Image-based deep learning reveals the responses of human motor neurons to stress and VCP-related ALS

Abstract

Aims: Although morphological attributes of cells and their substructures are recognised readouts of physiological or pathophysiological states, these have been relatively understudied in amyotrophic lateral sclerosis (ALS) research.

Methods: In this study, we integrate multichannel fluorescence high-content microscopy data with deep learning imaging methods to reveal-directly from unsegmented images-novel neurite-associated morphological perturbations associated with (ALS-causing) VCP-mutant human motor neurons (MNs).

Results: Surprisingly, we reveal that previously unrecognised disease-relevant information is withheld in broadly used and often considered 'generic' biological markers of nuclei (DAPI) and neurons ( $\beta$ III-tubulin). Additionally, we identify changes within the information content of ALS-related RNA binding protein (RBP) immunofluorescence imaging that is captured in VCP-mutant MN cultures. Furthermore, by analysing MN cultures exposed to different extrinsic stressors, we show that heat stress recapitulates key aspects of ALS.

Conclusions: Our study therefore reveals disease-relevant information contained in a range of both generic and more specific fluorescent markers and establishes the use of image-based deep learning methods for rapid, automated and unbiased identification of biological hypotheses.

Keywords: amyotrophic lateral sclerosis; deep learning; human induced pluripotent stem cells; immunofluorescence; motor neurons.