Using image classifiers to predict CMT2A disease-relevant mitochondrial motility phenotypes in iPSC motor neurons

Abstract

Charcot-Marie-Tooth disease type 2A (CMT2A) is a genetic disease characterized by autosomal dominant MFN2 mutations and dysregulated mitochondrial trafficking. While there is currently no FDA-approved CMT2A therapy, the recent development of iPSC motor neuron model systems, high-throughput imaging platforms, and CRISPR-based gene editing technologies holds promise for screening new therapies at scale in vitro. A critical roadblock for therapeutic screening is the development of scalable and robust computational methods to assess the mitochondrial trafficking phenotypes, healthy or diseased, of each iPSC motor neuron sample. To address this gap, we developed a vision transformer (ViT) based classification framework that predicts disease phenotypes using kymographs, an image transformation that captures particle movement along a prespecified path, such as mitochondrial movement along axons. We show that our classification approach more accurately discriminates healthy MFN2 wild-type (WT) from diseased MFN2 R364W-mutant (R364W) iPSCs than alternative summary statistics, such as mitochondrial speed and fraction of stationary mitochondria that are directly extracted from kymographs. Furthermore, we show that our model maintains high accuracy when deployed on a biological replicate holdout dataset. An analysis of ViT patch embeddings of the kymographs shows that mitochondria with highly variable sizes and many intersection events most strongly associate with R364W diseased kymographs. The computational approach demonstrated in this paper has broad applicability for future high-throughput screens where organelle trafficking along axons plays a key role in disease pathogenesis.