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Abstract Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to rare cell populations and leads to low reproducibility. Moreover, the human eye is not suited to detect shifts of cellular properties of entire populations. Hence, quantitative image analysis could improve the accuracy and reproducibility of MDS diagnosis. We used real-time deformability cytometry (RT-DC) to measure bone marrow biopsy samples of MDS patients and age-matched healthy individuals. RT-DC is a high-throughput (1000 cells/s) imaging flow cytometer capable of recording morphological and mechanical properties of single cells. Properties of single cells were quantified using automated image analysis, and machine learning was employed to discover morpho-mechanical patterns in thousands of individual cells that allow to distinguish healthy vs. MDS samples. We found that distribution properties of cell sizes differ between healthy and MDS, with MDS showing a narrower distribution of cell sizes. Furthermore, we found a strong correlation between the mechanical properties of cells and the number of disease-determining mutations, inaccessible with current diagnostic approaches. Hence, machine-learning assisted RT-DC could be a promising tool to automate sample analysis to assist experts during diagnosis or provide a scalable solution for MDS diagnosis to regions lacking sufficient medical experts.

Azacitidine (AZA) treatment is used for patients with myelodysplasias that cannot undergo bone marrow transplantation; however, AZA treatment is only partially effective. Here the authors show synergy of AZA with compounds inhibiting the chromatin regulators CBP and p300, which is mediated by the RNA-dependent functions of AZA affecting protein translation.

Abstract Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are clonal hematopoietic stem cell disorders with a poor prognosis, especially for elderly patients. Increasing evidence suggests that alterations in the non-hematopoietic microenvironment (bone marrow niche) can contribute to or initiate malignant transformation and promote disease progression. One of the key components of the bone marrow (BM) niche are BM stromal cells (BMSC) that give rise to osteoblasts and adipocytes. It has been shown that the balance between these two cell types plays an important role in the regulation of hematopoiesis. However, data on the number of BMSC and the regulation of their differentiation balance in the context of hematopoietic malignancies is scarce. We established a stringent flow cytometric protocol for the prospective isolation of a CD73+ CD105+ CD271+ BMSC subpopulation from uncultivated cryopreserved BM of MDS and AML patients as well as age-matched healthy donors. BMSC from MDS and AML patients showed a strongly reduced frequency of CFU-F (colony forming unit-fibroblast). Moreover, we found an altered phenotype and reduced replating efficiency upon passaging of BMSC from MDS and AML samples. Expression analysis of genes involved in adipo- and osteogenic differentiation as well as Wnt- and Notch-signalling pathways showed significantly reduced levels of DLK1, an early adipogenic cell fate inhibitor in MDS and AML BMSC. Matching this observation, functional analysis showed significantly increased in vitro adipogenic differentiation potential in BMSC from MDS and AML patients. Overall, our data show BMSC with a reduced CFU-F capacity, and an altered molecular and functional profile from MDS and AML patients in culture, indicating an increased adipogenic lineage potential that is likely to provide a disease-promoting microenvironment.