Biology subjects

Hood, B. L.

Publications and source records attributed to Hood, B. L..

8 recordsLinked to original sources

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

Integrated proteogenomics uncovers ancestry-specific and shared molecular drivers in localized prostate cancer

Our integrative proteogenomics (genome, proteome and phosphoproteome) of localized prostate cancer (PCa) in an equal-access Military Health System patient cohort (57 Black and 55 White) revealed significant ancestry-associated differences. Somatic and germline regulatory differences converged on androgen, metabolic, PI3K/AKT/mTOR, and DNA damage response (DDR) pathways, with ancestry-specific immune- and stromal-associated signals. Black patients displayed greater genomic variability, enhanced androgen response, fatty-acid metabolism, and epithelial-mesenchymal transition, while White patients showed prevalent DDRG alterations, activated oncogenic signaling (MYC, E2F, mTORC1), and cell cycle regulation. Phosphoproteomics highlighted distinct kinase activities and candidate druggable dependencies. Multiomics integration revealed three exploratory tumor subtypes whose distinct biological programs were reproducibly validated. Ancestry-associated eQTLs supported inherited regulation of the proteome independent of CNAs. Ancestry-specific CNA and protein panels improved progression risk prediction beyond PSA and pathology models. These findings provide a framework for ancestry-informed prognostic models and generate testable hypotheses for precision therapies to reduce outcome disparities.

cancer biology↗

bioRxiv2025

Development of a High-throughput Assay to Identify Novel Reactivators of Acetylcholinesterase

Acetylcholinesterase (AChE) is a key enzyme in the nervous system capable of breaking down acetylcholine (ACh). ACh is a critical neurotransmitter that signals between nerve cells and muscles. Organophosphates (OPs), used as insecticides and nerve agents, irreversibly inhibit AChE by forming a covalent bond with the enzymes active site. Since exposure to these nerve agents can quickly lead to death, many researchers have strived to discover new drugs that will reverse the inhibition of AChE by such agents. The goal was to develop a high-throughput miniaturized assay for testing large numbers of compounds for AChE reactivation quickly and reliably. Here we describe our miniaturized assay that utilizes the sarin surrogate nitrophenyl isopropyl methylphosphonate (NIMP) in an Amplex Red coupled assay and its validation with 42 compounds selected from an in silico virtual screen.

biochemistry↗

bioRxiv2024

Deep Quantitative Proteomics Identifies Conserved Proteome Alterations in Low and High-Grade Serous Ovarian Cancers

BackgroundLow-grade serous ovarian carcinoma (LGSOC) is a rare and largely chemoresistant subtype of epithelial ovarian cancer. Unlike treatment for high-grade serous ovarian cancer (HGSOC), management options for LGSOC patients are limited, in part, due to a lack of deep molecular characterization of this disease. To address this limitation, we aimed to define highly conserved proteome alterations in LGSOC by performing deep quantitative proteomic analysis of tumors collected from LGSOC (n=12) and HGSOC (n=24) patients or normal fallopian tube tissues (n=12) and validating proteins within two independent proteomic datasets of LGSOC (n=25) and HGSOC (n=49) tumors. ResultsOur efforts identified 275 protein alterations conserved between LGSOC and HGSOC tumors that exhibit high quantitative correlation between discovery and validation cohorts (Spearman Rho [≥] 0.82, P < 1E-4). Conserved proteins elevated in LGSOC tumors were enriched for pathways regulating cell adhesion and defective cellular apoptosis signaling and candidates mapping as putative drug targets included 5-nucleotidase/ cluster of differentiation 73 (NT5E/CD73). We also identified MUC16 (CA125) as significantly elevated in LGSOC versus HGSOC tumors and confirmed this by immunohistochemistry analysis between three independent reviewers. We also find that MUC16 exhibits a more apical versus membrane-staining pattern in LGSOC tumors, suggesting unique regulation of MUC16 in this disease subtype. ConclusionOur efforts define highly conserved protein alterations distinguishing LGSOC from HGSOC tumors, including CD73, as well as the novel identification that MUC16 is elevated and exhibits more apical staining pattern in LGSOC tumor tissues. These findings deepen our molecular understanding of LGSOC and provide unique insights into highly conserved proteome alterations in LGSOC tumors.

cancer biology↗

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↗

bioRxiv2020

Peptide Ancestry Informative Markers in Uterine Neoplasms from Women of European, African and Asian Ancestry

Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with molecular disease pathogenesis. Nonsynonymous single nucleotide polymorphisms (SNPs) encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. In silico analysis shows that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R2=0.9905). Multiplexed proteomic analysis of >100 human endometrial cancer cell lines and uterine leiomyoma tissues resulted in the quantitation of 62 pAIMs that correlate with self-described race and genotype-confirmed patient ancestry. Candidates include a D451E substitution in GC vitamin D-binding protein previously associated with altered vitamin D levels in African and European populations. These efforts describe a generalized set of markers for proteoancestry assessment that will further support studies investigating the impact of ancestry on the human proteome and how this relates to the pathogenesis of uterine neoplasms.

cancer biology↗

bioRxiv2020

Standardization and Harmonization of Distributed Multi-National Proteotype Analysis supporting Precision Medicine Studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrated robust, sensitive, and reproducible data generation across eleven sites in nine countries on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

molecular biology↗

bioRxiv2019

Extensive Intratumor Proteogenomic Heterogeneity Revealed by Multiregion Sampling in a High-Grade Serous Ovarian Tumor Specimen

Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten primary high-grade serous ovarian tumors and analyzed using proteomics (mass spectrometry and reverse phase protein microarray) and RNA-sequencing analyses. Comparative analyses of transcript and protein abundances revealed independent clustering of enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering between purified collections, driven by overall tumor purity. Comparison of historic prognostic molecular subtypes for HGSOC revealed protein and transcript expression from tumor epithelium correlated most strongly with the differentiated molecular subtype, whereas stromal proteins and transcripts most strongly correlated with mesenchymal subtype. Protein and transcript abundance in tumor epithelium and stromal collections from neighboring sections exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial protein and transcript expression heterogeneity within the tumor microenvironment that directly bears on prognostic signatures and underscore the need to enrich cellular subpopulations for expression profiling.

cancer biology↗