Biology subjects

Izzi, J. M.

Publications and source records attributed to Izzi, J. M..

4 recordsLinked to original sources

bioRxiv2023

An ex vivo model of interactions between extracellular vesicles and peripheral mononuclear blood cells in whole blood

Extracellular vesicles (EVs) can be loaded with therapeutic cargo and engineered for retention by specific body sites; therefore, they have great potential for targeted delivery of biomolecules to treat diseases. However, the pharmacokinetics and biodistribution of EVs in large animals remain relatively unknown, especially in primates. We recently reported that when cell culture-derived EVs are administered intravenously to Macaca nemestrina (pig-tailed macaques), they differentially associate with specific subsets of peripheral blood mononuclear cells (PBMCs). More than 60% of CD20+ B cells were observed to associate with EVs for up to 1 hr post-intravenous administration. To investigate these associations further, we developed an ex vivo model of whole blood collected from healthy pig-tailed macaques. Using this ex vivo system, we found that labeled EVs preferentially associate with B cells in whole blood at levels similar to those detected in vivo. This study demonstrates that ex vivo blood can be used to study EV-blood cell interactions.

immunology↗

bioRxiv2021

Pharmacokinetics and biodistribution of extracellular vesicles administered intravenously and intranasally to Macaca nemestrina

Extracellular vesicles (EVs) have great potential as novel drug carriers for the treatment of various diseases. These lipid bilayer vesicles are naturally abundant in mammalian tissues and circulation, can be loaded with therapeutic small molecule drugs, (si)RNA, proteins and CRISPR/Cas9, and may be engineered for retention by specific tissues. However, many questions remain on the optimal dosing, administration route, and pharmacokinetics of EVs. Previous studies have addressed biodistribution and pharmacokinetics in rodents, but little evidence is available from larger animals. Here, we investigated the pharmacokinetics and biodistribution of Expi293F-derived EVs labelled with a highly sensitive nanoluciferase reporter (palmGRET) in a non-human primate model (Macaca nemestrina), comparing intravenous (IV) and intranasal (IN) administration over a 125-fold dose range. We report that EVs administered IV had markedly longer circulation times in plasma than previously reported in mice, and were detectable in cerebrospinal fluid (CSF) after 30-60 minutes. Already after one minute following IV administration, we observed EV uptake by PBMCs, most notably B-cells. EVs were detected in liver and spleen within one hour of IV administration. None of the IN doses resulted in readily detectable EV levels in plasma, CSF, or organs, suggesting that IN delivery of EVs in large animals including humans may require reconsideration or pretreatment approaches. Furthermore, EV circulation times strongly decreased after repeated IV administration, possibly due to immune responses and with clear implications for xenogeneic EV-based therapeutics. We hope that our findings from this baseline study in macaques will help to inform future research and therapeutic development of EVs.

pharmacology and toxicology↗

bioRxiv2020

Combining In Vivo Corneal Confocal Microscopy with Deep Learning-based Analysis Reveals Sensory Nerve Fiber Loss in Acute SIV Infection

PurposeTo characterize corneal subbasal nerve plexus morphologic features using in vivo corneal confocal microscopy (IVCM) in normal and SIV-infected macaques and to implement automated assessments using novel deep learning-based methods customized for macaque studies. MethodsIn vivo corneal confocal microscopy images were collected from both male and female age-matched specific-pathogen free rhesus and pigtailed macaques housed at the Johns Hopkins University breeding colony using the Heidelberg HRTIII with Rostock Corneal Module. We also obtained repeat IVCM images of 12 SIV-infected animals including pre-infection and 10 day post-SIV infection time-points. All IVCM images were analyzed using a novel deep convolutional neural network architecture developed specifically for macaque studies. ResultsDeep learning-based segmentation of subbasal nerves in IVCM images from macaques demonstrated that corneal nerve fiber length (CNFL) and fractal dimension measurements did not differ between species, but pigtailed macaques had significantly higher baseline corneal nerve fiber tortuosity than rhesus macaques (P = 0.005). Neither sex nor age of macaques was associated with differences in any of the assessed corneal subbasal nerve parameters. In the SIV/macaque model of HIV, acute SIV infection induced significant decreases in both corneal nerve fiber length and fractal dimension (P= 0.01 and P= 0.008 respectively). ConclusionsThe combination of IVCM and objective, robust, and rapid deep-learning analysis serves as a powerful noninvasive research and clinical tool to track sensory nerve damage, enabling early detection of neuropathy. Adapting the deep-learning analyses to human corneal nerve assessments will refine our ability to predict and monitor damage to small sensory nerve fibers in a number of clinical settings including HIV, multiple sclerosis, Parkinsons disease, diabetes, and chemotherapeutic neurotoxicity.

neuroscience↗

bioRxiv2019

Deep Learning-Based Analysis of Macaque Corneal Sub-Basal Nerve Fibers in Confocal Microscopy Images

PurposeTo describe and assess different deep learning-based methods for automated measurement of macaque corneal sub-basal nerves using in vivo confocal microscopy (IVCM).\n\nMethodsThe automated assessment of corneal nerve fiber length (CNFL) in IVCM images is of increasing clinical interest. These measurements are important biomarkers in a number of diseases including diabetes mellitus, human immunodeficiency virus, Parkinsons disease and multiple sclerosis. Animal models of these and other diseases play an important role in understanding the disease processes as efforts toward developing new and effective therapeutics are made. And while automated methods exist for nerve fiber analysis in clinical data, differences in anatomy and image quality make the macaque data more challenging and has motivated the work reported here.\n\nToward this goal, nerves in macaque corneal IVCM images were manually labelled using an ImageJ plugin (NeuronJ). Different deep convolutional neural network (CNN) architectures were evaluated for accuracy relative to the ground truth manual tracings. The best performing model was used on separately acquired macaque ICVM images to additionally compare inter-reader variability.\n\nConclusionsDeep learning-based segmentation of sub-basal nerves in IVCM images shows excellent correlation to manual segmentations in macaque data. The technique is indistinguishable across readers and paves the way for more widespread adoption of objective automated analysis of sub-basal nerves in IVCM.\n\nTranslational RelevanceQuantitative measurements of corneal sub-basal nerves are important biomarkers for disease screening and management. This work reports on different approaches that, in using deep learning-based techniques, leverage state of the art analysis methods to demonstrate performance akin to human graders. In application, the approach is robust, rapid and objective, offering utility to a variety of clinical studies using IVCM.

neuroscience↗