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

Jenna Tomkinson, Cameron Mattson, Michelle Mattson-Hoss, Gwen Guzman, Herb Sarnoff, Stephanie J. Bouley, James A. Walker, Gregory P. Way,
High-content microscopy and machine learning characterize a cell morphology signature of NF1 genotype in Schwann cells,
Glial Health Research,
Volume 2,
2025,
100009,
ISSN 2950-4074,
https://doi.org/10.1016/j.ghres.2025.100009.
(https://www.sciencedirect.com/science/article/pii/S2950407425000050)
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 neurofibromas (pNF). pNFs are painful, disfiguring tumors with an approximately 1 in 5 chance of sarcoma transition. Selumetinib and mirdametinib are currently the only medicines approved by the U.S. Food and Drug Administration (FDA) for the treatment of pNFs. 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 in neurofibromin-deficient Schwann cells. We applied a fluorescence microscopy assay (called Cell Painting) to an isogenic pair of Schwann cell lines (derived from ipn02.3 2λ), one of wildtype (WT) 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 to perform quality control, illumination correction, segmentation, and cell morphology feature extraction. We segmented 20,680 NF1 WT and Null cells, measured 894 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. However, when applied to a new pair of Schwann cells, the model’s balanced accuracy dropped to 0.5, which is no better than random chance. This performance decline appears to result from morphology differences introduced by non-biological factors (cloning procedures, origin of parental cell line, and CRISPR procedures) of the second cell line pair. We next trained a new machine learning model using both isogenic cell line pairs, which rescued test-set performance. Taken together, we demonstrate that high-content microscopy and machine learning is a sensitive approach to modeling an NF1 morphology signature, which can be refined using a broader panel of Schwann cell lines in the future.
Keywords: High content microscopy; Machine learning; Neurofibromatosis Type 1; Schwann, cells; Cell Painting; Computational biology