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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RORB showed higher expression and a more distinctive cluster distribution in this case compared to previously published datasets.
“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]
The Results section (“Cell type and Regional Heterogeneity from snDrop-seq Data”; page unavailable) identifies three RORB-positive excitatory-neuron subpopulations, so the paper provides a relevant published dataset for comparison. It does not assess the citing authors’ case or establish that RORB expression is higher and its cluster distribution more distinctive in that case. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe expression of neurogenesis-related genes in these neurons can be contrasted to previous findings in control cases and decreased adult neurogenesis in AD patients.
“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]
The Results section on cell-type and regional heterogeneity reports gene-expression profiles for neuronal subtypes in adult human brain tissue, supporting their use as a previous comparison. But the paper’s explicit neurogenesis-related expression finding concerns oligodendrocyte precursor maturation, not those neurons. The decreased adult neurogenesis in AD is attributed to a different citation [25], not this source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredsnRNA-Seq has enabled detailed profiling of vulnerable neuronal subtypes and glial populations.
“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]
The Results section, “Cell type and Regional Heterogeneity from snDrop-seq Data” (page not available), describes detailed resolution of neuronal subtypes and glial populations. The paper also maps disease-risk variants to cell types, but it does not establish that the neuronal subtypes profiled were vulnerable to disease. (LLM, conf=0.84)
Experimental — accuracy not yet measuredBy isolating nuclei rather than whole cells, snRNA-Seq retains transcriptional fidelity while avoiding cell dissociation artifacts.
“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]
Recent advances in single-nucleus RNA sequencing (snRNA-Seq) have extended transcriptomic analysis to frozen or archived post-mortem brain tissues.
“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]
Human single nucleus RNA sequencing data from Mathys and colleagues and Lake and colleagues were obtained from The Myeloid Landscape 2 portal.
“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]
Apart from PSEN2, oligodendrocytes had elevated expression of all genes involved in amyloid beta production in human single-nucleus RNA sequencing datasets.
“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]
The citation in the printed sentence is attached to the parenthetical claim that PSEN2 is not essential for gamma-secretase function when PSEN1 is present; the elevated-expression finding is presented as the citing authors’ own result. Our copy’s Results section discusses oligodendrocyte expression in remyelination, but the passages found when searching for PSEN2, PSEN1, gamma-secretase, and amyloid-beta production do not address that parenthetical claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIn mouse and human nervous tissue, expression levels of amyloidogenic pathway genes (APP, BACE1, PSEN1 and PSEN2) are similar between neurons and oligodendrocytes.
“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]
The Results section on oligodendrocyte maturation reports myelin and glutamate-receptor gene expression, not a neuron–oligodendrocyte comparison of amyloidogenic genes (page unavailable). Searches for APP, BACE1, PSEN1, PSEN2, and amyloidogenic enzymes found no passage in our copy establishing that their transcript levels are similar in mouse or human nervous tissue. (LLM, conf=0.74)
Experimental — accuracy not yet measuredThe resource has single-cell RNA-seq data for 18,025 cells from PsychENCODE and 14,012 cells from outside sources.
“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]
Single-cell RNA-seq data from PsychENCODE and other brain studies were uniformly processed with standard pipelines.
“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]
The study's sample sizes were similar to or greater than those in previous COVID-19 or brain snRNA-seq studies.
“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]
No statistical method was used to predetermine sample sizes, but the sample sizes resemble those in earlier publications.
“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]
Although paired measurements of many modality combinations are possible, different modalities are still commonly measured independently, resulting in unpaired data.
“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]
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 brain.
“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]
TFs that regulate specific brain cell types can be identified by detecting enriched sequences within cCREs, and comparing them with known TF binding motifs.
“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
Partitioned heritability analysis can also use epigenomic data to link genetic risk variants with cell type-specific cCREs.
“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
Cellular heterogeneity in the human brain poses significant challenges for unraveling the gene regulatory architecture because the brain comprises heterogeneous cell populations with distinct gene expression and chromatin accessibility profiles.
“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]
Proportions of the different cell types were comparable to other snRNA-seq studies, with neurons (particularly excitatory neurons) over-represented relative to non-neuronal cell types.
“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]
A comprehensive molecular characterization of region-specific differences in Alzheimer's disease is currently lacking.
“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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Comprehensive Integration of Single-Cell Data
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Best practices for single-cell analysis across modalities
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“…that map multimodal data into a common feature space based on prior knowledge — such as assay for transposase-accessible chromatin (ATAC) regions to nearby transcripts — with subsequent application of single-cell data integration methods have been shown to result in information loss 135 .”
BackgroundComprehensive functional genomic resource and integrative model for the human brain
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A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
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Altered human oligodendrocyte heterogeneity in multiple sclerosis
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High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
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Self-citationSingle-cell genomics identifies cell type–specific molecular changes in autism
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Integrative functional genomic analysis of human brain development and neuropsychiatric risks
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A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk
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Brain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association
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Comparative cellular analysis of motor cortex in human, marmoset and mouse
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Self-citationSingle-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease
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Dysregulation of brain and choroid plexus cell types in severe COVID-19
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Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
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Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
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A multimodal cell census and atlas of the mammalian primary motor cortex
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Self-citationA Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation
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Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients
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Inherited and de novo genetic risk for autism impacts shared networks
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What is a cell type and how to define it?
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Comprehensive analysis of single cell ATAC-seq data with SnapATAC
Fang, R., Preissl, S., Li, Y., Hou, X., Lucero, J., Wang, X., Motamedi, A., Shiau, A. K., Zhou, X., Xie, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21583-9 · Cited by 455 · see citing works
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Single-cell epigenomics: Recording the past and predicting the future
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Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level
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An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
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Machine learning and integrative analysis of biomedical big data
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The triumphs and limitations of computational methods for scRNA-seq
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Self-citationTranscriptional signature in microglia associated with Aβ plaque phagocytosis
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Single-cell multiregion dissection of Alzheimer’s disease
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Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans
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“The Lake datasets both came from a single publication (Lake et al., 2018) which had data from frontal cortex, visual cortex and cerebellum.”
BackgroundSingle-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
Joint profiling of histone modifications and transcriptome in single cells from mouse brain
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“Two of the three studies - MSBB and Mayo RNaseq - obtained more than one tissue from the same donors.”
MethodInfluentialHighly scalable generation of DNA methylation profiles in single cells
Mulqueen, R. M., Pokholok, D., Norberg, S. J., Torkenczy, K. A., Fields, A. J., Sun, D., Sinnamon, J. R., Shendure, J., Trapnell, C., O'Roak, B. J., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4112 · Cited by 289 · see citing works
Tau Activation of Microglial cGAS–IFN Reduces MEF2C‐mediated Cognitive Resilience
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Single-cell sequencing techniques from individual to multiomics analyses
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Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
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Locus for severity implicates CNS resilience in progression of multiple sclerosis
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Microglia in Brain Development, Homeostasis, and Neurodegeneration
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Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex
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A shared disease-associated oligodendrocyte signature among multiple CNS pathologies
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Self-citationEarly Alzheimer’s disease pathology in human cortex involves transient cell states
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Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex
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An atlas of gene regulatory elements in adult mouse cerebrum
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Self-citationA comparative atlas of single-cell chromatin accessibility in the human brain
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Decoding gene regulation in the fly brain
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Decoding gene regulation in the fly brain
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Recent advances in single-cell sequencing technologies
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- 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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