4.6 Review

Genome-wide epigenomic profiling for biomarker discovery

期刊

CLINICAL EPIGENETICS
卷 8, 期 -, 页码 -

出版社

BMC
DOI: 10.1186/s13148-016-0284-4

关键词

Genome-wide epigenetic profiling; Biomarker discovery; Miniaturization; Automation; Single cell; DNA methylation; WGBS; ATAC-Seq; Stratification; Precision medicine

资金

  1. European Union grant BLUEPRINT [FP7/2011: 282510]
  2. SysStemCell [ERC-2013-ADG-339431]
  3. Netherlands Organization for Scientific Research) [NWO-VIDI 864.12.007]

向作者/读者索取更多资源

A myriad of diseases is caused or characterized by alteration of epigenetic patterns, including changes in DNA methylation, post-translational histone modifications, or chromatin structure. These changes of the epigenome represent a highly interesting layer of information for disease stratification and for personalized medicine. Traditionally, epigenomic profiling required large amounts of cells, which are rarely available with clinical samples. Also, the cellular heterogeneity complicates analysis when profiling clinical samples for unbiased genome-wide biomarker discovery. Recent years saw great progress in miniaturization of genome-wide epigenomic profiling, enabling large-scale epigenetic biomarker screens for disease diagnosis, prognosis, and stratification on patient-derived samples. All main genome-wide profiling technologies have now been scaled down and/or are compatible with single-cell readout, including: (i) Bisulfite sequencing to determine DNA methylation at base-pair resolution, (ii) ChIP-Seq to identify protein binding sites on the genome, (iii) DNaseI-Seq/ATAC-Seq to profile open chromatin, and (iv) 4C-Seq and HiC-Seq to determine the spatial organization of chromosomes. In this review we provide an overview of current genome-wide epigenomic profiling technologies and main technological advances that allowed miniaturization of these assays down to single-cell level. For each of these technologies we evaluate their application for future biomarker discovery. We will focus on (i) compatibility of these technologies with methods used for clinical sample preservation, including methods used by biobanks that store large numbers of patient samples, and (ii) automation of these technologies for robust sample preparation and increased throughput.

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