Walczak, P. | bioRxiv Science

bioRxiv2021

Reversible blood-brain barrier opening utilizing the membrane active peptide melittin in vitro and in vivo

The blood-brain barrier (BBB) tightly controls entry of molecules and cells into the brain, restricting the delivery of therapeutics. Blood-brain barrier opening (BBBO) utilizes reversible disruption of cell-cell junctions between brain microvascular endothelial cells to enable transient entry into the brain. Development of BBBO techniques has been hindered by a lack of physiological models for in vitro study. Here, we utilize an in vitro tissue-engineered microvessel model to demonstrate that melittin, a membrane active peptide present in bee venom, supports BBBO. From endothelial and neuronal viability studies, we identify the accessible concentration range for BBBO. We then use a tissue-engineered model of the human BBB to optimize dosing and elucidate the mechanism of opening. Melittin and other membrane active variants transiently increase paracellular permeability via disruption of cell-cell junctions. In mice, we demonstrate a minimum clinically effective intra-arterial dose of 3 M{middle dot}min melittin, which is reversible within one day and neurologically safe. Melittin-induced BBBO represents a novel platform for delivery of therapeutics into the brain.

bioengineering↗

bioRxiv2020

Inferring Maps of Cellular Structures from MRI Signals using Deep Learning

1H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each pixel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.

bioengineering↗