Cellquant: a vibecoder's guide to image analysis

Quantitative fluorescence microscopy is central to modern cell biology, yet extracting reproducible measurements from images remains a bottleneck for biologists without programming experience. Here we present cellquant, a single-script command line pipeline for multi-channel fluorescence images that performs cell segmentation, puncta quantification, colocalization analysis, and spatial proximity measurements. Because the interface is entirely text based, the exact command used to generate any result can be recorded and re-executed. We validate cellquant on two biological systems. In human HCT116 cells, the pipeline quantified arsenite-induced stress granule formation. In budding yeast, simultaneous measurement of nucleolar morphology, colocalization, and spatial proximity across a temperature gradient revealed a coordinated sequence of nucleolar reorganization. Applying PCA and UMAP to the multi-parameter output of cellquant resolved a continuous cell state transition across the temperature gradient, with condensate redistribution and nucleolar morphology defining orthogonal axes. The pipeline produces publication-ready quantification with visual quality control and statistically rigorous replicate analysis. All code, documentation, and example datasets are freely available.