Abstract 213: High-Throughput Analysis of Mitochondrial Morphology in Neurons Post-Mitochondrial Transplantation

Background: Mitochondrial transplantation (MTx) improves neurological and survival outcomes in rats after cardiac arrest. However, the impact of transferred mitochondria on native mitochondrial features in neurons remains poorly assessed. Evaluating mitochondrial morphology provides insights into post-MTx mitochondrial dynamics in recipient neurons, aiding the understanding of cellular processes and targeted therapy development. Aim: To investigate whether increased uptake of donor mitochondria influences the morphology of native mitochondrial networks in neurons. Methods: We conducted in vitro MTx using the HT-22 neuron cell line. Native mitochondria in neurons were pre-labeled with MitoTracker Green. Extracellular mitochondria were isolated from healthy adult rat muscle tissue, labeled with MitoTracker Deep Red, and co-cultured with neurons. Confocal microscopy captured 2D fluorescence images. MitoHacker, a computational program, facilitated high-throughput analysis of 27 mitochondrial morphometric markers. Results: We analyzed 62 images of cells ( Figure ). MitoHacker successfully identified features of native and transferred donor mitochondria. Linear regression analysis revealed that total donor mitochondrial area had positive correlations with native mitochondrial counts, total area, minor axis length of mitochondrion, mitochondrial averaged density, and mitochondrial branch length, and a negative correlation with distance between mitochondrial location and nucleus (all P<0.05). These findings suggest that increased amounts of transferred mitochondria can positively influence native mitochondrial biogenesis, proliferation, accumulation, and activated mitochondrial dynamics and networking in recipient neurons. Conclusion: Using the deep learning-based mitochondrial morphology analysis, our study provides evidence that MTx significantly impacts mitochondrial morphology and dynamics in recipient cells.