Technologies
This technology is micronuclAI, a pipeline for automated and reliable quantification of micronuclei of varying size, morphology, and location from nuclei-stained images to assess chromosomal instability, a hallmark of cancer.
Chromosomal instability is a hallmark of cancer resulting in accumulation of micronuclei. However, current methods for micronuclei quantification include labor intensive cytogenetics, quantitative imaging, and single cell genomics. Quantitative imaging microscopy is most widely used to quantify micronuclei and is manually scored. During manual quantification, the workflow of quantifying micronuclei is tedious, time-consuming and error-prone, and due to no universal method, quantification varies between observers of a single image. Additionally, some automated methods can quantify micronuclei, but they are not uniform across cell lines. There is a need for a pipeline that automatically quantifies nuclei across a wide range of cell lines.
This technology is micronuclAI, a deep learning-based pipeline that automatically quantifies micronuclei from nuclei-stained images for the assessment of chromosomal instability. The pipeline uses segmentation, nuclei isolation, and quantification to reliably quantify micronuclei across multiple cell lines that are ready to use in laboratories. A dataset using micronuclAI was compared against manual single-cell level counts by experts and against routinely used micronuclei ratio, where the classifier was able to achieve a weight F1 score of 0.937. This technology increases speed, accuracy, and robustness of chromosomal instability research.
This technology has been validated with multiple human and murine cancer cell lines.
IR CU24358
Licensing Contact: Joan Martinez