Reconstructing cellular crosstalk networks in tissue microenvironments
Keywords: Medicine. Department of Biomedical Data Science

Cell-cell interactions are crucial for the maintenance and progression of normal and diseased tissue regions. The cells within the regions typically communicate with one another via a ligand-receptor (LR) interaction to induce downstream pleiotropic phenotypic responses. In cancer, substantial work has been done to identify disease-specific malignant and immune cell interactions, leading to the development of innovative cancer checkpoint inhibitor immunotherapies. Despite these advances, disease recurrence remains an issue. We hypothesize that global cell-cell crosstalk adapts to the new microenvironment and enables the cancer cells to develop drug resistance. Mapping disease-specific interactomes remain a challenge due to the underlying heterogeneity of microenvironments since a single cell-cell interaction can have system-wide effects. This large combinatorial problem is difficult to test efficiently \textit{in-vitro} or \textit{in-vivo}. Instead, we aim to computationally infer potential cellular crosstalk patterns for hypothesis testing using transcriptomics data. This type of data provides us with a snapshot of the transcriptional abundance of all ligands and receptors within the system. Current computational approaches for inferring cellular crosstalk involve thresholding and correlation-based approaches, but do not capture the systems-level affect cell-cell interactions have upon one another. In addition, most transcriptomics datasets are high-dimensional but low sample size, which dampens the predictive power of many statistical approaches. In this dissertation, I present my work developing computational approaches to reconstruct complex cellular crosstalk networks within high-dimensional datasets of low sample size. I developed a novel network-based algorithm, REMI (REgularized Microenvironment Interactome), that predicts conditionally-dependent cell-cell interactions using transcriptomics data. I then applied REMI to a lung adenocarcinoma (LUAD) bulk flow-sorted RNA-sequencing dataset and identified disease progression-related cellular crosstalk signatures. I found that accounting for multi-cellular crosstalk interactions reduced false positives in predicted interactions through simulation analysis. To confirm the generalizability of REMI, I applied REMI to a head and neck squamous cell carcinoma (HNSCC) single-cell RNA-seq (scRNA-seq) atlas that performed with high specificity. In the last part of my thesis, I applied REMI to a bulk targeted-sequencing dataset from COVID-19 patients and identified cellular crosstalk patterns specific to early and late-stage SARS-CoV-2 disease progression. I also measured spatial crosstalk between cells by accounting for pairwise distances between cell types and multi-cellular neighborhood patterns. I conclude that spatial crosstalk is complex and the effect of all surrounding cell types should be accounted for to increase the accuracy of predictions of spatial crosstalk. My work demonstrates that cellular crosstalk exists in a group-wise manner and that extending our analysis beyond pairwise comparisons will greatly increase the accuracy of our predictions. These approaches will increase the accuracy of crosstalk inference, which will reduce misrepresented biological conclusions and accelerates the promise of precision medicine