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Simultaneous profiling of chromatin architecture and transcription in single cells

Nature Structural & Molecular Biology volume 30, pages 1393–1402 (2023)Cite this article

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Abstract

The three-dimensional structure of chromatin plays a crucial role in development and disease, both of which are associated with transcriptional changes. However, given the heterogeneity in single-cell chromatin architecture and transcription, the regulatory relationship between the three-dimensional chromatin structure and gene expression is difficult to explain based on bulk cell populations. Here we develop a single-cell, multimodal, omics method allowing the simultaneous detection of chromatin architecture and messenger RNA expression by sequencing (single-cell transcriptome sequencing (scCARE-seq)). Applying scCARE-seq to examine chromatin architecture and transcription from 2i to serum single mouse embryonic stem cells, we observe improved separation of cell clusters compared with single-cell chromatin conformation capture. In addition, after defining the cell-cycle phase of each cell through chromatin architecture extracted by scCARE-seq, we find that periodic changes in chromatin architecture occur in parallel with transcription during the cell cycle. These findings highlight the potential of scCARE-seq to facilitate comprehensive analyses that may boost our understanding of chromatin architecture and transcription in the same single cell.

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Fig. 1: Overview of scCARE-seq.
Fig. 2: CARE-seq provides an accurate method to simultaneously captures chromatin architecture and transcriptome in mESCs.
Fig. 3: scCARE-seq simultaneously captures high-quality chromatin architecture and transcriptome data in the same single cell.
Fig. 4: Transcriptional clustering reveals differences in 3D chromatin structure from 2i and serum single mESCs.
Fig. 5: 3D chromatin structure and expression have interrelated periodic changes in single cells during the cell cycle.

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Data availability

Sequencing data have been deposited at the NCBI GEO with accession number GSE211395, using mm10 reference genome. Other public datasets used in this study were downloaded from NCBI GEO with accession numbers as follows: ChIP-seq (GSE90895; CTCF and H3K27ac), in situ Hi-C (GSE124342), 2013-Nagano (GSE48262), 2017-Flyamer (GSE80006), 2017-Nagano (GSE94489), 2017-Steven (GSE80280), 2019-Tan (GSE121791), 2021-Tan (GSE162511), sci-CAR (GSE117089), SNARE-seq (GSE126074), CoTECH (GSE158435), Paired-Tag (GSE152020) and Paired-seq (GSE130399). Source data are provided with this paper.

Code availability

Custom scripts used in this study are available from https://github.com/jsun9003/scCARE-seq.

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Acknowledgements

We are grateful to members of the Ding laboratories for discussions on the paper. This research was funded by grants from the National Natural Science Foundation of China (grant nos 31970811 and 32170798), the Guangdong Basic and Applied Basic Research Foundation (grant no. 2021B1515120063), the Guangdong Regenerative Medicine and Health of Guangdong Laboratory Frontier Exploration Project (grant no. 2018GZR110105007) and the Guangdong Innovative and Entrepreneurial Research Team Program (grant no. 2016ZT06S029) to J.D.; the Natural Science Foundation of Guangdong Province, China (grant nos 2021A1515010938 and 2023A1515010148) to J.S.; the National Natural Science Foundation of China (grant no. 32100497), the Natural Science Foundation of Guangdong Province, China (grant no. 2023A1515010197) and Postdoctoral Program (grant no. 2021M703760) to C.W.; the Fundamental Research Funds for the Central Universities of Jinan University (Natural Science) (grant no. 2162004), China Postdoctoral Science Foundation (grant no. 2021M701441), China Postdoctoral Special Grant Foundation (grant no. 2022T150269), Guangdong Basic and Applied Basic Research Foundation (grant no. 2021A1515111056) and Guangzhou Basic and Applied Basic Research Foundation (grant no. 202201010961) to L.F.; and the National Natural Science Foundation of China (grant no. 32100927) to H.Y.

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Author notes

  1. These authors contributed equally: Jiale Qu, Jun Sun.

Authors and Affiliations

  1. RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Jiale Qu, Jun Sun, Cai Zhao, Xinyi Liu, Xinyao Zhang, Shaoshuai Jiang, Chao Wei & Junjun Ding

  2. Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Jiale Qu, Jun Sun, Cai Zhao, Xinyi Liu, Xinyao Zhang, Shaoshuai Jiang, Chao Wei & Junjun Ding

  3. Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Jiale Qu, Jun Sun, Cai Zhao, Xinyi Liu, Xinyao Zhang, Shaoshuai Jiang, Chao Wei & Junjun Ding

  4. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China

    Haopeng Yu, Xiaoxi Zeng & Junjun Ding

  5. Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China

    Lili Fan

  6. Department of Histology and Embryology, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China

    Junjun Ding

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Contributions

J.D. and J.Q. conceived and designed the study. J.Q. designed and performed all experiments. J.Q., J.S., X.L., C.Z. and X. Zhang performed the data analysis. J.Q., J.S. and C.Z. wrote the paper with input from all other authors. S.J. and C.W. discussed results and edited the manuscript. H.Y., X. Zeng and L.F. provided support. J.D. supervised the research.

Corresponding authors

Correspondence to Xiaoxi Zeng, Lili Fan or Junjun Ding.

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Competing interests

J.D., J.Q. and J.S. are listed as inventors of a patent application titled ‘Single-cell simultaneous detection of 3D chromatin structure and gene expression by sequencing’. The other authors declare no competing interests.

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Nature Structural & Molecular Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Dimitris Typas, in collaboration with the Nature Structural & Molecular Biology team.

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Extended data

Extended Data Fig. 1 Schematics for pre-amplification and construction of DNA/RNA libraries.

a, Pre-amplification was achieved by primers mix to amplify nucleic acid numbers. DNA/RNA libraries construction was worked by specific DNA or RNA primers. b, Scatter plots showing the mapped DNA reads of Hi-C data (left) and mapped RNA -reads of RNA-seq data (middle) in hg19 and mm10 for each cell. And the fraction of human reads in DNA and RNA libraries for each cell (right). HEK293T refers to human embryonic kidney 293 T, and mESCs refers to mouse embryonic stem cells.

Source data

Extended Data Fig. 2 Overview of our method to simultaneously detect 3D chromatin structure and transcription.

a,b, Track view displaying both contact matrices and RNA signals from 60-63 Mb of chromosome 10 (a) and 80-83 Mb of chromosome 15 (b).

Extended Data Fig. 3 Performance of CARE-seq in comparative analyses.

a, Saddle plots: average contact enrichment between pairs of 500 kb regions arranged by their compartment scores and the difference was Hi-C compared to CARE-seq. The upper right quarter represents A-A interactions, the bottom left quarter represents B-B interactions. b, The similarity of different bulk 3D chromatin structure data was evaluated by HiCRep at 500 kb resolution per euchromosome (n = 19). The values represented the mean Stratum-adjusted Correlation Coefficient (SCC). The boxplots were drawn from lower quartile (Q1) to upper quartile (Q3), with the middle line denoting the median, whiskers with maximum 1.5 interquartile range (IQR) and outliers were not indicated. c, Dependence of contact probability on genomic separation for single cells from CARE-seq data (orange) and Hi-C data (black). d, Insulation profiles of CARE-seq and Hi-C over 40 kb bins in chromosomes 2. e-g, Scatter plots show the strong concordance of gene expression signals from two technical replicates in nuclear mRNA (g) and total mRNA (f), and total mRNA versus CARE-seq (g).

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Extended Data Fig. 4 Comparison of CARE-seq and typical RNA-seq in gene expression profiles.

Representative regions showing a consistent pattern of gene expression across datasets.

Extended Data Fig. 5 Comparison of 2i and serum mESCs from scCARE-seq.

a, The tables summarized the Hi-C (top) and RNA-seq data (bottom) of Supplementary Table 2, respectively. b,c, Comparison of scCARE-seq data from 192 2i mESCs and 192 serum mESCs. The contacts numbers (left) and cis-to-trans ratio (right) (b); UMIs (left) and expressed gene numbers (right) (c). The boxplots were drawn from lower quartile (Q1) to upper quartile (Q3), with the middle line denoting the median, whiskers with maximum 1.5 interquartile range (IQR) and outliers were not indicated.

Source data

Extended Data Fig. 6 scCARE-seq data quality in mESCs.

a, Distribution of cis long-range interactions (>20 kb) in the scCARE-seq data (n = 384, median = 52.04%). b, The similarity between scCARE-seq Hi-C data and Hi-C or CARE-seq Hi-C data was evaluated by HiCRep at 500 kb resolution per euchromosome (n = 19). The values represented the mean Stratum-adjusted Correlation Coefficient (SCC). The boxplots were drawn from lower quartile (Q1) to upper quartile (Q3), with the middle line denoting the median, whiskers with maximum 1.5 interquartile range (IQR) and outliers were not indicated. c, Comparison of contact heatmap of chromosome 3 between scCARE-seq and CARE-seq, at 1 Mb resolution (left); 50–140 Mb/250 kb resolution (right). Matrix similarity is evaluated by HiCRep at the corresponding resolution. SCC, Stratum-adjusted Correlation Coefficient. d, Dependence of contact probability on genomic separation for single cells from scCARE-seq data (n = 192, yellow), combined scCARE-seq data from all cells (orange) and bulk CARE-seq data (black). e, Cumulative coverage percentage of genes detected in single cells compared to the bulk data. f, A representative region showing a consistent pattern of gene expression across datasets generated using scCARE-seq and CARE-seq. The transcriptional profiles are gene expression read counts from bulk (upper) and a total of 83 single cells (bottom).

Source data

Extended Data Fig. 7 The relationship between chromatin architecture and gene expression in the different cell clusters.

a,b, Pearson’s correlation matrixes from different cell clusters. Contacts numbers in different clusters were sampled to same numbers and plot the balanced matrixes in Juicebox (version 1.11.08). Pearson’s correlation coefficient was calculated under 1-Mb resolution. White frame shows the difference regions. c,d, Showing change in expression of the different clusters (top) in the compartment A to B (c) and compartment B to A (d), where the compartment switch was defined by published Hi-C data (bottom). 2i and serum have two replications, respectively. CS represents log2(compartment scores+1). The boxplots were drawn from lower quartile (Q1) to upper quartile (Q3), with the middle line denoting the median, whiskers with maximum 1.5 interquartile range (IQR) and outliers were not indicated.

Source data

Extended Data Fig. 8 The relationship between chromatin architecture and gene expression in the cell cycle.

a, Saddle plots: average contact enrichment between pairs of 100 kb regions arranged by their compartment scores in the different cell cycle phases. b, Uniform manifold approximation and projection (UMAP) embedding showing the clustering of single cells from scCARE-seq 3D chromatin structural profiles. Each dot represents an individual cell and each color represents a cell cluster. c, GO enriched by marker genes of clusters in b. d, Percentage of inter-chromosomal contacts per single mESC in 2i and serum were ordered by cell-cycle phasing and each cell was annotated by cell type colored the same as in a. The black line represents mean trend. Shadow represents the confidence intervals of 0.95. e, Similar to d, Contacts, E-P interactions, UMIs and number of expressed genes per single serum mESCs from left to right. f, Comparison of UMI in single cells of top and bottom group in 2i mESCs. The top and bottom groups were selected based on the top and the bottom each 48 single cells (25%) ranked by number of E-P interactions from highest to lowest. The boxplots were drawn from lower quartile (Q1) to upper quartile (Q3), with the middle line denoting the median, whiskers with maximum 1.5 interquartile range (IQR) and outliers were not indicated. P value, one-sided Wilcoxon signed-rank test. g, Venn diagrams showing the expressed genes in E-P interactions of Early-S and LateS-G2 in e (right). h, Partial gene ontology (GO) terms, enriched by specific genes of Early-S and LateS-G2 in g, respectively.

Source data

Supplementary information

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Table 1. The sequences of primers used in this study. Supplementary Table 2. The data quality of scCARE-seq DNA and RNA in this study. Supplementary Table 3. The phasing of cell cycle in scCARE-seq. Supplementary Table 4. The known cell cycle marker genes list.

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Qu, J., Sun, J., Zhao, C. et al. Simultaneous profiling of chromatin architecture and transcription in single cells. Nat Struct Mol Biol 30, 1393–1402 (2023). https://doi.org/10.1038/s41594-023-01066-9

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