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GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells

Nature Genetics (2024)Cite this article

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Abstract

The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34+ cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

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Fig. 1: Overview and validation of GAGE-seq.
Fig. 2: High-quality scHi-C and scRNA-seq data generated by GAGE-seq.
Fig. 3: Cell types in mouse cortex characterized by GAGE-seq scHi-C and scRNA-seq.
Fig. 4: 3D genome features inform cell-type-specific gene expressions in the mouse cortex.
Fig. 5: Integrative analysis of GAGE-seq and chromatin accessibility in the mouse cortex.
Fig. 6: Interplay between 3D genome variation and gene expression changes in human bone marrow differentiation.

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Data availability

All sequencing data from this study have been submitted to GEO under the accession number GSE238001. We use the following publicly available datasets in this work: in situ Hi-C datasets from Rao et al.3 (GSE: GSE63525); scHi-C datasets from Nagano et al.17 (GEO: GSE48262), Nagano et al.23 (GEO: GSE94489), Ramani et al.22 (GEO: GSE84920), Kim et al.37 (4DN Data Portal: 4DNES4D5MWEZ, 4DNESUE2NSGS, 4DNESIKGI39T, 4DNES1BK1RMQ and 4DNESTVIP977), Tan et al.26 (GEO: GSE117876), Tan et al.57 (GEO: GSE121791), Tan et al.27 (GEO: GSE162511), Flyamer et al.24 (GEO: GSE80006), Gassler et al.60 (GEO: GSE100569), Stevens et al.25 (GEO: GSE80280), Collombet et al.59 (GEO: GSE129029), Lee et al.44 (GEO: GSE124391), Liu et al.45 (GEO: GSE132489) and Mulqueen et al.58 (GEO: GSE174226); scRNA-seq datasets from Chen et al.62 (GEO: GSE126074), Plongthongkum et al.56 (GEO: GSE157660), Chen et al.55 (GEO: GSE178707), Ma et al.43 (GEO: GSE140203), Xu et al.65 (ArrayExpress: E-MTAB-11264), Xiong et al.66 (GEO: GSE158435), Zhu et al.63 (GEO: GSE130399), Zhu et al.52 (GEO: GSE152020), Cao et al.61 (GEO: GSE117089), Mimitou et al.64 (GEO: GSE126310) and Zhang et al.53 (GEO: GSE137864); HiRES co-assayed scHi-C and scRNA-seq datasets from Liu et al.35 (GEO: GSE223917); MERFISH spatial transcriptome datasets from Zhang et al.49 (Brain Image Library: cf1c1a431ef8d021); Paired-seq co-assayed scRNA-seq and scATAC-seq from Zhu et al.52 (GEO: GSE152020).

Code availability

The source code of the GAGE-seq data processing and analysis workflows can be accessed at: https://github.com/ma-compbio/GAGE-seq, which has also been deposited via Zenedo (https://doi.org/10.5281/zenodo.10888453)72. In our GitHub repository, we have provided notebooks (https://github.com/ma-compbio/GAGE-seq/tree/main/scripts_analysis) that detail the integration between GAGE-seq and Paired-seq data for single-cell joint analysis of 3D genome structure, chromatin accessibility and gene expression.

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Acknowledgements

We thank Y. Zhang for assistance with the figures. This work was primarily supported by the National Institutes of Health (NIH) grant no. R01HG012303 (J.M. and Z.D.), with additional funding, in part, provided by NIH Common Fund 4D Nucleome Program grant nos UM1HG011593 (J.M.) and UM1HG011586 (Z.D.), NIH Common Fund Cellular Senescence Network Program grant no. UG3CA268202 (J.M.) and NIH grant nos R01HG007352 (J.M.) and R61DA047010 (Z.D.). Z.D. was additionally supported by EvansMDS Discovery Research Grant 2019. J.M. received additional support from a Guggenheim Fellowship from the John Simon Guggenheim Memorial Foundation, a Google Research Collabs Award and a Single-Cell Biology Data Insights award from the Chan Zuckerberg Initiative. R.Z. was supported by the Eric and Wendy Schmidt Center at the Broad Institute. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Ruochi Zhang

    Present address: Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA

  2. Daniel Gao

    Present address: Department of Chemistry, Pomona College, Claremont, CA, USA

Authors and Affiliations

  1. Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Tianming Zhou, Ruochi Zhang & Jian Ma

  2. Department of Urology, University of Washington, Seattle, WA, USA

    Deyong Jia & Li Xin

  3. Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA

    Raymond T. Doty, Adam D. Munday, Janis L. Abkowitz & Zhijun Duan

  4. Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA

    Daniel Gao, Li Xin, Janis L. Abkowitz & Zhijun Duan

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Contributions

Z.D. and J.M. conceived and oversaw the project. Z.D. conceived and developed the GAGE-seq protocol with critical contributions from T.Z. and J.M. T.Z. developed the computational workflow and performed all the data analysis with assistance from R.Z., under the supervision of Z.D. and J.M. D.J. provided mice and dissected the mouse brain tissues, under the supervision of L.X. R.T.D. and A.D.M. prepared human PBMCs for method optimization, under the supervision of J.L.A. D.G. performed experiments under the supervision of Z.D. T.Z., Z.D. and J.M. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Zhijun Duan or Jian Ma.

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Competing interests

Z.D. is listed as the inventor on a provisional patent application that covered the GAGE-seq experimental protocol filed by the University of Washington. The other authors declare no competing interests.

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Nature Genetics thanks Fulai Jin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Methods, Results and Figs. 1–44.

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Supplementary Tables

Supplementary Table 1. Sequences of the GAGE-seq primers. Table 2. Metadata of the single cells detected in the K562-NIH3T3 library. Table 3. Metadata of the single cells detected in the K562-GM12878 library. Table 4. Metadata of the single cells detected in the MDS-L library. Table 5. Metadata of the single cells detected in the mBCortex libraries. Table 6. Metadata of the single cells detected in the hBMCD libraries.

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Zhou, T., Zhang, R., Jia, D. et al. GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01745-3

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