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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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2024-11-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/94689 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Kourosh Hayatigolkhatmi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-25ec1da52c74434895f994bf63041cdf |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-25ec1da52c74434895f994bf63041cdf2025-01-13T17:12:50ZengeLife Sciences Publications LtdeLife2050-084X2024-11-011310.7554/eLife.94689Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approachKourosh Hayatigolkhatmi0https://orcid.org/0000-0002-9910-9756Chiara Soriani1https://orcid.org/0000-0003-4363-6597Emanuel Soda2Elena Ceccacci3Oualid El Menna4Sebastiano Peri5Ivan Negrelli6Giacomo Bertolini7Gian Martino Franchi8Roberta Carbone9Saverio Minucci10Simona Rodighiero11https://orcid.org/0000-0003-4236-7823Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milan, ItalyDepartment of Experimental Oncology, European Institute of Oncology-IRCCS, Milan, ItalyDepartment of Experimental Oncology, European Institute of Oncology-IRCCS, Milan, ItalyDepartment of Experimental Oncology, European Institute of Oncology-IRCCS, Milan, ItalyDepartment of Experimental Oncology, European Institute of Oncology-IRCCS, Milan, ItalyDepartment of Experimental Oncology, European Institute of Oncology-IRCCS, Milan, ItalyTethis S.p.A., Milan, ItalyTethis S.p.A., Milan, ItalyTethis S.p.A., Milan, ItalyTethis S.p.A., Milan, ItalyDepartment of Experimental Oncology, European Institute of Oncology-IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, ItalyDepartment of Experimental Oncology, European Institute of Oncology-IRCCS, Milan, ItalyUnderstanding 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.https://elifesciences.org/articles/94689cell cyclelive-cell imagingnon-adherent cellscell trackingmachine learningAML |
| spellingShingle | 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 Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach eLife cell cycle live-cell imaging non-adherent cells cell tracking machine learning AML |
| title | Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach |
| title_full | Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach |
| title_fullStr | Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach |
| title_full_unstemmed | Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach |
| title_short | Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach |
| title_sort | automated workflow for the cell cycle analysis of non adherent cells using a machine learning approach |
| topic | cell cycle live-cell imaging non-adherent cells cell tracking machine learning AML |
| url | https://elifesciences.org/articles/94689 |
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