Coordinators: Christos Samakovlis und Herbert Schiller
Single cell genomics (SCG) is currently transforming every aspect of the life sciences by providing deep insights into previously hidden diversity of cell subtypes and functionally distinct cell states. Several large international consortia such as the LifeTime Initiative, the Human Cell Atlas and the NIH Human Biomolecular Atlas Program have been formed in recent years to create reference maps of the cellular circuits forming human tissues in health and disease. DZL researchers together with these consortia, and several other groups, have already established a first draft of such a cell atlas in mouse and human lung, and reported cellular and molecular changes associated with asthma, pulmonary fibrosis and aging. DZL researchers (Schiller, Samakovlis, Theis) also currently contribute to an ongoing project funded by the EU-H2020 program with the aim of building a first draft of an Atlas of the Human Lung as a 3D reconstruction of lung tissue architecture.
We propose that the DZL with its diverse disease areas is perfectly positioned to enrich the Human Lung Cell Atlas with data on lung disease and thereby also contribute to the LifeTime Initiative. As a unifying aim for the DZL 3.0 single cell working group we envision that data integration across disease areas will empower analysis on disregulation of cell plasticity in human lung disease. From single cell
transcriptomic data across disease areas we want to (1) perform integrated analysis to derive disease associated gene programs in the cell type context, followed by (2) spatial single cell analysis that localizes these gene programs in the tissue context and maps disease-associated alterations in cellcell communication niches. In order to (3) mechanistically dissect the function of individual cells and genes in the regulation of disease progression, we will use highly multiplexed chemical and genetic perturbations in ex vivo organotypic model systems of the human lung (e.g. precision cut lung slices and organoids) together with single cell readouts.
The analysis of single cell data across many different in vivo samples across disease areas and ex vivo perturbations empowers the computational analysis of gene programs. Using gene-gene correlation across individual single cells the co-expression of genes in specific conditions can be derived. The analysis across disease areas will reveal gene programs that are either highly disease specific (e.g. only in COPD but not any other lung disease), or shared in different diseases (e.g. vascular phenotypes shared in several lung diseases). This will provide a basis for studying the regulation of these programs in the context of the spatial tissue niche and individual mutations occurring in patients.
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