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

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

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...

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Bibliographic Details
Main Authors: Kourosh Hayatigolkhatmi, Chiara Soriani, Emanuel Soda, Elena Ceccacci, Oualid El Menna, Sebastiano Peri, Ivan Negrelli, Giacomo Bertolini, Gian Martino Franchi, Roberta Carbone, Saverio Minucci, Simona Rodighiero
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2024-11-01
Series:eLife
Subjects:
cell cycle
live-cell imaging
non-adherent cells
cell tracking
machine learning
AML
Online Access:https://elifesciences.org/articles/94689
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