51. Discerning cell types and states in spatial transcriptomics data using topic modelling

Abstract

Glioblastoma multiforme (GBM) is the most common adult brain tumour. GBM cells include a diverse collection of normal and tumour cells with distinct molecular capabilities and with differential levels of sensitivity to treatment that led to treatment resistance. Spatial transcriptomics technologies (e.g. 10X Visium) have enabled analyzing tumour-normal cells interactions in a physical tissue context. Using Visium, a well-annotated cohort of adult GBM patient samples grown as patient-derived xenograft (PDX) models were profiled at different time points of the disease. However, the identification of cell types or states of Visium spots is a challenging problem as each spot contains a mixture of cells. Current tools for cell-type deconvolution are limited in discovering new novel cell types because they require a well-annotated reference dataset. Moreover, the PDX data has a mixture of human and mouse cell types at the same spot. In this work, we have applied topic modelling, a well-known probabilistic approach in natural language processing to discover the distribution of topics in language documents. Using the analogy between documents and spatial transcriptomic data, we were able to identify the main cell types in spatial PDX data without gene expression references dataset of tumour states, normal mouse, and immune cell types. Finally, we compared various probabilistic topic modelling algorithms to fit the PDX data, and to understand the cell-to-cell interactions between tumour and normal cells.