Executive Summary : | The advent of high-throughput single cell omics platforms has revolutionized basic biology research by allowing for unprecedented resolution in investigating cellular genotype and phenotype. Single cell RNA sequencing (scRNA-seq) is the most popular omics technology, used for identifying tissue heterogeneity, spotting rare cell lineages, and discovering markers for clinically relevant cell subpopulations. It has also been used to model time-dependent biological processes like differentiation of embryonic stem cells, haematopoiesis, and clonal evolution of cancer cells. However, modeling changes in cellular states is computationally intractable due to the innumerable ways cells can be connected among each other. RNA velocity-based approaches, which infer a cell's future state based on its pre-mRNA composition, are considered gold standards in this field. Quantifying pre-mRNA in single cells is challenging due to the high cost of total RNA sequencing and the limited number of protein-coding genes in prokaryotes and lower eukaryotes. To enable directionality-resolved trajectory inference, a species-specific multiplex molecular network will be developed, encompassing all types of molecular interactions such as protein-protein, protein-DNA, protein-RNA, and small RNA-mediated regulations. Random walk-based proximity measures will be implemented to measure the likelihood of instructions flowing from one cellular state to another, resolving connectivities and directions associated with a cellular trajectory. This method will be used to study the emergence of drug tolerance in cancer cells in-vitro. |