An Overview of Genome Organization and How We Got There: from
FISH to Hi-C
James Fraser,a
Iain Williamson,b
Wendy A. Bickmore,b
Josée Dostiea
Department of Biochemistry, and Goodman Cancer Research Center, McGill University, Montréal, Québec, Canadaa
; MRC Human Genetics Unit, MRC Institute of Genetics
and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdomb
SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .347
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .348
THE GENOME IN A THREE-DIMENSIONAL NUCLEUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .348
The Nuclear Lamina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .348
The Nuclear Pore Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .349
The Nucleolus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .349
VISUAL AND MOLECULAR ANALYSIS OF GENOME ORGANIZATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .349
Visualizing Genome Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .349
2D-, 3D-, and cryo-FISH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .350
Inferring Genome Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .350
3C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .351
4C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .352
5C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .352
Hi-C, GCC, and TCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .352
ChIA-PET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .353
CHROMOSOME ORGANIZATION IN THE NUCLEAR SPACE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .353
Chromosome Territories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .353
Chromosome Compartments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .355
TADs and Sub-TADs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .355
Chromatin Looping and Looping Out. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .356
Transcription Factories and trans Contacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .357
New Insight from Superresolution Microscopy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .358
KEY REGULATORS OF GENOME ARCHITECTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .358
CTCF as a Master Genome Organizer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .358
Cohesin as a Cell Type-Speciﬁc Regulator of Chromatin Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .359
Roles of CTCF and Cohesin in TAD Formation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .359
FUTURE OUTLOOK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .360
Limitations of 3C-Type Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .360
The challenge of cell populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .360
Ploidy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .360
Improving Genome-Wide 3D Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .361
Alternative Uses of 3C Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .361
Karyotyping with Hi-C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .361
Genome and haplotype assembly with Hi-C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .361
CONCLUSION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .362
ACKNOWLEDGMENTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .362
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .362
AUTHOR BIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .371
SUMMARY
In humans, nearly two meters of genomic material must be folded
to ﬁt inside each micrometer-scale cell nucleus while remaining
accessible for gene transcription, DNA replication, and DNA repair.
This fact highlights the need for mechanisms governing genome
organization during any activity and to maintain the physical
organization of chromosomes at all times. Insight into the
functions and three-dimensional structures of genomes comes
mostly from the application of visual techniques such as ﬂuorescence
in situ hybridization (FISH) and molecular approaches including
chromosome conformation capture (3C) technologies.
Recent developments in both types of approaches now offer the
possibility of exploring the folded state of an entire genome and
maybe even the identiﬁcation of how complex molecular machines
govern its shape. In this review, we present key methodologies
used to study genome organization and discuss what they
reveal about chromosome conformation as it relates to transcription
regulation across genomic scales in mammals.
Published 29 July 2015
Citation Fraser J, Williamson I, Bickmore WA, Dostie J. 29 July 2015. An overview of
genome organization and how we got there: from FISH to Hi-C. Microbiol Mol Biol
Rev doi:10.1128/MMBR.00006-15.
Address correspondence to Wendy A. Bickmore,
Wendy.Bickmore@igmm.ed.ac.uk, or Josée Dostie, josee.dostie@mcgill.ca.
Copyright © 2015, American Society for Microbiology. All Rights Reserved.
doi:10.1128/MMBR.00006-15
crossmark
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INTRODUCTION
Human genomic DNA was serendipitously identiﬁed in 1869
by Friedrich Miescher while searching for new proteins in the
pus of wounded soldiers (reviewed in reference 1). We have since
come to recognize that DNA is the genetic material containing all
the information essential for life and the basis for heredity (2). The
human genome is divided into 46 DNA molecules, or chromosomes,
consisting of pairs of chromosomes 1 to 22 (autosomes),
numbered sequentially according to their size, and of two sex
chromosomes that determine whether an individual is male or
female (Fig. 1). Together, these molecules contain over 6 billion
letters that when joined would measure ϳ2 m in length. It stands
to reason that the human genome must be extensively packaged in
order to ﬁt inside the nucleus, the size of which is in the micrometer
range.
The physiological state of genomic DNA is in the form of chromatin,
where it is bound to histone and nonhistone proteins. Histones
are by far the most abundant proteins in chromatin and
bind DNA mainly as nucleosomes composed of two copies each of
H2A, H2B, H3, and H4. Wrapping of DNA around nucleosomes
represents the ﬁrst level in packaging, which effectively shortens
the length of chromosomes by 7-fold. Histones, particularly their
amino- and carboxy-terminal tails, are subject to posttranslational
modiﬁcations (PTMs) on multiple residues, including
methylation, acetylation, phosphorylation, sumoylation, ADP-ribosylation,
or ubiquitinylation (3–5). PTMs regulate the activity
of underlying genomic regions by altering how nucleosomes interact
with each other and the DNA, thereby controlling access to
given sequences, and/or by recruiting effector proteins that bind
PTMs directly and interpret whether a region should be active or
not. As such, chromatin could be considered the basic regulatory
unit of genomes, and further packaging within the conﬁnes of the
three-dimensional (3D) nuclear space can have a direct impact on
its activity. The importance of three-dimensional chromatin organization
both for reducing chromosome size and for other genome
functions such as transcription is indeed recognized.
In healthy cells, higher-order chromatin organization is necessarily
consistent with genome function and regulation. This level
of organization is poorly described, with even the fundamental
principles guiding interphase chromatin folding and unfolding
still being unknown. How the genome folds is particularly important
for transcription because control DNA elements and their
target genes are not always next to each other along the linear
genome sequence, a fact which has been apparent since Barbara
McClintock’s early studies on transposition (6). More recently, it
was found that gene regulation by distal control elements such as
enhancers is often associated with physical contacts between
them. Given that tissue speciﬁcity is achieved through the combined
action of regulatory sequences on target genes, this observation
raises a compelling conundrum about speciﬁcity: if elements
like enhancers can act long distance on genes located
anywhere in the genome, how is speciﬁcity achieved? Three-dimensional
genome organization appears to play an important role
in this process by both promoting and restricting the access of
control DNA elements to genes. This review provides an overview
of genome architecture in mammals and the methods used to
study its function and regulation. While some of the principles
derived from these studies might be applicable to different species,
they might not be applicable to others, including the budding
yeast Saccharomyces cerevisiae.
THE GENOME IN A THREE-DIMENSIONAL NUCLEUS
The Nuclear Lamina
In mammals, the genome is contained within the cell nucleus, a
double-membrane organelle that effectively segregates the transcription
machinery from the cytoplasm, where protein production
occurs (Fig. 1). At its lowest resolution, genome organization
is guided by contacts with several nuclear substructures. The nuclear
envelope (NE) and its lamina are such structures. The outer
nuclear membrane (ONM) and inner nuclear membrane (INM)
of the NE are populated by nuclear envelope transmembrane proteins
(NETs), which associate with the lamin proteins on the INM
face to form the nuclear lamina (reviewed in references 7 and 8).
FIG 1 Human genome organization in a three-dimensional nucleus. (A)
Chromosome territories observed during interphase. Nuclear pore complexes
are shown perforating the nuclear envelope. The nucleolus is shown in white.
The nuclear lamina is represented as a ﬁlamentous mesh inside the double
nuclear membrane. (B) Example of a normal female karyotype as would be
observed by SKY (322) of mitotic cells.
Fraser et al.
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The lamins are intermediate ﬁlament proteins, which, together
with NETs, can bind many different proteins, including chromatin
components such as heterochromatin protein 1 (HP1) and
histones. Interactions between the lamina and the chromatin can
regulate the position of chromosomes in the nucleus (9) and various
other activities (10–12).
Chromatin interacts with the nuclear lamina through laminassociated
domains (LADs) that vary in size from 0.1 to 10 Mb and
frequently contain transcriptionally inactive heterochromatin
(13, 14). LADs are generally conserved but can also be cell type
speciﬁc (15). While conserved LADs span regions with very low
GC content and are gene poor, cell type-speciﬁc LADs usually
have a higher GC content and correlate with tissue-speciﬁc gene
expression (16). These observations suggest that inactive chromatin
regions, even those that are gene rich, tend to localize at the
nuclear periphery or the nucleolus (see below). In fact, simply
relocating a given region to the nuclear lamina is often sufﬁcient to
reduce gene expression (17, 18), but this is not always the case
(19).
The Nuclear Pore Complex
The nuclear pore complex (NPC) is another nuclear substructure
involved in regulating gene expression that might play a role in
chromatin organization (20). NPCs are evolutionarily conserved
structures that mediate all transport between the nucleus and the
cytoplasm (21). They are very large, ranging in mass from ϳ60
million to 100 million Da, depending on the organism, and exhibit
an 8-fold symmetrical/cylindrical geometry around a central
transport channel. NPCs “perforate” the two lipid bilayers of the
nuclear envelope and are composed of Ͼ30 different nucleoporin
proteins. Nucleoporins either are part of the core (integral proteins),
form ﬁlaments extending from the NPC core toward the
cytoplasm or nucleoplasm (those containing FG repeats, or FGNups),
or are part of the nuclear ﬁbers (nuclear basket) (Fig. 1).
The chromatin environment around the nucleoplasmic face of
NPCs differs from that of the rest of the inner nuclear membrane
despite their physical proximity to the lamina. Early electron microscopy
studies of nuclei in higher eukaryotes revealed the presence
of heterochromatin exclusion zones (HEZs) around NPCs
(22, 23). These gaps in the heterochromatin landscape of the inner
membrane vary in size, tend to be cone-like, and are populated by
euchromatin that extends from the NPC ﬁbers to the nucleoplasm.
Interestingly, contacts between nucleoporins and chromatin
could be captured by double cross-linking (24, 25). Although
direct physical NPC-chromatin interactions cannot be concluded
from these experiments, they nonetheless suggest an intimate link
between chromatin and the nuclear transport machinery. Accordingly,
several nucleoporins have been involved in the activation of
transcription (26, 27), and although a large fraction of nucleoporins
is found free in the nucleoplasm, NUP98 has been shown to
bind genes on its own or as part of the NPC (28). Nucleoporins
might also contribute to the compartmentalization of chromatin
marks along human chromosomes given that they have been
linked to insulation in yeast (29) and are required for HEZ establishment
in human (30).
The Nucleolus
A third type of nuclear landmark involved in genome organization
is the nucleolus. Nucleoli are dense structures, visible by light
microscopy, where rRNA synthesis and preribosome assembly occur.
They form around grouped rRNA genes from different chromosomes
that are transcribed by RNA polymerase I (Pol I) and are
located where nascent rRNA transcripts are processed and packaged
into preribosomes. Several RNA polymerase II genes were
found to copurify with the nucleolus when transcriptionally inactive
(31, 32). These nucleolus-associated domains (NADs) significantly
overlap LADs (33) and have similar GC-poor and genepoor
contents. Loci were actually found to colocalize with either
NADs or LADs, suggesting that a certain amount of redistribution
occurs between the two regions after mitosis and possibly that
similar factors target the inactive chromatin to either the lamina
or nucleolus (33, 34). Other substructures have also been linked to
chromosome organization and include Cajal and promyelocytic
leukemia (PML) bodies (35, 36).
VISUAL AND MOLECULAR ANALYSIS OF GENOME
ORGANIZATION
In addition to anchors with nuclear landmarks, human genome
organization is guided by chromatin interactions within (cis) and
possibly between (trans) chromosomes. These interactions are
driven by the chromatin landscape and are thus often tissue speciﬁc
and regulated. Our current view of genome organization is
based largely on, and perhaps is limited by, data derived mainly
from only two types of approaches. A variety of microscopy techniques,
including several ﬂuorescence in situ hybridization (FISH)
procedures, visualized by conventional or superresolution light
microscopy, is currently used to directly measure the proximity
between DNA segments. These methods yield information-rich
data about genome topography in individual cells that are well
complemented by insights obtained from cell populations using
molecular techniques such as chromosome conformation capture
(3C) and its derivatives. This second type of technique infers DNA
proximity by quantifying the frequencies of contacts between
DNA segments and considering them to be inversely proportional
to their original distance in vivo. Representative methods from
each category are presented below.
Visualizing Genome Organization
Until the advent of molecular techniques such as 3C and its highthroughput
derivatives, the predominant method for determining
nuclear organization and chromatin conformation was FISH.
This cytogenetic approach has been used for a variety of applications,
from clinical diagnostics to the study of genome architecture.
Sensitivity and resolution are limiting factors to consider
when designing a FISH experiment. Sensitivity depends on the
light-capturing capability of a particular microscope, therefore
determining the size of the probe (larger probes will generally
produce stronger signals). Probe size then brings in one aspect of
resolution: being able to distinguish between two points along the
length of a chromosome. For example, fosmid probes are frequently
used to measure chromatin compaction or to identify the
colocalization of genes with remote regulatory elements. However,
due to their size (ϳ40 kb), genomic distances of Ͻ100 kb
cannot be resolved. However, oligonucleotide-based probes of
ϳ10 kb in size with high hybridization efﬁciencies that produce
strong signals allow the chromatin conformation of sub-100-kb
genomic regions to be determined (37–40). There is also the resolution
of the light microscope to consider: conventional light
microscopy cannot resolve structures with sizes of Ͻ200 nm in the
x and y planes and Ͻ500 nm in the z plane. However, superresoHuman
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lution microscopy is bringing the light diffraction limit down to
the tens-of-nanometers scale (see “New Insight from Superresolution
Microscopy,” below).
2D-,3D-,andcryo-FISH.Theobservationthatcomplementary
nucleotide sequences could hybridize to each other and form
more stable complexes than noncomplementary sequences was
the basis for the ﬁrst in situ hybridization analysis that identiﬁed
the position of ribosomal DNA within the nucleus of a frog egg
(41). Molecular cytogenetics is based on this procedure, with the
replacement of radioactive labels with more stable ﬂuorochromes
(42) providing improved safety and ease of detection. The FISH
technique, then, relies upon probe sequences that target genomic
DNA, which either are directly labeled with a ﬂuorochrome or
have been modiﬁed to contain a hapten (such as biotin) and are
then rendered ﬂuorescent indirectly by enzymatic or immunological
detection. Target and probe DNAs must go through a denaturation
step to allow target-probe hybridization. The ﬂuorescently
labeled genomic loci can then be visualized by using a
ﬂuorescence microscope.
There are now a diverse number of FISH assays that can be
applied to megabase (metaphase chromosomes), submegabase
(interphase chromosomes), and even nucleotide (oligonucleotide
arrays) resolutions (43, 44). The two-dimensional FISH (2DFISH),
3D-FISH, and cryo-FISH variations of the FISH process
have been used to directly visualize and measure the nuclear distance
between DNA segments, the nuclear location of DNA segments
or indeed whole chromosomes, and the location of a DNA
segment in relation to the rest of the chromosome (i.e., within or
“looped out” of the chromosome territory [CT]). In the 2D-FISH
procedure, cells are ﬁxed in methanol ascetic acid (MAA), which
generates looser chromatin packaging due to the ﬂattening out of
the cells on the slide; however, results are comparable to those for
paraformaldehyde (pFA) ﬁxation of the same cells (45–49). The
advantages conferred by 2D- over 3D-FISH are clearer visualization
and rapid image analysis. This technique has been predominantly
used for determining the nuclear location of genes or translocation
from the nuclear periphery to the center and vice versa
(17, 50) and for determining the location of a DNA segment in
relation to the rest of the chromosome (45, 48, 51). We have used
2D-FISH to determine changes in chromatin condensation at the
submegabase level, during differentiation and across a polarizing
axis during development (45, 49) or between wild-type and mutant
cells (46, 52).
Not all direct analyses of chromatin structure can be adequately
visualized by 2D-FISH. Colocalization of discrete genomic loci, for
example, such as promoter-enhancer interactions, requires 3D
reconstruction of nuclei. In 3D-FISH, cells or tissue sections are
ﬁxed in 2 to 4% pFA, and image capture requires confocal microscopy
or deconvolution software if images are taken with a wideﬁeld
ﬂuorescence microscope that has the capacity to generate
image stacks through the z dimension. Applying this method to
tissue sections and cell lines derived from embryonic day 10.5
(E10.5) limb buds of mouse embryos, we found that the colocalization
frequency of Hoxd13, crucial for distal limb development,
with a limb-speciﬁc long-range enhancer is increased in expressing
cells (49, 53). This type of analysis, in combination with 3C,
has also been done on the Shh locus at the same stage of limb
development (see below) (54) and has been used for visualizing
the colocalization of a single olfactory receptor allele and an enhancer
element in individual sensory neurons (55). More recently,
the Lomvardas laboratory identiﬁed the colocalization of multiple
putative enhancers with individual olfactory receptor (OR) alleles
by chromosome conformation capture-on-chip (4C) and 3DFISH
and determined by Hi-C that many of these colocalizations
occurred in trans, which was also conﬁrmed by FISH (56). Each
olfactory sensory neuron expresses only one of ϳ2,800 olfactory
receptor alleles, and by generating a DNA FISH probe that simultaneously
detected most OR loci, this group showed that the silent
OR alleles converge to form exclusive heterochromatic foci in a
cell type-speciﬁc and differentiation-dependent manner (57).
FISH has been combined with live-cell imaging to show that
targeted transcriptional activation of a chromosome locus can induce
movement from predominantly peripheral to more interior
nuclear locations (58, 59) and that chromatin movement is restrained
by the nuclear architecture (60). Live-cell imaging involves
the incorporation of LacO or TetO arrays into genomic
regions of interest and subsequent illumination through the binding
of a vector cassette containing LacR/TetR with a fused ﬂuorescent
protein. Tagged loci can then be visualized, and their movement
can be monitored while cells are maintained under suitable
conditions. Live-cell imaging of a whole chromosome has also
been achieved by combining LacO/LacI tagging with photoactivatable
histones (61). Three-dimensional FISH has been used in
conjunction with live-cell imaging and mathematical models to
probe chromatin topography at the immunoglobulin heavy-chain
locus. This combined approach has elucidated the compartmentalization
and large chromatin changes that occur during B lymphocyte
development and has generated a strong model for how
the widely spread Igh coding elements (within three domains over
Ͼ2 Mb) can frequently interact (62, 63). Spatial conﬁnement of
the interacting domain was posited to be the main driver in
genomic interactions between the Igh coding elements, which is a
scenario that we identiﬁed by 3D-FISH combined with chromosome
conformation capture carbon copy (5C) for increased gene
enhancer colocalization (53).
The cryo-FISH technique has been applied for determining the
spatial intermingling of interphase chromosome territories (64)
and for validating results from 4C technology that identiﬁed functionally
signiﬁcant long-range chromosomal interactions (65).
Cryo-FISH involves pFA ﬁxation and then embedding of cell pellets
in sucrose before cells are frozen in liquid nitrogen. Ultrathin
cryosections (150 to 200 nm) can be generated to allow 2D, wideﬁeld
microscopy analysis of sequential sections through nuclei
with no reduction in z resolution while improving the hybridization
efﬁciency and preserving the chromatin ultrastructure.
Inferring Genome Organization
Whereas distances between genetic loci can be measured directly
in single cells by microscopy, the physical proximity of chromatin
can also be deduced based on the frequency at which DNA segments
interact with each other in cell populations in vivo.
This approach is based on the premise that interactions between
close regions are more likely to be captured by cross-linking than
are those between regions located far away and that the contact
frequency over the cell population at a given time essentially reﬂects
how chromatin is organized in the nucleus of individual
cells. Genome architecture can be modeled with this type of data
by considering the frequency to be inversely proportional to the
physical distance. Several molecular techniques are available to
quantify chromatin contacts, including 3C and 3C-related methFraser
et al.
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ods (3C technologies) and chromatin interaction analysis by
paired-end tag sequencing (ChIA-PET). In contrast to FISH,
where analysis is performed at the single-cell level, these techniques
always capture contacts in cell populations and yield average
structure models, with the exception of one study where single
cells were analyzed (66).
3C. The “chromosome conformation capture” (3C) technique
was developed by Dekker et al. Ͼ10 years ago (67) and is routinely
used to study the organization of short genomic regions at high
resolution compared to the resolution of most visual techniques
(Fig. 2) (68). During 3C, a population of cells is ﬁrst chemically
ﬁxed with formaldehyde to create covalent bonds between chromatin
segments (67, 69, 70). The cross-linked chromatin is then
digested with a restriction enzyme, which cuts at speciﬁc sites
across the genome. The type of enzyme selected deﬁnes the resolution
of the 3C experiment, with those recognizing palindromes
of 4 bp yielding higher-resolution libraries (256 bp) than 6-cutters
(i.e., enzymes that recognize 6-bp sequences; 4,096 bp). The digested
DNA is next diluted, and ligase is added to join cross-linked
fragments pairwise. This step generates unique DNA junctions
measurable by various PCR methods (67, 71, 72).
As interactions are measured individually, 3C is generally used
for small-scale analysis and was ﬁrst applied to conﬁrm the Rabllike
organization of chromosomes in Saccharomyces cerevisiae (67,
73, 74). It was then used to explain long-range regulation at the
␤-globin cluster during erythroid differentiation (75, 76) in studies
that followed the very ﬁrst demonstration of enhancer-promoter
looping at the rat prolactin gene (77). Long-distance cis
and trans physical contacts have since been found genomewide
and regulate the activity of enhancers at promoters, insulator
function, transcriptional silencing, imprinting, and X inactivation
(68, 78, 79).
Although the data generated with 3C and its related technologies
have largely been corroborated by other methods like FISH, it
appears that we still have much to learn about how these methods
work (80). For instance, it was originally assumed that dilution of
FIG 2 Inferring chromatin organization. The original 3C method is outlined from top to bottom on the left. Formaldehyde cross-linking captures interactions
between DNA segments (blue and green lines) mediated by protein complexes (colored shapes). The chromatin is next digested with a restriction enzyme, and
the free DNA ends are joined by proximity ligation before reverse cross-linking and puriﬁcation. The genome-wide ChIA-PET and Hi-C techniques are related
to 3C, and key steps are shown from left to right. The Y-shaped molecule represents antibodies. Biotinylated nucleotides are shown as red dots. Streptavidin beads
are shown in brown. The genome-scale 4C and 5C methods indicated at the bottom require the production of 3C libraries, and speciﬁc key steps are outlined from
left to right. Green arrows represent PCR primers speciﬁc to the bait region. 5C primers used during the ligation-mediated ampliﬁcation step are illustrated with
green and blue lines, where the light and dark gray moieties represent universal primer sequences.
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the 3C reaction mixture prior to ligation was required to favor the
ligation of cross-linked restriction fragments. Recent work shows
that this is likely not the case, since a substantial portion of the
digested DNA remains trapped within the cross-linked nuclei
(81). The nucleus-bound DNA was found to actually contribute
most of the measured 3C signal, indicating that ligation occurs
mainly while the DNA is still bound to the nuclei rather than in
solution (81). Dilution of 3C reaction mixtures might therefore
simply be required to decrease SDS concentrations prior to ligation.
In another study, it was found that enhancer-promoter ligation
products actually represent Ͻ1% of all the restriction fragments
subjected to ligation (82). This result likely reﬂects the
frequency of interactions between regulatory DNA elements in
vivo; the many different types of products generated at the ligation
step; as well as the efﬁciency, speciﬁcity, and stability of the formaldehyde
cross-links formed during a 3C experiment (82, 83).
Also, we recently demonstrated that data from FISH and 5C are
sometimes discordant at high resolution, suggesting that parameters
other than distance might inﬂuence the interaction frequency
(53).
4C. The chromosome conformation capture-on-chip (4C)
techniques were the ﬁrst set of methods designed to improve the
throughput and resolution of 3C (55, 65, 84, 85). Each technique
was developed independently to identify all contacts between a
given region and the rest of the genome without any prior knowledge
of the contacting domains (68). Generally, these methods
work through the generation of very short ligation products between
a speciﬁc restriction fragment (the “bait” or “anchor”) and
the rest of the genome, which are quantiﬁed by using either microarrays
or sequencing.
The “open-ended” 3C methodology (84) was used to identify
HoxB1-associated loci throughout the genome in mouse embryonic
stem (ES) cells. This approach identiﬁed interacting DNA
segments by sequencing of short ligation products ampliﬁed from
the 3C library by inverse PCR with nested HoxB1 primers. The
Hoffman group developed the “associated chromosome trap”
(ACT) technique to ﬁnd genomic domains that interact with the
mouse insulin-like growth factor (Igf2)/H19 imprinting control
region (86). This method differed from the open-ended 3C technique
in that short 3C ligation products were ﬁrst ligated to linkers
before ampliﬁcation with linker primers and nested Igf2/H19speciﬁc
primers. The “circular chromosome conformation capture”
approach (85) used the mouse H19 imprinting control region
(ICR) as a general model to introduce the technique. It was
also similar to the open-ended 3C technique but used custom
arrays speciﬁc to the 4C libraries to quantify novel chromatin
interactions. In contrast, the 4C method (65) used microarrays
containing genome-wide probes, which were later phased out in
favor of the more sensitive high-throughput sequencing (Fig. 2)
(87).
The chromosome conformation capture-on-chip method (65)
was the only easily scalable 4C approach. Here, ligation products
from a 3C library are further digested with a restriction enzyme
that cuts more frequently and are religated into circular DNA.
PCR primers designed to face outwards on either side of the bait
are then used to simultaneously amplify all interacting fragments,
and the amplicons are quantiﬁed by deep sequencing. The application
of sequencing greatly increased the scale and sensitivity of
the 4C assay, allowing genome-wide proﬁling of interactions with
the bait region. No other 3C-related technique can yet generate
comparable high-resolution interaction proﬁles. 4C was also
modiﬁed to include an immunoprecipitation step before the ﬁrst
ligation reaction, which enables the capture of chromatin interactions
mediated by speciﬁc proteins (88, 89).
5C. Since 4C elucidates only interactions between a single restriction
fragment and the rest of the genome, it cannot be used to
predict the conformation of entire domains or chromosomes (90,
91). The chromosome conformation capture carbon copy (5C)
method, on the other hand, is suitable for this type of analysis, as it
can detect up to millions of 3C ligation junctions between many
restriction fragment pairs simultaneously (92–97).
5C was designed to increase the throughput and accuracy of 3C
by combining 3C with a modiﬁed version of the ligation-mediated
ampliﬁcation (LMA) technique (Fig. 2). For 5C, a series of primers
is computationally designed (98) at the restriction site of each
fragment in the region of interest. These 5C primers are then
pooled with a 3C library where they anneal to targeted 3C fragment
ends. Primers located next to each other across the 3C junction
are next ligated together by Taq ligase, generating new synthetic
DNA molecules. This 5C library is ampliﬁed by using the
common tails present in the 5C primers and is quantiﬁed by using
high-throughput sequencing. This process results in the interrogation
of all chromatin interactions between fragments represented
by 5C primers. As the LMA step quantitatively ligates primers
onto 3C junctions, the 5C library is essentially a “carbon copy”
of existing 3C products.
5C can be used at different scales to probe various biological
questions. That it uses predeﬁned primer sets to measure chromatin
contacts, however, implies that 5C can measure contacts only
for regions covered by the primer library. 5C has been used to
study chromosome-sized regions at high resolution (99) by multiplexing
large numbers of primers. We determined the physical
organization of the human Hox clusters in various cell systems
with 5C (90, 100–102) and used the data to model spatial chromatin
organization computationally (103, 104). Other groups have
used 5C to study the alpha-globin cluster (105), the three-dimensional
organization of the bacterial Caulobacter crescentus genome
(106), the regulatory landscape of mouse X inactivation (91), and
changes in developmentally regulated chromatin domains (107).
Hi-C, GCC, and TCC. The Hi-C technology, sometimes called
genome-wide chromosome conformation capture, uses highthroughput
sequencing to directly quantify proximity ligation
products in contact libraries and therefore can be used to probe
the spatial organization of an entire genome (108–110) (Fig. 2).
The type of data produced with Hi-C is therefore more comprehensive
than what other 3C-type methods usually yield, as was
previously described in detail (111). The production of Hi-C libraries
is similar to the production of 3C libraries: cell populations
are ﬁrst chemically ﬁxed with formaldehyde, and the chromatin is
digested with a restriction enzyme or DNase I, which was recently
reported to achieve higher resolutions (112). The restriction fragment
overhangs are then ﬁlled with Klenow enzyme and a mixture
of deoxynucleoside triphosphates (dNTPs) that includes biotin-
14-dCTP. This is followed by blunt-end ligation with T4 DNA
ligase to join cross-linked DNA fragments, reverse cross-linking,
and puriﬁcation. These unprocessed Hi-C libraries are then
sheared by sonication and size selected prior to pulldown on
streptavidin-coated beads, which enriches the samples for DNA
sequences containing the informative ligation junctions.
Illumina’s paired-end sequencing has so far been the method
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of choice to identify the sequences on either side of Hi-C junctions,
but longer read lengths will increasingly make single-readthrough
approaches viable alternatives for Hi-C analysis. Current
methods map each side of the sequence reads separately to a reference
genome, where the quantities of all valid Hi-C pairs are
catalogued in a matrix format spanning the entire genome. Since
the complexity of Hi-C libraries is very high, a sizeable amount of
sequencing is required. The data are typically binned at different
sizes based on sequence depth and library quality. The organizations
of large genomes such as those of human and mouse have
consequently been described mainly at resolutions ranging from
40 kb to 1 Mb (109, 113), except for a recent study where the Hi-C
protocol was modiﬁed to achieve kilobase resolutions (114). Hi-C
data have been used to correlate genome architecture with several
genomic features such as replication timing. It was found that
differential ﬁring at origins could be explained by the spatial compartmentalization
of origins into different units of three-dimensional
chromatin architectures (115, 116). Hi-C was also used to
show that the proximity of chromatin correlates well with the
incidence of intra- and interchromosomal translocations from
double-stranded DNA breaks (117). The generation of very-highresolution
Hi-C data sets is prohibitively expensive at present, and
until sequencing costs decrease or alternative Hi-C protocols are
developed, approaches such as 4C and 5C remain ideally suited for
high-resolution analyses.
Two techniques similar to Hi-C were reported at around the
same time as the original Hi-C report and were used to explore
the genome organization of the yeast Saccharomyces cerevisiae.
The “genome conformation capture” (GCC) technique reported a
global map of chromosomal interactions in yeast by shearing and
directly sequencing conventional 3C libraries (118). Since the majority
of the DNA in 3C libraries does not contain any 3C ligation
junctions, this approach yielded a small number of usable reads
that was nonetheless sufﬁcient to identify contact networks in the
small S. cerevisiae genome. GCC has since been used to map the
spatial organization of the Escherichia coli nucleoid (119). Another
group developed a 4C-inspired approach, which involved the ligation
of biotinylated adaptors to mark ligation junctions (120).
This study generated kilobase-resolution maps that were used to
construct a three-dimensional model of the yeast genome.
Hi-C itself has been modiﬁed into the “tethered conformation
capture” (TCC) method, which generates the same chromatin interaction
data but boasts higher signal-to-noise ratios (121). The
premise of TCC is that random intermolecular ligation products
between non-cross-linked DNA fragments are the largest source
of noise and appear most frequently as interchromosomal contacts
in the data (120, 122). In TCC, the frequency of this event was
decreased by biotinylating the cross-linked protein-DNA complexes
and tethering them to streptavidin-coated magnetic beads
prior to the Hi-C ligation step. This modiﬁcation was suggested to
improve the efﬁciency of ligation between cross-linked DNA
strands compared to that of traditional Hi-C ligation under diluted
conditions. Given that most contacts were recently shown to
form in the nucleus-bound fraction instead of in solution (81), it
will be interesting to see how immobilization of complexes on
beads decreases the incidence of nonspeciﬁc ligation products.
Comparable signal-to-noise ratios have since been obtained with
the original Hi-C procedure (113), but the fact that the levels of
random ligation products tend to vary signiﬁcantly between experiments
nonetheless highlights the importance of optimizing
this aspect of library production to improve read depth (123).
ChIA-PET. While Hi-C can be used to identify contacts genome-wide,
it does not provide information about the nature or
the function of these interactions. The “chromatin interaction
analysis by paired-end tag sequencing” (ChIA-PET) method was
developed to probe this type of question by mapping chromatin
networks associated with speciﬁc proteins (124, 125). ChIA-PET
is a genome-wide technique that uses a chromatin immunoprecipitation
(ChIP) step to isolate interactions between all regions
bound by a particular protein. Like Hi-C, ChIA-PET identiﬁes
contacts in cell populations ﬁxed with formaldehyde, but the ﬁxed
cells are sonicated and used ﬁrst for ChIP of the protein of interest.
Biotinylated DNA linkers are next added at the ends of the coimmunoprecipitated
DNA segments, and the resulting cross-linked
DNA fragments are ligated together intramolecularly. The ChIAPET
junctions generated by this process are then excised with
restriction sites featured in the linkers prior to puriﬁcation on
streptavidin beads and paired-end sequencing. Each half of the
ChIA-PET products is ﬁnally mapped to a reference genome and
joined to reveal the location of protein-mediated chromatin contacts.
This method has been used to map networks associated with
RNA polymerase II, CCCTC-binding factor (CTCF), and the estrogen
receptor genome-wide (124, 126, 127).
CHROMOSOME ORGANIZATION IN THE NUCLEAR SPACE
A general model of genome architecture wherein chromosomes
are organized in hierarchical length scales has recently emerged
(128). From low to high resolution, chromosomes ﬁrst fold to
occupy distinct territories and positions in the nuclear space deﬁned
in part by interactions with nuclear subdomains, including
heterochromatic regions. Individual chromosomes are then
folded into compartments A (open/active) and B (closed/silent)
that preferentially interact together, respectively (109) (Fig. 3).
Within compartments, the chromatin is packaged in the form of
topologically associated domains (TADs), largely conserved between
cell types and across species. The chromatin is further
folded into sub-TADs, the topologies of which can vary in a tissuespeciﬁc
manner. Ultimately, genomic DNA is wrapped around
nucleosomes, which represents the ﬁrst level of genome folding;
however, how DNA is packaged between the resolution of the
10-nm ﬁber and sub-TAD scales is still largely unknown. Below,
we explore the organization of chromatin across genomic scales,
from chromosome territories to individual genes.
Chromosome Territories
The work of Carl Rabl and Theodor Boveri suggested long ago that
animal interphase chromosomes adopt a form of territorial organization
where interchromosomal contacts are minimized. Chromosome
territories (CTs) were visualized under a light microscope
in these early studies and identiﬁed while the movement of
DNA during the cell’s life cycle was tracked (73, 129, 130). More
specialized approaches using UV irradiation and pulse labeling
later supported the existence of CTs by demonstrating the preferential
distribution of DNA aberrations within chromosomes
(131). FISH has since been used to visualize the location of chromosomes
and clearly demonstrates their propensity to form individual
domains (132–134). 3C-based data corroborate the existence
of CTs. For instance, 4C analyses and genome-wide Hi-C
studies capture more intrachromosomal contacts than interacHuman
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tions between chromosomes, even for loci hundreds of megabases
apart on a given chromosome (65, 87, 109). Accordingly, Heride
et al. demonstrated by 3D-FISH that homologous chromosomes
in a diploid cell are far apart from each other (135). Recent work
on haplotype reconstruction using Hi-C data supports these ﬁndings
by demonstrating that chromosome haplotypes in diploid
cells do not interact frequently with each other (136).
Although chromosomes mostly keep to themselves, they can
considerably interact with other CTs. For instance, contacts between
small, gene-rich chromosomes in Hi-C libraries of human
lymphocytes were shown to occur more frequently than would be
expected based on their size (109). Several loci were shown to loop
out of their chromosome territory, coinciding with both an open
conformation and active expression and suggesting that the space
between chromosome territories might be important (48, 85, 86,
137–141). It is important to note, however, that our understanding
of the structure and biology of CTs is derived largely from
FISH experiments using probe sets that do not cover entire chromosomes,
and thus, such looping out might sometimes reﬂect
only extrusion from the visualized regions rather than the actual
CT (142). Nevertheless, comparison of the FISH signals from conventional
whole-chromosome “painting” (i.e., hybridization with
ﬂuorescently labeled chromosome-speciﬁc probes) to those from
exome painting of the entire chromosome revealed that chromatin
segments at the surface of CTs are enriched for exons, residing
largely away from the more compact CT core, which is consistent
with looping out (39). Several groups demonstrated a signiﬁcant
amount of intermingling between different chromosome territories
on a cell-speciﬁc basis (64, 143, 144), although the extent of
these contacts remains an open question. Similarly, interchromosomal
contacts between a select set of highly transcribed regions
were captured by TCC, and it was suggested that access to the
transcription machinery, possibly within transcription factories,
can drive the formation of contacts (121). Other studies using
genome-wide Hi-C data from mouse and human show that physical
proximity prior to chromosomal rearrangement correlates
FIG 3 Chromatin organization across genomic scales. The chromatin ﬁber from one chromosome is unraveled to illustrate four different organization levels
described previously in the text. Chromatin conformations are presented from low (top) to high (bottom) resolutions. The chromatin ﬁber and corresponding
chromosome territory are shown in pink. A and B compartments (multimegabase scale) are shown separately to highlight their inherently distinct nature,
although there is no evidence that their conformations differ at the level of TADs (megabase scale). Three examples of chromatin looping (submegabase scale)
are shown: (i) enhancer-promoter, (ii) enhancer-silencer, and (iii) insulator-insulator. E, enhancer; P, promoter; S, silencer; I, insulator.
Fraser et al.
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well with the incidence of translocations genome-wide (117, 145–
147).
Chromosomes have preferred radial positions in the nucleus of
mammalian cells (132, 148). 3D-FISH and chromosome painting
analyses in various cell types showed that chromosomes tend to
localize at either the nuclear center or the periphery according to
gene density. Human chromosome 19, for example, is a small
gene-rich chromosome more frequently found at the center of the
nucleus than chromosome 18, which is similar in size but gene
poor (149–152). This behavior has been observed for multiple
species, including other primates, rodents, cattle, and birds, and
thus appears to reﬂect a general feature of eukaryotic nuclear organization
(153–157). Accordingly, Hi-C analysis showed that all
small gene-rich human chromosomes interact more frequently
with each other than with the similarly sized chromosome 18
(109). Computational modeling of TCC data further indicated
that gene-dense chromosomes tend to localize to the center of the
nucleus, while a group of gene-poor chromosomes localized to the
periphery (121). Other studies argue that transcription activity,
chromatin remodeling, replication timing, chromosome size, GC
content, and gene density within megabase-sized genomic windows
contribute to the preferential position of chromosomes relative
to the nucleus center (50, 132, 150, 155, 158–164).
Whether the position of a speciﬁc chromosome and its interacting
partners is functionally important is unknown. The position
of CTs was shown to be cell type speciﬁc, suggesting that boundaries
shared between a given chromosome and its neighbors, along
with their relative nuclear position, might be functionally relevant
(161, 165, 166). However, while the CT position tends to be stable
within a given cell (167), it can vary signiﬁcantly from cell to cell,
indicating that not all trans contacts are required and that an exact
nuclear position may not be essential (128). This is supported by
the observation that tethering to the nuclear periphery mostly
alters the expression of genes in cis, with little effect on transcription
from other chromosomes, even though trans contacts must
be altered in these experiments (17). The positions of CTs might
be inﬂuenced by their internal chromatin organization. Chromosome
folding into a CT was suggested to act as a barrier to internal
nuclear movement (128, 167, 168), and accordingly, genes embedded
deep within a chromosome territory are more difﬁcult to
activate (39, 169).
While the function of CTs as a ﬁrst level of compartmentalization
is recognized, there is at least one example where they can
serve a very different role. It was found that the CT position in the
rod photoreceptor cells of nocturnal mammals is inverted relative
to the conventional architecture seen in diurnal animals and most
eukaryotic cells (170). In nocturnal retina rod cells, the heterochromatin
localizes at the center of the nucleus, and the euchromatin
lines the nuclear periphery. Computational modeling of
this nuclear organization in the eye indicates that rod nuclei can
act as collecting lenses that efﬁciently channel light in this conﬁguration,
thus contributing to an adaptation to the nocturnal life-
style.
Chromosome Compartments
The original Hi-C study reported the genome-wide chromatin
organizations of two human cell lines at a resolution of 1 Mb. The
resulting Hi-C contact matrices displayed a type of checkerboardlike
contact pattern where multimegabase regions interact even
across large distances along chromosomes (109, 171). Principal
component analysis (PCA) of the Hi-C data was used to segregate
contact frequencies into pairwise states, which uncovered the existence
of two types of chromosome compartments (109). The
open “A” compartments include regions with high GC content
that are enriched in genes, transcription activity, DNase I hypersensitivity,
and histone modiﬁcations associated with active chromatin
(H3K36me3) and poised chromatin (H3K27me3). In contrast,
B compartments show higher interaction frequencies, a
stronger tendency toward self-association, and high levels of the
silencing H3K9me3 mark. The position of B compartments was
also found to be highly correlated with late replication timing and
LADs, suggesting a proximity to the nuclear periphery not observed
for A compartments (116).
The segregation of CTs into A and B compartments has thus far
been observed for all autosomes and for all mammalian cell types
examined. Their distribution along chromosomes is also highly
stable across cell types but can vary, pointing to a regulatory role
(172). However, contacts within compartments tend to be weak
and spread over large groups of restriction fragments throughout
the domains, suggesting that compartments may exist only transiently
or may even form simply as a consequence of shared fea-
tures.
TADs and Sub-TADs
While exploring chromosome organization at smaller scales using
5C and Hi-C, blocks of dense chromatin were identiﬁed in human,
mouse, and Drosophila melanogaster, which interact more
frequently within themselves than with neighboring regions (91,
113, 173, 174). The existence of topologically associating domains
(TADs) is supported by the ﬁnding that FISH probes intermingle
more frequently within TADs than between them (91). TADs are
clearly visible in 5C and Hi-C data and are deﬁned by sharp
changes in the contact frequency from one region to the next.
TADs are thought to partition genomes into distinct globular
units that can remain spatially distant even if they are adjacent
along chromosomes. Perhaps because of their small genome sizes,
TADs have not been identiﬁed in bacteria or yeast (106, 120). They
are also not found in the larger plant genomes, where other types
of chromatin domains exist (106, 120, 175).
The ﬁrst identiﬁed TADs had an average size of between 0.5 and
1 Mb and were argued to have distributions that are highly conserved
between cell types and species (113, 174). Similar-sized
domains had previously been observed by microscopy (176). The
domains were visible at the edges between pairs of chromosome
territories and were suggested to represent basic CT building
blocks (177, 178). The fact that both TADs and CT domains exist
at approximately the same scale suggests that they may be the same
chromatin structures (128).
The actual molecular makeup of TADs and the mechanisms by
which they are formed are the subjects of much investigation.
Genes within TADs tend to be coexpressed during differentiation,
and the position of TADs correlates well with the distribution of
activating and repressing histone marks (91). The boundaries delineating
the original TADs were found to be enriched in transcription
start sites (TSSs), active transcription and the corresponding
histone marks, housekeeping genes, tRNA genes, and
short interspersed nuclear elements (SINEs) (91, 107, 113, 174).
TAD boundaries are also enriched in binding sites for the architectural
proteins CTCF and cohesin (see below). The fact that
deletion of the sequence at a TAD boundary results in a partial
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fusion of the domains and affects nearby gene expression highlights
the importance of this level of compartmentalization in the
regulation of genes (91). Also, the observation that the positions of
TADs viewed at the megabase scale are largely conserved suggests
that sequence-related factors either within TADs or at their
boundaries are involved in their formation (179). This is supported
by the observation that most long-range gene regulation by
enhancers is constrained within TADs (180–182).
High-resolution 5C analysis later demonstrated that TADs are
further divided into submegabase-sized structures that are loosely
referred to as “sub-TADs” (107). Finer substructures were actually
already visible within the original lower-resolution TAD data
(113) but were subsumed owing to lower conﬁdence. The average
size and position of TADs appear to depend on both data resolution
and the parameters used to identify them. TADs are usually
deﬁned computationally by the position of their boundaries, identiﬁed
with a directionality index (DI) (113). The DI is a measure of
the difference between upstream and downstream interactions
along a chromosome, and boundaries are deﬁned as locations
where high contact frequencies shift from downstream to upstream
regions. As such, DI data can vary signiﬁcantly depending
on the sliding window size selected. Whereas small window sizes
return smaller TADs, larger ones yield larger TADs that often
contain groups of smaller domains (183). In fact, when the original
Hi-C data from which megabase-sized TADs were ﬁrst identiﬁed
(113) were reanalyzed with a different algorithm that uses
smaller window sizes, it was found that the larger conserved TADs
tend to consist entirely of smaller domains with an average size of
0.2 Mb (183). These domains were stable between cell lines and
persistent across resolutions, and their boundaries were also enriched
in CTCF binding and activating histone marks.
The ﬁnding that TADs can be substratiﬁed into smaller domains
displaying a pronounced hierarchical organization (183)
suggests that topologies from different length scales interact with
each other to yield functional genome architectures. However,
how domains at the sub-TAD scale relate to each other and the
more conserved TADs is unknown. The notion that domains
might sequentially interact with each other in progressively larger
structures challenges the functional signiﬁcance of domain classiﬁcation
based on size. In contrast to TADs, chromatin organization
at the submegabase scale was found to be more tissue speciﬁc
and mediated by various protein complexes (107). Whereas invariant
subdomains relied on CTCF and the cohesin complex forming
long-range interactions, the more tissue-speciﬁc enhancer/promoter
contacts within and across subdomains required Mediator
and cohesin. The unique topological signatures of sub-TADs
therefore appear to reﬂect the levels and types of genomic activities.
Whether boundaries at the invariant subdomains are functionally
distinct from the ones characterized at larger TADs is
unclear, and it will be interesting to see if the structures deﬁned by
them are one and the same.
Chromatin Looping and Looping Out
The ﬁner structures observed at the submegabase scale in highresolution
conformation data highlight the existence of longrange
contacts that either form the base of stable domains or are
directly involved in regulating processes such as transcription.
These long-distance interactions reﬂect the ability of chromatin
ﬁbers to fold into “loops.” Chromatin looping was evident as early
as 1878, when Walther Flemming ﬁrst reported the existence of
“strange and delicate structures” in the nucleus of amphibian
oocytes (184). It was J. Ruckert, however, who concluded that they
were looped chromosomes, calling them “lampbrush chromosomes”
for their resemblance to the bristled brushes then used to
clean the soot off oil-burning lamps. A detailed visual and biochemical
account of chromatin loops came only some 50 years
later through the work of Joseph Gall (185) and facilitated the
discovery of DNA loops in human cells, most of which are several
orders of magnitude smaller than the very large loops of the lampbrush
chromosomes containing upwards of several hundred kilobases
of DNA (186, 187).
As it relates to transcription in mammals, the function of chromatin
looping appears chieﬂy to either promote or prevent contacts
between gene promoters and regulatory elements, particularly
enhancers. Since their initial discovery in viral genomes (188,
189), cellular enhancers have been found genome-wide and are
largely responsible for the tissue-speciﬁc expression of genes (190,
191). Despite the fact that enhancers and promoters can each initiate
bidirectional transcription (192), enhancers can activate
transcription in either orientation, whereas promoters cannot.
Enhancers can also drive transcription from a position upstream
of, downstream of, or within target genes (193). Enhancers are
found at various distances from the promoters that they regulate
and can work over very long distances in cis or even from different
chromosomes (56, 86, 141, 194–196). The observation that chromosome
territories intermingle signiﬁcantly in the nuclei of human
cells (64) supports the existence of trans-regulatory mechanisms,
although the functional relevance of most contacts with
respect to transcription remains largely undeﬁned.
A striking example of long-range transcription regulation was
described for the mouse Sonic hedgehog (Shh) gene, which is activated
during limb development by an enhancer known as the zone
of polarizing regulatory sequence (ZRS; also known as MFCS1),
imbedded within the intron of another gene located Ͼ1 Mb away
(197, 198). 3C analysis revealed that Shh activation by the ZRS
correlates with physical contacts between them (54), suggesting a
looping of the chromatin path between the enhancer and Shh. A
study that combined genetic manipulation of the enhancer locus,
transgenics, and 3D-FISH showed that while the 5= end of the
ϳ800-bp ZRS is sufﬁcient to drive an adjacent reporter gene, the
3= end of the enhancer is required for long-range regulation and
full expression of Shh in the developing forelimbs and hind limbs
and, consequently, complete digit sets (199). The Shh locus was
further observed by 3D-FISH to loop out of its chromosome territory
when the gene is active (54). Such looping out suggests
extensive unfolding of the locus and should not be confused with
chromatin looping per se, which in essence refers to proximity
between distal regions.
Whether or not looping out of a CT accompanies the formation
of chromatin loops during long-range regulation might actually
depend on the type of enhancer mechanism used to activate transcription.
Indeed, while chromatin looping has been the preferred
model to explain how enhancers activate promoters, there are
several variations differing in the way in which the enhancer-promoter
interaction is established (200). Enhancers have been suggested
to use either free or facilitated diffusion to reach their promoters
or to use an active mechanism such as “tracking” (201) or
“oozing” (202–204). The process used by enhancers to ﬁnd their
targets might signiﬁcantly impact whether activation is accompanied
by looping out of CTs, since active mechanisms like tracking
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will invariably transform the chromatin composition, while others
involving diffusion might not.
Given that control elements are not necessarily next to each
other in the linear genome, mapping of physical contacts is particularly
important to deﬁne functional connectivity. It was previously
thought that enhancers mostly regulate and interact with
their nearest gene(s), provided that this interaction does not cross
sites bound by CTCF and cohesin (205). A survey of enhancerpromoter
contacts assessed by 5C in 1% of the human genome
sequence, which identiﬁed Ͼ1,000 long-range contacts in different
cell lines, indicated that this is not likely the case (206). It was
found that long-range promoter interactions were often not
blocked by CTCF and cohesin binding sites and that enhancers
physically interact with the nearest gene in only 7% of cases. Interactions
between promoters and sequences were most frequent
120 kb upstream of transcription start sites and were asymmetric,
suggesting a directionality of chromatin looping (111, 206). It is
important to note that in this study, as in all similar studies, chromatin
interaction peaks were depicted as loop conﬁgurations, i.e.,
two contacting regions with the intervening sequence excluded,
but that this is not the only possible interpretation, since physical
contacts are captured in cell populations by the 3C technologies
(207, 208). Peak 3C signals may reﬂect only the frequent occurrence
of interactions in the sample, particularly in the presence of
high cell-to-cell variation. Looping conformations within TADs
actually have not yet been shown by complementary methods
such as FISH.
The facts that one enhancer can have more than one target gene
and, conversely, that multiple enhancers can regulate a single gene
(190, 209) further highlight the need for mapping of chromatin
contacts to understand the functional connectivity of regulatory
elements. The ␤-globin locus provides a good example of this type
of complex regulatory network. The ␤-globin locus harbors multiple
␤-globin genes that are expressed sequentially and in a tissuespeciﬁc
manner during development. The ␤-globin protein produced
from this region along with ␣-globin together form the two
subunits of hemoglobin, which transports oxygen in the blood of
mammals. Not surprisingly, disruptions of the ␤-globin gene or
its regulation are known to cause diseases such as sickle cell anemia
and ␤-thalassemia (210, 211).
The ␤-globin locus and its regulation have been extensively
characterized and are highly conserved in human and mouse. The
locus spans a region of ϳ60 kb on human chromosome 11 and
mouse chromosome 7 and features an ϳ15-kb domain upstream
of the genes called the “locus control region” (LCR) (212, 213).
The LCR consists of numerous enhancers required for proper
␤-globin expression during development. Speciﬁc looping of the
LCR and expressed ␤-globin genes was ﬁrst demonstrated with 3C
in mouse erythroid cells, where chromatin around the LCR preferentially
interacted with the active genes compared to brain tissue
(76). A closer proximity between the LCR and expressed ␤-globin
genes was also inferred in a separate study of the locus using RNA
tagging and recovery of associated proteins (RNA-TRAP) in embryonic
liver cells (214).
These data strongly suggested that looping between the LCR
and genes was important for transcription. Further analyses
showed that insertion of the LCR into a gene-dense region affected
transcription at distances of up to 150 kb and was frequently associated
with positioning of the LCR outside its chromosome territory
(215). Contacts between the LCR and the active ␤-globin
genes persisted after transcription inhibition, demonstrating that
they form independently of RNA Pol II binding (216). Looping
between the LCR and its targets at the ␤-globin locus was later
shown to require several protein complexes. Erythroid-speciﬁc
transcription factors, which include GATA-1, EKLF1, and TAL1,
were independently found to be required for looping at the locus
(217–220). In one study, the Ldb1 protein complex composed of
GATA-1, TAL1, LMO2, and Ldb1 was shown to mediate loop
formation (221). The Ldb1 complex is known to promote the
transcription of numerous erythroid genes, including Myb, where
long-range interactions similar to the ones found at the ␤-globin
locus have been identiﬁed (222–224), and might therefore promote
loop formation genome-wide in erythrocytes.
Transcription factor complexes do not appear entirely responsible
for the chromatin conformation at the ␤-globin cluster. First,
the observation that the locus itself loops out of its chromosome
territory prior to gene activation, possibly toward more active
regions between chromosomes, suggests considerable changes in
chromatin composition during activation (225). Also, in addition
to contacts between the LCR and the active ␤-globin genes, a
network of interactions was found to link DNase I-hypersensitive
sites from both sides of the locus, the LCR, and the active gene(s)
(76, 226). A similar 3D clustering pattern, termed an “active chromatin
hub” (ACH), was observed at the active ␣-globin locus,
suggesting that both hemoglobin components are regulated by a
conserved spatial mechanism (105). The ACH is thought to reﬂect
the types of cis-acting regulatory elements that come together
in the three-dimensional space to coordinate gene expression, and
the nuclear compartmentalization provided by the formation of
an ACH was suggested to promote transcription irrespective of
the surrounding chromatin activity (75, 78, 227). CTCF binds at
hypersensitive sites on either side of the ␤-globin locus and is
required for ACH formation. Interestingly, depletion of CTCF
destabilized chromatin looping at the ␤-globin locus and altered
its histone acetylation and methylation proﬁles but did not significantly
affect gene expression, pointing to a predominant insulator
role in ACH function (228–231).
CTCF looping is also important to control the physical access of
enhancers to promoters. Studies have thus far identiﬁed many
more active enhancers than promoters in the human genome, and
contacts between them therefore must be tightly regulated (190,
191). Such regulation might be achieved by compartmentalizing
inactive genes away from enhancers by differential CTCF looping,
as was described for the apolipoprotein locus (232), or by domain
formation at the level of sub-TADs and TADs to insulate and/or
alter the three-dimensional path of chromatin.
Transcription Factories and trans Contacts
The physical clustering of actively transcribed genes into “transcription
factories” was ﬁrst observed when nascent transcripts
were monitored by pulse labeling in HeLa cells (233, 234). These
transcripts were shown to colocalize with RNA polymerase II in
foci that also contained splicing and transcription factors as well as
chromatin-remodeling enzymes (235–237). The number of foci
observed in interphase nuclei ranges from hundreds (238) to a few
thousand (239) and appears to depend on both the cell type and
the imaging technique used to detect them. In any case, multiple
genes can be predicted to share the same transcription factory
given that active genes far outnumber the total foci detected at any
given time in the nuclei of cells (240). Accordingly, active genes
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were shown to colocalize into factories (238), and the transient
crowding of enzymes at these sites is thought to enhance transcription
and splicing efﬁciency (241–243).
In some cases, gene activation has actually been found to correlate
well with relocalization into transcription factories containing
other active genes (225, 238, 244). Although some colocalized
genes may be coregulated by the same set of transcription factors
(88), there more often appears to be little in common between the
genes other than their transcription state. For instance, work on
the globin genes shows that they can localize in factories with
other active but unrelated genes (65, 105, 238, 245, 246), but they
have also been found around splicing speckles (239). Although
focal concentrations of RNA polymerase II have been shown to
occur transiently and thus may not always represent real factories
(241), genome-wide chromatin conformation analysis with TCC
supports their existence genome-wide as a cluster of active genes
without a shared purpose or function (121).
Long-range contacts are likely to constrain how genes are organized
in chromosomes and how chromosomes are positioned in
the nucleus. The overall tendency of active gene-rich regions to
cluster into transcription factories may thus play an important
role in genome organization as a source of chromatin loops and by
compartmentalizing coregulated genes (246–248). The extent to
which transcription factories may function in genome architecture
is unknown, but their limited number and spatial positions
were previously suggested to promote self-organization into tissue-speciﬁc
conformations (249). Regardless of the mechanisms
at play, three-dimensional modeling of chromatin interaction
data supports a major role for looping in genome organization
because of its considerable impact on the entropy of chromatin
ﬁbers (250).
New Insight from Superresolution Microscopy
Several ﬂuorescence imaging methods have overcome the diffraction
resolution limit of light (251). Of these, there are three main
techniques (required for imaging of internal cell structures):
structured illumination microscopy (SIM), stimulated emission
depletion (STED), and photoactivation localization microscopy/
stochastic optical reconstruction microscopy (PALM/STORM)
(252).
While STED and PALM/STORM have been used predominantly
to image large protein clusters and organelles located in/on
the cell membrane or in the cytoplasm, SIM has been employed to
gain increased insight into nuclear ultrastructures and interchromosomal
topography. SIM illuminates a sample with a series of
high-spatial-frequency stripes, recording a series of frames at different
stripe orientations with different shift positions, which results
in 9 to 15 frames per ﬁnal superresolution image (253). It is a
hybrid technique: the whole ﬁeld of view is imaged as in standard
wide-ﬁeld microscopy while the sample is being scanned with the
stripe pattern in the manner of confocal microscopy. By illuminating
the sample using a striped pattern while rotating the orientation,
ﬂuorescing signals can be captured at different times, thus
resolving structures that are closer to each other than would otherwise
be permitted by the actual light diffraction limit. The lateral
resolution limit is halved to ϳ100 nm, and indeed, the resolution
is doubled in all dimensions (252).
At the subnuclear level, SIM was ﬁrst used to resolve peripheral
nuclear ultrastructures such as the nuclear pore complex
and the nuclear lamina (254). The Cremer group combined
3D-SIM with 3D-FISH in a proof-of-principle study that
showed that key chromatin features were largely well preserved
(but with some perturbations) after 3D-FISH down to the resolution
limit imposed by 3D-SIM (255). This combined technique,
in conjunction with 5C, revealed that adjacent DNA
sequences within the same TAD colocalized to a greater extent
than did adjacent sequences in different TADs (91). Also, the
combination of 3D-SIM and 3D-FISH showed that chromatin
decompaction occurs at key differentiation genes and that
these genes migrate to the center of the nucleus as neural differentiation
progresses during embryonic development (256).
3D-SIM was also used to show a striking difference in the functional
organizations of transcriptionally active CTs and the
Barr body (257). In a novel approach based on SIM but replacing
the single light beam with a lattice light sheet that enabled
single-molecule live imaging, Sox2 binding sites were mapped
and shown to form discrete clusters in live ES cells (258).
The three techniques described here all have strengths and
weaknesses. The decision of which one to use depends on several
parameters such as resolution and whether the specimen is ﬁxed
or live imaging is to be done. So far, only SIM has been combined
with FISH to investigate chromatin ultrastructure in reported
studies; however, the other techniques should also be able to provide
further insight into chromatin topography, particularly
STED microscopy when looking at interprobe distances within
compact domains.
KEY REGULATORS OF GENOME ARCHITECTURE
Despite the fact that radial chromosome positions can vary
signiﬁcantly between generations (34, 167, 259), whether chromatin
organization is itself epigenetic at high resolution is unknown,
although it was recently suggested that by mutually
affecting each other, the chromatin state and architecture take
part in a self-enforcing feedback process to propagate cell fate
memory (260). Proteins that can both physically shape and
regulate the composition of chromatin are thus likely to play
important roles in spatial inheritance. The CTCF protein and
the cohesin complex are two chromatin components thought
to shape the human genome in hierarchical length scales, which
have been linked to transcription regulation, imprinting, and X
chromosome inactivation.
CTCF as a Master Genome Organizer
The CCCTC-binding factor (CTCF) is an essential protein that is
highly conserved from ﬂy to human (231, 261, 262). It is known to
exert vastly diverse nuclear functions (263), and its functional
diversity is thought to originate from the way in which it binds
DNA. CTCF binds genomic DNA through a central 11-zinc-ﬁnger
DNA binding domain with close to 100% homology between
chicken, mouse, and human. It can bind to a wide range of sequences
by the combinatorial use of its zinc ﬁngers, but most
binding sites (75 to 90%) contain a core consensus of 11 to 15 bp.
It was postulated that both CTCF and DNA adopt different conformations
upon binding to accommodate different zinc ﬁnger
combinations based on the underlying sequence and that these
allosteric shifts determine the kinds of proteins that can bind
CTCF (262). CTCF is thus viewed as a “multivalent factor” because
it binds to different proteins depending on the sequence
with which it interacts, leading to different posttranslational modFraser
et al.
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iﬁcations of itself and surrounding proteins and different functional
outcomes (262–264).
CTCF is a vertebrate protein shown to bind insulator sequences
directly and to help establish their activity (265). Insulators are
DNA elements that control transcription either by stopping the
spread of histone marks (266) or by preventing contacts that activate
transcription (267). CTCF is known to regulate gene expression
by both mechanisms (196, 215). For instance, it can act as an
insulator/barrier at heterochromatin boundaries and divide chromatin
into silent and active domains (268–270). It is also known
for its enhancer-blocking function to repress transcription (271,
272), an activity that likely plays a pivotal role in promoting or
preventing long-distance contacts between regulatory elements
and target genes.
Highlighting its importance in the regulation of gene expression,
CTCF was found to bind Ͼ30,000 sites across the human
genome, and many of these sites are conserved across cell types
and species (269, 273–276). Genome-wide analyses showed that
groups of genes within regions ﬂanked by CTCF are likely to be
coregulated, in contrast to gene pairs divided by CTCF binding, a
characteristic that may be linked to CTCF’s ability to demarcate
chromatin domains (276, 277). Regions that frequently bind
CTCF often contain genes featuring different tissue-speciﬁc promoters,
suggesting that it might be involved in the complex regulation
of such genes (276). CTCF also appears to be involved in the
formation of lamina-associated domains, as suggested by its enrichment
at LAD boundaries (13) and by ChIA-PET interaction
data (126). Together, these results point to a general role for CTCF
in genome function, partly by segregating transcriptional activity
to speciﬁc nuclear areas.
The exact mechanism(s) by which CTCF contributes to insulator
function is unknown, but evidence strongly suggests that it
involves the manipulation of chromatin architecture (263, 278).
Indeed, CTCF was shown to mediate long-range chromatin interactions
such as those observed during enhancer-promoter looping
(231, 232, 263, 279–282). In fact, CTCF’s roles in transcription,
imprinting, and X chromosome inactivation could likely be
explained mainly by its ability to form long-range DNA contacts
and spatially organize the chromatin. In addition, CTCF was
found to mediate contacts both within and between chromosomes
(85, 86, 283). One of the mechanisms by which CTCF
might physically recruit remote sites along and between chromosomes
is through its ability to oligomerize (284, 285). Exactly how
the protein achieves this is unknown, but CTCF was found to bind
asymmetrically across strong topological borders in a manner that
predicts the directionality of CTCF-CTCF interactions, suggesting
that the types of long-range contacts made by the protein are
deﬁned by the position and orientation of binding sites along the
linear sequence (114, 286).
One of the most well-characterized CTCF chromatin architectures
was identiﬁed at the imprinted Igf2/H19 locus and regulates
imprinting. It was found that Igf2 repression on the maternal allele
is achieved by preventing the interaction between the gene and
a distal enhancer through the formation of chromatin loops mediated
by CTCF (282, 287). In contrast, CTCF binding at the ICR
and insulator looping are prevented by DNA methylation on paternal
alleles, allowing the Igf2 gene to contact the distal enhancer
by transcription factor-mediated looping. These studies were the
ﬁrst ones to demonstrate cross talk between “classical” epigenetics
and spatial chromatin organization (282, 287, 288).
CTCF looping is essential for gene regulation and relevant to
human health. For example, mutations that affect CTCF binding
at the H19/Igf2 locus were shown to result in serious human syndromes
(289–291). Improper CTCF binding has also been linked
to other diseases, such as Huntington’s disease, where mutations
that destabilize CTCF binding sites appear to cause trinucleotide
repeat expansion (292–294).
Cohesin as a Cell Type-Speciﬁc Regulator of Chromatin
Organization
Cohesin is another important genome organizer involved in transcription
regulation. Cohesin is a multisubunit protein complex
composed of the Smc1A, Smc3, Rad21, and Stag1/2 (SA1/2) proteins.
It was initially recognized for its role in sister chromatid
cohesion, mitotic and meiotic chromosome segregation, and
DNA repair (295–297). The ﬁrst indication that cohesin regulates
transcription was the ﬁnding that mutations in Nipped-B facilitate
the activation of the Drosophila cut and Utrabithorax homeobox
genes by distal transcriptional enhancers (298). Nipped-B and
its human orthologue Nipped-B-like (NIPBL) are factors required
for the loading of cohesin onto the DNA that colocalize with
CTCF/cohesin but also bind at independent sites like promoters
(299). Mutations in the cohesin Smc1A or Smc3 subunit and in
the NIPBL gene are responsible for many cases of Cornelia de
Lange syndrome (CdLS) (300–303). Like CTCF, cohesin was also
found to bind thousands of sites in interphase nuclei, but its binding
is much more tissue speciﬁc (304–308). Cohesin was shown to
colocalize with Mediator genome-wide and facilitated enhancerpromoter
looping at the Nanog gene (304). Although it was originally
thought to be important for mouse stem cell maintenance
by directly regulating the Oct4 and Nanog pluripotent genes (304),
a more recent study indicates that this is not likely the case (309).
Cohesin colocalizes extensively with CTCF throughout the genome.
In fact, many of the original CTCF-mediated looping contacts
were later found to require cohesin. However, CTCF does
not exclusively colocalize with cohesin and vice versa. Also, sites
bound by CTCF and cohesin, colocalized or not, can each colocalize
with Mediator. Together, these ﬁndings point to the existence
of functionally distinct CTCF and cohesin complexes that
are DNA bound and involved in deﬁning the chromatin architecture.
How distinct CTCF/cohesin complexes relate to each other
and genes to coordinate architecture and transcription is unknown.
However, the fact that CTCF and cohesin were found to
be enriched at TAD boundaries suggests that they at least play a
role in partitioning the transcriptional landscape of the genome
(113).
Roles of CTCF and Cohesin in TAD Formation
The enrichment of CTCF and cohesin at TAD boundaries is one of
the most interesting TAD features and the subject of much scrutiny.
While clearly enriched at boundaries, the absolute number of
CTCF and cohesin binding sites within the TADs themselves is
much greater, suggesting multiple functions for the proteins
and/or that TAD structures may be more complex than currently
thought (113). CTCF is nonetheless required for proper TAD formation
since its depletion results in fewer intra-TAD contacts and
in more inter-TAD interactions (299).
If CTCF truly contributes to delineating TADs, it might do so by
mechanisms similar to the ones used at heterochromatin boundary
sites. Supporting this possibility is one study where deletion of
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a TAD boundary led to the formation of contacts across the deleted
region and transcription misregulation (91). The interaction
proﬁles of TADs point to higher enhancer-promoter interaction
frequencies within domains than between them. Thus, by physically
segregating chromatin regions into topological domains,
CTCF and cohesin might deﬁne functional microenvironments
for regulatory elements and target genes where contacts are more
easily nucleated while preventing chromatin states from spreading
and limiting contacts with the rest of the genome (128, 310). This
model is supported by the fact that partitioning of the genome into
TADs correlates with enhancer-promoter units, clusters of coregulated
promoters and enhancers (179, 311). Physical modeling
of 3C-type data that explored all possible TAD conformations
within population-averaged data sets further suggested that contacts
between control elements dynamically ﬂuctuate rather than
exist as stable structures (208).
The role of cohesin at TAD boundaries and its relationship with
CTCF are unclear. In contrast to CTCF, cohesin depletion only
reduces the intensity of intra-TAD interactions without affecting
the actual TAD location or organization (299, 312, 313), which is
consistent with a role for cohesin in mediating tissue-speciﬁc enhancer-promoter
contacts at the submegabase scale (107). Cohesin
is known to be required for CTCF-based insulation (113, 308,
314, 315), and depletion affects insulation patterns genome-wide,
correlates with global gene expression changes, and negatively affects
hierarchical long-range interactions between TAD boundaries
separated by multiple domains (312, 313). These ﬁndings collectively
suggest that cohesin may organize chromatin in such a
way as to prevent interactions between particular TADs and isolate
gene expression states from one another (312). The mechanism(s)
by which cohesin exerts this regulation on chromatin organization
remains unclear but might depend on CTCF.
FUTURE OUTLOOK
For over a decade, 3C-based approaches have frequently been revised
and improved upon to ﬁll certain roles and explore new
areas of the genome. 3C paved the way for the second-generation
technologies 4C and 5C, which in turn enabled the development
of Hi-C and related third-generation technologies. The introduction
of Hi-C presents a potentially scalable approach for enabling
genome-wide chromatin interaction analyses that can supersede
the beneﬁts of ﬁrst- and second-generation 3C technologies. Even
still, is it possible to improve upon Hi-C in much the same way
that it improved upon second-generation technologies? What
form might the fourth generation of 3C-based technologies take?
What else could they be used for?
Limitations of 3C-Type Analyses
The challenge of cell populations. The ultimate goal of studying
chromatin conformation is to understand how it behaves at the
single-cell level. As discussed above, FISH is perfectly suited for
this type of analysis, and the information that it provides is well
complemented by 3C-type analyses over large domains and even
genome-wide. Both FISH and 3C approaches have previously
pointed to signiﬁcant differences in chromatin conformations between
individual cells (14, 148, 167, 316, 317). Variations may
stem from multiple sources, including the cellular state and the
cell cycle stage.
Perhaps the most overlooked aspect of 3C-type data is the fact
that they are derived from populations of cells, and thus, these
data inherently reﬂect averaged chromatin interaction patterns.
This type of data does not provide information, for instance,
about the stability or the strength of interactions, whether these
interactions occur in all cells, or whether additional parameters
such as chromatin composition and ﬂexibility affect the contact
frequency. Deﬁning these aspects of chromatin interactions will
be essential to distinguish between what actually represents in vivo
chromatin architecture and the contacts captured by way of the
accessibility of chromatin fragments to each other, both of which
are likely important in the overall scheme of transcription regulation.
Also, during 3C-based analyses, only one ligation event is
ever possible for each restriction fragment end such that at most,
two different contacts are detectable for each fragment and from
each chromosome copy of a karyotypically normal cell. Thus, the
observation that contacts can be observed simultaneously for
most genomic regions in Hi-C data sets (109, 113) implies the
existence of many potential interaction partners and certain variability
in chromatin organization. A single study demonstrated
this variability by applying Hi-C at the single-cell level (66, 318).
Although little information about chromatin architecture is individually
contained in these data sets due to the pairwise nature of
Hi-C contacts, data pooling recapitulated population-based Hi-C
data remarkably well, including the existence of TAD-like struc-
tures.
A potentially major contributor to chromatin conformation
variability is the cell cycle stage. Cycling populations exist as a
mixture of G1-, S-, G2-, and M-phase (mitotic) cells, where the
entire genome is duplicated, folded into metaphasic chromosomes,
and unfolded upon cell cycle reentry. Few conformation
studies thus far have applied any form of synchronization or sorting
to obtain homogeneous cell populations. At least at the megabase
scale, interphase chromatin organization appears remarkably
stable, since the Hi-C contact proﬁles of G1-, S-, and G2-phase and
nonsynchronized HeLa S3 cells were shown to be highly correlated
(99). In contrast, the chromatin of mitotic cells, which represent
ϳ3% of asynchronous cell populations, was strikingly different,
having lost the hallmark plaid interaction patterns of
higher-order chromosome compartments and the smaller topologically
associating domains. A different study comparing the
Hi-C contact proﬁles of G1-sorted and unsynchronized neural
stem cells also found a high level of correlation (313). As both
studies focused on chromatin organization at larger scales, it will
be interesting to see how much change occurs at higher resolutions
where more differences would be expected.
Ploidy. For genomes such as those of human and mouse, all
autosomes exist as two copies, each inherited from separate parents
and bearing slightly different DNA sequences. Distinguishing
between haplotypes is generally difﬁcult without deep sequencing,
since sequence variations are rather small. For this reason, 3Ctype
analyses usually ignore the diploid nature of the samples, and
sequencing reads are averaged for each chromosome. In much the
same way, any differences arising from chromosome numbers and
structural abnormalities are simply combined and averaged based
on a reference genome, although such aberrations are easily identiﬁed
in the data (see below).
There have been surprisingly few Hi-C studies using haploid
cells, unlike 5C, which has been used to model the X chromosome
in male and female cells (91, 208). With the exception of one 4C
study using single nucleotide polymorphisms (SNPs) to tease sequence
reads apart to examine the inactive X chromosome (87),
Fraser et al.
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the chromosome organization in haploid cells has not yet been
explored. Such data would be highly valuable for generating accurate
chromatin models of individual chromosomes, which could
then be used as a reference to help elucidate their structure within
diploid cells.
Improving Genome-Wide 3D Mapping
While Hi-C provides genome-wide coverage, it does so at a cost,
and any advance improving sequencing depth will beneﬁt this
approach. The sequence complexity of Hi-C libraries is orders of
magnitude higher than that of the original genome sequence because
of the combinatorial nature of 3C products. This high complexity
currently demands very deep sequencing to obtain highresolution
data, and for this reason, techniques such as 4C and 5C
remain better suited for analysis at the submegabase scale. If progress
in high-throughput sequencing maintains its current pace, it
will soon be feasible to generate high-conﬁdence restriction fragment
contact maps of Hi-C libraries. Such advances in sequencing
depth would also make it possible to further increase resolution by
analyzing ultracomplex Hi-C libraries generated with combinations
of restriction enzymes or with enzymes that cut more frequently.
This type of assay might be necessary to understand the
true nature of TADs. Analysis of Hi-C libraries at this resolution
scale would require specialized bioinformatics tools, to deal with
both the sheer quantity of data and the potential additional biases
appearing at ultrahigh resolution (123).
Better genome-wide 3D maps might additionally be improved
by amending the Hi-C protocol. First, simply sequencing longer
paired-end reads (ϳ100 bp) that map more frequently than
shorter reads will produce more Hi-C read pairs. Increasing the
quality of Hi-C libraries will also directly inﬂuence data resolution.
Decreasing the incidence of random ligations, which materializes
as higher interchromosomal interaction frequencies and a
shift of the cis/trans contact ratio, would increase the amount of
usable Hi-C data (121). Although the presence of random ligation
products can be corrected during analysis (113), eliminating them
at the source would improve data quality.
Another way to increase the number of usable Hi-C reads is to
introduce a genome capture step in the Hi-C protocol. Most genomes
contain regions that cannot be mapped with high conﬁdence
because they either are highly repetitive or have low complexity.
A large portion of human Hi-C libraries is composed of
these regions, and removing them prior to sequencing will yield a
larger number of informative reads. Whole-genome capture
could be applied by hybridizing all nonrepetitive sequences onto
beads or by removing repetitive sequences from libraries. Alternatively,
selective genome capture may be performed with speciﬁc
oligonucleotide to bind Hi-C libraries. The latter approach, considered
a fourth-generation 3C technology, has already been successfully
used by several groups to study cis-regulatory landscapes
(capture-C [319]), to identify targets of breast cancer risk variants
(capture-Hi-C [320]), and to obtain more detailed maps of the
H19/Igf2 and ␤-globin networks (targeted chromatin capture
[321]).
Alternative Uses of 3C Technologies
Karyotyping with Hi-C. Karyotype abnormalities featuring one
or more chromosome aberrations are often found in human disease,
particularly in cancers. Chromosome anomalies can be numerical
or structural and lead to the misexpression of genes. The
large-scale translocations identiﬁed in leukemia, for example, can
be capitulated in the production of oncogenic fusion proteins
driving uncontrolled cell proliferation. Generally, these types of
aberrations can be visualized with a form of chromosome painting
called spectral karyotyping (SKY), where each chromosome is
represented by a different color (322). Translocations identiﬁed
with SKY are easily detected by microscopy as molecules containing
ﬂuorescence markers from different chromosomes.
While SKY is one of the more accurate techniques used to
identify structural abnormalities, its 1- to 2-Mb resolution limit
implies that structural defects smaller than 1 Giemsa band will not
be detected (322). However, these aberrations are well within the
resolution range of 3C-based data and could easily be identiﬁed by
using this type of technology. The value of 3C-based methods in
identifying chromosomal rearrangements was ﬁrst shown with 4C
by examining baits at frequently rearranged sites (320, 323). High
interaction frequencies are usually biased toward sites close to
each other along the linear genome and are not found between
chromosomes in cells with normal karyotypes. Thus, regions displaying
very high interaction frequencies must be next to each
other on the same DNA molecule. It was shown that 4C contact
proﬁles reﬂect the position of breakpoints as very high contact
frequencies between the bait region and the translocated domain
on the other side of the fusion site. Chromosomal rearrangements
can therefore be mapped genome-wide at high resolution with
Hi-C simply by identifying highly interacting regions that map to
different chromosomes on a reference genome (99, 324).
Genome and haplotype assembly with Hi-C. De novo sequencing
and assembly of genomes remain challenging, mainly because
the grouping of short reads into “contiguous sequences” (contigs)
is difﬁcult. Even though high sequence coverage is now possible
with deep sequencing technologies, the small size and generally
low quality of the reads render assembly difﬁcult and result most
often in fragmented genome maps, particularly for large and repeat-rich
genomes (325, 326). Centromeres, telomeres, and other
regions rich in repetitive sequences all contribute to this challenge.
The approaches currently used to build large-scale genome models
from short sequence reads actually rely on preexisting detailed
genetic and physical maps like the ones generated by the Human
Genome Project. Still, there remain unplaced contigs even in the
well-studied human reference genome (327). Analyses of lesswell-studied
organisms for which no such reference genome has
yet been established must therefore rely entirely on the billions of
short reads obtained with current deep sequencing approaches.
In 2013, two groups used slightly different approaches to explore
the value of three-dimensional chromatin data in deﬁning the positions
of contigs along chromosome sequences. In one study, a pro-
gramnamed“LACHESIS”(ligatingadjacentchromatinenablesscaffolding
in situ) was developed to assemble genomes de novo from
Hi-C and shotgun sequencing data (324). LACHESIS ﬁrst assembles
contigs by using shotgun sequencing data and aligns Hi-C read pairs
onto them. Hierarchical clustering is next applied to classify contigs
into chromosome groups, the number of which is initially speciﬁed.
Thelocationandorientationofeachcontigwithinachromosomeare
then identiﬁed, guided again by the Hi-C read pair information.
LACHESIS has 99% accuracy with regard to the order and orientation
of contigs, and errors tend to involve regions enriched in duplications
and repeats.
This approach works primarily because intrachromosomal
Hi-C interactions are more frequent than those between chromoHuman
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somes. Any interchromosomal interactions in the Hi-C data
would incorrectly be considered part of the same chromosome
and might lead to fusions. It is therefore not surprising that
LACHESIS generates such artifacts in the reconstruction of small
gene-rich human chromosomes, which are known to interact
more frequently with each other than their gene-poor counter-
parts.
A second group used Hi-C data in a similar way to map the
locations of 65 previously unplaced contigs in the human genome
(328). This group found that long-range Hi-C interactions between
regions located Ͼ1 Mb apart were sufﬁcient to place the
contigs. A comparison of this approach to another method that
uses admixture mapping and SNP data showed a high level of
similarity, validating the use of Hi-C (327). Although Hi-C appears
well suited to the assembly of genomes de novo, the 1 ϫ 107
cells required for a Hi-C experiment may not always be feasible
and limit its use (324, 329). More recently, however, Marbouty et
al. developed a metagenomic chromosome conformation capture
approach called “meta3C,” which can be used to deﬁne the average
chromosome organization of known and new microorganisms
in complex populations (330).
Because chromosomes fold into distinct territories, all of them,
including homologues, should remain physically separated from
each other, and 3C-type chromatin contacts should primarily derive
from within individual chromosomes. If this were the case,
separation of homologous pairs would be possible by using Hi-C
and whole-genome sequencing data (136). By using Hi-C data of
hybrid mouse embryonic stem cells with a known haplotype, it
was shown that only 2% of the intrachromosomal interactions
actually derive from between chromosome copies. A total of
99.5% of the known cell haplotypes could be reconstructed from
only the Hi-C data by using a modiﬁed version of HapCUT tuned
to deal with Hi-C data (136, 331). This included correctly linking
haplotypes across metacentric centromeres, rendered feasible by
the larger insert sizes of the Hi-C read pairs. These studies demonstrate
previously unrealized uses for Hi-C beyond 3D chromatin
organization.
CONCLUSION
Work from around the world altogether paints the human genome
as a complex molecular machine performing a myriad of
functions that is built on a four-letter language. Everything happening
within cells is ultimately derived from this simple language.
Regardless of what came ﬁrst, a change in what binds to the
DNA or in how the chromatin folds, it is now clear that chromosome
organization both reﬂects and guides transcription regulation.
Understanding the chromatin structure-function relationship
might therefore require a change in the types of questions that
we ask, from what the roles of contacts are to how they are formed.
A closer look at what leads to given structures might indeed point
to which control mechanisms could explain the RNA quantities
measured in cells. The only nontrivial issue remaining is that we
do not yet know how to read these mechanisms from 3C-type
data. Combining 3C-based approaches, paired or not with genome
capture, with other types of epigenomics data might help
bridge this knowledge gap and reveal fundamental principles underlying
genome structure and function.
ACKNOWLEDGMENTS
Work in the laboratory of J.D. is supported by the Canadian Cancer Society
(CCS) (grant number 702834) and the Canadian Institutes of Health
Research (CIHR) (MOP-115127). The laboratory of W.A.B. is funded by
the Medical Research Council UK.
J.D. is an FRSQ (Fonds de la Recherche en Santé du Québec) Research
Scholar.
We declare that we have no conﬂicts of interest.
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James Fraser is a senior Ph.D. student in the
laboratory of Dr. Josée Dostie from the department
of Biochemistry at McGill University. His
research has focused on exploring how threedimensional
chromatin organization changes
during cellular differentiation in both human
and mouse using the 5C and Hi-C techniques.
His interest lies particularly in the implementation
of new technologies at the bench and in the
development of new computational approaches
for the analysis of 3C-based genomics data.
Iain Williamson received his Ph.D. from the
University of Edinburgh, United Kingdom, in
2013 for work on long-range transcriptional
regulation of limb developmental genes in the
mouse under the supervision of Professors
Wendy Bickmore and Robert Hill. He is currently
a Postdoctoral Fellow in the laboratory of
Professor Wendy Bickmore, undertaking research
on how chromatin conformation inﬂuences
gene activity in development.
Wendy A. Bickmore received her Ph.D. (1986)
from the University of Edinburgh, for work on
the evolution of the X and Y chromosomes in
primates. She is currently Head of the Chromosomes
and Gene Expression Section at the MRC
Human Genetics Unit, Institute of Genetics and
Molecular Medicine, University of Edinburgh.
She showed that different human chromosomes
have preferred positions in the nucleus,
related to their gene content, and addressed
how genes are organized and packaged in the
nucleus and how they move in the cell cycle and during development. Current
research focuses on how the spatial organization of the nucleus inﬂuences
genome function in development and disease.
Continued next page
Human Genome Structure and Function across Size Scales
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Josée Dostie received her Ph.D. (2000) from
McGill University, Montréal, Canada, for work
conducted on mRNA translation and nucleocytoplasmic
transport under the supervision of
Dr. Nahum Sonenberg. She then completed her
ﬁrst postdoctoral training (2000 to 2004) with
Dr. Gideon Dreyfuss at the University of Pennsylvania,
where she studied the function of ribonucleoprotein
complexes. She was a Postdoctoral
Fellow (2004 to 2007) in the laboratory of
Dr. Job Dekker at the University of Massachusetts,
where she trained in genome organization. She is now Associate Professor
in the Biochemistry department at McGill and an Associate member of
the Goodman Cancer Center in Montréal. Her research interests include
how noncoding RNAs and chromatin structure regulate gene expression in
cancer and during development.
Fraser et al.
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