Wilson, K. N. | Search bioRxiv

bioRxiv2025

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

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.

cancer biology↗

bioRxiv2025

Field Evidence of Trypanosoma cruzi Infection, Diverse Host Use and Invasion of Human Dwellings by the Chagas Disease Vector in Florida, USA

Background: Triatomine bugs (Hemiptera, Reduviidae, Triatominae) are blood-sucking vectors of the protozoan parasite, Trypanosoma cruzi, which causes Chagas disease, a significant source of human morbidity and mortality in the Americas. Autochthonous transmission of Chagas disease in the United States is considered rare, despite evidence of Triatoma species harboring T. cruzi, invading homes, and biting occupants. In the southeastern United States, Triatoma sanguisuga is considered common, yet its distribution, host use, and T. cruzi infection are practically unknown in this region. Methodology/Principle Findings: Using field sampling and community science programs from 2013 to 2023, we collected triatomines from peridomestic and domestic settings, identified them to species, analyzed for bloodmeals, and screened for T. cruzi infection and Discrete Typing Units (DTUs) TcI - TcVI utilizing molecular techniques. Triatoma sanguisuga (n=310) were collected from 23 counties throughout the state, particularly in northern and central Florida. More than one third (34.6%) of T. sanguisuga were found inside a human dwelling, and 39.2% were collected by community members. T. cruzi infection was observed in 29.5% (88/298) of tested triatomines, with infection found in 12 of the 23 counties where triatomines had been collected. DTU-typing was successful for 47 of the T. cruzi-positive triatomines: 74.5% were infected with DTU TcI, 21.3% were infected with DTU TcIV, and 4.3% were co-infected with TcI and TcIV. Bloodmeal analysis of 144 T. sanguisuga found broad host use, including mammals (60%), ectothermic vertebrates (37%), and cockroaches (2.5%). Human blood meals contributed nearly a quarter (23%) of bloodmeals, indicating significant vector-human contact. Conclusion/Significance: Our field data from Florida demonstrate that T. sanguisuga is present near, and sometimes in, human dwellings, feeds upon humans, and is infected with multiple DTUs of T. cruzi. This indicates that the environment in the southeastern United States is suitable for autochthonous transmission of Chagas disease or that the human risk for T. cruzi infection is possible. The roles of ectotherms in T. sanguisuga and T. cruzi ecology also warrant further investigation. Author SummaryTriatomines, which can spread Chagas disease to humans and other animals, are found throughout many U.S. states. Little is known about triatomines in Florida - our study aimed to learn more about where triatomines can be found in Florida, how often they are infected with the parasite that causes Chagas disease, and which animals triatomines feed on in Florida, including humans. From 2013-2023, utilizing our own field work and community science programs, we collected 310 triatomines from various regions in Florida. Both adult and immature triatomines were found, and about one-third of the triatomines were found inside human homes (35%). About 30% of the triatomines tested were infected with the Chagas disease parasite, Trypanosoma cruzi. Blood-fed triatomines mostly fed on mammals (60%, including humans), amphibians (27%), and reptiles (11%), but not on birds. Nearly a quarter of bugs (23%) had fed on human blood. Our research raises concerns for possible transmission of Chagas disease to humans from triatomines in Florida.

ecology↗

bioRxiv2023

ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms, ESTIMATE and ConsensusTME (r [≥] 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomics data are available at (https://lmdomics.org/ProteoMixture/.

bioinformatics↗