G-quadruplex profiling in complex tissues using single-cell CUT&Tag

G-quadruplexes (G4) are non-canonical DNA structures that gained increasing attention for their potential roles in gene regulation, with implications in neurodegenerative diseases and cancer. Despite their biological significance, G4 structures have not been studied systematically across tissues and cell types. In this study, we employ G4 single-cell CUT&Tag (G4 scCUT&Tag) to characterize G4 landscapes in postnatal mouse brain cells, leveraging single-cell analytical approaches commonly used in scRNA-Seq and scATAC-Seq datasets. Using conventional single-cell omics workflows to process and explore our data, we distinguished different cell types based on G4 heterogeneity. Furthermore, we performed uncoupled multi-omics integration of G4 scCUT&Tag data with scRNA-Seq gene expression profiles, using both a covariance-based technique (canonical correlation analysis) and a transfer learning-based deep learning approach. These integrations not only revealed significant co-enrichment of G4 and gene expression signals, but demonstrated that G4 scCUT&Tag enables detailed examination of G4 heterogeneity in complex tissues and supports integrative analysis of G4 profiles with other omics layers, offering new insights into the epigenomic landscapes of the developing central nervous system.