Abstract:Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) – the causal agent in COVID-19 – affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.

Recent reports suggest an association between COVID-19 and altered olfactory function. Here we analyze bulk and single cell RNA-Seq datasets to identify cell types in the olfactory epithelium that express molecules that mediate infection by SARS-CoV-2 (CoV-2), the causal agent in COVID-19. We find in both mouse and human datasets that olfactory sensory neurons do not express two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. In contrast, olfactory epithelial support cells and stem cells express both of these genes, as do cells in the nasal respiratory epithelium. Taken together, these findings suggest possible mechanisms through which CoV-2 infection could lead to anosmia or other forms of olfactory dysfunction.

A growing number of single-cell sequencing platforms enable joint profiling of multiple omics from the same cells. We present , a novel method that not only allows for analyzing the data from joint-modality platforms, but provides a coherent framework for the integration of multiple datasets measured on different modalities. We demonstrate its performance on multi-modality data of gene expression and chromatin accessibility and illustrate the integration abilities of by jointly analyzing this multi-modality data with single-cell RNA-seq and ATAC-seq datasets.

No abstract

Understanding the complex interplay of factors affecting nanoparticle accumulation in solid tumors is a challenge that must be surmounted to develop effective cancer nanomedicine. Among other unique microenvironment properties, tumor vascular permeability is an important feature of leaky tumor vessels which enables nanoparticles to extravasate. However, permeability has thus far been measured by intravital microscopy on optical window tumors, which has many limitations of its own. Additionally, mathematical models of particle tumor transport are often too complicated to be accessible to most researchers. Here, we present a more simplified and accessible mathematical model based on diffusive flux, which uses particle tumor accumulation and plasma pharmacokinetics to yield effective permeability, P eff. This model, called diffusive flux modeling (DFM), allows effects from multiple parameters to be decoupled and is also the first demonstration, to the best our knowledge, of extracting P eff values from bulk biodistribution results (e.g., routine positron emission tomography studies). The DFM equation was used to explain in vivo results of sub-20 nm nanocarriers called three-helix-micelles (3HM), particularly 3HM’s selective accumulation in different tumor models. When DFM was applied to multiple published biodistribution data, a semiquantitative comparison of various tumor models, particle size, and active targeting strategies could be made. The analysis clearly pointed out the importance of balancing multiple characteristics of nanoparticles to ensure successful treatment outcome and highlights the usefulness of this simple model for initial particle design, selection, and subsequent optimization.