* 일시 : 11/27(목) 16:30 –
* 연사 : 김효빈 박사(Cedars-Sinai Medical Center)
* 장소 : 벤처관 711호
* 제목 : Decoding Context-Specific Cell–Cell Interaction Genes from Spatial Transcriptomics
*초록 :
Current approaches to inferring cell-cell interactions (CCIs) are largely constrained by predefined ligand-receptor databases, particularly for low-resolution spatial transcriptomics (ST) platforms such as Visium. Due to the difficulties in accurately resolving interacting cells at coarse spatial resolution, other modes of interaction are often overlooked. Low-resolution ST data, however, offer unbiased transcriptome profiling and can serve as an alternative to high-resolution ST, which suffers from low sensitivity, and to image-based ST, which is limited by restricted gene panels. Here, we present CellNeighborEX v2, a database-free framework that directly infers CCIs from ST data by detecting deviations between observed and expected gene expression at the spot-population level and attributing them to source cell types. This approach captures a broad spectrum of interactions, including both paracrine signaling and contact-dependent communication. Across datasets from hippocampus, liver cancer, colorectal cancer, ovarian cancer, and lymph node infection, CellNeighborEX v2 achieves high accuracy in identifying ligand-receptor pairs and contact-associated genes, uncovering context-specific and partner-specific CCIs even from Visium data. It reveals interaction dynamics undetectable by conventional methods or single-cell RNA sequencing alone, thereby expanding the analytical power of ST and deepening our understanding of the molecular language of intercellular communication.