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scGHOST: identifying single-cell 3D genome subcompartments

Nature Methods (2024)Cite this article

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Abstract

Single-cell Hi-C (scHi-C) technologies allow for probing of genome-wide cell-to-cell variability in three-dimensional (3D) genome organization from individual cells. Computational methods have been developed to reveal single-cell 3D genome features based on scHi-C, including A/B compartments, topologically associating domains and chromatin loops. However, no method exists for annotating single-cell subcompartments, which is important for understanding chromosome spatial localization in single cells. Here we present scGHOST, a single-cell subcompartment annotation method using graph embedding with constrained random walk sampling. Applications of scGHOST to scHi-C data and contact maps derived from single-cell 3D genome imaging demonstrate reliable identification of single-cell subcompartments, offering insights into cell-to-cell variability of nuclear subcompartments. Using scHi-C data from complex tissues, scGHOST identifies cell-type-specific or allele-specific subcompartments linked to gene transcription across various cell types and developmental stages, suggesting functional implications of single-cell subcompartments. scGHOST is an effective method for annotating single-cell 3D genome subcompartments in a broad range of biological contexts.

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Fig. 1: Overview of the scGHOST framework.
Fig. 2: scGHOST’s application to GM12878 single-cell Hi-C data showcases its accuracy in annotating single-cell subcompartments.
Fig. 3: scGHOST’s application to WTC11 scHi-C data and IMR90 single-cell 3D genome imaging data.
Fig. 4: scGHOST’s application to scHi-C data from the Lee et al. human PFC and Tan et al. developing mouse brain datasets.
Fig. 5: Application to HiRES data of developing mouse embryos.

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Data availability

In this work, we used several public datasets. scHi-C data for the GM12878 cell line12 were downloaded from the 4DN Data Portal29,40 (4DNES4D5MWEZ, 4DNESUE2NSGS and 4DNESTVIP977) in FASTQ format and were processed into contact maps at 500-kb resolution using the recommended processing pipeline (https://github.com/VRam142/combinatorialHiC)41 of the data source. The scHi-C dataset of the human prefrontal cortex14 was downloaded from the Gene Expression Omnibus (GEO) (GSE130711) in contact pairs format, which was then transformed into contact maps at 500-kb resolution. The WTC11 scHi-C dataset was downloaded from the 4DN Data Portal29 (4DNESF829JOW and 4DNESJQ4RXY5). All scHi-C datasets were imputed with Higashi17 (https://github.com/ma-compbio/Higashi)42 with default parameters. The Dip-C developing mouse brain dataset13 was downloaded from the GEO (GSE162511). The HiRES developing mouse embryos dataset33 was downloaded from the GEO (GSE223917). We downloaded the following ENCODE datasets: ENCFF167NBF, ENCFF171MDW, ENCFF803DJF, ENCFF776OVW, ENCFF001GNK, ENCFF001GNN, ENCFF001GOA, ENCFF001GNX, ENCFF001GNT, ENCFF001GNR, ENCFF001GRA, ENCFF001GRD, ENCFF001GRQ, ENCFF001GRM, ENCFF001GRJ, ENCFF001GRG, ENCFF834HNV, ENCFF066MEE, ENCFF366BVS, ENCFF050ZTH and ENCFF519FHW. The imaging dataset31 was obtained from Zenodo (https://doi.org/10.5281/zenodo.3928890)43. We also downloaded the scRNA-seq of multiple cortical areas of the human brain from the Allen Brain Map44,45. The marker genes for astrocyte (Astro), oligodendrocyte (ODC), oligodendrocyte progenitor cell (OPC), endothelial cell (Endo), microglia (MG) and neuron cell types were identified using Seurat46,47 with default parameters. For each cell type, the background was chosen as the rest of the cell types. When identifying marker genes for neuron subtypes, the background was chosen as the rest of the neuron cells. The genes were then ranked by the log fold change value between a specific cell type and the background.

Code availability

The source code of scGHOST can be accessed at https://github.com/ma-compbio/scGHOST (ref. 48), which has also been deposited to Zenodo (https://doi.org/10.5281/zenodo.10141210; ref. 49).

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Acknowledgements

This work was supported, in part, by National Institutes of Health Common Fund 4D Nucleome Program grant UM1HG011593 (J.M.); National Institutes of Health Common Fund Cellular Senescence Network Program grant UG3CA268202 (J.M.); and National Institutes of Health grants R01HG007352 (J.M.) and R01HG012303 (J.M.). J.M. was additionally supported by 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 additionally supported by funding from the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard.

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  1. Ruochi Zhang

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

  2. These authors contributed equally: Kyle Xiong, Ruochi Zhang.

Authors and Affiliations

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

    Kyle Xiong, Ruochi Zhang & Jian Ma

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Contributions

K.X. and J.M. conceived the development of this work. K.X. and R.Z. developed the software tools. K.X., R.Z. and J.M. conducted data analysis and investigation. K.X., R.Z. and J.M. wrote the paper. J.M. acquired funding to support this work.

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Correspondence to Jian Ma.

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Nature Methods thanks Ming Hu, Fulai Jin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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Xiong, K., Zhang, R. & Ma, J. scGHOST: identifying single-cell 3D genome subcompartments. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02230-9

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