Targeting integrins in drug‐resistant acute myeloid leukaemia

Ogana, Heather; Hurwitz, Samantha; Wei, Nathan; Lee, E.-J.; Morris, Karen; Parikh, Kaushal; Kim, Yong-Mi

doi:10.1111/bph.16149

Cited by 9 publications

(3 citation statements)

References 163 publications

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“…We deduce that this seeding protocol has the potential to be effectively employed with various non-adherent cell types. This is based on the principle that cell adhesion on SBS is facilitated by the nanostructure’s ability to engage with integrins (Schulte et al ., 2016), which are widely expressed in diverse cell types (Johansen et al ., 2018; Floren et al ., 2020; Kim et al ., 2020; Ogana et al ., 2023). The seeding protocol could also certainly be extended to other types of live cell imaging experiments, especially when the fluorescence intensity can be monitored for shorter durations, thereby circumventing the need for the addition of MC.…”

Section: Discussionmentioning

confidence: 99%

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

Hayatigolkhatmi,

Soriani,

Soda

et al. 2023

Preprint

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SummaryUnderstanding 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.

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Section: Discussionmentioning

confidence: 99%

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

Hayatigolkhatmi,

Soriani,

Soda

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, 3D models mirror the well-established therapy resistant effects previously reported for the TME. Ogana et al (2023) discussed targeting integrins to overcome TME-elicited drug resistance in acute myeloid leukaemia (AML).…”

mentioning

confidence: 99%

“…Furthermore, 3D models mirror the well‐established therapy resistant effects previously reported for the TME. Ogana et al (2023) discussed targeting integrins to overcome TME‐elicited drug resistance in acute myeloid leukaemia (AML). Although no integrin‐targeted treatments have been approved to date, this represents a potentially effective therapeutic approach not just in acute myeloid leukaemia but also for other blood cancers and solid cancers, to circumvent cell adhesion‐mediated drug resistance (CAM‐DR).…”

mentioning

confidence: 99%