Biology subjects

McCarty, O. J.

Publications and source records attributed to McCarty, O. J..

4 recordsLinked to original sources

bioRxiv2026

Whole organism 3D mapping reveals universal branching topology and biophysical optimization governs vascular and nervous system development

The vascular and nervous systems are transport networks essential for life, yet whether universal geometric and topological principles govern their formation remains unclear. The organism-wide molecular and biophysical coordination required to build and maintain these networks during embryonic development has inspired decades of theoretical work, offering predictions about their expected organization. However, the lack of complete three-dimensional (3D) data has limited validation to isolated structures, leaving whole-organism networks unexplored. Here, we developed a computational pipeline for whole-organism 3D imaging to reconstruct the complete vascular and nervous systems of rhesus macaque, mouse, and turtle embryos. Our analysis reveals that both networks share structural principles, including binary branching and scale-invariant bifurcation geometry, maintained across species and throughout development. Yet, from these shared rules emerge fundamentally different architectures. Vasculature exhibits fractal topology with a fractal dimension [~]3, forming space-filling trees that prioritize proximity to every cell in the body. Nervous system networks exhibit a fractal dimension [~]2, forming sheet-like arbors that prioritize electrical signal transmission. This architectural divergence originates from distinct biophysical constraints operating in bifurcations, where vascular junctions minimize energy expenditure while conserving fluid flow and nerve junctions maximize conduction velocity while conserving electrical current. These local optimization rules, iterated across generations, construct organism-wide networks governed by distinct physical constraints, revealing how evolution generated different solutions for fluid versus electrical transport.

developmental biology↗

bioRxiv2026

Bradykinin Contributes to Vasogenic Edema in Murine Experimental Cerebral Malaria

Cerebral malaria (CM) due to Plasmodium falciparum (Pf) infection is a major cause of death in African children. Bradykinin (BK) is a mediator of vasogenic edema. It could contribute to the pathogenesis of central nervous system malaria in Kenyan children and P. berghei ANKA (PbA) infected C57BL/6J mice with experimental cerebral malaria. Cleaved plasma high molecular weight kininogen (cHK) is a marker for prior BK release. 40% of children with central nervous system malaria had plasma cHK versus 18% of children with uncomplicated malaria. Wild-type PbA-infected mice had circulating plasma cHK, elevated BK levels, and reduced HK and prekallikrein levels. HK null (Kng1-/-), combined BK B1 and B2 receptor null (Bdkrb1-/- / Bdkrb2-/-), BK B2 (Bdkrb2-/-) or BK B1 (Bdkrb1-/-) receptor null mice were protected from neurologic deterioration and brain edema compared to wild-type mice. F12-/-mice were not protected from neurological deterioration. Prekallikrein null (Klkb1-/-), prolylcarboxypeptidase hypomorphs (Prcpgt/gt), and brain endothelial cell conditional knockout of PRCP (Prcpfl/fl Cre) mice had reduced neurologic deterioration and brain edema. Adjuvant plasma kallikrein inhibition combined with artesunate treatment of PbA-infected mice reversed neurologic deterioration and brain edema and prolonged survival relative to artesunate alone. BK-induced vasogenic edema contributes to human and murine CM.

pathology↗

bioRxiv2025

CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images

Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training of segmentation algorithms requires advanced programming skills and intricate workflows, limiting their accessibility to many investigators. Here, we present a protocol and software for automatic segmentation of medical images guided by a graphical user interface (GUI) using the CODAvision algorithm. This workflow simplifies the process of semantic segmentation of microanatomical structures by enabling users to train highly customizable deep learning models without extensive coding expertise. The protocol outlines best practices for creating robust training datasets, configuring model parameters, and optimizing performance across diverse biomedical image modalities. CODAvision enhances the usability of the CODA algorithm (Nature Methods, 2022) by streamlining parameter configuration, model training, and performance evaluation, automatically generating quantitative results and comprehensive reports. We expand beyond the original implementation of CODA to serial histology by demonstrating robust performance across numerous medical image modalities and diverse biological questions. We provide sample results in data types including histology, magnetic resonance imaging (MRI), and computed tomography (CT). We demonstrate the diverse use of this tool in applications including quantification of metastatic burden in in vivo models and deconvolution of spot-based spatial transcriptomics datasets. This protocol is designed for researchers with interest in rapid design of highly customizable semantic segmentation algorithms and a basic understanding of programming and anatomy.

bioinformatics↗

bioRxiv2023

Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures

Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.

developmental biology↗