Bioimage analysis in deep visual proteomics: Advancing transparency, reproducibility, and FAIR principles

Deep Visual Proteomics (DVP) combines high-resolution microscopy, computational instance segmentation, laser capture microdissection (LMD), and ultrasensitive mass spectrometry to enable spatially resolved molecular analysis of defined cellular populations. Within this workflow, LMD plays a central role by physically isolating microscopy-defined regions of interest for downstream proteomic profiling. However, accurate image-to-stage transfer remains challenging due to limitations in imaging resolution, tissue preparation variability, laser cutting parameters, and partially manual alignment procedures. Here, we present an open-source semi-automated image-to-LMD interoperability framework for DVP that integrates fiducial marker detection, coordinate transformation, and segmentation-driven contour export for reproducible microdissection. Using neutrophil isolation from murine urinary bladder tissue as a representative use case, we demonstrate robust alignment of computationally defined cellular boundaries with LMD-guided excision. By improving interoperability, transparency, and workflow standardisation, this framework strengthens the integration of microscopy and spatial proteomics and supports reproducible analysis of spatially defined immune cell populations.