Cecilia Lindskog (Presenting Author) | Department of Immunology Genetics and Pathology, Cancer Precision Medicin research unit | Uppsala university, 75185, Uppsala, Sweden
Abstract
For a fundamental understanding of human health, molecular medicine and targeted treatment, it is necessary to map processes unique to each tissue and cell type. Here, we used scRNA-seq, spatial proteomics and machine learning algorithms to generate high-resolution spatio-temporal maps of human tissues. We identified distinct cell states, such as stem cells, linked to certain biological processes and molecular functions, including cell division and meiosis, transport or signaling pathways involved in disease. By utilizing a large-scale multiplex immunofluorescence pipeline, we then performed an in-depth characterization of >1000 proteins to map their exact spatial localization. The integrated data allowed us to study mRNA and protein expression along with maturation processes and draw trajectories of temporal and dynamic gene expression. We identified which mRNAs are consistently translated into proteins from those that vary from a spatio-temporal aspect, and were able to assign presumed functions to numerous uncharacterized proteins not previously described in the context of the specific tissues. In summary, we aim to present a spatio-temporal single-cell type reference map of human tissues that links quantitative data with tissue morphology. The data contribute to valuable insights into molecular function and form the basis for further understanding of processes linked to disease.
