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Understanding and Engineering
Chromatin as a Dynamical System
across Length and Timescales
Christopher P. Johnstone,1,4 Nathan B. Wang,1,4 Stuart A. Sevier,2,3,* and Kate E. Galloway1,*
1Department of Chemical Engineering, MIT, 25 Ames St., Cambridge, MA 02139, USA
2Department of Systems Biology, Harvard Medical School, Boston, MA, USA
3Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
4These authors contributed equally
*Correspondence: s.a.sevier@gmail.com (S.A.S.), katiegal@mit.edu (K.E.G.)
https://doi.org/10.1016/j.cels.2020.09.011
SUMMARY
Connecting the molecular structure and function of chromatin across length and timescales remains a grand
challenge to understanding and engineering cellular behaviors. Across five orders of magnitude, dynamic
processes constantly reshape chromatin structures, driving spaciotemporal patterns of gene expression
and cell fate. Through the interplay of structure and function, the genome operates as a highly dynamic feed-
back control system. Recent experimental techniques have provided increasingly detailed data that revise
and augment the relatively static, hierarchical view of genomic architecture with an understanding of how dy-
namic processes drive organization. Here, we review how novel technologies from sequencing, imaging, and
synthetic biology refine our understanding of chromatin structure and function and enable chromatin engi-
neering. Finally, we discuss opportunities to use these tools to enhance understanding of the dynamic inter-
relationship of chromatin structure and function.The regulation of the human genome in three-dimensional
space is staggeringly complex. The genome self-organizes
over five orders of magnitude of space and time, with dynamic
processes such as transcription, cellular division, and differenti-
ation constantly reshaping structures (Phillips et al., 2012).
Robust cellular function requires proteins to access genetic in-
formation in an ever-shifting landscape of structures. Chromatin
(see Glossary), the chromosomal DNA polymer wrapped around
nucleosomes, is dynamically remodeled during replication and
transcription. Emergent hierarchies of actively folded structures
may dynamically facilitate or impede access to different regions
across these scales (Zhang et al., 2019). Understanding the
mechanisms of chromatin organization is crucial to understand-
ing how cells function (Figure 1).
With increasing resolution, the relatively static, hierarchical
view of genomic architecture is being revised with an under-
standing of how dynamic processes drive organization across
the breadth of length and timescales. The local and global state
of chromatin is dynamically altered through a combination of
induced forces, torques, and chemical alterations which both
operate on, and bridge, the respective length and timescales
of cellular function (see Box 1). These effects provide a scaffold
for epigenetic memory and a channel for information transmis-
sion that must work collectively for the emergence of spatial
and temporal fidelity of crucial cellular functions such as replica-
tion and transcription (Rivera-Mulia et al., 2019). Gaps remain in
our understanding of how transcription and transcriptional regu-
lation impact structures to enable the emergence of new tran-
scriptional states. Within these gaps, there exist opportunities424 Cell Systems 11, November 18, 2020 ª 2020 The Author(s). Pub
This is an open access article under the CC BY license (http://creativefor new technologies and integrative approaches to bridge
models between imaging and sequencing technologies, be-
tween static and dynamic observations, and between single-
cell and bulk population analyses. Recent experimental develop-
ments have begun to bridge this divide, ushering in the opportu-
nity to extract greater understanding of how organizational struc-
tures contribute to functional behavior across various scales.
Here, we provide an introduction to chromatin and the modes
of chromatin regulation to acquaint new researchers in the field.
With this background, we review emerging technologies in
sequencing and imaging that are forging new models for how
chromatin is dynamically regulated in space and time. Addition-
ally, we examine how engineered chromatin systems, from syn-
thetic transcription factors to synthetic contact domains, engage
emerging questions in gene regulation and chromatin dynamics
from a bottom-up approach. Finally, we present a discussion of
the emerging questions in chromatin regulation and opportu-
nities to deploy novel technologies to address these questions.
INTRODUCTIONTOCHROMATINANDDYNAMICMODES
OF GENE REGULATION
Chromatin can facilitate pattern formation and information trans-
fer across multiple time and length scales (Figure 1A). Nano-
meter-scale interactions such as transcription factor binding
occur on the order of milliseconds (Fierz and Poirier, 2019), while
chromosome territories, the largest subnuclear structures,
persist over many cell divisions (Dekker and Mirny, 2016).
Because of the short-ranged stochastic nature of DNA-proteinlished by Elsevier Inc.
commons.org/licenses/by/4.0/).
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Figure 1. Scales and Modes of Chromatin Structure and Organization
The organization of cellular chromosomes extends over multiple overlapping genomic and physical length, and timescales.
(A) Time and genomic length scales: Numerous forms of organization have been identified ranging from small-scale patterns of histone positioning at the 1-kb-
length scale, mechanical domains extending into the 10-kb scale, contact domains on the scale of 100s of kb and whole regions of chromosomes on the 1-Mb
scale. Dynamic processes affecting each length scale occur on timescales that typically scale with the relevant length scale. A combination of chemical effects,
mechanical forces, and architectural proteins contributes to the formation of each organizational mode. The distinct but overlapping modes help to link DNA
base-pair-level activities, such as transcription and DNA binding, to large-scale structures such as DNA loops and chromosomal compartments. These links
provide important mechanisms for the dynamic interactions of chromosomal structures and functions.
(B) Physical length scales: Multiple layers of organization also occur at different physical length scales spanning 5 orders of magnitude. At the largest scale, the
nucleus is organized into chromosome territories and subnuclear structures like nucleoli. At the smallest scale of 1–5 nm, DNA-protein interactions are relevant
(Dekker and Mirny, 2016).
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Box 1. Mechanical Epigenetics
Many DNA-based processes such as transcription, replication, and histone dynamics both control and are controlled by the me-
chanical state of DNA. We refer to this important layer of regulation and feedback as mechanical epigenetics.
To understand the role of mechanical epigenetics in gene regulation, it is important to highlight the physical properties of chro-
matin. DNA is composed of individual strands of covalently bonded nucleotides, which are then non-covalently paired with com-
plementary strands (of order kbT, see Glossary) to from the double helix. This relatively weak bonding makes local melting of the
DNA possible through molecular interactions and use of ATP and NTP (~20 kbT ) which occur on the same energy scale (Marko,
2018). The helical nature of naked DNA introduces both substantial topological and mechanical properties into the polymeric
behavior of DNAwith bending and twisting persistence on the nm scale (Marko, 2018). To form chromatin, DNAmust be physically
wrapped around nucleosomes, which requires the chemical energy of nucleosome-DNA attraction to overcome mechanical en-
ergy associated with wrapping (Fierz and Poirier, 2019). In yeast and human cells, transcriptionally induced supercoiling demarks
bounds of gene activity (Achar et al., 2020; Baranello et al., 2018; Corless et al., 2014; Naughton et al., 2013) and has been impli-
cated in important structural (Le and Laub, 2016) and functional (El Houdaigui et al., 2019; Kim et al., 2019a) properties of bacterial
transcription. Biophysical models of gene expression and DNA replication predict changes in chromatin structure that feed back
into changes in transcriptional activity (Sevier, 2020; Sevier and Levine, 2018). The propagation of local mechanical forces and
torques applied to DNA have the ability to influence DNA-bound processes at long distances (>10 kb). Thus, mechanical epige-
netics create a hidden, dynamic interaction across many processes and scales. For instance, RNAPII is capable of exerting forces
of up to 50 pN and torques of over 10 pN–nm, sufficiently forceful to influence histone binding and induce folded, buckled regions
of chromatin (Fierz and Poirier, 2019; Le et al., 2019; Ma and Wang, 2016; Teves and Henikoff, 2014). Recent experiments have
started to reveal a rich relationship between transcription and nucleosome positioning and the underlying mechanical state of
DNA (Hsieh et al., 2020; Naughton et al., 2013). These phenomena collectively link dynamics on the timescale of DNA twisting
(sub-second) to dynamics on the timescale of DNA bending (seconds to minutes) (Marko, 2018), which can be further integrated
by histone and large-scale chromatin conformational changes (hours) (Fierz and Poirier, 2019). Thus, mechanical effects provide
an important avenue for information storage between DNA-bound processes and chromatin organization, leading to a funda-
mental connection between chromosome function and structure.
(Continued on next page)
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Box 1. Continued
As yet, few genomic analyses incorporate supercoiling assays into integrative analyses due to the relatively low signal of the assay
(Corless et al., 2014; Teves and Henikoff, 2014). As improved supercoiling probes are developed, supercoiling structures may
explain the connectivity between transcription and small-scale structures that predict differences in gene expression. Connecting
single-cell tracking of transcriptional activity and small-scale structural features will provide better resolution of the casual link be-
tween these structures and gene expression.interactions (1–5 nm) (Dekker andMirny, 2016), cellular functions
spanning long timescales are dynamically mediated by overlap-
ping scales of genome organization. For mammals, the total
chromosomal length exceeds 1 billion nucleotides. If arranged
end-to-end and stretched-out length wise, the combined length
of human chromosomes can span two meters (Alberts et al.,
2002; Milo and Phillips, 2015). Each macroscopically long chro-
mosome is packaged into a nucleus with a 10-mm diameter,
requiring a compaction of 5 orders of magnitude (Figure 1B)
(Milo and Phillips, 2015). Encoded DNA sequences determine
DNA base pairing, transcription factor recognition and binding,
and local DNA structures. Biochemical modification of DNA via
methylation and other functional groups impacts protein binding
and recognition in ways that can be propagated over many cell
cycles (Liu et al., 2016; Seisenberger et al., 2013).
Beyond the linear sequence, the first major building block of
chromatin organization is the nucleosome. Nucleosomes are
10-nm-sized protein complexes composed of eight histones
wrapped ~1.6 times by a ~146-bp length of DNA (Fierz and Poi-
rier, 2019; Luger et al., 1997). The position and geometry of nu-
cleosomes within a chromosome represents a primary layer for
encoding spatial and structural information in the genome that
forms the basis of nucleosome-mediated epigenetics (Lai and
Pugh, 2017). Two factors control the occupancy and positioning
of histones: the affinity of the histone to DNA and the energy
required to bend the DNA around the histone (Zuiddam et al.,
2017). Histone modifications, such as acetylation and methyl-
ation on specific histone residues, alter both the physicochem-
ical properties of the histone and the affinity of histone-DNA
binding. While early in vitro studies of chromosomes at high
salt concentrations revealed periodic organization of many his-
tones into a 30-nm fiber, recent experiments reveal an inter-
phase nucleus that is decorated with heterogeneous fibers of
chromatin composed of clusters of nucleosomes (discussed
further alongside imaging methods) (Maeshima et al., 2019; Ou
et al., 2017).
On the length scale of tens of kb, multiple patterns of
increased chromosomal contact exist between and within cod-
ing and noncoding regions of the genome. At this scale, many
genomic elements support important cell-type-specific behav-
iors (Beagan et al., 2020; Kragesteen et al., 2018). These include
changes involving coding elements, such as increased physical
interactions between promoters and enhancers, as well as addi-
tional influences from long noncoding RNA (lncRNA), insulators
and silencers. Consequently, methods of assaying proximity
by identifying DNA contacts have driven recent efforts to under-
stand gene regulation in eukaryotes (Figure 2) (Beagan et al.,
2020; Hsieh et al., 2020; Lu et al., 2020; Oudelaar et al., 2020).
Cells manipulate the local and global mechanical state of chro-
matin using enzymes and energy to induce forces and torqueson chromatin. Mechanical forces are transmitted to the chemical
state of chromatin by altering the distribution of DNA and nucle-
osome bound molecules. Recent sequencing-based techniques
have revealed contact domains (see Glossary) of chromatin
spanning 5-kb genomic distances that are highly correlated
with active transcription (Hsieh et al., 2020; Krietenstein et al.,
2020; Rowley et al., 2017). This length scale coincides with
observed regions of chromatin over and under twisting (Bara-
nello et al., 2018). This largely hidden phenomena, referred to
as DNA supercoiling (see Glossary), can mechanically influence
higher-order histone structures across many kilobases (Ban-
caud et al., 2006; Le et al., 2019) and dynamically alters and is
altered byDNA-based processes such as transcription and repli-
cation (Figure 1; Box 1) (Ma and Wang, 2016). The role of me-
chanical effects such as supercoiling have long been linked to
transcription (Ma and Wang, 2016) and chromatin organization
(Baranello et al., 2018; Corless and Gilbert, 2016). Recent
studies have implicated transcriptional mechanics in the struc-
ture (Le and Laub, 2016; Le et al., 2019; Marinov et al., 2020)
and function (El Houdaigui et al., 2019; Kim et al., 2019a; Yeung
et al., 2017) of lower organisms and mammalian systems (Desai
et al., 2020; Leidescher et al., 2020).
At the 100-kb length scale, larger contact domains are referred
to as topologically associated domains (TADs, see Glossary)
(Rowley et al., 2017). Contact domains are often marked by
point-like contacts between domain boundaries (‘‘corner dots’’
in Hi-C maps). In mammals, domain boundaries often display
high association with cohesin, the structural maintenance of
chromosomes (SMCs) protein, which forms loops by binding
two strands of chromatin. These contact domains are dubbed
loop domains (Brandão et al., 2019; Fudenberg et al., 2016;
Rowley et al., 2017; Sanborn et al., 2015). Enrichment of intra-
domain contacts seen inside TADs may explain how the effects
of cis-regulatory elements like enhancers are restricted to spe-
cific contact domains (Schoenfelder and Fraser, 2019; Sym-
mons et al., 2014). Beyond providing boundaries and structure,
the functional importance of TADs on gene regulation remains
unclear (Beagan and Phillips-Cremins, 2020).
At the Mb length scale, areas of active and inactive chromatin
spatially segregate into micron-sized regions called compart-
ments (see Glossary) (Mirny et al., 2019). Early nomenclature
dubbed regions of chromatin as A or B compartments if they
contained accessible or inaccessible chromatin, respectively
(Rowley et al., 2017). Early genome-wide contact maps were
only able to detect contacts between large Mb regions, resulting
in large A/B compartments (Rao et al., 2014). However, higher
resolution techniques identify finer A/B-like compartments,
called compartmental domains. Compartmental domains more
closely correlate with transcriptionally active and inactive re-
gions than the originally characterized larger A/B compartmentsCell Systems 11, November 18, 2020 427
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A
B
Figure 2. Sequencing-Based Approaches to Probing Chromatin Structure and Organization
The increasing resolution of 3C-derived sequencing methods have revealed different principles of chromatin organization at increasingly fine length scales.
(A) Proximity capture workflow: All 3C-derived methods use a proximity capture workflow to produce a library suitable for next generation sequencing by
crosslinking chromatin to nearby neighbors, digesting it into fragments, followed by label, ligation, and pull-down steps to isolate loci of interest.
(B) (Bi) Hi-C: Early Hi-C protocols ligated fragments under dilute conditions and with 6-bp cutters, limiting the structures observed to large triangular Mb regions,
called TADs. (Bii) 4C and in situ Hi-C: 4C (top) detects long-range chromatin contacts, often attributed to looping, with a single viewpoint. In situ Hi-C (bottom)
(legend continued on next page)
428 Cell Systems 11, November 18, 2020
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Review OPEN ACCESS(Hsieh et al., 2020). Compartmental domains of both types (e.g.,
A/B) exist within single loop domains and outside of loop do-
mains (Rao et al., 2014). Compartments also display long-range
attraction, microphase separation (see Glossary), and nested hi-
erarchies (Mirny et al., 2019; Rowley et al., 2017; Rowley and
Corces, 2018).
Given the diverse scales and temporally distinct modes of
gene regulation, understanding how chromatin’s dynamic struc-
ture determines gene regulation and cellular identity remains a
grand challenge. Recent advances in technologies for studying
chromatin structure are illuminating the principles and scales
of organization with increasing resolution. While real-time obser-
vations shed invaluable light on the dynamic nature of chromatin,
static pictures can shed light on entire regions of chromatin at
multiple length scales. The emergent structures revealed in
static observations provide insights into the size, scale, control,
and components of chromatin organization. However, perturba-
tive bottom-up methods in conjugation with mechanistic theo-
retical connections must bemade to connect dynamic and static
perspectives. Thus, a complete picture of chromatin as a
dynamical system involves the blending of both dynamic and
static observations from both top-down and bottom-upmethod-
ologies (see Glossary).
SEQUENCING-BASED METHODS UNCOVER MULTIPLE
LENGTHS SCALES OF GENOME ORGANIZATION
One major class of methods to map 3D genome organization re-
lies on sequencing-based techniques. Although the 3D genome
is structurally dynamic, snapshots of physical contacts between
chromatin in 3D space can reveal important aspects of chro-
matin organization. Contacts are commonly detected with chro-
mosome conformation capture (3C)-based methods that use
proximity ligation of physically close DNA fragments to measure
contact frequencies between loci of interest (Figure 2A) (Dekker
et al., 2002). Though these methods only reveal static configura-
tions of chromatin, they have proven invaluable in revealing the
diversity of structures present. Various 3C-based techniques
(e.g., 3C/4C/5C/Hi-C) differ in the number of genomic loci from
which contacts can be simultaneously detected (Dostie et al.,
2006; Lieberman-Aiden et al., 2009; Zhao et al., 2006). For
example, 4C probes all contact partners for a locus of interest
(i.e., one to all), whereas Hi-C detects all contacts genome
wide (i.e., all to all). Other techniques like HiChIP and ChIA-
PET enrich for contacts bearing specific DNA-protein interac-
tions (Li et al., 2014; Mumbach et al., 2016). All 3C-based
methods require the generation and sequencing of DNA frag-
ments for proximity ligation. Therefore, sequencing depth and
fragment length determine the smallest detectable units of struc-
tural organization (i.e., maximum level of resolution) obtained
(Denker and de Laat, 2016).
Hi-C methods generate population-averaged genome-wide
contact frequency maps (Figure 2) (Dixon et al., 2012; Lieber-
man-Aiden et al., 2009). The sequencing depth of an early Hi-Cimproved the resolution of Hi-C, leading to the detection of finer compartmental do
and blue triangles), and loop domains (dark red dot) occurring inside larger TADs
by convergent CTCF-binding sites (purple arrows). (Biii) Micro-C: At amaximum re
connect E-P and P-P sites, enclosing as little as 1 to 2 genes.study enabled identification of 1-Mb regions with increased con-
tact frequencies (Lieberman-Aiden et al., 2009). With 1-Mb res-
olution, large 1 Mb to >10 Mb regions that interact over long
length scales (creating a characteristic checkerboard pattern in
contact frequency heatmaps) were identified as corresponding
to transcriptionally active and inactive A and B compartments,
respectively (Figure 2Bi) (Lieberman-Aiden et al., 2009). At 40-
kb resolution, triangle patterns ranging from 100 kb to 5 Mb
emerge on contact heatmaps, indicating increased contact fre-
quencies between loci within those regions (Dixon et al., 2012).
Due to their proximity and putative physical association that is
facilitated by chromatin folding, these domains were named
TADs (Dixon et al., 2012; Nora et al., 2012). Enrichment of
CCCTC-binding factor (CTCF) often marks the borders of these
TADs, which display some conservation across cell types (Bea-
gan and Phillips-Cremins, 2020; Bonev et al., 2017).
Original 3C methods used 6-bp restriction enzyme cutters to
produce DNA fragments and performed proximity ligation under
dilute conditions. By using a 4-bp cutter to generate shorter DNA
fragments and by performing ligation in intact nuclei (in situ prox-
imity ligation, see Glossary) to reduce noise, the maximum reso-
lution of Hi-C drastically increases to 1 kb (Rao et al., 2014). In
situHi-C reveals finer domain boundaries (Figure 2Bii), indicating
that many TADs are hierarchically organized into smaller 40 kb to
3Mb nested contact domains (sometimes called subTADs) (Phil-
lips-Cremins et al., 2013; Rao et al., 2014). Many of these smaller
contact domains display specificity that is linked to developmen-
tally regulated genes and cell types (Phillips-Cremins et al.,
2013). Furthermore, a number of these finer domains are marked
by strong corner dots, thought to be caused by chromatin loops
(Beagan and Phillips-Cremins, 2020).
Recently, through an improved fragmentation protocol, the
resolution of contact maps has increased 5-fold (Hsieh et al.,
2016). Previously used restriction enzymes generate heteroge-
neous distributions of fragments and are limited to a maximum
resolution of 1 kb, even in the absence of limitations such as
sequencing depth and cell number (Hsieh et al., 2016, 2020). In
contrast, Micro-C uses micrococcal nuclease (MNase) to frag-
ment DNA into evenly spaced mononucleosomes, achieving
200-bp resolution with only tens of thousands of cells while still
capturing structures over 1 Mb (Figure 2Biii). Enhanced mapping
via Micro-C identified previously undetected features, such as
10 kb to 50 kb nested contact map stripes (Hsieh et al., 2020).
Many of these stripes are punctuated by dots when they inter-
sect and connect enhancer-promoter (E-P) sites or promoter-
promoter (P-P) sites in so-called E-P domains (Hsieh et al.,
2020). Furthermore, finer-scale domain boundaries (<1 kb) en-
compassing only 1–2 genes in E-P domains were found exclu-
sively in active compartment A regions and were marked by
different biochemical and functional features (e.g., transcription
factor-binding sites, embryonic-stem-cell-specific domains,
etc.) (Hsieh et al., 2020). If these newly detected Micro-C dots
correspond to transient loops and how these domains relate to
larger stable loop domains such as TADs remains unclearmains (red and blue eigenvector regions), nested contact domains (light orange
(green, blue, and orange triangles). Mammalian loop domains are often flanked
solution of ~200 bp, Micro-C detects E-P contact domains and E-P stripes that
Cell Systems 11, November 18, 2020 429
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tions also highlight how increasingly high-resolution contact
maps can help discover previously undetected, dynamic,
genome-wide interactions (e.g., E-P domains, Micro-C-spe-
cific dots).
Although 3C-based methods are widely used to probe 3D
genome organization, the proximity ligation step required in
3C-derivativesmaymiss important interactions that do not occur
close enough for molecules to proximity ligate to one another
(Beagrie et al., 2017; Quinodoz et al., 2018, 2020). Thus, non-
proximity methods may enable mapping of structures beyond
this limit. Genome architecture mapping (GAM) isolates thin cry-
osections of nuclei, then amplifies and sequences the DNA in
each nuclear slice to identify interacting genomic loci (Beagrie
et al., 2017, 2020). Another sequencing technique that avoids
proximity ligation relies on split-pool recognition of interactions
by tag extension (SPRITE). Specifically, SPRITE crosslinks
RNA and DNA within the nucleus together, fragments it, then
uses a ‘‘split-pool tagging’’ process to uniquely barcode each
crosslinked fragment. This split-pool barcoding approach allows
both RNA and DNA to be directly read, enabling the simulta-
neous identification of DNA-DNA, RNA-DNA, and RNA-RNA in-
teractions (Quinodoz et al., 2018, 2020). Average 3D chromatin
structures are similarly recapitulated by Hi-C, GAM, and SPRITE
methods (Fiorillo et al., 2020). However, recent improvements to
SPRITE have greatly increased the detection efficiency of low-
abundance RNAs (e.g., noncoding RNAs and nascent mRNAs),
revealing the importance of RNA in guiding other RNAs and pro-
teins to specific subnuclear structures and genomic loci (Quino-
doz et al., 2020). These observations highlight the importance of
orthogonal approaches to studying 3D genome organization.
While high-resolution contact maps have enabled the identifi-
cation of 3D chromatin structures at extremely fine scales, many
3C-based techniques average sparse contacts over a single
temporal snapshot of millions of cells (Beagan and Phillips-Cre-
mins, 2020). Given that 3D genome organization is highly dy-
namic, it is unsurprising that single-cell Hi-C data and computa-
tional methods to compute polymer models consistent with this
data reveal that inter-domain interactions occur more frequently
and with larger heterogeneity than suggested by population
maps (Flyamer et al., 2017; Stevens et al., 2017; Tan et al.,
2018). Thus, the domains identified from population-averaged
contact maps, such as compartmental and contact domains,
putatively represent the most coherent or probable structures
of an entire structural ensemble. Combining sparse single-cell
Hi-C data with imaging techniques confirm that loop domains
do not form in all cells and are heterogeneously located (Flyamer
et al., 2017; Stevens et al., 2017). Studies like these demonstrate
how combined sequencing and imaging-based single-cell
methods may improve our understanding of dynamic nuclear ar-
chitecture at the single-cell level.SINGLE-CELL IMAGING METHODS REVEAL EXTENSIVE
HETEROGENEITY AND DYNAMIC BEHAVIOR AT THE
VARIOUS LENGTH SCALES OF GENOMIC
ARCHITECTURE
Imaging methods constitute a complementary category of ap-
proaches to mapping nuclear architecture. Imaging methods430 Cell Systems 11, November 18, 2020provide static and dynamic single-cell examinations of nuclear
architecture beyond averaged chromosomal points of contact
(Figures 3 and 4). Chromatin imaging methods can be broadly
classified into three categories: fluorescence in situ hybridization
(FISH) methods (see Glossary), which collect the fine-scale
multi-probe spatial localization of static chromatin conforma-
tions; fluorescent protein methods, which can track dynamic
live-cell chromatin motion; and non-fluorescent methods, which
offer extremely detailed insight into static configurations of
chromatin.
Fluorescent protein tags and DNA or RNA FISH probes can be
imaged via diffraction-limited fluorescencemicroscopy (Lakada-
myali and Cosma, 2020). FISH relies on probes hybridizing to
their targets in the nucleus and thus requires fixed cells, as the
fixation process ensures that the probes can permeate to the nu-
cleus and that chromatin structure remains intact during the
high-temperature denaturing step. Initial FISH studies revealed
large-scale organization such as lamina-associated domains
(LADs), regions of the genome that preferentially interact with
the nuclear lamina and are often transcriptionally silenced (Gue-
len et al., 2008). Initial FISH-based methods used a probe library
directly conjugated to reporter fluorophores, involving an expen-
sive library construction step and limiting multiplexing without a
harsh washout step to the number of distinguishable colors
(Joyce et al., 2012; Raj et al., 2008). Oligopaint and its descen-
dent methods circumvent expensive library creation by con-
structing and simultaneously incubating an entire library of iden-
tically labeled DNA probes (Beliveau et al., 2012). By labeling Mb
regions of chromosomes with FISH probes, chromosome terri-
tories were directly imaged with Oligopaint in fixed cells (Beli-
veau et al., 2014, 2015). By separating theDNA-binding function-
ality from the imaging functionality, derivative methods use
secondary fluorescent probes that hybridize to DNA target
probes and perform strand displacement at room temperature,
allowing successive regions as small as 10 kb to be distin-
guished with microscopy without harsh wash conditions
(Nir et al., 2018). When combined with super-resolution micro-
scopy (see Glossary), this class of methods, including both
OligoSTORM and optical reconstruction of chromatin architec-
ture (ORCA), can be used to determine the 3D spatial location
of sequential chromosomal segments as short as several kb
within the nucleus (Figure 3A) (Bintu et al., 2018; Mateo et al.,
2019; Nir et al., 2018).
By using the median spatial distance between pairs of
genomic loci to generate a Hi-C like contact map, structures
that look like Hi-C contact domains (e.g., triangles) appear (Bintu
et al., 2018). Single-cell imaging indicates that cohesin depletion
results in variable domain boundaries, while leaving the Mb-
sized A/B compartments relatively unaffected. When averaged
over the entire population, this randomization of domain bound-
aries is responsible for the near-complete reduction in TADs
observed in population-level Hi-C data (Bintu et al., 2018).
Here, the dynamic motion of contact domain boundaries is in-
ferred from the multi-cell ensemble, instead of being directly
measured.
In contrast to the FISH-derived methods, protein-based
methods capture nuclear real-time dynamics in living cells (Gu
et al., 2018; Shaban et al., 2018). Protein fluorophores offer low
quantum yield compared with the small-molecule dyes used in
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C
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Figure 3. Imaging-Based Approaches to Probing Chromatin Structure and Organization
The heterogeneity of nuclear architecture at the single-cell level is revealed by various imaging methods. The relevant chromatin structures that eachmethod can
observe is determined by multiplexing ability and maximum imaging resolution (shown here both for super-resolution and diffraction-limited cases).
(A) Multiplexed FISH: By separating fluorescent readout and target binding into separate probes as in OligoSTORM and ORCA, successive genetic loci across
megabase regions can be visualized (middle). Localizations can be summarized by median distance between loci pairs, giving a single-cell contact map matrix
(left). Averaging single-cell contact map matrices from many cells reveals ensemble behavior similar to that observed with Hi-C.
(B) Targeted-loci protein methods: Target loci in live cells can be imaged via protein fusions of DNA-binding domains and fluorescent proteins. Both native loci and
inserted recognition sites can be visualized. Multiplexing is limited by fluorescent protein color, with only around four simultaneous probes remaining distinguishable.
(C) Tagged histones: Histones taggedwith fluorescent proteins can be visualized across the entire nucleus. In addition to imagingmotion of large-scale chromatin
structures, super-resolution allows identification of heterogeneous nucleosome clutches.
(D) EM/SXT: non-fluorescent methods including electron microscopy and soft X-ray tomography allow for maximum resolution approaching one nanometer,
uncovering heterogeneities at the smallest relevant scale.
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A B C
Figure 4. Sequencing- and Imaging-Based Approaches: An Integrated View to Understand Dynamic Chromatin Structures and Organization
(A–C) Dynamic chromatin architecture at various relevant length scales (B, center) is best understood via complementary information revealed by sequencing (A)
and imaging methods (C).
(A) Sequencing methods: 3C-derived methods give population-level view of chromatin architecture. Initial Hi-C methods revealed large-scale compartments,
LADs, and loop domains, whereas modern techniques including Micro-C reveal fine structures at the 200-bp level and can still probe large-scale structures.
(B) Relevant chromatin structures: Chromatin structures are nested and interlinked on length scales spanning over five orders of magnitude. Over 1 Mb:
Chromatin segregates into chromosome territories, LADs, and Mb-sized A/B compartments. 100 kb to 1 Mb: Long-range looping interactions and large TADs,
identified as triangles in Hi-C data, were first identified at this scale. 1 to 100 kb: Smaller nested contact domains exist at this scale. Dynamic processes such as
transcription, enhancer-promoter interactions, and phase condensates reshape these contact domains. 100 bp to 1 kb: At the length scale of nucleosomes,
chromatin is highly heterogeneous and is shaped by physical processes with energies not exceeding a few kT.
(C) Imaging methods: Microscopy techniques allow visualization of chromatin structures in single cells, revealing heterogeneity both in the boundaries of large
contact domains and at the length scale of individual nucleosomes.
432 Cell Systems 11, November 18, 2020
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Review OPEN ACCESSFISHmethods (Giepmans et al., 2006). A variety of methods exist
to recruit multiple fluorophores to targets to improve detection
(Figure 3B) (Lakadamyali and Cosma, 2020). In tiled dCas9-,
TetR-TetO, and TALE-based systems (see Glossary), multiple
proteins are recruited to adjacent locations on the genome
(Chen et al., 2013; Miyanari et al., 2013; Straight et al., 1996; Ta-
san et al., 2018; Zhu and Cheng, 2020). In contrast, methods like
ANCHOR recruit fluorophores by oligomerizing multiple fluores-
cent proteins to a single-protein-bound DNA site, allowing tar-
geting of a single non-repetitive genetic locus (Germier et al.,
2017). These methods allow for live measurement of the location
and mobility of genomic elements (e.g., promoters and their cis-
regulatory elements) for seconds to minutes. Dynamics at this
scale display multiple modes of behavior from generic confine-
ment (Khanna et al., 2019) to decreasedmobility of a gene’s pro-
moter region with increasing activity (Germier et al., 2017). How-
ever, themobility of cis enhancers increases as transcription rate
increases, consistent with a model where active transcription-
linked processes increase the frequency of interactions with reg-
ulatory elements (Gu et al., 2018). The dynamic behavior of the
entire nucleus can also be studied through these protein-based
methods. One study used fluorescently tagged histone proteins
to characterize whole-nucleus chromatin motion and found that
chromatin motion was correlated over length scales equal to
nearly a tenth of the nuclear diameter (Figure 3C) (Shaban
et al., 2018). Furthermore, inhibiting RNA polymerase II (RNAPII)
elongation resulted in a decrease of both correlated chromatin
motion and diffusion, suggesting that transcription is an impor-
tant regulator of coordinated chromatin motion (Shaban et al.,
2018, 2020). These protein-based imaging techniques will be
invaluable in uncovering transcription dynamics and other pro-
cesses that cannot be interrogated in fixed cells.
At the smallest scale, nuclear architecture can be captured via
fluorescent and non-fluorescent techniques. Histone-fusion pro-
teins allow for super-resolution live-cell imaging of the entire nu-
cleus at 20-nm resolution (Figure 3C) (Ricci et al., 2015). Fluores-
cent, super-resolution, live-cell studies demonstrated that
nucleosomes were arranged in visually distinct grouping called
nucleosome clutches that vary in density along chromatin fibers
(Lakadamyali and Cosma, 2020; Ricci et al., 2015). Both cell type
and local transcriptional activity affected the density of these
nucleosome clutches (Ricci et al., 2015).
Sub-nucleosome resolution up to 1 nm can be achieved
via non-fluorescent electron microscopy techniques such as
ChromEMT or via soft X-ray tomography (Figure 3D) (Le Gros
et al., 2016; Ou et al., 2017; Smith et al., 2014). In ChromEMT, di-
aminobenzidine polymerized onto chromatin is visualized by
staining. ChromEMT images of human cell nuclei revealed that,
in contrast to the original in vitro understanding of hierarchical
30-nm chromatin fibers, chromatin in interphase chromosomes
is organized as distributed chromatin fibers of varying diameter.
These observations confirm the heterogeneous distribution of
nucleosomes observed as ‘‘clutches’’ in both imaging and
sequencing technologies, in static and dynamic observations,
and in single-cell and bulk structure analyses. Although non-fluo-
rescent methods allow 1-nm resolution, these methods neces-
sarily provide a static view of the nucleus. Recent advances in
super-resolution microscopy extend live-cell imaging to the
1 nm level (Gwosch et al., 2020). Because processes affectingchromatin at this scale operate on the timescale of milliseconds
to seconds (such as nucleosome binding and transcription elon-
gation), live-imagingmethodswill be critical in understanding the
finest scales of intricate nuclear dynamics.
ADVANCES IN ENGINEERING CHROMATIN AND
NUCLEAR ARCHITECTURE
Simultaneously tracking all relevant phenomena impacting chro-
matin structure makes conventional top-down deconstruction of
chromatin’s structure-function relationship challenging. Alterna-
tively, synthetic, bottom-up approaches for constructing syn-
thetic models of chromatin may lead to invaluable insight into
how structures emerge from functions. These insights may iden-
tify mechanisms that feedback into chromosome function by
revealing the causal processes underlying chromatin dynamics
through perturbative experiments.
ENGINEERING TRANSITIONS IN CHROMATIN STATE
AND CELL FATE VIA SYNTHETIC TRANSCRIPTION
FACTORS
Unique transcriptional and epigenetic profilesmark andmaintain
cellular identities (Spivakov and Fisher, 2007). Transitions in cell
fate require chromatin remodeling which may be facilitated by
transcription factors (Black et al., 2016; Ieda et al., 2010; Vierbu-
chen et al., 2010). However, understanding how small-scale
(10 bp), high-frequency transcription factor dynamics induce
stable cell-fate changes is poorly understood. In reprogramming
and direct conversion, transcription factors drive changes in
chromatin structure directly by binding to cognate sequences,
recruiting transcriptional machinery, and inducing transcription
(Di Giammartino et al., 2019; Stadhouders et al., 2018). Addition-
ally, transcription factors drive indirect changes in chromatin
structure via the activation of transcriptional networks, inducing
expression of additional transcription factors and epigenetic reg-
ulators (Cahan et al., 2014). Consequently, efforts to drive
cellular transitions have focused on transcription factor cocktails
to induce specific cell fates. While directed differentiation via
small molecules and exogenous factors represents the most
common method of generating somatic cells types from iPSCs,
overexpression of lineage-specifying transcription factors is
increasingly employed to guide cells to specific identities (Flitsch
et al., 2020; Nickolls et al., 2020; Wang et al., 2017). The devel-
opment of synthetic transcription factors expands the ensemble
of tools beyond native transcription factors to enable precise sin-
gle-locus and multi-locus targeting of regulatory nodes. These
efforts may reveal themechanistic connections between proper-
ties of transcription factors, chromatin dynamics, and cell fates.
Synthetic transcription factors are composed of two primary
domains: a DNA-binding domain and an activation domain
(Figure 5A). Selection of the DNA-binding domain determines
sequence specificity, allowing focused activation of a single lo-
cus or connection to broader transcriptional networks via native
transcription factor-binding sites. CRISPR activator (CRISPRa)
systems employ programmable gRNAs to target specific regions
(Chavez et al., 2016). CRISPRa proteins are composed of cata-
lytically inactive Cas9 (dCas9) fused to various activation do-
mains (Figure 5A). Activation domains of CRISPRa includeCell Systems 11, November 18, 2020 433
ll
OPEN ACCESS Review
A
B
C
D E F
Figure 5. Engineering Chromatin
(A) By fusing various DNA-binding domains to known transcription regulators, synthetic transcription factors can selectively regulate native and synthetic gene
circuits. These transcriptional regulators can be recruited to specific single- and multi-loci targets. Recruitment of regulatory domains can be enhanced via the
SunTag system.
(legend continued on next page)
434 Cell Systems 11, November 18, 2020
ll
Review OPEN ACCESSvariants of VP16 and p65 (e.g., VP64, VPR) as well as p300 that
are either fused to dCas9 or recruited to dCas9 via protein-pro-
tein or protein-RNA interactions. Design of gRNAs of varying af-
finity provides a method for tuning CRISPRa-induced gene
expression tomore precisely control activation of various loci uti-
lizing a single CRISPRa protein (Jost et al., 2020). Activation do-
mains induce transcription by recruiting transcriptional machin-
ery to transcription start sites and enhancers. Multimerization
of activation domains via multi-valency domains enhances
recruitment and gene activation (Tanenbaum et al., 2014). Off-
target effects of CRISPRa systems appear to be minimal
compared with off-target effects of CRISPR screening systems
(Gilbert et al., 2014).
Synthetic transcription factors can replace overexpression of
native transcription factors to induce reprogramming. Key plu-
ripotency transcription factors such as Oct4 and Sox2 are over-
expressed in order to reprogram fibroblasts into induced plurip-
otent stem cells (iPSCs). Instead of overexpressing these genes,
targeted activation of Oct4 and Sox2 via CRISPRa successfully
reprograms iPSCs (Balboa et al., 2015; Liu et al., 2018). Similarly,
targeting CRISPRa to the myogenic transcription factor Myod1
enables direct conversion of mouse embryonic fibroblasts
(MEFs) to skeletal muscle (Black et al., 2016; Chakraborty
et al., 2014). Activation of other key transcription factors with
CRISPRa can convert MEFs into cardiomyocyte progenitor cells
or neural cells (Black et al., 2016; Wang et al., 2020b). Together
these data suggest that nucleated activation at key regulatory
nodes is sufficient to induce the massive chromatin remodeling
required for cellular reprogramming. Reprogramming remains a
rare event within a population of cells. Insight into the mecha-
nisms that limit reprogramming may provide opportunities for
synthetic transcription factors to target additional regulatory no-
des in the epigenome.
While CRISPRa techniques provide programmable site-spe-
cific activation, activity often requires multiple guides per locus
which makes scaling to larger transcriptional networks chal-
lenging. Additionally, the large size of dCas9 (4.5 kb gene) limits
adoption into viral vectors with smaller cargo limits (e.g., adeno-
associated virus [AAV]). Single guide strategies with smaller Cas
variants may enable improved flexibility of CRISPRa (Pausch
et al., 2020; Zetsche et al., 2015). Alternatively, sequence spec-
ificity can also be programed through smaller native transcription
factors (Kabadi et al., 2015). Beyond native transcription factors,
directed evolution of native factors or artificial zinc fingers li-
braries has generated synthetic transcription factors that can
replace reprogramming factors (Eguchi et al., 2016; Veerapan-
dian et al., 2018). Improvement in synthetic transcription factors
used for cellular reprogramming may benefit from insights from
synthetic transcription factors developed to recognize orthog-
onal synthetic sequences and activate gene expression (Dona-
hue et al., 2020; Keung et al., 2014; Khalil et al., 2012). For(B) Fusing DNA-binding domains to native and engineered chromatin regulatory
marks, including various histone modifications (addition or removal of acetyl and
(C) Insertion or deletion of SMC and CTCF-binding sites can modify loop domain
spacing, direction, and adjacency of genomic elements within a contact domain
(D) Using protein domains targeting specific loci, synthetic loop domains can be
(E) Targeting fusion proteins to genomic loci can be used to specifically position
(F) Recruitment of proteins with intrinsically disordered regions (IDRs) to specific g
the role of condensates in enhancer-promoter contacts and transcriptional reguexample, the number and spacing of binding sites can be used
to tune the level of gene expression (Donahue et al., 2020). In
native systems, valency provides an important metric for
inducing nuclear structures that are associated with transcrip-
tion and splicing (Guo et al., 2019; Shrinivas et al., 2019). With
increased understanding of the dynamics of chromatin struc-
tures that enable cell-fate transitions, engineered transcription
factors may guide cells through rate-limiting steps in chromatin
remodeling as well as target loci that remain refractory to reprog-
ramming.
ENGINEEREDBIOCHEMICALSYSTEMSOFCHROMATIN
REGULATION
Biochemical modifications of chromatin on the scale of 100 bp
serve as important regulatory elements that can affect the chro-
matin dynamics and structure onmultiple length scales (Figure 1)
(Holtzman and Gersbach, 2018; Rao et al., 2014). Although
biochemical marks like histone modifications correlate with spe-
cific functions (e.g., poised versus active enhancers), causal re-
lationships remain obscured in native systems (Creyghton et al.,
2010; Wapinski et al., 2013). Engineered systems of chromatin
regulation offer novel opportunities to define how biochemical
modifications and gene expression influence one another.
Engineered chromatin regulator (CR) proteins read, write, and
erase biochemical modifications (Figure 5B). Like synthetic tran-
scription factors, CRs activate gene expression by recruiting
transcriptional machinery to specific loci to promote or interfere
with biochemical modifications. For example, fusing dCas9 with
CRs such as p300, BAF, or Tet1, activates gene expression from
specific loci by inducing modifications such as histone acetyla-
tion (p300) and DNA demethylation (Tet1) (Braun et al., 2017; Hil-
ton et al., 2015; Josipovic
 et al., 2019; Liu et al., 2016). Using this
approach, a myogenic enhancer was selectively demethylated
via transient dCas9-Tet1 recruitment enabling reprogramming
of fibroblasts into myoblasts (Liu et al., 2016). Furthermore, de-
methylation and successive methylation of a sialytransferase
gene showed that transient CR expression can cause stable,
yet reversible changes to gene expression (Marx et al., 2018).
Beyondmanipulating gene expression, incorporation of native
CRs into synthetic proteins and gene circuits (see Glossary) en-
ables characterization of these elements in orthogonal contexts
(Van et al., 2020). With these engineered systems, the dynamic
interplay between transcription, biochemical mark placement,
and mark removal can be observed (Liu et al., 2016; Molina
et al., 2016). For example, integrating CRs with a reporter circuit
enables tracking of biochemical modification turnover due to
exposure to CRs over periods of hours to days (Bintu et al.,
2016). Retention kinetics of modifications in synthetic circuits
depend on the specific biochemical modification and mirror
the properties observed in native systems (Bachman et al.,domains allows targeted modification and reading of important biochemical
methyl marks on key residues) and DNA methylation.
dynamics, potentially affecting enhancer-promoter interactions. Modifying the
can also affect gene expression.
formed to increase intra-domain contacts between enclosed elements.
these loci to nuclear regions, such as the nuclear lamina and Cajal bodies.
enomic loci can induce phase separation, giving a model system to investigate
lation.
Cell Systems 11, November 18, 2020 435
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OPEN ACCESS Review2001; Bintu et al., 2016; Guelen et al., 2008; Seki et al., 2005;
Zhao et al., 2005). One study used a synthetic writer circuit
and found that the boundaries of repressive heterochromatic re-
gions were determined by a dynamic competition between mark
writing and transcription-induced turnover (Figure 5B) (Hath-
away et al., 2012). Biochemical marks can also directly influence
RNAPII behavior; one histone acetylation mark, H3K27ac, was
found to increase the rate of RNAPII elongation but does not
affect the rate of RNAPII initiation (Stasevich et al., 2014).
RNAPII elongation can in turn induce mark placement, high-
lighting the dynamic interplay at work between transcription
and epigenetic modification (Morillon et al., 2005).
While engineered circuits utilizing native CRs may be affected
by endogenous regulatory mechanisms, synthetic CRs provide
an orthogonal alternative (Park et al., 2019; Tekel et al., 2018).
Incorporation of bacterial methylation enzymes enabled con-
struction of an orthogonal mammalian epigenetic system (Park
et al., 2019). Synthetic CR enzymes methylate desired loci,
read methyl marks to regulate gene expression, and passively
spread existing methylation marks to adjacent unmarked chro-
matin. Other work has engineered and validated chromatin
reader domains that recognize combinations of native chromatin
marks including trimethylation of H3K4, 9, and 27 in addition to
CpG DNA methylation (Villaseñor et al., 2020). Through sensing
and actuation on native and synthetic biochemical signals, these
CR systems may be deployed as synthetic epigenetic control
systems.
Biochemical methods of marking histones provide an addi-
tional layer of control to the synthetic circuit design toolkit,
enabling reversible, short-term control and long-term memory
storage that can be combined within additional layers of chro-
matin engineering.
ENGINEERING CONTACT DOMAIN AND
COMPARTMENTALIZATION MODIFICATIONS
At the largest scale of organization, the dynamic behavior of con-
tact domains and compartmentsmay control the communication
between regions of chromatin separated by distances of more
than 100 kb. In native systems, genetic editing of contact do-
mains via CRISPR has been demonstrated to affect the activity
of endogenous enhancers. Limb morphogenesis requires pre-
cise tissue-specific chromatin organization for enhancer regula-
tion (Infante et al., 2013; Lewandowski et al., 2015; Sheth et al.,
2016). For example, Pitx1 is regulated by the Pen enhancer.
Despite expression of Pen in both the fore and hindlimbs, Pen
only activates Pitx1 expression in the hindlimbs due to tissue-
specific chromatin organization (Andrey et al., 2017; Kraft et al.,
2015; Kragesteen et al., 2018). Flipping the orientation of the
genomic region containing Pen brings Pitx1-Pen in close prox-
imity, inducing the formation of a hindlimb-like chromatin hub
including both Pen and Pitx1. Mice bearing this inversion ectop-
ically express Pitx1 in the forelimb and develop an ectopic pa-
tella. In humans, this malformation disease is known as Lieben-
berg syndrome (Al-Qattan et al., 2013; Alvarado et al., 2011;
Kragesteen et al., 2018). These studies highlight that chromatin
structure around enhancers support complex tissue develop-
ment. Additionally, genome editing enables precise investigation
into the structure-function relationship of chromatin.436 Cell Systems 11, November 18, 2020Genome engineering elucidates the relationship between
genome structure and transcriptional dynamics including tran-
scriptional bursting. Proximity of genes to enhancers may influ-
ence competition between genes for the same enhancer. Inves-
tigation of genes under shared enhancer control revealed that
inverting one gene to place its promoter further from the
enhancer caused the genes to switch from competitive tran-
scriptional bursting to coordinated bursting (Figure 5C, right) (Fu-
kaya et al., 2016). Another study demonstrated that deleting a
contact domain boundary only affected the transcription of a
gene within the domain but not outside (Figure 5C) (Yokoshi
et al., 2020). Moreover, both studies found that changes in over-
all mRNA levels are best explained by changes in transcriptional
burst frequency rather than burst size. In mammals, cohesin-
mediated chromatin looping can also be modified by inserting,
deleting, or inverting CTCF-binding sites (Beagan and Phillips-
Cremins, 2020; de Wit et al., 2015; Jia et al., 2020). CTCF site
orientation determines DNA looping and chromatin architecture
and can regulate gene expression (Guo et al., 2015). Tandemly
oriented CTCF-binding sites separating promoters control the
relative activity of distal and proximal promoters by changing
which promoter statistically prefers to dynamically contact a
shared enhancer with a convergent CTCF-binding site (Jia
et al., 2020). Together, these studies demonstrate that gene
expression and behavior (e.g. cooperative versus competitive
bursting) can be engineered through elements of chromatin
structure as well as by manipulating E-P regulation via changes
in dynamic chromatin structures.
While genetic modifications such as CTCF site inversion can
alter statistically preferred chromatin structures, several syn-
thetic systems induce forced looping to examine dynamics
(Bartman et al., 2016; Deng et al., 2012, 2014; Kim et al.,
2019b; Morgan et al., 2017). These synthetic looping systems
use DNA-binding domains to target two genomic loci of interest.
Fusing the DNA-binding domains to dimerization domains en-
ables induction of forced looping upon addition of a dimerizing
agent (e.g., small molecule or light) (Figure 5D) (Kim et al.,
2019b; Morgan et al., 2017). Forced looping results in modest in-
creases in gene expression. In mouse cells, induced looping
increased burst fraction (related to burst frequency) but not burst
size (Bartman et al., 2016). Overall, these observations suggest
that gene expression is very sensitive to changes in burst fre-
quency that may be increased by loop-mediated boundary rein-
forcement between enhancers and promoters.
Synthetic systems have also been developed to allow dynamic
restructuring of 3D genome organization via membraneless or-
ganelles at the 100-nm to 1-mm scale. For instance, dCas9-
based systems have been used to reposition specific loci to sub-
nuclear structures (e.g., Cajal bodies [CBs], nuclear lamina),
enabling repression of gene expression (Figure 5E) (Wang
et al., 2018). dCas9-based systems have been shown to initiate
the formation of membraneless structures like CBs and synthetic
condensates at specific loci (Shin et al., 2018; Wang et al., 2018).
Recently, structures formed via liquid-liquid phase separation
(LLPS) have gained much attention for their ability to segregate
chromatin into different phases and promote cellular processes,
such as transcription, by enabling high local concentrations of
transcriptional machinery (Bracha et al., 2018, 2019; Gibson
et al., 2019; Hnisz et al., 2017; Shin et al., 2017). Synthetic
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Review OPEN ACCESScondensates canmechanically restructure local chromatin orga-
nization and store memory of spatial stimuli (Figure 5F) (Dine
et al., 2018; Shin et al., 2018). Although LLPS is often implicated,
there exists alternate mechanisms to compartmentalization and
chromatin segregation. Notably, ectopic overexpression of
HP1a, a protein necessary for heterochromatin formation, re-
sults in a switch-like compaction where chromatin is either
compact or decompact, existing within the same rather than
separate liquid phase (Erdel et al., 2020).
Overall, condensates have been shown to alter chromatin or-
ganization and promote cellular processes like transcription,
creating a feedback between structure and function. Future
studies will clarify how biophysical principles of non-equilibrium
processes are utilized by cells in concert with equilibrium-driven
LLPS to organize the 3D genome and regulate gene expression.
RESOLVING THE INTERRELATIONSHIP BETWEEN
CHROMATIN STRUCTURE AND GENOME FUNCTION
With novel tools from imaging, sequencing, genome engineer-
ing, and synthetic biology, the field of chromatin organization
has witnessed accelerated discovery and innovation. Increasing
levels of sophistication and resolution in imaging and
sequencing methodologies have revealed increasingly detailed
levels of nuclear architecture (Bintu et al., 2018; Flyamer et al.,
2017; Hoffman et al., 2020; Hsieh et al., 2016; Stevens et al.,
2017; Su et al., 2020). However, many open questions remain:
how does transcription influence chromatin during cell-fate tran-
sitions?What are the implications for dynamic chromatin bound-
aries? Do enhancers directly contact promoters or do they
recruit promoters to enhancer hubs? How can synthetic chro-
matin systems and gene circuits enable mechanistic insight
into chromatin dynamics? In light of the emerging models, how
do we engineer chromatin to regulate gene expression and con-
trol cell fate? A dynamic, unified view of nuclear architecture will
be key both to understanding native processes and engineering
novel synthetic systems (Figure 6).
RESOLVING SINGLE-CELL AND POPULATION-LEVEL
ANALYSES
Most 3C-derived analyses capture the average profile of millions
of cells, obscuring the relationship between cell state and chro-
matin dynamics. Tracking dynamics in heterogenous popula-
tions compounds the problem (Figures 6A and 6B). Single-cell
imaging combined with cell-state reporters may enable more
precise definition of the temporal profile of chromatin states
that generates specific behaviors. For instance, high-resolution
imaging methods show that contact domains do exist in single
cells (Bintu et al., 2018). While single-cell domain boundaries
are more heterogeneously distributed than population-averaged
contact maps suggest, this indicates that contact domains iden-
tified through Hi-C are not simply artifacts of population aver-
aging. Rather, they represent statistically preferred, coherent
domain boundaries for the ensemble of dynamic chromatin
states within a population (Bintu et al., 2018). Given the dynamic
binding and fractional occupancy of CTCF, heterogeneous dis-
tributions should be expected (Cattoglio et al., 2019; Holzmann
et al., 2019). However, these analyses do not address whetherparticular states (or distributions) are the result of randomness
in chromatin conformations or differences in cell state such as
cell-cycle phase, transcription rate, metabolism rate, or cell-
fate trajectory. Integrating live or fixed readouts will enable the
isolation of patterns that drive changes from those that do not.
To connect chromatin dynamics with gene regulation and cell
fate, synthetic systems such as cellular reprogramming provide
unique contexts to study chromatin dynamics in concert with
transcriptional dynamics during cell-fate transitions (Box 2).
With recent developments in technologies, opportunities exist
for examining chromatin dynamics with unprecedented resolu-
tion and mapping encoded sequences and structures to func-
tional outcomes. In order to investigate transient phenomena
such as enhancer-promoter contacts, methods of mapping
chromatin dynamics via sequencing are emerging. Techniques
such as Micro-C and Tiled-C can achieve high-resolution con-
tact maps with only thousands of cells that may enable chro-
matin organization analysis of rare subpopulations of cells (Hsieh
et al., 2020; Oudelaar et al., 2020).
Polymer simulations of chromatin across multiple time and
length scales provide an important bridge between observations
and theory (Jost et al., 2018). Simulations using Hi-C maps as
constraints can successfully reproduce ensemble chromatin
conformations (Beagan et al., 2020; Di Pierro et al., 2016; Di Ste-
fano et al., 2016, 2020; Nuebler et al., 2018; Paulsen et al., 2017;
Serra et al., 2017). While useful, these models use population-
averaged snapshots, possibly missing dynamics present in sin-
gle cells (Figures 6B and 6C). Time-point Hi-C and FISH experi-
ments, when combined with modeling, have begun to decrease
this gap (Abbas et al., 2019; Di Stefano et al., 2020). However,
even detailed single-cell sequencing experiments preclude live
observation and tracking. If scalable, single-cell microscopy an-
alyses will enable the identification of causal principles and
mechanisms underlying dynamic genomic architecture regula-
tion (Su et al., 2020). Future work in modeling to bridge the divide
between single-cell and population-level observations will rely
on a combination of bottom-up mechanistic studies (Brackey
et al., 2020) as well as integration of observations across many
scales (Moller and de Pablo, 2020).
CONNECTING DYNAMICS IN CHROMATIN STRUCTURE
AND GENE REGULATION
Resolving the dynamic structure-function relationship of chro-
matin remains a grand challenge in understanding gene regulation
and resulting cell fates. Chromatin structure both affects and is
affected by gene expression, presenting opportunities for redraft-
ing the traditional arrow connecting structure to function into a
feedback loop. SMC degradation studies highlight the need for
revising the model of how structure supports function. For
example, degradation of SMCs eliminates the formation of stable
population TAD structures, but acute loss of SMCs only modestly
perturbs gene expression profiles (Nora et al., 2017; Rao et al.,
2017; Schwarzer et al., 2017). However, chronic loss of CTCF re-
sults in changes in proliferation and viability, making experimental
inferences over physiological relevant time periods challenging
(Nora et al., 2017). However, in light of massive structural changes
resulting in minimal perturbations of gene expression, how do we
understand the role of structure in regulating gene expression?Cell Systems 11, November 18, 2020 437
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OPEN ACCESS Review
A B
C D
E
Figure 6. Resolving the Interrelationship between Chromatin Structure and Genome Function
(A) Within tissues and cell lines, cells adopt different states of chromatin organization. Gaps remain in our understanding of how heterogeneity of chromatin states
varies across populations dynamically.
(B) Structures vary dynamically in response to diverse stimuli including stage of cell cycle, paracrine signaling, and interaction with the extracellular matrix.
(C) Marked by unique chromatin states, cell-fate transitions such as differentiation and reprogramming require epigenetic remodeling. Capacity for remodeling
may vary dynamically by chromatin state (B).
(D) Induction of gene expression alters chromatin structure, changing the connectivity of enhancers and promoters. Two models capture diverse changes in
proximity between enhancers and promoter. In the proximity model, transcriptional activity correlates with increased enhancer-promoter proximity. In the
transcriptional hub model, promoter-enhancer proximity may increase as protein and RNAs are recruited to regions of active transcription.
(E) Nuclear structures associated with transcription and splicing contain high concentrations of proteins and RNAs that form phase-separated compartments
called nuclear condensates.Recent work examining loss of CTCF in reprogramming and the
immune response provides some insight into how CTCF differen-
tially impacts cell-fate transitions and cell-specific functions. Loss438 Cell Systems 11, November 18, 2020of CTCF does not impact B cell transdifferentiation into induced
macrophages (iMacs) (Stik et al., 2020). However, when chal-
lenged with endotoxins, iMacs lacking CTCF fail to induce a full-
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Review OPEN ACCESS
Box 2. Capturing Chromatin Dynamics during Cell-Fate Transitions
Cells integrate a vast array of external and intrinsic cues into cellular decision-making events during the transition from one identity
to another. Despite these myriad of influences, emerging evidence identifies chromatin remodeling as a key step in adoption of a
new cell fate. Overexpression of lineage-specifying transcription factors drives changes in domain interactions (Dall’Agnese et al.,
2019; Di Giammartino et al., 2019; Stadhouders et al., 2018). Notably, chromatin rewiring in noncoding regions precedes changes
in genic regions in the conversion of fibroblasts to iPSCs (Di Giammartino et al., 2019). Similar to reprogramming, transformation in
cancer initiates from early epigenetic changes. Rewiring of chromatin structure precedes the induction of oncogenesis andmetas-
tasis (Denny et al., 2016; Park et al., 2018; Schuijers et al., 2018). Beyond primary tumors, dynamic epigenetic state switching en-
ables persistence of drug-tolerant subpopulations and metastases (Denny et al., 2016; Sharma et al., 2010).
Within populations of cells, unique processes mark privileged populations capable of mediating the massive epigenetic transition
to reprogram (Babos et al., 2019; Guo et al., 2014). The subpopulation of fast-cycling cells reprogram to highly proliferative iPSCs
and post-mitotic neurons at much higher rates (5–100-fold) than slower cycling cells (Babos et al., 2019; Guo et al., 2014). Reprog-
ramming of somatic nuclei via fusion with ESCs requires DNA synthesis, suggesting that S phase may provide a unique environ-
ment for reprogramming (Tsubouchi et al., 2013). Additionally, cell-cycle stage correlates with chromatin folding state (Zhang et al.,
2019). Together these observations suggest that proliferation may promote reprogramming via cell-cycle-induced resetting and
restructuring of chromatin. Additionally, high transcription rates in hyperproliferative cells result in near-deterministic reprogram-
ming (Babos et al., 2019). Hypertranscription may facilitate rapid, extensive chromatin remodeling to drive cellular reprogramming.
Given the rarity of privileged cells across populations, novel low-input 3C-based methods and single-cell imaging methods will be
key to understanding the dynamic and highly heterogeneous processes underlying cell-fate transitions (Wang et al., 2020).
Knowing how dynamic chromatin rewiring in single cells affects gene regulation may explain how transient subpopulations
contribute to cell-fate transitions.blown immune response. While large-scale structures such as
TADs may be dispensable for cell-fate transitions, these struc-
tures may uniquely support temporally specific functions (e.g.,
infection, neuronal stimulation, and acute stress) (Cuartero et al.,
2018). Thus, the deficits associated with loss of these structures
would not be observed except under unique conditions, poten-
tially explaining the lack of correlation observed between the de-
gree of difference in structural deficits and gene expression (Nora
et al., 2017; Rao et al., 2017; Schwarzer et al., 2017). The chal-
lenge remains in identifying cell- and context-specific functions
of chromatin to examine this hypothesis.If changes in the most probable large-scale structures only
introduce small expression perturbations, how are smaller,
more dynamic structures linked to gene regulation? One study
in Drosophila, a species that lacks mammalian SMC-driven
loop domains, integrated nascent transcript analysis with Hi-C
contact data, histone mark profiling, and accessible chromatin
assays and found that small (<10kb) contact domains formed
based on transcriptional state (Rowley et al., 2017). In fact, pro-
files of nascent transcripts are sufficiently informative to recapit-
ulate contact maps, chromatin profiles, and the strength of con-
tact domain interactions (Rowley et al., 2017; Wang et al.,Cell Systems 11, November 18, 2020 439
ll
OPEN ACCESS Review2020a). It has even been reported that transcription-linked chro-
matin reconfiguration occurs on timescales as short as neuronal
stimulation (Beagan et al., 2020). In mouse embryonic stem cells
(ESCs), RNAPII-mediated activity drives formation of a subset of
similar small contact domains (gene-to-gene, enhancer-to-pro-
moter) with little impact on higher-order structures such as
TADs, emphasizing the role that transcription plays in establish-
ing dynamic structures on the kilobase length scale (Hsieh
et al., 2020).
If changes in transcription influence chromatin structure, how
do we integrate recent work from cellular reprogramming that in-
dicates that changes in chromatin topology precede changes in
gene expression? One potential answer lies in transcription of
noncoding RNAs. Transcription of noncoding regions such as
lncRNAs and enhancer (eRNAs) may precede and induce topo-
logical changes in chromatin that in turn induce changes in genic,
coding sequences (Klattenhoff et al., 2013; Puc et al., 2015; Stad-
houders et al., 2018). Induction of androgen signaling recruits
topoisomerase I to induce eRNA transcription, resulting in upre-
gulation of coding genes (Puc et al., 2015). Additionally, RNA
binding promotes the function of canonical mediators of chro-
matin structure. For example, CTCF binds RNA and loss of
RNA-binding regions or transcriptional inhibition disrupts chro-
matin loops, resulting in nuclear reorganization (Hansen et al.,
2019; Saldaña-Meyer et al., 2019). These studies suggest that
dynamic changes in the concentrations of proteins and RNAs
facilitate nuclear reorganization. Waves of transcription from
noncoding to coding may guide cell-fate transitions through re-
modeling chromatin structures by increasing local concentration
of RNAs and inducing mechanical changes in chromatin state.
Chromatin states at smaller scales are significantly impacted
by transcription-mediated mechanical epigenetics (Box 1).
Recent experiments have started to reveal a rich relationship be-
tween transcription and nucleosome positioning and the under-
lying mechanical state of DNA (Hsieh et al., 2020; Naughton
et al., 2013). These phenomena collectively indicate the ability
of DNA-bound processes to influence chromatin organization,
leading to a fundamental connection between chromosome
function and structure. Resolving the impact of feedback be-
tween transcription-generated structure on subsequent tran-
scriptional dynamics may illuminate principles for engineering
transcriptional regulation via genetically encoded dynamic struc-
tures. While some computational work has been done to recon-
cile observations of chromatin dynamics (Khanna et al., 2019;
Lampo et al., 2016; Di Pierro et al., 2018; Polovnikov et al.,
2018), future work is needed to fully connect the dynamics of
chromatin function and structure (Brackey et al., 2020).
UNDERSTANDINGMODESOF ENHANCERREGULATION
Enhancers confer cell-type specificity by maintaining the unique
expression profile that characterizes each cell type and confers
unique sensitivity during development and disease (Furlong and
Levine, 2018; Lu et al., 2020; Sakabe et al., 2012). During differen-
tiation, chromatin remodeling enables enhancer engagement with
promoters to initiate transcription of gene regulatory networks that
confer a new cell identity. However, the mechanisms by which
engagement occurs and how enhancers influence chromatin
structure and dynamics to confer cell identity remains poorly un-440 Cell Systems 11, November 18, 2020derstood (Di Giammartino et al., 2019; Mateo et al., 2019). Though
a consensus perspective has yet to emerge, enhancers are tradi-
tionally defined as noncoding regulatory sequences of DNA that
can increase expression of genes via recruitment of transcrip-
tional machinery to promoters (Pennacchio et al., 2013). A subset
of enhancers induce transcription through a proximity-based
model that requires contact with cognate promoters (Chen
et al., 2018); others act in a proximity-independent fashion (Fig-
ures 6D and 6E) (Benabdallah et al., 2019). Proximity-independent
models suggest that enhancers may induce promoter activity
through distinct mechanisms including noncoding transcription
at enhancers, the direct alteration of chromatin structure, scaf-
folding of regulatory elements, and the formation of condensates
(Bracha et al., 2019; Shin et al., 2018; Shrinivas et al., 2019; Zamu-
dio et al., 2019). These mechanisms recruit transcriptional regula-
tors including RNA, mediator, and RNAPII, which in turn increase
the local concentration of transcriptional machinery at enhancers
and distally located promoters (Cho et al., 2018; Shrinivas et al.,
2019; Zamudio et al., 2019). By forming subnuclear regions with
high protein and RNA concentration, these biomolecular conden-
sates (sometimes called ‘‘hubs’’) may enable indirect enhancer-
promoter regulation across larger distances (e.g., up to a micron)
(Benabdallah et al., 2019; Cho et al., 2018; Zamudio et al., 2019).
How these condensates form, shape chromatin, and regulate
gene expression via their interactions with enhancers remains
an active area of research.
Beyond impacting supercoiling structures, transcription at en-
hancers may impact chromatin organization by increasing the
local concentration of RNA. Emerging evidence suggests non-
coding RNAs shape chromatin structure (Nozawa and Gilbert,
2019). Loss of noncoding RNAs reduces differentiation capacity
of ESCs toward different lineages, suggesting locus-specific ef-
fects for noncoding RNAs that contribute to adoption of new
transcriptional states (Guttman et al., 2011). Improved imaging
and sequencing methods, as well as live-cell tracking of identity
and transcription, will connect transcription to enhancer activity
in single cells, clarifying the mechanism by which enhancers
enable transcriptional control as well as highlighting fragile regu-
latory points sensitive to local perturbations.
Additional regulatory elements that may function similarly to
enhancers include insulators and silencers. Insulators were orig-
inally recognized as elements that blocked heterochromatin
mark spreading and have now also been implicated in the forma-
tion of some contact domain boundaries (Khurana et al., 2016;
Yang and Corces, 2011). Identified insulator regions include
the binding sites of architectural proteins such as CTCF, the
binding sites of other DNA-binding proteins, and certain highly
expressed tRNA genes (Chen and Corces, 2001; Hou et al.,
2012; Liao et al., 2018). Silencers actively decrease transcription
of encoded genes (Pang and Snyder, 2020). While some well-
known insulators and silencers such as CTCF and Polycomb
have been characterized, the mechanistic function and genomic
distribution of this class of elements are not fully understood
(Cao et al., 2002; Fukaya et al., 2016; Pang and Snyder, 2020).
CONCLUDING THOUGHTS
Combining synthetic bottom-up approaches with top-down
genome engineering, novel imaging and sequencing techniques
ll
Review OPEN ACCESSwill improve the molecular detail with which we understand the
role of chromatin regulation on gene expression and cellular
behavior. With a wealth of new tools, our ability to understand
the interrelationship between chromatin structure and function
will rapidly accelerate through the thoughtful integration of
methods and approaches. Already new tools highlight transcrip-
tion as a key regulator of chromatin dynamics and structure. In
the future, as our understanding of how transcription shapes
chromatin in native systems expands, synthetic biologists will
apply this understanding to develop strategies to sense and con-
trol chromatin structures to regulate gene expression. By har-
nessing an important layer of native gene regulation, engineering
chromatin-mediated control of gene expression will improve the
reliability of gene- and cell-based therapies.GLOSSARY
A and B Compartments
Originating from PCA of early Mb-sized compartments, the A
and B naming convention is used to refer to transcriptionally
active and inactive regions, which are now known to go down
to the kb scale and result in checkerboard patterns on
genome-wide contact maps (related: compartmental domains,
eigenvector, microphase separation).Bottom-up
‘‘Bottom-up’’ approaches involve forward engineering of sys-
tems to achieve specific design objectives. Bottom-up ap-
proaches may include addition of native or synthetic binding
sites, inclusion of larger noncoding regulatory sequences,
design of synthetic regulatory regions that insulate or interact
with native chromatin structures.Chromatin
The most elementary component of genetic material in eukary-
otic organisms consisting primarily of DNA and histones. It
serves as the first layer of DNA organization whereby genetic in-
formation stored in the dsDNA base pairs is dynamically wrap-
ped around histones to form nucleosomes. The basic composi-
tion of chromatin (such as where nucleosomes exist and their
affinity for DNA) serves as the first layer of epigenetic regulation,
influencing the local steric and mechanical state of DNA near
important areas such as transcription start sites. Additionally,
chromatin serves as the polymeric substance used to form
higher-order chromosomal structures.Compartmental Domain
Large-Mb-scale A/B compartments are actually composed of
much finer ~5 kb compartmental domains that can only be de-
tected by high-resolution contact maps.Contact Domain
A type of chromatin organization where certain genomic regions
experience increased physical proximity resulting in decreased
spatial distances and increased chromatin contact frequency.
They are manifested as on-diagonal triangles in Hi-C maps
(related: loop domain, TAD).Corner Dot
A punctate cluster of significantly increased contact frequencies
relative to neighboring loci, appearing as a dot at the corner of
triangle domains in Hi-C contact maps. Contact domains with
corner dots are typically annotated as loop domains.
CTCF
CCCTC-binding factor (CTCF) is an architectural protein with
multiple regulatory roles and blocks loop extrusion when bound
to convergently oriented CTCF-binding sites in mammals.
dCas9
A nuclease-deficient Cas9 enzyme that is capable of recognizing
guide-RNA-encoded targets but cannot introduce DNA strand
breaks. When combined with other techniques, dCas9 can be
used to recruit various fluorescent probes to specific
genomic loci.
Diffraction-Limited
Imaging techniques where the maximum resolution is deter-
mined by the physical spreading of a point source signal due
to diffraction.
DNA Supercoiling
The over or under winding of DNA resulting in local and global
changes in DNA topology and mechanics. Active processes
such as transcription will both influence it due to mechanical
coupling between elongation and DNA rotation, as well as be
influenced by it due to altered transcription initiation and elonga-
tion bubble stability (see Box 1).
Eigenvector
Eigenvectors give underlying information of a system assuming it
behaves linearly. The principal component in PCA is simply an
eigenvector of the covariance matrix, which can be used to
assign A and B compartments.
FISH
FISH, a method for fluorescently tagging specific DNA or RNA
targets.
Histone
Fundamental protein complexes found in eukaryotes. Through
various biochemical modifications they display variable affinity
for having DNA wrapped around them, forming nucleosomes.
In Situ Proximity Ligation
An improved version of proximity ligation where the ligation step
is performed in intact crosslinked nuclei to reduce noise, replac-
ing older protocols which lysed nuclei open and diluted cross-
linked chromatin prior to ligation (related: 3C-based methods).
kbT Energy
A unit of energy frequently used to scale energies of single mol-
ecules. Watson-crick base pairing is roughly ~2.5 to 4 kbT.
Level of Resolution
Contact frequencies aremeasured by sequencing proximity liga-
tion products and binning them into regions of DNA, where theCell Systems 11, November 18, 2020 441
ll
OPEN ACCESS Reviewbin size is the level of resolution of the contact map (related: 3C-
based methods).
Loop Domain
A type of contact domain resulting from loop extrusion, usually
indicated by the existence of a TAD which has strong point-like
corners in Hi-C (related: loop extrusion, corner dots).
Loop Extrusion
The process by which chromatin is expelled from active
anchoring factors to form loops.
Microphase Separation
The underlying physical mechanism thought to be responsible
for the formation of compartments, whereby active and inactive
regions of chromatin form copolymer structures, which physi-
cally segregate due to a number of potential mechanisms.
Micro-C
The highest resolution version of Hi-C where a micrococcal
nuclease digest is used to generate the DNA fragments, yielding
evenly spacedmononucleosomes and thus 200-bp contact map
resolution.
Oligopaint
A technique used for creating a fluorescently labeled heteroge-
nous DNA or RNA probe library.
RNA Polymerase II (RNAPII)
Eukaryotic RNA polymerase responsible for transcription of cod-
ing genes. RNAPII transcripts include a 50 cap and poly-adenyla-
tion tail.
Structural Maintenance Complexes (SMCs)
A family of ATP-consuming protein structures that is responsible
for many aspects of chromatin organization including the forma-
tion of the mitotic chromosomal as well as loop extruding factors
Super-Resolution
Imaging techniques where point sources are only transiently
active, which allow for precise calculation of the center of a dif-
fracted point source.
Synthetic Biology
The systematic engineering of native biological systems to
extract design principles (e.g., build-to-understand) as well as
the forward design of novel, synthetic tools, devices, and sys-
tems. More simply, synthetic biology can be defined as ‘‘engi-
neering biology’’ with a special emphasis on molecular-scale en-
gineering (related: bottom-up).
Synthetic Gene Circuit
Interlinked synthetic transcriptional networks regulating gene
expression.
TALE (Transcription Activator-Like Effectors):
Proteins that contain subdomains that specifically bind to single
base pairs. Multiple subdomains can be combined in a fusion442 Cell Systems 11, November 18, 2020protein in order to recognize genomic loci with base pair speci-
ficity
Topologically Associated Domain (TAD)
A type of contact domain that occurs on the genomic scale of
100 kb.
Top-Down
‘‘Top-down’’ approaches involve reverse-engineering of sys-
tems by systematic subtraction of elements followed by charac-
terization of function. From a biological perspective, top-down
approaches may involve loss of function mutant, deletion of pro-
tein domains, or removal of DNA-binding sites.
Transcriptional Element
A single component that is transcribed. Synthetic transcriptional
elements often include a regulatory element (e.g., promoter),
transcription start site, and gene (e.g., coding or noncoding)
and poly-adenylation sequence.
3C-Based Methods
The pioneering 3C method uses proximity ligation to quantify
how frequently 2 genomic loci are close together in a ‘‘one-to-
one’’ manner. Subsequent derived methods have expanded
how many pairwise contacts can be simultaneously probed,
such as 4C (‘‘one to all’’), 5C (‘‘many to many’’), and Hi-C (‘‘all
to all’’) and are collectively called 3C-based methods (related:
in situ proximity ligation, level of resolution).
ACKNOWLEDGMENTS
We thank AdamBeitz, Hugo Brandao, Anders Hansen, andMirna Kheir Gouda
for useful discussions and feedback. Funding: N.B.W. is supported by the Na-
tional Science Foundation Graduate Research Fellowship Program under
grant no. 1745302.
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