Hongxiang Fu

Methods for analyzing DNA methylation data via Most Recurrent Methylation Patterns (MRMPs). Supports cell-type annotation, spatial deconvolution, unsupervised clustering, and cancer cell-of-origin inference. Includes C-backed summaries for YAME “.cg/.cm” files (overlap counts, log2 odds ratios, beta/depth aggregation), an XGBoost classifier, NNLS deconvolution, and plotting utilities. Scales to large spatial and single-cell methylomes and is robust to extreme sparsity.

Decoding DNA methylomes for biological insights is critical in epigenetics research. We present KnowYourCG (KYCG), a data interpretation framework designed for functional DNA methylation analysis. Unlike existing tools that target genes or genomic intervals, KYCG features direct base-level screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type–specific methylation, and trait associations. Through implementing efficient infrastructure that rapidly screens and investigates thousands of knowledgebases, KYCG addresses the challenges of data sparsity in various methylation datasets, including low-pass or single-cell DNA methylomes, 5-hydroxymethylation (5hmC) profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies. Applying KYCG to these datasets provides valuable insights into cell differentiation, cancer origins, epigenome-trait associations, and technical issues such as array artifacts, single-cell batch effects, and Nanopore 5hmC detection accuracy. Our tool simplifies large-scale methylation analysis and integrates seamlessly with standard assay technologies.

The spatial resolution of omics analyses is fundamental to understanding tissue biology ^1–3 . The capacity to spatially profile DNA methylation, which is a canonical epigenetic mark extensively implicated in transcriptional regulation ^4,5 , is lacking. Here we introduce a method for whole-genome spatial co-profiling of DNA methylation and the transcriptome of the same tissue section at near single-cell resolution. Applying this technology to mouse embryogenesis and the postnatal mouse brain resulted in rich DNA–RNA bimodal tissue maps. These maps revealed the spatial context of known methylation biology and its interplay with gene expression. The concordance and distinction in spatial patterns of the two modalities highlighted a synergistic molecular definition of cell identity in spatial programming of mammalian development and brain function. By integrating spatial maps of mouse embryos at two different developmental stages, we reconstructed the dynamics that underlie mammalian embryogenesis for both the epigenome and transcriptome, revealing details of sequence-, cell-type- and region-specific methylation-mediated transcriptional regulation. This method extends the scope of spatial omics to include DNA cytosine methylation, enabling a more comprehensive understanding of tissue biology across development and disease.

SPIE Digital Library Proceedings