Title: Gain spatial biology insight via graph-based artificial intelligence
Abstract:
Cell–cell communications are vital for biological signaling and play important roles in complex diseases. Recent advances in single cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand–receptor (L–R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. To address these challenges, we have proposed a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L–R signaling axis across neighboring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. spaCI achieves to reveal hidden L–R interactions and their upstream transcription factors from different types of SCST data such as seqFISH+ and NanoString CosMx Spatial Molecular Imager (SMI) data. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.
