ShapeUpLMD: Automated Spatial Optimization of Laser Microdissection for Tissue Sampling
Description
Laser microdissection (LMD) enables enrichment of defined cellular populations from heterogeneous tissues but lacks standardized approaches for generating optimized regions of interest (ROIs) for scalable spatial multi-omic workflows. We developed ShapeUpLMD, an open-source tool that optimizes digitally annotated ROIs for automated spatial LMD workflows. Fresh-frozen uterine serous carcinoma tumor (n=11) tissue sections were pathologist-annotated, and tumor-specific HALO classifier generated ROIs were refined by ShapeUpLMD and collected using a Leica LMD7. Classifier-estimated tumor ROIs increased effective tumor purity by 86 ± 6% relative to whole tissue while maintaining strong concordance between predicted and collected regions (accuracy = 0.90 ± 0.05). Data-independent acquisition mass spectrometry quantified approximately 6,500 proteins, revealing regionally coherent clustering and patterns consistent with tumor and non-tumor cell admixture. ShapeUpLMD provides an automated, scalable framework linking digital pathology with high-fidelity LMD for standardized spatial multi-omic profiling. ShapeUpLMD is freely available at https://github.com/GYNCOE/ShapeUpLMD.