Magnetic particle imaging (MPI) is an emerging ionizing radiation-free biomedical tracer imaging technique that directly images the intense magnetization of superparamagnetic iron oxide nanoparticles (SPIOs). MPI offers ideal image contrast because MPI shows zero signal from background tissues. Moreover, there is zero attenuation of the signal with depth in tissue, allowing for imaging deep inside the body quantitatively at any location. Recent work has demonstrated the potential of MPI for robust, sensitive vascular imaging and cell tracking with high contrast and dose-limited sensitivity comparable to nuclear medicine. To foster future applications in MPI, this new biomedical imaging field is welcoming researchers with expertise in imaging physics, magnetic nanoparticle synthesis and functionalization, nanoscale physics, and small animal imaging applications.

Viral engineered chimeric antigen receptor (CAR) T cell therapies are potent, targeted cancer immunotherapies, but their permanent CAR expression can lead to severe adverse effects. Nonviral messenger RNA (mRNA) CAR T cells are being explored to overcome these drawbacks, but electroporation, the most common T cell transfection method, is limited by cytotoxicity. As a potentially safer nonviral delivery strategy, here, sequential libraries of ionizable lipid nanoparticle (LNP) formulations with varied excipient compositions were screened in comparison to a standard formulation for improved mRNA delivery to T cells with low cytotoxicity, revealing B10 as the top formulation with a 3-fold increase in mRNA delivery. When compared to electroporation in primary human T cells, B10 LNPs induced comparable CAR expression with reduced cytotoxicity while demonstrating potent cancer cell killing. These results demonstrate the impact of excipient optimization on LNP performance and support B10 LNPs as a potent mRNA delivery platform for T cell engineering.

Background and aims:The atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability are not yet available. We sought to develop a scRNA-seq analysis workflow to investigate this environment and uncover potential therapeutic approaches. We designed a user-friendly, reproducible workflow that will be applicable to other disease-specific scRNA-seq datasets. Methods: Here we incorporated automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into a ready-to-deploy workflow. We applied this pipeline to further investigate a previously published human coronary single-cell dataset by Wirka et al. Notably, we developed an interactive web application to enable further exploration and analysis of this and other cardiovascular single-cell datasets.Results: We revealed distinct derivations of fibroblast-like cells from smooth muscle cells (SMCs), and showed the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several avenues for future pharmacological development for precision medicine. Further, our interactive web application, PlaqView (www.plaqview.com), allows lay scientists to explore this and other datasets and compare scRNA-seq tools without prior coding knowledge. Conclusions: This publicly available workflow and application will allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. Our analysis pipeline provides many hypothesis-generating tools to unravel the etiology of coronary artery disease. We also highlight potential

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COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV).