ShapeUpLMD: An Automated Pipeline for Spatially Optimized Laser Microdissection in Multi-omic Tissue Profiling

Schaaf, J. P.; Mitchell, D.; Ogata, J.; TaQee, S. A.; Wilson, K. N.; Conrads, K. A.; Loffredo, J.; Hood, B. L.; Abulez, T.; Hunt, A. L.; Langeland, A.; Mhawech-Fauceglia, P.; Darcy, K. M.; Tarney, C. M.; Maxwell, G. L.; Conrads, T. P.; Bateman, N. W.

doi:10.64898/2025.12.19.695433

bioRxiv · 10.64898/2025.12.19.695433

ShapeUpLMD: An Automated Pipeline for Spatially Optimized Laser Microdissection in Multi-omic Tissue Profiling

Schaaf, J. P.·Mitchell, D.·Ogata, J.·TaQee, S. A.·Wilson, K. N.·Conrads, K. A.·Loffredo, J.·Hood, B. L.·Abulez, T.·Hunt, A. L.·Langeland, A.·Mhawech-Fauceglia, P.·Darcy, K. M.·Tarney, C. M.·Maxwell, G. L.·Conrads, T. P.·Bateman, N. W.

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Abstract

Laser microdissection (LMD) enables enrichment of defined cellular populations from heterogeneous tissues, providing histologically-resolved molecular profiles with spatial resolution. However, the absence of standardized methods for generating optimized regions of interest (ROIs) limits reproducibility and scalability for spatial multi-omic workflows. To address this gap, we developed ShapeUpLMD, an open-source software tool and integrated workflow that automates the optimization of spatially defined ROIs for LMD, directly from digital pathology annotations. Fresh-frozen uterine serous carcinoma (USC) tumors (n = 11) were sectioned onto polyethylene naphthalate slides, scanned and subsequently annotated by an expert pathologist. Tumor and non-tumor ROIs were used to train tumor-specific classifiers in HALO (Indica Labs). Classified tumor or unbiased whole tissue ROIs were refined and optimized by ShapeUpLMD prior to automated collection on a Leica LMD7 microscope. Across the cohort, predicted tumor ROIs increased effective tumor purity by 86 {+/-} 6% relative to whole tissue while maintaining high spatial concordance between predicted and collected ROIs (accuracy = 0.9 {+/-} 0.05). Data-independent acquisition mass spectrometry quantified upwards of [~]6,500 proteins across spatially resolved ROIs, revealing regionally coherent clustering of adjacent tumor regions and abundance patterns consistent with tumor and non-tumor cell admixture in an unbiased spatial proteomic application. ShapeUpLMD provides an automated, reproducible, and scalable framework that bridges digital pathology with LMD enabling high-fidelity spatial enrichment for multi-omic analyses. This workflow increases throughput, reduces inter-operator variability, and supports standardized, regionally resolved tissue collection for spatial systems biology applications. The software is available at https://github.com/GYNCOE/ShapeUpLMD.

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Schaaf, J. P., Mitchell, D., Ogata, J., TaQee, S. A., Wilson, K. N., Conrads, K. A., Loffredo, J., Hood, B. L., Abulez, T., Hunt, A. L., Langeland, A., Mhawech-Fauceglia, P., Darcy, K. M., Tarney, C. M., Maxwell, G. L., Conrads, T. P., Bateman, N. W.. 2025-12-22. ShapeUpLMD: An Automated Pipeline for Spatially Optimized Laser Microdissection in Multi-omic Tissue Profiling. https://doi.org/10.64898/2025.12.19.695433

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