Genomic analysis of human brain metastases treated with stereotactic radiosurgery reveals unique signature based on treatment failure.

Stereotactic radiosurgery (SRS) has been shown to be efficacious for the treatment of limited brain metastasis (BM); however, the effects of SRS on human brain metastases have yet to be studied. We performed genomic analysis on resected brain metastases from patients whose resected lesion was previously treated with SRS. Our analyses demonstrated for the first time that patients possess a distinct genomic signature based on type of treatment failure including local failure, leptomeningeal spread, and radio-necrosis. Examination of the center and peripheral edge of the tumors treated with SRS indicated differential DNA damage distribution and an enrichment for tumor suppressor mutations and DNA damage repair pathways along the peripheral edge. Furthermore, the two clinical modalities used to deliver SRS, LINAC and GK, demonstrated differential effects on the tumor landscape even between controlled primary sites. Our study provides, in human, biological evidence of differential effects of SRS across BM's.

SpatialView: an interactive web application for visualization of multiple samples in spatial transcriptomics experiments.

MOTIVATION: Spatial transcriptomics (ST) experiments provide spatially localized measurements of genome-wide gene expression allowing for an unprecedented opportunity to investigate cellular heterogeneity and organization within a tissue. Statistical and computational frameworks exist that implement robust methods for pre-processing and analyzing data in ST experiments. However, the lack of an interactive suite of tools for visualizing ST data and results currently limits the full potential of ST experiments. RESULTS: To fill the gap, we developed SpatialView, an open-source web browser-based interactive application for visualizing data and results from multiple 10× Genomics Visium ST experiments. We anticipate SpatialView will be useful to a broad array of clinical and basic science investigators utilizing ST to study disease. AVAILABILITY AND IMPLEMENTATION: SpatialView is available at https://github.com/kendziorski-lab/SpatialView (and https://doi.org/10.5281/zenodo.10223907); a demo application is available at https://www.biostat.wisc.edu/˜kendzior/spatialviewdemo/.

Rhinovirus infection of airway epithelial cells uncovers the non-ciliated subset as a likely driver of genetic susceptibility to childhood-onset asthma.

Asthma is a complex disease caused by genetic and environmental factors. Epidemiological studies have shown that in children, wheezing during rhinovirus infection (a cause of the common cold) is associated with asthma development during childhood. This has led scientists to hypothesize there could be a causal relationship between rhinovirus infection and asthma or that RV-induced wheezing identifies individuals at increased risk for asthma development. However, not all children who wheeze when they have a cold develop asthma. Genome-wide association studies (GWAS) have identified hundreds of genetic variants contributing to asthma susceptibility, with the vast majority of likely causal variants being non-coding. Integrative analyses with transcriptomic and epigenomic datasets have indicated that T cells drive asthma risk, which has been supported by mouse studies. However, the datasets ascertained in these integrative analyses lack airway epithelial cells. Furthermore, large-scale transcriptomic T cell studies have not identified the regulatory effects of most non-coding risk variants in asthma GWAS, indicating there could be additional cell types harboring these "missing regulatory effects". Given that airway epithelial cells are the first line of defense against rhinovirus, we hypothesized they could be mediators of genetic susceptibility to asthma. Here we integrate GWAS data with transcriptomic datasets of airway epithelial cells subject to stimuli that could induce activation states relevant to asthma. We demonstrate that epithelial cultures infected with rhinovirus significantly upregulate childhood-onset asthma-associated genes. We show that this upregulation occurs specifically in non-ciliated epithelial cells. This enrichment for genes in asthma risk loci, or 'asthma heritability enrichment' is also significant for epithelial genes upregulated with influenza infection, but not with SARS-CoV-2 infection or cytokine activation. Additionally, cells from patients with asthma showed a stronger heritability enrichment compared to cells from healthy individuals. Overall, our results suggest that rhinovirus infection is an environmental factor that interacts with genetic risk factors through non-ciliated airway epithelial cells to drive childhood-onset asthma.

Targeted inhibition of BET proteins in HPV-16 associated head and neck squamous cell carcinoma reveals heterogeneous transcription response.

Integrated human papillomavirus (HPV-16) associated head and neck squamous cell carcinoma (HNSCC) tumors have worse survival outcomes compared to episomal HPV-16 HNSCC tumors. Therefore, there is a need to differentiate treatment for HPV-16 integrated HNSCC from other viral forms. We analyzed TCGA data and found that HPV+ HNSCC expressed higher transcript levels of the bromodomain and extra terminal domain (BET) family of transcriptional coregulators. However, the mechanism of BET protein-mediated transcription of viral-cellular genes in the integrated viral-HNSCC genomes needs to be better understood. We show that BET inhibition downregulates E6 significantly independent of the viral transcription factor, E2, and there was overall heterogeneity in the downregulation of viral transcription in response to the effects of BET inhibition across HPV-associated cell lines. Chemical BET inhibition was phenocopied with the knockdown of BRD4 and mirrored downregulation of viral E6 and E7 expression. Strikingly, there was heterogeneity in the reactivation of p53 levels despite E6 downregulation, while E7 downregulation did not alter Rb levels significantly. We identified that BET inhibition directly downregulated c-Myc and E2F expression and induced CDKN1A expression. Overall, our studies show that BET inhibition provokes a G1-cell cycle arrest with apoptotic activity and suggests that BET inhibition regulates both viral and cellular gene expression in HPV-associated HNSCC.

Rhinovirus C causes heterogeneous infection and gene expression in airway epithelial cell subsets.

Rhinoviruses infect ciliated airway epithelial cells, and rhinoviruses' nonstructural proteins quickly inhibit and divert cellular processes for viral replication. However, the epithelium can mount a robust innate antiviral immune response. Therefore, we hypothesized that uninfected cells contribute significantly to the antiviral immune response in the airway epithelium. Using single-cell RNA sequencing, we demonstrate that both infected and uninfected cells upregulate antiviral genes (e.g. MX1, IFIT2, IFIH1, and OAS3) with nearly identical kinetics, whereas uninfected non-ciliated cells are the primary source of proinflammatory chemokines. Furthermore, we identified a subset of highly infectable ciliated epithelial cells with minimal interferon responses and determined that interferon responses originate from distinct subsets of ciliated cells with moderate viral replication. These findings suggest that the composition of ciliated airway epithelial cells and coordinated responses of infected and uninfected cells could determine the risk of more severe viral respiratory illnesses in children with asthma, chronic obstructive pulmonary disease, and genetically susceptible individuals.

SpatialCorr identifies gene sets with spatially varying correlation structure.

Recent advances in spatially resolved transcriptomics technologies enable both the measurement of genome-wide gene expression profiles and their mapping to spatial locations within a tissue. A first step in spatial transcriptomics data analysis is identifying genes with expression that varies spatially, and robust statistical methods exist to address this challenge. While useful, these methods do not detect spatial changes in the coordinated expression within a group of genes. To this end, we present SpatialCorr, a method for identifying sets of genes with spatially varying correlation structure. Given a collection of gene sets pre-defined by a user, SpatialCorr tests for spatially induced differences in the correlation of each gene set within tissue regions, as well as between and among regions. An application to cutaneous squamous cell carcinoma demonstrates the power of the approach for revealing biological insights not identified using existing methods.

Normalization by distributional resampling of high throughput single-cell RNA-sequencing data.

MOTIVATION: Normalization to remove technical or experimental artifacts is critical in the analysis of single-cell RNA-sequencing experiments, even those for which unique molecular identifiers are available. The majority of methods for normalizing single-cell RNA-sequencing data adjust average expression for library size (LS), allowing the variance and other properties of the gene-specific expression distribution to be non-constant in LS. This often results in reduced power and increased false discoveries in downstream analyses, a problem which is exacerbated by the high proportion of zeros present in most datasets. RESULTS: To address this, we present Dino, a normalization method based on a flexible negative-binomial mixture model of gene expression. As demonstrated in both simulated and case study datasets, by normalizing the entire gene expression distribution, Dino is robust to shallow sequencing, sample heterogeneity and varying zero proportions, leading to improved performance in downstream analyses in a number of settings. AVAILABILITY AND IMPLEMENTATION: The R package, Dino, is available on GitHub at https://github.com/JBrownBiostat/Dino. The Dino package is further archived and freely available on Zenodo at https://doi.org/10.5281/zenodo.4897558. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Novel immortalized human vocal fold epithelial cell line: In vitro tool for mucosal biology.

Study of vocal fold (VF) mucosal biology requires essential human vocal fold epithelial cell (hVFE) lines for use in appropriate model systems. We steadily transfected a retroviral construct containing human telomerase reverse transcriptase (hTERT) into primary normal hVFE to establish a continuously replicating hVFE cell line. Immortalized hVFE across passages have cobblestone morphology, express epithelial markers cytokeratin 4, 13 and 14, induced hTERT gene and protein expression, have similar RNAseq profiling, and can continuously grow for more than 8 months. DNA fingerprinting and karyotype analysis demonstrated that immortalized hVFE were consistent with the presence of a single cell line. Validation of the hVFE, in a three-dimensional in vitro VF mucosal construct revealed a multilayered epithelial structure with VF epithelial cell markers. Wound scratch assay revealed higher migration capability of the immortalized hVFE on the surface of collagen-fibronectin and collagen gel containing human vocal fold fibroblasts (hVFF). Collectively, our report demonstrates the first immortalized hVFE from true VFs providing a novel and invaluable tool for the study of epithelial cell-fibroblast interactions that dictate disease and health of this specialized tissue.