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Joint profiling of histone modifications and transcriptome in single cells from mouse brain

Nature Methods volume 18, pages 283–292 (2021)Cite this article

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Abstract

Genome-wide profiling of histone modifications can reveal not only the location and activity state of regulatory elements, but also the regulatory mechanisms involved in cell-type-specific gene expression during development and disease pathology. Conventional assays to profile histone modifications in bulk tissues lack single-cell resolution. Here we describe an ultra-high-throughput method, Paired-Tag, for joint profiling of histone modifications and transcriptome in single cells to produce cell-type-resolved maps of chromatin state and transcriptome in complex tissues. We used this method to profile five histone modifications jointly with transcriptome in the adult mouse frontal cortex and hippocampus. Integrative analysis of the resulting maps identified distinct groups of genes subject to divergent epigenetic regulatory mechanisms. Our single-cell multiomics approach enables comprehensive analysis of chromatin state and gene regulation in complex tissues and characterization of gene regulatory programs in the constituent cell types.

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Fig. 1: Overview of Paired-Tag.
Fig. 2: Histone modification-based cell clustering recapitulates transcriptomic-based cell clustering with varying degrees of success.
Fig. 3: Integrative analysis of chromatin states at promoters and gene expression across mouse brain cell types.
Fig. 4: Characterization of chromatin state at distal candidate cis-regulatory elements across brain cell types.
Fig. 5: Correlative analysis of chromatin state and gene expression links distal candidate cis-regulatory elements to putative target genes.

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Data availability

The sequencing data obtained in this study have been deposited at the NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE152020. The processed data can be accessed from the web portal (http://catlas.org/pairedTag). All other data are available upon request. CEMBA datasets are available from NEMO with the accession number RRID SCR_016152 (http://data.nemoarchive.org/biccn/grant/cemba/ecker/chromatin/scell/raw/). ENCODE (https://www.encodeproject.org/) datasets were downloaded with the accession numbers: H3K4me1 (ENCSR000APW), H3K27ac (ENCSR000AOC), H3K27me3 (ENCSR000DTY), H3K9me3 (ENCSR000AQO) and DNase-seq (ENCSR959ZXU). The other external datasets were downloaded from NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/), with the accession numbers: SPLiT-seq (GSE110823), CoBATCH (GSE129335), itChIP (GSE109762) and HT-scChIP-seq (GSE117309). 10x scRNA-seq datasets were downloaded from the 10x Genomics website (https://www.10xgenomics.com/). Source data are provided with this paper.

Code availability

Custom scripts used in this study are available from https://github.com/cxzhu/Paired-Tag.

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Acknowledgements

We thank B. Li for bioinformatic support, and S. Kuan for assistance with DNA sequencing. We thank QB3 MacroLab for the protein A-Tn5 enzyme. We thank S. Preissl, X. Hou, H. Huang, M. Yu and J. Song for discussion. This study was funded by grant nos. 1U19 MH114831-02, U01MH121282 and R01AG066018 and the Ludwig Institute for Cancer Research (to B.R.); grant no. 1K99HG011483-01 (to C.Z.); grant no. 1K99CA252020-01 (to Y.Z.); and grant no. R01MH112763 (to M.M.B.). This publication includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health SIG grant (no. S10 OD026929).

Author information

Authors and Affiliations

  1. Ludwig Institute for Cancer Research, La Jolla, CA, USA

    Chenxu Zhu, Yanxiao Zhang & Bing Ren

  2. Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA

    Yang Eric Li & Bing Ren

  3. Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA

    Jacinta Lucero & M. Margarita Behrens

  4. Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA

    Bing Ren

  5. Institute of Genomic Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA

    Bing Ren

  6. Moores Cancer Center, University of California San Diego School of Medicine, La Jolla, CA, USA

    Bing Ren

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  1. Chenxu Zhu
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Contributions

B.R. and C.Z. conceived and designed the study and wrote the manuscript. C.Z. performed the Paired-Tag experiments. C.Z., Y.Z. and Y.E.L. performed the data analysis. Y.E.L. set up the web portal. J.L. and M.M.B. harvested and dissected the mouse brain tissues. All authors discussed results and edited the manuscript. B.R. supervised the research.

Corresponding author

Correspondence to Bing Ren.

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Competing interests

B.R. is a co-founder and consultant for Arima Genomics, Inc., and a co-founder of Epigenome Technologies, Inc. B.R. and C.Z. are listed as inventors of a provisional patent titled ‘Parallel analysis of individual cells for RNA expression and DNA from targeted tagmentation by sequencing’.

Additional information

Peer review information Nature Methods thanks Andrew Adey, Steven Henikoff and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Overview of Paired-Tag.

a, Schematics for 2nd adaptor tagging of DNA and RNA libraries. For DNA libraries, amplified products were digested with a type IIS restriction enzyme – FokI, and the cohesive end was then used to ligate the P5 adaptor. For RNA libraries, N5 adaptor was added by tagmentation. b, Table showing the numbers of overlapped peaks across different histone marks between Paired-Tag and ENCODE ChIP-seq or DNase-seq datasets. The total numbers of peaks identified for each dataset are also indicated. c, d, Heatmaps showing the Pearson’s Correlation Coefficients of genome-wide reads distribution (in 10-kb bins) (c) between Paired-Tag datasets and ENCODE ChIP-seq or DNase-seq datasets, and (d) between replicates of Paired-Tag datasets and ENCODE ChIP-seq datasets from HeLa cells. e, Scatter plot showing the Pearson’s correlation coefficients of Paired-Tag RNA dataset and in-house generated nuclei RNA-seq from HeLa cells.

Extended Data Fig. 2 Performances of Paired-Tag in single-nucleus analysis from mouse brain.

a, Schematics showing the sample multiplexing strategy in this study. Different samples or replicates were labeled by the 1st round of Paired-Tag cellular barcode (Sample Barcode) located in reverse transcription primers and transposome oligos. b, Heatmap showing the pair-wise Pearson’s correlation coefficients of genome-wide reads distribution for different histone marks from single-cell Paired-Tag datasets (indicated with ‘sc’, aggregated from all cells shown in Fig. 2a) and bulk datasets. c, Boxplots showing the mapping rates (upper panels) and the fraction of reads uniquely mapped to the reference genome (bottom panels) of DNA profiles of different histone marks and RNA profiles in frontal cortex and hippocampus. The boxes were drawn from lower quartile (Q1) to upper quartile (Q3) with the middle line denote the median, whiskers with maximum 1.5 IQR, outliers were indicated with dots. For Frontal Cortex, n = 7,781 (H3K4me1), 3,509 (H3K4me3), 7,584 (H3K27ac), 3,891 (H3K27me3), 6,560 (H3K9me3), 6,551 (ChromAcc) from dissections of 2 different mice s and n = 35,876 (RNA) from dissections of 4 different mice; for Hippocampus, n = 5,181 (H3K4me1), 3,956 (H3K4me3), 4,165 (H3K27ac), 2,643 (H3K27me3), 5,484 (H3K9me3), 7,544 (ChromAcc) from dissections of 2 different mice and n = 28,973 (RNA) from dissections of 4 different mice. d, Scatter plots showing the proportion of human and mouse RNA reads in each cell (left panel) and the fraction of human reads in DNA and RNA libraries for each cell (right panel) in the species-mixing experiment. Barcodes with less than 80% reads from the same species were identified as mixed cells, the 230 mixed cells of RNA profiles (left panel) were excluded from plotting of the right panel. e, Numbers of unique loci per nucleus for deeply sequenced H3K4me1, H3K4me3, H3K27ac, H3K27me3 and H3K9me3 DNA profiles down-sampled to different levels. f, Numbers of unique loci per nucleus for deeply sequenced Paired-seq DNA profiles down-sampled to different levels. g, Numbers of UMI per nucleus for the deeply sequenced RNA sub-library down-sampled to different levels. For comparison, the numbers of unique loci per cell from the stand-alone high-throughput scChIP-seq assays and the numbers of UMI per cell from scRNA-seq assays were also shown, indicated by dots with labels. h, Violin plots showing the numbers of unique loci mapped per nucleus for all sequenced DNA libraries (average 35k sequenced reads/nuclei with ~40–60% PCR duplication rates). Median numbers, H3K4me1: 5,770 and 5,443, H3K4me3: 1,392 and 1,081, H3K27ac: 1,842 and 1,803, H3K27me3: 904 and 925, H3K9me3: 6,563 and 7,182, chromatin accessibility: 3,170 and 4,381, for frontal cortex and hippocampus, respectively. i, Violin plots showing the fraction of reads inside peaks for different histone marks and brain regions. For Frontal Cortex, n = 7,781 (H3K4me1), 3,509 (H3K4me3), 7,584 (H3K27ac), 3,891 (H3K27me3), 6,560 (H3K9me3), 6,551 (ChromAcc) from dissections of 2 different mice; for Hippocampus, n = 5,181 (H3K4me1), 3,956 (H3K4me3), 4,165 (H3K27ac), 2,643 (H3K27me3), 5,484 (H3K9me3), 7,544 (ChromAcc) from dissections of 2 different mice. j, Violin plots showing the numbers of UMI and genes detected per nucleus for all sequenced RNA libraries (average 30k sequenced reads/nuclei with ~40–60% PCR duplication rates). Median numbers, 4,215 and 3,568 RNA UMI per nucleus for frontal and hippocampus, respectively. k,l, Violin plots showing the (k) fraction of reads mapped to annotated gene regions (GENCODE GRCm38.p6) and (l) fraction of intronic reads for Paired-Tag RNA datasets and 10X scRNA-seq datasets (10k Brain Cells from an E18 Mouse, V3). n = 35,876 (Frontal Cortex) and 28,973 (Hippocampus) from dissections of 4 different mice. For (h-l), the violin plots were drawn from lower quartile (Q1) to upper quartile (Q3) with the middle line denote the median, whiskers with maximum 1.5 IQR, outliers were indicated with dots.

Source data

Extended Data Fig. 3 Annotation of cell types by Paired-Tag transcriptomic profiles.

a, UMAP embedding of Paired-Tag transcriptomic profiles and stacked bar plots showing the fraction of cells from different regions or replicates (dissections from different mice) in each cell type. b, UMAP embedding of transcriptomic profiles from individual Paired-Tag and Paired-seq datasets. The color of cell types was the same as in Fig.1d. c, Dot plots showing the expression of marker genes for each mouse brain cell type measured from Paired-Tag RNA profiles. The size of the dots represents the fraction of cells positively detect the transcripts and the color of the dots represents the average. d, UMAPs showing the co-embedding of single-nucleus gene expression from Paired-Tag RNA profiles and the previously published scRNA-seq datasets of the same tissues. e, Heatmaps showing the confusion matrices of the overlap between cell type annotations based on Paired-Tag RNA profiles and from the previously published scRNA-seq datasets. The circles left side indicating RNA clusters and were colored according to Fig.1d. f, Boxplots showing the Pearson’s correlation coefficients for all genes, variable genes and invariable genes for matched and non-matched cell types between Paired-Tag RNA profiles and the previously published scRNA-seq. The boxes were drawn from lower quartile (Q1) to upper quartile (Q3) with the middle line denote the median, whiskers with maximum 1.5 IQR, outliers were indicated with dots. n = 22 cell types. g, Scatter plot showing the expression levels of variable genes in Astrocytes measured by Paired-Tag RNA profiles and the published scRNA-seq datasets.

Source data

Extended Data Fig. 4 Histone marks-based single-cell clustering.

a-e, UMAP embeddings based on (a) H3K4me1, (b) H3K4me3, (c) H3K27ac, (d) H3K27me3 and (e) H3K9me3 DNA profiles and stacked bar plots showing the fraction of cells from each region or replicate. f, UMAP embeddings based on Paired-Tag H3K27ac DNA profiles down-sampled to different sequencing depth (11,749 nuclei, 100–1,500 loci/nuclei). g, UMAP embeddings based on Paired-Tag H3K27ac DNA profiles of different numbers of sub-sampled nuclei (median 1,826 loci/nuclei, 200–10,000 nuclei). For visualization, cells were colored according to clustering results from Fig. 1d.

Source data

Extended Data Fig. 5 Gene expression and promoter epigenetic states.

a, Violin plots showing reads densities of the five histone marks in Group II-a and III-b promoters. Colors represent cell types the same as in (e). Group II-a promoters were repressed by H3K27me3 in all cell types; genes in III-b were activated by H3K27ac in neuron cells, with comparable H3K27me3 levels in all cell groups. b, Boxplots showing the expression levels of genes grouped by their promoter DNA reads densities for different histone marks. The boxes were drawn from lower quartile (Q1) to upper quartile (Q3) with the middle line denote the median, whiskers with maximum 1.5 IQR, outliers were indicated with dots. n = 2,900 genes for the first 5 groups and n = 2,898 for the 6th group of each histone modification. c, Heatmap showing the Spearman’s correlation coefficients of gene expression and promoter histone modification levels within each cell type. d, Genome browser view of aggregated Paired-Tag profiles showing the three Olfr gene clusters in chr7 was silenced by H3K9me3. e, 3D-scatter plot showing the PCA embedding of aggregated RNA profiles. PC1 differs neuron cells from glial cells and PC2 mainly separates different non-neuron cell types. f, Scatter plot showing the loadings of the first 2 PCs for each gene. Genes from group II-b and III-d were colored in brown and blue, respectively. g, UMAP embedding of 4,659 OPC and Oligodendrocytes nuclei used for pseudotime analysis. h, Expression of marker genes alone the pseudotime.

Source data

Extended Data Fig. 6 Histone modification states in mouse neuron cell types.

a, Stacked bar plots showing the fraction of genomic regions for CREs of each group in Fig. 4a and b. b, Line plots showing the densities of CREs from different groups around CpG islands. c, Scatter plot showing the Spearman’s correlation coefficients of TF motif enrichment and TF gene expression across cell types. TFs with significant positive correlations (FDR < 0.05) between expression and motif enrichment for both H3K4me1 and H3K27ac were colored in red. d, Heatmap showing the emission probability of each histone mark across the 8 chromatin states identified by chromHMM. e, Heatmap showing the fold enrichment of the 8 chromatin states around transcription start sites of FC L2/3 cell cluster.

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Extended Data Fig. 7 Identification of putative CRE-gene pairs.

a, Bar charts showing the fraction of predicted H3K27ac- and H3K27me3- associated cCRE-gene pairs supported by the CEMBA datasets. P-value, two-tailed Fisher’s exact test. b–e, Bar charts showing the numbers of cCREs per targeted genes for (b) H3K27ac- and (c) H3K27me3- associated cCRE-gene pairs, and the numbers of predicted targeted genes per cCRE for (d) H3K27ac- and (e) H3K27me3- associated cCRE-gene pairs. f, Representative genome browser view of Gad2 gene locus, both H3K27ac- and H3K27me3-associated cCREs were shown. TSS-proximal region is indicated with green box and cCREs are marked with blue (H3K27ac-specific), brown (H3K27me3-specific) or purple (shared) boxes. g, Top enriched de novo TF motifs and GO terms of cCREs in H3K27ac-specific, shared and H3K27me3-specific pairs. h, Stacked bar plots showing the fraction of genomic regions for cCREs with potential active and repressive functions. P-value, two-tailed Fisher’s exact test. i,j, Bar charts showing the distribution of the distance between cCRE and TSS of predicted target genes from (i) H3K27ac- and (j) H3K27me3- associated cCRE-gene pairs. k, Spearman’s correlations coefficients between reads densities of cCREs and promoters of putative target genes across cell types for H3K4me1 and H3K9me3. P-value, two-tailed Wilcoxon signed-rank test. The boxes were drawn from lower quartile (Q1) to upper quartile (Q3) with the middle line denote the median, whiskers with maximum 1.5 IQR. n = 22 cell types. l, Heatmap showing the histone modification levels at cCREs with potential repressive roles in expression of putative target gene. cCRE were grouped using K-means clustering based on histone modification levels. m, Heatmap showing the expression levels of corresponding putative target genes of cCREs in (l). n, Top enriched Gene Ontology terms for genes in (m) and the top enriched de novo motifs for cCREs from each group in (l).

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Supplementary information

Supplementary Information

Supplementary Notes.

Reporting Summary

Supplementary Table 1

Paired-Tag primer sequences. This table shows the primer and barcode sequences used in Paired-Tag experiments.

Supplementary Table 2

Nuclei metadata. This table shows the sequencing quality, mapping status, clustering and annotation information of single nuclei in this study.

Supplementary Table 3

Marker genes by clusters. This table shows the differentially expressed genes between the clusters obtained from Paired-Tag RNA profiles. P value: two-sided Wilcoxon rank sum test and adjusted by Bonferroni correction using all features in the dataset.

Supplementary Table 4

Promoters by groups. This table lists the genes of different groups classified by epigenetic states of the corresponding promoters described in Fig. 3.

Supplementary Table 5

Gene Ontology analysis of genes from different groups. This table summarized the Gene Ontology analysis results for genes from each group in Fig. 3. P value: one-sided binomial test.

Supplementary Table 6

cis-Regulatory elements by groups. This table lists the CREs of different groups classified by their epigenetic states described in Fig. 4.

Supplementary Table 7

Known Motifs Enrichment analysis of CREs from different groups. This table summarizes the enrichment of known motifs for CREs from each group in Fig. 4. P value: one-sided binomial test.

Supplementary Table 8

TF motif enrichment—gene expression correlation. This table lists Spearman’s correlation coefficients between motif enrichment (chromVAR deviations) and gene expression (RPKM) across clusters for 342 transcription factors.

Supplementary Table 9

Predicted cCRE–gene pairs. This table lists the predicted H3K27ac- and H3K27me3-associated cCRE–gene pairs in Fig. 5.

Supplementary Table 10

Predicted target genes by groups. This table lists the predicted target genes from both H3K27ac- and H3K27me3-associated pairs for each group in Fig. 5.

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Zhu, C., Zhang, Y., Li, Y.E. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat Methods 18, 283–292 (2021). https://doi.org/10.1038/s41592-021-01060-3

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