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Article . 2015 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
Methods
Article . 2015
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Single molecule and single cell epigenomics

Authors: Byung-Ryool, Hyun; John L, McElwee; Paul D, Soloway;

Single molecule and single cell epigenomics

Abstract

Dynamically regulated changes in chromatin states are vital for normal development and can produce disease when they go awry. Accordingly, much effort has been devoted to characterizing these states under normal and pathological conditions. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most widely used method to characterize where in the genome transcription factors, modified histones, modified nucleotides and chromatin binding proteins are found; bisulfite sequencing (BS-seq) and its variants are commonly used to characterize the locations of DNA modifications. Though very powerful, these methods are not without limitations. Notably, they are best at characterizing one chromatin feature at a time, yet chromatin features arise and function in combination. Investigators commonly superimpose separate ChIP-seq or BS-seq datasets, and then infer where chromatin features are found together. While these inferences might be correct, they can be misleading when the chromatin source has distinct cell types, or when a given cell type exhibits any cell to cell variation in chromatin state. These ambiguities can be eliminated by robust methods that directly characterize the existence and genomic locations of combinations of chromatin features in very small inputs of cells or ideally, single cells. Here we review single molecule epigenomic methods under development to overcome these limitations, the technical challenges associated with single molecule methods and their potential application to single cells.

Related Organizations
Keywords

Epigenomics, Chromatin Immunoprecipitation, Microfluidics, DNA Methylation, Single-Cell Analysis, Mass Spectrometry, Software, Workflow

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    33
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Average
Top 10%
Top 10%
bronze