key: cord-0841660-5odudeav authors: Finlay, John B.; Brann, David H.; Abi-Hachem, Ralph; Jang, David W.; Oliva, Allison D.; Ko, Tiffany; Gupta, Rupali; Wellford, Sebastian A.; Moseman, E. Ashley; Jang, Sophie S.; Yan, Carol H.; Matusnami, Hiroaki; Tsukahara, Tatsuya; Datta, Sandeep Robert; Goldstein, Bradley J. title: Persistent post-COVID-19 smell loss is associated with inflammatory infiltration and altered olfactory epithelial gene expression date: 2022-04-18 journal: bioRxiv DOI: 10.1101/2022.04.17.488474 sha: c12c8ed2744b98ab22383def9e6343402be3bcf9 doc_id: 841660 cord_uid: 5odudeav Most human subjects infected by SARS-CoV-2 report an acute alteration in their sense of smell, and more than 25% of COVID patients report lasting olfactory dysfunction. While animal studies and human autopsy tissues have suggested mechanisms underlying acute loss of smell, the pathophysiology that underlies persistent smell loss remains unclear. Here we combine objective measurements of smell loss in patients suffering from post-acute sequelae of SARS-CoV-2 infection (PASC) with single cell sequencing and histology of the olfactory epithelium (OE). This approach reveals that the OE of patients with persistent smell loss harbors a diffuse infiltrate of T cells expressing interferon-gamma; gene expression in sustentacular cells appears to reflect a response to inflammatory signaling, which is accompanied by a reduction in the number of olfactory sensory neurons relative to support cells. These data identify a persistent epithelial inflammatory process associated with PASC, and suggests mechanisms through which this T cell-mediated inflammation alters the sense of smell. The olfactory sensory neurons detect volatile odors via olfactory receptors (ORs) localized to the neuronal cilia at the nasal airspace 10 , and are replenished following damage by neurogenic basal cells [11] [12] [13] [14] . Evidence from animal models suggests that acute transient anosmia may be due to sustentacular cell dysfunction or loss, and inflammation, both of which drive transient gene expression changes in olfactory neurons or alter the character of the mucus layer surrounding neuronal cilia 2, 15 . In addition, polymorphisms in the UGT2A1/UGT2A2 locus, whose gene product is expressed in sustentacular cells, are associated with elevated risk of COVID-19-related loss of smell or taste 16 . With viral clearance, it is likely that normal epithelial reparative processes reconstitute the sustentacular cell population, restoring function 13 . However, in the subset of subjects with lasting olfactory loss, what prevents recovery? There are several non-mutually-exclusive possibilities, including severe initial epithelial damage that eliminates the basal stem cell pools that normally are activated to reconstitute the neuroepithelium; infiltration of the OE by immune cell populations such that neuroinflammation or auto-immune phenomena perturb normal olfactory function and homeostasis through alterations in gene expression or other means; or central mechanisms that cause derangements in the olfactory bulbs of the brain or olfactory cortex. Evidence from animal models and human autopsies suggests that severe initial epithelial loss is unlikely, and that CNS involvement appears limited. However, no direct examination of olfactory tissue (including single cell RNA-sequencing) from humans suffering from PASC-olfactory dysfunction has been reported. Here, we obtained OE biopsies from subjects with lasting post-COVID olfactory loss, defined by objective olfactory testing, and examined samples using single cell RNA-sequencing (scRNA-seq) and immunohistochemistry to identify the cell populations and transcriptional alterations associated with PASC-olfactory dysfunction. We obtained nasal biopsy samples from subjects reporting olfactory dysfunction persisting at least 4 months since the onset of COVID-19 (Figure 1a and Table S1 ). Olfactory function was assessed with psychophysical testing (SIT, Smell Identification Test, Sensonics, Inc, Haddon Heights, NJ) confirming hyposmia. Most subjects also reported subjectively some component of parosmia, or distorted odor perceptions. Endoscopic olfactory mucosa biopsies were obtained either in the otolaryngology clinic, or in the operating room in subjects undergoing unrelated trans-sphenoidal procedures to access the pituitary for benign disease. Sinusitis or other known sinonasal disease was excluded by endoscopic exam and imaging, excluding bacterial infection, edema, polyposis. Biopsies were processed immediately for scRNA-seq analysis, as we have described previously 14, 17 . Additional surgical olfactory samples were processed for histologic analysis, including a PASC hyposmic and a post-COVID normosmic (1.5-2 months post-COVID-19). Samples for scRNA-seq included 6 PASC hyposmics (age range 22-58 years; 5 female, 1 male, 4-16 months post-COVID-19 onset). For comparison, we analyzed 3 normosmic control samples (age range 51-71 years; 2 female, 1 male); to bolster these control data, we combined them with previously published datasets from normosmic and presbyosmic patients to generate an integrated single-cell sequencing dataset from a total of 16 subjects, permitting robust cluster annotation from >124,000 cells (Figure 1b) . In a separate group of subjects, olfactory cleft mucus was collected from PASC hyposmics (n=15 subjects) or normosmic controls (n=13 subjects) for cytokine/chemokine assays. Nasal biopsies from control or PASC hyposmic subjects capture olfactory neuroepithelial cells. The OE is a self-renewing pseudostratified epithelium comprised of apical sustentacular and microvillar cells, mature OSNs, and immature OSNs emerging from basal stem and progenitor cells termed globose basal cells (GBCs) or horizontal basal cells (HBCs) (Figure 1a) . Commonly, patches of respiratory epithelium are interspersed within the olfactory cleft region, comprised of secretory cells, ciliated cells and basal cells. Submucosal Bowman's glands, vascular or stromal cells, and immune cell populations are abundant as well. To examine cell states and transcriptional profiles, we rapidly dissociated biopsies and processed live cell suspensions for scRNA-seq ( Figure 1b ). Uniform manifold approximation projection (UMAP) plots confirmed that the expected distribution of olfactory, respiratory and immune cells were captured for analysis. Importantly, we did not detect SARS-CoV-2 transcripts in scRNA-seq biopsy samples from any PASC hyposmics aligned to the viral reference genome; thus olfactory dysfunction in these patients is unlikely to reflect ongoing infection with SARS-CoV-2. Pseudotime analysis confirmed the expected OE lineage relationships and marker gene expression in the OE and in the neurogenic adult OE niche (Figure 1c-f ). Consistent with prior work exploring the acute effects of SARS-CoV-2 infection in human nasal epithelia, we observed changes in several transcripts associated with olfactory function in neuron clusters from PASC hyposmics compared to controls, including a reduction in the key adenylyl cyclase (ADCY3) that couples ORs to action potentials 15 (Figure 1g) . To quantify OSN numbers, we normalized OSN counts to sustentacular cell counts (Figure 1h ; n=5 PASC hyposmic, 5 control, p=0.034, 2-tailed t test); we used this approach since OE biopsies can be variable and because there are often patches of respiratory-like metaplasia in biopsies. When normalized in this manner, OSNs were reduced relative to sustentacular cells in the PASC hyposmic samples compared to controls. However, despite the reduction in OSN number, in the PASC samples we observed no differences relative to control in the frequency of cells expressing ORs, the expression levels of the OR genes, or the distribution of ORs In a separate cohort of PASC hyposmics and controls, olfactory mucus was assayed to measure cytokines and chemokines ( Figure S3 ). Consistent with a lack of severe cytotoxic inflammation, we verified that there were no markedly elevated changes in IL-1b, or TNFa. We confirmed the presence of IFN-g, although no significant difference was apparent in mucus, despite differences in IFN-g secreting cells identified by scRNA-seq. Finally, we qualitatively confirmed the T cell infiltrates observed by sc-Seq through immunohistochemistry on biopsies from control or post-COVID-19 hyposmic subjects (Figure 3h) . The COVID hyposmic staining exhibits dense CD45 + immune infiltration, and prominent CD3 + lymphocytic infiltration, both of which are absent in the normosmic. CD68 myeloid labeling pattern appears qualitatively similar in both conditions. Together, we identify in PASC hyposmic OE evidence of a persistent lymphocytic response that may provide signaling sufficient to drive localized IFNresponse phenotypes in sustentacular cells. This observation suggests a coupling between immune infiltration and sustentacular cell gene expression that could influence olfactory function. Understanding PASC pathophysiology is a global priority, as lasting dysfunction following SASR-CoV-2 infection may involve multiple systems, including olfactory, neuropsychiatric, respiratory, or cardiovascular 21, 22 . Current evidence for COVID-19 damage within the human OE comes largely from autopsy studies from subjects who died from severe acute COVID-19 3, 15 . These studies lack objective measurements of smell, and samples were obtained in the context of significant prior medical intervention. While those studies provide valuable insights into the cell types infected by SARS-CoV- Samples were processed for single cell analysis as described previously 17 Illumina base call files were converted to FASTQ files and processed through CellRanger Counts 6.1.2 (10X Genomics), aligned to either a human reference genome (GRCh38) or a combined reference genome containing human and SARS-CoV-2 genomes 27 . Starting from the raw cell by gene count matrices, data integration and preprocessing were performed using Scanpy (v1.8.2) and scvi-tools (v0.15.2). For accurate cell type identification, the data generated in this study were combined with our published human olfactory datasets 14, 17 . Highly-variable genes (HVGs) were identified using the scvi-tools "poisson_gene_selection" function (with patient id as the batch key), and the raw counts for these gene subsets were used as the input to the variational autoencoder (scVI) model. An scVI model (using the top 3000 HVGs) was trained for 500 unsupervised epochs with the default learning rate (with early stopping when the ELBO validation metric did not improve for at least 20 epochs) with the default parameters (10 latent dimensions, 128 nodes per hidden layer), a negative binomial observation model (gene_likelihood="nb"), the percentage of mitochondrial genes as a continuous covariate, and categorical covariate keys for the patient condition and patient id categorical variables (which thus performed dataset integration and batch correction for the purposes of cell type identification). A k-nearest neighbor graph was constructed from the resulting 10-dimensional latent embedding (using k=15 neighbors). The knn graph which was used for cell type clustering via the Leiden algorithm (resolution=1. 2) and as the input to the UMAP algorithm (with min_dist=0.5) for visualization. Clusters of dying cells containing high percentages of mitochondrial genes and low total counts as well as a cluster of cell doublets were removed, and the above procedure starting from the HVG identification was repeated (but with Leiden clustering resolution=1.6). The resulting cell type clusters were merged and manually annotated based on known cell type markers. After identifying and annotating the broad clusters, cell types of interest were further subclustered in an iterative manner, using the same scVI embedding approach, starting For additional plots, such as differential expression analysis and further subsetting and analysis of immune cell populations, filtered outputs were analyzed in R (v4.1.1) using the Seurat toolkit (v4.1.0) 28 . Processed anndata objects from Scanpy were converted to R objects preserving all meta data (including scVI clusters) using the LoadH5Seurat function from SeuratDisk. Data were normalized using relative counts normalization prior to differential expression analysis. Differentially expressed genes were found using the FindMarkers function with default settings (Wilcoxon Rank-Sum) and plotted using ggplot2 (identifying significant DE genes with >log2 fold-change, adjusted p<0.05). Cluster markers of lymphocyte subsets were identified using FindAllMarkers with default settings. NicheNet analysis was conducted in R with the nichenetr package (V 1.1.0) using the default ligand-target prior model, ligand receptor network, and weight integrated networks 29 . Specifically, cell populations of interest (i.e. lymphocyte clusters, OSNs, and sustentacular cells) with normalized gene expression were subset out from processed R objects (from anndata) and used as input for the appropriate receiver and sender populations. Samples for histology were collected in Hanks' Balanced Salt Solution (HBSS, Gibco) + 10% FBS. Tissues were fixed with 4% paraformaldehyde (Sigma, St. Louis) in phosphate buffered saline (PBS) for 4 hours at room temperature. Samples were washed with PBS and then incubated on a rocker at 4°C for 5-7 days in 30% sucrose, 250mM EDTA, and PBS. Samples were then flash frozen in OCT compound (VWR, Radnor, PA), sectioned at 10µm on a cryostat (CryoStar NX50, Thermo Fisher) and collected on Superfrost plus slides (Thermo Fisher). Tissue sections were rehydrated in PBS and blocked in 5% normal goat serum in PBS with 0.1% Triton X-100. Anti-Tubulin β3 (BioLegend, clone TUJ1, 1:500), Anti-CD45 (BioLegend, clone HI30, 1:100), Anti-CD3 (BioLegend, clone HIT3a, 1:100), Anti-CD68 (BioLegend, clone BL13756, 1:100) or anti-ERMN (Thermo, #PA5-58327, 1:500) primary antibodies diluted in blocking buffer were incubated on tissue sections for 1 hour at room temperature or overnight at 4 degrees. Following PBS washes, tissues were incubated with fluorescent conjugated secondary antibodies for 45 minutes (Jackson ImmunoResearch, West Grove, PA). Vectashield with DAPI (Vector Laboratories, Burlingame, CA) was applied to each section prior to coverslip. All images were acquired on a Leica DMi8 microscope system (Leica Microsystems). Images were analyzed using ImageJ software (V 2.3.0), and scale bars were applied using metadata from the original Leica acquisition software files. Mucus was obtained from the olfactory cleft using absorbant filter paper under endoscopic guidance per an approved IRB protocol at UC San Diego (#210078). Cohorts included PASC hyposmics (n=15 patients) or control normosmics (n=13), based on psychophysical testing using the SIT. A fluorescent bead-based multiplex assay (LegendPlex, Biolegend) was used to quantify 13 cytokines/chemokines through flow cytometry. All sequencing data set analyses were performed in Python or R using the toolkits and packages described above. Plots were produced using Scanpy, or ggplot2 in associated R toolkits 30 , or Graphpad Prism 9. Cell phenotype comparisons between PASC hyposmic and control samples were performed using unpaired 2-tailed t-test, with significance defined as p<0.05. Error bars represent standard error of the mean. DE gene sets were analyzed for gene ontology, cellular pathway or tissue output terms using ToppGene Suite 31 . Study Approval: All human subjects studies were performed under protocols approved by Institutional Review Boards. Disclosures: BJG discloses advising for Mirodia Therapeutics and consulting for Frequency Therapeutics. Table S1 . Biopsy samples processed for scRNA-seq. Immunohistochemistry (IHC); single cell RNA-sequencing (scRNA-seq). 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We appreciate the expert technical assistance of the Duke Molecular Genomics Core, and bioinformatics assistance from Vaibhav Jain. We also thank Clinical Research Coordinators, Amy Walker and Victoria Eifert, for expert assistance. Graphical schematics were created with Biorender.com. Data sets from scRNA-seq are deposited in GEO (accession numbers will be available upon publication).Code Availability: Any requests regarding code used here should be directed to the corresponding author.