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Analysis of single cell gene expression reveals cellular heterogeneity and differentiation dynamics of an healthy airway epithelia

Abstract : Improvements made in nucleic acid sequencing and cell handling technologies now offer the opportunity to analyze simultaneously the content of numerous single cells (RNA, DNA, ...) by global and unbiased approaches. This single-cell ‘omics’ revolution provides a new framework to revisit the “Cell Theory”, elaborated over several centuries, and essentially based on morphological and functional features. The many cell modalities now accessible at single- cell level, such as their transcriptome, spatial localization, developmental trajectories, enrich considerably this definition, and set a renewed context to precisely reassess the definition of ‘cell types’, ‘cell states’ as well as their different interactions and fates.My thesis work initially set up ad hoc approaches and statistical framework to analyze appropriately these single-cell data, which deeply differ from standard bulk RNA-seq. High variance, presence of a huge percentage of null values, large volume of data are among the specific characteristics of these datasets. My work was centered on the main experimental model of my host laboratory, e.g. the human airway epithelium. Human airways are lined by a pseudostratified epithelium mainly composed of basal, secretory, goblet and multiciliated cells. Airways also constitute a true cellular ecosystem, in which the epithelial layer interacts closely with immune and mesenchymal cells. This coordination between cells ensures proper defense of the respiratory system and its correct regeneration in case of external aggression and injuries. A better understanding of the operating sequences in normal and physiopathological situations is relevant in pathologies such as chronic obstructive pulmonary disease, asthma or cystic fibrosis.First, I characterized at a single cell level the precise and cell-specific sequence of events leading to functional regeneration of the epithelium, using a 3D model of human cells. I then built a single-cell atlas of the different cell types that are lining healthy human airways from the nose to the 12th generation of bronchi.By applying computational and statistical approaches, I have identified cell lineage hierarchies and was able to reconstruct a comprehensive cell trajectory roadmap in human airways. I not only confirmed previously described cell lineages, but I have also discovered a novel trajectory that links goblet cells to multiciliated cells, identifying novel cell populations and molecular interactors involved in the process of healthy human airway epithelium regeneration. The profiling of 12 healthy volunteers then generated a dataset of 77,969 cells, derived from 35 distinct locations. The resulting atlas is composed of more than 26 epithelial, immune and stromal cell types demonstrating the cellular heterogeneity present in the airways. Its analysis has revealed a strong proximo-distal gradient of expression in suprabasal, secretory, or multiciliated cells between the nose and lung airways. My work has also improved the characterization of rare cells, including “hillock” cells that have been previously described in mice.In conclusion, this work probably represents one of the first single-cell investigations in human airways. It brings original contributions to our understanding of differentiation’s dynamics and cellular heterogeneity in healthy human airways. The resulting resource will be extremely useful for any future single-cell investigators and also for establishing a very useful joint between clinical and biological works. As such, it will constitute a reference in any future project aiming to precisely analyze specific disease conditions.
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Submitted on : Friday, September 25, 2020 - 10:59:06 AM
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Marie Deprez. Analysis of single cell gene expression reveals cellular heterogeneity and differentiation dynamics of an healthy airway epithelia. Cellular Biology. COMUE Université Côte d'Azur (2015 - 2019), 2019. English. ⟨NNT : 2019AZUR6022⟩. ⟨tel-02948927⟩



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