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Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation and accumulation of immature myeloblasts, which impair normal hematopoiesis. While this definition categorizes the disease into a distinctive group, the large number of different genetic and epigenetic alterations actually suggests that AML is not a single disease, but a plethora of malignancies. Still, most AML patients are not treated with targeted medication but rather by uniform approaches such as chemotherapy. The identification of novel treatment options likely requires the identification of cancer cell vulnerabilities that take into account the different genetic and epigenetic make-up of the individual tumors. Here we show that STK3 depletion by knock-down, knock-out or chemical inhibition results in apoptotic cells death in some but not all AML cell lines and primary cells tested. This effect is mediated by a premature activation of cyclin dependent kinase 1 (CDK1) in presence of elevated cyclin B1 levels. The anti-leukemic effects seen in both bulk and progenitor AML cells suggests that STK3 might be a promising target in a subset of AML patients.
Current metabolomics approaches utilize cellular metabolite extracts, are destructive, and require high cell numbers. We introduce here an approach that enables the monitoring of cellular metabolism at lower cell numbers by observing the consumption/production of different metabolites over several kinetic data points of up to 48 hours. Our approach does not influence cellular viability, as we optimized the cellular matrix in comparison to other materials used in a variety of in‐cell NMR spectroscopy experiments. We are able to monitor real‐time metabolism of primary patient cells, which are extremely sensitive to external stress. Measurements are set up in an interleaved manner with short acquisition times (approximately 7 minutes per sample), which allows the monitoring of up to 15 patient samples simultaneously. Further, we implemented our approach for performing tracer‐based assays. Our approach will be important not only in the metabolomics fields, but also in individualized diagnostics.