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

• DOI: 10.7554/elife.94689.1
• Publication Date: 2024-04-15T10:25:08Z
• Authors: Kourosh Hayatigol...; Chiara Soriani; Emanuel Soda; Elena Ceccacci; Oualid El Menna; Sebastiano Peri; Ivan Negrelli; Giacomo Bertolini; Gian Martino Franchi; Roberta Carbone; Saverio Minucci; Simona Rodighiero
• Keywords: Cell synchronization

Understanding the details of cell cycle at level individual cells is critical for both cellular biology and cancer research. While existing methods using specific fluorescent markers have advanced our ability to study in that adhere surfaces, there a clear gap when it comes non-adherent cells. In this study, we combine specialized surface improve attachment, genetically-encoded FUCCI(CA)2 sensor, an automated image processing analysis pipeline, custom machine-learning algorithm. This combined approach allowed us precisely measure duration different phases non-adherent, as well adherent cells.Our method provided detailed information from hundreds under experimental conditions fully manner. We validated two acute myeloid leukemia lines, NB4 Kasumi-1, which unique distinct characteristics. also measured how drugs influence properties affect each phase cycles these lines. Importantly, system freely available has been use with cells.In summary, article introduces comprehensive, studying cells, offering valuable tool biology, research drug development.