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Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq

Nature Biotechnology (2024)Cite this article

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Abstract

Dynamic 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications to DNA regulate gene expression in a cell-type-specific manner and are associated with various biological processes, but the two modalities have not yet been measured simultaneously from the same genome at the single-cell level. Here we present SIMPLE-seq, a scalable, base resolution method for joint analysis of 5mC and 5hmC from thousands of single cells. Based on orthogonal labeling and recording of ‘C-to-T’ mutational signals from 5mC and 5hmC sites, SIMPLE-seq detects these two modifications from the same molecules in single cells and enables unbiased DNA methylation dynamics analysis of heterogeneous biological samples. We applied this method to mouse embryonic stem cells, human peripheral blood mononuclear cells and mouse brain to give joint epigenome maps at single-cell and single-molecule resolution. Integrated analysis of these two cytosine modifications reveals distinct epigenetic patterns associated with divergent regulatory programs in different cell types as well as cell states.

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Fig. 1: Base resolution, joint analysis of 5mC and 5hmC in single cells.
Fig. 2: Analysis of 5mC and 5hmC from the same molecules revealed multiple 5hmC types associated with active chromatin.
Fig. 3: Cell-type-specific 5mC and 5hmC landscapes in human PBMCs.
Fig. 4: Single-cell joint analysis of 5mC and 5hmC from the mouse brain.

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Data availability

Raw sequencing and processed data generated in this study are available from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSE197740 (ref. 113). Other external datasets were downloaded from the GEO with the following accession numbers: WGBS and TAPS of mESC55 (GSE112520), RNA sequencing of mESC (2i and serum)66 (GSE23943), TAB-seq of mESC46 (GSE36173), Joint-snhmC-seq of mouse brain82 (GSE236798), Paired-Tag of mouse brain83 (GSE152020); ArrayExpress with the following accession numbers: scRNA-seq of mESC114 (E-MTAB-2600); 10x Genomics website: scRNA-seq of PBMCs (https://www.10xgenomics.com); ENCODE with the following accession numbers: DNase-seq ChIP-seq of E14 mESC (ENCSR000CMW), H3K4me1 ChIP-seq of E14 mESC (ENCSR000CGN), H3K27ac ChIP-seq of E14 mESC (ENCSR000CGQ), H3K4me1 ChIP-seq of immune cells (ENCSR777RWW (CD4+ T cell), ENCSR631BPS (CD8+ T cells), ENCSR214VUB (B cells), ENCSR963TKB (NK cells) and ENCSR400VWA (monocytes)), H3K4me3 ChIP-seq of immune cells (ENCSR263WLD (CD4+ T cells), ENCSR231FDF (CD8+ T cells), ENCSR269OVV (B cells), ENCSR570AUC (NK cells) and ENCSR796FCS (monocytes)), H3K27ac ChIP-seq of immune cells (ENCSR546SDM (CD4+ T cells), ENCSR835OJV (CD8+ T cells), ENCSR191ZQT (B cells), ENCSR391EQV (NK cells) and ENCSR012PII (monocytes)), H3K27me3 ChIP-seq of immune cells (ENCSR043SBG (CD4+ T cells), ENCSR797GOJ (CD8+ T cells), ENCSR522EGW (B cells), ENCSR939JZW (NK cells) and ENCSR080XUB (monocytes)), H3K9me3 ChIP-seq of immune cells (ENCSR453GNY (CD4+ T cells), ENCSR905SHH (CD8+ T cells), ENCSR295PSK (B cells), ENCSR021FSY (NK cells) and ENCSR236JVK (monocytes)), H3K36me3 ChIP-seq of immune cells (ENCSR828WZG (CD4+ T cells), ENCSR694CDP (CD8+ T cells), ENCSR789RGI (B cells), ENCSR519SOC (NK cells) and ENCSR244XWL (monocytes)) and ChromHMM states of NK cells (ENCSR972ZND).

Code availability

Custom scripts used for analyzing SIMPLE-seq datasets are available from GitHub (https://github.com/cxzhu/SIMPLE-seq)115.

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Acknowledgements

We thank Y. Zhuang, J. Song, H. Zeng, C.-G. Ji, H.-W. Meng and Z.-R. Xu for technical assistance; P. Du and F.-C. Tang (Peking University) for providing mESCs; G.-L. Xu (Chinese Academy of Sciences) for providing mTET1CD plasmid; J.-Y. Xiao (Peking University) for protein purification assistance; and Z. Xi (Nankai University) for discussion. We thank the National Center for Protein Sciences at Peking University for technical help. We carried out data analysis on the High-Performance Computing Platform at the School of Life Sciences, Peking University. This study is supported by the Ministry of Science and Technology of China (no. 2023YFC3402200, no. 2019YFA0110900 and no. 2019YFA0802201 to C.Y.), the Beijing Natural Science Foundation (no. Z220013 to C.Y.) and the National Natural Science Foundation of China (no. 91953201 and no. 21825701 to C.Y.).

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Authors and Affiliations

  1. State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China

    Dongsheng Bai, Xiaoting Zhang & Chengqi Yi

  2. Department of Obstetrics and Gynecology, First Affiliated Hospital of Anhui Medical University, Anhui, China

    Huifen Xiang

  3. NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Anhui, China

    Huifen Xiang

  4. State Key Laboratory of Coordination Chemistry, Coordination Chemistry Institute, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China

    Zijian Guo

  5. New York Genome Center, New York, NY, USA

    Chenxu Zhu

  6. Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA

    Chenxu Zhu

  7. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Chengqi Yi

  8. Department of Chemical Biology and Synthetic and Functional Biomolecules Center, College of Chemistry and Molecular Engineering, Peking University, Beijing, China

    Chengqi Yi

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  1. Dongsheng Bai
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Contributions

D.B., C.Z. and C.Y. conceived and designed the study and wrote the paper. D.B. developed and optimized the SIMPLE-seq protocol and generated the data. C.Z. performed pilot labeling experiments, with help from D.B., and data analysis, with help from D.B. and X.Z. All authors discussed the results and edited the paper. C.Y. supervised the study.

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Extended data

Extended Data Fig. 1 Sequential chemical labeling enables simultaneous detection of 5mC and 5hmC bases on the same molecules.

a, Overview of TAPS and hmC-CATCH. b, Sanger sequencing results showing the ‘C-to-T’ conversion signal from model oligonucleotide sequence (T5MH) before treatment, after 5hmC labeling, and after both 5hmC and 5mC labeling. c, Schematics of chemical labeling for 5hmC and 5mC. d, qPCR result of lambda DNA (25,086bp-25,308bp) before and after potassium ruthenate (K2RuO4) treatment; n = 3 (Treated), n = 3 (Control). Data are presented as 6.020 ± 0.006 (Treated) and 5.957 ± 0.026 (Control). e, Agarose gel images of dsDNA of fragmented lambda DNA treated with 5hmC-labelling reaction only (left panel) and sequential 5hmC and 5mC-labelling (right panel). Experiment was performed once. f, Barplot showing the distribution of C-to-T mutation rates for unmethylated CH sites and methylated CG sites. g, C-to-T mutation signals on both strands of T1M spike-in model DNA, symmetric methylated CG sites are indicated in red on the sequences below. h, Genome browser view showing the sequenced reads aligned to spike-in model DNA (upper: T1M with 5mCG, positive and negative strands are separately displayed, bottom left: T2H with a single 5hmCG site, bottom right: T3MH with both 5mC site and 5hmCG site at known position). C-to-T is colored in red for positive strands, and G-to-A is colored in green for negative strands, respectively. Conversion rates are estimated from all the modified cytosines of spike-in model DNA. i, Nuclei clumps and resolved single nuclei suspension after brief sonication under bright field microscope. Experiment was performed once. Scale bars, 100 μm. j, Enrichment analysis of genome coverage by SIMPLE-seq and DNase-seq on DHS (DNase I hypersensitive sites). k, A table showing tagmentation efficiency under different Tn5 reaction conditions, including Tn5 with hyperactive mutations, working concentrations and reaction buffers; right-sided showing the fragments analysis result under optimal condition. l-n, Standard curves for mixed oligo DNA with (l) 5mC and C, (m) 5hmC and C, (n) 5mC and 5hmC, were plotted based on gradient mixing ratios (0:10; 2:8; 4:6; 6:4; 8:2; 10:0). o, C-to-T mutation rate estimated from 10-kb non-overlap bins across the whole genome. For both boxplots, hinges were drawn from the 25th to 75th percentiles, with the middle line denoting the median, whiskers with maximum 2× interquartile range (IQR). For 5mC, minima = 1.12%, maxima = 1.47%; for 5hmC, minima = 0.75%, maxima = 2.43%. 5mC, n = 156,755; 5hmC, n = 159,962. p and q, Scatter plot showing (p) the number of 5hmC reads mapped to human and mouse genome and (q) the fraction of 5mC and 5hmC reads mapped to human and mouse genome in each cell from the species-mixing experiment with twin axes. r, Stacked barplot showing the fraction of reads mapped to the reference genome and assigned to 5mC, 5hmC, or cannot be assigned. s-v, Comparisons between SIMPLE-seq and published single-cell and bulk 5mC and 5hmC sequencing methods: (s) fraction of mappable reads, (t) the number of 5mCG sites detected and total sequenced reads in each study, and (u) the number of 5hmCG sites detected and total sequenced reads in each study, (v) Dot plot showing the average number of covered CGs and sequenced reads for each cell in each study. w, Line plots showing the number of unique mapped reads or CG dinucleotides with at different sequenced read depths per cell in each study.

Extended Data Fig. 2 Joint analysis of 5mC and 5hmC from single cells.

a, Barplot showing the enrichment of 5mCG and 5hmCG sites detected by SIMPLE-seq, WGBS and TAB-seq over different genomic regions. b, Boxplots showing the 5mC or 5hmC modification levels on different genomic regions from bisulfite sequencing, TAB-seq and SIMPLE-seq. For all boxplots, hinges were drawn from the 25th to 75th percentiles, with the middle line denoting the median, whiskers with maximum 2× IQR. The minima/maxima/numbers of elements of all boxplots: 60.76%/74.60%/157,324 (Alu,(1)), 58.52%/68.61%/137,869 (Alu,(2)), 3.71%/10.71%/37,810 (Alu,(3)), 0.00%/10.91%/8,332 (Alu,(4)), 14.71%/67.84%/5,828 (CGI,(1)), 12.36%/54.72%/3,662 (CGI,(2)), 1.96%/5.87%/1,059 (CGI,(3)), 1.45%/5.24%/304 (CGI,(4)), 6.02%/72.62%/45,014 (Intron,(1)), 8.48%/79.35%/30,294 (Intron,(2)), 4.19%/5.01%/7,333 (Intron,(3)), 3.41%/4.85%/1,669 (Intron,(4)), 70.19%/75.69%/124,777 (L1,(1)), 64.68%/94.66%/103,247 (L1,(2)), 6.89%/10.32%/22,350 (L1,(3)), 4.90%/8.53%/5,414 (L1,(4)), 67.40%/75.88%/26,295 (L2,(1)), 68.26%/87.71%/18,819 (L2,(2)), 1.86%/7.30%/4,342 (L2,(3)), 2.20%/6.31%/875 (L2,(4)), 64.16%/73.22%/909 (LCP,(1)), 57.34%/79.78%/739 (LCP,(2)), 2.88%/10.32%/171 (LCP,(3)), 2.56%/7.71%/62 (LCP,(4)), 70.15%/75.10%/194,933 (LINE,(1)), 65.93%/81.40%/177,226 (LINE,(2)),3.79%/7.49%/34,376 (LINE,(3)), 3.99%/5.61%/9,412 (LINE,(4)), 68.59%/75.74%/189,543 (LTR,(1)), 60.01%/72.15%/170,132 (LTR,(2)), 2.85%/7.75%/32,147 (LTR,(3)), 3.24%/5.59%/10,073 (LTR,(4)), 68.60%/74.58%/47,771 (MIR,(1)), 66.88%/85.86%/30,355 (MIR,(2)), 2.88%/9.75%/7,707 (MIR,(3)), 0.00%/7.62%/1,520 (MIR,(4)), 62.68%/74.58%/325,867 (SINE,(1)), 58.25%/73.48%/302,881 (SINE,(2)), 4.93%/9.37%/56,712 (SINE,(3)), 0.00%/9.52%/19,715 (SINE,(4)), 62.82%/79.57%/4,651 (H3K9me3,(1)), 48.15%/94.31%/3,514 (H3K9me3,(2)), 3.79%/12.09%/862 (H3K9me3,(3)), 0.00%/11.97%/226 (H3K9me3,(4)), 14.28%/69.99%/21,711 (DNase,(1)), 12.70%/70.84%/15,340 (DNase,(2)), 0.00%/10.11%/3,961 (DNase,(3)), 0.00%/6.10%/973 (DNase,(4)). c, Venn plot showing the 5mCG sites overlap between SIMPLE-seq and TAPS. P-value, two-sided Fisher’s exact test. d, Venn plot showing the 5hmCG sites overlap between SIMPLE-seq and TAB-seq. P-value, two-sided Fisher’s exact test. e, Stacked barplot showing the fraction of called 5hmC sites overlapped with 5mC (grey, 5hmC-shared) and 5hmC-sites did not overlapped with a called 5mC sites (blue, 5hmC-only). f, Boxplot showing the 5hmC modification levels of 5mC-5hmC shared sites and the 5hmC-only sites. For both boxplots, hinges were drawn from the 25th to 75th percentiles, with the middle line denoting the median, whiskers with maximum 2× IQR. For 5mC-5hmC shared sites, minima = 0.01, maxima = 1.00, sites number n = 622,323, and for 5hmC-only sites, minima = 0.01, maxima = 1.00, sites number n = 435,143. P-value, two-sided Fisher’s exact test. g, Barplot showing the enrichment of 5mC-5hmC shared sites and the 5hmC-only sites over different genomic regions. h, Barplot showing the relative enrichment of 5hmC-only sites over 5mC-5hmC shared sites on different genomic regions. i-k, UMAP embedding showing cells based on their (i) 5mCG, (j) 5mCHG and (k) 5hmCHG levels (in 100-kb non-overlapping bins). Each dot represents a single cell and is colored according to its original identity. l, Assignment of 2i mES cells and serum mES cells into two distinct clusters grouped by unsupervised clustering. m, Silhouette plot to evaluate the degree of separation of the clusters based on 5mC or 5hmC. n, Line plots showing the cumulated coverages of 10-kb non-overlapping bins with different depths for 5mC (green) and 5hmC (blue) from different numbers of single cells. The shadowed area showing the error ranges from 5 randomly sampled cell sets. o, Smoothed line plots showing the 5hmCG levels around genic regions of genes with different expression levels (using the smooth.spline function with parameter df = 30). p-q, Line plots showing the relationships between promoter 5mCG and 5hmCG modification levels with gene expression levels in (p) 2i mES cells, (q) serum mES cells.

Extended Data Fig. 3 Analysis of 5mC and 5hmC from the same molecules revealed multiple 5hmC types.

a, Heat maps showing the 5mCHG and 5hmCHG TF motif enrichments along with TF expression level during mESC 2i state to serum state transition. b, Dotplots showing promoter methylation levels for genes associated with cell proliferation during mESC 2i to serum state transition. c, Cytosine modification entropies in single cells. Each dot represents a single cell and is colored and ordered according to its pseudotime score. Grey dots are background entropy levels estimated by shuffled the cell barcodes of called modification sites. d, Violin plots showing the fraction of 5mC and 5hmC reads with paired modality in single cells. For both Violin plots, hinges were drawn from the 25th to 75th percentiles, with the middle line denoting the median, whiskers with maximum 2× IQR. For 5mC modality, minima = 1.94%, maxima = 9.12%; for 5hmC modality, minima = 6.93%, maxima = 20.75%; n = 300 randomly sampled cells. e, Barplot showing the distributions of 5hmCG sites with different fractions of cells with detected 5mCG-associated 5hmCG sites. False positive detection rates (FDR) based on the fraction of averaged false detected (Type 2) sites from shuffled groups in total detected sites (FDR = 0.0467 was selected). f, UMAP embedding showing 5hmC levels of different types in single cells. Each dot represents a single cell and is colored according to the average 5hmC level. g, Top enriched GREAT GO terms for different types of 5hmCG sites. P-value, one-sided Fisher’s exact test. h, Histograms and heatmaps showing the relationship between two types of 5hmCG with mESC DNase-seq signals from ENCODE (ENCSR000CMW). i, Fraction of Type 1 and Type 2 5hmCGs in detected from low-entropy (0–75%) or high-entropy (75%-100%) cells. j, Barplot showing the enrichment of 5hmCG sites detected in low-entropy or high-entropy cells over different genomic regions.

Extended Data Fig. 4 SIMPLE-seq generates cell-type-specific 5mC and 5hmC profiles from human PBMC.

a, Heatmap showing the gene expression and promoter methylation levels of marker genes for the five major cell types. b and c, UMAP embedding showing the single-cell clustering based on (b) 5mCHG and (c) 5hmCHG levels (in 100-kb non-overlapping bins) from PBMC. Each dot represents a single cell and is colored according to its annotation based on 5mCG.d, Silhouette plot showing the degree of separation of the PBMC clusters based on 5mC, 5hmC or joint 5mCG-5hmCG. e, UMAP embedding showing the single-cell clustering based on joint 5mCG-5hmCG levels (in 100-kb non-overlapping bins) from PBMC. Each dot represents a single cell and is colored according to its annotation based on 5mCG. f and g, UMAP embedding showing the single-cell clustering based on (f) 5mCG levels of H3K4me1 regions (g) 5hmCG levels of H3K4me1 regions from PBMC. Each dot represents a single cell and is colored according to its annotation based on 5mCG. h, Violin plots showing the modification levels of 5mCG, 5hmCG, 5mCHG and 5hmCHG in different cell types. i, The heatmap showing the numbers of pairwise differentially methylated (5mCG) regions across cell types (in 5-kb non-overlapping bins). j, Heatmap showing the methylation levels of 5mCG DMRs of a representative group (CD4+ T cells and Monocytes). k-m, The enrichment analysis of (k) know motifs, (l) top enriched de novo motifs, P-value, one-sided Fisher’s exact test, and (m) top enriched GREAT GO terms, P-value, one-sided Fisher’s exact test.

Extended Data Fig. 5 Comparison of conserved and differential cytosine states among immune cells.

a, Violin plots showing the genomic coverages of the 10 cytosine states. b, Heatmap showing the enrichment of different cytosine state regions around TSS and TES sites. c-e, Scatter plot showing the fraction of genome regions overlapped with peaks of different histone marks in conserved and differential (c) E5 (d) E7 and (e) E9 state regions. P-value, two-sided t-test. f, Top enriched GREAT GO terms for conserved and differential regions of E5, E7 and E9 in representative cell types. P-value, two-sided t-test.

Extended Data Fig. 6 SIMPLE-seq generates cell-type-specific 5mC and 5hmC profiles from mouse brain.

a, Violin plots showing the fraction of reads mapped to reference mouse genome for 5mC and 5hmC in SIMPLE-seq (this study) and Joint-snhmC-seq (GSE236798) datasets. b, Violin plots showing the numbers of unique reads per cell for 5mC and 5hmC in SIMPLE-seq (this study) and Joint-snhmC-seq (GSE236798) datasets. For fair comparisons, Joint-snhmC-seq dataset was down sampled to the same per-cell depth in SIMPLE-seq. Data are presented as 57,563 ± 4,260 (SIMPLE-seq, 5mC), 35,283 ± 552 (joint-snhmC-seq, 5mC), 39,374 ± 4,553 (SIMPLE-seq, 5hmC), 20,538 ± 565 (joint-snhmC-seq, 5hmC). Cell number n = 4,767 (SIMPLE-seq, 5mC and 5hmC), n = 552 (joint-snhmC-seq, 5mC and 5hmC). c and d, UMAP embedding showing the single-cell clustering based on (c) 5mCG and (d) 5hmCG levels (in 100-kb non-overlapping bins) from mouse brain cells. Each dot represents a single cell and is colored according to its annotation based on joint 5mCG-5hmCG clustering. e, Silhouette plot showing the degree of separation of the mouse brain cell clusters based on 5mC, 5hmC or joint 5mCG-5hmCG. f, Dot plots showing the genebody 5hmCG levels of representative marker genes in the detected cell types. g, The distribution of H3K4me1 and H3K27me3 reads densities around the 5mCG sites or 5hmCG sites from EXC1. h, Line plots showing the relationships between gene body 5mCG and 5hmCG levels with gene expression levels in EXC2 and ASC1 cell types i, Heatmap showing the 5mCG modification levels of cell type-specific 5mCG across the 11 cell types. j, Top enriched de novo motifs (left) and top enriched GO terms (right) for each cell type were also shown. P-value, one-sided Fisher’s exact test.

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Bai, D., Zhang, X., Xiang, H. et al. Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02148-9

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