A single-cell atlas of the human healthy airways

Abstract

248 words) Rationale: The respiratory tract constitutes an elaborated line of defense based on a unique cellular ecosystem. Single-cell profiling methods enable the investigation of cell population distributions and transcriptional changes along the airways. Methods:We have explored cellular heterogeneity of the human airway epithelium in 10 healthy living volunteers by single-cell RNA profiling. 77,969 cells were collected by bronchoscopy at 35 distinct locations, from the nose to the 12 th division of the airway tree. Results:The resulting atlas is composed of a high percentage of epithelial cells (89.1%), but also immune (6.2%) and stromal (4.7%) cells with peculiar cellular proportions in different sites of the airways. It reveals differential gene expression between identical cell types (suprabasal, secretory, and multiciliated cells) from the nose (MUC4, PI3, SIX3) and tracheobronchial (SCGB1A1, TFF3) airways. By contrast, cell-type specific gene expression was stable across all tracheobronchial samples. Our atlas improves the description of ionocytes, pulmonary neuroendocrine (PNEC) and brush cells, which are likely derived from a common population of precursor cells. We also report a population of KRT13 positive cells with a high percentage of dividing cells which are reminiscent of "hillock" cells previously described in mouse. Conclusions:Robust characterization of this unprecedented large single-cell cohort establishes an important resource for future investigations. The precise description of the continuum existing from nasal epithelium to successive divisions of lung airways and the stable gene expression profile of these regions better defines conditions under which relevant tracheobronchial proxies of human respiratory diseases can be developed. Results Building a molecular cell atlas of the airways in healthy volunteers Data collectionCells were analyzed by droplet-based single-cell RNA sequencing (scRNA-seq), after isolation from 4 distinct locations using 2 sampling methods: (i) nasal biopsies (3 samples) and (ii) nasal brushings (4 samples), (iii) tracheal biopsies (carina, 1 st division, 9 samples), (iv) intermediate bronchial biopsies (5-6 th divisions, 10 samples), (v) distal brushings (9-12 th divisions, 9 samples) in 10 healthy volunteers ( Figure 1A, 1B, Figure E1A, Table E1). Optimized handling and dissociation protocols allowed the profiling of 77,969 single cells which were collected at 35 distinct positions of the airways, resulting in the detection of an average of 1,892 expressed genes per cell with 7,070 UMI per cell ( Figure E2A).Following batch correction and graph-based clustering, cell types were assigned to each cluster using well-established sets of marker genes ( Figure 1C, Figure E3). We identified 14 epithelial cell types, including 12 for the surface epithelium and 2 for submucosal glands, which collectively represented 89.1% of total cells ( Figure 1C-1E, Table E2; See also our interactive web tool https://www.genomique.eu/cellbrowser/HCA/?ds=HCA_airway_epithelium).Strom...

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia

“…It was recently reported that nasal RE has higher expression of CoV-2 entry genes than RE of the trachea or lungs 45 , and we therefore asked where OE fell within this previously established spectrum of expression. We took advantage of recently published data from Deprez and colleagues 46 in which biopsies of healthy human donors that spanned the nasal RE to the distal lung were subject to scSeq. To compare the gene expression levels in this respiratory dataset to those in the nasal mucosa-specific samples in the Durante et al dataset 48 , we sought a mapping between cells in these two datasets to establish a common scale of gene expression.…”

“…Data from Deprez et al 46 were downloaded from the European Genomephenome Archive at accession EGAS00001004082. A subset of these data was combined with a subset of the Durante data for mapping between cell types.…”

“…To establish a cell type mapping: 1) Durante 48 and Deprez 46 data were combined and total counts normalized so that all cells across datasets had the same total counts. PCA was then performed using highly variable genes and PCA-batch-correction with the Deprez data as a reference set.…”

“…It has recently been demonstrated through single cell sequencing analysis (scSeq) that cells from the human upper airway -including nasal RE goblet, basal and ciliated cells -express high levels of ACE2 and TMPRSS2, suggesting that these RE cell types may serve as a viral reservoir during CoV-2 infection 45 . However, the analyzed samples did not include any OSNs or sustentacular cells, suggesting that tissue sampling in these experiments was limited to the RE and did not access the OE 46,47 .…”

“…It therefore remains unclear whether any cell types in the OE express ACE2, TMPRSS2 or other molecules involved in coronavirus entry, which would render them susceptible to direct infection by CoV-2. To address the hypothesis that CoV-2 causes anosmia through primary effects on the OE, we queried publicly available mouse, nonhuman primate and human RNA sequencing (RNASeq) and scSeq datasets that include definitive OE populations to assess CoV-2-related gene expression 37,46,[48][49][50][51][52][53][54][55] . We also mapped scSeq data across different datasets to compare observed gene expression levels from nasal samples to samples obtained from other airway tissues.…”