To comprehensively understand human health and applications of molecular and precision medicine, it is critical to characterize biological processes at the tissue and cellular level. During this webinar, Dr. Cecilia Lindskog, research group leader and associate professor from Uppsala University, describes how scRNA-seq, spatial proteomics, and machine learning algorithms can be used to generate high-resolution spatio-temporal maps of human tissues.
Using these approaches, distinct subpopulations of cells linked to pathways involved in normal and disease states were identified. Through a large-scale multiplex immunofluorescence pipeline, deep characterization of >500 proteins was performed to map the spatial localization. Combined analysis of mRNA and protein expression enabled the mapping of temporal and dynamic changes in gene expression and mRNAs that exhibited variable spatio-temporal expression patterns. Furthermore, putative functions were assigned to numerous uncharacterized proteins within a tissue-specific context.
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