Abstract
Deciphering mechanisms in cell-fate decisions requires single-cell holistic reconstructions of multidimensional epigenomic states in transcriptional regulation. Here we develop CoTECH, a combinatorial barcoding method allowing high-throughput single-cell joint detection of chromatin occupancy and transcriptome. We used CoTECH to examine bivalent histone marks (H3K4me3 and H3K27me3) with transcription from naive to primed mouse embryonic stem cells. We also derived concurrent bivalent marks in pseudosingle cells using transcriptome as an anchor for resolving pseudotemporal bivalency trajectories and disentangling a context-specific interplay between H3K4me3/H3K27me3 and transcription level. Next, we revealed the regulatory basis of endothelial-to-hematopoietic transition in two waves of hematopoietic cells and distinctive enhancer-gene-linking schemes guiding hemogenic endothelial cell emergence, indicating a unique epigenetic control of transcriptional regulation for hematopoietic stem cell priming. CoTECH provides an efficient framework for single-cell coassay of chromatin occupancy and transcription, thus enabling higher-dimensional epigenomic reconstructions.
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Data availability
Sequencing data have been deposited at the NCBI GEO with accession number GSE158435. Other public datasets used in this study were downloaded from NCBI GEO with accession numbers as follows: scCC (GSE148448), sci-CAR (GSE117089), Paired-seq (GSE130399), SNARE-seq (GSE126074), bulk H3K4me3 ChIP-seq in NIH 3T3 (GSM1544520), bulk RNA-seq in NIH 3T3 (GSM970853), bulk H3K4me3 ChIP-seq in mESC (GSM1000124), bulk H3K27me3 ChIP-seq in mESC (GSM1000089), bulk RNA-seq in mESC (GSM723776), bulk H3K27ac ChIP-seq in NIH 3T3 (GSM801538), bulk RNA-seq in 293T (GSM2258993) and scATAC-seq in H1ESC (GSE65360). Source data are provided with this paper.
Code availability
Custom scripts used in this study are available from https://github.com/Helab-bioinformatics/CoTECH.
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Acknowledgements
We thank all members of the He laboratory for critical comments on this manuscript. Part of the analyses was performed on the High-Performance Computing Platform of the Center for Life Sciences, Peking University. We thank the flow cytometry core at the National Center for Protein Sciences at Peking University, particularly L. Du and H. Yang, for technical help. A.H. was supported by the National Key Research and Development Program of China (2019YFA0801802 and 2017YFA0103402), the National Natural Science Foundation of China (32025015 and 31771607), the Peking-Tsinghua Center for Life Sciences and the 1000 Youth Talents Program of China.
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A.H. conceived and designed the study. Y.L. and Q.W. designed and performed all experiments, assisted by X.Y. H.X. performed the computational analyses, supervised by A.H. H.X., Y.L. and A.H. wrote the paper with input from all other authors. All authors participated in data discussion and interpretation.
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Peer review information Nature Methods thanks Andrew Adey 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.
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Extended data
Extended Data Fig. 1 Overview of the DNA- and RNA-devoted library preparation of CoTECH.
a, Molecular structure of the CoTECH DNA-partition library. Following tagmentation and pre-amplification, the DNA-partition library was prepared by the 2-round PCR enrichment, resulting in the molecular constructs ready for sequencing using the standard Illumina Truseq recipe for paired-end 150-bp reads on Hiseq X-ten or Novaseq 6000 platform. b, Molecular structure of the CoTECH RNA-partition library. The mRNA was captured and reverse transcribed as described in a modified Smart-seq2 protocol. The reverse-transcribed cDNA was amplified by IS + 3’P2 primers and tagmented by Tn5-MEA. The resulting library was enriched by Nextera i5 and Truseq i7 primers and sequenced for paired-end 150-bp reads by loading both Nextera and Truseq read primers on the Novaseq 6000 platform.
Extended Data Fig. 2 Data quality of DNA and RNA profiles of CoTECH data.
a, Track view displaying both H3K27ac ChIP and RNA signal at representative locus in NIH 3T3 (left) and 293T (right) cells. Bulk H3K27ac ChIP-seq data in NIH 3T3 and 293T cells were downloaded from GSM801538 and ENCODE, respectively. Bulk RNA data in NIH 3T3 and 293T cells were downloaded from GSM970853 and GSM2258993. b-c, Species-mixing experiment of H3K27ac-RNA CoTECH in NIH 3T3 and 293T cells. Scatter plots showing the unique mapped reads (b) and UMI (c) rate for cells in which DNA and RNA profiles were obtained. d, Cross-cell contamination in the species-mixing dataset (n = 180 and 158 for mouse and human cells. The contamination was accurately quantified and estimated by DecontX. e, Venn diagram showing peak overlapping between CoTECH-H3K4me3 and bulk ChIP-seq data. Bulk data was from GSM1544520. f-g, Heatmap showing CoTECH-H3K4me3 and bulk H3K4me3 ChIP signals at 10-kb peak regions. The rows were sorted by the descending signals in 25,311 bulk peaks (f) and 23,149 CoTECH-H3K4me3 peaks (g). h-i, Violin plot showing the FRiP (h) and sensitivity (i) for single cells of CoTECH data (n = 844), scATAC-seq data (H1ESC, GSE65360) (n = 96) and simulated random genomic regions. The 25,311 bulk ChIP peaks were used to calculate the FRiP and sensitivity. j, Comparison of two CoTECH experiments with respect to mapping rate and exonic rate in NIH 3T3 cells (n = 844). k, Characterization of CoTECH, scDam&T-seq and scCC. l, Violin plot showing the distribution of non-duplicated reads (DNA), gene number (RNA), and UMI (RNA) in different datasets across SNARE-seq (n = 1,047, 10,309, and 5,081 for CellLineMixture, AdBrainCortex and P0_BrainCortex dataset, respectively), Paired-seq (n = 3,359, 25,845 and 15,191 for Cell_Mix, Fetal_Forebrain and Adult_Cerebrail_Cortex dataset, respectively), sci-CAR (n = 6,085 and 13,395 for A549 and mouse_kidney dataset, respectively), and CoTECH (n = 844 and 6,993 for NIH 3T3 and mESC dataset, respectively). Processed data were downloaded from GSE126074, GSE130399, and GSE117089, respectively. The boxes in violin plots of Extended Data Fig. 2d, h-j and l indicate upper and lower quartiles (25th and 75th percentiles).
Extended Data Fig. 3 Optimization of read number with/without pre-amplification of DNA fragments and detection of gene numbers.
a, Distribution of non-duplicated reads (DNA), gene number (RNA), and UMI (RNA) of 96 single cells in K562 between two independent CoTECH libraries. Red line indicates the median. For DNA pre-amplification, primer pairs Connector A/B (Supplementary Table 1) were added to amplify CoTECH DNA fragments for another 5 cycles following 4+7 cycles for full-length cDNA pre-amplification by primers IS and P2. For RNA partition, reads mapped to gene exons were used for calculation here. b, Violin plot showing the distribution of gene number calculated using reads mapping to exons (spliced mRNAs) and reads mapped to both exons and introns (precursor and spliced mRNAs) detected in RNA partition of CoTECH data in K562 cells without (left) or with pre-amplification (right) (n = 96 for each condition). The boxes in violin plots indicate upper and lower quartiles (25th and 75th percentiles).
Extended Data Fig. 4 CoTECH Data quality and relationship between H3K4me3 and RNA in mESCs.
a, Hierarchical clustering of DNA profiles of different groups by using the genome-wide signals in non-overlapping 5-kb windows. b, Pearson correlation of gene expression (n=24,413 genes) between four CoTECH assays. Aggregated transcript reads were log10 normalized. Two-sided correlation coefficients, p value < 2.2e-16. c, Comparison of CoTECH assays with respect to mapping rate and exonic rate (n = 3,907 and 3,086 cells for CoTECH-RNA (H3K4me3) and CoTECH-RNA (H3K27me3), respectively). The boxes in violin plots indicate upper and lower quartiles (25th and 75th percentiles). d, Dimensionality reduction for visualizing 6,993 single cells (both DNA and RNA detected) from different replicates in mESCs using UMAP. e, UMAP showing cellular heterogeneity of individual cells using CoTECH-H3K4me3 profiles colored by different replicates of mESCs and NIH 3T3. f-g, Cell-normalized topic scores of specific topics for NIH 3T3 (f) and mESCs (g) identified by cisTopic using CoTECH-H3K4me3 data. The colors from grey to red indicate the topic scores from low to high. h, GO terms enriched in the NIH 3T3- and mESC-specific topic corresponding to (f) and (g). Top 5 terms of biological process were shown. P value was calculated by two-sided binomial test. Gene Ontology (GO) term enrichment analysis of each module was performed by GREAT. i, Heatmap showing the expression of feature genes in mESCs. Color bars indicate identified clusters (top row) as in Fig. 3b. The columns represent single cells ordered by pseudotime. j, Analytic strategy for examining H3K4me3-RNA concordance in a varying number of clusters of mESCs along pseudotime. k, Fraction of cells with concordance of RNA-H3K4me3 in a varying number of clusters of mESCs along pseudotime. l-m, Pseudotime of 6,993 single cells by monocle 2. Cells are colored by clusters (l) as in Fig. 3b and pseudotime (m). n, Heatmaps showing the normalized H3K4me3 signals (left) and gene expression (right) of genes upon changes in H3K4me3 signal across 3,907 mESCs. The columns represent single cells ordered by pseudotime. o, Scatter plot showing the pseudotime defined by H3K4me3 and RNA profiles for the same cells of mESCs. The scatters were fitted by locally weighted regression to a red curve.
Extended Data Fig. 5 Co-occupancy analysis of H3K4me3 and H3K27me3 in mESCs.
a, Track view displaying H3K4me3 and H3K27me3 signals at three different representative loci in mESC. b, Bivalency score at the promoter regions (TSS ± 3 kb) of genes. H3K4me3 and H3K27me3 signals were normalized using RPKM. 2,706 reported bivalent genes were colored with red. Bulk H3K4me3, H3K27me3 ChIP-seq and bulk RNA-seq data were downloaded from GSM1000124, GSM1000089 and GSM723776, respectively. c-d, Dimensionality reduction for visualizing both 6,993 single cells and 350 pseudosingle cells of mESCs colored by clusters (c) and pseudotime (d) ordered by RNA profiles. e, The pseudotemporal dynamics of H3K4me3, H3K27me3, RNA, and bivalency scores of representative pluripotency genes. f, UMAP plot showing bivalency scores (up) and gene expression (down) in 350 pseudosingle cells of mESCs. Notably, bivalent genes are typically silenced or expressed at a very low level in mESCs.
Extended Data Fig. 6 Multi-omics factor analysis reveals coordinated epigenetic and transcriptomic variation during endothelial-to-hematopoietic transition between YS and AGM.
a, The variance explained by DNA and RNA profiles of CoTECH experiments for all AGM cells. b, Scatter plot of MOFA factor 1 (x axis) and MOFA factor 2 (y axis) for all AGM cells. Cells are colored as in Fig. 6. c, UMAP visualization of expression of marker genes for AGM cells. d, The variance explained by DNA and RNA profiles of CoTECH experiments for all YS cells. e, Scatter plot of MOFA factor 1 (x axis) and MOFA factor 2 (y axis) for all YS cells. Cells are colored as in Fig. 6. f, UMAP visualization of expression of marker genes for YS cells. g, Heatmaps showing RNA (left) and H3K27ac (right) signals at gene-correlated peaks in AGM cells along the pseudotime during EHT. The rows represent 2,219 non-differential genes. h, Line plots showing average RNA and H3K27ac signals of three gene clusters ranked by gene expression levels as in (g). i, Heatmaps showing RNA (left) and H3K27ac (right) signals at gene-correlated peaks in YS cells along the pseudotime during EHT. The rows represent 2,008 non-differential genes. j, Line plots showing average RNA, H3K27ac signals of three gene clusters ranked by gene expression levels as in (i). k-l, Heatmaps showing the TF motif scores calculated using chromVAR with default parameters (g) and gene expression (j) during AGM-EHT. i-j, Heatmaps showing the TF motif scores calculated using chromVAR with default parameters (i) and gene expression (j) during YS-EHT. k, Inferred enhancer-to-target links for Runx1 in YS-EC, YS-HEC, AGM-earlyEC, and AGM-HEC. The enhancer-to-gene relationships were computed using enhancerToGene function in ScoMAP, and the links were plotted by Cicero.
Supplementary information
Supplementary Information
Supplementary Note
Supplementary Table 1
The sequences of custom primers used in this work.
Supplementary Table 2
Data quality of all CoTECH experiments in this study.
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Xiong, H., Luo, Y., Wang, Q. et al. Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions. Nat Methods 18, 652–660 (2021). https://doi.org/10.1038/s41592-021-01129-z
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DOI: https://doi.org/10.1038/s41592-021-01129-z
