Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2 + TMPRSS2 + cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.
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...
The emergence and quick spread of SARS-CoV-2 has pointed at a low capacity response for testing large populations in many countries, in line of material, technical and staff limitations. The traditional RT-qPCR diagnostic test remains the reference method and is by far the most widely used test. These assays are limited to a couple of probe sets, require large sample PCR reaction volumes, along with an expensive and time-consuming RNA extraction steps. Here we describe a quantitative nanofluidic assay that overcomes some of these shortcomings, based on the Biomark instrument from Fluidigm. This system offers the possibility of performing 4608 qPCR end-points in a single run, equivalent to 192 clinical samples combined with 12 pairs of primers/probe sets in duplicate, thus allowing the monitoring in addition to SARS-CoV-2 probes of other pathogens and/or host cellular responses (virus receptors, response markers, microRNAs). Its 10 nL range volume is compatible with sensitive and reproducible reactions that can be easily and cost-effectively adapted to various RT-qPCR configurations and sets of primers/probe. Finally, we also evaluated the use of inactivating lysis buffers composed of various detergents in the presence or absence of proteinase K to assess the compatibility of these buffers with a direct reverse transcription enzymatic step and we propose several procedures, bypassing the need for RNA purification. We advocate that the combined utilization of an optimized processing buffer and a high-throughput real-time PCR device would contribute to improve the turn-around-time to deliver the test results to patients and increase the SARS-CoV-2 testing capacities.
In the version of this Letter initially published online and in print, an article by Lizé et al. (Cell Cycle 9, 4579-4583; 2010), which reports that miR-449 microRNAs accumulate during mucociliary differentiation of human airway epithelia, was inadvertently omitted from the references list. On pages 1-2, the following text has replaced the previous text: "miR-449a, miR-449b and miR-449c (collectively named miR-449), constitute by far the most strongly induced microRNAs during epithelium differentiation in both species. Although representing less than 0.01% of all microRNA sequences in proliferating HAECs, miR-449 accounted for more than 8% of the microRNA reads in differentiated HAECs (Fig. 1a and Supplementary Fig. S1c,d; see also ref. 13
This protocol provides details on the cell dissociation that should be performed to obtain single-cell suspensions from nasal epithelium brushings. Cell dissociation is performed at 4°C to avoid gene expression alterations and maximize viability. The typical cell number recovery is 200 000 - 300 000 for one brushing. Cell suspensions are suitable for single-cell RNA-sequencing protocols.
Mammary carcinoma, including triple-negative breast carcinomas (TNBC) are tumortypes for which human and canine pathologies are closely related at the molecular level.Low-passage, primary carcinoma cells from TNBC versus non-TNBC were used to compare the efficacy of an oncolytic vaccinia virus (VV). We show that non-TNBC cells are 28 times more sensitive to VV than TNBC cells in which VV replication is impaired.Single-cell RNA-seq performed on two different TNBC cell samples infected or not with VV highlighted three distinct populations: naïve cells, bystander cells, defined as cells exposed to the virus but not infected and infected cells. The transcriptome of these three populations showed striking variations in the modulation of pathways regulated by cytokines and growth factors. We hypothesized that the pool of genes expressed in the bystander populations was enriched in antiviral genes. Bio-informatic analysis suggested that the reduced activity of the virus was associated with a higher mesenchymal status of the cells. In addition, we demonstrate experimentally that high expression of one gene, DDIT4, is detrimental to VV production. Considering that DDIT4 is associated with a poor prognosis in various cancers including TNBC, out data highlight DDIT4 as a candidate resistance marker for oncolytic poxvirus therapy. This information could be used to design new generations of oncolytic poxviruses. Beyond the field of gene therapy, this study demonstrate that single-cell transcriptomics can be used to identify cellular factors influencing viral replication Cambien et al.3 Author summaryThe identification of cellular genes influencing viral replication/propagation have been studied using hypothesis-driven approaches and/or high-throughput RNA interference screens. In the present report, we propose a methodology based on single-cell transcriptomic. We have studied, in the context of oncolytic virothepary, the susc eptibility of primary, low-passage mammary carcinoma cells of canine origin from different grades to an oncolytic vaccinia virus (VV). We highlight a fault in replication of VV in cells originated from high-grade triple-negative breast carcinomas (TNBC).Single-cell RNA-seq performed on TNBC cell samples infected with VV suggested that the reduced activity of the virus was associated with a higher mesenchymal status of the cells. We also demonstrate that high expression of one gene, DDIT4, is detrimental to VV production. Considering that DDIT4 is associated with a poor prognosis in various cancers including TNBC, out data highlight DDIT4 as a candidate resistance marker for oncolytic poxvirus therapy. Beyond the field of cancer gene therapy, we demonstrate here that single-cell transcriptomics increases the arsenal of tools available to identify cellular factors influencing viral replication.Cambien et al.
Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.