A single fiber view of the nucleosome organization in eukaryotic chromatin

Mark Boltengagen; Daan Verhagen; Michael Roland Wolff; Elisa Oberbeckmann; Matthias Hanke; Ulrich Gerland; Philipp Korber; Felix Mueller-Planitz

doi:10.1093/nar/gkad1098

. 2023 Nov 22;52(1):166–185. doi: 10.1093/nar/gkad1098

A single fiber view of the nucleosome organization in eukaryotic chromatin

Mark Boltengagen ¹, Daan Verhagen ^2,^3,², Michael Roland Wolff ^4,^5,², Elisa Oberbeckmann ⁶, Matthias Hanke ⁷, Ulrich Gerland ⁸, Philipp Korber ⁹, Felix Mueller-Planitz ^10,^✉

PMCID: PMC10783498 PMID: 37994698

Abstract

Eukaryotic cells are thought to arrange nucleosomes into extended arrays with evenly spaced nucleosomes phased at genomic landmarks. Here we tested to what extent this stereotypic organization describes the nucleosome organization in Saccharomyces cerevisiae using Fiber-Seq, a long-read sequencing technique that maps entire nucleosome arrays on individual chromatin fibers in a high throughput manner. With each fiber coming from a different cell, Fiber-Seq uncovers cell-to-cell heterogeneity. The long reads reveal the nucleosome architecture even over repetitive DNA such as the ribosomal DNA repeats. The absolute nucleosome occupancy, a parameter that is difficult to obtain with conventional sequencing approaches, is a direct readout of Fiber-Seq. We document substantial deviations from the stereotypical nucleosome organization with unexpectedly long linker DNAs between nucleosomes, gene bodies missing entire nucleosomes, cell-to-cell heterogeneity in nucleosome occupancy, heterogeneous phasing of arrays and irregular nucleosome spacing. Nucleosome array structures are indistinguishable throughout the gene body and with respect to the direction of transcription arguing against transcription promoting array formation. Acute nucleosome depletion destroyed most of the array organization indicating that nucleosome remodelers cannot efficiently pack nucleosomes under those conditions. Given that nucleosomes are cis-regulatory elements, the cell-to-cell heterogeneity uncovered by Fiber-Seq provides much needed information to understand chromatin structure and function.

Graphical Abstract

Introduction

Nucleosomes exert profound control over transcription, replication, recombination, and repair in eukaryotes. Cells arrange nucleosomes along DNA like beads on a string, forming extended arrays. These arrays consist of evenly spaced nucleosomes and are aligned (‘phased’) next to barriers in the genome, which are often located near functionally important elements such as transcription start sites (TSSs) or replication origins. Arrays decorate most gene bodies (1,2) but not promoter regions, which are characterized by a nucleosome depleted region (NDR; Figure 1A).

Figure 1. — MNase-Seq cannot reliably map the nucleosome organization. (A) Textbook schematic of nucleosomes around promoters (top). Arrays are phased with respect to the TSS of genes (broken arrow) and form regularly spaced arrays with a defined Nucleosome Repeat Length (NRL) downstream of the Nucleosome Depleted Region (NDR). MNase-Seq can visualize these phased regularly spaced arrays (bottom). (B–D) Unphased but regularly spaced arrays (B), irregularly spaced arrays (C) and low or variable nucleosome occupancy (D) cannot be distinguished by MNase-Seq. In essence, neither cell-to-cell heterogeneity nor nucleosome occupancy can be directly observed by MNase-Seq. (E) Fiber-Seq can measure site-averaged (bottom) and read-averaged occupancies (right).

The nucleosome organization of DNA is disrupted by several processes such as DNA transcription, replication and repair. Repair, for instance, induces removal of nucleosomes from DNA and therefore drastically lowers the fraction of DNA that is occupied by nucleosomes (3). Replication duplicates the DNA and thereby rapidly creates massive amounts of free DNA that needs to be assembled into nucleosomes. Lastly, transcription disrupts the regular spacing between nucleosomes (4). It can also impair the nucleosome structure and even evict entire nucleosomes from DNA (5).

These disruptive forces are efficiently countered by cellular factors that regenerate the proper nucleosome organization (4). Several families of ATP-hydrolyzing nucleosome remodeling enzymes are key in this effort. Some, like the INO80 complex, participate in positioning the first nucleosome downstream of the TSS, the so called +1-nucleosome (6,7). Others, including remodelers of the ISWI and CHD1 families, are thought to induce even spacing between nucleosomes in gene bodies (4,8–10). Mechanistic details of this nucleosome spacing reaction remain debated. One intriguing possibility is that these ‘spacing’ enzymes slide nucleosomes along DNA and then position (‘clamp’) them to a neighboring nucleosome at a characteristic remodeler-specified distance (9,11–13).

Micrococcal nuclease digestion of chromatin followed by high-throughput sequencing (MNase-Seq) is a widely used technique to map the nucleosome organization (14). It has been instrumental in the discovery of phased arrays of regularly spaced nucleosomes (Figure 1A). It also can be used to measure the average distance between nucleosomes, the so-called nucleosome repeat length (NRL), and to infer the degree of phasing and regularity of arrays over each gene (8,15).

However, MNase cleaves the DNA that flanks nucleosomes so that information about the positions of the neighboring nucleosomes on the same DNA molecule is lost. Consequently, the structure of individual nucleosome fibers, including the proposed regularity of nucleosome arrays, cannot be directly observed by MNase-Seq, severely diminishing our knowledge of the actual nucleosome organization present in individual cells.

In fact, regularly spaced nucleosomes are considered a hallmark of chromatin organization but may be much rarer than thought. Individual arrays could consist of randomly (statistically) positioned nucleosomes downstream of a genomic barrier (16). At high densities, nucleosomes can then only populate a restricted set of locations on the DNA next to the barrier. Averaging the nucleosome positions over many cells would reveal those locations, and it appears as if phased regularly spaced arrays existed.

Moreover, a substantial fraction of genes lacks phased arrays to begin with as evident from an MNase-Seq signal without clear peaks (Figure 1B, C). However, the absence of peaks does not prove irregular nucleosome spacing. It could also indicate lack of phasing of perfectly spaced nucleosomes (Figure 1B) (17,18). The underlying cause for the poorly defined nucleosome signal in MNase-Seq data thus remains unclear.

The nucleosome density constitutes another fundamental property of chromatin organization that remains unresolved by MNase-Seq (Figure 1D, E) (19). Nucleosome density is likely an important and regulated property of chromatin. It substantially drops during DNA damage and cellular senescence, for instance (3,20). Of note, a low or variable nucleosome density across the cell population could be yet another underlying cause for the lack of defined peaks in MNase-Seq data (Figure 1D).

Nucleosome density along the DNA is often expressed as the fraction of DNA that is occupied by nucleosomes. MNase-Seq cannot measure nucleosome occupancy primarily because MNase digests away the nucleosome-free parts of the DNA so that the fraction of nucleosomal and non-nucleosomal DNA can no longer be determined. In addition, MNase liberates nucleosomes preferentially in the vicinity of NDRs, it possesses a DNA-sequence bias, and it readily overdigests and thereby destroys nucleosomes (21,22). The MNase-Seq signal therefore heavily depends on the chosen MNase digestion degree. As the MNase-seq signal varies with conditions, MNase-seq cannot quantitatively report on the nucleosome occupancy (23).

In summary, the true nature of the nucleosome fiber organization remains hidden when using conventional high throughput nucleosome mapping technology such as MNase-Seq. It therefore remains an important goal to develop assays that can map all nucleosome locations on individual chromatin fibers. Such assays provide insights into the cell-to-cell heterogeneity of the nucleosome landscape and could directly determine array regularity on single DNA molecules. The same assays would also natively determine nucleosome occupancy. They could do so at individual genomic sites or for individual chromatin fibers, parameters we termed site- and read-occupancy, respectively (Figure 1E).

Here, we present advances towards the goal to study nucleosome organization at the single fiber level. We adapted and further developed a methylation footprinting approach (19,24,25) to obtain information about the positions of nucleosomes on long, individual chromatin fibers by exploiting long-read, single-molecule sequencing technology (26–30). In this approach, dubbed Fiber-Seq (26), chromatin is isolated and treated with an enzyme that methylates the DNA bases that are not protected by nucleosomes. The nucleosomes thereby leave a footprint of unmethylated DNA. The methylated DNA is then directly sequenced without prior PCR amplification by Oxford Nanopore technology, which natively detects the methylated DNA bases. As each sequencing read came from a single cell, this methodology uncovers cell-to-cell heterogeneity and the intriguing complexity of the nucleosome organization. Large fractions of the genome deviated from the stereotypical promoter-proximal nucleosome organization in Saccharomyces cerevisiae (Figure 1A). For instance, we document substantial levels of unphased nucleosome arrays (Figure 1B), irregularly spaced arrays (Figure 1C), and cell-to-cell heterogeneity with regards to array organization and nucleosome occupancy (Figure 1D). Moreover, acute histone depletion in combination with deletion of remodeling enzymes allowed us to study how nucleosomes redistribute upon experimental nucleosome depletion and test current models for the nucleosome spacing reaction catalyzed by nucleosome remodelers.

Materials and methods

Materials availability

Plasmids and yeast strains generated in this study are available upon request.

Expression and purification of core histones

Core histones were expressed and purified under denaturing conditions as previously described (31). Briefly, plasmids for expression of Drosophila core histones (H2A pFMP128, H2B pFMP129, H3 pFMP186, H4 pFMP187) were separately transformed into BL21-Gold (DE3) cells (Agilent Technologies). Expression was induced using 1 mM isopropyl β-d-1-thiogalactopyranoside (IPTG) for 2–3 h at 37°C, 180 rpm. Cells were harvested by centrifugation at 4000 rpm for 20 min at 4°C. After that, cells were washed with cold water, centrifuged and snap-frozen in liquid nitrogen before storage at -80°C. Before purification, cells were thawed quickly and resuspended in 35 ml 6–7 M urea, 200 mM NaCl, 5 mM β-mercaptoethanol, 0.2 M PMSF, 1 mg/ml Aprotinin, 1 mg/ml Leupeptin, 1 mg/ml Pepstatin, 40 mM NaOAc pH 5.2, 1 mM EDTA pH 8, 10 mM Lysine. Cell walls were broken with at least 2 runs on a Microfluidizer LM10 at a pressure of 1200 bar. Cell extracts were pelleted for 30 min at 40 000 × g at 4°C. Supernatants were filtered through 0.45 μm HPF Millex 50 ml syringe filter and subjected to chromatography through a Q-Sepharose column stacked on top of a SP-Sepharose column (5 ml each; GE Healthcare/Cytiva). The Q column was removed after sample application. The SP Sepharose column was washed and then eluted by a NaCl salt gradient. Histone purity was assessed using 18% SDS PAGE. Fractions containing core histones were pooled, dialyzed against water and stored at –80°C.

Histone octamer reconstitution and 601 array preparation

Core histones were lyophilized, dissolved in unfolding buffer (7 M guanidinium–HCl, 20 mM Tris pH 7.5, 10 mM DTT), mixed in a ratio H2A/H2B/H3/H4 1.2:1.2:1:1 to a final concentration 4 mg/ml, and dialyzed (MWCO 3500 Da) in refolding buffer (2M NaCl, 10 mM Tris pH 7.5, 1 mM EDTA, 1 mM DTT). Reconstituted histone octamers were purified by HiLoad 16/60 Superdex 200 chromatography. The quality of histone octamers was assessed using 18% SDS PAGE. Histone octamers were concentrated using 30K Amicon Ultra 4 ml (Merck) at 3000 rpm at 4°C. Purified histone octamers were snap-frozen in liquid nitrogen and stored at -80°C.

Preparation of the 601 25x nucleosome array was done as previously described (32). Briefly, plasmid DNA (pFMP232) was digested with EcoRI-HF, HincII and AseI. The completeness of the digestion was assessed by 1% agarose gel electrophoresis. Digested fragments were purified by phenol/chloroform extraction using MaXtract High Density tubes (Qiagen #129056). DNA was ethanol precipitated and resuspended in TE buffer. 0.15 mg/ml of digested plasmid DNA was mixed with histone octamers at a molar ratio of octamer to 601-sites from 0.7 to 1.7 in 2 M NaCl, 10 mM Tris–HCl pH 7.6 (final concentrations) in a volume of 50–100 μl. Reactions were transferred into Slide-A-Lyzer MINI dialysis device (MWCO 7 kDa, 0.1 ml, Thermo Fisher Scientific, #69560). Dialysis devices were placed into 0.2 l of high salt buffer (10 mM Tris–HCl pH 7.4, 2 M NaCl, 1 mM EDTA pH 8.0, 0.01% NP-40, 1 mM DTT). No salt buffer (10 mM Tris–HCl pH 7.4, 1 mM EDTA pH 8.0, 0.01% NP-40, 1 mM DTT) was gradually added to the dialysis device using a peristaltic pump for 24 h. During the initial 6 hours, the flow rate was 50 ml/h. After that, the flow rate was increased to 100 ml/h. After incubation the dialysis device was transferred into 1 l of low salt buffer (10 mM Tris–HCl pH 7.4, 50 mM NaCl, 1 mM EDTA, 0.01% NP-40, 1 mM DTT) and dialyzed for 2 h at 4°C. The reactions were then transferred into low-bind tubes (Eppendorf, #0030108.051). Assembled nucleosome arrays were further purified by Mg²⁺-precipitation as described in (32). The final concentration of the nucleosome arrays was assessed by measuring the absorbance at 260 nm assuming that 1 OD₂₆₀ corresponds to 50 μg/ml 25-mer array DNA. Quality of arrays was assessed using restriction digests of accessible and inaccessible sites as described (32). Arrays were stored at 4°C.

Growth of cells

Genotypes of yeast strains are provided in Table S1. Wildtype and NHP6a/b-deletion strains cells were grown at 30°C in YPAD (1% yeast extract, 2% peptone, 2% glucose, 60 mg/l adenine hemisulfate). For histone depletion experiments, cells were initially grown overnight to OD₆₀₀ 1.0 in 2% galactose-containing YNB synthetic media, washed with pre-warmed YNB media without carbon source (6.7 g/l yeast nitrogen base, 1.6 g/l amino acid dropout-mix (-His, -Leu, -Ura, -Trp), 84 mg/l His/Trp/Ura, 168 mg/l Leu) as previously described (15) and then dissolved in pre-warmed YNB media supplemented with 2% glucose to OD₆₀₀ 0.5. Cells were grown at 30°C for 3 h and harvested. Cells grown in parallel with 2% galactose instead of glucose were used as controls for histone depletion. NHP6a/b deletion strains were fixed by addition of 1% formaldehyde to the medium for 5 min at RT with shaking.

Nuclei preparation from S. cerevisiae

For NHP6a/b deletion strains and the isogenic control strain, nuclei were prepared from crosslinked cells as described (19). WT, HD and TKO HD nuclei were prepared largely as described (15,33). Cells were grown overnight in the 0.5 l of YPAD complete media to OD₆₀₀ 0.8–1.2 and harvested at 4000 rpm for 30 min at 4°C (Heraeus Cryofuge 6000i). The pellet was washed once in 45 ml of cold deionized water and harvested by centrifugation (4000 rpm, 5 min, 4°C; Thermo Fisher Scientific, TX-1000). The cell pellet was resuspended in 2 ml of preincubation solution (0.7 M ß-mercaptoethanol, 2.8 mM EDTA pH 8.0) per 1 g of wet weight. Cells were incubated for 30 min at 30°C whilst shaking at 130 rpm, then pelleted at 4000 rpm for 5 min at 4°C. The cell pellet was washed in 40 ml cold 1 M sorbitol before cells were harvested at 4000 rpm for 5 min at 4°C. Cells were then resuspended in 5 ml sorbitol-β-ME (1 M sorbitol, 5 mM β-mercaptoethanol) solution per 1 g of wet weight, and 100 μl of 2% Zymolyase 100T (Nacalai Tesque Inc. #07665) was added. Cells were spheroplasted for 20–25 min at 30°C whilst shaking at 130 rpm. Spheroplasts were pelleted at 4000 rpm for 8 min at 4°C, then washed in 40 ml cold 1 M sorbitol. Spheroplasts were pelleted as above and resuspended in 7 ml Ficoll buffer (18% Ficoll Type 400 (Sigma, #F4375), 20 mM KH₂PO₄, 1 mM MgCl₂, 0.25 mM EGTA pH 8.0, 0.25 mM EDTA pH 8.0) per gram wet weight and aliquoted into desired amounts (typically 0.5 g). The crude nuclei were centrifuged using Beckman Coulter JA 20.1 rotor at 12 000 × g for 30 min at 4°C. Pellets were shock frozen in a mixture of dry ice and ethanol and stored at –80°C.

Fiber-Seq

Crude nuclei were thawed on ice for 20 min, then resuspended by vortexing in 10 ml 1× M.SssI buffer per gram of nuclei's wet weight. For non-crosslinked nuclei, the following 1× M.SssI buffer was used: (10 mM Tris- HCl pH 7.9, 50 mM NaCl, 10 mM MgCl₂, 10 mM BSA, 1 mM DTT). For crosslinked nuclei, the 1x M.SssI buffer was composed of 20 mM HEPES–NaOH pH 7.5, 70 mM NaCl, 0.25 mM EDTA, 0.5 mM EGTA, 0.5% glycerol 1 mM DTT, 0.25 mM PMSF (19). The typical wet weight for a methylation reaction with two timepoints was 0.5 g. Resuspended nuclei were incubated for 3 min on ice and subsequently centrifuged at 4000 rpm for 6 min at 4°C using Heraeus Multifuge X3R. The pellet was resuspended in 1 ml 1× M.SssI buffer per 1 g of cell's wet weight and placed on ice. Slide-A-Lyzer MINI Dialysis Devices (3.5K MWCO, 0.5 ml, Thermo Fisher Scientific, #88400) were pre-hydrated in 14 ml of 1× M.SssI buffer. The DNA concentration in crude nuclei was approximated by 1:20 and 1:40 dilution in 1× M.SssI buffer using a Qubit 3.0 fluorometer (ThermoFisher Scientific). For each methylation reaction, approximately 25 μg of DNA was transferred to a clean LoBind (Eppendorf, #30108051) tube. To this volume, 20 μl RNase A of a 10 mg/ml stock, 10 μl M.SssI methyltransferase (20.000 U/ml; NEB, #M0226M), 601-nucleosome arrays (75–100 ng DNA) as an internal spike-in and cold 1× M.SssI buffer to a final volume of 397.5 μl was added. Each reaction was gently mixed and placed on ice for 5 min. To the 14 ml 1x M.SssI buffer, in which the Slide-A-Lyzer tubes were hydrated, 87.5 μl S-Adenosyl methionine (SAM; 32 mM stock; NEB, #B9003) was added. Samples were transferred to the pre-hydrated dialysis devices, a micro stir bar (VWR 5 × 2 mm) was added, and the reaction was stirred for 2 min to reach room temperature. Stirring was set to the lowest speed necessary to keep nuclei in suspension. Methylation was started by adding 2.5 μl of SAM from the 32 mM stock, and incubated for 30–60 min at room temperature. Additional 2.5 μl SAM and 2.5 μl M.SssI were supplemented after 30- and 60-min incubation. Typically, two 190 μl time points were taken, one after 45–60 min and one at 90–120 min. To each time point, 15 μl (2 mM final concentration) of S-(5′-adenosyl)-l-homocysteine (SAH) (26 mM stock, Sigma, #A9384) was added, and tubes were placed at 65°C for 15 min to quench the methylation reaction. Samples were then placed at 25°C with mild shaking to continue RNase A digestion until all time points were collected. To all samples, 25 μl 10× STOP buffer (50 mM Tris–Cl pH 8.0, 4% SDS, 100 mM EDTA pH 8.0) and 25 μl 10 mg/ml Proteinase K solution was added. Samples were incubated at 37°C for 2 h.

Genomic DNA isolation

After incubation with Proteinase K, 150 μl sample was mixed with 150 μl of ROTI®Phenol:Chloroform:Isoamylalcohol (Carl Roth, #A156) were added to the Fiber-Seq samples. Samples were vortexed for 3 s and transferred to a MaXtract High Density tube (Qiagen, #129056). Samples were centrifuged at 16 000 × g for 5 min. The upper layer was transferred to clean 1.5 ml Eppendorf tubes. NaCl (0.2 M–final concentration) and 2.5 volumes of ethanol were added, and samples were incubated on ice for 30 min. Samples were centrifuged for 25 min at 15 000 × g at 4°C. DNA was washed with 1.5 ml 70% EtOH and centrifuged for 5 min at 15 000 rpm. DNA pellets were dried for 8 min at 37°C, then resuspended in 55 μl TE buffer (10 mM Tris–HCl, pH 8.0, 1 mM EDTA pH 8.0) and incubated at 4°C overnight to assure complete dissolution. The concentration and yield of purified DNA were assessed using Qubit dsDNA HS Assay Kit. Quality of DNA were assessed by running 1 μg of DNA on 0.7% agarose gel electrophoresis.

Nanopore library preparation and sequencing

Library preparation of isolated DNA was performed with the SQK-LSK109 ligation sequencing kit (Oxford Nanopore Technologies) according the manufacturer's protocol. For multiplexing up to 24 samples, native barcode expansion kits (EXP-NBD104/114) were used. Libraries were loaded onto MinION R9.4.1 flow cells (Oxford Nanopore Technologies) and run for 72 h using MinKNOW software (version 20.06.4) with disabled basecalling, barcoding and alignment. Output was saved in FAST5 format.

Basecalling and demultiplexing

Basecalling and demultiplexing were done using ONT guppy software by Oxford Nanopore Technologies (version 5.0.16) (34). First, DNA sequences were called by guppy_basecaller using the following parameters:

–config dna_r9.4.1_450bps_hac.cfg –fast5_out –device auto –gpu_runners_per_device 32

Resulting fastq files were split per barcode by guppy_barcoder_out using the following parameters:

–barcode_kits ‘EXP-NBD104 EXP-NBD114’ –device auto

FASTQ files were merged into a single file for each barcode using a custom bash script.

Sequence alignment and selection of genomic features

Demultiplexed FASTQ files for each barcode were mapped to the SacCer3 (R64-1-1) (https://www.ncbi.nlm.nih.gov/assembly/GCF_000146045.2/) reference genome by minimap2 (version 2.24, https://github.com/lh3/minimap2) (35), using the following parameters: minimap2 -ax map-ont. Spike-in DNA sequences were added to the reference genome as a separate chromosome. For the alignment of the 601-nucleosome array, the plasmid sequence was used as a genome reference. Resulting alignments were further filtered, sorted and indexed by Samtools (version 1.9, https://github.com/samtools/samtools) (36) using the following parameters: samtools view –F 2052 –q 30 –b –S; samtools sort –o; samtools index –b.

Genomic coordinates for subtelomeric regions, mating type loci and Ty1 transposones were taken according to http://sgd-archive.yeastgenome.org for R64-1-1 SacCer3 reference sequence.

Methylation calling

CpG methylation for each barcode were called by Nanopolish (version 0.13.2, https://github.com/jts/nanopolish) (37), using default parameters for ‘cpg’ motif with DNA sequences in FASTQ files, mapping results in BAM files and the same genome reference as was used for the sequence alignment as inputs.

Methylation states for each CpG, derived by Nanopolish, were defined by log likelihood using the following thresholds: methylated (≥2.5), unmethylated (≤–2.5) and ambiguous (between –2.5 and 2.5). Methylation rate was defined as a ratio of number of methylated sites per read to the total number of either methylated or unmethylated sites in the read. Reads longer than 1000 bp and containing methylation rates between 0.1 and 0.9 were selected for the further analysis in R (version 4.0.2). Of the 936 352 mapped reads across 15 WT samples (median length 2611 bp), 60.3% of the reads (564 764; median length of 5126 bp) were retained after filtering.

Alignment of reads to the stereotypic average in vivo +1 nucleosome and generation of composite plots

Genomic coordinates of reads were set to 0 relative to the ensemble-averaged in vivo +1-nucleosome genomic position by using H3Q85C chemical mapping dataset for 5542 annotated genes (38). Composite plots of aligned reads were generated by calculating the binned occupancy for each CpG as a proportion of the number of unmethylated CpG sites to the total number of both methylated and unmethylated CpG sites with a 10 bp bin size.

MNase-Seq composite plots

Wild type MNase-Seq data (GSE141007) from eight independent experiments were processed as previously described (15) and merged. Briefly, reads from merged FASTQ files were mapped to the SacCer3 genome using bowtie2 (version 2.4.5) (39) with parameters -X 500 –very-sensitive –no-discordant –no-mixed. Mitochondrial (chrM) and rDNA reads (chrXII 451000:469000) were excluded from further analysis. Mapped reads were further processed using Samtools (version 1.15.1). Reads were sorted by samtools sort and duplicated reads were marked using GATK (version 4.2.6.1) (40). Reads were further selected using samtools view -h -b -F 1024, samtools view -hb -f 2 -F 1804 -q 20, and after sorting, reads were additionally selected using samtools view -bl. Nucleosome dyad coverage were generated by taking the center of 130–160 bp fragments and resizing to 100 bp at the dyad center. All samples were randomly sub-sampled to 50 million reads with replacement. Nucleosome dyad coverages were further aligned relative to the ensemble-averaged dyad coordinate of +1-nucleosomes (38) and the binned mean signal was plotted for a chosen genomic region with bin size 10 bp.

Selection of ‘Gene Body Reads’

To restrict certain analyses to gene bodies, we deleted all other regions from individual reads, thereby generating Gene Body Reads. For each of the 5542 annotated genes that are larger than 500 bp, we selected a region between two offsets: M bp upstream from the ensemble-averaged +1-nucleosome dyad coordinate (38) (to either cover or exclude the NDR) and N bp upstream from the Transcription Termination Site (TTS) (to exclude the region at the 3′ end of genes). The choice of offsets depended on downstream analyses and is described in the following sections. CpG sites within the chosen region for each gene were identified for each read. The read length was defined as the distance between the most distant CpG sites in a single read for the chosen region. Reads shorter than 500 bp as well as reads with ambiguity rate greater than 0.75 were excluded from the further analysis. Next, the resolution ratio for each read was calculated as the relation of sum of distances between neighbouring methylated or/and unmethylated CpG sites greater than 150 bp to the read length. Reads with resolution ratio >0.75 were excluded from further analyses. Genomic coordinates for selected reads for each gene were set to 0 relative to the ensemble-averaged +1 dyad coordinate as described above. Genomic coordinates for reverse genes were inverted relative to 0 so that all genes had the same direction.

Global occupancies

Global occupancies were calculated as the total number of unmethylated sites divided by the total number of both methylated and unmethylated CpG sites in methylation data for the entire genome. Occupancies in Gene Body Reads were calculated as the total number of unmethylated sites divided by the total number of both methylated and unmethylated CpG sites in Gene Body Reads selected with following offsets: 0 bp from the ensemble-averaged +1 dyad and 0 bp from the TTS. Occupancies from multiple samples were plotted using geom_jitter function from ggplot2 R package (https://ggplot2.tidyverse.org) with position_jitter parameter 0.2. Mean and standard deviation values for each group were overlayed using geom_pointrange function (ggplot2 R package). The difference between samples were assessed using Welch's t-test in R (version 4.0.2).

Occupancy density plots

Occupancy per read were calculated from the number of unmethylated CpG sites divided by the total number of both methylated and unmethylated CpG sites for each single read in methylation data. The density of 100 000 randomly selected, with replacement, reads for each sample was calculated using geom_density function from ggplot2 R package.

M-islands density plot

M-islands were defined as the genomic range of regions covered by ≥2 methylated CpG sites with no unmethylated or ambiguous sites in between and with a maximal distance between any neighbouring CpG sites of less than 120 bp. M-islands were detected from selected Gene Body Reads for 5542 annotated genes as described above with the following offsets: 0 from ensemble averaged +1 dyad coordinate and 100 bp upstream from the TTS. Density distributions for all m-islands were calculated using geom_density function from ggplot2 R package. The rate of m-islands was calculated as a relation of number of m-islands longer than either 50 bp or 150 bp to the total number of m-islands.

Footprint density plots

Footprint sizes were defined as the distances in bp between two methylated CpG sites with at least one unmethylated site in between and were calculated from methylation data. Density distributions for all footprints were calculated using geom_density function from ggplot2 R package. Where indicated, the Footprint center was calculated as the central genomic position between two methylated CpG cites in the footprint relative to the ensemble-averaged +1-nucleosome dyad coordinate, rounded to the closest integer. Footprints were plotted as footprint sizes in bp against the footprint centers (bp from the ensemble-averaged +1-nucleosome dyad coordinate) using geom_bin2d function with binwidth parameter (10,10) and scale_fill_viridis colour scheme from ggplot2 R package.

Generation of single-molecule plots

Reads fully covering the plotted genomic region were selected. Reads were aligned to the ensemble-averaged +1-nucleosome as described above. Borders between patches of either methylated or unmethylated CpG sites were detected as the centers between marginal CpG sites of opposite methylation states (methylated and unmethylated).

Computational phasing

Computational phasing was done on Gene Body Reads, selected as described above with the following offsets: 500 bp upstream of the +1-nucleosomes and 0 bp from the TTS. 147 ± 20 bp footprint sizes (mononucleosomal footprint) were selected for each read. Footprint sizes were determined as above. Computation phasing was done for +1, +2, +3 or +4 nucleosomes as follows. The mononucleosome-sized footprint whose midpoint fell into the windows occupied by either +1, +2, +3 or +4 nucleosomes (0, 166, 332 or 498 bp, respectively, relative to the ensemble +1-nucleosome coordinate) ±75 bp was determined. Genomic coordinates of the reads selected for each gene were shifted to the midpoint of the detected footprint.

Computational phasing of genes belonging to gene quartiles with highest and lowest array regularity as classified by MNase-Seq was done as follows. WT samples from previously published MNase-Seq dataset (GSE141007) (15) were first processed as described in MNase-Seq composite plots section. Regularity score was calculated for each gene as correlation coefficient between nucleosome dyad coverages and the closest Gaussian distribution with fixed repeat length between peaks for the range 200 bp upstream and 600 bp downstream from the ensemble +1-nucleosome coordinate as previously described (15). Genes with length between 500 and 1500 bp were sorted by regularity score and 10% genes with highest and lowest score were selected using quantile function.

Computational phasing of reads to footprint midpoints at any genomic region were done as follows. The midpoint position of every nucleosomal footprint in a single gene body read were used as a value to shift genomic coordinates of this read so that the center of the footprint assumed the 0 coordinate. Composite plots after phasing were calculated as above, after genes with opposite strand direction were inverted to have the same direction.

Selection of reads with footprints within the NDR

The distribution of footprint sizes in the NDR was calculated as follows: Gene Body Reads were selected for all genes with the following offsets: 500 bp from the ensemble-averaged +1-coordinate and 0 bp from the TTS. Reads that contained 40–185 bp long footprints, whose midpoints are within 140 ± 20 bp upstream from the ensemble-averaged +1-coordinate, were selected and ordered by footprint sizes. Composite plots for selected reads were generated as described in Generation of aligned composite plots. These reads were compared with reads selected in the same way at +1, +2 and +3 nucleosomes positions (0, 166, 332 bp downstream from the ensemble-averaged +1-coordinates respectively).

Selection of reads with ‘well-positioned’ and ‘shifted’ +1-nucleosomes

Selection of reads with well-positioned and shifted +1-nucleosomes used in Figure 3C and Figure 4B–D was done as follows: Gene Body Reads were selected for all genes with the following offsets: 500 bp upstream from the ensemble-averaged +1-coordinate and 0 bp from the TTS. Reads containing midpoints of mononucleosome-sized footprints (165 ± 20 bp) within a ±20 bp window either from the ensemble +1-nucleosome dyad location (‘well-positioned’) or 83 bp downstream of it (‘shifted +1-nucleosomes’) were selected.

Reads were further computationally phased as described in Computational phasing section at ensemble-averaged +1-coordinate, and composite plots were generated as described in Generation of aligned composite plots section with bin size 20 bp.

To calculate the number of reads containing midpoints of mononucleosome-sized footprints (165 ± 20 bp) within ±20 bp window, the window midpoint was moved from 0 to 750 bp from the ensemble +1-nucleosome at 1 bp resolution and the number of selected reads was normalized to the maximal value.

Selection of reads that were ‘inaccessible’ or ‘accessible’ at ensemble +1-coordinates

Selection of (in-) accessible reads shown in Figure 3D and Figure 4B–D was done as follows: Gene Body Reads were selected for all genes with the following offsets: 500 bp upstream of the ensemble +1 dyad coordinate and 0 bp from the TTS. Reads were selected that contained at least one CpG within ±20 bp from ensemble +1-nucleosome positions. In case multiple CpGs were present in this window, we required all CpGs to have the same methylation state, either fully methylated (accessible) or fully unmethylated (inaccessible). All other reads were not considered for this analysis. Reads were further computationally phased as described in Computational phasing section at ensemble-averaged +1-coordinate, and composite plots were generated as described in Generation of aligned composite plots section with bin size 20 bp.

Statistical positioning model fit

Starting point is the statistical positioning model introduced in (41). We use model with parameters and w = 83 and find the external potential to match the average peak to peak distance of the one-particle density (i.e. the dyad density) of the model with the average peak to peak distance of computationally phased WT data (based on phased reads with 165 ± 20 bp mum length resulted in 164.25 bp average peak to peak distance).

The dyad density needs to be folded with the footprint pattern specific to the experimental method to obtain an occupancy pattern comparable with the experimentally measure occupancy. For this we fold the dyad density with a constant nucleosome footprint (occupancy 1 for the length of the footprint) tuning the footprint length such that the average occupancy (measured between the N +2 peak and the linker between N +6 and N +7 (at around 900 bp) to avoid any bias due to the offset that is present between the peak positions) matches the experimental WT value. This results in footprint length of 128 bp. Note that this 128 bp footprint is smaller than the footprint measured at the N +1 position: 142 bp. This might be explainable by extra factors bound next to the N +1. We thus off set the models' occupancy by the difference of 14 bp downstream in the plots, which leads to better agreeing peak positions in the WT case.

To obtain a statistical position model for the HD case we retuned the external potential such that the average model occupancy, obtained by folding the dyad density with the same footprint as above, matches with the computationally phased HD occupancy in region of 600 bp to 1000 bp.

Correlation of +1 shifted nucleosomes with transcription activity or gene classes

The fraction of reads with shifted +1-nucleosomes was calculated for each gene separately. Genes were sorted according to either transcription quartiles (8) (GEO: GSE69400) or UNB, STM, TFO, RP gene classes (1), and ratio of +1 shifted reads were calculated and plotted.

rDNA analyses

The read-average methylation over either RDN37-1 or RDN37-2 neighbouring loci was calculated. Individual reads were sorted according to their read-averaged methylation the two neighbouring loci, and then hierarchically clustered based on distances between summed methylated states in 10 bp bins using the ‘manhattan’ method. The transcriptional independency between two neighbouring loci was tested as the enhancement, which is defined as the conditional probability distribution of one locus being active whilst the neighbouring locus is active as well. This probability is divided by the marginal probability distribution of one locus being active whilst ignoring the neighbouring locus. To measure this observable, we utilize the following equation: r = P(active locus 2 | active locus 1)/P(active locus 2). Independency of the combined methylation state of both rDNA loci on a single read were performed using a Fisher's exact test.

Results

Fiber-Seq as tool to map gene-to-gene and cell-to-cell heterogeneity of the nucleosome organization

To visualize nucleosome architecture by Fiber-Seq, we exploited the lack of endogenous CpG methylation in baker's yeast (42). We treated nuclei with the DNA methyltransferase M.SssI, which methylates cytosines in CpG dinucleotides, yielding 5-methyl-cytosine (5mC). Nucleosomes leave a footprint of unmethylated cytosines (Figure 2A). We paid particular attention to let the methylation reaction go to completion by always collecting at least two methylation timepoints, adding fresh enzyme during the reaction time course, and internally controlling the methylation reactions through spike-ins with in-vitro assembled 601 nucleosome arrays. Complete methylation enhances the resolution of the method and interpretability of the data because footprints become more sharply defined. Moreover, complete methylation is a prerequisite for accurately measuring nucleosome occupancy (Figure 1E) (19).

5mC is traditionally detected after bisulfite conversion followed by PCR amplification and short read sequencing. We circumvented these steps and directly sequenced the methylated DNA molecules by Oxford Nanopore technology, which can directly detect 5mC (28–30). Importantly, this technology provides several kb long reads (5126 bp median length), allowing us to footprint entire nucleosome arrays on the same chromatin fiber. The methylation data for the spiked-in 25mer 601-arrays revealed that this technology faithfully recovered the expected methylation pattern (32,43) (Figure 2B). Unmethylated 601-array DNA, in contrast, showed little methylation (<1.2%; Figure 2C).

Fiber-Seq also reproducibly recapitulated the stereotypical nucleosome organization at 5′ ends of genes in wild-type (WT) yeast (Figure 2D). NDRs featured a low level of protection against methylation. Throughout this study, we used the level of protection as a measure for nucleosome occupancy, which we express as the fraction of unmethylated CpGs (Figure 1E). Over gene bodies, we obtained a periodic protection pattern, consistent with phased, regularly spaced arrays of nucleosomes. Notably, the maxima of the periodic pattern remained comparably high throughout the gene body. Nucleosome occupancy is therefore uniformly high over the entire gene body, consistent with our earlier results (19). MNase-Seq, in contrast, shows a steady decline of peak heights downstream of promoters (19,44), which is caused by MNase biases. Fiber-Seq therefore provides a more faithful depiction of the nucleosome occupancy landscape than MNase-Seq.

Inspection of single reads revealed substantial heterogeneity in nucleosome array organization (Figure 2E). As each read stems from a single haploid cell, individual reads over a particular genomic region reveal cellular heterogeneity, information that is lost by ensemble methods including MNase-Seq. Pronounced deviations from the stereotypic nucleosome organization became visible when inspecting individual genes. Several coding regions harbored methylated patches of DNA spanning > 150 bp of DNA, much longer than the average linker DNA in yeast (∼19 bp; Figure 2F, G, Figure S1A, B) (15). These patches existed for the majority of reads over these genes and were evidently nucleosome-free even though they are large enough to accommodate a nucleosome.

To estimate the prevalence of such uncharacteristically long linker regions across all gene bodies, we searched for ‘m-islands’. We defined m-islands as continuous stretches of methylated cytosines (Figure 2A; Materials and methods). Their size distribution revealed that 23.4% of m-islands inside of gene bodies exceeded 50 bp in length (Figure 2H), demonstrating that a sizeable portion of yeast genes harbors surprisingly long linkers in their nucleosome arrays. 1.69% of m-islands even exceeded 150 bp, a region long enough to accommodate an entire nucleosome. Affected genes were often located in subtelomeric regions (Table S2). Nucleosome-free gaps therefore exist in gene bodies and are unexpectedly abundant.

Some of these uncharacteristically long linkers were present only in some cells but not in others. About half of the reads over the FMT1 gene, for instance, exhibited a strong methylation signal between ∼900 and ∼1000 bp downstream of the TSS (Figure 2G). Cell-to-cell variability existed also for YOR084W and YCL029C (Figure S1C, D; between ∼500 to ∼650 bp and ∼1100 to ∼1250 bp, respectively). Promoter NDRs also experienced heterogeneity (Figure S1C,E). We conclude that cell-to-cell and gene-to-gene variabilities can be substantial and that Fiber-Seq is instrumental to their discovery.

To probe the array organization up- and downstream of long m-islands, we sorted fibers according to their m-islands sizes over the +1/+2 and the +2/+3 linker DNA regions. The quartile of fibers with the longest m-islands (Q4) had a substantially diminished array regularity compared to the shortest quartile (Q1; Figure S2A). Fibers with long m-islands therefore possessed a disrupted array architecture.

Fiber-Seq also allows direct quantification of nucleosome occupancies. Genome-wide, we measured 70.2 ± 1.5% occupancy in WT cells (Figure 2I). In gene bodies, the occupancy was 77.9 ± 1.4% (Figure 2I). The latter value falls short of the theoretical expectation of ∼89% occupancy (one nucleosome every 165 bp), and thereby bolsters our finding that a sizable fraction of arrays contains unexpectedly long m-islands. Occupancies measured by Fiber-Seq are consistent with data obtained by independent technologies (19).

+1-Nucleosomes can produce unusually large footprints

The size distribution of footprints, which we calculated from the distance between two 5mC sites that surround at least one unmethylated CpG, revealed well-defined peaks consistent with mono- and oligonucleosomes (Figure 3A). The methylation signal therefore largely reflected the nucleosome organization.

A fraction of footprints, however, were smaller than a nucleosome (<140 bp; 14.0 ± 0.76%, N = 15, P = 8.9E–16). These footprints were particularly prevalent in the NDR. 82% of all footprints in the NDR were smaller than expected for a nucleosome compared to ≤ 26% in gene bodies (Figure S2B). The footprints in the NDR also had a continuum of sizes. The NDR is therefore populated by a heterogeneous mix of factors, which likely include a combination of DNA-bound factors and subnucleosomes (1,45). Gene body footprints were much more homogeneous in size, in line with canonical nucleosomes predominantly populating gene bodies (Figure S2B).

Intriguingly, +1-nucleosomes had a special footprint size distribution compared to downstream nucleosomes (Figure 3B). Two populations of footprints existed for +1-nucleosomes. One population had the same size distribution as downstream nucleosomes (dashed lines), with a peak at 158 bp, and the centers of the footprint coinciding with the ensemble in vivo +1-nucleosome positions. The second population protected more DNA (175 bp; arrow) and was upstream-shifted. We speculate that chromatin factors were bound upstream of the +1-nucleosome and thereby contributed to the enlarged footprint of the second population.

The +1-nucleosomes are particularly dynamic (46) possibly due to transcription shifting or disrupting the nucleosome (47,48). Supporting +1-dynamics, the composite plots revealed that the +1-nucleosome had a reproducibly lower amplitude compared to its +2 neighbor (mean values of 0.861 versus 0.881, respectively; P = 0.00029, Welch's t-test; Figure 2D). We therefore investigated whether +1-nucleosomes were differently positioned or even missing in a fraction of reads. We selected fibers with a mononucleosome-sized footprint centered around ensemble +1-coordinates (Figure 3C, left) (38) or centered further downstream (Figure 3C, right; Figure S3A). 16.3% of fibers experienced a shift in their +1-nucleosome dyad location of >41 bp (Figure S3A). 7.66% even had a dyad location 83 bp downstream of ensemble +1-coordinates. In other words, these nucleosomes were maximally out of phase with the ensemble. In a complementary approach, we determined what fraction of fibers were accessible to methylation over a 40 bp window centered around ensemble +1-dyad coordinates (Figure 3D). Only fibers with unambiguous methylation status in this window were considered (Materials and methods). 13.9% of fibers were accessible at the dyad location of ensemble +1-nucleosomes. We conclude that the +1-nucleosome frequently shifts downstream into the gene, exposing the underlying DNA.

Our Fiber-Seq approach provided the unique opportunity to investigate whether heterogeneity in the +1-positioning affects nucleosome patterns downstream. Fibers with well-positioned +1-nucleosomes or inaccessible ensemble +1-dyads had discernible regularity over the gene body as seen in individual reads and the composite signal (Figure 3C, left; Figure 3D, left). In contrast, fibers with +1-nucleosomes shifted by 83 bp downstream (Figure 3C, right) and reads with accessibility over ensemble +1-coordinates (Figure 3D, right) displayed a visibly more heterogeneous methylation pattern of individual fibers indicating substantial cell-to-cell heterogeneity. Composite plots also revealed little regularity (Figure 3C, D; Figure S3B). The entire nucleosome array, not only the +1-nucleosome, therefore assumed heterogeneous nucleosome patterns in these fibers.

Accessible +1-coordinates were frequently observed in highly expressed genes (Figure 3E), consistent with results on heat shock-induced genes (49) and the notion that transcription can evict nucleosomes (5). The STM promoters were particularly enriched in fibers with accessible ensemble +1-dyads (Figure 3F). STM promoters are inducible and named after SAGA, Mediator and Tup1 that bind upstream of the TSS, often together with other factors (1). TFO and UNB promoters on the other hand, which do not bind these factors, were depleted of fibers with accessible ensemble +1 dyads. The results are consistent with STM factors directly or indirectly pushing +1-nucleosomes away from their ensemble locations.

A single molecule view of seemingly irregularly spaced arrays

A low periodicity in MNase-Seq data, as indicated by small peak-to-peak amplitudes in composite plots, is often interpreted as lack of regularly spaced nucleosomes. However, it could also result from lack of phasing (Figure 1B, C) (4,17,18). We leveraged the single fiber resolution of our assay to differentiate between these models. First, we identified, on each read individually, the footprint of the +1-nucleosome. We then used the midpoints of the footprints to computationally align the reads, an approach we dubbed computational phasing (Figure 4A). We did so for the read categories defined above (Figure 3C, D).

As expected, computational phasing did not affect the composite signal of fibers that had already a well-positioned +1-nucleosome (Figure S3C). In contrast, computational phasing improved the peak-to-peak amplitude for fibers with accessibility at +1-coordinates (Figure 4B). Therefore, arrays were more regular on these reads than the composite signal prior phasing suggested, providing evidence for the scenario depicted in Figure 1B.

Nevertheless, even computational phasing (Figure 4B) did not increase the amplitude to levels seen in the control group of reads, which feature inaccessibility at +1-coordinates (Figure 4C). Similarly, computational phasing did not reveal any signs of array regularity on reads with shifted +1-nucleosomes (Figure 4D). The simplest model to explain these results is that irregularly spaced arrays decorate fibers with a non-stereotypic placement of the +1-nucleosome (Figure 1C). However, we cannot rule out an alternative model, in which high array regularity over these reads is obscured by variable NRLs.

Independent evidence for the existence of irregularly spaced arrays came from the footprint size distribution calculated from the same four read categories used above (Figure 4E). Whereas fibers with stereotypically positioned nucleosomes (‘well-positioned +1’ and ‘inaccessible +1-dyad’) displayed pronounced peaks, the patterns for fibers with non-stereotypically positioned +1-nucleosomes (‘shifted +1’ and ‘inaccessible +1-dyad’) were notably fuzzier, as would be expected for irregularly spaced arrays.

A low nucleosome density could cause high array irregularity because each nucleosome has more space to explore along the DNA. Consistent with this model, fibers with non-stereotypically positioned +1-nucleosomes had reduced read occupancies over gene bodies (Figure 4F). An intriguing possibility is that fibers with non-stereotypically positioned +1-nucleosomes represent transient products of disruptive nuclear processes such as replication and transcription, which reduce nucleosome density and can destroy array regularity (15,48,49), both features of these fibers.

We inspected next regions of the genome that ensemble approaches would predict to harbor irregularly spaced nucleosome arrays. To this end, we selected deciles of genes that scored highest and lowest for array regularity as estimated by MNase-Seq (15). Composite plots of the methylation signal of the low-regularity decile showed low amplitudes as expected (Figure 4G). This amplitude increased after computational phasing (Figure 4G, left), indicating the presence of unphased but regularly spaced arrays over genes that MNase-seq would classify as ‘low regularity’. It did not reach the amplitude seen for genes classified as high regularity, though (Figure 4G, right). The results again point to the presence of irregularly spaced arrays decorating at least some genes classified as low regularity by ensemble approaches.

Array irregularity is also visually evident from the large accessible regions in the middle of some gene bodies (Figure 2G; Figure S1A–C). The long tail in the m-island distribution provides additional strong evidence for its existence (Figure 2H).

In summary, the stereotypical picture of genic nucleosome organization (Figure 1A) does not represent substantial parts of the genome. Lack of phasing (Figure 1B) exists and multiple lines of evidence support that arrays can be truly irregular (Figure 1C), complicating interpretation of data from ensemble methods.

The direction of transcription does not influence array regularity

Composite plots of MNase-Seq data show a drop in amplitude from the TSS to TTS in all tested organisms including yeast (14) suggesting that array regularity is highest close to TSSs. Transcription elongation could be involved in installing this apparent directional asymmetry (50).

We leveraged our computational phasing approach to directly test if array regularity varies throughout the gene body. Composite plots of reads aligned to midpoints of footprints observed for the +1 and +4 nucleosomes overlapped, demonstrating that arrays downstream of the +4-nucleosome have the same level of regularity as arrays downstream of the +1-nucleosomes (Figure 5A). Alignments to +2 and +3-nucleosomes showed analogous results (Figure S3D). Arrays therefore possess translational symmetry. We next tested if the direction of transcription may influence the degree of regularity as suggested (50). We first classified all reads as either forward or reverse with respect to transcription. Within both read categories, we then aligned all reads to all footprint midpoints found inside gene bodies. The corresponding composite plots of forward and reverse reads superimposed well, ruling out that transcription direction is an important factor for regularity of arrays over the majority of the genome (Figure 5B).

Figure 5. — The nucleosome organization is indistinguishable throughout the gene body and with respect to the direction of transcription. (A) Computational phasing of reads to midpoints of nucleosome footprints that belong to +1- or +4-nucleosomes. (B) Computational phasing to midpoints of footprints that are located in gene bodies. Shown is a comparison of reads that are forward and reverse with respect to the direction of transcription.

In summary, we conclude that the average nucleosome organization of the average gene does not appreciably vary throughout the gene body at steady state, neither in terms of occupancy, regularity and orientation relative to transcription. When looking at individual genes, however, strong, idiosyncratic differences to the stereotypic architecture become evident (see sections above).

Neighboring rDNA repeats exhibit transcriptional coupling

Transcription of ribosomal DNA (rDNA) by RNA polymerase I heavily affects chromatin architecture. rDNA exists as repeats of >100 copies in yeast but only a fraction of them is transcriptionally active. Unlike inactive repeats, active repeats are devoid of nucleosomes over the 35S rRNA gene (51). Fiber-Seq readily visualized this drastic dichotomy of nucleosomal versus nucleosome-depleted 35S rDNA. Striking differences in accessibility were visible over the 35S rDNA but not in the intervening regions (Figure 6A). The distribution of read-averaged methylation over 35S rDNA showed two broad peaks, with 67 ± 0.3% of reads exhibiting an occupancy >50% (Figure 6B). These results suggested that roughly two-thirds of rDNA repeats were transcriptionally inactive in our strain (W303a) and growth conditions.

The high-occupancy rDNA fibers had a nucleosome organization indistinguishable from other gene bodies (Figure 6C, D; Figure S4A). The low occupancy fibers, on the other hand, were strongly enriched with small, non-nucleosomal footprints (Figure 6D), consistent with a strong depletion of nucleosomes and binding of the high-mobility group protein Hmo1 in these transcriptionally active repeats (51).

Transcription of adjacent 35S rDNA has been suggested to occur independently of each other (52,53). Many of our reads spanned neighboring 35S rDNA, allowing us to directly visualize the chromatin states of neighboring repeats. Visual inspection showed four broad clusters in which the 35S genes on neighboring repeats were both inaccessible, both accessible, or in mixed states (Figure 6A). After classification of each 35S accessibility states (Figure S4B), we found that neighboring repeats assumed the same chromatin state on average ∼1.7-fold more often than predicted by random chance. This enhancement relative to random chance was observable across all 15 replicates with high statistical significance (Figure S4C, D). We therefore suggest that transcriptional states of neighboring repeats are coupled.

Reads with an accessible 35S region featured a lowly methylated region just upstream of the TSS. This footprint did not overlap with a Reb1 binding site (28). It coincides instead with the binding site for Upstream Activating Factor (UAF; purple in Figure 6E), which aids in the formation of the transcription preinitiation complex of RNA polymerase I. In contrast, closed 35S rDNA genes showed strong accessibility at this location suggesting that these closed repeats are poised to assemble the UAF complex. This open UAF binding site upstream of closed 35S rDNA indicates that mechanisms exist that prevent nucleosomes from spilling over from the neighboring nucleosomal regions.

The nucleosome organization over heterochromatic and repetitive DNA regions

The long reads of Fiber-Seq provide the opportunity to shed light on the nucleosome organization of repetitive DNA. Short-read technologies such as MNase-Seq have difficulties here because the short reads cannot be uniquely mapped to the genome. As a workaround, non-uniquely mapping MNase-Seq reads are sometimes simply averaged, providing only the average nucleosome organization. When we inspected repetitive Ty1 retrotransposons, we observed a strong fiber-to-fiber heterogeneity over the first 1000 bp from their 5′ LTRs (Figure 7A, B). This region constitutes the Ty1 promoter and contains more than a dozen binding sites for activator and repressor proteins (54). Methylation accessibility over this region correlated with retrotransposon expression (r = 0.67; Figure 7C), underscoring the functional importance of this region. Notably, nucleosome occupancy and the nucleosome footprint distribution were indistinguishable from gene bodies (Figure 7D, E), suggesting that Ty1 nucleosome organization does not deviate much from the average yeast gene.

Figure 7. — Nucleosome organization over repetitive DNA regions. (A, B) Single fibers and composite plots over two exemplary Ty1 retrotransposons. As MNase-Seq did not provide uniquely mappable reads (black line in similar plots elsewhere), the MNase-Seq tracks represent an average of multiple Ty1 elements (grey lines). The dashed lines demarcate the first 1000 bp from the 5′ LTRs that contain the promoters. (C) Promoter accessibility correlates with expression level of individual Ty1 elements. The fraction of fibers with a methylation accessibility >0.25 in the promoter was calculated for each Ty1 element and plotted against Ty1 expression levels (75). (D) Nucleosome occupancy over Ty1 elements exceeds the genome average but is indistinguishable from gene bodies. (E) Comparison of footprint size distributions over Ty1 elements and gene bodies. (F) Occupancies over the heterochromatic mating cassettes HML and HMR are indistinguishable from gene bodies.

We also inspected the repetitive HML and HMR mating cassettes. They are silent heterochromatic regions, yet their average occupancies were indistinguishable from euchromatic gene bodies (Figure 7F). Thus, yeast eu- and heterochromatin nucleosome densities may not substantially differ. Due to a sparse CpG density over parts of these regions (Figure S5), orthogonal methods will have to be used to reconstruct the nucleosome organization in greater detail (see Discussion).

Fiber-Seq but not MNase-Seq reads also uniquely mapped to the highly repetitive chromosome ends (Figure S6). Visual inspection of single reads revealed little cell-to-cell heterogeneity. The average nucleosome occupancy over subtelomeres approximated the genome-average, not gene bodies (Figure S6E), probably because subtelomeres contain several NDRs (Figure S6A–D).

Nucleosome redistribution upon acute nucleosome depletion

Fiber-Seq's ability to directly detect changes in nucleosome occupancy makes it an ideal method to study how the nucleosome landscape responds to an acute drop of nucleosome density, a situation cells encounter during replication, DNA damage and cellular senescence (3,20). Of particular interest is whether remodelers can pack nucleosomes tightly even at lower nucleosome densities. Such an activity, termed clamping (Figure 8A), was seen for several remodelers in vitro (9,12). To which extent clamping contributes to the nucleosome organization in vivo has remained unclear.

We used yeast cells deleted for nhp6aΔ and nhp6bΔ that reportedly lost 35% of their nucleosomes (55). By Fiber-Seq, however, we did not detect any measurable decrease in nucleosome occupancy (Figure S7). As an alternative, we employed an inducible nucleosome depletion system, in which the expression of histones H3 and H4 can be repressed upon switching cells from galactose- to glucose-containing media (56). Unlike MNase-Seq (15,57,58), Fiber-Seq allowed us to directly detect and quantitate changes in nucleosome occupancy upon histone depletion (HD). HD decreased the occupancy by 46% relative to the WT levels (Figure 8B), consistent with previous Western blot results (15). Most cells responded to HD treatment because the entire distribution of read-occupancies shifted to lower occupancy values upon HD (Figure 8C).

Nucleosomes across the entire gene body suffered from loss of occupancy (Figure 8D; Figure S8A,B). The +1-peak dropped from ∼86% in WT cells to ∼51% occupancy in HD cells (Figure 8B), which corresponds to a loss of only 40% occupancy, less than the 46% loss that the entire gene body experiences. We observed a similar effect in isw1Δ, isw2Δ, chd1Δ triple knockout (TKO) HD cells, which will be further discussed at the end of this section. Glucose treatment of TKO HD cells reduced the occupancy by ∼41%, slightly less efficiently than in HD cells (46%; Figure 8B–D). But like HD cells, TKO HD cells also preferentially retained +1-nucleosomes, whose occupancy dropped by only 32% (Figure 8B). +1-nucleosomes are therefore preferentially retained after HD at the expense of nucleosomes downstream, and this retention is independent of the three remodelers missing in TKO HD cells. We conclude that mechanisms exist that maintain the +1-nucleosome. They likely involve a combination of nucleosome-attracting DNA sequences and ATP-dependent processes (6,7,15,59). This conclusion is consistent with the previously described 5′ bias of retaining nucleosomes over gene bodies (11,60).

The composite plots for HD cells revealed lower periodicity compared to WT (Figure 8D), consistent with earlier MNase-Seq data (15,57,58). Computational phasing of reads to the midpoints of +1-nucleosome footprints did not improve the periodicity much, as judged by the amplitude and correlation distance of the peaks in composite plots (Figure 8E). We can therefore rule out a model, in which a lack of phasing prevented us from detecting large amounts of evenly spaced nucleosomes in ensemble data (15).

The lower nucleosome occupancy after HD led to a wider NRL (Figure 8E). A similar correlation could be observed when sorting fibers from WT cells according to their read occupancies. NRLs were larger for fibers with lower occupancies (Figure 8F). These results do not support clamping to be responsible for the tight NRL seen in WT yeast.

Nevertheless, clamping could favor a wide NRLs, wider than what is observed in WT cells. It could thus contribute to the NRL and residual array regularity in HD cells. If this were the case, HD cells should possess a different NRL than predicted by statistical positioning as the null model. Arrays should also be more pronounced than in the null model. Computational simulations of statistical positioning unfortunately did not help us to differentiate between clamping and statistical positioning. On the one hand, the experimental NRL seen for HD cells was shorter than the NRL from HD simulations, consistent with a mechanism that packs nucleosomes more densely than predicted by statistical positioning (Figure S8C) (11). On the other hand, the experimental amplitude was smaller than the simulated one, not predicted by clamping.

Also sorting HD fibers according to their read occupancies did not help in distinguishing statistical positioning from clamping. The quartile of HD fibers with the highest occupancies possessed the highest peak-to-peak amplitudes and longest arrays as predicted by both models. Noise in the data prevented us, though, from assessing whether the NRL remained constant across all quartiles as predicted by clamping (Figure 8G).

To experimentally test if HD cells contained higher levels of evenly spaced nucleosomes than expected from statistical positioning, we compared the HD and TKO HD datasets. TKO HD cells lack all ISWI and Chd1-type spacing remodelers. Consistent with a spacing activity of the remodelers, composite plots revealed lower periodicity in TKO HD cells than in HD cells (Figure 8D). The results independently validated our earlier observations using MNase-seq (15). Note that the lower periodicity was observable in TKO HD even though these cells possessed a higher nucleosome occupancy (Figure 8B), which would result in more pronounced arrays due to statistical positioning. Compared to HD cells, reduced periodicity in TKO HD was discernible also after computational phasing and sorting fibers according to their read-occupancies (Figure 8E,H). The signal difference between TKO HD and HD cells was small, however, indicating that the activity of ISWI- and Chd1-type remodelers could hardly overcome the strong dispersive forces acting on nucleosomes in HD cells.

Discussion

The nucleosome landscape shapes the function and fate of eukaryotic cells. A single-cell view of the nucleosome organization and how it affects cellular function in health and disease remains a formidable goal in chromatin research. Techniques to visualize all nucleosomes in single cells however do not exist so far. Single-cell MNase-Seq is a promising technology but so far suffers from sparse nucleosome sampling (61). Most approaches instead reveal the average nucleosome locations across the cell population, and only for well-positioned nucleosomes. Importantly, even the most basic organization of the nucleosome landscape, that of a nucleosome array, is mostly inferred from ensemble-averaged short-read sequencing data but not directly visualized.

Long-read technology offers unique insights into the nature of the nucleosome organization. Array-Seq, for instance, determines the length distribution of oligonucleosomes generated by partial MNase digests (18). This technology essentially estimates the locations of the two end-positioned nucleosomes in an oligonucleosome molecule and can deduce regular spacing independent of phasing. Fiber-Seq, a combination of nucleosome footprinting (24) and long-read sequencing, provides an even more comprehensive picture, as it sheds light on the nucleosome locations also inside of long nucleosome arrays (26–30).

These features make Fiber-Seq ideally suited to study how nucleosomes redistribute upon nucleosome depletion, a situation that cells must cope with during DNA damage and cellular senescence (3,20). We find that our HD conditions roughly halved the nucleosome occupancy, which massively impaired nucleosome array organization. Nevertheless, residual levels of array regularity persevered after HD, and this regularity depended on bona fide spacing remodelers of the ISWI and Chd1 families. We therefore propose that these remodelers try to reestablish order but can only weakly overcome the strong dispersive forces acting on nucleosomes after extensive histone depletion in living cells. Transcription, which is upregulated in HD conditions (62), likely helps disperse nucleosomes and maximize their positional entropy (15).

Fiber-Seq also lends itself to test mechanistic proposals for the mechanism of spacing remodelers (12). In vitro, evidence exist for and against a clamping activity of remodelers (9,11,12,63,64). Our data do not support clamping being the main mechanism to establish the tight NRL in WT cells because NRLs were inversely correlated with nucleosome occupancies. Clamping as a mechanistic basis for nucleosome spacing should not be ruled out, however. For instance, remodelers may favor an NRL that is wider than the tight NRL seen in WT. Also, clamping may be obscured by disruptive processes such as transcription in the cell. Preventing interference from those processes in combination with higher-resolution footprinting methods (see below) would help to further test the mechanism of spacing in vivo. Clamping would become visible either as predicted before from constant spacing at varying nucleosome densities (9,12) or more generally from remodeler-induced deviations from an NRL that can be expected from pure statistical positioning or a linker-length equilibration mechanism (63).

Promoter proximal arrays in WT cells are suggested to feature the highest level of regularity, perhaps because the +1-nucleosome is particularly well-positioned and thus provides a barrier for downstream nucleosomes, or because transcription elongation fosters array formation via histone deposition and nucleosome remodeling machineries (11,14,50). We leveraged the long-read capabilities of Fiber-Seq to carefully characterize arrays over gene bodies. Arrays are translationally symmetrical meaning that they are the same on 5′ ends of the gene and inside gene bodies. They are also symmetrical with regards to the direction of transcription. Array regularity is thus thoroughly equilibrated throughout the average gene body negating an argument previously used to support a model of elongation-associated array synthesis (50).

We find substantial heterogeneity of the +1-nucleosomes in WT cells. Fast turnover rates (46), gene regulation (49,65), and positioning away from the thermodynamically most stable positions (15) probably contribute to this heterogeneity. All the more surprising is the finding that +1-nucleosomes remain astonishingly well positioned (15) and preferentially retained after an acute decrease in nucleosome density in HD cells. Cells thus appear to employ mechanisms to place +1-nucleosomes, which may involve remodelers, DNA sequence and transcription factors (4,6,7,50,66).

With Fiber-Seq, it also becomes possible to estimate the length distribution of linker DNAs in vivo, and we were surprised to find sizable amounts of linkers inside gene bodies that were much longer than the average linker lengths determined from ensemble data. We speculate that some of these wide linkers allow cryptic initiation of transcription (67).

A notable feature of Fiber-Seq is that it natively detects nucleosome occupancy, i.e. the fraction of DNA that is protected by nucleosomes. Whereas HD is readily visualizable by Fiber-Seq, we unexpectedly did not detect reduced occupancy in NHP6a/b-deleted cells under our growth conditions, urging caution when using these cells as models of reduced nucleosome density. Fiber-Seq also effortlessly visualizes the drastic dichotomy of nucleosome occupancy over the 35S rDNA gene (28). Notably, our analysis of > 17000 individual rDNA fibers indicated that transcription of neighboring 35S rDNA genes are coupled as they possess the same transcriptional state ∼1.7-fold more often than expected from random chance. Detection and quantitation of such a modest effect size demands high throughput methods such as Fiber-Seq, explaining why this effect can be readily missed (52,53). We predict that Fiber-Seq will be instrumental to dissect the underlying mechanism.

More generally, we think that nucleosome occupancy is likely a fundamentally important yet understudied feature in chromatin organization because robust readouts of occupancy have only recently been developed (19). Transcription by RNA polymerase II, for instance, may be able to evict nucleosomes over coding regions (5), and DNA damage and cellular senescence reportedly reduce nucleosome occupancy globally (3,20). Fiber-seq's ability to probe individual chromatin fibers could be used to detect these dynamic states of chromatin. Importantly, Fiber-Seq measures occupancy without the need for normalizations. We therefore expect that it will replace more traditional normalization-dependent approaches including Western blots, mass spectrometry and spike-in normalized MNase-Seq analyses (23).

As a single-molecule method, Fiber-Seq can characterize the nucleosome organization even if ensemble methods provide little insights. In fact, ensemble nucleosome profiles can be deceptive and difficult to interpret due to unknown levels of phasing and nucleosome occupancy (Figure 1) (17,18). Lack of phasing is no issue for Fiber-Seq, as it can be rectified by computational phasing (this study) or by estimating nucleosome regularity of individual fibers (27), and read-occupancy is a direct readout. In fact, fibers with low nucleosome occupancy also possess the lowest regularity, drawing a mechanistic link between the two features.

The nucleosome organization around gene promoters appears to be much more heterogeneous than the textbook picture suggests (compare Figures 1A, 2E). We find some arrays to be simply unphased, whereas others are truly irregularly spaced and strongly deviate from the idealized textbook picture, featuring nucleosome-free regions in arrays in a large proportion of cells.

Importantly, Fiber-Seq can document cell-to-cell variability, including rare chromatin states. We find evidence for cell-to-cell heterogeneity for instance in the NDR (68), with some NDRs being fully occluded in a fraction of cells. Other NDRs host a heterogeneous mix of DNA-bound factors and subnucleosomes (1,45). Also, the +1-nucleosome experiences strong heterogeneity (46). Considering the importance of cell-to-cell heterogeneity for cellular activity (69), we are looking forward to studies dissecting the functional consequences of heterogeneity. For instance, we expect heterogeneity in the NDR and +1-nucleosome to result in cell-to-cell variability of TSS selection and transcription activity (30).

Footprinting methodologies with a high spatial resolution will be instrumental to follow up on some of our findings. Currently, the resolution is limited by the distance between CpG dinucleotides, with a median value of one CpG every 23 bp in the yeast genome, limiting the precision of placing nucleosomes into footprints. A cocktail of multiple methyltransferases (28) or enzymes that nonspecifically methylate adenine residues have been successfully used to achieve higher resolution already and make Fiber-seq compatible with genomes that possess endogenous CpG methylation (26,27). Higher resolution will also benefit efforts to study co-occupancy of the transcription factors (70) with nucleosomes and other DNA-bound factors (1) to arrive at a comprehensive single-molecule map of chromatin (71).

Supplementary Material

gkad1098_Supplemental_File

Click here for additional data file.^{(2.5MB, pdf)}

Acknowledgements

We thank Ashish K. Singh, Petra Vizjak, Joachim Griesenbeck, Stefan Hamperl and Stefan Krebs for discussions, Tamas Schauer and Tobias Straub for help with bioinformatic questions, Matthias Hanke for help with coding, and Stefan Krebs and Helmut Blum (LAFUGA, Gene Center, LMU Munich) for initial Nanopore sequencing. We are grateful to Maryam Khatami for bioinformatic contributions, to Silvia Härtel, Madhura Khare and Andrea Schmid for technical help and the Müller-Planitz lab for discussions. D.V. and E.O. acknowledge support from the IRTG SFB 1064.

Author contributions: Conceptualization: F.M.-P., U.G., P.K.; Software: M.R.W., M.B., D.V., M.H.; Formal analysis: M.B., D.V., M.R.W., M.H.; Investigation: M.B., D.V., M.R.W., E.O.; Data curation: M.B., D.V., M.R.W.; Writing – of original draft: F.M.-P., M.B.; Writing – Review and editing: all authors; Visualization: M.B., D.V., M.R.W.; Funding acquisition: F.M.-P., U.G., P.K.

Notes

present address: Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, 37077 Göttingen, Germany

Contributor Information

Mark Boltengagen, Institute of Physiological Chemistry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany.

Daan Verhagen, Institute of Physiological Chemistry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany; Molecular Biology, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany.

Michael Roland Wolff, Institute of Physiological Chemistry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany; Physics of Complex Biosystems, Physics Department, Technical University of Munich, Garching, Germany.

Elisa Oberbeckmann, Molecular Biology, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany.

Matthias Hanke, Physics of Complex Biosystems, Physics Department, Technical University of Munich, Garching, Germany.

Ulrich Gerland, Physics of Complex Biosystems, Physics Department, Technical University of Munich, Garching, Germany.

Philipp Korber, Molecular Biology, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany.

Felix Mueller-Planitz, Institute of Physiological Chemistry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany.

Data availability

The next generation sequencing data are available in the Gene Expression Omnibus under accession number GSE224713. Publicly available high-throughput sequencing datasets were retrieved using the following Gene Expression Omnibus (GEO) accession numbers: PolII ChIP-Seq (GSE69400) (8), Reb1 SLIM-ChIP (GSE108948) (72), UAF30 ChIP-Seq (GSE116661) (73) and UAF30 ChIP-Exo (GSE147927) (1), WT MNase-Seq (GSE141007) (15). Scripts used for the data analysis in this study are available in Zenodo at https://doi.org/10.5281/zenodo.10039450.

Supplementary data

Supplementary Data are available at NAR Online.

Funding

Deutsche Forschungsgemeinschaft [MU3613/3-1, MU3613/8-1, SFB1064 A04, SFB1064 A07]. Funding for open access charge: SLUB Dresden.

Conflict of interest statement. None declared.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

gkad1098_Supplemental_File

Click here for additional data file.^{(2.5MB, pdf)}

Data Availability Statement

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PERMALINK

A single fiber view of the nucleosome organization in eukaryotic chromatin

Mark Boltengagen

Daan Verhagen

Michael Roland Wolff

Elisa Oberbeckmann

Matthias Hanke

Ulrich Gerland

Philipp Korber

Felix Mueller-Planitz

Abstract

Graphical Abstract

Graphical Abstract.

Introduction

Figure 1.

Materials and methods

Materials availability

Expression and purification of core histones

Histone octamer reconstitution and 601 array preparation

Growth of cells

Nuclei preparation from S. cerevisiae

Fiber-Seq

Genomic DNA isolation

Nanopore library preparation and sequencing

Basecalling and demultiplexing

Sequence alignment and selection of genomic features

Methylation calling

Alignment of reads to the stereotypic average in vivo +1 nucleosome and generation of composite plots

MNase-Seq composite plots

Selection of ‘Gene Body Reads’

Global occupancies

Occupancy density plots

M-islands density plot

Footprint density plots

Generation of single-molecule plots

Computational phasing

Selection of reads with footprints within the NDR

Selection of reads with ‘well-positioned’ and ‘shifted’ +1-nucleosomes

Figure 3.

Figure 4.

Selection of reads that were ‘inaccessible’ or ‘accessible’ at ensemble +1-coordinates

Statistical positioning model fit

Correlation of +1 shifted nucleosomes with transcription activity or gene classes

rDNA analyses

Results

Fiber-Seq as tool to map gene-to-gene and cell-to-cell heterogeneity of the nucleosome organization

Figure 2.

+1-Nucleosomes can produce unusually large footprints

A single molecule view of seemingly irregularly spaced arrays

The direction of transcription does not influence array regularity

Figure 5.

Neighboring rDNA repeats exhibit transcriptional coupling

Figure 6.

The nucleosome organization over heterochromatic and repetitive DNA regions

Figure 7.

Nucleosome redistribution upon acute nucleosome depletion

Figure 8.

Discussion

Supplementary Material

Acknowledgements

Notes

Contributor Information

Data availability

Supplementary data

Funding

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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