A semi-automated machine learning-aided approach to quantitative analysis of centrosomes and microtubule organization.

ABSTRACT: Microtubules (MTs) promote important cellular functions including migration, intracellular trafficking, and chromosome segregation. The centrosome, comprised of two centrioles surrounded by the pericentriolar material (PCM), is the cell's central MT-organizing center. Centrosomes in cancer cells are commonly numerically amplified. However, the question of how the amplification of centrosomes alters MT organization capacity is not well studied. We developed a quantitative image-processing and machine learning-aided approach for the semi-automated analysis of MT organization. We designed a convolutional neural network-based approach for detecting centrosomes, and an automated pipeline for analyzing MT organization around centrosomes, encapsulated in a semi-automatic graphical tool. Using thi

SUBMITTER: Sankaran DG 

PROVIDER: S-EPMC7406313 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature