors cause DNA methylation changes, and cell types themselves exhibit methylation heterogeneity.[15][17] Cellular DNA methylation heterogeneity occurs to regulate gene expression for the conservation of cell-specific phenotypes and morphology.[1] Hypermethylation and hypomethylation cause gene suppression and activation, respectively.[1] This can uncover unique cellular patterns that genetic data alone cannot provide, such as in Ewing sarcoma, where patients exhibit similar mutational profiles, but methylation differences influenced cell signatures and were associated with severity.[18]

Technological advances to preserve spatial information

Sequencing technologies have emerged that aim to capture the underlying biology of the cell and its interaction with its surroundings, such as the development of bulk sequencing for capturing tissue-level properties to single-cell sequencing technologies, improving resolution and addressing the major limitation of bulk sequencing, which averages data to its tissue of origin.[19] Although single-cell technologies can improve the scientific understanding of cell types and tissues, the spatial context with which these cells reside cannot be inferred, which led to the development of spatial technologies to address this issue by incorporating imaging, barcoding, and sequencing together.[19]

Spatial transcriptomics, spatial epigenomics, and spatial proteomics are prominent examples of spatial technologies that have provided novel insight into expression, epigenetic, and protein differences within tissues, respectively.[19] These work by either barcoding the molecule of interest to be sequenced so that the original location in the tissue can be identified, or through imaging of tissues with probes bound to targeted molecules.[20][21]

Methods

Sequencing-based Methods

Spatial Joint Profiling of the DNA methylome and transcriptome

Spatial joint profiling of the DNA methylome and transcriptome, or spatial-DMT, is a recently developed protocol for sequencing both DNA methylation and expression simultaneously in tissues by the Deng laboratory at the University of Pennsylvania, and is the first to capture spatial DNA methylation at high resolution, similar to other spatial sequencing technologies.[22] Motivation for this protocol arose from the lack of sequencing technologies that specifically measure spatial DNA methylation patterns, as well as the need to investigate transcription and methylation together to understand the relationship between them that cannot be accurately captured with other technologies.[22]

In summary, the protocol begins with fixing the tissue and adaptor ligation to genomic DNA (gDNA) using the tn5 transposome, as well as the simultaneous generation of a cDNA library from mRNAs.[22] Next, spatial barcodes are added to the cDNA and gDNA to capture the location of these molecules using microfluidic in situ barcoding, then the two DNA types are separated to individually sequence the methylome and transcriptome.[22] For methylome profiling, they chose enzymatic methyl-seq, which involves a preparation step that uses the enzyme ten-eleven translocation methylcytosine dioxygenase 2 (TET2) to preserve methylated cytosines, and uracil is used to replace unmethylated cytosines.[22][23] Afterwards, they add a PCR adaptor to these gDNA fragments to create the finalised gDNA fragment for sequencing, which has the information of cytosine methylation and its location in tissue encoded within it that can be sequenced and analysed to uncover tissue-specific methylation differences in conjunction with expression levels of RNA at the same coordinates.[22]

This protocol has been used for the investigation of whole-genome spatial DNA methylation patterns in conjunction with the transcriptome with success at high resolution.[22] However, it cannot be used to identify different types of methylation marks, highlighting a need to advance this technology for improving specificity.[22]

Laser capture microdissection & DNA methylation profiling

Laser capture microdissection (LCM) followed by DNA methylation profiling has been used in various contexts.[24] This involves the selection of a region of interest within tissue, DNA extraction, conversion of unmethylated cytosines to uracil through bisulfite conversion, and either investigation of a specific region through PCR amplification or next-generation sequencing.[24]

Laser capture microdissection-reduced representation bisulfite sequencing (LCM-RRBS) is one example of an approach that aims to capture spatial DNA methylation patterns at higher throughput, developed by the Mitra lab at the Washington University School of Medicine.[25] This technique involves the extraction of tissue from specific regions within a fixed tissue sample using LCM.[25] Next, adaptors are added to the DNA following extraction and preparation, and the DNA fragments undergo bisulfite conversion to capture the methylation patterns.[25] Last, sample barcodes are added to track the region from which the sample was obtained.[25] Although this method does provide some spatial context to methylation to resolve differences across tissues, resolution is low as the patterns acquired are limited to the size of the sample obtained using LCM, and cannot be resolved to the single-cell level.[25]

Imaging-Based methods

Methylation Mark immunostaining

Antibody binding to 5-methylcytosine (5mc), a DNA methylation modification on cytosine, was one of the first DNA methylation marker assays that could be used to detect DNA methylation differences between cells and tissues.[26] This method involves immunostaining using the antibody to target the 5mc marks in a tissue section, fluorescence imaging, and measuring signal intensity to determine 5mc localization.[27] Other work has also utilised antibodies targeting other DNA methylation marks such as 5-formylcytosine and 5-carboxylcytosine, to identify methylation differences at the tissue and cell levels.[28] This approach provides a visual representation of DNA methylation abundance for the markers of interest; however it is not fully quantitative and it cannot be used to identify the DNA sequence the methylation marks come from.[29]

Methylation-Specific Fluorescence in situ Hybridization (MeFISH)

Methylation-specific fluorescence in situ hybridization (MeFISH) is a technique that also targets methylation marks such as 5mc and 5hmc, but instead is used to target methylation in satellite DNA repeats.[30] Fluorescent labels are attached to Interstrand Complexes formed by Osmium and Nucleic Acids ("ICON") probes that bind to the 5mc or 5hmc methylation marks using in situ hybridization, and are visualised using microscopy to identify these sites within tissues.[30][31] This is similar to methylation mark immunostaining in that it uses fluorescence detection of methylation marks, but it can also provide insight into the sequence itself, due to the specific sites it is used to detect.[30] However, it cannot be used to detect other sites, and relies solely on fluorescence and accessibility can be limited.[30]

Related technologies

Spatial Epigenomic Profiling

Spatial DNA methylation profiling is still in its infancy, with the techniques described above providing a glimpse into the beginning of a new field to capture one of the known epigenetic to understand gene regulation.[22] However, other technologies exist to address numerous other epigenetic marks that are of interest. For example, spatial-CUT&Tag and Epigenomic MERFISH are techniques used to profile histone modifications while capturing the spatial and cell context the modifications are in.[32][33] Spatial ATAC-seq is used for determining chromatin accessibility in the genome while preserving spatial context.[21] Overall, spatial epigenomics enhances the understanding of gene regulation to better understand various biological processes.

Applications & limitations

Developmental biology

Spatial DMT has been applied to mouse embryos to map tissue-specific methylation patterns in early development.[22] Targeting embryos during development allows tracking of lineage-specific epigenetic modifications and understanding the coordination of methylation and transcription.[22] Also, researchers can observe how epigenetic modifications can shape organogenesis.[34]

Cancer biology

Tumours exhibit changing methylation patterns in different spatial contexts.[35] Spatial profiling can help to identify distinct tumor subclones via distinct epigenetic modifications, map the invasive modifications, and detect the differences between the tumors and the changing epigenetic modifications in cells surrounding them.

Disease modelling & aging

5mC immunostaining has been used to look at the changing methylation states during both embryonic development and cellular senescence.[36] Spatial approaches can help add information to distinguish between localized epigenetic modifications that cause disruptions associated with aging and disease.[37]

Limitations

Despite the rapid advances and promise the field holds, several limitations remain:[38]

  • Trade-offs between resolution and genome coverage
  • Limited ability to detect diverse methylation changes
  • High cost
  • High technical complexity
  • DNA degradation during tissue processing

Note

ChatGPT was used as a search engine to search for sources related to the topic.[39] All sources were read and verified by authors of this article.

References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 1.5 Moore, Lisa D.; Le, Thuc; Fan, Guoping (January 2013). "DNA Methylation and Its Basic Function". Neuropsychopharmacology. 38 (1): 23–38. doi:10.1038/npp.2012.112.
  2. ↑ 2.0 2.1 2.2 2.3 Jang, Hyun; Shin, Woo; Lee, Jeong; Do, Jeong (2017-05-23). "CpG and Non-CpG Methylation in Epigenetic Gene Regulation and Brain Function". Genes. 8 (6): 148. doi:10.3390/genes8060148. ISSN 2073-4425. PMC 5485512. PMID 28545252.
  3. ↑ Tibben, Bailey M.; Rothbart, Scott B. (2024). "Mechanisms of DNA Methylation Regulatory Function and Crosstalk with Histone Lysine Methylation". Journal of Molecular Biology. 436 (7). doi:10.1016/j.jmb.2023.168394. PMC 10957332 Check |pmc= value (help). PMID 38092287 Check |pmid= value (help).
  4. ↑ 4.0 4.1 4.2 Mattei, Alexandra L.; Bailly, Nina; Meissner, Alexander (2022). "DNA methylation: a historical perspective". Trends in Genetics. 38 (7): 676–707. doi:10.1016/j.tig.2022.03.010.
  5. ↑ Vanyushin, B. F.; Tkacheva, S. G.; Belozersky, A. N. (1970). "Rare Bases in Animal DNA". Nature. 225 (5236): 948–949. doi:10.1038/225948a0. ISSN 1476-4687.
  6. ↑ Adams, R.L.P. (1971). "The relationship between synthesis and methylation of DNA in mouse fibroblasts". Biochimica et Biophysica Acta (BBA) - Nucleic Acids and Protein Synthesis. 254 (2): 205–212. doi:10.1016/0005-2787(71)90829-x. ISSN 0005-2787.
  7. ↑ Vanyushin, B.F.; Mazin, A.L.; Vasilyev, V.K.; Belozersky, A.N. (1973). "The content of 5-methylcytosine in animal DNA: The species and tissue specificity". Biochimica et Biophysica Acta (BBA) - Nucleic Acids and Protein Synthesis. 299 (3): 397–403. doi:10.1016/0005-2787(73)90264-5. ISSN 0005-2787.
  8. ↑ Bird, Adrian P.; Taggart, Mary H.; Smith, Barbara A. (1979). "Methylated and unmethylated DNA compartments in the sea urchin genome". Cell. 17 (4): 889–901. doi:10.1016/0092-8674(79)90329-5. ISSN 0092-8674.
  9. ↑ 9.0 9.1 Shi, Xianglin; Lu, Xiaodong; Chen, Xinyue; Yu, Shaojun; Arnold, Rebecca; Master, Viraj; Zhao, Jonathan (2025-03-26). "Impact of Nanopore Flow Cell Types on DNA Methylation Detection". Serican Journal of Medicine. 2 (2). doi:10.17161/sjm.v2i2.23664. ISSN 3066-3377. PMC 12880221 Check |pmc= value (help). PMID 41658019 Check |pmid= value (help).
  10. ↑ 10.0 10.1 Liu, Fang; Wang, Yunfei; Gu, Hongcang; Wang, Xiaoxue (2023). "Technologies and applications of single-cell DNA methylation sequencing". Theranostics. 13 (8): 2439–2454. doi:10.7150/thno.82582. ISSN 1838-7640. PMC 10196823 Check |pmc= value (help). PMID 37215576 Check |pmid= value (help).
  11. ↑ Anaparthy, Naishitha; Ho, Yu-Jui; Martelotto, Luciano; Hammell, Molly; Hicks, James (2019). "Single-Cell Applications of Next-Generation Sequencing". Cold Spring Harbor Perspectives in Medicine. 9 (10). doi:10.1101/cshperspect.a026898. ISSN 2157-1422. PMC 6771363 Check |pmc= value (help). PMID 30617056. Unknown parameter |article-number= ignored (help)
  12. ↑ Lee, Chin Nien; Fu, Hongxiang; Cardilla, Angelysia; Zhou, Wanding; Deng, Yanxiang (2025-10-30). "Spatial joint profiling of DNA methylome and transcriptome in tissues". Nature. 646 (8087): 1261–1271. doi:10.1038/s41586-025-09478-x. ISSN 0028-0836. PMC 12571926 Check |pmc= value (help). PMID 40903587 Check |pmid= value (help).
  13. ↑ Mancilla, Viviana J.; Peeri, Noah C.; Silzer, Talisa; Basha, Riyaz; Felini, Martha; Jones, Harlan P.; Phillips, Nicole; Tao, Meng-Hua; Thyagarajan, Srikantha; Vishwanatha, Jamboor K. (2020-08-20). "Understanding the Interplay Between Health Disparities and Epigenomics". Frontiers in Genetics. 11. doi:10.3389/fgene.2020.00903. ISSN 1664-8021. PMC 7468461 Check |pmc= value (help). PMID 32973872 Check |pmid= value (help).
  14. ↑ Ladisa, Giorgio; Montenegro, Francesca; Picerno, Angela; Nigro, Alessio; Cicirelli, Antonella; Stasi, Alessandra; Fiorentino, Marco; Pontrelli, Paola; Gesualdo, Loreto; Sallustio, Fabio (2026-02-06). "A New Complexity Layer: DNA Methylation and the Predictive Impact of Epigenetic Tests". International Journal of Molecular Sciences. 27 (3): 1611. doi:10.3390/ijms27031611. ISSN 1422-0067. PMC 12897665 Check |pmc= value (help). PMID 41684028 Check |pmid= value (help).
  15. ↑ 15.0 15.1 Smith, Zachary D.; Hetzel, Sara; Meissner, Alexander (January 2025). "DNA methylation in mammalian development and disease". Nature Reviews Genetics. 26 (1): 7–30. doi:10.1038/s41576-024-00760-8.
  16. ↑ Wenger, Anna; Ferreyra Vega, Sandra; Kling, Teresia; Bontell, Thomas Olsson; Jakola, Asgeir Store; Carén, Helena (6 May 2019). "Intratumor DNA methylation heterogeneity in glioblastoma: implications for DNA methylation-based classification". Neuro-Oncology. 21 (5): 616–627. doi:10.1093/neuonc/noz011.
  17. ↑ Bell, Christopher G.; Lowe, Robert; Adams, Peter D.; Baccarelli, Andrea A.; Beck, Stephan; Bell, Jordana T.; et al. (November 2019). "DNA methylation aging clocks: challenges and recommendations". Genome Biology. 20 (1). doi:10.1186/s13059-019-1824-y.
  18. ↑ Sheffield, Nathan C; Pierron, Gaelle; Klughammer, Johanna; Datlinger, Paul; Schönegger, Andreas; Schuster, Michael; et al. (March 2017). "DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma". Nature Medicine. 23 (3): 386–395. doi:10.1038/nm.4273.
  19. ↑ 19.0 19.1 19.2 Lee, Yoonji; Lee, Mingyu; Shin, Yoojin; Kim, Kyuri; Kim, Taejung (22 April 2025). "Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications". International Journal of Molecular Sciences. 26 (9): 3949. doi:10.3390/ijms26093949.
  20. ↑ Moffitt, Jeffrey R.; Lundberg, Emma; Heyn, Holger (December 2022). "The emerging landscape of spatial profiling technologies". Nature Reviews Genetics. 23 (12): 741–759. doi:10.1038/s41576-022-00515-3.
  21. ↑ 21.0 21.1 Deng, Yanxiang; Bartosovic, Marek; Ma, Sai; Zhang, Di; Kukanja, Petra; Xiao, Yang; et al. (8 September 2022). "Spatial profiling of chromatin accessibility in mouse and human tissues". Nature. 609 (7926): 375–383. doi:10.1038/s41586-022-05094-1.
  22. ↑ 22.00 22.01 22.02 22.03 22.04 22.05 22.06 22.07 22.08 22.09 22.10 Lee, Chin Nien; Fu, Hongxiang; Cardilla, Angelysia; Zhou, Wanding; Deng, Yanxiang (30 October 2025). "Spatial joint profiling of DNA methylome and transcriptome in tissues". Nature. 646: 1261–1271. doi:10.1038/s41586-025-09478-x.
  23. ↑ Zhang, Xinchao; Zhang, Yue; Wang, Chaofu; Wang, Xu (11 August 2023). "TET (Ten-eleven translocation) family proteins: structure, biological functions and applications". Signal Transduction and Targeted Therapy. 8 (1). doi:10.1038/s41392-023-01537-x.
  24. ↑ 24.0 24.1 Bhamidipati, Theja; Sinha, Mithun; Sen, Chandan K.; Singh, Kanhaiya (February 2022). "Laser Capture Microdissection in the Spatial Analysis of Epigenetic Modifications in Skin: A Comprehensive Review". Oxidative Medicine and Cellular Longevity. 2022 (1). doi:10.1155/2022/4127238.
  25. ↑ 25.0 25.1 25.2 25.3 25.4 Schillebeeckx, Maximiliaan; Schrade, Anja; Löbs, Ann-Kathrin; Pihlajoki, Marjut; Wilson, David B.; Mitra, Robi D. (June 2013). "Laser capture microdissection–reduced representation bisulfite sequencing (LCM-RRBS) maps changes in DNA methylation associated with gonadectomy-induced adrenocortical neoplasia in the mouse". Nucleic Acids Research. 41 (11): e116. doi:10.1093/nar/gkt230.
  26. ↑ Sano, H; Royer, H D; Sager, R (June 1980). "Identification of 5-methylcytosine in DNA fragments immobilized on nitrocellulose paper". Proceedings of the National Academy of Sciences. 77 (6): 3581–3585. doi:10.1073/pnas.77.6.3581.
  27. ↑ Singh, Ratnesh K.; Diaz, Pablo E.; Binette, François; Nasonkin, Igor O. (29 August 2018). "Immunohistochemical Detection of 5-Methylcytosine and 5-Hydroxymethylcytosine in Developing and Postmitotic Mouse Retina". Journal of Visualized Experiments (138). doi:10.3791/58274.
  28. ↑ Abakir, Abdulkadir; Wheldon, Lee; Johnson, Andrew D.; Laurent, Patrick; Ruzov, Alexey (16 August 2016). "Detection of Modified Forms of Cytosine Using Sensitive Immunohistochemistry". Journal of Visualized Experiments (114). doi:10.3791/54416.
  29. ↑ Çelik, Selcen (January 2015). "Understanding the complexity of antigen retrieval of DNA methylation for immunofluorescence-based measurement and an approach to challenge". Journal of Immunological Methods. 416: 1–16. doi:10.1016/j.jim.2014.11.011.
  30. ↑ 30.0 30.1 30.2 30.3 Thompson, Anna-Lee C; Wopereis, Judith L M; Tekle, Yonas I; Katz, Laura A (April 2025). "Visualizing Epigenetics: A Review of Microscopy Techniques for Investigating DNA Methylation Patterns, Chromatin Structure, and Gene Expression". Microscopy and Microanalysis. 31 (2). doi:10.1093/mam/ozaf017.
  31. ↑ Li, Yufeng; Miyanari, Yusuke; Shirane, Kenjiro; Nitta, Hirohisa; Kubota, Takeo; Ohashi, Hirofumi; et al. (October 2013). "Sequence-specific microscopic visualization of DNA methylation status at satellite repeats in individual cell nuclei and chromosomes". Nucleic Acids Research. 41 (19): e186. doi:10.1093/nar/gkt766.
  32. ↑ Deng, Yanxiang; Bartosovic, Marek; Kukanja, Petra; Zhang, Di; Liu, Yang; Su, Graham; et al. (10 February 2022). "Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level". Science. 375 (6581): 681–686. doi:10.1126/science.abg7216.
  33. ↑ Lu, Tian; Ang, Cheen Euong; Zhuang, Xiaowei (November 2022). "Spatially resolved epigenomic profiling of single cells in complex tissues". Cell. 185 (23): 4448–4464.e17. doi:10.1016/j.cell.2022.09.035.
  34. ↑ Moosavi, Azam; Motevalizadeh Ardekani, Ali (2016-11-01). "Role of Epigenetics in Biology and Human Diseases". Iranian Biomedical Journal (5): 246–258. doi:10.22045/ibj.2016.01. ISSN 1028-852X. PMC 5075137. PMID 27377127.
  35. ↑ Ferreyra Vega, Sandra; Wenger, Anna; Kling, Teresia; Olsson Bontell, Thomas; Jakola, Asgeir Store; Carén, Helena (2022). "Spatial heterogeneity in DNA methylation and chromosomal alterations in diffuse gliomas and meningiomas". Modern Pathology. 35 (11): 1551–1561. doi:10.1038/s41379-022-01113-8. PMC 9596370 Check |pmc= value (help). PMID 35701666 Check |pmid= value (help).
  36. ↑ Kharat, Suhas S.; Sharan, Shyam K. (2025-11-17). "5-Hydroxymethylcytosine: a key epigenetic mark in cancer and chemotherapy response". Epigenetics & Chromatin. 18 (1). doi:10.1186/s13072-025-00636-z. ISSN 1756-8935. PMC 12621374 Check |pmc= value (help). PMID 41243111 Check |pmid= value (help).
  37. ↑ An, Yongpan; Wang, Qian; Gao, Ke; Zhang, Chi; Ouyang, Yanan; Li, Ruixiao; Ma, Zhou; Wu, Tong; Zhou, Lifan; Xie, Zhengwei; Zhang, Rui; Wu, Guojun (2025). "Epigenetic Regulation of Aging and its Rejuvenation". MedComm. 6 (9). doi:10.1002/mco2.70369. ISSN 2688-2663. PMC 12402629 Check |pmc= value (help). PMID 40904701 Check |pmid= value (help).
  38. ↑ Choe, Kyongho; Pak, Unil; Pang, Yu; Hao, Wanjun; Yang, Xiuqin (2023-01-12). "Advances and Challenges in Spatial Transcriptomics for Developmental Biology". Biomolecules. 13 (1): 156. doi:10.3390/biom13010156. ISSN 2218-273X. PMC 9855858 Check |pmc= value (help). PMID 36671541 Check |pmid= value (help).
  39. ↑ "ChatGPT". ChatGPT. Retrieved 20 February 2026.


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