Boying Gong

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.

A growing number of single-cell sequencing platforms enable joint profiling of multiple omics from the same cells. We present Cobolt, 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 Cobolt by jointly analyzing this multi-modality data with single-cell RNA-seq and ATAC-seq datasets.

Whole-genome bisulfite sequencing (WGBS) provides a precise measure of methylation across the genome, yet presents a challenge in identifying differentially methylated regions (DMRs) between different conditions. Many methods have been developed, which focus primarily on the setting of two-group comparison. We develop a DMR detecting method MethCP for WGBS data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. For simple two-group comparison, we show that our method outperforms developed methods in accurately detecting the complete DMR on a simulated data set and an Arabidopsis data set. Moreover, we show that MethCP is capable of detecting wide regions with small effect sizes, which can be common in some settings, but existing techniques are poor in detecting such DMRs. We also demonstrate the use of MethCP for time-course data on another data set after methylation throughout seed germination in Arabidopsis.

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, Peff. 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 Peff 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.

Abstract A growing number of single-cell sequencing platforms enable joint profiling of multiple omics from the same cells. We present Cobolt, 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 Cobolt by jointly analyzing this multi-modality data with single-cell RNA-seq and ATAC-seq datasets.