METHOD Open Access
Precise genome-wide mapping of single
nucleosomes and linkers in vivo
Răzvan V. Chereji1†
, Srinivas Ramachandran2†
, Terri D. Bryson2
and Steven Henikoff2*
Abstract
We developed a chemical cleavage method that releases single nucleosome dyad-containing fragments, allowing
us to precisely map both single nucleosomes and linkers with high accuracy genome-wide in yeast. Our single
nucleosome positioning data reveal that nucleosomes occupy preferred positions that differ by integral multiples of
the DNA helical repeat. By comparing nucleosome dyad positioning maps to existing genomic and transcriptomic
data, we evaluated the contributions of sequence, transcription, and histones H1 and H2A.Z in defining the
chromatin landscape. We present a biophysical model that neglects DNA sequence and shows that steric occlusion
suffices to explain the salient features of nucleosome positioning.
Keywords: Chemical cleavage mapping, Linker quantization, Biophysical modeling
Background
Nucleosomes are the basic units of DNA packaging in eukaryotes.
They contain ~ 147 bp of DNA, wrapped around a
histone octamer, in about 1.7 superhelical turns [1–3].
Nucleosomes have multiple roles inside the nucleus. In
addition to DNA packaging, nucleosome organization plays
a crucial role in controlling DNA accessibility of many
DNA-binding proteins to regulatory elements on the chromosomes.
Nucleosome positions influence gene expression
regulation [4–7], DNA replication [8, 9], DNA repair [8, 10],
and DNA recombination [11]. The importance of nucleosome
position in understanding these processes has fueled a
debate as to whether the position of a nucleosome is determined
in part by inherent sequence preferences [12–16].
The most generally accepted view is that nucleosome positioning
is determined by a combination of DNA sequence,
ATP-dependent remodeling enzymes, transcription factors,
and elongating RNA polymerases [15]. However, the validity
of this paradigm depends on the accuracy and precision with
which the in vivo positions of nucleosomes are mapped.
To map nucleosome positions, the chromatin fiber is
typically digested with micrococcal nuclease (MNase)
followed by high-throughput sequencing (MNase-seq).
MNase generates a wide range of fragment sizes even at
well-positioned nucleosomes due to unwrapping dynamics
of DNA at the entry and exit of the nucleosome
compared to the DNA near the dyad axis. Fragments
that range in size between 130 and 160 bp may represent
intact nucleosomes, where the variation in the length of
DNA that wraps around a histone octamer could represent
internal cleavages on one end and linker cleavage
on the other end. Thus, due to the very nature of MNase
cleavage, there is a degree of “fuzziness” in the nucleosome
positions determined by MNase-seq.
Linker DNA is about 25 times more susceptible to
MNase cleavage, compared to nucleosomal DNA [17],
implying that after an extensive digestion, most of the
linker DNA will be digested and the remaining DNA will
be highly enriched in mononucleosomal fragments.
However, MNase digestion produces DNA fragments
that are protected both by nucleosomes and by nonhistone
DNA-binding proteins [18]. MNase also has a
strong DNA sequence specificity [19–21]. Furthermore,
measuring nucleosome occupancy using MNase has
been a problem, and questions have been raised about
the degree to which MNase protection measures nucleosome
occupancy as opposed to DNA accessibility [22].
Alternative methods have been introduced that promise
to circumvent these issues involving the use of
MNase to map nucleosomes [23, 24]. In the H4S47C
cleavage mapping method [23], substitution of a cysteine
for a serine at position 47 of histone H4 converts it into
* Correspondence: steveh@fhcrc.org
†
Equal contributors
2
Howard Hughes Medical Institute and Basic Sciences Division, Fred
Hutchinson Cancer Research Center, Seattle, WA 98109, USA
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Chereji et al. Genome Biology (2018) 19:19
DOI 10.1186/s13059-018-1398-0
a site-specific nuclease by coupling the cysteine to phenanthroline
ex vivo. The phenanthroline chelates a copper
ion, which in the presence of peroxide cleaves DNA in its
local vicinity. Cleavages due to H4S47C-phenanthroline-
Cu+
occur close to the pseudo-dyad axis, where the DNA
has the least mobility. The ends of the resulting DNA
fragments approximate very well the positions of the
dyads corresponding to the nucleosomes that produced
the cleavage. Thus, the H4S47C cleavage method maps
nucleosome dyad positions with base-pair resolution.
Initially, this method was used to map nucleosomes
in vitro [3, 25]. More recently, this method was successfully
applied in vivo in Saccharomyces cerevisiae [23, 26],
S. pombe [27, 28], and mouse embryonic stem cells [29].
H4S47C-anchored chemical cleavage occurs at positions
±1 and ±6 bp from the nucleosomal center. This results in
a fragment that spans from one side of the dyad axis of a
nucleosome to the dyad axis of the next nucleosome or to
a non-specific cleavage at a nucleosome-depleted region
(NDR) due to free phenanthroline. The pattern of cleavages
around the dyad axis was used to develop a Bayesian
deconvolution algorithm in order to infer the most likely
positions for the nucleosome centers [23]. Thus, in
H4S47C cleavage mapping, the nucleosome positions
were derived by considering clusters of many cleavages
originating from different cells and predicting the most
probable nucleosome locations. However, background
cleavages in nucleosome-depleted and linker regions can
complicate the use of the method for determining nucleosome
occupancy. Moreover, nucleosomes can form at different
alternative positions in different cells [30–32],
which motivates the development of a method that maps
individual nucleosomes rather than relying on inferred coordinates
for the most probable location of their centers.
Here we introduce an alternative chemical cleavage
method that avoids the complications inherent in H4S47C
cleavage mapping. With H4S47C cleavages, the fragment
between two cleavages in a single nucleosome is at most
12 bp and is too short to be part of the sequencing library.
We hypothesized that moving the cysteine to a DNAproximal
position on a histone that is farther away from
the dyad would release a fragment long enough to be
sequenced and uniquely mapped. This fragment would represent
a single nucleosome and directly indicate that single
nucleosome’s position at base-pair resolution. In this study,
we describe a new histone mutant, H3Q85C, which when
present in two copies in the nucleosome releases a 51-bp
DNA fragment from each nucleosome following a chemical
cleavage reaction. This 51-bp fragment from a single yeast
cell enables the direct and precise assignment of the single
nucleosome position. Using H3Q85C cleavage data, we
have mapped nucleosome dyad positions, nucleosome occupancies,
NDRs, and linker lengths with unprecedented
accuracy. Using these data, we resolve features of the
chromatin landscape with high precision and propose a
biophysical model of nucleosome organization that predicts
the major features of nucleosome positioning despite ignoring
DNA sequence.
Results
H3Q85C cleavage mapping precisely locates dyads of
single nucleosomes
H4S47C positions are derived by averaging many cleavages.
There are also non-specific cleavages in linkers and NDRs
due to free phenanthroline, contributing to noise in
H4S47C-anchored cleavage mapping (Fig. 1a, tracks 1–5).
To obtain direct measurements of dyad positions and reduce
the noise due to non-specific cleavages, we tested mutants
at positions farther away from the dyad so that two
cleavages on either side of the dyad would give rise to a
fragment that is long enough to be sequenced. We found
that H3Q85C cleaves nucleosomal DNA and releases 51-bp
fragments, as expected from the position of this residue
close to the DNA minor groove based on the crystal
structure of the nucleosome (Additional file 1: Figure S1).
Cleavages on either side of the dyad of a nucleosome result
in a 51-bp fragment and drastically reduce the noise due to
non-specific cleavages. As each 51-bp fragment arises from
a single nucleosome, its midpoint corresponds to the precise
position of a nucleosome and directly informs about
the distribution of nucleosome centers on the genome
without the need to average cleavages from multiple nucleosomes.
By using 51-bp fragments generated by H3Q85C
cleavages for mapping rather than the linker-spanning
fragments generated by H4S47C cleavages, we avoid the
background due to cleavages within linkers, which is especially
important for distinguishing regions that are merely
nucleosome-depleted from those that are virtually
nucleosome-free (Fig. 1a; compare tracks 6–8 with tracks
1–5). This reduction in noise is reflected in the average
nucleosome distribution relative to the transcription start
site (TSS) (Fig. 1b) by the reduced nucleosome density at
promoters and increased peak heights at the typical nucleosome
positions. Wild-type control cells treated similarly to
the mutants show a high density of cleavages at TSSs and
linker regions, evidence that the elevated level of cleavages
over TSSs using the H4S47C mutant is caused by free
phenanthroline.
Although, in addition to intranucleosomal fragments
(~51 bp long), H3Q85C cleavages also produce longer
internucleosomal fragments, we can easily purify the
intranucleosomal fragments using polyacrylamide gel
electrophoresis (PAGE) (Fig. 1c). With this increased
sensitivity in detecting H3-containing nucleosomes, we
asked if this data set could clarify nucleosome occupancy
at other genomic landmarks. One model for the budding
yeast centromere posited a Cse4-H3 heterotypic nucleosome
[33]. With the new H3 localization data, we are
Chereji et al. Genome Biology (2018) 19:19 Page 2 of 20
0.00
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H3Q85CH4S47C
Distance from CEN center (bp) Distance from CEN center (bp)
Fragmentlength(bp)
-1000 -500 0 500 1000
Position relative to TSS (bp)
0
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-500 0 500
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WT control
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YBR083W YBR084W
Gene
Promoter
H4-S47C
Brogaard et al. (1.5 min)
H4-S47C
Brogaard et al. (20 min)
H4-S47C, Replicate 1
H4-S47C, Replicate 2
H4-S47C, Replicate 3
H3-Q85C, Replicate 1
H3-Q85C, Replicate 2
H3-Q85C, Replicate 3
MNase-ChIP-seq, H4 IP
Sonication-ChIP-seq, H4 IP
[0 - 2.7]
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Fig. 1 (See legend on next page.)
Chereji et al. Genome Biology (2018) 19:19 Page 3 of 20
able to directly interrogate the presence of histone H3 at
yeast centromeres and directly distinguish between these
alternative models. When we compared the heat map of
fragment ends mapping around the ~ 80-bp centromere
DNA element (CDEII) sequence of all yeast chromosomes
from our previous H4S47C data [26] and our
present H3Q85C data, we observed striking differences
(Fig. 1d). The “railroad track” in the H4 data denotes the
precise positioning of H4S47C at the centromere. In
contrast, we observe no cleavages at the centromere in
the H3 data, but uniform coverage of reads adjacent to
the centromere, as seen also by averaging the fragments
relative to CDEII (Fig. 1e). Thus, H3Q85C mapping
rules out the presence of H3 at yeast centromeres.
Similarly, at the transfer RNA (tRNA) genes, we see a
striking nucleosome depletion in H3Q85C maps (Fig. 1f),
whereas H4S47C maps show a high level of cleavages
that were likely generated by free phenanthroline
(Fig. 1f). Finally, the higher signal-to-noise ratio of
H3Q85C enables us to discern alternate rotational positions
for nucleosomes defined by H4S47C mapping. We
plotted the distribution of nucleosome dyads deduced
either from the centers of ~ 51-bp fragments produced
by H3Q85C cleavages, or from the individual cleavages
produced by H4S47C, relative to the +1 nucleosome positions
defined using published H4S47C nucleosome
calls (Fig. 1g). Not only did we observe a higher signal at
the called dyad itself, but we also observed at least three
alternate rotational positions upstream and downstream
of the +1 dyad that are invisible to H4S47C mapping. In
summary, relying on two intranucleosomal cleavages in
H3Q85C mapping enables the generation of a superior
nucleosome map in budding yeast.
H3Q85C cleavage mapping identifies rotational
positioning preferences of all nucleosomes in the
genome
Within the DNA sequences that are wrapped around the
histone octamer, there is a compositional preference for
WW dinucleotides (AA, TT, AT, or TA) to occur where
the minor grooves of DNA directly interact with histones,
while SS dinucleotides (CC, GG, CG, or GC)
occur where the major grooves of DNA are facing the
histones [15]. These alternating DNA sequences have a
higher affinity for nucleosome formation [34], and they
have been observed among the nucleosomal sequences
in different organisms [35–37]. Evolution of this weak
compositional preference has a structural rationale, in
which more bendable sequences tend to be in contact
with the histones every 10 bp and less bendable sequences
tend to be solvent-exposed, to facilitate the tight
wrapping around the nucleosome core [15]. Originally
described using only 177 MNase-generated chicken nucleosomes
[36], this enrichment has been confirmed for
the yeast genome using MNase-seq [38] and is also seen
using H4S47C-anchored cleavage [23]. These analyses
are based on called nucleosome positions, which represent
a small subset of nucleosomes that can be identified
based on their high signal-to-noise ratio using these
methods. However, with H3Q85C cleavage mapping,
each ~ 51-bp fragment identifies one unique nucleosome
without requiring any additional filtering or statistical inference.
Thus, we calculated the dinucleotide preference
based on every ~ 51- bp fragment. We observed that nucleosome
centers determined by H3Q85C-anchored cleavage
mapping also show a cumulative rotational phasing of
WW and SS dinucleotides (Fig. 2a). The power of this
(See figure on previous page.)
Fig. 1 Concurrent chemical cleavages of H3 histones eliminate the background noise in the chemical mapping experiments. a Integrative Genomics Viewer
browser snapshot illustrating nucleosome maps obtained by different techniques. Previous chemical maps, using mutated histones H4S47C (tracks 1–5), are
affected by increased background of non-specific cleavages produced by free phenanthroline. Tracks 1 and 2 show the nucleosome occupancy inferred from
H4S47C cleavages reported by [23] by symmetrically extending each cleavage position to a footprint of 101 bp (to emphasize the linker positions) between
neighboring nucleosomes. Tracks 3–5 show similar data from [26]. The background from the promoter of YBR084W is strongly reduced when we consider only
concurrent chemical cleavages, both produced by H3Q85C (tracks 6–8). Tracks 6–8 show the occupancy produced by symmetrically extending the ~ 50-bp
fragment centers to a footprint of 101 bp. The absence of histones from the promoter is confirmed by two alternative chromatin immunoprecipitation
sequencing (ChIP-seq) methods (tracks 9 and 10) (data from [51, 60]). To make the occupancy profiles comparable, the MNase-ChIP-seq nucleosome map was
generated by symmetrically extending the position of the dyad to a footprint of 101 bp. For sonication-ChIP-seq profiles, the occupancy was computed by
stacking all the fragments with length between 50 and 200 bp, as in this case the centers of the fragments do not necessarily represent nucleosome dyads.
b Average dyad distributions obtained from our H3Q85C cleavage experiment show reduced background noise compared to the map obtained from the
H4S47C cleavage experiment. c Length distribution of DNA fragments before and after gel filtration. d Left-right V-plot display of chemical cleavages, where the
X-axis shows the position of the center of all 16 aligned centromeres and the Y-axis shows the length of each fragment; red pixels represent the left fragment
ends and blue pixels represent the right ends. The two edges of the ~ 80-bp centromere DNA element II (CDEII) are indicated by dotted lines. For H4S47C, the
red and blue vertical features between the dotted lines imply that cleavages occur at two distinct positions within CDEII over the population. However, for
H3Q85C, no cleavages are seen within the centromeric region, indicating that centromeric nucleosomes do not contain H3 histones. e Cleavage density
(left) and average occupancy (right) plots of the data shown in d, comparing H4S47C and H3Q85C cleavage data. Mock control data were obtained by
phenanthroline treatment and cleavage reactions performed using wild-type cells as described [26]. f Cleavage density plots comparing H4S47C, H3Q85C,
and Mock control cleavage data over aligned transfer RNA (tRNA) genes. g The preferred rotational positions are more evident in the H3S47C cleavage
data, even when comparing the nucleosome positions called from the H4S47C cleavage experiment. All dyad distributions were normalized such that the
average dyad density equals 1 for every chromosome
Chereji et al. Genome Biology (2018) 19:19 Page 4 of 20
analysis is apparent when we compare the WW/SS distributions
generated from every read of H3Q85C mapping to
WW/SS distributions generated from every read of
H4S47C mapping (Fig. 2b). The WW/SS distributions appear
stronger and phased for H4S47C mapping only when
limited to the strong “called” nucleosome positions (Fig. 2c),
representing a subset of the nucleosomes compared to
H3Q85C mapping, which in principle represents all nucleosomes
genome-wide.
Precise identification of NDRs and flanking nucleosomes
Most yeast genes have a stereotypical nucleosome
organization in their promoters, characterized by an
NDR just upstream of the TSS, which is flanked by
regular arrays of nucleosomes [39–43]. Various factors
are partly responsible for this organization: (1) nonhistone
proteins bind to promoters and compete with
nucleosome formation [44–46]; (2) some DNA sequence
motifs present in promoters are unfavorable
for nucleosome formation [47, 48]; (3) chromatin
organization is disrupted by transcription [49–51] and
replication [52–55]; and (4) nucleosome positioning is
maintained by chromatin remodelers [56–60].
The lack of background cleavages at promoters using
H3Q85C mapping (Fig. 1a, tracks 6–8) facilitates identification
of the exact locations of every NDR and the corresponding
flanking nucleosomes (+1 and –1 nucleosomes).
Using a previously described procedure [60], we identified
all NDRs and the precise locations of +1 and –1 nucleosomes
in S. cerevisiae (Additional file 2: Table S1). We
then aligned all NDRs and sorted them according to their
widths (Fig. 3a, b). Figure 3b demonstrates the high resolution
of our nucleosome positioning data, in which we
can distinguish even alternative rotational positions that
are occupied by nucleosomes in different cells. These alternative
positions from different cells differ by the magnitude
of the DNA helical twist (~10 bp), so they can only
be distinguished if the accuracy of the nucleosome
positioning data has a resolution < 5 bp (the separation between
a maximum and a minimum in the dyad distribution),
which is not attainable using MNase-seq.
Analyzing other published data sets by aligning the
promoters in the same way, we observed that most of
the NDRs are flanked by H2A.Z-containing nucleosomes
(Fig. 3c) (data from [61]) and are bound by a multitude
of proteins, e.g., TATA-binding protein (TBP, data from
[62]), Reb1 (data from [63]), and Sth1, Snf2, Ioc3 (data
from [64]) (Fig. 3d-h). As expected, the NDRs are also
DNase I hypersensitive (data from [65]) and accessible
to transposase Tn5 (data from [66]) (Fig. 3i, j), indicating
that our identification of NDRs captures known properties
of yeast promoters.
The precise positions of +1 and –1 nucleosomes can be
used to get insights about important processes that involve
nucleosomes, e.g., to study the nucleosome shifts, nucleosome
eviction, and spacing changes produced by chromatin
remodelers with high resolution at the genome-wide scale,
or to study the precise locations of pausing when polymerases
transcribe through nucleosomes. As a proof of concept,
we re-analyzed the nucleosome shifts produced
through depletion of RSC from yeast cells. Previously, it
was shown that in rsc8-depleted cells, nucleosomes near
the gene promoters invade the NDR, reducing its width
[58], although the precise mechanism of nucleosome
displacement remains unknown. We wondered whether
the +1 and –1 nucleosomes shift toward the NDRs by a
random number of base pairs, or whether they change their
locations among the alternative rotational positions observed
in Fig. 3b by shifting by an integral number of helical
twists along the DNA, thus maintaining the same rotational
setting. Using the reference positions that we identified for
+1 and –1 nucleosomes in wild-type cells and the MNaseseq
data for rsc8-depleted and wild-type cells [58], we made
an unexpected discovery. The +1 and –1 nucleosomes preferentially
occupy the same rotational positions, both in
wild-type and in rsc8-depleted cells (Fig. 3k), and it is only
Position relative to dyad (bp)
Relativedinucleotidefrequencies
H3Q85C (all fragments)
Position relative to dyad (bp)
Relativedinucleotidefrequencies
H4S47C (all fragments)
Position relative to dyad (bp)
Relativedinucleotidefrequencies
WW
SS
WS/SW
H4S47C (called nucleosomes)a b c
Fig. 2 H3Q85C cleavages map rotational positions genome-wide. Nucleosome dyad positions based on raw H3Q85C (a) and H4S47C (b) cleavage
data were aligned and dinucleotide frequencies were mapped at each base pair position. The genome-wide H4S47C rotational positioning
signature is irregular and much weaker than the H3Q85C signal. A strong and more regular positioning signature (c) is obtained for H4S47C data
by using the “called” subset H4S47C nucleosome dyads based on stringent cutoffs [23]
Chereji et al. Genome Biology (2018) 19:19 Page 5 of 20
TBP (Zentner et al. - MCB 2013)
0
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H2A.Z (Woo et al. - Bioinformatics 2013)
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DNase I cuts (Hesselberth et al. - Nat. Methods 2009)
0
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ATAC-seq cuts (Schep et al. - Genome Res. 2015)
0
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Reb1 (Zentner et al. - Nat Commun 2015)
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Sth1 (Parnell et al. - eLife 2015)
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Snf2 (Parnell et al. - eLife 2015)
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Ioc3 (Parnell et al. - eLife 2015)
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Nucleosome dyads
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Position relative to NDR center (kb)
0
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G/Ccontent
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Averagedyaddensity
Wild type
rsc8 depleted
G/Ccontent
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NDR
Wild type
rsc8
Fig. 3 (See legend on next page.)
Chereji et al. Genome Biology (2018) 19:19 Page 6 of 20
the relative occupancy of each of these alternative positions
that changes in the mutant cells. This suggests that RSC
maintains the proper NDR width by shifting nucleosomes
in steps equal to the helical twist, maintaining at the same
time the preferred rotational orientation of the DNA relative
to the histone core. Aligning all DNA sequences of +1
and –1 nucleosomes, we observed that the average G/C
content of these two loci is also oscillatory (Fig. 3k, bottom
heat maps). This suggests that at the single-nucleosome
level, the histone core is able to sample an ensemble of favorable
rotational positions, determined by the underlying
DNA sequence, and chromatin remodelers affect the stochastic
process of selecting the individual positions of nucleosomes
in each cell.
To better understand the nucleosome phasing patterns
near gene promoters, we realigned all promoters at the +1
nucleosomes, and sorted the genes according to the distance
between the +1 nucleosome and the NDR of the
nearest upstream gene (Fig. 4a). We also separated the
divergent genes (top part of Fig. 4a) from the tandem genes
(bottom part of Fig. 4a). To maximize the signal, we
combined dyads from our previously published H4S47C
cleavage data (~ 300 million mapped nucleosomes, [26])
with the current H3Q85C cleavage data (~ 10 million
mapped nucleosomes). The nucleosomes on the gene
bodies are well phased relative to the NDR, as indicated by
the regular pattern of vertical red stripes in Fig. 4a. The nucleosomes
form regular arrays on both sides of the NDRs.
Between neighboring NDRs, the nucleosome distribution
produces an interference pattern, in which overlapping
signals originate from each NDR [58]. When the space
between the neighboring NDRs is a multiple of the typical
nucleosome repeat length (NRL) (~ 165 bp in yeast [60]),
we observe constructive interference of the two phasing
signals, and well-positioned nucleosomes form a long regular
array from one NDR to another. From Fig. 4a, we observe
that nucleosome organization near gene promoters
depends on the location and orientation of the upstream
genes, and so averaging all configurations into an aggregate
plot as is commonly done can be misleading, especially for
the region upstream of the TSS.
We performed a similar analysis of the 3’ ends of the
genes. It is known that the transcription termination site
(TTS) is also a region of reduced nucleosome density, so
the TTS could be another nucleosome phasing element,
similar to the TSS. To test this hypothesis, we realigned
all yeast genes at their TTSs and sorted according to the
distance between the TTS and the NDR of the downstream
gene (Fig. 4b). To distinguish the role of TTS per
se, we separated the convergent genes (top part of
Fig. 4b) from the tandem genes (bottom part of Fig. 4b),
which typically have their TTS very close to the TSS of
the neighboring downstream gene. We observed that the
TTS of convergent genes is not characterized by strong
nucleosome depletion. For tandem genes, strong nucleosome
depletion is observed only when the TSS of the
downstream gene is very close to the TTS of the reference
gene. Moreover, the TTS is not a phasing element,
and the only nucleosome phasing observed near the
TTS is due to the presence of other TSSs in their
vicinity. In the case of convergent genes, which have
their TTS far from interfering TSSs of downstream
genes, we do not observe vertical red stripes, which
would indicate regular arrays of nucleosomes, phased
relative to the TTS.
The presence of regular nucleosome arrays flanking
the NDRs raises some interesting questions. Is this nucleosome
organization encoded in the DNA sequence?
Is the DNA sequence the most important factor that
dictates the nucleosome positions in vivo? In order to
answer these questions, we re-analyzed the nucleosome
organization obtained by in vitro reconstitution [13].
Aligning the genes as in Fig. 4a, b, we see that reconstituted
nucleosomes do not form the phased regular arrays
flanking the NDRs (Fig. 4c, d), as observed in vivo
(Fig. 4a, b). Instead, the nucleosome distribution is much
more disorganized in vitro. Weak NDRs were observed
in the reconstituted chromatin at both gene ends, with a
stronger depletion present at the TTS. Surprisingly,
strong NDR positions are observed for aligned TTSs in
vitro, even though no strong NDRs are present in vivo.
The locations of these strong in vitro NDRs coincide
with the A/T-rich regions (Fig. 4e, f — genes aligned as
in panels a, c and b, d, respectively). Although it is possible
that there is reduced nucleosome formation at A/
T-rich regions, the well-known preference of MNase for
A/T-rich regions is sufficient to account for the NDRs at
TTSs (Additional file 1: Figure S2) [20, 67]. In vitro,
(See figure on previous page.)
Fig. 3 Characterization of yeast promoters. a Heat map of the nucleosome dyad distribution at yeast promoters. Each row represents a 2-kb window
focused on the NDR centers of yeast genes, which are sorted according to the NDR width. b Zoomed-in view of a 500-bp region around
the NDR center, including only the +1 and –1 nucleosomes of all yeast genes. The high resolution of the nucleosome positioning data allows the
identification in the ensemble of cells of alternative rotational positions (parallel sigmoidal stripes corresponding to +1 and –1 nucleosomes),
which differ by multiples of the helical twist. c NDRs are flanked by H2A.Z-containing nucleosomes (data from [61]). NDRs are generally bound by
TATA-binding protein (d) (TBP, data from [62]), Reb1 (e) (data from [63]), remodelers RSC, Snf2, and ISW1 (f-h) (data from [64]). NDRs are accessible
to DNase I (i) (data from [65]) and transposase Tn5 (j) (data from [66]). k In rsc8-depleted cells, nucleosomes +1 and –1 shift by a multiple of the
helical twist, maintaining the same rotational setting. Heat maps showing the average G/C content of the preferred dyad locations. MNase-seq
data from [58]
Chereji et al. Genome Biology (2018) 19:19 Page 7 of 20
-4 -3 -2 -1 0 1
Position relative to +1 nuc. (kb)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Nucleosome dyads
-1 0 1 2 3 4
Position relative to TTS (kb)
Nucleosome dyads
-4 -3 -2 -1 0 1
Position relative to +1 nuc. (kb)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Nucleosome dyads (Kaplan et al. - Nature 2009)
-1 0 1 2 3 4
Position relative to TTS (kb)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Nucleosome dyads (Kaplan et al. - Nature 2009)
-4 -3 -2 -1 0 1
Position relative to +1 nuc. (kb)
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
A/T-content
-1 0 1 2 3 4
Position relative to TTS (kb)
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
A/T-content
Divergent genes
Tandem genes Tandem genes
Convergent genes
a
c
e
b
d
f
Fig. 4 (See legend on next page.)
Chereji et al. Genome Biology (2018) 19:19 Page 8 of 20
nucleosomes would form with similar affinities on every
DNA sequence, but during the mapping process involving
chromatin digestion by MNase, the nucleosomes
from A/T-rich regions are digested faster, and are underrepresented
in the sequenced library after extensive digestion
(Additional file 1: Figure S2). In vivo, MNase
sensitivity of nucleosomes wrapped by A/T-rich DNA
sequences was previously reported both in S. cerevisiae
[68] and in Drosophila [69].
H3Q85C cleavage mapping demonstrates linker
“quantization”
The precise measurements of dyad positions and nucleosome
occupancy obtained using H3Q85C cleavage mapping
also provide a rigorous method for investigating
linker lengths and nucleosome crowding in vivo [70].
We addressed these issues using our full cleavage data
sets, which were derived from DNA samples that had
not undergone gel purification and so included dyad-todyad
fragments spanning all linker lengths. We aligned
the positions of all +1 nucleosomes and computed the
relative occupancy generated by the DNA fragments of
each different size (Fig. 5a). The short fragments (~ 50 bp)
were concentrated around the locations of nucleosome
dyads (Fig. 5a), and they originate from two cleavages inside
the same nucleosome (red segments in Fig. 5b). The
medium fragments (~ 70–130 bp) were located between
the consecutive nucleosome dyad positions (Fig. 5a),
which indicates that they were the fragments resulting
from two cuts inside neighboring nucleosomes produced
at consecutive cleavage sites (black segments in Fig. 5b).
The long fragments (~ 135–175 bp) overlap both with
short and medium fragments (Fig. 5a), because they derive
from cuts made by different nucleosomes at alternate
cleavage sites (yellow segments in Fig. 5b).
During the cleavage reaction and library preparation
steps, the ends of the DNA fragments resulting from a cut
are slightly shortened, and a small gap arises at the location
of the cleavage site (Fig. 5b). In order to estimate the sizes
of these gaps, we computed the cross-correlations between
the ends of different groups of DNA fragments. To obtain
the left gap between the short fragments and the medium
or long fragments, we computed the cross-correlations between
the right ends of the short fragments (44–58 bp) and
the left ends of the medium fragments (130–170 bp) or the
left ends of the long fragments (135–175 bp), respectively.
The overall maxima of the cross-correlations correspond to
a lag of 5 bp, which is equivalent to a gap of 4 bp between
the fragments (Additional file 1: Figure S3). The same value
was obtained for the second gap from Fig. 5, by calculating
the cross-correlations between the left ends of the short
fragments and the left ends of the medium or long fragments.
This gap is expected between two cleavages by a
single cysteine-phenanthroline on opposite strands 5 bp
apart according to our published structural model [26].
Knowing the lengths of the sequenced fragments and
the gaps between them, we next estimated the typical
NRLs and linker lengths in S. cerevisiae. From the histogram
of the DNA fragment lengths (Fig. 5b) we inferred
that the most abundant lengths of the long fragments
are 147 bp, 157 bp, and 167 bp. From Fig. 5b we see that
the NRL can be computed as the sum of the gap size
and the length of a long fragment, so we obtain for the
most frequent NRLs the following values: 151 bp,
161 bp, and 171 bp. As the NRL is the sum of the length
of the nucleosomal DNA fragment and the linker length,
and nucleosomal DNA is ~ 146–147 bp long [1, 2], we
conclude that the most frequent linker lengths in
budding yeast are 5, 15, 25 or 4, 14, 24, depending on
the exact value for the length of the nucleosomal DNA.
This proves that the linker lengths are “quantized,” in
the sense that the most favored values for the linker
lengths are discretized and separated by 10 bp.
Linker quantization at the gene level
The discretization of the linker lengths according to the
rule L = 10 n + 5, at the global level, poses the following
questions: Do some genes have linkers of 5 bp, others
(See figure on previous page.)
Fig. 4 Nucleosome phasing patterns are characteristic of 5’ but not 3’ gene ends. a Nucleosome dyads from combined H4S47C- and H3Q85C-anchored cleavage
data. Gene promoters aligned at +1 nucleosomes. Divergent (top) and tandem (bottom) genes are separated and sorted according to the distance
between the +1 nucleosome and the NDR of the upstream gene. The vertical red stripes from the gene bodies indicate strong nucleosome phasing relative to
the NDR. The nucleosome distribution in the region between neighboring NDRs is characterized by an interference pattern generated by phasing signals
originating from both flanking NDRs. b Nucleosome distribution at the 3’ gene ends aligned at the transcription termination site (TTS). Convergent (top) and
tandem (bottom) genes are separated and sorted according to the distance between the TTS and the NDR of the downstream gene. The absence of vertical
red stripes at the TTS of convergent genes indicates that the TTS is not a nucleosome phasing element, unless it overlaps with the TSS of the downstream
gene, as in the case of some of the tandem genes. Even in the case of tandem genes, it is evident that nucleosomes are not phased relative to the TTS but
relative to the position of the nearby TSSs, as the red stripes are not vertical but bent according to the positions of the downstream genes. c, d Organization of
in vitro reconstituted nucleosomes (data from [13]) near gene ends shows that DNA sequence is not sufficient to dictate nucleosome phasing patterns. In vitro,
NDRs are formed on the regions of high A/T content, suggesting that they may be artifacts of MNase-seq, introduced by the MNase sensitivity of the
nucleosomes located on A/T-rich sequences [68, 69], which are easily overdigested. e, f A/T content (ratio of nucleotides A or T) near gene ends shows that
TTSs are among the most A/T-rich regions in the yeast genome. In vitro, NDRs near TTSs form at A/T-rich regions and not at the positions where they are
observed in live cells
Chereji et al. Genome Biology (2018) 19:19 Page 9 of 20
15 bp, and others 25 bp? Or do individual genes also
have a wide range of linker lengths, similar to the genomewide
linker length distribution? Are the linker lengths of an
individual gene also discretized? Fortunately, our H3Q85C
cleavage data permit the investigation of all these questions,
as we can precisely map each internucleosomal DNA fragment
to the correct location on the genome.
Yeast genes are often very small and are typically
wrapped by only a few nucleosomes. In such cases, the
number of reads mapped to the gene body is too low for
reliably estimating linker sizes. Nevertheless, there are
many yeast genes for which we obtained thousands of
sequencing reads that allowed us to accurately estimate
the linker length distribution for these genes.
We also analyzed the lengths of the long fragments
(135–175 bp) that map to the bodies of single genes
with > 5000 sequenced fragments (Fig. 6). We observed
that individual genes are also characterized by a wide
range of fragment lengths, similar to the genome-wide
distribution (red curve in Fig. 6). This shows that in
different cells and at different times during the cell cycle,
individual genes can have somewhat different nucleosome
organizations and NRLs, suggesting that chromatin
organization is reacting to the stochastic processes
that take place inside the nuclei.
Correlations among nucleosome spacing, transcription
rate, and NDR width
One of the factors shown to have an effect on the nucleosome
spacing is the level of transcription of a gene [71, 72].
In general, highly transcribed genes are characterized by a
decreased nucleosome spacing [72], and the inactivation of
RNA polymerase II via a temperature-sensitive mutation
was shown to increase the nucleosome spacing [71]. As
Relative occupancy (%)
0 1
0 1000
0 1000
2070120170
0.0 0.4 0.8
2070120170
2070120170
Fragmentlength(bp)
2D occupancy
Length
histogram
Position relative to +1 nuc. (bp) Percentage (%)
Short
(intra-nucleosomal)
DNA fragments:
Medium
(inter-nucleosomal)
Long
(inter-nucleosomal)
Nucleosome array
+1 +2 +3 +4 +6
Nucleosome dyad Nucleosome dyad NucleosomeChemical cleavages
Short
(intra-nucleosomal)
Resulting DNA fragments:
Digested DNA (gap)
Medium
(inter-nucleosomal)
Long
(inter-nucleosomal)
DNA fragment size distribution
50 100 150 200
DNA fragment size (bp)
0
0.5
1
1.5
2
2.5
3
3.5
4
Numberoffragments(x105
)
DNA
a
b
Fig. 5 H3Q85C cleavage data prove linker “quantization.” a A two-dimensional occupancy heat map and length histogram show the coverage of
DNA fragments of different sizes relative to +1 nucleosomes. There are three major groups of fragment sizes. Small fragments (~ 50 bp long)
originate from two cuts inside the same nucleosome; their centers indicate the position of a nucleosome dyad. Medium fragments (~ 70–130 bp
long) and long fragments (~ 135–175 bp long) originate from two cuts inside neighboring nucleosomes, produced at consecutive or alternate
cleavage sites, respectively. b Scheme of the cleavage sites and the possible resulting fragments, together with the histogram of DNA fragment
lengths obtained in the experiment
Chereji et al. Genome Biology (2018) 19:19 Page 10 of 20
previously noted, the nucleosome phasing pattern can be
explained to a first approximation by statistical positioning
against promoter barriers [46]. The number of barrier complexes
that are bound to a promoter is presumably correlated
with the width of the accessible region, the NDR
width. To test for possible relationships between NDR
widths, transcription rates, and nucleosome spacing, we
computed for each gene the average size of the long fragments
(135–175 bp) from H3Q85C cleavage data, and we
re-sorted the yeast genes according to this score (Fig. 7a;
the genes with the longest average length of the H3Q85C
cleavage fragments are at the bottom). This sorting score is
a direct measure of the average nucleosome spacing, and it
is closely related to the average linker length for each gene.
As expected, the heat map confirmed that the arrays of
nucleosomes at the top are more crowded, while the nucleosome
arrays at the bottom are less crowded (toward the
bottom of the heat map, the red stripes indicating nucleosome
dyads bend to the right). This increase in nucleosome
spacing was also observed when we grouped the genes
from the heat map into five groups: quintile 1 containing
the top fifth of the rows, quintile 5 containing the bottom
fifth of the rows, etc. (Fig. 7b). The first quintile, with the
most crowded nucleosomes, also had the widest NDR
(Fig. 7b), which is characteristic of highly transcribed genes
[51, 71]. To further test for a correlation between the chromatin
organization and the transcription levels, we analyzed
the levels of nascent transcripts obtained from native
elongating transcript sequencing (NET-seq) data [73]. Indeed,
transcription levels correlate with chromatin
organization; the more active genes are the ones at the top
of the heat map, corresponding to the genes with more
crowded nucleosome arrays and wider NDRs (Fig. 7a).
Chromatin organization and nucleosome spacing also
correlate with the amounts of linker histone H1 (Hho1
in yeast) present on the gene bodies and the level of incorporation
of the H2A.Z variant into the +1 nucleosomes.
Linker histone H1 is preferentially deposited on
the genes with increased nucleosome spacing (Fig. 7c),
in agreement with observations from multicellular organisms,
where histone H1 and its variants have an important
role in nucleosome spacing [74, 75]. Moreover,
genes with increased nucleosome spacing also contain
the +1 nucleosomes with the highest incorporation of
H2A.Z. This agrees with the previous observation that,
in yeast, H2A.Z is localized to the promoters of less active
genes [76, 77], suggesting that during transcription,
histone variant H2A.Z is preferentially evicted compared
to the canonical histone H2A. As the genes with the
more crowded nucleosome arrays from the top of the
heat map are more transcribed, their gene bodies are
marked by characteristic histone modifications, such as
H3K4me3 (Fig. 7c), and their promoters contain higher
amounts of TBP, which is probably an underlying cause
of the extended NDRs of these genes. For each quintile
shown in Fig. 7b, we computed the following quantities
(Fig. 7d): average nucleosome spacing and average NDR
width, estimated from Fig. 7b; average NET-seq signal
on the gene bodies (data from [73]); average Rpb1 density
on the gene bodies (data from [78]); average Rpb3
density on the gene bodies (data from [79]); average H1
cross-link density in the window (D-73, D + 750) bp,
where D is the dyad position for +1 nucleosome (data
from [80]); average H2A.Z occupancy for the +1 nucleosomes
(data from [61]); average H3K4me3 occupancy
computed in the same range as for the H1 density (data
140 150 160 170
01234
All genes
Percentage(%)
140 150 160 170
01234
YOR396W
Fragment length (bp)
140 150 160 170
01234
YIL177C
140 150 160 170
01234
YLR467W
Fragment length (bp)
140 150 160 170
01234
YLR106C
Fragment length (bp)
140 150 160 170
01234
YKR054C
Fragment length (bp)
Percentage(%)
140 150 160 170
01234
YHR099W
140 150 160 170
01234
YDR545W
Fig. 6 Nucleosome spacing and linker lengths are “quantized” even at the gene level. Length distribution of the long DNA fragments (135–
175 bp) resulting from H3Q85C cleavages. Examples of yeast genes among the ones containing the highest numbers of reads over their gene
bodies show that the distributions of fragment lengths from individual genes (blue curves) resemble the genome-wide distribution (red curve). This
indicates that the same gene can have different levels of nucleosome compaction in different cells and at different times
Chereji et al. Genome Biology (2018) 19:19 Page 11 of 20
from [81]); average TBP occupancy in the window (D-500,
D-73) bp, where D is the dyad position for +1 nucleosome
(data from [62]). The strong positive and negative correlations
suggested by the heat maps in Fig. 7 are confirmed
for all possible comparisons by Pearson correlation coefficients
and scatter plots (Additional file 1: Figure S4). In
particular, the five quintiles show near-perfect correlations
between the average NDR width, transcription level (estimated
by the average NET-seq, Rpb1, and Rpb3 signals on
the gene bodies, r = 0.980–0.988), average TBP occupancy
at promoters (r = 0.988), and average density of H3K4me3
marks (r = 0.989). All these quantities are almost perfectly
anti-correlated with the average nucleosome spacing of
the corresponding genes, average enrichment of histone
variant H2A.Z in the +1 nucleosomes, and with the average
density of linker histone H1 (r = –0.945 to 0.998).
A biophysical model for nucleosome positioning
In general, promoters are depleted of nucleosomes and are
occupied by other non-histone protein complexes (Fig. 3).
This competition among diverse proteins for binding to the
same DNA can be modeled using statistical mechanics
a b
c
d
Fig. 7 Genes sorted by average size of the long fragments from H4Q85C cleavage data. a Genes aligned at the +1 nucleosome and sorted
according to the average size of the long fragments (135–175 bp) that were produced by H3Q85C cleavage. The transcription levels, as measured
by the levels of nascent transcripts obtained from NET-seq data [73], indicate that the more transcribed genes have more compact nucleosome
arrays. b Average nucleosome dyad density for the yeast genes separated into five quintiles: quintile 1, the top fifth from a, through quintile 5,
the bottom fifth from a. The decrease in nucleosome spacing correlates with a widening of the NDR, which is characteristic for the highly
transcribed genes. c The distribution of linker histone H1 (Hho1 data from [80]) is also correlated with nucleosome spacing: the longer the
nucleosome spacing, the more H1 is bound to the corresponding nucleosomes. The H2A.Z variant is enriched in the +1 nucleosomes (data from
[61]); the less active genes appear to have more H2A.Z histones incorporated into their +1 nucleosomes. The more active genes contain more
H3K4me3 marks on their bodies (data from [81]) and more TBP bound to their promoters (data from [62]). d Average properties for the
five quintiles
Chereji et al. Genome Biology (2018) 19:19 Page 12 of 20
methods [46, 69, 70]. Briefly, nucleosomes are modeled as a
system of non-overlapping one-dimensional (1D) particles
of length a = 147 bp. The particles are confined to 1D lattices
of different lengths, representing the 16 yeast chromosomes.
The energy of the system is given by the sum of
one-particle energies (the binding energies corresponding
to each nucleosome, u(i), where i represents the coordinate
of the corresponding nucleosome dyad) and the two-body
interactions, Φ(i, j), where i and j represent the positions of
the neighboring nucleosome dyads. We assume a hard-core
interaction between the nucleosomes (infinite if nucleosomes
overlap, and zero if they do not overlap) which guarantees
that only the valid non-overlapping nucleosomal
configurations have finite energies and non-vanishing probabilities.
This is also known as a Tonks lattice gas model
[82]. The effect of non-histone barrier complexes that bind
to promoters and create the NDRs is modeled by an
external energy barrier that increases the binding energy of
nucleosomes located in the promoters, preventing
nucleosome formation at these loci. Following the formalism
described in [69] and illustrated in Additional file 1:
Figure S5, we enumerate all valid nucleosome configurations
and their corresponding energies and probabilities in
a recursive way. Once we compute the normalization factor
of the Boltzmann weights corresponding to each state (the
grand canonical partition function, Z), we predict the distribution
of nucleosome dyads and the nucleosome occupancy
by summing the probabilities of all states that
contain a nucleosome at a given position, or a nucleosome
covering a specified base pair, respectively. Using the nucleosome
distribution on chromosome I for training the
model, we calculated the model parameters — the chemical
potential, μ, related to the overall nucleosome density, the
average nucleosome binding energy, u, and the energy barrier
parameters, H, σ, x0 (Fig. 8a) — such that the predicted
nucleosome organization on chromosome I fits the data.
The fitted parameters were then used to predict the
genome-wide nucleosome organization.
Figure 8b shows the predicted nucleosome distribution
at the gene ends, which are aligned as in Fig. 4. In this
model the effect of the entire environment upon nucleosome
formation is reduced to the presence of simple energy
barriers at promoters, and the sequence-dependent
contribution to nucleosome affinity is completely
neglected. It is striking that the model produces a close
approximation of the nucleosome phasing pattern.
Although the energy barriers are placed only at the 5’
ends of the genes, they are enough to also generate the
correct organization at the 3’ ends of the genes (Fig. 8c),
which supports the hypothesis that TTSs are not
0
4
5
6
u(k
B
T)
Position (bp)
+1-1
H = 7.88 kB
T
x0
= 68 bp x0
= 27.64 bp
NDR width
-4 -3 -2 -1 0 1
Position relative to +1 nuc. (kb)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Predicted nucleosome dyads
-1 0 1 2 3 4
Position relative to TTS (kb)
Predicted nucleosome dyads
Divergent genes
Tandem genes Tandem genes
Convergent genes
b C
a
Fig. 8 Nucleosome organization modeled by statistical mechanics predicts the correct phasing pattern and NDR locations. a Fitted parameters
for the energy barrier localized at the gene promoters. The barrier consists of two halves of a Gaussian distribution (H = 7.88 kBT; σ = 27.64 bp)
with the centers shifted by x0 = 68 bp from the dyads of +1/–1 nucleosomes and a flat energy barrier in between. The additional fitting
parameter, the binding energy for a nucleosome away from the barrier, u − μ = –2.79 kBT. b Predicted nucleosome organization in gene
promoters. Promoters are aligned at +1 nucleosomes and sorted exactly as in Fig. 4a. c Predicted nucleosome distribution at the 3’ ends of
genes, which are aligned at the TTS. Genes are sorted as in Fig. 4b. A discussion of the applications of Tonks gas modeling of nucleosome
positions is presented elsewhere [105]
Chereji et al. Genome Biology (2018) 19:19 Page 13 of 20
nucleosome phasing elements. Therefore, the nucleosome
organization at the 3’ ends of genes is also dictated
by phasing elements at the 5’ ends of genes.
In vivo, there are also other processes that cannot be
easily modeled within the framework of statistical positioning
models. Transcription is a major nucleosome
disrupting process [49–51, 83], and chromatin remodelers
were shown to have a major impact on nucleosome
organization [56, 58–60]. In order to model these processes,
non-equilibrium statistical mechanics models are
necessary, but single-molecule experiments that could
be used to train such models are not yet available.
Discussion
We have developed an improved in vivo chemical cleavage
method that allows simultaneous precise mapping of both
nucleosomes and DNA linkers. Our H3Q85C cleavage
method determines individual nucleosome positions with
high accuracy, while avoiding complications due to differential
MNase sensitivity of nucleosomes [67]. Unlike the
earlier H4S47C cleavage method, H3Q85C cleavage maps
individual nucleosomes, which avoids phenanthrolinemediated
linker noise and greatly increases coverage, resulting
in robust detection of rotational phasing. Our method
also detects pairs of neighboring nucleosomes from the
same cell without relying on any statistical inference model
— each sequencing read precisely determines either the
position of a single nucleosome or the spacing between two
neighboring nucleosomes. As opposed to the rough estimations
of the overall NRL that one could obtain from gel
electrophoresis of MNase-digested chromatin [84], our
method offers high-resolution measurements of the NRL at
the single-gene level, and even at the single-linker level,
using increased sequencing depth.
In addition to providing improved nucleosome maps, our
method can address outstanding biological questions that
could not be resolved using previous methods. Our
H3Q85C maps revealed the absence of histone H3 at yeast
centromeres, ruling out a model that proposed a Cse4-H3
heterotypic nucleosome [33] at each yeast centromere. We
also showed that tRNA genes are depleted of nucleosomes,
which could not be determined using the H4S47C method,
presumably because of phenanthroline-mediated background
at NDRs. Single nucleosome mapping also led to
the discovery of three alternative rotational positions on either
side of the central dyad position and rotational positioning
of nucleosomes remodeled by RSC, demonstrating
that variation in the position of the dyad follows integral
multiples of helical twist. Our results suggest that at the
single-nucleosome level, the histone core is able to sample
an ensemble of favorable rotational positions determined
by the underlying DNA sequence, and that chromatin
remodelers affect the stochastic process of selecting the individual
positions of nucleosomes in each cell.
Given the high resolution of our measurements, we have
been able to show that linkers between neighboring nucleosomes
have a wide distribution of lengths, with preferred
discrete lengths according to the rule L = 10 n + 5, where
n is an integer [85]. Linker discretization was previously
inferred from analysis of dinucleotide periodicities of 296
mononucleosomal DNA sequences [86], but direct measurements
of dinucleosomal data at the genome-wide
scale are not yet available to confirm this linker
discretization rule. The same linker discretization rule was
suggested in more recent studies [23, 27], where this rule
was obtained from the “unique nucleosome positions” defined
by clustering the chemical cleavages and inferring
the most frequent positions among a population of cells.
Again, dinucleosomal fragments originating from the
same cell were not used to test this hypothesis. When the
paired-end H4S47C cleavage data [23] were re-analyzed
without neglecting the joint distribution of the pairs of
neighboring cleavages originating from the same cells
[70], a linker discretization rule was not found. Nucleosomal
dimer fragments from rat liver chromatin, obtained
by MNase digestion combined with exonuclease III and
S1 nuclease trimming, were also used to suggest that the
lengths of internucleosomal DNA are integral multiples of
the helical repeat plus 5 bp [87], although the oscillatory
signal obtained in the distribution of dinucleosomal fragment
lengths was weak [87] and even absent in other virtually
identical studies [88, 89].
Our H3Q85C cleavage method allows precise measurements
both of individual nucleosome positions and
individual linker lengths between neighboring nucleosomes
originating from the same cell. We have shown
that the linker discretization rule holds not only at the
genome-wide level, but also at the single-gene level, indicating
significant heterogeneity of nucleosome configurations
on the coding regions within a cell population.
A similar high variability in nucleosome positions among
individual cells was previously reported for the PHO5
promoter, the 5’ end of the CHA1 gene, and the promoter
of CYS3 [32].
It is known that chromatin packaging in different
organisms is characterized by a variety of NRLs, ranging
from 154 bp in Aspergillus nidulans [90] to 260
in Strongylocentrotus purpuratus sperm [91] (for an
extended list of NRLs in different organisms see [85, 92]).
Our data indicate that even individual genes of the same organism
may have a wide range of different NRLs among a
population of cells. This suggests that in different cells the
same gene may have different chromatin conformations,
depending on the corresponding linker lengths [92, 93].
Using the precise measurements of linker lengths at
the single-gene level, we were able to rank yeast genes
according to the typical degree of nucleosome crowding,
which allowed the further investigation of the role of
Chereji et al. Genome Biology (2018) 19:19 Page 14 of 20
nucleosome spacing in other biological processes. As a
demonstration of the validity of the method, we recapitulated
the well-known correlations between nucleosome
spacing and multiple chromatin properties: depletion of
nucleosomes from promoters, incorporation of histone
variant H2A.Z in the +1 nucleosome and of linker histone
H1 in the coding regions, and transcription level.
We confirmed that the most transcribed genes have a
decreased nucleosome spacing and a larger NDR width
[71, 72]. This indicates that during transcription initiation
promoters of highly transcribed genes are occupied
by other protein complexes that compete with nucleosome
formation, increasing the NDR width.
We have shown that a simple biophysical model can
explain the formation of NDRs in promoters and the nucleosome
phasing patterns observed near both gene
ends. We have used statistical mechanics to model the
distribution of hard rods, representing nucleosomes, in
1D lattices, representing chromosomes [46, 94]. As opposed
to previous genome-wide nucleosome predictions
[38, 94], we designed a minimal model that does not rely
on fitting sequence-dependent nucleosome affinities, but
uses the steric exclusion principle as the only requirement,
in order to explain the nucleosome phasing near
external energy barriers located in the gene promoters
(representing the competition with other DNA-binding
proteins that are found in promoters). This Tonks gas
system was previously used to explain the oscillatory
nucleosome distribution near a perfect barrier [44] and
near TSSs [95]. By fitting the energy barrier from
promoters, we extend the prediction of nucleosome
positions genome-wide and show that the complex
nucleosome phasing pattern can be explained at the
genome-wide scale, including the 3’ ends of genes, where
no phasing element was imposed. Using the smallest
yeast chromosome alone, we trained our model and inferred
the energy barriers created by other DNA-binding
proteins, which compete with histones and prevent nucleosome
formation in the NDRs. Our genome-wide
prediction of nucleosome organization is in excellent
agreement with the experimental data, indicating that
barrier complexes that occupy promoters are essential
for establishing the nucleosome organization in vivo,
and our minimal biophysical model is able to explain the
pattern of nucleosome phasing genome-wide.
Our biophysical model explains the salient features
of nucleosome positioning genome-wide, while ignoring
DNA sequence, thus simplifying our current understanding
of the rules for nucleosome positioning
[15]. Our model also can account for previous observations
that DNA sequence is unable to generate the
stereotypical nucleosome organization near TSSs,
without help from ATP-dependent chromatin remodelers
[57, 96]. Further improvements in the prediction
of nucleosome positions might be obtained by including
in the model additional energy barriers at other
NDRs, such as those observed at tRNA genes [97]
and origins of replication [98].
Conclusions
Our introduction of a new method for mapping nucleosomes
that is not subject to the uncertainties inherent in
MNase-seq and H4S47C cleavage mapping has led to new
insights into the forces governing nucleosome positioning.
Our approach provides high-confidence validation of relationships
between nucleosome spacing, NDR width, transcription,
and chromatin features genome-wide. We have
shown that rotational positioning is simply described as
A/T out of phase with G/C, that TTSs are not positioning
elements, and that linkers are quantized on the level of a
gene. Our minimal biophysical model, which neglects
sequence-dependent nucleosome affinities, can explain
the formation of NDRs and nucleosome phasing patterns
and implies that steric exclusion plays a central role in establishing
nucleosome phasing.
Methods
H3Q85C cleavage mapping
Strain construction
Point mutations in histone H3 were introduced into the
HHT2/HHF2 plasmid, which contains a URA3 selectable
marker [99]. The locus with homology arms to the genomic
HHT2/HHF2 locus was excised together with URA3
from the mutant plasmid using BciIV and transformed
into the W1588-4C strain with the HHT1/HHF1 locus
deleted (MATa ade2-1 can1-100 his3-11, 15 leu2-3, 112
trp1-1 ura3-1 RAD5+ - Δhht1/hhf1::kanMX4). Transformed
cells were selected for on minimal medium-uracil
plates to obtain cells with single copies of the histone H3/
H4 genes with the required mutations in histone H3.
Cleavage mapping experiments
The yeast strain with HHT1/HHF1 deleted and carrying
the H3Q85C mutation was grown in a volume of ~
166 ml in yeast extract peptone dextrose (YPD) medium
up to optical density (OD)600 = 0.4–0.6. Cells were collected
by centrifugation at 3000 g for 5 min. Cells were
resuspended with 1 ml of 1 M sorbitol and spun again at
3000 g for 5 min. Cells were resuspended with 1 ml of
spheroplasting buffer containing lyticase (1 M sorbitol,
5 mM β-mercaptoethanol, 10 mM Tris, 10 mg/ml lyticase)
and gently rotated at room temperature for 5 min.
Cells were pelleted at 3000 g for 5 min and washed twice
with 1.5 ml of sorbitol/NP-40 buffer (1 M sorbitol, 0.2%
NP-40 (# 74385, Sigma-Aldrich, St. Louis, MO, USA),
and 10 mM Tris pH 7.5). The cell pellet was resuspended
in 400 μl of labeling buffer (1 M sorbitol,
50 mM NaCl, 10 mM Tris-HCl pH 7.5, 5 mM MgCl2,
Chereji et al. Genome Biology (2018) 19:19 Page 15 of 20
0.5 mM spermidine, 0.15 mM spermine, 0.2% NP-40,
0.1 mM ethylenediaminetetraacetic acid (EDTA)). At this
point, the tubes were covered with aluminum foil to protect
them from light. The phenanthroline label, N-(1,10phenanthroline-5-yl)iodoacetamide
(# 92015, Biotium,
Fremont, CA, USA) stock solution was made fresh by dissolving
the lyophilized label to a concentration of 7 mM
in dimethyl sulfoxide (DMSO). The label was added to
cells for a final concentration of 0.14 mM. The labeling
mixture was rotated at room temperature for 2 h and then
at 4 °C overnight. The cells were washed three times in
1.5 ml of sorbitol/NP-40 buffer to remove excess label.
Comparison of cells before and after phenanthroline labeling
by MNase-seq showed no evidence of changes in nucleosome
position or occupancy [26].
Phenanthroline-labeled cells were resuspended in 300 μl
of mapping buffer (2.5 mM NaCl, 50 mM Tris-HCl pH 7.5,
5 mM MgCl2, 1 M sorbitol, 0.5 mM spermidine, 0.15 mM
spermine, 0.2% NP-40), CuCl2 was added to a final concentration
of 150 μM, and the cells were incubated at room
temperature for 2 min. Cells were spun down for 3 min at
3400 g and then washed three times with 1.5 ml of mapping
buffer. After the final wash, the cells were resuspended
in 300 μl of mapping buffer. 3-Mercaptopropionic acid was
then added to a final concentration of 6 mM followed by
H2O2 to a final concentration of 6 mM. The reaction was
incubated at room temperature for 15 min, quenched by
the addition of neocuproine to 0.28 mM, and the cells were
spun down at 3000 g for 5 min. The reaction was performed
four more times starting with resuspension in mapping
buffer. The cells were pelleted after five reactions and
resuspended in 400 μl of Tris-EDTA (TE) buffer (10 mM
Tris-HCl pH 8.0, 1 mM EDTA). EDTA was supplemented
to a final concentration of 10 mM and sodium dodecyl sulfate
was added to a final concentration of 0.5%. Twenty μg
of proteinase K was added, and the samples were incubated
at 55 °C for 2 h. To extract DNA, two volumes (800 μl) of
phenol:chloroform:isoamyl alcohol (PCI, 25:24:1) was
added to the samples and mixed well, and the samples were
spun at max speed for 5 min. The aqueous phase was separated
and the DNA precipitated with three volumes of
100% ethanol and 30 μg of GlycoBlue as a carrier, for
30 min at 4o
C, maximum speed. DNA pellets were washed
with 1 ml of 75% ethanol and spun again. The pellets were
air dried and resuspended in 50–100 μl of TE buffer.
Then 90 μg of RNase A was added and the samples
incubated at 42 °C for 30 min. We analyzed 2 μg of
cleavage material and 0.5 μg of starting material by PAGE
(10% acrylamide). If the 50-bp band was observed, a final
PCI extraction, chloroform cleanup, and ethanol precipitation
were performed before proceeding to library preparation.
For one of the samples, the 50-bp fragment band
was excised from a polyacrylamide gel and purified before
making libraries.
Sequencing
Cluster generation and 25 rounds of paired-end sequencing
were performed by the Fred Hutchinson Cancer Research
Center (FHCRC) Genomics Shared Resource
with an Illumina Hi-Seq 2500. Base calling, data processing,
and analysis were performed as described [18].
Cleavage data analysis
Paired-end reads were aligned to the S. cerevisiae reference
genome sacCer3, using Bowtie2 [100] with parameters
-X 1000 –very-sensitive, to map sequences up to
1 kb with maximum accuracy. DNA fragments of
lengths 51 ± 7 bp were selected and their centers used to
define the nucleosome dyads. To estimate the nucleosome
occupancy, each dyad was extended 50 bp in both
directions, resulting in a footprint of 101 bp for each nucleosome.
The occupancy profiles obtained by stacking
the reduced footprints (< 147 bp) are useful for emphasizing
the linkers between neighboring nucleosomes
when they occupy alternative positions in different cells.
The detection of NDRs and flanking (+1/–1) nucleosome
positions was done as in [60]. Heat maps representing
different genomic data, aligned at various
references (TSS, TTS, and NDR center) were generated
in MATLAB using the Bioinformatics toolbox to import
data and the heatmap plotting function (http://
www.mathworks.com/matlabcentral/fileexchange/24253customizable-heat-maps).
To visualize specific loci, the
Integrative Genomics Viewer (IGV) browser [101] was
used, loading the tracks (tdf files) created with igvtools.
Transcript end coordinates (TSS and TTS) were obtained
from a previous work [102].
Precise identification of the +1 and –1 nucleosomes
Using a previously described procedure [60], we first
identified all NDRs and the typical locations of +1 and –
1 nucleosomes in S. cerevisiae. Nucleosomes do not occupy
identical positions in all cells, and for each locus
occupied by nucleosomes we observed clusters of alternative
rotational positions in the ensemble of cells. To
identify the center of the cluster of alternative positions,
we searched in a window of 50 bp around the previously
identified positions, and we refined the positions of the
+1 and –1 nucleosomes by maximizing the crosscorrelation
between the cumulative profile of dyad
counts and the following oscillatory pattern:
D0 xð Þ ¼ G x; 0; 2ð Þ þ 0:75 G x; 10; 2ð Þ þ 0:50 G x; 20; 2ð Þ
þ0:25 G x; 30; 2ð Þ þ 0:75 G x; −10; 2ð Þ
þ0:50 G x; −20; 2ð Þ þ 0:25 G x; −30; 2ð Þ;
where Gðx; c; σÞ ¼ e
−ðx−cÞ2
2 σ2
represents a Gaussian distribution
with mean c and standard deviation σ. The crossChereji
et al. Genome Biology (2018) 19:19 Page 16 of 20
correlations were computed using the xcorr function
from the Signal Processing toolbox of MATLAB.
Two-dimensional occupancy analysis
To study the origin of the fragments of different sizes
that resulted in the chemical cleavage reaction, we
used two-dimensional (2D) occupancy analysis [68].
Briefly, for each fragment size, we computed the relative
occupancy due to all DNA fragments of this size.
Then we created a matrix with the rows representing
the relative occupancy corresponding to each DNA
fragment size, and the columns representing the
number of fragments of different sizes covering each
base pair in promoter regions. This 2D representation
of occupancy (Fig. 5a) allowed us to distinguish fragments
of different sizes and to identify their origin as
a function of their lengths. The columns of the 2D
occupancy profile (Fig. 5a) mimic the separation of
DNA fragments in a gel electrophoresis experiment,
while the rows provide information on their genomic
locations. The marginal distribution obtained by summing
all values from each column of this matrix will
generate the usual 1D occupancy, while the marginal
distribution obtained by summing all values from
each row of this matrix will generate the DNA fragment
length histogram (Fig. 5a, right panel).
The software used for computing and visualizing the
2D occupancies is freely available on the GitHub repository
page at https://github.com/rchereji/plot2DO. For
more information on usage options, consult the online
documentation from the GitHub page.
Analysis of the missing gaps between chemical cleavage
fragments
Two neighboring DNA fragments result from each chemical
cleavage. During the cleavage process or throughout
the library preparation steps, a few nucleotides between
the two resulting fragments are lost. The size of the gap is
important in order to estimate the NRL, and we estimate
this by computing the lag that maximizes the crosscorrelation
between the ends of the two sets of resulting
DNA fragments: short fragments (51 ± 7 bp) originating
from two cleavages in the same nucleosome, and longer
fragments (70–130 bp) originating from two cleavages on
distinct neighboring nucleosomes (Fig. 5b). The crosscorrelations
are computed using the xcorr function from
the Signal Processing toolbox of MATLAB.
Statistical mechanics model
The statistical mechanics formalism used to predict the
nucleosome organization near the energy barriers located
in the gene promoters is described in [69]. Briefly,
a chromosome containing L bp is modeled as a 1D lattice
of length L, which is populated by hard rods of size
a = 147 bp. Each position i where a nucleosome can
form along the chromosome is characterized by a binding
energy u(i), which is constant everywhere along the
chromosome, except for the positions at promoters,
where the potential barriers dictate the binding energy.
Assigning to each valid configuration (in which the hard
rods do not overlap) a Boltzmann weight according to
the total energy of the system, and taking into account
all the possible configurations of the system with a variable
number of hard rods, we compute the grand canonical
partition function [103] as described in [69]. The
partition function can be computed in a recursive way,
and from this function we obtain the probability for a
nucleosome dyad to occupy at position i, n(i), and the
probability that base pair i is occupied by a nucleosome,
or nucleosome occupancy:
Occ ið Þ ¼
Xiþ73
k¼i−73
n kð Þ:
This way, knowing the potential barriers from promoters,
we can predict the distribution of nucleosomes on
the whole genome. We train the model by fitting the nucleosome
distribution on the shortest yeast chromosome,
and with the energy parameters that we obtained (Fig. 8a)
we predict the genome-wide nucleosome organization
(Fig. 8b, c). A generalized formalism, in which generic
two-body interactions are considered between neighboring
nucleosomes [46] or nucleosomes can be partially unwrapped
[70], is also available.
Model training
In order to train the model and to obtain the parameters
shown in Fig. 8a, a global optimization was performed using
the genetic algorithm optimization function ga from the
Global Optimization toolbox of MATLAB. The residue
function that was minimized by the global optimization
function consisted of the negative of the 2D correlation coefficient
(corr2 function from the Image Processing toolbox in
MATLAB) between the real and the predicted nucleosome
dyad distributions on chromosome I, aligned at the +1 nucleosome
(as in Fig. 8b, but using only the rows/genes corresponding
to chromosome I).
Additional files
Additional file 1: Figure S1. Chemical cleavages produced by H3Q85C.
Figure S2. H3Q85C cleavage mapping distinguishes between real NDRs
and MNase artifacts (MNase-sensitive nucleosomes). Figure S3.
Determination of the gap size using cross-correlations between the
distributions of the DNA fragment ends. Figure S4. Correlations among
nucleosome spacing, NDR width, transcription levels, and ChIP-seq data.
Figure S5. Sketch of the statistical mechanics formalism for predicting
nucleosome occupancy. (PDF 2035 kb)
Chereji et al. Genome Biology (2018) 19:19 Page 17 of 20
Additional file 2: Table S1 The coordinates of the dominant positions
of +1 and –1 nucleosomes identified in this study. (XLSX 396 kb)
Abbreviations
MNase: Micrococcal nuclease; MNase-seq: Micrococcal nuclease followed by
high-throughput sequencing; NDR: Nucleosome-depleted region;
NRL: Nucleosome repeat length; TSS: Transcription start site;
TTS: Transcription termination site
Acknowledgements
We thank Christine Codomo for preparing sequencing libraries, the Fred
Hutch Genomics Shared Resource for sequencing and data processing, and
David Clark, Steven Hahn, and Alan Hinnebusch for discussions and helpful
comments on the manuscript.
Funding
Work in the Henikoff lab is funded by the Howard Hughes Medical Institute.
R.V.C. was supported by the Intramural Research Program of the National
Institutes of Health (NIH). This study utilized the computational resources of
the NIH High-Performance Computing (HPC) Biowulf cluster (http://
hpc.nih.gov).
Availability of data and materials
Chemical cleavage data from this study have been submitted to the National
Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO,
http://www.ncbi.nlm.nih.gov/geo) under accession number [GEO:GSE97290]
[104].
Other data sets used in this study were downloaded using the following
GEO accession numbers: GSE25107, NET-seq data [73]; GSE74787, Rpb3 ChIPseq
data [79]; GSE47073, H2A.Z ChIP-seq [61]; GSE44200, TBP ChIP-seq [62];
GSE41424, H3K4me3 ChIP-seq [81]; GSE66386, ATAC-seq data [66]; GSE67453,
ChEC-seq data [63]; GSE65594, RSC, Snf2, and ISW1 ChIP-seq data [64]. Three
data sets were downloaded from the NCBI Sequence Read Archive, using
the following accession numbers: SRP033438, Rpb1 ChIP-seq data [78];
SRA059355, H1 ChIP-exo data [80]; SRP000620, DNase-seq data [65].
Authors’ contributions
SR and TDB performed the experiments; RVC and SR analyzed the data; RVC
developed the biophysical model; RVC, SR, and SH wrote the manuscript,
which was read and approved by all authors.
Ethics approval and consent to participate
No ethical approval was needed to perform this study.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Division of Developmental Biology, Eunice Kennedy Shriver National
Institute for Child Health and Human Development, National Institutes of
Health, Bethesda, MD 20892, USA. 2
Howard Hughes Medical Institute and
Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
98109, USA.
Received: 10 December 2017 Accepted: 24 January 2018
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