The molecular mechanisms governing the transition from hematopoietic stem cells (HSCs) to lineage-committed progenitors remain poorly understood. Transcription factors (TFs) are powerful cell intrinsic regulators of differentiation and lineage commitment, while cytokine signaling has been shown to instruct the fate of progenitor cells. However, the direct regulation of differentiation-inducing hematopoietic TFs by cell extrinsic signals remains surprisingly difficult to establish. PU.1 is a master regulator of hematopoiesis and promotes myeloid differentiation. Here we report that tumor necrosis factor (TNF) can directly and rapidly upregulate PU.1 protein in HSCs in vitro and in vivo. We demonstrate that in vivo, niche-derived TNF is the principal PU.1 inducing signal in HSCs and is both sufficient and required to relay signals from inflammatory challenges to HSCs.

Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism’s potential or actual metabolic operations. We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of Escherichia coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E.coli’s metabolic capabilities. Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta. Supplementary data are available at Bioinformatics online.

Growth rate is a widely studied parameter for various cell-based biological studies. Growth rates of cell populations can be monitored in chemostats and micro-chemostats, where nutrients are continuously replenished. Here, we present an integrated microfluidic platform that enables long-term culturing of non-adherent cells as well as parallel and mutually independent continuous monitoring of (i) growth rates of cells by means of impedance measurements and of (ii) specific other cellular events by means of high-resolution optical or fluorescence microscopy. Yeast colonies were grown in a monolayer under culturing pads, which enabled high-resolution microscopy, as all cells were in the same focal plane. Upon cell growth and division, cells leaving the culturing area passed over a pair of electrodes and were counted through impedance measurements. The impedance data could then be used to directly determine the growth rates of the cells in the culturing area. The integration of multiple culturing chambers with sensing electrodes enabled multiplexed long-term monitoring of growth rates of different yeast strains in parallel. As a demonstration, we modulated the growth rates of engineered yeast strains using calcium. The results indicated that impedance measurements provide a label-free readout method to continuously monitor the changes in the growth rates of the cells without compromising high-resolution optical imaging of single cells.