Tanimura, S. | Search bioRxiv

bioRxiv2026

Cytoplasmic mRNA granules regulate cell fate decisions during PINK1/Parkin mitophagy

Mitophagy is generally considered to promote cell survival by removing damaged mitochondria in response to mitochondrial stress, whereas apoptosis occurs during prolonged stress. However, the mechanisms that determine cell survival and cell death under these stress conditions remain poorly understood. Here, we showed that cytoplasmic mRNA granules, designated as mitophagy-induced mRNA granules (mitoRGs), were formed transiently and played an important role in cell fate decisions during PINK1/Parkin-dependent mitophagy. Although some components, such as G3BP1, were shared with stress granules (SGs), mitoRGs were distinct from SGs because mitoRG assembly required the mitochondrial protein phosphatase PGAM5. In response to mitochondrial stress, PGAM5 was released into the cytosol from mitochondria and incorporated into mitoRGs, but was then released back into the cytosol during mitoRG disassembly following prolonged mitochondrial stress, corresponding with the induction of apoptosis. Impairment of mitoRG assembly through G3BP1 depletion sensitized cells to apoptosis during mitophagy in a PGAM5-dependent manner. These results suggest that mitoRGs regulate cell fate decisions by spatiotemporally controlling PGAM5 and its pro-apoptotic activity during PINK1/Parkin mitophagy.

cell biology↗

bioRxiv2024

OrgaMeas: A pipeline that integrates all the processes of organelle image analysis

Although image analysis has emerged as a key technology in the study of organelle dynamics, the commonly used image-processing methods, such as threshold-based segmentation and manual setting of regions of interests (ROIs), are error-prone and laborious. Here, we present a highly accurate high-throughput image analysis pipeline called OrgaMeas for measuring the morphology and dynamics of organelles. This pipeline mainly consists of two deep learning-based tools: OrgaSegNet and DIC2Cells. OrgaSegNet quantifies many aspects of different organelles by precisely segmenting them. To further process the segmented data at a single-cell level, DIC2Cells automates ROI settings through accurate segmentation of individual cells in differential interference contrast (DIC) images. This pipeline was designed to be low cost and require less coding, to provide an easy-to-use platform. Thus, we believe that OrgaMeas has potential to be readily applied to basic biomedical research, and hopefully to other practical uses such as drug discovery.

cell biology↗