Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach.

ABSTRACT: Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent markers have improved the study of adherent cells, non-adherent cells remain challenging. In this study, we addressed this gap by combining a specialized surface to enhance cell attachment, the FUCCI(CA)2 sensor, an automated image analysis pipeline, and a custom machine learning algorithm. This approach enabled precise measurement of cell cycle phase durations in non-adherent cells. This method was validated in acute myeloid leukemia cell lines NB4 and Kasumi-1, which have unique cell cycle characteristics, and we tested the impact of cell cycle-modulating drugs on NB4 cells. Our cell cycle analysis system, which is also compatible with adheren

SUBMITTER: Hayatigolkhatmi K 

PROVIDER: S-EPMC11584176 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature