Deconvolving the Transcriptomic Signatures of Somatic Expansion in Huntington’s Disease

Abstract

Huntington’s disease (HD) is driven by somatic CAG repeat expansions, but measuring expansion length alongside transcriptomes at single-cell resolution is experimentally challenging. In this thesis I test DCAG, a deep generative model that predicts somatic expansion stage from single-cell RNA-seq data by disentangling expansion-associated transcriptional variability from confounding factors such as cell type and sequencing technology. In the supervised setting, DCAG outperforms baseline models in balanced accuracy while producing interpretable latent representations. It also shows good performance in the semi-supervised setting trained with cells from different sequencing technologies. The model enables propagation of expansion labels to unlabeled datasets, facilitating mutation-length-aware analyses of existing and future experiments. While developed for HD, the used methodology is generalizable to other polyglutamine disorders or staged molecular processes, providing a computational tool for linking transcriptomic signatures to disease-relevant molecular features.