Skip to main content

Advertisement

Log in

Cell Types in Environmental Epigenetic Studies: Biological and Epidemiological Frameworks

  • Early Life Environmental Health (H Volk and J Buckley, Section Editors)
  • Published:
Current Environmental Health Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

This article introduces the roles of perinatal DNA methylation in human health and disease, highlights the challenges of tissue and cellular heterogeneity to studying DNA methylation, summarizes approaches to overcome these challenges, and offers recommendations in conducting research in environmental epigenetics.

Recent Findings

Epigenetic modifications are essential for human development and are labile to environmental influences, especially during gestation. Epigenetic dysregulation is also a hallmark of multiple diseases. Environmental epigenetic studies routinely measure DNA methylation in readily available tissues. However, tissues and cell types exhibit specific epigenetic patterning and heterogeneity between samples complicates epigenetic studies. Failure to account for cell-type heterogeneity limits identification of biological mechanisms and biases study results.

Summary

Tissue-level epigenetic measures represent a convolution of epigenetic signals from individual cell types. Tissue-specific epigenetics is an evolving field and the use of disease-affected target, surrogate, or multiple tissues has inherent trade-offs and affects inference. Likewise, experimental and bioinformatic approaches to accommodate cell-type heterogeneity have varying assumptions and inherent trade-offs that affect inference. The relationships between exposure, disease, tissue-level DNA methylation, cell type–specific DNA methylation, and cell-type heterogeneity must be carefully considered in study design and analysis. Causal diagrams can inform study design and analytic strategies. Properly addressing cell-type heterogeneity limits sources of potential bias, avoids misinterpretation of study results, and allows investigators to distinguish shifts in cell-type proportions from direct changes to cellular epigenetic programming, both of which provide insights into environmental disease etiology and aid development of novel methods for prevention and treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Greally JM. A user’s guide to the ambiguous word “epigenetics.”. Nat Rev Mol Cell Biol. 2018;19:207–8. https://doi.org/10.1038/nrm.2017.135.

    Article  CAS  PubMed  Google Scholar 

  2. Deichmann U. Epigenetics: the origins and evolution of a fashionable topic. Dev Biol. 2016;416:249–54. https://doi.org/10.1016/j.ydbio.2016.06.005.

    Article  CAS  PubMed  Google Scholar 

  3. Goldberg AD, Allis CD, Bernstein E. Epigenetics: a landscape takes shape. Cell. 2007;128:635–8. https://doi.org/10.1016/j.cell.2007.02.006.

    Article  CAS  PubMed  Google Scholar 

  4. Reik W, Dean W, Walter J. Epigenetic reprogramming in mammalian development. Science. 2001;293:1089–93. https://doi.org/10.1126/science.1063443.

    Article  CAS  PubMed  Google Scholar 

  5. Smith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013;14:204–20. https://doi.org/10.1038/nrg3354.

    Article  CAS  PubMed  Google Scholar 

  6. Khavari DA, Sen GL, Rinn JL. DNA methylation and epigenetic control of cellular differentiation. Cell Cycle. 2010;9:3880–3. https://doi.org/10.4161/cc.9.19.13385.

    Article  CAS  PubMed  Google Scholar 

  7. Virani S, Colacino JA, Kim JH, Rozek LS. Cancer epigenetics: a brief review. ILAR J. 2012;53:359–69. https://doi.org/10.1093/ilar.53.3-4.359.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Berson A, Nativio R, Berger SL, Bonini NM. Epigenetic regulation in neurodegenerative diseases. Trends Neurosci. 2018;41:587–98. https://doi.org/10.1016/j.tins.2018.05.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ordovás JM, Smith CE. Epigenetics and cardiovascular disease. Nat Rev Cardiol. 2010;7:510–9. https://doi.org/10.1038/nrcardio.2010.104.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Feil R, Fraga MF. Epigenetics and the environment: emerging patterns and implications. Nat Rev Genet. 2012;13:97–109. https://doi.org/10.1038/nrg3142.

    Article  CAS  PubMed  Google Scholar 

  11. Gluckman PD, Hanson MA, Buklijas T, Low FM, Beedle AS. Epigenetic mechanisms that underpin metabolic and cardiovascular diseases. Nat Rev Endocrinol. 2009;5:401–8. https://doi.org/10.1038/nrendo.2009.102.

    Article  CAS  PubMed  Google Scholar 

  12. Smith ZD, Chan MM, Humm KC, Karnik R, Mekhoubad S, Regev A, et al. DNA methylation dynamics of the human preimplantation embryo. Nature. 2014;511:611–5. https://doi.org/10.1038/nature13581.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Messerschmidt DM, Knowles BB, Solter D. DNA methylation dynamics during epigenetic reprogramming in the germline and preimplantation embryos. Genes Dev. 2014;28:812–28. https://doi.org/10.1101/gad.234294.113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chaligné R, Heard E. X-chromosome inactivation in development and cancer. FEBS Lett. 2014;588:2514–22. https://doi.org/10.1016/j.febslet.2014.06.023.

    Article  CAS  PubMed  Google Scholar 

  15. Doi A, Park I-H, Wen B, Murakami P, Aryee MJ, Irizarry R, et al. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat Genet. 2009;41:1350–3. https://doi.org/10.1038/ng.471.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bakulski KM, Halladay A, Hu VW, Mill J, Fallin MD (2016) Epigenetic research in neuropsychiatric disorders: the “tissue issue.” Curr Behav Neurosci Rep 3:264–274. https://doi.org/10.1007/s40473-016-0083-4

  17. Hannon E, Lunnon K, Schalkwyk L, Mill J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics. 2015;10:1024–32. https://doi.org/10.1080/15592294.2015.1100786.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Walton E, Hass J, Liu J, Roffman JL, Bernardoni F, Roessner V, et al. Correspondence of DNA methylation between blood and brain tissue and its application to schizophrenia research. Schizophr Bull. 2016;42:406–14. https://doi.org/10.1093/schbul/sbv074.

    Article  PubMed  Google Scholar 

  19. Wang T, Pehrsson EC, Purushotham D, et al. The NIEHS TaRGET II consortium and environmental epigenomics. Nat Biotechnol. 2018;36:225–7. https://doi.org/10.1038/nbt.4099.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Joubert BR, Felix JF, Yousefi P, Bakulski KM, Just AC, Breton C, et al. DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis. Am J Hum Genet. 2016;98:680–96. https://doi.org/10.1016/j.ajhg.2016.02.019.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sikdar S, Joehanes R, Joubert BR, Xu CJ, Vives-Usano M, Rezwan FI, et al. Comparison of smoking-related DNA methylation between newborns from prenatal exposure and adults from personal smoking. Epigenomics. 2019;11:1487–500. https://doi.org/10.2217/epi-2019-0066.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bakulski KM, Dou J, Lin N, London SJ, Colacino JA. DNA methylation signature of smoking in lung cancer is enriched for exposure signatures in newborn and adult blood. Sci Rep. 2019:9. https://doi.org/10.1038/s41598-019-40963-2.

  23. Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature. 2008;454:766–70. https://doi.org/10.1038/nature07107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlén SE, Greco D, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One. 2012;7:e41361. https://doi.org/10.1371/journal.pone.0041361.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Holbrook JD, Huang R-C, Barton SJ, Saffery R, Lillycrop KA. Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies? Epigenomics. 2017;9:1143–50. https://doi.org/10.2217/epi-2017-0032.

    Article  CAS  PubMed  Google Scholar 

  26. Jaffe AE, Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014;15:R31. https://doi.org/10.1186/gb-2014-15-2-r31.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:3156. https://doi.org/10.1186/gb-2013-14-10-r115.

    Article  Google Scholar 

  28. Jylhävä J, Pedersen NL, Hägg S. Biological age predictors. EBioMedicine. 2017;21:29–36. https://doi.org/10.1016/j.ebiom.2017.03.046.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bauer M, Fink B, Thürmann L, Eszlinger M, Herberth G, Lehmann I. Tobacco smoking differently influences cell types of the innate and adaptive immune system—indications from CpG site methylation. Clin Epigenetics. 2016;8. https://doi.org/10.1186/s13148-016-0249-7.

  30. • Su D, Wang X, Campbell MR, et al. Distinct epigenetic effects of tobacco smoking in whole blood and among leukocyte subtypes. PLOS ONE. 2016;11:e0166486. https://doi.org/10.1371/journal.pone.0166486This study revealed cell type–specific associations between smoking and DNA methylation in multiple leukocyte subpopulations. Furthermore, DNA methylation fine-mapping and discordant gene expression changes provide evidence that disease etiology should be evaluated in a lineage-specific matter.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Bauer M, Linsel G, Fink B, Offenberg K, Hahn AM, Sack U, et al. A varying T cell subtype explains apparent tobacco smoking induced single CpG hypomethylation in whole blood. Clin Epigenet. 2015;7:81. https://doi.org/10.1186/s13148-015-0113-1.

    Article  CAS  Google Scholar 

  32. Lappalainen T, Greally JM. Associating cellular epigenetic models with human phenotypes. Nat Rev Genet. 2017;18:441–51. https://doi.org/10.1038/nrg.2017.32.

    Article  CAS  PubMed  Google Scholar 

  33. Herzenberg LA, Parks D, Sahaf B, Perez O, Roederer M, Herzenberg LA. The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin Chem. 2002;48:1819–27.

    Article  CAS  PubMed  Google Scholar 

  34. Karemaker ID, Vermeulen M. Single-cell DNA methylation profiling: technologies and biological applications. Trends Biotechnol. 2018;36:952–65. https://doi.org/10.1016/j.tibtech.2018.04.002.

    Article  CAS  PubMed  Google Scholar 

  35. Tanay A, Regev A. Scaling single-cell genomics from phenomenology to mechanism. Nature. 2017;541:331–8. https://doi.org/10.1038/nature21350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Shen-Orr SS, Gaujoux R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol. 2013;25:571–8. https://doi.org/10.1016/j.coi.2013.09.015.

    Article  CAS  PubMed  Google Scholar 

  37. Teschendorff AE, Zheng SC. Cell-type deconvolution in epigenome-wide association studies: a review and recommendations. Epigenomics. 2017;9:757–68. https://doi.org/10.2217/epi-2016-0153.

    Article  CAS  PubMed  Google Scholar 

  38. Houseman EA, Kelsey KT, Wiencke JK, Marsit CJ. Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective. BMC Bioinformatics. 2015;16:95. https://doi.org/10.1186/s12859-015-0527-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. •• Gervin K, Salas LA, Bakulski KM, et al. Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data. Clin Epigenetics. 2019;11. https://doi.org/10.1186/s13148-019-0717-yThis study shows that combining cell type–specific DNA methylation references across multiple studies can improve deconvolution and provides guidelines for conducting reference-based deconvolution in umbilical cord blood that may be extended to other tissues.

  40. Gervin K, Page CM, Aass HCD, Jansen MA, Fjeldstad HE, Andreassen BK, et al. Cell type specific DNA methylation in cord blood: a 450K-reference data set and cell count-based validation of estimated cell type composition. Epigenetics. 2016;11:690–8. https://doi.org/10.1080/15592294.2016.1214782.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bakulski KM, Feinberg JI, Andrews SV, Yang J, Brown S, L. McKenney S, et al. DNA methylation of cord blood cell types: applications for mixed cell birth studies. Epigenetics. 2016;11:354–62. https://doi.org/10.1080/15592294.2016.1161875.

    Article  PubMed  PubMed Central  Google Scholar 

  42. de Goede OM, Lavoie PM, Robinson WP. Characterizing the hypomethylated DNA methylation profile of nucleated red blood cells from cord blood. Epigenomics. 2016;8:1481–94. https://doi.org/10.2217/epi-2016-0069.

    Article  CAS  PubMed  Google Scholar 

  43. Lin X, Tan JYL, Teh AL, Lim IY, Liew SJ, MacIsaac JL, et al. Cell type-specific DNA methylation in neonatal cord tissue and cord blood: a 850K-reference panel and comparison of cell types. Epigenetics. 2018;13:941–58. https://doi.org/10.1080/15592294.2018.1522929.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol. 2018;19:1–14. https://doi.org/10.1186/s13059-018-1448-7.

    Article  CAS  Google Scholar 

  45. Guintivano J, Aryee MJ, Kaminsky ZA. A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics. 2013;8:290–302. https://doi.org/10.4161/epi.23924.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zheng SC, Webster AP, Dong D, Feber A, Graham DG, Sullivan R, et al. A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix. Epigenomics. 2018;10:925–40. https://doi.org/10.2217/epi-2018-0037.

    Article  CAS  PubMed  Google Scholar 

  47. Liang L, Cookson WOC. Grasping nettles: cellular heterogeneity and other confounders in epigenome-wide association studies. Hum Mol Genet. 2014;23:R83–8. https://doi.org/10.1093/hmg/ddu284.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3:e161. https://doi.org/10.1371/journal.pgen.0030161, 1724, 1735.

  49. •• Houseman EA, Kile ML, Christiani DC, et al. Reference-free deconvolution of DNA methylation data and mediation by cell composition effects. BMC Bioinformatics. 2016;17:259. https://doi.org/10.1186/s12859-016-1140-4This study introduces an indirect, reference-free deconvolution method with interpretable biological outputs, including cell-type proportions, that also explicitly quantitates mediation by cell composition in phenotypic associations with DNA methylation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zheng SC, Beck S, Jaffe AE, Koestler DC, Hansen KD, Houseman AE, et al. Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses. Nat Methods. 2017;14:216–7. https://doi.org/10.1038/nmeth.4187.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Greenland S, Pearl J, Robins J. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48.

    Article  CAS  PubMed  Google Scholar 

  52. Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol. 2008;8:70. https://doi.org/10.1186/1471-2288-8-70.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Bianco-Miotto T, Craig JM, Gasser YP, van Dijk SJ, Ozanne SE. Epigenetics and DOHaD: from basics to birth and beyond. J Dev Orig Health Dis. 2017;8:513–9. https://doi.org/10.1017/S2040174417000733.

    Article  CAS  PubMed  Google Scholar 

  54. Godfrey KM, Lillycrop KA, Burdge GC, Gluckman PD, Hanson MA. Epigenetic mechanisms and the mismatch concept of the developmental origins of health and disease. Pediatr Res. 2007;61:5–10. https://doi.org/10.1203/pdr.0b013e318045bedb.

    Article  Google Scholar 

  55. Baron RM, Kenny DA. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82. https://doi.org/10.1037/0022-3514.51.6.1173.

    Article  CAS  PubMed  Google Scholar 

  56. • Barker ED, Walton E, CAM C. Annual Research Review: DNA methylation as a mediator in the association between risk exposure and child and adolescent psychopathology. Journal of Child Psychology and Psychiatry. 2018;59:303–22. https://doi.org/10.1111/jcpp.12782This article reviews the evidence available to evaluate a DNA methylation conceptual mediation framework for early-life exposures and developmental psychopathology. The article underscores the paucity of longitudinal study designs adequate to assess mediation by DNA methylation.

    Article  PubMed  Google Scholar 

  57. Lin VW, Baccarelli AA, Burris HH. Epigenetics—a potential mediator between air pollution and preterm birth. Environ Epigenet. 2016;2. https://doi.org/10.1093/eep/dvv008.

  58. Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods. 2010;15:309–34. https://doi.org/10.1037/a0020761.

    Article  PubMed  Google Scholar 

  59. VanderWeele TJ. Mediation and mechanism. Eur J Epidemiol. 2009;24:217–24. https://doi.org/10.1007/s10654-009-9331-1.

    Article  PubMed  Google Scholar 

  60. Bansal A, Simmons RA. Epigenetics and developmental origins of diabetes: correlation or causation? American Journal of Physiology-Endocrinology and Metabolism. 2018;315:E15–28. https://doi.org/10.1152/ajpendo.00424.2017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Saffery R. Epigenetic change as the major mediator of fetal programming in humans: are we there yet? ANM. 2014;64:203–7. https://doi.org/10.1159/000365020.

    Article  CAS  Google Scholar 

  62. •• Liu Y, Aryee MJ, Padyukov L, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31:142–7. https://doi.org/10.1038/nbt.2487This study applied a mediation causal inference approach to test whether DNA methylation mediates the genetic risk of rheumatoid arthritis. Causal diagrams were employed to inform study design and analysis. Furthermore, the investigators accounted for cell-type heterogeneity by employing a reference-based deconvolution method, explained their choice of target tissue, and ruled out epigenetic changes thought to be a consequence of disease status.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Meehan RR, Thomson JP, Lentini A, Nestor CE, Pennings S. DNA methylation as a genomic marker of exposure to chemical and environmental agents. Curr Opin Chem Biol. 2018;45:48–56. https://doi.org/10.1016/j.cbpa.2018.02.006.

    Article  CAS  PubMed  Google Scholar 

  64. Shenker N, Ueland P, Polidoro S, et al. DNA methylation as a long-term biomarker of exposure to tobacco smoke. Epidemiology. 2013;24:712–6. https://doi.org/10.1097/EDE.0b013e31829d5cb3.

    Article  PubMed  Google Scholar 

  65. Guerrero-Preston R, Goldman LR, Brebi-Mieville P, Ili-Gangas C, LeBron C, Witter FR, et al. Global DNA hypomethylation is associated with in utero exposure to cotinine and perfluorinated alkyl compounds. Epigenetics. 2010;5:539–46. https://doi.org/10.4161/epi.5.6.12378.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ladd-Acosta C, Shu C, Lee BK, Gidaya N, Singer A, Schieve LA, et al. Presence of an epigenetic signature of prenatal cigarette smoke exposure in childhood. Environ Res. 2016;144:139–48. https://doi.org/10.1016/j.envres.2015.11.014.

    Article  CAS  PubMed  Google Scholar 

  67. Breton CV, Siegmund KD, Joubert BR, Wang X, Qui W, Carey V, et al. Prenatal tobacco smoke exposure is associated with childhood DNA CpG methylation. PLoS One. 2014;9:e99716. https://doi.org/10.1371/journal.pone.0099716.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615–25. https://doi.org/10.1097/01.ede.0000135174.63482.43.

    Article  PubMed  Google Scholar 

  69. Simmons SO, Fan C-Y, Yeoman K, Wakefield J, Ramabhadran R. NRF2 oxidative stress induced by heavy metals is cell type dependent. Curr Chem Genomics. 2011;5:1–12. https://doi.org/10.2174/1875397301105010001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Michels KB, Binder AM, Dedeurwaerder S, Epstein CB, Greally JM, Gut I, et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat Methods. 2013;10:949–55. https://doi.org/10.1038/nmeth.2632.

    Article  CAS  PubMed  Google Scholar 

  71. Schulte PA, Perera FP (2012) Molecular epidemiology: principles and practices. Academic Press.

  72. Mayeux R. Biomarkers: potential uses and limitations. NeuroRx. 2004;1:182–8.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Udali S, Guarini P, Moruzzi S, Choi SW, Friso S. Cardiovascular epigenetics: from DNA methylation to microRNAs. Mol Asp Med. 2013;34:883–901. https://doi.org/10.1016/j.mam.2012.08.001.

    Article  CAS  Google Scholar 

  74. Goud Alladi C, Etain B, Bellivier F, Marie-Claire C. DNA methylation as a biomarker of treatment response variability in serious mental illnesses: a systematic review focused on bipolar disorder, schizophrenia, and major depressive disorder. Int J Mol Sci. 2018;19:3026. https://doi.org/10.3390/ijms19103026.

    Article  CAS  PubMed Central  Google Scholar 

  75. Mikeska T, Craig JM. DNA methylation biomarkers: cancer and beyond. Genes. 2014;5:821–64. https://doi.org/10.3390/genes5030821.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Chu T, Burke B, Bunce K, Surti U, Allen Hogge W, Peters DG. A microarray-based approach for the identification of epigenetic biomarkers for the noninvasive diagnosis of fetal disease. Prenat Diagn. 2009;29:1020–30. https://doi.org/10.1002/pd.2335.

    Article  CAS  PubMed  Google Scholar 

  77. • Küpers LK, Monnereau C, Sharp GC, et al. Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight. Nat Commun. 2019;10:1–11. https://doi.org/10.1038/s41467-019-09671-3This meta-analysis shows an association between birthweight and neonatal blood DNA methylation sites. The investigators employed a basic conceptual model to inform careful inference of study results, recognizing the limitations and assumptions of their approach.

    Article  CAS  Google Scholar 

  78. Levenson VV. DNA methylation as a universal biomarker. Expert Rev Mol Diagn. 2010;10:481–8. https://doi.org/10.1586/erm.10.17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. EpideHmiology. 2009;20:488–95. https://doi.org/10.1097/EDE.0b013e3181a819a1.

    Article  Google Scholar 

  80. Tomlinson MJ, Tomlinson S, Yang XB, Kirkham J (2012) Cell separation: terminology and practical considerations: journal of tissue engineering. https://doi.org/10.1177/2041731412472690.

  81. Akman K, Haaf T, Gravina S, Vijg J, Tresch A. Genome-wide quantitative analysis of DNA methylation from bisulfite sequencing data. Bioinformatics. 2014;30:1933–4. https://doi.org/10.1093/bioinformatics/btu142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Schultz MD, He Y, Whitaker JW, Hariharan M, Mukamel EA, Leung D, et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature. 2015;523:212–6. https://doi.org/10.1038/nature14465.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Welch JD, Kozareva V, Ferreira A, et al (2019) Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177:1873-1887.e17. https://doi.org/10.1016/j.cell.2019.05.006.

  84. Kapourani C-A, Sanguinetti G. Melissa: Bayesian clustering and imputation of single-cell methylomes. Genome Biol. 2019;20:61. https://doi.org/10.1186/s13059-019-1665-8.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86. https://doi.org/10.1186/1471-2105-13-86.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7. https://doi.org/10.1038/nmeth.3337.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Koestler DC, Jones MJ, Usset J, Christensen BC, Butler RA, Kobor MS, et al. Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL). BMC Bioinformatics. 2016;17:120. https://doi.org/10.1186/s12859-016-0943-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Teschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinformatics. 2017;18:105. https://doi.org/10.1186/s12859-017-1511-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Teschendorff AE, Zhuang J, Widschwendter M. Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics. 2011;27:1496–505. https://doi.org/10.1093/bioinformatics/btr171.

    Article  CAS  PubMed  Google Scholar 

  90. Gagnon-Bartsch JA, Speed TP. Using control genes to correct for unwanted variation in microarray data. Biostatistics. 2012;13:539–52. https://doi.org/10.1093/biostatistics/kxr034.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Zou J, Lippert C, Heckerman D, Aryee M, Listgarten J. Epigenome-wide association studies without the need for cell-type composition. Nat Methods. 2014;11:309–11. https://doi.org/10.1038/nmeth.2815.

    Article  CAS  PubMed  Google Scholar 

  92. Houseman EA, Molitor J, Marsit CJ. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics. 2014;30:1431–9. https://doi.org/10.1093/bioinformatics/btu029.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, et al. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat Methods. 2016;13:443–5. https://doi.org/10.1038/nmeth.3809.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Rahmani E, Schweiger R, Shenhav L, Wingert T, Hofer I, Gabel E, et al. BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference. Genome Biol. 2018;19:141. https://doi.org/10.1186/s13059-018-1513-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

K. A. C. was supported by the National Institutes of Health (T32 HG00040) and Michigan State University’s Environmental Influences on Child Health Outcomes program (UG3 OD023285, UH3 OD023285). J. A. C. was supported by the Ravitz Family Foundation, the Forbes Institute for Cancer Discovery at the University of Michigan Rogel Cancer Center, and the National Institutes of Health (grant numbers R01 ES028802, U01 ES026697, P30 ES017885, and P30 CA046592). S. K. P. declares no funding sources. K. M. B. was supported by research grants from the National Institute of Environmental Health Sciences (R01 ES025531; R01 ES025574), National Institute of Aging (R01 AG055406), National Institute on Minority Health and Health Disparities (R01 MD013299), and ALS Association (20-IIA-532).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kyle A. Campbell.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Early Life Environmental Health

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Campbell, K.A., Colacino, J.A., Park, S.K. et al. Cell Types in Environmental Epigenetic Studies: Biological and Epidemiological Frameworks. Curr Envir Health Rpt 7, 185–197 (2020). https://doi.org/10.1007/s40572-020-00287-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40572-020-00287-0

Keywords

Navigation