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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 adherent cells, is fully automated and freely available, providing detailed insights from hundreds of cells under various conditions. This report presents a valuable tool for advancing cancer research and drug development by enabling comprehensive, automated cell cycle analysis in both adherent and non-adherent cells. Cells regularly grow and divide through a process called the cell cycle. It includes rest periods where no growth or division occurs. When the cells are ready to divide, they duplicate their DNA, so each new cell gets a complete set of instructions. Finally, the cell splits into two new cells through a process called cytokinesis. This whole process can take hours or days to complete, depending on the cell type. Many things can go wrong during these processes, impairing healing or causing tumor formation. Learning more about these processes could help scientists better understand healing and diseases like cancer. Emerging imaging and data analysis tools allow scientists to observe cell-growth processes and watch errors as they occur. But, doing so requires sophisticated equipment and can be time and labor-intensive. Especially, if scientists are trying to track the cell cycle in a large number of cells. It can also be challenging to track free-moving cells, like blood or immune cells. New tools and techniques are needed to help scientists overcome these challenges. Hayatigolkhatmi, Soriani, Soda et al. developed a method in which a sticky surface is used to grow blood cancer cells that allows them to observe the cell cycle in large numbers of the cells at the same time. In the experiments, blood cancer cells were grown on a nano-material-coated surface that kept the usually free-floating cells still. The team compared gene expression in the cells before and after raising them on the surface to confirm that confining the cells did not alter their gene expression or disrupt their normal life cycle. Then, the researchers developed machine learning software that monitors the cell cycle in hundreds of individual cells, quantifies cell cycle phases and analyzes data with minimal human intervention. Usually, it would take a scientist 40-50 hours to oversee the cell cycle in a single experimental condition. This time was reduced to approximately 2 hours for a complete experiment using their pipeline. Finally, they validated their tools by monitoring different types of cancer cells under various treatment conditions. The tools developed by Hayatigolkhatmi, Soriani, Soda et al. provide researchers with a fast, easy and cost-effective tool for studying the cell cycle. It could help scientists study early development and how cells differentiate, grow or age. It could also be helpful for scientists studying cancer and how to treat it or scientists studying the healing process.

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 adherent cells, is fully automated and freely available, providing detailed insights from hundreds of cells under various conditions. This report presents a valuable tool for advancing cancer research and drug development by enabling comprehensive, automated cell cycle analysis in both adherent and non-adherent cells. Cells regularly grow and divide through a process called the cell cycle. It includes rest periods where no growth or division occurs. When the cells are ready to divide, they duplicate their DNA, so each new cell gets a complete set of instructions. Finally, the cell splits into two new cells through a process called cytokinesis. This whole process can take hours or days to complete, depending on the cell type. Many things can go wrong during these processes, impairing healing or causing tumor formation. Learning more about these processes could help scientists better understand healing and diseases like cancer. Emerging imaging and data analysis tools allow scientists to observe cell-growth processes and watch errors as they occur. But, doing so requires sophisticated equipment and can be time and labor-intensive. Especially, if scientists are trying to track the cell cycle in a large number of cells. It can also be challenging to track free-moving cells, like blood or immune cells. New tools and techniques are needed to help scientists overcome these challenges. Hayatigolkhatmi, Soriani, Soda et al. developed a method in which a sticky surface is used to grow blood cancer cells that allows them to observe the cell cycle in large numbers of the cells at the same time. In the experiments, blood cancer cells were grown on a nano-material-coated surface that kept the usually free-floating cells still. The team compared gene expression in the cells before and after raising them on the surface to confirm that confining the cells did not alter their gene expression or disrupt their normal life cycle. Then, the researchers developed machine learning software that monitors the cell cycle in hundreds of individual cells, quantifies cell cycle phases and analyzes data with minimal human intervention. Usually, it would take a scientist 40-50 hours to oversee the cell cycle in a single experimental condition. This time was reduced to approximately 2 hours for a complete experiment using their pipeline. Finally, they validated their tools by monitoring different types of cancer cells under various treatment conditions. The tools developed by Hayatigolkhatmi, Soriani, Soda et al. provide researchers with a fast, easy and cost-effective tool for studying the cell cycle. It could help scientists study early development and how cells differentiate, grow or age. It could also be helpful for scientists studying cancer and how to treat it or scientists studying the healing process.

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

Understanding the details of the cell cycle at the level of individual cells is critical for both cellular biology and cancer research. While existing methods using specific fluorescent markers have advanced our ability to study the cell cycle in cells that adhere to surfaces, there is a clear gap when it comes to non-adherent cells. In this study, we combine a specialized surface to improve cell attachment, the genetically-encoded FUCCI(CA)2 sensor, an automated image processing and analysis pipeline, and a custom machine-learning algorithm. This combined approach allowed us to precisely measure the duration of different cell cycle phases in non-adherent, as well as adherent cells.Our method provided detailed information from hundreds of cells under different experimental conditions in a fully automated manner. We validated this approach in two different acute myeloid leukemia cell lines, NB4 and Kasumi-1, which have unique and distinct cell cycle characteristics. We also measured how drugs that influence cell cycle properties affect the duration of each phase in the cell cycles of these cell lines. Importantly, our cell cycle analysis system is freely available and has also been validated for use with adherent cells.In summary, this article introduces a comprehensive, automated method for studying the cell cycle in both non-adherent and adherent cells, offering a valuable tool for cellular biology, cancer research and drug development.Competing Interest StatementThe authors have declared no competing interest.

In the article titled “Circulating Cancer Stem Cell-Derived Extracellular Vesicles as a Novel Biomarker for Clinical Outcome Evaluation” [1], the author Piero del Boccio was affiliated to Department of Medical, Oral and Biotechnological Sciences, University “G. D’Annunzio” of Chieti-Pescara, Analytical Biochemistry and Proteomics Laboratory, Chieti, Italy, which is incorrect. The corrected list of affiliations is shown above.