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
Published in:	eLife Vol. 13
Main Authors:	Hayatigolkhatmi, Kourosh, Soriani, Chiara, Soda, Emanuel, Ceccacci, Elena, El Menna, Oualid, Peri, Sebastiano, Negrelli, Ivan, Bertolini, Giacomo, Franchi, Gian Martino, Carbone, Roberta, Minucci, Saverio, Rodighiero, Simona
Format:	Journal Article
Language:	English
Published:	England eLife Sciences Publications Ltd 22.11.2024
Subjects:	Acute myeloid leukemia Adherent cells AML Automation Automation - methods Cancer research Cell Adhesion Cell Cycle Cell Line, Tumor cell tracking Cyclin-dependent kinases Data analysis Data smoothing Drug development Experiments Fluorescent indicators Humans Image processing Image Processing, Computer-Assisted - methods Kinases Learning algorithms Leukemia, Myeloid, Acute - pathology live-cell imaging Machine Learning Microscopy non-adherent cells Proteins Time series Tumor cell lines Workflow Fiji AML cell biology non-adherent cells cell cycle cell tracking live-cell imaging cancer biology machine learning human
ISSN:	2050-084X, 2050-084X
Online Access:	Get full text
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