Single-cell Epigenetics of Tau Dementia | Part 2 - Exploring Disease-associated Dynamic Peaks, Functional Variants, eQTLs, and Cellular Subcluster Diversity

Understanding Tauopathies Through Single-cell Epigenetic and Transcriptomic Analyses

Epigenome Technologies Blog brings you part two of a three-part summary of a recent single-cell epigenetics preprint article from researchers led by Jessica E. Rexach (University of California, Los Angeles), who sought to define cell-type-specific cis-regulatory elements (CREs) via chromatin accessibility (snATAC-seq) and gene expression (snRNA-seq) analysis in single nuclei across 6 cell types and 50 subclasses in samples from brain regions with distinct vulnerabilities in Alzheimers disease (AD), Picks disease (PiD), and progressive supranuclear palsy (PSP) patients to understand the regulatory circuitry of non-coding genetic variants underlying risk-associated cell states (Han et al.).

The authors provide a cross-disorder atlas linking gene regulation, chromatin dynamics, and cellular functions across tau-related disorders to highlight disorder-specific glial states of differential resilience. In doing so, they enhance our understanding of disease regulatory circuits by uncovering epigenomic dynamics and mapping genetic variants to their target through CREs, prioritize genes for validation to inform causal mechanisms and therapeutic strategies by identifying molecular targets linked to polygenic disease risk, enhance our understanding of glial contributions to tauopathies at the single-cell level, and underscore the importance of cross-disorder, cell-specific chromatin profiling in brain regions with moderate pathology.

Exploring Disease-associated Dynamic Peaks, Functional Variants, eQTLs, and Cellular Subcluster Diversity

Can Disease-associated Dynamic Peaks Explain Disease-associated Genetic Variants?

• Interrogating whether disease-responsive dynamic peaks captured and interpreted heritability differentially when compared to condition-stable cell type-specific peaks integrated genome-wide association study (GWAS) data from AD, FTD, and PSP studies
• Individual genetic variants revealed similar distribution patterns across consensus peaks and dynamic peaks; however, combined heritability partitioning across different peak sets revealed notable differences
• Different cell types captured the most significant amount of heritability of diseases over disease-dynamic peaks
• The most significant heritability enrichment occurred for dynamic peaks in microglia, inhibitory neurons, and oligodendrocytes in PiD; oligodendrocytes and astrocytes in AD; and neurons in PSP
• Accessible regions whose chromatin accessibility changed in microglia strongly captured PiD heritability
• Peaks that gained chromatin accessibility in PiD microglia displayed the greatest overall PiD GWAS heritability score; meanwhile, significant changes in heritability-associated accessibility occurred in astrocytes in AD and PSP
• Peaks losing accessibility in PSP neurons displayed the most significant heritability of any cell type
• These findings suggest dynamic gene regulatory elements in disease-relevant tissue contexts - characterized by the loss of regulatory elements in neurons and glia during degeneration and a predominant gain in glia - influence disease heritability
• These alterations differentially affect cell types in a disease context-specific fashion that further relates to differential cellular vulnerability

Exploring Massively Parallel Reporter Assay-Validated Functional Variants within Dynamic Peaks

• Integrating CREs with massively parallel reporter assay data on human microglial-relevant genetic variants identified 3,325 functional variants (frVars), with the highest concentration found in microglia enhancers
• This data linked neurodegeneration-associated genes to distinct CREs in different cell types in a disease-specific manner, highlighting the importance of context and disease-specific data in identifying candidate functional regulatory elements
• Transcription factor (TF) analysis for dynamic vs. stable peaks containing frVars across cell types revealed distinct patterns of enrichment
• TFs in dynamic peaks associated with frVars involving those managing cellular responses and maintaining homeostasis, while TFs in stable peaks associated with frVars that spanned a broader range of biological processes
• Assessing frVar enrichment in dynamic vs. stable peaks across peak types in each cell type revealed the significant enrichment of frVars in dynamic peaks in microglia (regardless of peak type), suggesting that frVars modulate gene regulation by perturbing dynamic chromatin regions
• An enrichment analysis of genes linked to dynamic frVar-containing CREs to identify genes affected in each disease revealed the enrichment of phagosome-related genes in PiD and PSP, lysosome- and immune regulation-associated genes in PSP, and microtubule binding-associated genes in AD
• Attempts to understand the functional implications of variants in microglia states extracted and partitioned CREs harboring frVars into co-accessible regulatory modules across microglia subtypes based on chromatin accessibility
• CRE modules exhibited subtype-specific activity and displayed enrichment for distinct biological functions
• Module 5 became specifically activated in C4-subtype microglia, which involved lysosome function, sphingolipid catabolic process, and synaptic vesicle endocytosis driven by the MEF2C TF
• CREs within the C4-subtype microglia-specific module harbored MEF2C binding sites associated with frVars and linked to genes enriched in endocytosis and pathways of neurodegenerative diseases
• These results highlight coordinated context-dependent gene regulation in microglia, identify TF drivers based on frVar mapping, and support genetic variation in modulating microglial stress responses in the diseased brain

Assessing the Enrichment of Single-Nucleus Quantitative Trait Loci in Dynamic Chromatin

• The analysis of single-nucleus expression quantitative trait loci (eQTL) datasets from the brain assessed whether disease context-specific dynamic peaks in cross-disorder brain datasets display any enrichment for regulatory variants and evaluated effects on brain cell gene expression
• Assessing eQTL enrichment in dynamic versus stable peaks across different peak types in each cell type revealed that dynamic peaks in microglia and oligodendrocytes consistently exhibited significant eQTL enrichment, particularly within CREs plus gene bodies and across all peaks
• The data suggest that microglia and oligodendrocytes serve as common targets in tauopathies, where common regulatory variants modulate gene regulation by perturbing dynamic chromatin regions
• Analysis of CREs harboring eQTLs identified a C4-subtype microglia-specific regulatory module
• Importantly, the activation of eQTL module-10 in C4-subtype microglia associated with lysosomal membrane function and protein kinase binding
• TF analysis over linked enhancers identified nine key regulators driving module-10 activation, targeting CREs linked to vesicle-mediated transport, lysosomal membrane, and cytoskeleton organization genes
• These findings suggest lysosome-related dysregulation in disease-associated C4-subtype microglia
• The combined eQTL and MPRA-based frVar annotation identified a series of candidate TF and target genes poised to regulate microglial lysosomal function and variation in the human brain
• These findings support roles for human genetic variation in affecting stress response pathways in glial cells that display significant dysregulation in tauopathies

How Does the Diversity of Cellular Subclusters Change Across Brain Regions?

• After identifying dynamic peaks including functional variants linked to risk genetic factors, the study sought to understand their organization across cell subclusters representing cellular biological responses to tauopathies
• This approach employed 50 total subclusters - 10 astrocytes, 10 microglia, 12 neurons, 8 oligodendrocytes, and 10 oligodendrocyte precursor cells (OPC) - achieved by combining all cells from a given major cell type across brain regions and disorders for high-resolution cellular subtyping
• This analysis reproduced astrocyte subclusters associated with homeostasis, reactive states (C5, C7, C8, and C9), synaptic transmission (C2), antigen presentation (C3), inflammation (C10), and metabolism (C11), and microglia subclusters related to homeostasis (C14 and C16), inflammation (C11 and C13), disease (C12), synaptic transmission (C4 and C7), and T cell activation (C9)
• In addition, the study identified two myelin-related astrocyte subclusters (C1 and C4) and microglia subclusters expressing PLP1 and SOX10(C4 and C6)
• The tau-encoding gene MAPT displayed upregulated expression in C4-subtype microglia and differential expression in PSP in the insula in C1-subtype astrocytes, suggesting a link between these two subclusters with tau dysregulation
• Chromatin tracks revealed that marker peaks of marker genes displayed consistent upregulation in distinct astrocyte subcluster

What Can Paired-Tag from Epigenome Technologies Do for Your Research?

Paired-Tag from Epigenome Technologies generates joint epigenetic and transcriptomic profiles at single-cell resolution and detects histone modifications and RNA transcripts in individual nuclei with comparable efficiency to single-nucleus RNA-seq/ChIP-seq assays while avoiding the need for cell sorting. The implementation of Paired-Tag technology may enable researchers to make significant strides in understanding gene regulation and improving the management of diseases, such as the neurodegenerative tauopathies explored in this exciting preprint.