BACKGROUND: Epigenetic mechanisms play an important role in prenatal development, but fetal tissues are not readily accessible. Fetal DNA molecules are present in maternal plasma and can be analyzed noninvasively. METHODS: We applied genomewide bisulfite sequencing via 2 approaches to analyze the methylation profile of maternal plasma DNA at single-nucleotide resolution. The first approach used maternal blood samples and polymorphic differences between the mother and fetus to analyze the fetal methylome across the genome. The second approach used the methylation profile of maternal blood cells and the fractional fetal DNA concentration in maternal plasma to deduce the placental methylomic profile from maternal plasma DNA-sequencing data. RESULTS: Because of the noninvasive nature of these approaches, we were able to serially assess the methylation profiles of fetal, placental, and maternal plasma with maternal blood samples collected in the first and third trimesters and after delivery. Gestation-related changes were observed. The fetal methylation profile deduced from maternal plasma data resembled that of the placental methylome, both on a genomewide level and per CpG site. Imprinted genes and differentially methylated regions were identified from the maternal plasma data. We demonstrated one potential clinical application of maternal plasma bisulfite sequencing with the successful detection of fetal trisomy 21. CONCLUSIONS: We successfully analyzed fetal and placental methylomes on a genomewide scale, noninvasively and serially. This development offers a powerful method for research, biomarker discovery, and clinical testing for pregnancy-related disorders.
BACKGROUND: Epigenetic mechanisms play an important role in prenatal development, but fetal tissues are not readily accessible. Fetal DNA molecules are present in maternal plasma and can be analyzed noninvasively. METHODS: We applied genomewide bisulfite sequencing via 2 approaches to analyze the methylation profile of maternal plasma DNA at single-nucleotide resolution. The first approach used maternal blood samples and polymorphic differences between the mother and fetus to analyze the fetal methylome across the genome. The second approach used the methylation profile of maternal blood cells and the fractional fetal DNA concentration in maternal plasma to deduce the placental methylomic profile from maternal plasma DNA-sequencing data. RESULTS: Because of the noninvasive nature of these approaches, we were able to serially assess the methylation profiles of fetal, placental, and maternal plasma with maternal blood samples collected in the first and third trimesters and after delivery. Gestation-related changes were observed. The fetal methylation profile deduced from maternal plasma data resembled that of the placental methylome, both on a genomewide level and per CpG site. Imprinted genes and differentially methylated regions were identified from the maternal plasma data. We demonstrated one potential clinical application of maternal plasma bisulfite sequencing with the successful detection of fetal trisomy 21. CONCLUSIONS: We successfully analyzed fetal and placental methylomes on a genomewide scale, noninvasively and serially. This development offers a powerful method for research, biomarker discovery, and clinical testing for pregnancy-related disorders.
Authors: Stephanie C Y Yu; K C Allen Chan; Yama W L Zheng; Peiyong Jiang; Gary J W Liao; Hao Sun; Ranjit Akolekar; Tak Y Leung; Attie T J I Go; John M G van Vugt; Ryoko Minekawa; Cees B M Oudejans; Kypros H Nicolaides; Rossa W K Chiu; Y M Dennis Lo Journal: Proc Natl Acad Sci U S A Date: 2014-05-19 Impact factor: 11.205
Authors: Kun Sun; Peiyong Jiang; K C Allen Chan; John Wong; Yvonne K Y Cheng; Raymond H S Liang; Wai-kong Chan; Edmond S K Ma; Stephen L Chan; Suk Hang Cheng; Rebecca W Y Chan; Yu K Tong; Simon S M Ng; Raymond S M Wong; David S C Hui; Tse Ngong Leung; Tak Y Leung; Paul B S Lai; Rossa W K Chiu; Yuk Ming Dennis Lo Journal: Proc Natl Acad Sci U S A Date: 2015-09-21 Impact factor: 11.205
Authors: Jason C H Tsang; Joaquim S L Vong; Lu Ji; Liona C Y Poon; Peiyong Jiang; Kathy O Lui; Yun-Bi Ni; Ka Fai To; Yvonne K Y Cheng; Rossa W K Chiu; Yuk Ming Dennis Lo Journal: Proc Natl Acad Sci U S A Date: 2017-08-22 Impact factor: 11.205
Authors: K C Allen Chan; Peiyong Jiang; Carol W M Chan; Kun Sun; John Wong; Edwin P Hui; Stephen L Chan; Wing Cheong Chan; David S C Hui; Simon S M Ng; Henry L Y Chan; Cesar S C Wong; Brigette B Y Ma; Anthony T C Chan; Paul B S Lai; Hao Sun; Rossa W K Chiu; Y M Dennis Lo Journal: Proc Natl Acad Sci U S A Date: 2013-11-04 Impact factor: 11.205