Trained Classifier Imaging for Healthy and Diseased Myotube Detection
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Solution Overview
Problem
Current methods struggle to reliably assess the activity of therapeutic agents in patient-derived muscle cells under physiological conditions and target therapies to patient subpopulations that have the greatest chance to benefit from them, due to challenges in accurately distinguishing between healthy and diseased myotubes and the lack of sensitivity in in vitro studies using population-level transcriptional and proteomic profiling techniques.
Innovation Solution
A computer-implemented method using a trained classifier to distinguish between healthy and diseased myotubes based on imaging markers and labelling agents, with a training process achieving an accuracy of at least 0.9 F-score, and applying this classifier for assessing compound potency, monitoring therapeutic responses, and selecting suitable patients for treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If population-level transcriptional and proteomic profiling techniques are used, then throughput is improved, but measurement precision deteriorates due to lack of sensitivity in distinguishing healthy and diseased myotubes
Solution Approach 1:
The patent segments the muscle tissue into individual myotube level analyses using immunofluorescence microscopy and image analysis. This allows detection of disease-specific markers at the single-cell level, achieving both high throughput (processing many myotubes in parallel) and high sensitivity (detecting individual diseased myotubes among healthy ones), thereby resolving the contradiction between throughput and measurement precision.
2Measurement precision
If image analysis with trained classifier is used, then measurement precision is improved for distinguishing healthy and diseased myotubes, but device complexity increases
Solution Approach 1:
The patent introduces a trained machine learning classifier as an intermediary between the image acquisition system and the analysis output. This classifier processes images of myotubes stained with disease-specific markers, automatically distinguishing healthy from diseased cells with high precision. The intermediary handles the complexity of pattern recognition and classification, simplifying the overall workflow while maintaining high differentiation accuracy.
3Adaptability or versatility
If therapeutic compound assessment is performed in patient-derived myotubes, then adaptability to patient subpopulations is improved, but reliability of activity assessment deteriorates due to difficulty in accurately distinguishing healthy and diseased cells
Solution Approach 1:
The patent applies local quality by staining myotubes with disease-specific markers and using image analysis to identify and select only those myotubes with specific disease characteristics for therapeutic assessment. This ensures that therapeutic compounds are evaluated in the correct patient subpopulation (e.g., myotubes with specific protein markers or morphological features), improving both adaptability to patient subpopulations and reliability of activity assessment by ensuring homogeneous, disease-relevant cell selection.
Data Source
AI summary
The present invention relates to methods using trained classifier for assessing potency of a compound to revert the phenotype of a myotube exhibiting features of a neuromuscular disorder of interest into a healthy phenotype, for predicting the ability of a compound to treat a neuromuscular disorder of interest, for monitoring the response to a therapeutic compound of a patient affected with a neuromuscular disorder of interest, for selecting a patient affected with a neuromuscular disorder of interest for a treatment with a therapeutic compound or for determining whether a patient affected with a neuromuscular disorder of interest is susceptible to benefit from a treatment with a therapeutic compound, or for diagnosing a neuromuscular disorder of interest.