Innovative Computerized Dystrophin Quantification Method Based on Spectral Confocal Microscopy.
Anna CodinaMonica RoldánDaniel Natera-de BenitoCarlos OrtezRobert PlanasLeslie MatalongaDaniel CuadrasLaura CarreraJesica Expósito-EscuderoJesus MarquezCecilia Jimenez-MallebreraJosep M PortaAndres NascimentoCristina JouPublished in: International journal of molecular sciences (2023)
Several clinical trials are working on drug development for Duchenne and Becker muscular dystrophy (DMD and BMD) treatment, and, since the expected increase in dystrophin is relatively subtle, high-sensitivity quantification methods are necessary. There is also a need to quantify dystrophin to reach a definitive diagnosis in individuals with mild BMD, and in female carriers. We developed a method for the quantification of dystrophin in DMD and BMD patients using spectral confocal microscopy. It offers the possibility to capture the whole emission spectrum for any antibody, ensuring the selection of the emission peak and allowing the detection of fluorescent emissions of very low intensities. Fluorescence was evaluated first on manually selected regions of interest (ROIs), proving the usefulness of the methodology. Later, ROI selection was automated to make it operator-independent. The proposed methodology correctly classified patients according to their diagnosis, detected even minimal traces of dystrophin, and the results obtained automatically were statistically comparable to the manual ones. Thus, spectral imaging could be implemented to measure dystrophin expression and it could pave the way for detailed analysis of how its expression relates to the clinical course. Studies could be further expanded to better understand the expression of dystrophin-associated protein complexes (DAPCs).
Keyphrases
- muscular dystrophy
- duchenne muscular dystrophy
- end stage renal disease
- poor prognosis
- clinical trial
- ejection fraction
- chronic kidney disease
- newly diagnosed
- optical coherence tomography
- prognostic factors
- binding protein
- squamous cell carcinoma
- machine learning
- high throughput
- computed tomography
- photodynamic therapy
- deep learning
- randomized controlled trial
- patient reported
- long non coding rna
- single molecule
- study protocol
- label free