Abstract
Nucleosome positioning is critical to chromatin accessibility and is associated with gene expression programs in cells1,2,3. Previous nucleosome mapping methods assemble profiles from cell populations and reveal a cell-averaged pattern: nucleosomes are positioned and form a phased array that surrounds the transcription start sites of active genes3,4,5,6 and DNase I hypersensitive sites7. However, even in a homogenous population of cells, cells exhibit heterogeneity in expression in response to active signalling8,9 that may be related to heterogeneity in chromatin accessibility10,11,12. Here we report a technique, termed single-cell micrococcal nuclease sequencing (scMNase-seq), that can be used to simultaneously measure genome-wide nucleosome positioning and chromatin accessibility in single cells. Application of scMNase-seq to NIH3T3 cells, mouse primary naive CD4 T cells and mouse embryonic stem cells reveals two principles of nucleosome organization: first, nucleosomes in heterochromatin regions, or that surround the transcription start sites of silent genes, show large variation in positioning across different cells but are highly uniformly spaced along the nucleosome array; and second, nucleosomes that surround the transcription start sites of active genes and DNase I hypersensitive sites show little variation in positioning across different cells but are relatively heterogeneously spaced along the nucleosome array. We found a bimodal distribution of nucleosome spacing at DNase I hypersensitive sites, which corresponds to inaccessible and accessible states and is associated with nucleosome variation and variation in accessibility across cells. Nucleosome variation is smaller within single cells than across cells, and smaller within the same cell type than across cell types. A large fraction of naive CD4 T cells and mouse embryonic stem cells shows depleted nucleosome occupancy at the de novo enhancers detected in their respective differentiated lineages, revealing the existence of cells primed for differentiation to specific lineages in undifferentiated cell populations.
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Change history
06 November 2018
Change history: In Fig. 1c of this Letter, the two graphs were duplicates. The right panel of Fig. 1c has been corrected online.
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Acknowledgements
We thank B. Z. Stanton for critical reading of the manuscript, B. Z. Stanton and J. Cooper for discussions, the National Heart, Lung, and Blood Institute DNA Sequencing Core Facility for sequencing the libraries, the National Heart, Lung, and Blood Institute Flow Cytometry Core facility for sorting the cells, and the National Institutes of Health Biowulf High Performance Computing Systems for computing service. The work was supported by Division of Intramural Research, National Heart, Lung and Blood Institute.
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Nature thanks M. Buck and the other anonymous reviewer(s) for their contribution to the peer review of this work.
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Authors and Affiliations
Contributions
K.Z. conceived the project. Q.T. and K.Z. performed the scMNase-seq experiments for NIH3T3 cells. K.C., W.G. and W.X. performed the scMNase-seq experiments for naive T cells and mouse ESCs. B.L. analysed the data. W.J., G.H. and B.N. contributed to the data analysis. B.L. and K.Z. wrote the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Characterizing scMNase-seq datasets.
a, Mapping rates of reads from 100 human cells (two experiments on the left) or 100 mouse cells (three experiments on the right) against human genome (blue) and mouse genome (orange) are shown. The cells were sorted from pre-mixed and MNase-digested human and mouse cells. These data show that there is little contamination of DNA of one cell from another cell. b, Fragment-length density of pooled scMNase-seq for NIH3T3 cells, pooled scMNase-seq and bulk-cell MNase-seq for T cells and mouse ESCs. c, Box plots of fragment ratio (subnucleosome-sized particle-to-nucleosome) for NIH3T3 cell, naive CD4 T cell and mouse ESC scMNase-seq libraries. Single-cell libraries were grouped by biologically independent experiments. Supplementary Table 1 gives the library number for each group. Centre line, median; boxes, first and third quartiles; whiskers, 1.5× interquartile range. d, Plot of non-redundant (NR) read number (x axis) and sequencing redundancy (y axis) for single NIH3T3 cells, CD4 T cells and mouse ESCs. e, Plot of non-redundant nucleosome reads (x axis) and percentage of nucleosomes with overlapping piles ≥ 3 (y axis). The plot suggests the polyploidy of NIH3T3 cells. f, Nucleosome density at different genomic regions for NIH3T3 cell, CD4 T cell and mouse ESC scMNase-seq libraries reveals that the nucleosomes in different genomic regions were similarly detected and represented by scMNase-seq.
Extended Data Fig. 2 Characterizing pooled scMNase-seq data and subnucleosome-sized particles.
a, Average density profiles of nucleosomes (red) and subnucleosome-sized particles (blue) relative to the TSSs of active genes (left) and CTCF-binding sites (right) for bulk-cell naive CD4 T cell MNase-seq data. b, Average density profiles of nucleosomes (red) and subnucleosome-sized particles (blue) relative to the TSSs of active genes (left) and CTCF-binding sites (right) for pooled mouse ESC scMNase-seq data (top) and bulk-cell mouse ESC MNase-seq data (bottom). c, Smoothed scatter plot for the fraction of nucleosome occupied at 8,929 DHS centres (selected from the top 10,000 DHSs (see Supplementary Methods for criteria)) for pooled scMNase-seq (x axis) versus bulk-cell MNase-seq (y axis) for T cells (left). Pearson correlation coefficient is indicated. As a positive control, the scatter plots for two bulk-cell MNase-seq replicates are also shown (right). d, Pearson correlation coefficient for the fraction of nucleosomes occupied at the DHS centre between pooled sub-sampled CD4 T cell scMNase-seq libraries and bulk-cell MNase-seq, as a function of sub-sampled cell number (left). Percentages of top 10,000 DHSs represented in the comparison—that is, the sample size in the top panel—as a function of sub-sampled cell number are also shown (right). e, Smoothed scatter plot for subnucleosome-sized particle density at 83,229 DHSs for pooled scMNase-seq (x axis) versus bulk-cell MNase-seq (y axis) for T cells (left). Pearson correlation coefficient is indicated. As a positive control, the scatter plots for two bulk-cell MNase-seq replicates are also shown (right). f, Pearson correlation coefficient for subnucleosome-sized particle density between pooled sub-sampled T cell scMNase-seq libraries and bulk-cell MNase-seq at 83,229 DHSs, as a function of sub-sampled cell number. g, h, Smoothed scatter plot for the fraction of nucleosomes occupied at 8,449 DHS centres (selected from top-10,000 DHSs (see Supplementary Methods for criteria)) (g) and subnucleosome-sized particle density at 94,250 DHSs (h) for pooled scMNase-seq (x axis) versus bulk-cell MNase-seq (y axis) for mouse ESCs. Pearson correlation coefficient is indicated. As a positive control, the scatter plots for two bulk-cell MNase-seq replicates are also shown. i, j, Average density profiles of subnucleosome-sized particles around TSSs for gene subgroups with different expression levels (i) and around DHSs for DHS subgroups with different DNase I tag densities (j). k, Table showing the mapping statistics for 198 mouse ESC scMNase-seq libraries and 96 previously published10 mouse ESC scATAC-seq libraries. l, m, Scatter plots of the number of non-redundant reads (l, y axis) and percentage of recovered DHSs (m, y axis) versus sequencing redundancy (x axis) for scMNase-seq subnucleosome-sized particles (red, n = 198 single-cell libraries) and scATAC-seq reads (grey, n = 96 single-cell libraries). Box plots (right) show the values from scatter plots (left) for cells with redundancy that ranges from 50% to 70% (blue rectangle in the left panel; red, n = 49; grey, n = 58) for the two methods. n, Scatter plot showing the percentage of recovered DHSs (y axis) versus number of non-redundant reads for scMNas-seq subnucleosome-sized particles (red, n = 198 single-cell libraries) and scATAC-seq reads (grey, n = 87 single-cell libraries). o, Aggregated nucleosome profile similarity score at DHSs for different groups of DHSs (left) and two promoter groups (right), for comparison between pooled scMNase-seq and bulk-cell MNase-seq (top) and between two bulk-cell MNase-seq replicates (bottom). The DHS groups are classified by three positioning-stringency levels (low, positioning score < 0.45; moderate, 0.45 < positioning score < 0.65, high, positioning score > 0.65) and three nucleosome coverage levels (high, ≥15; moderate, 10–15; low, 5–9). The DHS numbers for each group are: low positioning score and high coverage, n = 803; low positioning score and moderate coverage, n = 531; low positioning score and low coverage, n = 450; moderate positioning score and high coverage, n = 701; moderate positioning score and moderate coverage, n = 592; moderate positioning score and low coverage, n = 588; high positioning score and high coverage, n = 162; high positioning score and moderate coverage, n = 230; high positioning score and low coverage, n = 395. The number of promoters for each group: active, n = 6,777; silent, n = 418. In box plots in i, m, o, centre line, median; boxes, first and third quartiles; whiskers, 1.5× interquartile range; notch, 95% confidence interval of the median.
Extended Data Fig. 3 Measuring uniformity in nucleosome spacing in single cells.
a, Cartoon illustrates that uniformity in nucleosome spacing can be measured by nucleosome-to-nucleosome distance density: uniformly spaced nucleosomes in a single array result in sharp and high peaks, whereas non-uniformly spaced nucleosomes result in flat peaks or no peaks. Nucleosomes from mixed arrays also result in flat peaks, even if they are uniformly spaced. b, The nucleosome space phasing and relative peak height gradually decreased as the number of mixed cells increases, which indicates cellular heterogeneity of nucleosome organization across different cells. c, Nucleosome space phasing and relative peak height do not change when reducing the library size (number of sequence reads) to 1/2, 1/3 and 1/4. d, Density plots of nucleosome-to-nucleosome distance (top) and relative peak height in density plot (bottom) for nucleosomes with different positioning stringency for bulk-cell MNase-seq, pooled 48 single cells, one representative single cell and 48 single-cell scMNase-seq datasets.
Extended Data Fig. 4 Uniformity in nucleosome spacing is higher in silent heterochromatin regions than in active chromatin regions.
a, b, Density plots of nucleosome-to-nucleosome distance (top) and relative peak height (bottom) for nucleosomes at active or silent promoters and DHS or non-DHS regions for T cells (a) and mouse ESCs (b). c, Relative peak height of density plots for nucleosome-to-nucleosome distance for nucleosomes in DHS (red) and non-DHS regions (blue) for low-coverage cells (top) and high-coverage cells (bottom). d, Density plots of nucleosome-to-nucleosome distance (top) and relative peak height (bottom) for diploid (black) and haploid (red) mouse ESCs. e, Density plots of nucleosome-to-nucleosome distance (top) and relative peak height (bottom) at DHS and non-DHS regions for haploid mouse ESCs. f, Mapped nucleosome count normalized by chromosome length at chromosome 1, X and Y for mouse ESCs and CD4 T cells suggests that mouse ESCs are derived from male mouse. g, Density plots of nucleosome-to-nucleosome distance at DHS and non-DHS regions at chromosome X for mouse ESCs. h, Violin plots of library size (total non-redundant reads) for NIH3T3 scMNase-seq libraries treated with three MNase concentrations (0.1 unit (0.1 U), 0.6 unit (0.6 U) and 2.4 unit (2.4 U) MNase per million cells). Each condition has 10 single-cell libraries. In the violin plots, centre dot, mean; inner layer, the interquartile range. i, Fragment-length density of pooled scMNase-seq data with three MNase concentrations. j, k, Average density profiles of all reads (j, k, left) and nucleosome reads with length between 140 and 180 bp (j, k, right) around the TSSs of active genes (j) and CTCF-binding sites (k) for pooled scMNase-seq with three MNase concentrations. l, m, Density plots of nucleosome-to-nucleosome distance (l) and relative peak height (m) at DHS (red) and non-DHS (blue) regions for scMNase-seq treated by 0.6 U (left) and 2.4 U (right) MNase concentrations. n, o, Density plots of nucleosome-to-nucleosome distance (n) and relative peak height (o) for nucleosomes within genomic regions marked by different histone modifications.
Extended Data Fig. 5 Variation in nucleosome positioning within a cell or across different single cells around the centre of DHS or histone modification peaks.
a, Cartoon illustrating the definition of nucleosome variance within a cell or across different single cells. b, c, Heat maps showing the distribution of nucleosome variance at the position relative to DHS centre within a cell (b) or across different single cells (c). d, Nucleosome variance within a cell (red) and across single cells (blue) becomes smaller when getting closer to the DHS centre. e, Calculations of mean value of nucleosome variance from two ranges (3–82 bp and 0–82 bp) reveal the same trend of increase when nucleosomes become farther away from DHS centre. f, g, Average profiles of nucleosome variance at the position relative to the centre of histone modification peaks within a cell (f) or across different cells (g).
Extended Data Fig. 6 Nucleosomes show a synchronized shift in silent-gene promoters and heterochromatin regions, and show compressed spacing where they flank DHS centres.
a, Cartoon illustrates synchronized shift of adjacent nucleosomes within single nucleosome arrays. b, Bar plot showing synchronized shift score for different genomic regions. Silent promoter, silent-gene promoter; active promoter, active-gene promoter; not marked, regions not marked by any histone modifications as shown; DHS: ±2,000-bp region surrounding DHS centre; non-DHS, intervals of DHS regions. c, Synchronized shift score for nucleosome pairs with different distances of nucleosome space. d, Density plot of nucleosome-to-nucleosome distance in single cells reveals dominant nucleosome space at about 182 bp. e, Density plot of nucleosome spacing in the regions flanking strong and weak DHSs as well as non-DHSs. f, Distances between each pair of nucleosomes in the chromatin regions flanking strong DHS, weak DHS or non-DHS, described in e.
Extended Data Fig. 7 Heterogeneity of nucleosome spacing and positioning around DHS across different single cells.
a, Heat maps showing DHS frequency as a function of number of cells with the narrow spacing (x axis) and number of cells with the wide spacing (y axis) for four DHS subgroups with different DNase I tag densities. Numbers indicate the percentages of DHSs that have more wide space than narrow space. b, c, Box plots showing the accessibility level from cell population, measured by DNase-seq tag density (b) and pooled scMNase-seq subnucleosome-sized particle density (c), for five groups of DHSs defined by fraction of wide space. Data represent values on 612, 2,088, 3,858, 2,500 and 1,586 DHSs (from left to right). d, Scatter plot of the ratio of wide-to-narrow space at the DHS in a single cell (x axis) and fragment-size ratio of subnucleosome-sized particles to nucleosomes (y axis) on 48 NIH3T3 scMNase-seq libraries. Pearson correlation coefficient and P value are indicated. P value is the probability that one would have found the current result if the correlation coefficient were zero (null hypothesis), and was calculated using R. e, Box plot showing fraction of cells with positioned nucleosomes around a DHS for different groups of DHSs. DHSs were grouped on the basis of the number of cells detected as DHS in a previously published scDNase-seq experiment12. Number of DHSs for each group was 44,040, 15,622, 11,056, 8,009, 4,063 and 1,180 (from left to right). f, Box plot showing fraction of cells with a positioned +1 nucleosome for two groups of genes sorted by expression variation (low, n = 1,171; high, n = 1,174). g, Gene Ontology analysis of top-1,000 active genes with the smallest nucleosome variance across cells. Significant Gene Ontology terms with P value are reported by David Bioinformatics Resources (v.6.7). h, Density plot showing nucleosome variance around DHSs within a cell (n = 73,274 nucleosome pairs) and across different cells (n = 752,398 nucleosome pairs). i, Cumulative density plot for nucleosome variation at +1 nucleosome relative to the TSSs of active genes within a cell (red, n = 11,388 nucleosome pairs) and across cells (blue, n = 237,006). j, Box plot showing nucleosome variance around DHSs across cells for within a cell type (NIH3T3–NIH3T3 cells, n = 1,128 nucleosome pairs; T cells–T cells, n = 23,936; ESCs–ESCs, n = 5,775) and across different cell types (NIH3T3–T cells, n = 11,856; NIH3T3–ESCs, n = 6,962; T cells–ESCs, n = 20,442). k, Heat map reveals clustering of NIH3T3 cells, T cells and mouse ESCs based on cell-to-cell nucleosome dissimilarity score around DHSs. Colour bar on the right indicates cell types and colour bars on the bottom indicate experiment time and fragment-size ratio. P values in panels b, c, e, f, h and i were calculated using one-sided Mann–Whitney U-test. In box plots, centre line, median; boxes, first and third quartiles; whiskers, 1.5× interquartile range; notch, 95% confidence interval of the median.
Extended Data Fig. 8 Cell-to-cell single-base variation is associated with variation in nucleosome positioning and variation in gene expression across different single cells.
a, CC, GG and GC frequency is higher in the nucleosome-occupied region than in the flanking region, whereas AA, TT, AT and TA frequency shows the opposite pattern. b, CC, GG and GC frequency in flanking regions increases as nucleosome variance within a cell (left) or across different single cells (right) increases. c, AA, TT, AT and TA frequency in flanking regions decreases as nucleosome variance within a cell (left) or across different single cells (right) increases. d, Nucleosome variances within a cell and across different single cells are reversely correlated with the percentage of AA, TT, AT and TA in flanking regions. e, Weblogos sequences logos for sequence preferences across MNase cleavage sites are shown for subgroups of nucleosomes with different positioning variance across cells. f, An example showing a CTCF motif with the reference base (green) in some cells and alternative base (red) in other cells. scMNase-seq data show that the reference base is associated with subnucleosome-sized particles, whereas the alternative base is associated with the nucleosome structure. Fragments from DNase-seq and CTCF ChIP-seq datasets within the window are also shown with the bases at single-nucleotide polymorphism location highlighted. Tracks for tag densities of CTCF ChIP-seq, DNase-seq, and nucleosomes and subnucleosome-sized particles from pooled single cells are shown in a zoomed-out window. g, The number of CTCF-motif matches containing alternative or reference bases at the single-nucleotide polymorphism locus occupied by nucleosomes, subnucleosome-sized particles, sequence reads obtained by DNase-seq and by CTCF ChIP-seq. P value was calculated using one-sided Fisher’s exact test. The ratio between alternative and reference bases is also shown (bottom). h, Single-nucleotide polymorphism frequency is correlated with nucleosome variation across different single cells. Variant frequencies at each position relative to nucleosome midpoint for four nucleosome subgroups with different levels of nucleosome variance across cells are shown. i, Single-nucleotide polymorphism frequency within transcription-factor motifs at DHSs for four DHS subgroups, sorted by nucleosome variance around DHS across different single cells (each subgroup has 22,139 DHSs that contains at least one transcription-factor motif match). j, Single-nucleotide polymorphism frequency within transcription-factor motifs at DHSs in promoters for gene subgroups, sorted by expression variation across different single cells (each subgroup has 2,136 genes). P value in i, j is defined as the probability of observing a larger difference than current result between two groups by random. P value calculation is described in Supplementary Methods. SNP, single-nucleotide polymorphism.
Extended Data Fig. 9 Characterization of primed enhancers in undifferentiated naive CD4 T cells.
a, Heat maps show H3K27ac in naive T cells and p300 in TH1 and TH2 cells around naive T cell-specific, TH1-specific and TH2-specific enhancers. b, Profile of nucleosome occupancy from pooled naive T cell scMNase-seq around T cell-specific, TH1-specific and TH2-specific enhancers. c, Normalized nucleosome occupancy within ± 200 bp of the centre of de novo TH1 enhancers (left) or de novo TH2 enhancers (right) for subgroups of T cells primed for TH1 cells (green), TH2 cells (blue) or none (black). d, e, Plots of fragment-size ratio of subnucleosome-sized particles-to-nucleosomes versus nucleosome occupancy score at de novo TH1 (d) and TH2 (e) enhancers for 237 naive CD4 T cells reveal that nucleosome occupancy score is not correlated with fragment-size ratio. Pearson correlation coefficient and P value are indicated. P value is the probability that one would have found the current result if the correlation coefficient were zero (null hypothesis), and was calculated using R. f, Subgroups of naive CD4 T cells primed for TH1 and TH2 do not have much overlap. g, Plots of de novo TH1 enhancers ranked on the basis of differences in nucleosome occupancy between pooled primed cells and the non-primed cells (y axis, see Supplementary Methods). Enhancers associated with key genes for TH1 were labelled by genes along with ranks. h, Nucleosome positions in pooled or single primed (red) and non-primed (blue) cells at de novo TH1-specific enhancers for Ifng gene. i, Plots of de novo TH2 enhancers ranked on the basis of differences in nucleosome occupancy between pooled primed cells and the non-primed cells (y axis, see Supplementary Methods). Enhancers associated with key genes for TH2 were labelled by genes along with ranks. j, Nucleosome positions in pooled or single primed (red) and non-primed (blue) cells at de novo TH2-specific enhancers for Il4 gene. k, Motifs enriched in the top 1,000 TH1/TH2-primed enhancers are shown. l, m, Gene Ontology analysis for top 1000 TH1-primed (l) and TH2-primed (m) enhancers. Significant Gene Ontology terms with P values are reported using GREAT v.3.0.0.
Extended Data Fig. 10 Characterization of primed enhancers in undifferentiated mouse ESCs.
a, Heat maps show H3K27ac in mouse ESCs and EB cells and p300 in EB cells around ESC-specific and EB-specific enhancers. b, Profile of nucleosome occupancy from pooled mouse ESC scMNase-seq around mouse ESC-specific and EB-specific enhancers. c, Normalized nucleosome occupancy within ± 200 bp of the centre of de novo EB-specific enhancers for subgroups of mouse ESCs that are primed for EB (red) or not primed for EB (black). d, Plots of fragment-size ratio of subnucleosome-sized particles-to-nucleosomes versus nucleosome occupancy score at de novo EB-specific enhancers for 144 mouse ESCs. Pearson correlation coefficient and P value are indicated. P value is the probability that one would have found the current result if the correlation coefficient were zero (null hypothesis), and was calculated using R. e, Plots of de novo EB-specific enhancers ranked on the basis of difference in nucleosome occupancy between pooled primed cells and the non-primed cells. Enhancers associated with key genes for EB cells were labelled by genes along with ranks. f, Nucleosome positions in pooled or single primed (red) and non-primed (blue) cells at de novo EB-specific enhancers for Brachyury gene. g, Gene Ontology analysis for top-1,000 EB-primed enhancers. Significant Gene Ontology (GO) terms with P values are reported using GREAT v.3.0.0.
Supplementary information
Supplementary Methods
This file contains detailed methods for reproducing this study
Supplementary Table 1
This files contains the scMNase-seq data sets summary in the study.
Supplementary Table 2
The file contains the data sets used in this study.
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Lai, B., Gao, W., Cui, K. et al. Principles of nucleosome organization revealed by single-cell micrococcal nuclease sequencing. Nature 562, 281–285 (2018). https://doi.org/10.1038/s41586-018-0567-3
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DOI: https://doi.org/10.1038/s41586-018-0567-3
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