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

OBJECTIVES: Although morphological attributes of cells and their substructures are recognized readouts of physiological or pathophysiological states, these have been relatively understudied in amyotrophic lateral sclerosis (ALS) research. MATERIALS AND 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 unrecognized disease-relevant information is withheld in broadly used and often considered 'generic' biological markers of nuclei (DAPI) and neurons (β 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.