Morphological profiling by cell painting in human neural progenitor cells classifies hit compounds in a pilot drug screen for Alzheimer's disease

Alzheimer's disease (AD) accounts for 60-70% of dementia cases. Current treatments are inadequate and there is a need to develop new approaches to AD drug discovery. We chose to develop a cell phenotype-based drug screen centred on the AD-risk gene, SORL1, which encodes the protein SORLA. Increased AD risk has been repeatedly linked to variants in SORL1, particularly those that confer loss of, or decreased, SORLA. This is consistent with the lower SORL1 levels observed in post-mortem brain samples from individuals with AD. Consistent with its role in the endolysosomal pathway, deletion of SORL1 is associated with enlarged endosomes in neural progenitor cells (NPCs) and neurons. We, therefore, hypothesised that multiparametric, image-based phenotyping would identify features characteristic of SORL1 deletion. An automated morphological profiling assay (known as Cell Painting) was adapted to wild-type and SORL1-/- NPCs. This methodology was used to determine the phenotypic response of SORL1-/- NPCs to treatment with compounds from a small FDA-approved drug library (TargetMol, 330 compounds). We detected distinct phenotypic signatures for SORL1-/- NPCs compared to isogenic wild-type controls. Furthermore, we identified 16 FDA-approved drugs that reversed the mutant morphological signatures in NPCs derived from 3 SORL1-/- subclonal iPSC lines. Network pharmacology analysis revealed the 16 compounds belonged to five mechanistic groups: 20S proteasome, aldehyde dehydrogenase, topoisomerase I and II, and DNA synthesis inhibitors. Enrichment analysis confirmed targeting to gene sets associated with these annotated targets, and to pathways/biological processes associated with DNA synthesis/damage/repair, Proteases/proteasome and metabolism. Prediction of novel targets for some compounds revealed enrichment in pathways associated with neural cell function and AD. The findings suggest that image-based phenotyping by morphological profiling distinguishes SORL1-/- NPCs from isogenic wild-type lines, and predicts treatment responses that rescue SORL1-/--associated cellular signatures that are relevant to both SORLA function and AD. Overall, this work suggests that i) a quantitative phenotypic metric can distinguish iPSC-derived SORL1-/- NPCs from isogenic wild-type control and ii) phenotypic screening combined with multiparametric high-content image analysis is a viable option for drug repurposing and discovery in this human neural cell model of AD. ### Competing Interest Statement The authors have declared no competing interest.