Domain-specific AI segmentation of IMPDH2 rod/ring structures in mouse embryonic stem cells

Abstract

Background

Inosine monophosphate dehydrogenase 2 (IMPDH2) is an enzyme that catalyses the rate-limiting step of guanine nucleotides. In mouse embryonic stem cells (ESCs), IMPDH2 forms large multi-protein complexes known as rod-ring (RR) structures that dissociate when ESCs differentiate. Manual analysis of RR structures from confocal microscopy images, although possible, is not feasible on a large scale due to the quantity of RR structures present in each field of view. To address this analysis bottleneck, we have created a fully automatic RR image classification pipeline to segment, characterise and measure feature distributions of these structures in ESCs.

Results

We find that this model can automatically segment images with a Dice score of over 80% for both rods and rings for in-domain images compared to expert annotation, with a slight drop to 70% for datasets out of domain. Important feature measurements derived from these segmentations show high agreement with the measurements derived from expert annotation, achieving an R² score of over 90% for counting the number of RRs over the dataset.

Conclusions

We have established for the first time a quantitative baseline for RR distribution in pluripotent ESCs and have made a pipeline available for training to be applied to other models in which RR remain an open topic of study.