Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Data

TL;DR

This paper employs deep learning and explainable AI techniques on high-dimensional imaging data to better understand mitochondrial disease pathology directly from raw tissue images, bypassing manual annotation.

Contribution

It introduces a novel application of deep learning and explainability to analyze high-dimensional IMC data for mitochondrial disease research, eliminating manual pre-processing.

Findings

Deep learning models achieved high accuracy on IMC data.

Explainable maps reveal features consistent with mitochondrial disease hypotheses.

The approach enables direct analysis of raw tissue images without manual annotation.

Abstract

Mitochondrial diseases are currently untreatable due to our limited understanding of their pathology. We study the expression of various mitochondrial proteins in skeletal myofibres (SM) in order to discover processes involved in mitochondrial pathology using Imaging Mass Cytometry (IMC). IMC produces high dimensional multichannel pseudo-images representing spatial variation in the expression of a panel of proteins within a tissue, including subcellular variation. Statistical analysis of these images requires semi-automated annotation of thousands of SMs in IMC images of patient muscle biopsies. In this paper we investigate the use of deep learning (DL) on raw IMC data to analyse it without any manual pre-processing steps, statistical summaries or statistical models. For this we first train state-of-art computer vision DL models on all available image channels, both combined and individually. We observed better than expected accuracy for many of these models. We then apply state-of-the-art explainable techniques relevant to computer vision DL to find the basis of the predictions of these models. Some of the resulting visual explainable maps highlight features in the images that appear consistent with the latest hypotheses about mitochondrial disease progression within myofibres.