It has long been recognized that the function of many biological systems depends on the spatial regulation of many genes. For instance, morphogen gradients in embryos are tightly regulated to ensure the correct cell type differentiation in time and space; the liver lobule is divided in labor according to distance from the portal triad. Therefore, studying the spatial architecture of a tissue can reveal how cells interact and organize across the entire tissue landscape. In cancer research it can help to unravel how the different subtypes of infiltrating immune cells are selected or recruited to tumours, and then develop therapies that stimulate the immune system to fight cancer. Recently, single-cell sequencing technologies have uncovered previously unappreciated levels of cancer and immune cell heterogeneity. However, because this type of analysis demands that the tissue is dissociated and the cells segregated, their spatial context is lost. Spatially resolved genomic methods offer the solution to this challenge, providing genome-scale omics measurements while pre-serving spatial context. While the field is moving forward at rapid pace, those technologies still face multiple challenges, including sensitivity, labor intensiveness, and cost. My research focuses on developing a novel gene expression platform for spatially resolving single-cell transcriptomes using an in-situ capturing technology. It involves the use of high-resolution microarrays printed with anchored oligo-dT capture probes with a unique spatial barcode for in situ cDNA synthesis. Concomitant with hardware developments, I aim to build a data-analysis strategy for scalable gene function evaluation of the resulting data.
Master of Bioinformatics, 2021