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In recent years, the study of epigenetics and its role in disease has increased dramatically. DNA methylation, which influences the ability of proteins to bind the DNA and thereby regulates gene expression and genomic stability, is the most widely known and studied epigenetic modification. Analyzing methylation can provide markers of disease progression or uncover mechanisms of gene regulation, revealing potential therapeutic targets.

The most common methods for analyzing methylation, however, profile cells in bulk. Although this has revealed biological insights, bulk analysis has limited ability to detect cell type-specific methylation patterns and can mask critical changes in subpopulations of cells (Figure 1). Although computational deconvolution methods exist, they often require reference datasets and may not be able to identify novel or unknown cell types. Single-cell analysis of DNA methylation is critical for gaining epigenetic insight at cellular resolution.

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Figure 1

This article covers several cases where single-cell DNA methylation analysis is critical for resolving complex biology, spanning from cancer to neurodegeneration research.

Resolving tissue heterogeneity to improve epigenetic disease signatures

Alzheimer’s disease (AD) is a progressive neurodegenerative disease. Although many genetic and non-genetic risk factors have been identified, the molecular mechanisms driving AD pathology remain elusive.

Recently, Shireby and colleagues used fluorescence-activated nuclei sorting (FANS) prior to DNA methylation analysis to gain cell-type resolution in AD.1 After sorting cortex nuclei into three populations (neuronal-enriched, oligodendrocyte-enriched, and microglia- and astrocyte- enriched) they performed an epigenome-wide association study (EWAS) from 631 donors and combined with meta-analysis from previous studies. Their results identified 334 differentially methylated positions, including loci not previously implicated in AD pathology. Surprisingly, single-cell analysis revealed the majority of previously identified disease-associated methylation in cortex were actually associated with non-neuronal cells. Even when using a deconvolution method, the authors were not able to identify disease-associated DNA methylation occurring in specific cell types from “bulk” data (Figure 2). This study demonstrates the importance of characterizing methylation profiles on refined cell populations. Additional resolution at the single-cell level may uncover novel biology and further enhance our understanding of epigenetic signatures in AD and other neurodegenerative diseases.

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Figure 2

Understanding tumor cell states and epigenetic plasticity

Tumors are often a complex mixture of malignant and normal cells. Single-cell RNA sequencing studies have identified extensive transcriptional diversity in cancer cells from the same tumor. In glioma, for example, cells follow a neurodevelopmental trajectory defined by distinct transcriptional states.2 The transcriptional diversity cannot be solely attributed to genetic diversity, raising the question of how these cell states are encoded.

To understand the transcriptional diversity within individual glioma tumors, Chaligne and colleagues performed single-cell RNA-seq and single-cell DNA methylation analysis.3 They found extensive intratumor heterogeneity of DNA methylation profiles, which reflect cellular states and provide evidence for epigenetic encoding. When classifying cells using previously defined glioma tumor subtypes,4 single cells within the individual tumors spanned multiple subtypes, suggesting tumor subtyping based on bulk analysis may not be capturing the full complexity of tumor dynamics. As bulk DNA methylation profiling is increasingly used for clinical classification, this study demonstrates the critical need to better understand intratumor DNA methylation using single-cell studies to better serve patients.

To help address these and other challenges, Scale Biosciences has launched the first commercial solution for single-cell DNA methylation. The Single Cell DNA Methylation Kit delivers unbiased whole genome coverage to maximize discovery power, or the use of target capture enrichment to tailor experiments to regions of interest. The streamlined workflow and powerful chemistry enable the analysis of tens of thousands of cells in a single experiment. Complemented by an easy-to-use data analysis pipeline, the end-to-end solution enables efficient analysis of single-cell methylomes at scale.

 

References

  1. Shireby G, et al. DNA methylation signatures of Alzheimer’s disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types. Nat Commun. 2022 Sep 24;13(1):5620.
  2. Tirosh I et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Science. 2016 Nov 10; 539(7628): 309–313.
  3. Chaligne R, et al, Epigenetic encoding, heritability, and plasticity of glioma transcriptional states. Nat Genet. 2021 Oct;53(10):1469-1479.
  4. Ceccarelli M et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell. 2016 Jan 28; 164(3): 550–563.

 

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