Fast photochemical oxidation of proteins (FPOP) is a powerful, mass spectrometry (MS)-based, biophysical method used to probe protein structure, interactions, and conformations. FPOP was recently extended into cells (IC-FPOP) and can modify thousands of proteins in a single experiment, enabling proteome-wide structural biology. Although IC-FPOP can reveal critical structural information in 2D cell culture, the conditions do not emulate an in-vivo environment. To address this, we propose to develop a mass spectrometry-based protein footprinting method that assesses the varying protein heterogeneity in 3D cell culture; Spheroid-FPOP. IC-FPOP on intact spheroids was performed using a patented PIXY platform which brought automation to IC-FPOP. Spheroid-FPOP coupled with serial trypsinization to obtain spatial resolution, revealed modifications in three distinct spheroid regions; the outer inner and core. Native oncogenic pathways were interrogated through this study showing its value in disease pathogenesis and treatment. Though progressive for FPOP, the extension into 3D model systems generated three times the samples and data compared to typical IC- or IV-FPOP experiments. This shed light to FPOP workflow limitations. The research herein responds to those challenges by developing an automated sample preparation workflow by coupling a sample handling robot with Thermo’s sample preparation kit. These modifications robustly improve the workflow by significantly reducing the manual labor, execution time, and variability of samples processed and data acquired. After workflow optimization FPOP, we apply the optimized method more complex biological sample. In our case, 3D bioprinted Huh-7 liver organoids were generated for IC-FPOP. To obtain spatial resolution within the model, we integrated cryosectioning of the top, middle and bottom layers of the organoid. Peptide level analysis revealed differences in the extent of modification for peptides identified in each region of the organoid, which confirms the acquisition of structural information. However further optimization was required to increase proteome depth. By coupling the organoid model with IC-FPOP we aim further validate its implementation for complex proteome-wide structural studies. In all, this research is focused on advancing the applications and processing workflows for IC-FPOP.