High-throughput single-cell RNA-seq (scRNA-seq) is used to describe complex tissues by characterizing transcriptional states of individual cells. Defining a cell’s position, both in regard to tissue margins and its social context, is essential for understanding the intrinsic and extrinsic variables that affect the transcriptional identity of individual cells. Conventional high-throughput scRNA-seq assays, however, decouple cells from their original locations within tissues. In situ hybridization readouts of gene expression and in situ sequencing preserve spatial information in tissues, but currently have a lower total read threshold than NextGen sequencing – imposing a restriction on either cell throughput or transcriptional breadth. Combining the above methods or using regional barcodes to define 2D positions have spatially reconstructed tissue regions but are seldom employed in unspecialized laboratories. In this thesis, I describe SEgmentation by Exogenous Perfusion (SEEP), a rapid, integrated, method for providing 3D spatial-segmentation to scRNA-seq data. Tissues are divided into layers based on the accessibility of a fluorescent dye allowing sorted cells to be characterized by transcriptomic and regional identity. I use SEEP to explore how the transcriptional states of cells in high-grade serous ovarian cancer vary with respect to intratumoural position. I describe an integrated method for correlating 3D radial-spatial cell positions with scRNA-seq data. By employing a basic stain-and-sort method using an off-the-shelf live-dead stain, I correlate transcriptional profiles with radial-spatial positions in spheroid and organoid tumour models. After discussing the utility of SEEP to explore the spatially dynamic transcriptional states of spheroid and organoid ovarian cancer tumour models, I describe Punch-SEEP, an extension of the SEEP method to characterize large and/or asymmetric tissue samples through an engineered dye-perfusing biopsy punch. This method presents a dye-saturated gelatin plug to tissue surfaces upon the harvesting on biopsy plugs. In doing so, it established a dye gradient within the biopsy plugs that can be used to segment tissues based on distances to the native tissue surface. SEEP and Punch-SEEP use imaging-based calibration steps to inform the parameters of FACS and sequencing based measurement steps. As a cross-model analysis, I explored regional transcriptional similarities between spheroid, organoid, and PDX tumor models of high grade serous ovarian cancer. Across model systems, I found a similar surface transcriptional profile characterized by inflammation, interferon response, immune response, and active epithelial to mesenchymal transition. I found a second set of surface-residing cells that were enriched for genes involved in DNA repair and transcriptional targets of the MYC oncogene. On cells found deep within the core of the tumour models analysed, I found a transcriptional enrichment for genes involved in Heme metabolism and mTORc1 signaling. This cross-model analysis demonstrated the utility of SEEP to spatially segment tissue samples so that spatially defined cells could be characterized by transcriptional identity and used to make meaningful comparisons across systems. In addition to limitations regarding spatial-resolved characterization, conventional scRNA-seq assays impose a limitation on the number of sequencing reads explored per experiment. This is manifest as a tradeoff between the numbers of genes and the number of cells analysed per sequencing run. The last project in this thesis describes a proof-of-concept method termed trapFISH. TrapFISH offers a protocol to make transcript abundance measurements in single cells by capturing cells in hydrogel beads and arraying the hydrogels on a series of microfluidic traps. Each hydrogel acts as a scaffold to display the captured transcriptome of individual cells. Transcript abundance measurements across a panel of genes can then be quantified through iterative fluorescent hybridization.