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

Abstract

Microtubules (MTs) perform important cellular functions including migration, intracellular trafficking, and chromosome segregation. The centrosome, comprised of two centrioles surrounded by the pericentriolar material (PCM), is the cells central MT organizing center. The PCM proteins, including{gamma} -tubulin and Pericentrin, promote MT nucleation and organization. Centrosomes in cancer cells are commonly numerically amplified. However, the question of how amplification of centrosomes alters the MT organization capacity is not well-studied. We developed a quantitative image-processing and machine learning-aided approach for the 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 this tool, we analyzed the spatial distribution of PCM proteins, the growing ends of MTs and the total MT density in breast cancer cells. We find that breast cancer cells with supernumerary centrosomes not only have increased PCM protein but also exhibit expansion in PCM size. Moreover, centrosome amplified cells have a greater MT density and more growing MT ends near centrosomes than unamplified cells. The semi-automated approach developed here enables facile, unbiased and quantitative measurements of centrosome aberrations. We show that these aberrations increase MT nucleation and promote changes to MT density and the spatial distribution of MTs around amplified centrosomes.