title: Comparison of single cell transcriptomics technologies  and their application to investigate  cellular heterogeneity in healthy and diseased lung
creator: Trefzer, Timo Benedikt
subject: ddc-500
subject: 500 Natural sciences and mathematics
description: Multicellular organisms rely on the concerted interaction of a multitude of cells,  which are often highly specialised and give rise to complex tissues. As vastly different  cellular phenotypes emerge from the same genotype that is shared across all cells of  an organism, the transcriptome represents a key mediator driving different cell types  and cell states that give rise to functional tissues. These are also subject to  environmental factors or intrinsic changes that may disrupt homeostasis and lead to  disease. In the human lung, the effects of tobacco smoke exposure, still the greatest  risk factor for lung cancer, have not been fully resolved at the cellular level.  Moreover, cellular heterogeneity may be significant for the emergence of lung cancer  in never smokers, a growing proportion of global cases. A focused investigation of  cellular heterogeneity in the healthy lung and lung cancers is therefore highly  warranted.  During the last decade, technological advancements have made it possible to  interrogate the transcriptome of single cells by novel next generation sequencing  approaches. While previous studies were limited to averaging transcriptome  information over many cells, single cell RNA sequencing (scRNA-seq) technologies  are now enabling the investigation of cellular phenotypes in healthy and diseased  tissues at unprecedented resolution.  In this thesis, I adapt different scRNA-seq technologies to process fresh or biobanked  samples from different tissues and species, thus enabling comparisons across diverse  origins. We identify specific advantages, limitations and experimental challenges  associated with each technology.  I then perform a comprehensive single-cell transcriptomics study of healthy lung and  lung adenocarcinoma (LADC). Based on twelve healthy lung samples, we generate  a reference cell atlas that provides a rich resource for investigating cellular diversity  in the human alveolar lung. Its utility is demonstrated by probing the expression of  genes that are implicated in host cell entry of SARS-CoV-2 virus, thereby  vi  contributing to our understanding of coronavirus infections. By comparing single cell  profiles from smokers and never smokers, we also resolve the involvement of distinct  cell types in the maintenance of an inflammatory state in smoker lungs, and we  identify key mediators of inflammatory processes induced by tobacco smoke exposure  in fibroblasts and endothelial cells.  To investigate cell type diversity and microenvironment interactions in LADC, I  analyse 26 tumour tissue samples and resolve functional malignant cell  subpopulations linked by a differentiation hierarchy in both smokers and never  smokers. They comprise proliferating and intermediate undifferentiated cells as well  as two differentiated tumour cell states implicated in cancer progression and  invasiveness. Distinct macrophage and fibroblast subpopulations which contribute  to a tumourigenic environment are also detected. A subset of proliferating tumour  cells show differential immune modulating activity dependent on smoking status,  with implications for future treatment approaches.  Taken together, these results provide a comparison of rapidly developing scRNA-seq  technologies for use in further studies and demonstrate their utility to dissect cellular  heterogeneity and identify transcriptional programmes in the healthy and diseased  lung. By applying these technologies, I add to our understanding of SARS-CoV-2  entry into human lung cells, define the alveolar lung cell types affected by tobacco  smoke exposure, and provide deeper insight into cellular heterogeneity of LADC and  the tumour microenvironment. These findings represent a valuable reference for  future translational studies.
date: 2022
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/31433/1/PhD_Thesis_TBTrefzer_pdfa.pdf
identifier: DOI:10.11588/heidok.00031433
identifier: urn:nbn:de:bsz:16-heidok-314337
identifier:   Trefzer, Timo Benedikt  (2022) Comparison of single cell transcriptomics technologies and their application to investigate cellular heterogeneity in healthy and diseased lung.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/31433/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng