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Histone deacetylases (HDACs) are a key component of the epigenetic machinery regulating gene expression, and behave as oncogenes in several cancer types, spurring the development of HDAC inhibitors (HDACi) as anticancer drugs. This review discusses new results regarding the role of HDACs in cancer and the effect of HDACi on tumour cells, focusing on haematological malignancies, particularly acute myeloid leukaemia. Histone deacetylases may have opposite roles at different stages of tumour progression and in different tumour cell sub-populations (cancer stem cells), highlighting the importance of investigating these aspects for further improving the clinical use of HDACi in treating cancer.

The lysine-specific histone demethylase 1 A (LSD1) is involved in antitumor immunity; however, its role in shaping CD8 + T cell (CTL) differentiation and function remains largely unexplored. Here, we show that pharmacological inhibition of LSD1 (LSD1i) in CTL in the context of adoptive T cell therapy (ACT) elicits phenotypic and functional alterations, resulting in a robust antitumor immunity in preclinical models in female mice. In addition, the combination of anti-PDL1 treatment with LSD1i-based ACT eradicates the tumor and leads to long-lasting tumor-free survival in a melanoma model, complementing the limited efficacy of the immune or epigenetic therapy alone. Collectively, these results demonstrate that LSD1 modulation improves antitumoral responses generated by ACT and anti-PDL1 therapy, providing the foundation for their clinical evaluation. The lysine-specific histone demethylase 1 A (LSD1) can regulate cytotoxic CD8 T cell (CTL) responses and anti-tumor immunity. Here the authors show that ex vivo epigenetic reprogramming with a LSD1 inhibitor enhances cell persistence and anti-tumor activity of adoptively transferred CD8 T cells in preclinical tumor models.

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.

During prenatal life, the foetal liver is colonised by several waves of haematopoietic progenitors to act as the main haematopoietic organ. Single cell (sc) RNA-seq has been used to identify foetal liver cell types via their transcriptomic signature and to compare gene expression patterns as haematopoietic development proceeds. To obtain a refined single cell landscape of haematopoiesis in the foetal liver, we have generated a scRNA-seq dataset from a whole mouse E12.5 liver that includes a larger number of cells than prior datasets at this stage and was obtained without cell type preselection to include all liver cell populations. We combined mining of this dataset with that of previously published datasets at other developmental stages to follow transcriptional dynamics as well as the cell cycle state of developing haematopoietic lineages. Our findings corroborate several prior reports on the timing of liver colonisation by haematopoietic progenitors and the emergence of differentiated lineages and provide further molecular characterisation of each cell population. Extending these findings, we demonstrate the existence of a foetal intermediate haemoglobin profile in the mouse, similar to that previously identified in humans, and a previously unidentified population of primitive erythroid cells in the foetal liver.

In the mouse embryo, endothelial cell (EC) progenitors almost concomitantly give rise to the first blood vessels in the yolk sac and the large vessels of the embryo proper. Although the first blood cells form in the yolk sac before blood vessels have assembled, consecutive waves of hematopoietic progenitors subsequently bud from hemogenic endothelium located within the wall of yolk sac and large intraembryonic vessels in a process termed endothelial-to-hematopoietic transition (endoHT). The receptor tyrosine kinase KIT is required for late embryonic erythropoiesis, but KIT is also expressed in hematopoietic progenitors that arise via endoHT from yolk sac hemogenic endothelium to generate early, transient hematopoietic waves. However, it remains unclear whether KIT has essential roles in early hematopoiesis. Here, we have combined single-cell expression studies with the analysis of knockout mice to show that KIT is dispensable for yolk sac endoHT but required for transient definitive hematopoiesis in the fetal liver.

The histone demethylase LSD1 is over-expressed in hematological tumors and has emerged as a promising target for anticancer treatment, so that several LSD1 inhibitors are under development and testing, in preclinical and clinical settings. However, the complete understanding of their complex mechanism of action is still unreached. Here, we unraveled a novel mode of action of the LSD1 inhibitors MC2580 and DDP-38003, showing that they can induce differentiation of AML cells through the downregulation of the chromatin protein GSE1. Analysis of the phenotypic effects of GSE1 depletion in NB4 cells showed a strong decrease of cell viability in vitro and of tumor growth in vivo. Mechanistically, we found that a set of genes associated with immune response and cytokine-signaling pathways are upregulated by LSD1 inhibitors through GSE1-protein reduction and that LSD1 and GSE1 colocalize at promoters of a subset of these genes at the basal state, enforcing their transcriptional silencing. Moreover, we show that LSD1 inhibitors lead to the reduced binding of GSE1 to these promoters, activating transcriptional programs that trigger myeloid differentiation. Our study offers new insights into GSE1 as a novel therapeutic target for AML.

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.

According to a hierarchical model, targeting leukemia-initiating cells (LICs) was speculated to achieve complete remission (CR) or cure. Nonetheless, increasing evidence emphasized the plasticity of differentiated blasts undergoing interconversion into LICs. We exploited murine models of acute promyelocytic leukemia (APL), a subtype of acute myeloid leukemia driven by the promyelocytic leukemia/retinoic acid receptor (PML-RARα) oncofusion protein, which recruits histone deacetylase (HDAC)-containing complexes. We studied APLs with different LIC frequencies and investigated the effect of two HDAC inhibitors: valproic acid (VPA), with relative selectivity towards class I HDAC enzymes and vorinostat/suberoylanilide hydroxamic acid (SAHA) (pan-HDAC inhibitor) in combination with all-trans retinoic acid (ATRA), on the bulk APL cells and APL LICs. Indeed, we found that while VPA differentiates the bulk APL cells, SAHA selectively targets LICs. ATRA + VPA + SAHA combination efficiently induced CR in an APL model with lower LIC frequency. Substituting ATRA with synthetic retinoids as etretinate which promotes APL differentiation without downregulating PML/RARα compromised the therapeutic benefit of ATRA + VPA + SAHA regimen. Altogether, our study emphasizes the therapeutic power of co-targeting the plasticity and heterogeneity of cancer –herein demonstrated by tackling LICs and bulk leukemic blasts - to achieve and maintain CR.

During prenatal life, the foetal liver is colonised by several waves of haematopoietic stem and progenitor cells (HSPCs) to act as the main haematopoietic organ. Single cell (sc) RNA-seq has been used to identify foetal liver cell types via their transcriptomic signature and to compare gene expression pattern as haematopoietic development proceeds. To obtain a refined single cell landscape of haematopoiesis in the foetal liver, we have generated a novel scRNA-seq dataset from whole mouse E12.5 liver that includes a larger number of cells than prior datasets at this stage and was obtained without cell type preselection to include all liver cell populations. We combined mining of this dataset with that of previously published datasets at other developmental stages to follow transcriptional dynamics as well as cell cycle state of developing haematopoietic lineages. Our findings corroborate several prior reports on the timing of liver colonisation by HSPCs and the emergence of differentiated lineages and provide further molecular characterisation of each cell population. Extending these findings, we demonstrate the existence of a foetal intermediate haemoglobin profile in the mouse, similar to that previously identified in humans, and a previously unidentified population of primitive erythroid cells in the foetal liver.Competing Interest StatementThe authors have declared no competing interest.

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.