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The authors thank the Knut and Alice Wallenberg Foundation, the
Wellcome Trust, and the Swedish Foundation for Strategic
Research for their support (S.L. and L.E.B.) and the Allen Institute for
Brain Science founders, P. G. Allen and J. Allen, for their vision,
encouragement, and support (E.L.).
Single-cell epigenomics: Recording
the past and predicting the future
Gavin Kelsey,1,2*† Oliver Stegle, 3, 4*† Wolf Reik1,2, 5†
Single-cell multi-omics has recently emerged as a powerful technology by which different
layers of genomic output—and hence cell identity and function—can be recorded
simultaneously. Integrating various components of the epigenome into multi-omics
measurements allows for studying cellular heterogeneity at different time scales
and for discovering new layers of molecular connectivity between the genome and its
functional output. Measurements that are increasingly available range from those
that identify transcription factor occupancy and initiation of transcription to long-lasting
and heritable epigenetic marks such as DNA methylation. Together with techniques in
which cell lineage is recorded, this multilayered information will provide insights into a
cell’s past history and its future potential. This will allow new levels of understanding of cell
fate decisions, identity, and function in normal development, physiology, and disease.
The discovery and description of individual cells in the body has fascinated biologists and pathologists since the cell was discovered (1). With the advent of molecular cell biology, methods have been developed for measuring
properties and functions of single cells at increasing resolution. This includes, among others, fluorescent protein reporters and single-molecule
detection of RNA or DNA. Only recently however, have high-throughput
sequencing methods allowed
us more comprehensive access to genomic information
in single cells. Hence, single-cell RNA sequencing has revealed how heterogeneous
the transcriptome of individual cells can be within
a seemingly homogeneous
cell population or tissue, providing insights into cell identity, fate, and function in the context of both normal biology and
pathology [Stubbington et al. (2) and Lein et al.
( 3)]. A few years from now, we likely will have
access to total RNA, small and long noncoding
RNA, and transcriptional initiation output of
the transcriptome (in addition to the stable
cytoplasmic component). The development of
single-cell RNA sequencing was followed by
single-cell genome sequencing, which has provided new insights into genomic stability and
genomic variations that occur in physiology and
in disease—for example, in cancer, reproductive
medicine, or microbial genetics ( 4).
Epigenetics connects the genome with its functional output (Fig. 1). Various epigenetic marks
have been described, ranging from DNA (such as
DNA methylation) to histone modifications, which
can affect the way the cell reads its genome and
hence its transcriptional output. Transcription
factors that bind to DNA can create or alter
epigenetic states (e.g., open or closed chromatin
and higher-order chromatin conformation), or
their binding can be sensitive to preexisting epigenetic
states. Some epigenetic marks
can also be heritable from one
cell generation to the next (
during mitosis) or from one organism generation to the next
[intergenerational or trans-generational epigenetic inheritance ( 5)]. However, there
are key questions in epigenetics that can only be addressed by determining
the epigenome in single cells. For example, how is
transcriptional heterogeneity between cells connected with epigenetic heterogeneity (if it is),
do changes in transcription precede or follow
epigenetic marks when cells change their fate or
function, and are epigenetic states better or worse
identifiers of rare cell populations and transitional states than the transcriptome? The recent
development of single-cell epigenomics methods
is beginning to allow us to address these fundamental questions.
Single-cell epigenome methods can identify
open or closed chromatin, including nucleosome
positioning ( 6–11). From these, one can infer the
likelihood of certain transcription factors to bind
or not bind to specific DNA sequences within individual cells, and methods are being developed that allow for assaying transcription factor
binding directly—for example, single-cell chromatin immunoprecipitation sequencing (
ChIP-seq). Thus, one can currently measure (albeit
imperfectly) the heterogeneity in a cell population
of key histone marks associated with transcriptional states, such as H3K4me3, which indicates
1Epigenetics Programme, Babraham Institute, Cambridge
CB22 3AT, UK. 2Centre for Trophoblast Research, University
of Cambridge, Cambridge CB2 3EG, UK. 3European
Molecular Biology Laboratory, European Bioinformatics
Institute, Wellcome Genome Campus, CB10 1SD Hinxton,
Cambridge, UK. 4European Molecular Biology Laboratory,
Genome Biology Unit, Heidelberg 69117, Germany. 5Wellcome
Trust Sanger Institute, Cambridge CB10 1SA, UK.
*These authors contributed equally to this work. †Corresponding
author. Email: firstname.lastname@example.org (G.K.); oliver.
email@example.com (O.S.); firstname.lastname@example.org (W.R.)
“[T]oday we can probe
the majority of