Fermat's Library | 3D structures of individual mammalian genomes studied by single-cell Hi-C annotated/explained version.

This papers main contribution is the first high resolution map of t...

This paper introduces a novel pipeline to create 3D structures of i...

Although DNA structure is frequently thought of as a double helix, ...

Most of what we know about cells comes from looking at them with ad...

Watch the video the authors created for their predicted 3D genomes:...

It's very impressive that their pipeline, by pairing Hi-C with imag...

Resolution on the 100-kb scale (100K bases) is actually an amazing ...

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6 APRIL 2017 | VOL 544 | NATURE | 59

ARTICLE

doi:10.1038/nature21429

3D structures of individual mammalian

genomes studied by single-cell Hi-C

Tim J. Stevens

1,2

*, David Lando

*, Srinjan Basu

*, Liam P. Atkinson

, Yang Cao

, Steven F. Lee

, Martin Leeb

†, Kai J. Wohlfahrt

Wayne Boucher

, Aoife O’Shaughnessy-Kirwan

1,4

, Julie Cramard

, Andre J. Faure

, Meryem Ralser

, Enrique Blanco

Lluis Morey

†, Miriam Sansó

, Matthieu G. S. Palayret

, Ben Lehner

5,6,7

, Luciano Di Croce

5,6,7

, Anton Wutz

†, Brian Hendrich

1,4

Dave Klenerman

& Ernest D. Laue

Our understanding of nuclear architecture has been built on electron

and light microscopy studies that suggest the existence of territories

pervaded by an inter-chromosomal space through which molecules

diffuse to and from their sites of action

. In parallel, biochemical studies,

in particular chromosome conformation capture experiments (such as

3C and Hi-C) in which DNA sequences in close spatial proximity in

the nucleus are identified after restriction enzyme digestion and DNA

ligation, have provided molecular information about chromosome

folding

. At the megabase scale, Hi-C experiments have partitioned

the genome into two (A or B) compartments

. In addition, they have

provided evidence for 0.5–1.0-Mb topological-associated domains

(TADs)

4–6

, as well as smaller loops (hundreds of kilobases)

. 3C-type

experiments have further shown that enhancers make direct physical

interactions with promoters, and that these interactions are stabi-

lized by a network of protein–protein interactions involving CTCF,

cohesin and Mediator

8,9

. Although probabilistic methods can be used to

calculate ensembles of low-resolution models that are consistent with

population Hi-C data

10,11

, understanding genome structure at higher

resolution requires the development of single-cell approaches.

In mitotic cells, both TADs and A/B compartments disappear

and thus the structural complexity of interphase chromosomes is re-

established during the G1 phase. To study interphase genome structure,

we have combined imaging with an improved Hi-C protocol (Fig. 1a)

to determine whole-genome structures of single G1-phase haploid

mouse embryonic stem (ES) cells at the 100-kb scale. The structures

allow us to study TAD and loop structure genome-wide, to analyse

the principles underlying genome folding, and to understand which

factors may be important for driving chromosome/genome structure.

We also illustrate how combining single-cell genome structures with

population-based RNA sequencing (RNA-seq) and chromatin immu-

noprecipitation followed by high-throughput sequencing (ChIP–seq)

The folding of genomic DNA from the beads-on-a-string-like structure of nucleosomes into higher-order assemblies is

crucially linked to nuclear processes. Here we calculate 3D structures of entire mammalian genomes using data from a new

chromosome conformation capture procedure that allows us to first image and then process single cells. The technique

enables genome folding to be examined at a scale of less than 100 kb, and chromosome structures to be validated. The

structures of individual topological-associated domains and loops vary substantially from cell to cell. By contrast, A and

B compartments, lamina-associated domains and active enhancers and promoters are organized in a consistent way on

a genome-wide basis in every cell, suggesting that they could drive chromosome and genome folding. By studying genes

regulated by pluripotency factor and nucleosome remodelling deacetylase (NuRD), we illustrate how the determination

of single-cell genome structure provides a new approach for investigating biological processes.

Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.

MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical

Campus, Cambridge CB2 0QH, UK.

Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.

Wellcome Trust – MRC Stem Cell Institute, University of

Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK.

EMBL-CRG Systems Biology Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain.

Universitat Pompeu Fabra, 08003

Barcelona, Spain.

Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain. †Present addresses: Max F. Perutz Laboratories, University of Vienna, Vienna Biocenter,

Dr. Bohr-Gasse 9/3, 1030 Vienna, Austria (M.L.); Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Department of Human Genetics, Miami, Florida 33136,

USA (L.M.); Inst. f. Molecular Health Sciences, ETH Zurich, HPL E 12, Otto-Stern-Weg 7, 8093 Zürich, Switzerland (A.W.).

* These authors contributed equally to this work.

In-nucleus Hi-CIdentify contactsCompute structure

Cut and

purify

Add

adaptors

Map ends

to genome

Use contact

as restraintsDigestion

Biotin

end-ll Ligation

Imaging

Amplify and

sequence

Chr 10

(5 models)

Nuclear position

Chr 10

Cell 1 – all chromosomes

Restraints

Chr 10

FACS

Cell 1Cell 2 Cell 3 Cell 4Cell 5Cell 6Cell 7Cell 8

Chr 1–19,X

020406080100

Inter-chromosome contact density (% max.):

Figure 1 | Calculation of 3D genome structures from single-cell Hi-C

data. a, Schematic of the protocol used to image and process single nuclei.

b, Colour-density matrices representing the relative number of contacts

observed between different pairs of chromosomes. c, Five superimposed

structures from a single cell, from repeat calculations using 100-kb

particles and the same experimental data, with the chromosomes coloured

differently. An expanded view of chromosome 10 (Chr 10) is shown,

coloured from red to purple (centromere to telomere), together with an

illustration of the restraints determining its structure.

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reSeArcH

60 | NATURE | VOL 544 | 6 APRIL 2017

data provides insights into the organization of pluripotency factor- and

nucleosome remodelling deacetylase (NuRD)-regulated genes.

Intact genome structures from single-cell Hi-C data

We imaged haploid mouse ES cell nuclei, expressing fluorescently

tagged CENP-A (the centromeric histone H3 variant) and histone H2B

proteins, to select G1-phase cells (Extended Data Fig. 1a) and later val-

idate the structures. Hi-C processing of eight individual mouse ES cells

yielded 37,000–122,000 contacts (Extended Data Table 1), representing

1.2–4.1% recovery of the total possible ligation junctions. In single cells,

unlike in population data, Hi-C contacts are observed between distinct

and different sets of chromosomes (Fig. 1b and Extended Data Fig. 1b).

Using a particle-on-a-string representation and an extended simu-

lated annealing protocol, we calculated highly consistent 3D genome

structures (ensemble root mean square deviation (r.m.s.d.) < 1.75

particle radii) with discrete chromosome territories (Fig. 1c and

Supplementary Videos 1, 2). The structures were calculated with an

average of 1–3 Hi-C contact-derived restraints for each 100-kb particle

(with a total of 26,000–75,000 restraints; Extended Data Table 2

and Extended Data Fig. 1c). Recalculation after randomly omitting

10–70% of the data reliably generated the same folded conformation

(r.m.s.d. < 2.5 particle radii). Moreover, structure calculations after

randomly merging half of the data from two different cells resulted in

a vast increase in the number of violated experimental restraints (37.4%

have a distance of more than 4 particle radii, compared with 5–6% for

the separate data), and generated compacted, highly inconsistent struc-

tures (Extended Data Fig. 1d). Thus, single-cell Hi-C datasets cannot

result from independent sampling of contacts from a single underlying

conformation. In addition, cells with either a broken/recombined

chromosome (Extended Data Fig. 1e) or a duplicated chromosome

(Extended Data Fig. 1f) can be immediately recognized from the data.

Structure validation and contact coverage

A consistent Rabl configuration (with centromeres and telomeres clus-

tered on opposite sides of the nucleus) was observed in all G1-phase

ES cells, strongly validating the structures (Fig. 2a, Extended Data

Fig. 2a and Supplementary Video 3). Figure 2b shows two examples

of CENP-A image superposition with the corresponding genome

structure from the same cell, providing independent evaluation of the

reliability of the structures. Cell 7 shows typical clustering of the peri

centromeric regions in a cavity on one side of the structure, which is

clearly supported by the centromere positions in the CENP-A image. In

cell 8, the centromeres are more diffusely distributed in both the image

and the structure. The structures were also validated by comparison

A/B in nucleus

High RNA expression

Cell 1

Cell 2

High RNA expression

LADs

B compartmentA compartment

Chromosome

territories

Chromosome territories

A/B compartments

Cell 1

(Chr 9)

Cell 2

(Chr 9)

Cell 1

Sequence position A/B compartments

LADs

Cell 3

(Chr 3)

Cell 3

(Chr 7)

Cell 3

(Chr 3)

Cell 3

(Chr 9)

Cell 3

(Chr 18)

Centromere

Telomere

90°

Cell 1

Pericentromeric ends

CENP-A uoresence

Cell

ell 8

90°

Figure 2 | Large-scale structure of the genome. a, Five superimposed

structures from a single cell in three different orientations, with the

chromosomes coloured from red to purple (centromere to telomere).

b, Superposition of two single-cell structures with images of mEos3.2-

tagged CENP-A recorded from the same single cells. The centromeres

from the images are shown as yellow spheres and the centromeric ends

of the chromosomes are coloured red. The same structures after rotation

by 90° are shown below. c, 3D structure of a haploid mouse ES genome

with expanded views of the separate chromosome territories (left), and

the spatial distribution of the A (blue) and B (red) compartments (right).

d, Structure of chromosome 9 from two different cells coloured from

red to purple (centromere to telomere) (left), or according to whether

the sequence is found in either the A (blue) or the B (red) compartments

(right). e, Cross-sections through five superimposed 3D structures from

two different cells, coloured according to: whether the sequence is in the A

or B compartment (left); whether the sequence is part of a cLAD (yellow)

or contains highly expressed genes (blue) (centre); and chromosome

identity (right). f, Structures of selected chromosomes from a single cell

illustrating the different ways chromosomes can contribute to the A and B

compartments. g, Chromosome 3 from a single cell with the positions of

highly expressed genes shown as blue circles (larger circles indicate higher

expression) and lamina-associated regions shown in yellow (left), and in

which the sequence is coloured according to whether it is in the A or B

compartment (right).

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reSeArcH

6 APRIL 2017 | VOL 544 | NATURE | 61

with previous imaging studies, and with both our own and previous

DNA-FISH experiments, and by testing structural predictions using

super-resolution microscopy (see below).

The single-cell Hi-C data shows fairly uniform coverage of long-

range contacts across both the A and B compartments, suggesting

similar restriction enzyme/ligase accessibility in each (Extended Data

Fig. 2b). Notably, the contact probability is preserved for all nearby

particles, showing that the entire structure is consistent with the Hi-C

contact data (Extended Data Fig. 2c). We noticed an increase in con-

tact density in some regions that coincided with sites of early DNA

replication

, but after studying violated experimental restraints we

were unable to identify any region that cannot be described by a single

structural conformation, that is, in which replication appeared to have

begun (Extended Data Fig. 2b).

Comparison of haploid and diploid mouse ES cells using RNA-seq

and ChIP–seq experiments, respectively, showed that the levels of

gene expression are highly correlated with each other (Spearman’s

rho = 0.97, P < 10

−15

) (Extended Data Fig. 2d) and that protein–

genome interactions are highly similar (Extended Data Fig. 2e). This

allowed us to use published ChIP–seq data when analysing the haploid

structures.

3D genome architecture is conserved in all cells

Discrete chromosome territories can be seen in all the intact genome

structures (Fig. 2c and Supplementary Video 1), although there is a

considerable degree (5–10%) of chromosome intermingling (Extended

Data Fig. 3a). While chromosome structure varies markedly from cell

to cell, we find that regions belonging to the A or B compartments

always cluster together and A segregates from B (Fig. 2d and Extended

Data Fig. 3b). This is supported by recent imaging experiments showing

that A- and B-compartment TADs are organized in a spatially polarized

manner in single chromosomes

, providing further validation of our

structures. In all cells, the chromosomes then pack together to give

an outer B compartment ring, an inner A compartment ring, and an

internal B compartment region around the hollow nucleoli (Fig. 2e,

Extended Data Fig. 3c and Supplementary Video 4). The nucleolus is

often close to the nuclear membrane with the A compartment forming

a bowl-like structure. To achieve this organization, chromosomes can

fold in from the surface towards the nucleoli, or fold in and back out

again, or go all the way through the nucleus (Fig. 2f and Supplementary

Video 5). Chromatin states computed from the genome-wide associ-

ation of post-translationally modified histones in mammalian cells

(a completely independent method) also show a similar organization

(Extended Data Fig. 3d). Likewise, constitutive lamina-associated

domain (cLAD) regions

16,17

are confined to either the nuclear mem-

brane or the nucleolar periphery in every cell, consistent with reshuf-

fling between these regions at each cell cycle

18,19

. Highly expressed

genes, however, mostly lie in the inner A compartment ring (Fig. 2e, g,

Extended Data Fig. 3c, e, f and Supplementary Videos 6, 7).

By mapping ChIP–seq data onto the single-cell genome structures,

we observed 3D clustering of histones H3K4me1, H3K27ac and

H3K4me3, consistent with the presence of enhancer/promoter clus-

ters or transcription factories (Extended Data Fig. 4a, b). Annotating

enhancers and promoters for activity (see Supplementary Methods)

showed that active enhancers spatially associate most strongly with

each other, followed by active enhancers with active promoters (Fig. 3a).

We also found a pronounced clustering of highly expressed genes, in

single cells, after mapping nuclear RNA-seq data onto the structures

(Fig. 2g), and the greater the level of gene expression the larger the

effect (Fig. 3b). Genome-wide analysis also showed that active/poised

enhancers and active/bivalent promoters have a clear preference for

being located at chromosomal interfaces (Extended Data Fig. 4c).

Notably, there are very clear correlations between the expression level

of a gene, and both localization to a chromosomal interface and depth

within the A compartment (Fig. 3c and Extended Data Fig. 4d, e). We

also related the preferred positions of pluripotency genes

to gene

expression and found that two highly expressed genes, Zfp42 (also

known as Rex1) and Nanog, have variable positions in our structures

(Fig. 3d). They are either found near the nuclear membrane or buried.

DNA-FISH experiments, in which Pou5f1 (also known as Oct4) is a typ-

ical highly expressed (and usually buried) gene control, verified these

conclusions and provided further validation of the structures (Fig. 3e).

Notably, the A and B compartments, cLAD, ChIP–seq and RNA-seq

data were all determined from populations of cells. Their consistent

organization in every cell suggests that overall chromosome and

genome conformation may be driven by a combination of interactions

of LADs with the nuclear membrane/nucleolus and the clustering of

active enhancers/promoters, which can be modulated by chromatin

remodelling

. That genome structure is driven by transcription is

supported by live-cell imaging of histone–GFP fusion proteins during

Caenorhabditis elegans development, which shows that knockdown of

RNA polymerase II leads to a collapse of chromatin to a ring inside the

nuclear membrane

Folding of chromosomes into TADs or CTCF/cohesin loops

As in previous studies

5,9,23

, we observed an alignment between highly

expressed genes and both A/B compartment and TAD boundaries (Fig. 4a

and Extended Data Fig. 5a). Analysis of two pairs of TADs, either side

of highly expressed genes (regions 1 and 2 in Fig. 4a), illustrates that

in some cells a particular TAD is compacted, often such that its two

boundaries are close enough to interact, whereas in others it is com-

pletely extended. This difference is not due to a lack of data because

the structures obtained from repeated calculations using identical

experimental restraints are very well defined (Fig. 4b and Extended

Data Fig. 5b).

We systematically studied compaction in chromosome 12 TADs

(Extended Data Fig. 5a) by computing the radius of gyration

()

after

excluding possible sites of early DNA replication where TAD structure

might be disrupted. As with previous studies of the Tsix TAD

abc

RNA-seq quintiles

Chromosomal

interface

A compartment

depth

R = –0.52

R = 0.75

80–100

60–80

40–60

20–40

0–20

0-20

20-40

40-60

60-80

80-100

E active

P active

P inactive

P bivalent

E active

P active

P inactive

P bivalent

E poised

P v. active

E poised

0.64

0.48

0.32

0.16

0.00

3210

Median particle depth (particle radii)

Depth s.d. (particle radii)

1234567

information gainSpatial density enrichment

DNA-FISH Structure

Depth (%)

Nanog

Zfp42

Pou5f1

Gm27037

Zfp42

Gm27037

100

Nanog

Pou5f1

Zfp42

Gm27037

Nanog

Pou5f1

Gm27037

Tfcp2l1

Klf5

Klf4

Klf2

Esrrb

Sall4

Pou5f1

Gbx2

Sox2

Tcf3

Stat3

Zfp42

Nr0b1

Nanog

Mbd3

Figure 3 | Relationship between genome folding and gene expression.

a, b, The enrichment in spatial density of: enhancers (E) and promoters (P)

annotated using ChIP–seq data (a); and gene expression determined

from nuclear RNA-seq data (b), with genes separated according to their

relative level of expression. Data in a and b are presented in hierarchical

cluster order, grouping the most similar datasets together. c, The

enrichment in the spatial density of gene expression versus distance from

the nearest inter-chromosomal interface (left) and the outer surface of

the A compartment (right). d, Median versus standard deviation of the

depth from the nuclear periphery for particles in the A (blue) or B (red)

compartments. Particles containing pluripotency genes are indicated

by yellow circles; the sizes illustrate relative levels of expression.

e, Comparison of nuclear depth in either the 3D structures (n = 8) or DNA-

FISH analysis of the Nanog (n = 84 cells) and Zfp42 (n = 142 cells) genes,

with Pou5f1 (n = 189 cells) as a control. The pseudo-gene Gm27037 (n = 16

cells) was an additional non-pluripotency factor control. Scale bars, 2 μ m.

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62 | NATURE | VOL 544 | 6 APRIL 2017

individual TAD compaction varies widely from highly extended to

compacted states (Fig. 4c), consistent with ligation occurring between

almost every site in population Hi-C data. The structures of both

compact and extended TADs are well defined and there is little corre-

lation between the

and Hi-C contact density (Extended Data

Fig. 5c), further showing that extended TAD structures do not result

from a lack of experimental contacts. Analysis of TAD structure in all

the other chromosomes gave analogous results (Extended Data Fig. 6).

It is noteworthy that compaction in the structures often appears to

involve the formation of loops within a TAD (see Fig. 4b, Extended

Data Fig. 5b and Supplementary Videos 8–11), and it will be interesting

to investigate whether these structures are related to supercoiling

25,26

or loop extrusion

27–29

We found that CTCF/cohesin loops identified in high-resolution

Hi-C data from mouse B lymphoblasts

mostly involve interac-

tions in which at least one end of the loop is in (or very near to) the

A compartment (Extended Data Fig. 5d). Considering the 88 largest

loops from 2,823 in total (with sequence separation of greater than

600 kb), we found that 33% do not form in any of the cells, whereas the

boundaries of the remainder contact each other in 12–62% of the cells

(Fig. 4d). Extending this analysis to all 2,823 loops in 8 cells showed

that the boundaries interact in 62.1% of cells (Extended Data Fig. 7).

Our genome-wide results suggest that TADs and CTCF/cohesin loops

do not form in all cells, in agreement with previous DNA-FISH exper-

iments

in which four representative loops were shown to form in only

a proportion of cells.

Our structures provide snapshots of genome folding at a par-

ticular time in different cells, and thus do not provide information

about dynamics. They are, however, markedly consistent with what

is expected from recently proposed loop-extrusion models, in which

TADs and CTCF/cohesin loops might be expected to have highly

dynamic and variable structures as cohesin rings are driven to stable

Figure 4 | Structure of TADs and CTCF/cohesin loops. a, Part of the

Hi-C contact map from chromosome 12 showing: contacts observed in

three different single cells (coloured red, yellow and blue; above the

diagonal); and the corresponding population Hi-C data (below the

diagonal). TADs identified previously

are shown in dark blue, and the

two regions analysed in b are shown in magenta. b, Ensembles of five

superimposed structures showing: two B-compartment TADs (region 1

in a; left); and TADs either side of an A/B-compartment boundary (region

2 in a; right). The TADs are coloured according to whether they are in the

A (blue) or B (red) compartments, with white indicating a transitional

segment (between A and B). Boundaries are marked by asterisks. c, The

mean radius of gyration

()

of chromosome 12 TADs ± s.e.m. Data are

scaled according to TAD size, and presented as quantile values for the

chromosome. Values below and above the fiftieth percentile value are

coloured blue and red, respectively. The

values for multiple cells are

presented in hierarchical cluster order, grouping the most similar cell

traces together. A schematic illustrating the calculation of the

as a

measure of the compaction of a particle chain is shown below. d, Analysis

illustrating whether CTCF/cohesin loops with sequence separation of

more than 600 kb identified previously

could be formed in the different

single cells. Black squares indicate that a loop could be formed; white

squares indicate that the two relevant particles are too far apart in the

structure. The bar chart across the top shows the probability, for each loop,

of random particles (pairs with the same sequence separation) forming the

same number of contacts, or better.

= 1.5 R

= 3.4 R

= 5.5

Chr 12 TAD R

percentiles

Below median

Above median

100

chart key:

Percentile

TADs

Comp

100 120

Chr 12 62.9–66.9 Mb

Cell 4

Cell 5

Chr 12 67.2–70.5 Mb

Cell 4 Cell 5

Prob.

1.0

0.5

[

Region 1 Region 2

Cell 1

Cell 2

Cell 3

Cell 4

Cell 5

Cell 6

Cell 7

Cell 8

Cells

Single cells ×3

Region 1

Region 2

Population

RNA-seq

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6 APRIL 2017 | VOL 544 | NATURE | 63

binding sites

7,27–29

. It is not known what drives the movement of

cohesin rings in mammalian cells, but previous studies in yeast suggest

that it might be RNA polymerase molecules and transcription

. This

would be consistent with our observation that CTCF/cohesin loops

are mostly found in the A compartment (in which transcription levels

are higher), studies in Drosophila suggesting that TADs result from the

compaction of chromatin due to transcription

31,32

, and recent studies

of the inactive mouse X chromosome that show a global loss of TAD

structure except at expressed genes

33,34

Understanding the gene networks in single ES cells

In addition to CTCF, cohesin and Mediator, previous studies have

implicated key pluripotency factors as well as the Polycomb complexes

(PRC1 and PRC2) in organizing 3D genome structure in mouse ES

cells. Analysis of one of the published 4C Nanog gene-interaction net-

works

showed that only one (or two) of the previously identified 4C

contacts can be identified in each single-cell structure, showing that the

propensity for particular genes to interact is low (Fig. 5a and Extended

Data Fig. 8a, b). Analysis of Pou5f1 gene-interacting regions

gave very

similar results (Extended Data Fig. 8c).

We mapped ChIP–seq data for different pluripotency factors onto

the single-cell genome structures and showed that, in single cells,

Klf4 spatially clusters strongly with itself, H3K4me1, H3K27ac and

H3K4me3, that is, with active enhancers and promoters (Extended

Data Fig. 4a, b). This analysis also suggested 3D clustering of histone

H3K27me3 (a marker for Polycomb complexes), but lower levels of

3D clustering of Nanog, both with itself and with H3K27me3. These

results are consistent with previous ES-cell imaging experiments

36,37

and strongly validate our single-cell structures. They support the pro-

posal that Klf4 organizes long-range chromosomal interactions

, and

suggest that the observed large-scale 3D segregation of Nanog and

H3K27me3 (ref. 37) mostly results from Nanog and PRC complexes

binding to separated sequences in chromosomes. However, although

they suggest that Klf4-bound genes cluster, they also show that there

is little propensity for particular Klf4-bound genes to interact with

each other.

Next, we used the structures to study genes regulated by the NuRD

complex, which has a key role in controlling the earliest stages of

differentiation of mouse ES cells

. ChIP–seq experiments showed

that although the chromatin-remodelling component CHD4 and the

deacetylase component MBD3 (ref. 39) are widely distributed (data

not shown), marked 3D clustering of NuRD-regulated genes occurs

(Fig. 5b, c). Super-resolution microscopy and single-particle track-

ing using photo-activated light microscopy in fixed and live cells,

respectively, showed clustering of both the chromatin-remodelling

and deacetylase sub-modules (as illustrated by the mEos3.2-tagged

CHD4 and MBD3 proteins, respectively), consistent with the 3D

clustering of NuRD-regulated genes (Fig. 5d and Extended Data

Fig. 8d). Notably, although our structures show that the regions con-

taining highly NuRD-regulated genes cluster, the actual regions that

interact vary from cell to cell (Fig. 5e). In addition, we found that

most genes are upregulated or downregulated in either the CHD4-

depletion experiment or the MBD3-knockout cells, but not in both

(Fig. 5c), suggesting that the chromatin-remodelling and deacetylase

sub-modules may function separately. However, despite regulating

different sets of genes, it is notable that genes that are downregulated

in the MBD3-knockout cells cluster more strongly than those that are

upregulated, and genes that are downregulated in the MBD3-knockout

cluster more strongly with genes that are upregulated in the CHD4-

knockdown (and vice versa) (Fig. 5b). Although further work is neces-

sary to understand what drives the formation of NuRD clusters, the 3D

clustering of CHD4 and MBD3 with active enhancers and promoters

is noteworthy (Fig. 5b).

Conclusion

The structures allow one of the first genome-wide analysis of 3D

interactions of individual regulatory elements and genes in single

cells. In combination with 3D imaging the data show that although

Klf4- and NuRD-regulated genes interact and cluster to form foci,

the genes they bring together are very variable. Our combination of

imaging with determination of genome structure will allow further

studies of these and many other biological processes. In addition, the

finding that chromosomes have a Rabl configuration in mammalian

G1-phase cells may underlie slight preferences in long-range chro-

mosomal interactions, such as those leading to translocation events

involved in disease

Online Content Methods, along with any additional Extended Data display items

and Source Data, are available in the online version of the paper; references

unique to these sections appear only in the online paper.

Data Availability The ChIP–seq, RNA-seq and Hi-C data, structures and

images reported in this study have been made available at the Gene

Expression Omnibus (GEO) repository under accession code GSE80280.

Received 18 March 2016; accepted 26 January 2017.

Published online 13 March; corrected online 5 April 2017

(see full-text HTML version for details).

MBD3

2,505 Ĺ

CHD4-KD

1,708 Ļ

CHD4-KD

1,320 Ļ

1,477 Ĺ

MBD3-KO

MBK3-KO

389

change in both

ChIP–seq

0.64

0.56

0.48

0.40

0.32

0.24

0.16

0.08

Klf4

Ep300

Oct4

Nanog

E active

P active

P v. active

E poised

P inactive

P bivalent

GenesĹ CHD4-KD

Chd4

Mbd3

GenesĻ CHD4-KD

GenesĻ MBD3-KO

GenesĹ MBD3-KO

Klf4

Ep300

Oct4

Nanog

E active

P active

P v. active

E poised

P inactive

P bivalent

Genes

Ĺ CHD4-KD

Chd4

Mbd3

GenesĻ CHD4-KD

GenesĻ MBD3-KO

GenesĹ MBD3-KO

Chr 16 3.7–5.8 Mb

Cell 1

Cell 3

Chr 6 28.5–30.0 Mb

Chr 16 33.4–34.0 Mb

Nanog gene

Nanog-interaction points

Cell 1

(Chr 6)

MBD3–mEos3.2

No. of molecules

CHD4–mEos3.2

2 μm 2 μm

Spatial density enrichment

information gain

Figure 5 | Understanding the nature of gene networks in mouse

ES cells. a, Structure of an individual cell illustrating the interactions

identified between the Nanog gene (highlighted in yellow) in chromosome

6 (coloured blue) and other regions of the genome (red circles) in a

population 4C experiment

. b, The spatial density enrichment of NuRD

components (CHD4 and MBD3), pluripotency factors and NuRD-

regulated genes, as well as annotated enhancers and promoters defined

using ChIP–seq data. c, Pie chart showing the numbers of NuRD-regulated

genes in different classes. CHD4-KD, CHD4-knockdown; MBD3-KO,

MBD3-knockout. d, Heat map showing clustering of CHD4 and MBD3

molecules in 2D super-resolution photo-activated light microscopy

in fixed mouse ES cells. e, Structures of a region of chromosome 16 in

two different cells, showing clustering of regions containing genes that

are highly regulated by NuRD (highlighted in yellow). The positions of

genes in either the CHD4-knockdown or MBD3-knockout cells that are

downregulated (red circles) or upregulated (blue circles) are indicated

(larger circles for more highly regulated).

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64 | NATURE | VOL 544 | 6 APRIL 2017

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Supplementary Information is available in the online version of the paper.

Acknowledgements We thank A. Riddell for cell sorting, P. Humphreys for

confocal microscopy, A. Peter Gunnarson for the density mapping software,

the CRUK Cambridge Institute for DNA sequencing, T. Nagano and

P. Fraser for processing the preliminary haploid mouse ES cells, and W. Dean,

S. Schoenfelder and S. Wingett for advice. We thank the Wellcome Trust

(082010/Z/07/Z), the EC FP7 4DCellFate project (277899) and the MRC

(MR/M010082/1) for financial support.

Author Contributions D.L., S.B. and Y.C. developed the protocol and carried

out imaging/Hi-C processing. T.J.S. developed the software with assistance

from L.P.A., W.B. and K.J.W. A.O’S.-K., J.C., M.R. and B.H. carried out the CHD4/

MBD3 depletion experiments, associated RNA-seq and ChIP–seq, and created

the mEos3.2-Halo tagged ES cell lines. M.L. and A.W. provided the initial

samples of haploid mouse ES cells. S.F.L., M.G.S.P. and D.K. designed and

built the microscope. L.M., M.S. and L.D.C. carried out ChIP–seq and RNA-seq

experiments, while A.J.F., E.B. and B.L. carried out bioinformatics analysis. T.J.S.

and E.D.L. designed experiments, analysed the results and wrote the manuscript

with contributions from all the other authors.

Author Information Reprints and permissions information is available at

www.nature.com/reprints. The authors declare no competing financial interests.

Readers are welcome to comment on the online version of the paper.

Correspondence and requests for materials should be addressed to

E.D.L. (e.d.laue@bioc.cam.ac.uk).

Reviewer Information Nature thanks W. Huber and the other anonymous

reviewer(s) for their contribution to the peer review of this work.

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Extended Data Figure 1 | See next page for caption.

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Extended Data Figure 1 | Quality control for Hi-C processing and

3D structure calculation. a, Comparison of 3D images of CENP-A in

haploid mouse ES cell nuclei, expressing mEos3.2-tagged CENP-A and

tandem infrared fluorescent protein (iRFP)-tagged histone H2B, with

their corresponding white-light images. b, Comparison of three single-

cell Hi-C contact maps (above the diagonal; contacts coloured red,

yellow and blue) with the population Hi-C map (below the diagonal).

c, An analysis of the accuracy and precision of the 100-kb structure

calculation procedure for cell 1. The graphs show how the global (dis)

similarity of structures is affected by: the total number of contacts (left);

the number of inter-chromosomal contacts (middle); and the number

of random noise contacts (right). Mean r.m.s.d. values for all pairs of

conformations ± s.e.m. are shown for: the precision within ensembles

arising from ten re-calculations using the same contacts (red); the

variation across ensembles that arises from different random resampling

(blue); and, as a measure of accuracy, the similarity to the best ensemble

of structures (yellow). d, An example of a structure calculation carried

out using either a single dataset, or after randomly merging 50% of the

data from two different cells. Strongly violated experimental restraints

(> 4 particle radii apart) are shown in red. The plot (right) shows the

probability of any two particles connected by an experimental restraint

being violated to different degrees. e, Left, the structure of chromosome 1

from cell 6, where part of the chromosome lies at the opposite side of the

genome structure, with no intermediate chromosome folding, illustrating

the presence of a chromosomal break or recombination event. Right, the

contact map shows that there are no contacts from the disconnected region

to any other part of chromosome 1, but clear contacts to chromosomes 3

and 7. f, An example of an attempted calculation of the haploid genome

structure for a cell containing a duplicated chromosome 2 shows many

violations of the experimental restraints for that chromosome and a much

more compacted structure (here compared with chromosomes 1 and 3).

The structures are coloured according to position in the chromosome

sequence from red to purple (centromere to telomere).

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Extended Data Figure 2 | See next page for caption.

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Extended Data Figure 2 | Validation and analysis of single-cell

contacts. a, Structure of the entire haploid mouse ES cell genome from

cells 2 to 8. The structural ensemble is represented by five superimposed

conformations from repeat calculations, and is shown in three different

orientations (after rotation through 90° relative to each other), with the

chromosomes coloured according to their position in the chromosome

sequence from red to purple (centromere to telomere). b, Correspondence

between the distribution of Hi-C contacts (both cis and trans), violations

of the distance restraints in the 3D structures, and DNA replication

timing

for a representative chromosome (chromosome 12). c, Left,

log-scale plots of contact probability (P

cont

) against sequence separation

(S). The slopes for a power law relationship (P

cont

∝ S

) in which α is

either −1.0 or −1.5 are also indicated. Data are shown for the combined

single-cell Hi-C contact data, for all of the non-sequential particles that

are close to each other in the structures (< 2 particle radii apart), and

for the population Hi-C data. Right, the distribution in the number of

intra-chromosomal (cis) or inter-chromosomal (trans) contacts between

100-kb regions in the single-cell Hi-C data are shown for both the A

and B compartments. d, Correlation of gene expression levels (left), and

hierarchically clustered heat maps showing the pairwise enrichment of

ChIP–seq peak overlaps between haploid and diploid ES cells (centre),

and Nanog ChIP–seq peak overlaps between haploid and diploid ES cells

used in this study, as well as that previously published from diploid ES cells

(right)

41. Murakami, K. et al. NANOG alone induces germ cells in primed epiblast in vitro

by activation of enhancers. Nature 529, 403–407 (2016).

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Extended Data Figure 3 | See next page for caption.

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Extended Data Figure 3 | Chromosome interactions. a, Violin plot

showing the proportion of each chromosome that intermingles with other

chromosomes. b, Pairwise comparison of the chromosome structure

in different cells by r.m.s.d. analysis. Four models of chromosome 9

from a selection of different cells are shown, coloured according to the

chromosome sequence (from red to purple, centromere to telomere),

together with a table showing the r.m.s.d. values between the chromosomal

3D coordinates for each cell (bottom). c, Further cross-sections through

the structures of haploid genomes from cells 3–8 through the structures

of haploid genomes (see Fig. 2e), coloured according to: whether the

sequence is in the A or B compartment (top); whether the sequence is part

of a cLAD or contains highly expressed genes (coloured yellow and blue,

respectively) (centre); and the identity of the chromosomes (bottom).

In each case, an ensemble of five superimposed conformations arising

from repeat calculations starting from different randomly generated sets

of coordinates is shown. d, An analysis of the genome depth of various

chromatin class categories, determined by k-means clustering of 100-kb

segments according to the presence of histone H3 ChIP–seq data

. The

active class is associated with H3K4me3, Polycomb with H3K27me3, the

inactive class with H3K9me3, and null denotes the remainder. Left, the

probability distribution for each of the categories at different normalized

nucleus depths. Right, the divergence of the probability distribution for

each category from the whole-genome average. Data are shown for the

genome structures of all cells. e, An analysis of the genome depth for

regions with differing levels of gene expression, as measured by nuclear

RNA-seq. Here, RNA-seq signal peaks were ranked and split into five

classes. As in d, the probability distribution for each class with regard

to genome depth is shown (left), together with the divergence of each

distribution from the genome as a whole (right). f, Further comparisons

of the structure of chromosome 3 from different cells, coloured according

to whether the sequence is part of the cLAD domains (yellow), with the

positions of highly expressed genes indicated by the blue rings (larger

circles indicate higher expression).

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Extended Data Figure 4 | See next page for caption.

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Extended Data Figure 4 | Relationship between genome folding and

gene expression. a, Calculation of 3D spatial clustering compared to a

random hypothesis in which the same data were circularly permuted

around the sequence, and repeating the calculations, using the same

structure. Two examples, with strong (Klf4/H3K4me1) and weaker

(Nanog/H3K27me3) spatial co-localization compared to random,

are shown. b, The enrichment in spatial density (after removal of any

clustering expected from their being located in the same chromosome

sequence) of histone H3 with various post-translational modifications

and selected pluripotency factors as determined by ChIP–seq data. The

enrichment is calculated over all cells as the Kullback–Liebler divergence

of the normalized spatial density distribution from a random, circularly

permuted, expectation (see Supplementary Methods for more details),

and the data are presented in hierarchical cluster order, grouping the most

similar datasets together. c, Box and whisker plots showing enhancer,

promoter and repetitive sequence content (bottom), and the enrichment

in spatial density of different types of enhancer, promoter and repetitive

sequence (top), after the data have been divided into ten groups based

on increasing distance from the nearest inter-chromosomal interface.

Whiskers represent the tenth and ninetieth percentiles, boxes represent the

range from the twenty-fifth to the seventy-fifth percentile, and outliers are

shown as dots. Mean and median values are shown by black crosses and

bars, respectively. The Rvalues are the Pearson’s correlation coefficient

on the underlying, unranked data. d, Plots of the level of gene expression

as measured by the nuclear RNA-seq signal within 1-Mb regions against

distance from the nearest inter-chromosomal interface (left) and the outer

surface of the A compartment (right). e, Examples of inter-chromosomal

interfaces from two different cells in which the chromosomes are coloured

increasingly bright red for higher enrichment in the density of gene

expression, compared to what would be expected for a given sequence

separation. The remainder of the two chromosomes is coloured grey, and

the positions of promoters are indicated by blue circles. The same views

are shown with the two different chromosomes coloured yellow and blue

(top), or with their regions in the A and B compartments coloured blue

and red (bottom).

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Extended Data Figure 5 | See next page for caption.

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Extended Data Figure 5 | Chromosome folding into compartments,

TADs and loops. a, A contact map showing the population Hi-C data

for chromosome 12 with TADs identified using the directionality index

as blue squares. On the left and at the bottom, data tracks are shown

identifying the A and B compartments (in blue and red, respectively), and

highly expressed genes (in magenta). b, Further comparisons (see Fig. 4b)

showing the structures (and their variability) of two B compartment

TADs either side of a highly expressed gene(s) in a short region of A

compartment (top), or at a boundary between the A and B compartments

(bottom). Ensembles of five superimposed conformations, from repeat

calculations using the same experimental data, are shown with pairs of

TADs highlighted and coloured according to whether they are in the A

or B compartments (blue and red, respectively), with white indicating a

transitional segment (between A and B). TAD boundaries are marked by

asterisks. c, Scatter plots of the mean radius of gyration for 1-Mb regions

of genome structure compared to the average number of single-cell Hi-C

contacts, within the same region, considering a 1-Mb sliding analysis

window. Data are shown for all genome structures and split according to

cis (left) and trans (right) contacts. d, Structure of chromosome 12, with

the A compartment coloured blue and positions of CTCF/cohesin loops

identified previously

indicated by dotted red lines. The pie chart shows

the numbers of loops between sequences in the A and B compartments.

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Extended Data Figure 6 | Chromosome folding into TADs. Bar charts of

the mean

values of TADs identified using the directionality index

for

all the different chromosomes. The data are mean values over all structure

conformations, scaled according to TAD size, and presented as quantile

values for the chromosome. The fiftieth percentile value corresponds to

the central grey line. Values below and above this are coloured blue and

red respectively. TADs that contain both regions of early replication timing

(above the ninetieth percentile) and moderate restraint violation (see

Extended Data Fig. 2b) are excluded from the calculation. The errors

in the

are the percentiles ± the s.e.m. Values for multiple cells are

presented in hierarchical cluster order, grouping the most similar cells

together.

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Extended Data Figure 7 | Chromosome folding into loops. A genome-

wide analysis illustrating whether CTCF/cohesin loops

could be formed

in the different single cells, in each chromosome. A black square indicates

that the two boundaries in the loop could interact, a white square indicates

that the two relevant particles are too far apart in the structure. The loop

boundary separation, in particles, is shown along the x axis. The bar chart

across the top shows the probability, for each loop, of random particles

(pairs with the same sequence separation) forming the same number of

contacts, or better. The probability of choosing a set of loop boundary

points, which interact more frequently than we observed is 0.00072

(see Supplementary Methods).

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Extended Data Figure 8 | Understanding the nature of gene networks

in mouse ES cells. a, Structures of cells 2–8 illustrating the interactions

identified between the Nanog gene and other regions of the genome

by population 4C (ref. 34). Chromosome 6 is coloured blue, with the

position of the Nanog gene highlighted in yellow, and the remainder of

the chromosomes are coloured grey. Interacting positions in the genome

are indicated by red circles. b, Heat map showing the number of times

a particular interaction is detected between two of the 4C Nanog

gene-interacting points

. c, Heat map showing the number of times a

particular interaction is detected between two of the 4C Pou5f1 gene-

interacting points

. In b and c, the interaction points are presented in

hierarchical order, grouping the regions that show the most interactions

together. d, 2D single-molecule tracking using photo-activated light

microscopy in live mouse ES cells shows clustering of CHD4 and MBD3.

In both cases, a heat map of a single cell is shown in which the pixels have

been colour-coded according to the density of molecules detected in that

region.

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Extended Data Table 1 | Summary of data for the eight cells analysed, and statistics for the sequence analysis

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Extended Data Table 2 | Summary of statistics from the genome structure calculation process for the eight cells analysed#

Discussion

Watch the video the authors created for their predicted 3D genomes: https://www.youtube.com/watch?v=1Fyq9ul9N9Q I'm very excited about the possibilities of high resolution 3D genomes for elucidating the epigenome! It's very impressive that their pipeline, by pairing Hi-C with imaging, is able to obtain high resolution 3D structures of the genome using only 1.2 to 4.1 % of total junctions in their genomes (compared to other Hi-C methods that require much more data) One potential limitation is that the authors only apply their method on eight cells (and they are all mouse ES cells), in future work they should validate on more cells across different cell types. Resolution on the 100-kb scale (100K bases) is actually an amazing feat, because most other Hi-C experiments (that typically employ pooled cells rather than doing single cell sequencing), have 1MB or higher resolution. Most of what we know about cells comes from looking at them with advanced microscopes. We could use microscopy to observe the physical locations of different fluorescently labeled DNA regions on chromosomes, but the resolution of this images are very low. As a result, methods like chromosome capture techniques have emerged, that need to be coupled with structure prediction algorithms. This papers main contribution is the first high resolution map of the 3D genome and a novel pipeline for building 3D genome models. The authors cleverly annotate the genomes in their analysis and come up with several potential functional relationships between transcription and structure. This paper introduces a novel pipeline to create 3D structures of individual mammalian genomes studied by single-cell Hi-C, and produces a genome-wide analysis of the 3D interactions of regulatory elements and genes at the single cell level. Hi-C experiments have been widely used for chromosome capture since they were first introduced by Lieberman-Aiden et al in 2009, and have helped uncover the two compartments of the genome (A and B), evidence of .5-1-Mb topological-associated domains (TADS), and smaller loops in the genome. Similar biochemical experimental methods like 3C have uncovered structural information about the interactions between enhancers and promoters. Although DNA structure is frequently thought of as a double helix, that is actually it’s form when it’s changing phases, and it’s typical form is actually much more complex in 3 Dimensions. To understand 3D genome architecture, researchers use chromosome conformation capture techniques (like Hi-C) that find DNA contacts (places where DNA strands touch one another). The next step is a computational problem-> how can you reconstruct the structure/dynamics of a genome from this partial information of DNA contacts.

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