Divya Ganapathi Sankaran
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Divya Ganapathi Sankaran has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Bharath Hariharan
Cornell University Cornell University
1 shared publication - Chad G. Pearson
Department of Cell and Developmental Biology, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045
1 shared publication
Latest Publications
- Open Access
- 3 January 2020
- Published by Cold Spring Harbor Laboratory in bioRxiv
A semi-automated machine learning-aided approach to quantitative analysis of centrosomes and microtubule organization
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 cell’s central MT organizing center. The PCM proteins, includingγ-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.
- 3 January 2020
- Published by Cold Spring Harbor Laboratory in bioRxiv
Open AccessA semi-automated machine learning-aided approach to quantitative analysis of centrosomes and microtubule organization
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 cell’s central MT organizing center. The PCM proteins, includingγ-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.