Granuloma Annulare Exhibits Mixed Immune and Macrophage Polarization Profiles with Spatial Transcriptomics.

Granuloma annulare (GA) is an idiopathic condition characterized by granulomatous inflammation in the skin. Prior studies have suggested that GA develops from various triggers, leading to a complex interplay involving innate and adaptive immunity, tissue remodeling, and fibrosis. Macrophages are the major immune cells comprising GA granulomas, however, the molecular drivers and inflammatory signaling cascade behind macrophage activation is poorly understood. Histologically, GA exhibits both palisaded and interstitial patterns on histology, however the molecular composition of GA at the spatial level remains unexplored. GA is a condition without FDA-approved therapies despite the significant impact of GA on quality of life. Spatial transcriptomics is a valuable tool for profiling localized, genome-wide gene expression changes across tissue with emerging applications in clinical medicine. To improve our understanding of the spatially localized gene expression patterns underlying GA, we profiled the spatial gene expression landscape from six patients with GA. Our findings revealed mixed Th1 and Th2 signals comprising the GA microenvironment and spatially distinct M1 and M2 macrophage polarization characteristics. IFN-γ and TNF signals emerged as important regulators of GA granulomatous inflammation and interleukin-32 emerged as a key driver of granulomatous inflammation. Overall, our spatial transcriptomics data indicate that GA exhibits mixed immune and macrophage polarization.

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.

Transcriptomic and Proteomic Spatial Profiling of Pediatric and Adult Diffuse Midline Glioma H3 K27-Altered, Reveals Region Specific Differences and Limited Overlap between mRNA and Protein.

Diffuse midline glioma, H3 K27-altered (DMG-Alt) are highly aggressive malignancies of the central nervous system (CNS) that primarily affect the pediatric population. Large scale spatial transcriptomic studies have implicated that tumor microenvironmental landscape plays an important role in determining the phenotypic differences in tumor presentation and clinical course, however, data connecting overall transcriptomic changes to the protein level is lacking. The NanoString GeoMx™ Digital Spatial Profiler platform was used to determine the spatial transcriptomic and proteomic landscape in a cohort of both pediatric and adult H3 K27-altered DMG biopsy samples. Three fluorescently labeled antibodies targeting immune cells (CD45), epithelial cells (PanCK), tumor cells (H3 K27M) and a nucleic acid stain (SYTO-13) were used to establish regions of interest (ROI) for genomic and proteomic analysis. We found genetic alterations within the tumor which can be delineated across patient age and spatial location. We show that the H3 K27M mutation itself has a profound impact on tumor cells transcriptomics and interestingly we found limited fidelity between overall transcriptome and proteome. Our data also validate the previously described OPC like precursor signature at the proteomic level and reveal a special shift in the signature based on the local TME composition.

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/.

Non-Metastatic Clear Cell Renal Cell Carcinoma Immune Cell Infiltration Heterogeneity and Prognostic Ability in Patients Following Surgery.

Predicting which patients will progress to metastatic disease after surgery for non-metastatic clear cell renal cell carcinoma (ccRCC) is difficult; however, recent data suggest that tumor immune cell infiltration could be used as a biomarker. We evaluated the quantity and type of immune cells infiltrating ccRCC tumors for associations with metastatic progression following attempted curative surgery. We quantified immune cell densities in the tumor microenvironment and validated our findings in two independent patient cohorts with multi-region sampling to investigate the impact of heterogeneity on prognostic accuracy. For non-metastatic ccRCC, increased CD8+ T cell infiltration was associated with a reduced likelihood of progression to metastatic disease. Interestingly, patients who progressed to metastatic disease also had increased percentages of exhausted CD8+ T cells. Finally, we evaluated the spatial heterogeneity of the immune infiltration and demonstrated that patients without metastatic progression had CD8+ T cells in closer proximity to ccRCC cells. These data strengthen the evidence for CD8+ T cell infiltration as a prognostic biomarker in non-metastatic ccRCC and demonstrate that multi-region sampling may be necessary to fully characterize immune infiltration within heterogeneous tumors. Tumor CD8+ T cell infiltration should be investigated as a biomarker in adjuvant systemic therapy clinical trials for high-risk non-metastatic RCC.

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.

Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough?

Spatial transcriptomics, the technology of visualizing cellular gene expression landscape in a cells native tissue location, has emerged as a powerful tool that allows us to address scientific questions that were elusive just a few years ago. This technological advance is a decisive jump in the technological evolution that is revolutionizing studies of tissue structure and function in health and disease through the introduction of an entirely new dimension of data, spatial context. Perhaps the organ within the body that relies most on spatial organization is the brain. The central nervous system's complex microenvironmental and spatial architecture is tightly regulated during development, is maintained in health, and is detrimental when disturbed by pathologies. This inherent spatial complexity of the central nervous system makes it an exciting organ to study using spatial transcriptomics for pathologies primarily affecting the brain, of which Glioblastoma is one of the worst. Glioblastoma is a hyper-aggressive, incurable, neoplasm and has been hypothesized to not only integrate into the spatial architecture of the surrounding brain, but also possess an architecture of its own that might be actively remodeling the surrounding brain. In this review we will examine the current landscape of spatial transcriptomics in glioblastoma, outline novel findings emerging from the rising use of spatial transcriptomics, and discuss future directions and ultimate clinical/translational avenues.

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.

Genomic Analysis of Human Brain Metastases Treated with Stereotactic Radiosurgery Under the Phase-II Clinical Trial (NCT03398694) Reveals DNA Damage Repair at the Peripheral Tumor Edge.

Stereotactic Radiosurgery (SRS) is one of the leading treatment modalities for oligo brain metastasis (BM), however no comprehensive genomic data assessing the effect of radiation on BM in humans exist. Leveraging a unique opportunity, as part of the clinical trial (NCT03398694), we collected post-SRS, delivered via Gamma-knife or LINAC, tumor samples from core and peripheral-edges of the resected tumor to characterize the genomic effects of overall SRS as well as the SRS delivery modality. Using these rare patient samples, we show that SRS results in significant genomic changes at DNA and RNA levels throughout the tumor. Mutations and expression profiles of peripheral tumor samples indicated interaction with surrounding brain tissue as well as elevated DNA damage repair. Central samples show GSEA enrichment for cellular apoptosis while peripheral samples carried an increase in tumor suppressor mutations. There are significant differences in the transcriptomic profile at the periphery between Gamma-knife vs LINAC.

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.

Transcriptome Dynamics in the Developing Larynx, Trachea, and Esophagus.

The larynx, trachea, and esophagus share origin and proximity during embryonic development. Clinical and experimental evidence support the existence of neurophysiological, structural, and functional interdependencies before birth. This investigation provides the first comprehensive transcriptional profile of all three organs during embryonic organogenesis, where differential gene expression gradually assembles the identity and complexity of these proximal organs from a shared origin in the anterior foregut. By applying bulk RNA sequencing and gene network analysis of differentially expressed genes (DEGs) within and across developing embryonic mouse larynx, esophagus, and trachea, we identified co-expressed modules of genes enriched for key biological processes. Organ-specific temporal patterns of gene activity corresponding to gene modules within and across shared tissues during embryonic development (E10.5-E18.5) are described, and the laryngeal transcriptome during vocal fold development and maturation from birth to adulthood is characterized in the context of laryngeal organogenesis. The findings of this study provide new insights into interrelated gene sets governing the organogenesis of this tripartite organ system within the aerodigestive tract. They are relevant to multiple families of disorders defined by cardiocraniofacial syndromes.

ß Cell-specific deletion of Zfp148 improves nutrient-stimulated ß cell Ca2+ responses.

Insulin secretion from pancreatic ß cells is essential for glucose homeostasis. An insufficient response to the demand for insulin results in diabetes. We previously showed that ß cell-specific deletion of Zfp148 (ß-Zfp148KO) improves glucose tolerance and insulin secretion in mice. Here, we performed Ca2+ imaging of islets from ß­Zfp148KO and control mice fed both a chow and a Western-style diet. ß-Zfp148KO islets demonstrated improved sensitivity and sustained Ca2+ oscillations in response to elevated glucose levels. ß-Zfp148KO islets also exhibited elevated sensitivity to amino acid-induced Ca2+ influx under low glucose conditions, suggesting enhanced mitochondrial phosphoenolpyruvate-dependent (PEP-dependent), ATP-sensitive K+ channel closure, independent of glycolysis. RNA-Seq and proteomics of ß-Zfp148KO islets revealed altered levels of enzymes involved in amino acid metabolism (specifically, SLC3A2, SLC7A8, GLS, GLS2, PSPH, PHGDH, and PSAT1) and intermediary metabolism (namely, GOT1 and PCK2), consistent with altered PEP cycling. In agreement with this, ß-Zfp148KO islets displayed enhanced insulin secretion in response to l-glutamine and activation of glutamate dehydrogenase. Understanding pathways controlled by ZFP148 may provide promising strategies for improving ß cell function that are robust to the metabolic challenge imposed by a Western diet.

SpotClean adjusts for spot swapping in spatial transcriptomics data.

Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies.

Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization.

Considerable effort has been devoted to refining experimental protocols to reduce levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.

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.

Identification of direct transcriptional targets of NFATC2 that promote ß cell proliferation.

The transcription factor NFATC2 induces ß cell proliferation in mouse and human islets. However, the genomic targets that mediate these effects have not been identified. We expressed active forms of Nfatc2 and Nfatc1 in human islets. By integrating changes in gene expression with genomic binding sites for NFATC2, we identified approximately 2200 transcriptional targets of NFATC2. Genes induced by NFATC2 were enriched for transcripts that regulate the cell cycle and for DNA motifs associated with the transcription factor FOXP. Islets from an endocrine-specific Foxp1, Foxp2, and Foxp4 triple-knockout mouse were less responsive to NFATC2-induced ß cell proliferation, suggesting the FOXP family works to regulate ß cell proliferation in concert with NFATC2. NFATC2 induced ß cell proliferation in both mouse and human islets, whereas NFATC1 did so only in human islets. Exploiting this species difference, we identified approximately 250 direct transcriptional targets of NFAT in human islets. This gene set enriches for cell cycle-associated transcripts and includes Nr4a1. Deletion of Nr4a1 reduced the capacity of NFATC2 to induce ß cell proliferation, suggesting that much of the effect of NFATC2 occurs through its induction of Nr4a1. Integration of noncoding RNA expression, chromatin accessibility, and NFATC2 binding sites enabled us to identify NFATC2-dependent enhancer loci that mediate ß cell proliferation.

The Anna Karenina Model of ß-Cell Maturation in Development and Their Dedifferentiation in Type 1 and Type 2 Diabetes.

Loss of mature ß-cell function and identity, or ß-cell dedifferentiation, is seen in both type 1 and type 2 diabetes. Two competing models explain ß-cell dedifferentiation in diabetes. In the first model, ß-cells dedifferentiate in the reverse order of their developmental ontogeny. This model predicts that dedifferentiated ß-cells resemble ß-cell progenitors. In the second model, ß-cell dedifferentiation depends on the type of diabetogenic stress. This model, which we call the "Anna Karenina" model, predicts that in each type of diabetes, ß-cells dedifferentiate in their own way, depending on how their mature identity is disrupted by any particular diabetogenic stress. We directly tested the two models using a ß-cell-specific lineage-tracing system coupled with RNA sequencing in mice. We constructed a multidimensional map of ß-cell transcriptional trajectories during the normal course of ß-cell postnatal development and during their dedifferentiation in models of both type 1 diabetes (NOD) and type 2 diabetes (BTBR-Lepob/ob ). Using this unbiased approach, we show here that despite some similarities between immature and dedifferentiated ß-cells, ß-cell dedifferentiation in the two mouse models is not a reversal of developmental ontogeny and is different between different types of diabetes.

Microglial responses to CSF1 overexpression do not promote the expansion of other glial lineages.

BACKGROUND: Colony-stimulating factor 1 (CSF1) expression in the central nervous system (CNS) increases in response to a variety of stimuli, and CSF1 is overexpressed in many CNS diseases. In young adult mice, we previously showed that CSF1 overexpression in the CNS caused the proliferation of IBA1+ microglia without promoting the expression of M2 polarization markers. METHODS: Immunohistochemical and molecular analyses were performed to further examine the impact of CSF1 overexpression on glia in both young and aged mice. RESULTS: As CSF1 overexpressing mice age, IBA1+ cell numbers are constrained by a decline in proliferation rate. Compared to controls, there were no differences in expression of the M2 markers ARG1 and MRC1 (CD206) in CSF1 overexpressing mice of any age, indicating that even prolonged exposure to increased CSF1 does not impact M2 polarization status in vivo. Moreover, RNA-sequencing confirmed the lack of increased expression of markers of M2 polarization in microglia exposed to CSF1 overexpression but did reveal changes in expression of other immune-related genes. Although treatment with inhibitors of the CSF1 receptor, CSF1R, has been shown to impact other glia, no increased expression of oligodendrocyte lineage or astrocyte markers was observed in CSF1 overexpressing mice. CONCLUSIONS: Our study indicates that microglia are the primary glial lineage impacted by CSF1 overexpression in the CNS and that microglia ultimately adapt to the presence of the CSF1 mitogenic signal.

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.