High-content microscopy and machine learning characterize a cell morphology signature of NF1 genotype in Schwann cells

Abstract

Neurofibromatosis type 1 (NF1) is a multi-system, autosomal dominant genetic disorder driven by the systemic loss of the NF1 protein neurofibromin. Loss of neurofibromin in Schwann cells is particularly detrimental, as the acquisition of a second-hit (e.g., complete loss of NF1) can lead to the development of plexiform neurofibroma tumors. Plexiform neurofibromas are painful, disfiguring tumors with an approximately 1 in 5 chance of sarcoma transition. Selumetinib is currently the only medicine approved by the U.S. Food and Drug Administration (FDA) for the treatment of plexiform neurofibromas in a subset of patients. This motivates the need to develop new therapies, either derived to treat NF1 haploinsufficiency or complete loss of NF1 function. To identify new therapies, we need to understand the impact neurofibromin has on Schwann cells. Here, we aimed to characterize differences in high-content microscopy imaging in neurofibromin-deficient Schwann cells. We applied a fluorescence microscopy assay (called Cell Painting) to two isogenic Schwann cell lines, one of wildtype genotype (NF1+/+) and one of NF1 null genotype (NF1-/-). We modified the canonical Cell Painting assay to mark four organelles/subcellular compartments: nuclei, endoplasmic reticulum, mitochondria, and F-actin. We utilized CellProfiler pipelines to perform quality control, illumination correction, segmentation, and cell morphology feature extraction. We segmented 22,585 NF1 wildtype and null cells, utilized 907 significant cell morphology features representing various organelle shapes and intensity patterns, and trained a logistic regression machine learning model to predict the NF1 genotype of single Schwann cells. The machine learning model had high performance, with training and testing data yielding a balanced accuracy of 0.85 and 0.80, respectively. All of our data processing and analyses are freely available on GitHub. We look to improve upon this preliminary model in the future by applying it to large-scale drug screens of NF1 deficient cells to identify candidate drugs that return NF1 patient Schwann cells to phenocopy NF1 wildtype and healthier phenotype.