Abstract 225: Single Cell DNA methylomes from multiple tissues demonstrate tissue heterogeneity and target enrichment as a driver of read utility
Authors: Austin Holmes, Camden Jansen, Eric Pu, Sanika Khare, Dominic Skinner, Bryce Alves, Jerushah Thomas, Dmitry Pokholok, Felix Schlesinger
Journal: Cancer Research (2025)

Abstract Comparative analysis of single cell gene expression technologies is helpful in choosing a platform that fits experimental needs. However, emerging single cell (sc) technologies, like scDNA methylation, are currently unavailable across platforms and are unable to benefit from such analysis. Rather than comparing the performance of one tissue across multiple technologies, here we present one technology and a comparative analysis across multiple tissues, and sequencing read depths, with target enrichment. We integrate bulk-DNA methylation with single cell and transform methylation data to gene level metrics on par with current technologies. These datasets give guidelines on how to maximize the data per cell without excessive sequencing, allowing experiments to scale up. Single cell DNA methylation adds value to current genetic and epigenetic modalities as an orthogonal dataset that further characterizes functional genomics. Scale Bio offers a kitted platform for library generation and analysis of thousands of single cell methylomes. DNA methylation from bulk samples has been widely demonstrated to be a gene regulatory mechanism in health and disease, however large-scale single cell datasets are limited. At the onset of new technologies sequencing depth and cell number often exceed requirements for the most basic single cell analyses such as cell clustering, cell annotation and calling differentially methylated regions. At these high read depths and high cell # per sample, sample multiplexing in a biologically relevant way becomes difficult. We investigated six single cell methylomes (solid tumor, blood tumor, young PBMCs, old PBMCs, human brain and mouse brain) generated with Scale Bio scMet kit. For the tumor samples, we conducted copy number variation analysis alongside down-sampled read depth and cell number. For all samples, we cross compared cell clustering and cell annotation/recovery at various sequencing depths and cell number demonstrating that brain tissue tolerates much lower read depths. Each of these libraries were further enriched using the Twist Human methylome panel, demonstrating that an enrichment of regulatory regions allows for even lower read depths while maintaining a high number of significant DMRs. Lastly, we generated a bulk-DNA methylation library from the blood tumor sample to overlap with the most differentially methylated regions resolved at the single cell, revealing the limitations of bulk DNA compared to single cell. In conclusion these datasets and analyses conclude that tissue heterogeneity and the enrichment of known differentially methylated regions is a critical factor when considering sequencing depth required and the cell number per sample. However, emerging single cell methylomes continue to fine tune known DNA regulatory elements as well as facilitate discovery of de novo DNA methylation events at the single cell level. Citation Format: Austin Holmes, Camden Jansen, Eric Pu, Sanika Khare, Dominic Skinner, Bryce Alves, Jerushah Thomas, Dmitry Pokholok, Felix Schlesinger. Single Cell DNA methylomes from multiple tissues demonstrate tissue heterogeneity and target enrichment as a driver of read utility [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 225.