The innate immune system processes pathogen-induced signals into cell fate decisions. How information is turned to decision remains unknown. By combining stochastic mathematical modelling and experimentation, we demonstrate that feedback interactions between the IRF3, NF-κB and STAT pathways lead to switch-like responses to a viral analogue, poly(I:C), in contrast to pulse-like responses to bacterial LPS. Poly(I:C) activates both IRF3 and NF-κB, a requirement for induction of IFNβ expression. Autocrine IFNβ initiates a JAK/STAT-mediated positive-feedback stabilising nuclear IRF3 and NF-κB in first responder cells. Paracrine IFNβ, in turn, sensitises second responder cells through a JAK/STAT-mediated positive feedforward pathway that upregulates the positive-feedback components: RIG-I, PKR and OAS1A. In these sensitised cells, the ‘live-or-die’ decision phase following poly(I:C) exposure is shorter—they rapidly produce antiviral responses and commit to apoptosis. The interlinked positive feedback and feedforward signalling is key for coordinating cell fate decisions in cellular populations restricting pathogen spread.

While shear emulsification is a well understood industrial process, geometrical confinement in microfluidic systems introduces fascinating complexity, so far prohibiting complete understanding of droplet formation. The size of confined droplets is controlled by the ratio between shear and capillary forces when both are of the same order, in a regime known as jetting, while being surprisingly insensitive to this ratio when shear is orders of magnitude smaller than capillary forces, in a regime known as squeezing. Here, we reveal that further reduction of—already negligibly small—shear unexpectedly re-introduces the dependence of droplet size on shear/capillary-force ratio. For the first time we formally account for the flow around forming droplets, to predict and discover experimentally an additional regime—leaking. Our model predicts droplet size and characterizes the transitions from leaking into squeezing and from squeezing into jetting, unifying the description for confined droplet generation, and offering a practical guide for applications.

Stored red blood cells (RBCs) incur biochemical and morphological changes, collectively termed the storage lesion. Functionally, the storage lesion manifests as slower oxygen unloading from RBCs, which may compromise the efficacy of transfusions where the clinical imperative is to rapidly boost oxygen delivery to tissues. Recent analysis of large real-world data linked longer storage with increased recipient mortality. Biochemical rejuvenation with a formulation of adenosine, inosine, and pyruvate can restore gas-handling properties, but its implementation is impractical for most clinical scenarios. We tested whether storage under hypoxia, previously shown to slow biochemical degradation, also preserves gas-handling properties of RBCs. A microfluidic chamber, designed to rapidly switch between oxygenated and anoxic superfusates, was used for single-cell oxygen saturation imaging on samples stored for up to 49 days. Aliquots were also analyzed flow cytometrically for side-scatter (a proposed proxy of O2 unloading kinetics), metabolomics, lipidomics, and redox proteomics. For benchmarking, units were biochemically rejuvenated at 4 weeks of standard storage. Hypoxic storage hastened O2 unloading in units stored to 35 days, an effect that correlated with side-scatter but was not linked to posttranslational modifications of hemoglobin. Although hypoxic storage and rejuvenation produced distinct biochemical changes, a subset of metabolites including pyruvate, sedoheptulose 1-phosphate, and 2/3 phospho-d-glycerate, was a common signature that correlated with changes in O2 unloading. Correlations between gas handling and lipidomic changes were modest. Thus, hypoxic storage of RBCs preserves key metabolic pathways and O2 exchange properties, thereby improving the functional quality of blood products and potentially influencing transfusion outcomes.

Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI—statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-κB single—cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory. In light of single-cell, live-imaging experiments understanding of how cells transmit information about identity and quantity of stimuli is incomplete. When exposed to the same stimulus individual cells exhibit substantial cell-to-cell heterogeneity. Besides, stimuli have been shown to regulate temporal profiles of signaling effectors. Therefore, it is, for instance, not entirely clear whether single-cell responses are binary or contain more information about the quantity of stimuli. The above questions resulted in a considerable interest to study cellular signaling within the framework of information theory. Unfortunately, the utilization of the information-theoretic perspective is handicapped in part by the lack of suitable methods that account for multivariate signaling data. Here, we propose a novel algorithm that breaks a considerable computational barrier by allowing the effective information-theoretic analysis of highly-dimensional single-cell measurements. Our approach is computationally efficient, robust and straightforward to use. Moreover, we provide a simple R-package implementation.

The results of synthesis of the fluorescent carbon dots (CDots) from graphite target and reduced graphene oxide (rGO) nanoparticles performed by the nanosecond laser ablation in polyethylene glycol 200 (PEG₂₀₀) are shown. Two-step laser irradiation (first graphite target, next achieved suspension) revealed a very effective production of CDots. However, the ablation in PEG appeared to be effective with 1064 nm laser pulse in contrast to the ablation with 355 nm laser pulse. In the case of rGO nanoparticles similar laser irradiation procedure was less efficient. In both cases, received nanoparticles exhibited strong, broadband photoluminescence with a maximum dependent on the excitation wavelength. The size distribution for obtained CDots was evaluated using the DLS technique and HRTEM images. The results from both methods show quite good agreement in nanoparticle size estimation although the DLS method slightly overestimates nanoparticle’s diameter.