Chemical genetic screens reveal defective lysosomal trafficking as synthetic lethal with NF1 loss

Bouley, Stephanie J.; Grassetti, Andrew V.; Allaway, Robert J.; Wood, Matthew D.; Hou, Helen W.; Burdon Dasbach, India R.; Seibel, William; Wu, Jimmy; Gerber, Scott A.; Dragnev, Konstantin H.; Walker, James A.; Sanchez, Yolanda

doi:10.1242/jcs.262343

Cited by 2 publications

References 72 publications

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High-content microscopy and machine learning characterize a cell morphology signature ofNF1genotype in Schwann cells

Tomkinson,

Mattson,

Mattson-Hoss

et al. 2024

Preprint

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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 treatNF1haploinsufficiency or complete loss ofNF1function. 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 ofNF1null 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 22,585NF1wildtype 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 theNF1genotype 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.

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High-content microscopy and machine learning characterize a cell morphology signature ofNF1genotype in Schwann cells

Tomkinson,

Mattson,

Mattson-Hoss

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

First person – Stephanie Bouley

2024

Journal of Cell Science

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First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping researchers promote themselves alongside their papers. Stephanie Bouley is first author on ‘ Chemical genetic screens reveal defective lysosomal trafficking as synthetic lethal with NF1 loss', published in JCS. Stephanie conducted the research described in this article while a PhD candidate in Yolanda Sanchez's lab at Geisel School of Medicine at Dartmouth College, Hanover, USA. She is now a post-doctoral research fellow in the lab of James A. Walker at Massachusetts General Hospital Center for Genomic Medicine, Boston, MA USA, where she is interested in identifying novel therapeutic targets for treating neurofibromatosis type 1-associated tumors.

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Chemical genetic screens reveal defective lysosomal trafficking as synthetic lethal with NF1 loss

Cited by 2 publications

References 72 publications

High-content microscopy and machine learning characterize a cell morphology signature ofNF1genotype in Schwann cells

High-content microscopy and machine learning characterize a cell morphology signature ofNF1genotype in Schwann cells

First person – Stephanie Bouley

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