FusionX: Automated, Standardized quantification of cell-to-cell fusion across diverse systems

typically relies on calculating the percentage of nuclei within multinucleated cells or using indirect genetic reporters. However, existing methods are laborious and error-prone, which limits standardization, accuracy, and throughput, thereby hindering meaningful mechanistic analyses. Here, we present FusionX, an AI-powered image analysis pipeline that enables automated and robust quantification of cell fusion across diverse cell types using standard membrane and nuclear dyes. FusionX integrates CellX, a fine-tuned Segment Anything Model that segments the cell boundaries of mono- and multi-nucleated cells, with Cellpose for accurate nuclear detection, enabling high-throughput and detail-rich analysis. Importantly, by extracting precise cell boundaries, FusionX provides the number of nuclei per cell together with additional single-cell parameters such as cell size and shape. Benchmarking demonstrates that FusionX delivers human-level accuracy, dramatically increases speed, and generalizes across systems, from viral fusogen-induced fusion to myogenic differentiation. By eliminating the need for specialized reporters and subjective manual quantification, FusionX paves the way for reproducible, scalable and multiparametric quantification of cell fusion in a wide range of biological contexts.