Fully automated software for quantitative measurements of mitochondrial morphology

Mitochondria undergo dynamic changes in morphology in order to adapt to changes in nutrient and oxygen availability, communicate with the nucleus, and modulate intracellular calcium dynamics. Many recent papers have been published assessing mitochondrial morphology endpoints. Although these studies have yielded valuable insights, contemporary assessment of mitochondrial morphology is typically subjective and qualitative, precluding direct comparison of outcomes between different studies and likely missing many subtle effects. In this paper, we describe a novel software technique for measuring the average length, average width, spatial density, and intracellular localization of mitochondria from a fluorescent microscope image. This method was applied to distinguish baseline characteristics of Human Umbilical Vein Endothelial Cells (HUVECs), primary Goto-Kakizaki rat aortic smooth muscle cells (GK SMCs), primary Wistar rat aortic smooth muscle cells (Wistar SMCs), and SH-SY5Ys (human neuroblastoma cell line). Consistent with direct observation, our algorithms found SH-SY5Ys to have the greatest mitochondrial density, while HUVECs were found to have the longest mitochondria. Mitochondrial morphology responses to temperature, nutrient, and oxidative stressors were characterized to test algorithm performance. Large morphology changes recorded by the software agreed with direct observation, and subtle but consistent morphology changes were found that would not otherwise have been detected. Endpoints were consistent between experimental repetitions (R=0.93 for length, R=0.93 for width, R=0.89 for spatial density, and R=0.74 for localization), and maintained reasonable agreement even when compared to images taken with compromised microscope resolution or in an alternate imaging plane. These results indicate that the automated software described herein allows quantitative and objective characterization of mitochondrial morphology from fluorescent microscope images. Keywords: Image processing; Microscopy; Mitochondria; Morphology.