Lo, J. O. | bioRxiv Science

bioRxiv2025

A Zika Virus-Like Particle Vaccine Mitigates Early Pregnancy Loss In Rhesus Macaques

Zika virus (ZIKV) is an arthropod-borne Orthoflavivirus that caused a major outbreak in the Americas in 2015-16. In Brazil, up to 46% of ZIKV positive pregnancies resulted in congenital Zika syndrome (CZS). CZS is characterized by a wide range of neurologic birth defects and miscarriage in up to 7.6% of affected pregnancies. With no current licensed ZIKV vaccines, we sought to evaluate a Zika virus-like particle (VLP) vaccine candidate in a rhesus macaque (RM) pregnancy model. VLPs were produced in mammalian cells expressing the pre-membrane-envelope region of the Asian-lineage ZIKV strain PRVABC59, which belongs to the Asian ZIKV lineage that is associated with outbreaks of congenital disease. To evaluate vaccine protection against adverse pregnancy complications, two cohorts of female RM were vaccinated with ZIKV-VLP with adjuvant Alhydrogel (alum) or adjuvant alone prior to mating. At gestational day (GD) 30 (early first trimester), pregnant animals were challenged with ZIKV-DAK 41524, an African-lineage strain shown to induce 1st-trimester fetal demise in 78% (n=11/14 animals) of RM, making it an ideal and stringent model for evaluating ZIKV vaccines. Within the vaccinated cohort, 2 of 3 animals reached the study endpoint of GD 90 with no observed adverse pregnancy outcomes. The third animal experienced pregnancy loss at GD 49 (18 d post infection), although no infectious virus was detected in placental or fetal tissues. In the unvaccinated cohort, two animals had severe adverse events. One animal experienced preterm labor, and another developed early-onset hydrops fetalis with widespread ZIKV-RNA detected via RNAscope and extensive placental damage. These results confirm a significant risk for early pregnancy loss in RM infected with ZIKV-DAK 41524. This model can be further used to understand the complexities of placental immunological features underlying stillbirth and miscarriage following infection. Our findings indicate that this ZIKV-VLP vaccine candidate protected pregnant macaques against fetal demise associated with highly pathogenic ZIKV challenge. Author SummaryZika virus (ZIKV) infection during pregnancy is associated with pregnancy loss, severe birth defects, including microcephaly and developmental delays, and other subtle neurologic changes. Although vaccine development efforts have been ongoing since the 2015/2016 ZIKV outbreak, no approved vaccine is currently available. Many existing studies have tested vaccines in animal models using strains such as ZIKV-PR that cause mild or moderate pregnancy complications at similar rates to human cases. Using challenge strains that only cause mild, or moderate pregnancy complications makes it difficult to rigorously evaluate vaccine efficacy. In this study, we tested a virus-like particle (VLP) vaccine, a safe and effective method for use during pregnancy as it is replication-deficient and only contains viral structural antigens. We found that the VLP vaccine, when paired with an adjuvant (alum), induced strong antibody responses in mice and controlled viral dissemination following challenge in nonpregnant macaques. To evaluate the protective efficacy of the ZIKV-VLP vaccine against in utero infection and disease, we used a stringent model of ZIKV infection during early pregnancy in rhesus macaques that is associated with high rates of fetal demise. In pregnant macaques, the vaccine reduced maternal viremia, limited viral spread to fetal and placental tissues, and conferred protection against virus-mediated fetal demise and placental damage. This is the first study to evaluate a VLP-based vaccine in a consistent pregnancy loss model of ZIKV infection. These findings support the continued development of VLP vaccines as a safe and effective strategy for protecting pregnant individuals and their developing fetuses from ZIKV.

microbiology↗

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↗