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Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
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Abstract
Detailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, as well as computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for the study of complex processes in the brain, such as genetic programs that coordinate adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, which provided insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.
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“we identified that RORB showed higher expression and a more distinctive cluster distribution (Supp. Figure 12, online resource).”
From Distinct tau neuropathology and cellular profiles of an APOE3 Christchurch homozygote protected against autosomal dominant Alzheimer’s dementia (2022) · cites this paper as [20]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“The expression of neurogenesis-related genes in these neurons can be contrasted to previous findings in control cases [20, 21] and decreased adult neurogenesis in AD patients [25].”
From Distinct tau neuropathology and cellular profiles of an APOE3 Christchurch homozygote protected against autosomal dominant Alzheimer’s dementia (2022) · cites this paper as [20]
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“This technique has enabled detailed profiling of vulnerable neuronal subtypes and glial populations.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [155]
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“By isolating nuclei rather than whole cells, snRNA-Seq retains transcriptional fidelity while avoiding cell dissociation artifacts155,165.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [155]
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“Recent advances in single-nucleus RNA sequencing (snRNA-Seq) have extended transcriptomic analysis to frozen or archived post-mortem brain tissues.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [155]
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“Human single nucleus RNA sequencing data from Mathys and colleagues [5] and Lake and colleagues [12] were obtained from The Myeloid Landscape 2 portal (http://research-pub.gene.com/BrainMyeloidLandscape) [70].”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [12]
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“Although some brain regions relevant to AD have been studied individually at scale or jointly in samples from a few individuals8–16, a comprehensive molecular characterization of region-specific differences in AD is currently lacking”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [10]
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“We found that, apart from PSEN2 (which is not essential for gamma secretase function when PSEN1 is present [13]), oligodendrocytes had elevated expression of all of these genes (Fig 1A–1D).”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [12]
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“transcripts of amyloid precursor protein (APP) and amyloidogenic enzymes are equally abundant in oligodendrocytes (OLs).”
From Oligodendrocytes produce amyloid-β and contribute to plaque formation alongside neurons in Alzheimer’s disease model mice (2024) · cites this paper as [14]
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“The resource also has single-cell RNA-seq data for 18,025 cells from PsychENCODE and 14,012 cells from outside sources (20).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [20]
In the citing paper
“we used standard pipelines to uniformly process single-cell RNA-seq data from PsychENCODE, in conjunction with other single-cell studies on the brain (14, 16, 20).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [20]
In the citing paper
“Our sample sizes were similar to or greater than those reported in previous COVID-19 or brain single-nucleus RNA-sequencing (snRNA-seq) studies24-26.”
From Dysregulation of brain and choroid plexus cell types in severe COVID-19 (2021) · cites this paper as [24]
In the citing paper
“We did not use statistical methods to predetermine sample sizes, but they are similar to those reported in previous publications24,25,49.”
From Dysregulation of brain and choroid plexus cell types in severe COVID-19 (2021) · cites this paper as [24]
In the citing paper
“Although advancements in experimental assays allow for the paired measurements of many modality combinations246, different modalities are still commonly measured independently, resulting in unpaired data247.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [247]
In the citing paper
“Similarly, single-cell DNA methylation and chromatin accessibility studies have begun to reveal cell-type-specific genome-wide epigenetic landscapes and gene regulatory networks in the brain12–15.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [14]
In the citing paper
“The TFs that regulate specific brain cell types can be identified by detecting enriched sequences within cCREs, and comparing them with known TF binding motifs (Lake et al., 2018; Li et al., 2020; Liu et al., 2020; Luo et al., 2017; Preissl et al., 2018).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Lake et al., 2018
In the citing paper
“Partitioned heritability analysis can also use epigenomic data to link genetic risk variants with cell type-specific cCREs (Lake et al., 2018; Luo et al., 2019).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Lake et al., 2018
In the citing paper
“However, cellular heterogeneity poses significant challenges for unraveling the complexity in the gene regulatory architecture of the human brain, as it is comprised of heterogeneous cell populations, mostly different types of neurons and glia, each of which display distinct gene expression3,8–10 and chromatin accessibility profiles11–14.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [11]
In the citing paper
“Proportions of the different cell types were comparable to other snRNA-seq studies (8,23) with neurons (particularly excitatory neurons) being over-represented relative to non-neuronal cell types ( Fig S2 ).”
From Single cell transcriptome profiling of the human alcohol-dependent brain (2020) · cites this paper as [23]
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Sos, B. C., Fung, H.-L., Gao, D., Osothprarop, T., Kia, A., He, M. M., & Zhang, K. (2016) · Genome biology · doi:10.1186/s13059-016-0882-7 · Cited by 52
Self-citationImproved genome sequencing using an engineered transposase
Kia, A., Gloeckner, C., Osothprarop, T., Gormley, N. A., Bomati, E. K., Stephenson, M., Goryshin, I. Y., & He, M. M. (2017) · BMC Biotechnology · doi:10.1186/s12896-016-0326-1 · Cited by 43
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Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Single-cell transcriptomic analysis of Alzheimer’s disease
Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019) · Nature · doi:10.1038/s41586-019-1195-2 · Cited by 2,568
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775
Mullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00857-4 · Cited by 1,517
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460
Metadata
- Authors
- Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., & Zhang, K.
- Affiliations
- University of California San Diego (US)
- Harvard University (US)
- Sanford Burnham Prebys Medical Discovery Institute (US)
- Corresponding author
- Chun, Jerold
- Kharchenko, Peter V
- Zhang, Kun
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2017-12-11
- Volume
- 36
- Issue
- 1
- Pages
- 70-80
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC5951394
- OpenAlex
- W2952602120
- Semantic Scholar
- 25648a2211b20fada4e722c14d7adb29d924e89c
- Cited by
- 983
- References
- 67
- Citations by year
- 2026: 65 · 2025: 91 · 2024: 99 · 2023: 117 · 2022: 153 · 2021: 163 · 2020: 147 · 2019: 98 · 2018: 47 · 2017: 3
- FWCI
- 33.27
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute on Aging (P50AG005131, T32AG000216)
- National Heart, Lung, and Blood Institute (R01HL131768)
- National Institute of Mental Health (P50MH106933, U01MH098977)
- National Cancer Institute (F99CA222750, F31CA206236)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (L40 HD093312, K12HD000850)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- DNA accessibility, epigenetics, cell type annotation, transcription factors, regulatory elements, visual cortex, frontal cortex, cerebellum, remyelination, multi-omics integration
- MeSH terms
- Adult, Brain, Cerebellum, Frontal Lobe, Humans, Visual Cortex, Sequence Analysis, RNA, Epigenesis, Genetic, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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