Unraveling Biology's Heterogeneity through Single Cell Analysis

Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for resolving cell type and state heterogeneity in both normal and pathological biological systems. Recent advancements in computational tools have significantly enhanced the analytical capabilities of scRNA-seq and enabled even deeper insights into cellular heterogeneity and function than has been possible before. Leveraging these resources, I investigated one of the most fundamental biological processes ubiquitous to every cell: the cell cycle. Decades of research have thoroughly characterized the molecules and regulatory mechanisms involved in cell cycle progression, leading to the well-established model that we utilize today. However, the in-depth characterization of one particular cell cycle state—G0, involved in development, tissue homeostasis, therapy resistance, and other important processes—has been hindered by the absence of molecular markers to identify it, and thus, has not been fully studied. Therefore, I developed a cell cycle classifier using scRNA-seq data from human neural stem cells that can identify a quiescent-like G0 state in neuroepithelial-derived cells and glioma. This allowed a more in-depth study of the functional impact of the G0 cell subpopulation and led to the discovery of its protective role in patients with Glioblastoma Multiforme (GBM). Moreover, I show that by integrating cell cycle classification with experimental validation through fluorescence-activated cell sorting (FACS) and RNA-seq, I can extend the identification of G0 cells to diverse cell populations beyond those initially included in the classifier training. In addition to probing cell cycle states, I highlight the importance of accurately modeling disease systems like GBM by incorporating various microenvironment cell types and properties into a representative 3D organotypic microfluidic platform. Using scRNA-seq, I identified key ligand-receptor pairs involved in glioma stem cell invasion within the platform, which revealed promising targets for intervention. Because of the groundbreaking technology of scRNA-seq and the sophisticated computational tools for analysis, we can now identify novel cellular states linked to better patient outcomes, and key pathways of cellular communication that, when disrupted, could reduce the invasiveness of even the most aggressive forms of brain cancer.