AI Insight
Researchers developed and validated a standardized imaging and analysis workflow to quantitatively evaluate cutaneous neurofibromas in a mouse model of Neurofibromatosis type 1 (NF1). Using automated ImageJ scripts, they achieved excellent reproducibility and strong correlation with manual analysis methods while reducing analysis time up to 100-fold. The workflow combines macroscopic measurements (tumor count, fluorescent surface area, intensity) with microscopic analysis (cell composition, area quantification) in mice with targeted Nf1 gene inactivation in Schwann cells.
Why it matters
This standardized methodology provides a reliable framework for testing potential drug therapies for cutaneous neurofibromas in preclinical studies, addressing a critical need since no approved pharmacological treatments currently exist. The time-efficient and reproducible approach will accelerate therapeutic development and improve cross-study comparability in NF1 research.
Understand the Science
by Laura Fertitta, Fanny Coulpier, Layna Oubrou, Xavier Decrouy, Etienne Audureau, Nicolas Ortonne, Pierre Wolkenstein, Piotr Topilko
Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder in which cutaneous neurofibromas (cNFs) represent one of the most common and burdensome manifestations. No approved pharmacological treatment exists. Preclinical studies are essential to evaluate candidate therapies, but reliable outcome and endpoint measures for cNFs in animal models remain limited. We developed and validated a standardized methodology to assess drug efficacy in the Prss56Cre Nf1-KO mouse model which recapitulates key features of cNFs. In this model, Nf1 inactivation and tdTomato (Tom) reporter expression were specifically targeted to Schwann cells (SCs) responsible for cNF development. This approach enables real-time monitoring, isolation, and manipulation of tumor SCs at any time. We defined macroscopic (tumor count, total Tom+ fluorescent surface area, fluorescence intensity) and microscopic (cell-type composition defined by immunolabeling with a panel of specific markers, area quantification) endpoints, developed dedicated ImageJ scripts for automated image analysis, and compared the results with those obtained using the conventional manual method. Both automated measurements showed excellent reproducibility (ICC = 1) and strong correlation with manual analysis (Spearman’s coefficient > 0.90), while significantly reducing analysis time (up to 100-fold faster). Bland–Altman analyses confirmed the absence of systematic bias compared with manual scoring. The standardized image naming and metadata integration further facilitated data consolidation and statistical analysis. This validated approach provides a reliable, reproducible, and time-efficient framework for evaluating drug effects on cNFs in preclinical studies. It establishes a foundation for robust efficacy testing of candidate therapies, facilitates cross-study comparability, and accelerates therapeutic development and clinical translation.