A deep learning workflow for quantification of micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation.

Anand Panchbhai
,
Munuse C. Savash Ishanzadeh
,
Ahmed Sidali
,
Nadeen Shaikh Solaiman
,
Smarana Pankanti
,
Kanagaraj Radhakrishnan

Research Output: Contribution to journal Article Peer-review

Open access

Sustainable Development Goals

SDG 3 Good Health and Well

Abstract

The cytokinesis block micronucleus assay is widely used for measuring/scoring/counting micronuclei, a marker of genome instability in cultured and primary cells. Though a gold standard method, this is a laborious and time-consuming process with person-to-person variation observed in quantification of micronuclei. We report in this study the utilisation of a new deep learning workflow for detection of micronuclei in DAPI stained nuclear images. The proposed deep learning framework achieved an average precision of >90% in detection of micronuclei. This proof of principle investigation in a DNA damage studies laboratory supports the idea of deploying AI powered tools in a cost-effective manner for repetitive and laborious tasks with relevant computational expertise. These systems will also help improving the quality of data and wellbeing of researchers.