Computational approaches for the analyses of spatial profiling technologies

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In a tumor ecosystem, the location and spatial interactions among diverse cell populations are determining the tumor progression and therapy outcome. However, conventional genomics approaches, such as bulk sequencing and single-cell profiling, cannot capture critical spatial information. Recent years have seen the rapid progress of spatial technologies for both proteomics and transcriptomics profiling. These technologies generate new data types, which may have a lower resolution than current data, but contains spatial dynamics patterns. Therefore, I plan to develop bioinformatics methods to enable knowledge mining for spatial proteomics and transcriptomics data. Our primary focus is to develop an automatic annotation framework to identify cancer, stroma, and immune regions from the spatial transcriptomic data. Meanwhile, our method will also decompose the detailed lineages of immune cells and analyze the spatial interaction enrichments.