Abstract LB193: Enhanced single cell DNA methylation analysis using combinatorial indexing

Abstract The epigenetic landscape of the human brain undergoes a plethora of changes during malignant transformation. In recent years, DNA methylation-based epigenetic modifications have been widely studied using traditional techniques like bisulfite sequencing and enzymatic methyl sequencing (EM-seq). However, these methods analyze bulk cell populations and lack the granularity of single-cell analysis. Although advances in single-cell analysis have revolutionized our understanding of cellular heterogeneity and functional diversity within complex biological systems, they are still costly, low throughput, and laborious. To address these challenges, ScaleBio has pioneered combinatorial indexing technology, enabling a significant increase in cell throughput. This method utilizes the cell itself as a compartment to perform 2-3 rounds of sequential barcoding in a plate-based workflow, eliminating the need for complex instrumentation. This technology has been successfully adapted to assess DNA methylation at the single-cell level offering a robust, affordable, high-throughput protocol that enhances yield, diversity, and coverage. In this study we used ScaleBio's single-cell methylation kit to investigate DNA methylation patterns during oncogenesis using cancer cells with widespread DNA methylation changes, such as human isocitrate dehydrogenase (IDH) mutant glioma cells at the single-cell level. The IDH gene family, comprising of IDH1, IDH2, and IDH3, encodes enzymes involved in the tricarboxylic acid (TCA) cycle. IDH1/2 mutations are present in over 80% of low-grade gliomas while IDH-mutant gliomas constitute about 1/5th of all adult diffuse gliomas, thus making them one of the most common brain tumor subtypes. As such, IDH mutations have emerged as attractive therapeutic targets for glioma treatment. By uncovering DNA methylation patterns at the single cell level, we provide here an epigenetic map to better understand this complex disease and better inform clinical discoveries. We achieved high cell recovery and robust cytosine coverage throughout our analysis of single cell methylomes isolated from human glioma tumor tissue. Using this data we generated a ranked list of the top hypo- and hypermethylated genomic regions and identified cell type specific clusters seen in different pathological states by looking at Differentially Methylated Regions (DMR) uncovering unique single-cell methylation profiles that may be obscured by bulk or pseudo-bulk analysis. These data show that the ScaleBio single-cell methylation workflow offers increased sensitivity, specificity, and accuracy in identifying DNA methylation sites when compared to other techniques while offering a comprehensive view of methylomes and providing insights into cellular heterogeneity and trajectories. Citation Format: Sanika Khare, Dominic Skinner, Maggie Nakamoto, Hosu Sin, Ashley Woodfin, Eric Pu, Lin Lin, Jason Koth, Beth Walczak. Enhanced single cell DNA methylation analysis using combinatorial indexing abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (7Suppl): Abstract nr LB193.