Single nucleus multi-omics identifies human cortical cell regulatory genome diversity
Cell Genom. 2022-03-09;2(3):100107.
- Abstract
- Single-cell technologies measure unique cellular signatures but are typically limited to a single modality. Computational approaches allow the fusion of diverse single-cell data types, but their efficacy is difficult to validate in the absence of authentic multi-omic measurements. To comprehensively assess the molecular phenotypes of single cells, we devised single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) and applied it to postmortem human frontal cortex tissue. We developed a cross-validation approach using multi-modal information to validate fine-grained cell types and assessed the effectiveness of computational data fusion methods. Correlation analysis in individual cells revealed distinct relations between methylation and gene expression. Our integrative approach enabled joint analyses of the methylome, transcriptome, chromatin accessibility, and conformation for 63 human cortical cell types. We reconstructed regulatory lineages for cortical cell populations and found specific enrichment of genetic risk for neuropsychiatric traits, enabling the prediction of cell types that are associated with diseases.
Related data
- Available data
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- Data summary
- Raw and processed data included in this study were deposited to NCBI GEO/SRA with accession number GSE140493. Methylome and transcriptomic profiles generated by snmCAT-seq from H1 and HEK293T cells can be visualized at http://neomorph.salk.edu/Human_cells_snmCT-seq.php.
- Available data
- website
- Data summary
- snmCAT-seq generated from brain tissues can be visualized at http://neomorph.salk.edu/human_frontal_cortex_ensemble.php.
- Available data
- website
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- snRNA-seq data is available for download from the Neuroscience Multi-omics Archive (https://assets.nemoarchive.org/dat-s3creyz).
- Available data
- website
- Data summary
- The code for SingleCellFusion is available from https://github.com/mukamel-lab/SingleCellFusion.
- Available data
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- Data summary
- The code benchmarking computational integration methods are available from https://github.com/lhqing/snmCAT-seq_integration.
- Available data
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- The code reproducing the over- and under-splitting analysis are available from https://github.com/FangmingXie/mctseq_over_under_splitting/blob/master/over-under-splitting-analysis.ipynb.
- Available data
- website
- Data summary
- A detailed bench protocol for snmCAT-seq and future updates to the method can be found at https://www.protocols.io/view/snmcat-v1-bwubpesn. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.