@article{popowski_moatti_scull_silkstone_lutz_lópez de juan abad_george_belcher_zhu_mei_et al._2022, title={Inhalable dry powder mRNA vaccines based on extracellular vesicles}, volume={5}, ISSN={2590-2385}, url={http://dx.doi.org/10.1016/j.matt.2022.06.012}, DOI={10.1016/j.matt.2022.06.012}, abstractNote={Respiratory diseases are a global burden, with millions of deaths attributed to pulmonary illnesses and dysfunctions. Therapeutics have been developed, but they present major limitations regarding pulmonary bioavailability and product stability. To circumvent such limitations, we developed room-temperature-stable inhalable lung-derived extracellular vesicles or exosomes (Lung-Exos) as mRNA and protein drug carriers. Compared with standard synthetic nanoparticle liposomes (Lipos), Lung-Exos exhibited superior distribution to the bronchioles and parenchyma and are deliverable to the lungs of rodents and nonhuman primates (NHPs) by dry powder inhalation. In a vaccine application, severe acute respiratory coronavirus 2 (SARS-CoV-2) spike (S) protein encoding mRNA-loaded Lung-Exos (S-Exos) elicited greater immunoglobulin G (IgG) and secretory IgA (SIgA) responses than its loaded liposome (S-Lipo) counterpart. Importantly, S-Exos remained functional at room-temperature storage for one month. Our results suggest that extracellular vesicles can serve as an inhaled mRNA drug-delivery system that is superior to synthetic liposomes.}, number={9}, journal={Matter}, publisher={Elsevier BV}, author={Popowski, Kristen D. and Moatti, Adele and Scull, Grant and Silkstone, Dylan and Lutz, Halle and López de Juan Abad, Blanca and George, Arianna and Belcher, Elizabeth and Zhu, Dashuai and Mei, Xuan and et al.}, year={2022}, month={Sep}, pages={2960–2974} }
 @article{cai_zhang_kovalsky_ghashghaei_greenbaum_2021, title={Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet}, volume={16}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0257426}, abstractNote={The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.}, number={9}, journal={PLOS ONE}, author={Cai, Yuheng and Zhang, Xuying and Kovalsky, Shahar Z. and Ghashghaei, H. Troy and Greenbaum, Alon}, year={2021}, month={Sep} }