Fluorescent nanosensors are powerful tools for basic research and bioanalytical applications. Individual nanosensors are able to detect single molecules, while ensembles of nanosensors can be used to measure the bulk concentration of an analyte. Collective imaging of multiple nanosensors could provide both spatial and temporal chemical information from the nano- to the microscale. This type of chemical imaging with nanosensors would be very attractive to study processes such as chemical signaling between cells (e.g., neurons). So far, it is not understood what processes are resolvable (concentration, time, space) and how optimal sensors should be designed. Here, we develop a theoretical framework to simulate the fluorescence image of arrays of nanosensors in response to a concentration gradient. For that purpose, binding and unbinding of the analyte is simulated for each single nanosensor by using a Monte Carlo simulation and varying rate constants (k, k). Multiple nanosensors are arranged on a surface and exposed to a concentration pattern c(x,y,t) of an analyte. We account for the resolution limit of light microscopy (Abbe limit) and the acquisition speed and resolution of optical setups and determine the resulting response images ΔI(x,y,t). Consequently, we introduce terms for the spatial and temporal resolution and simulate phase diagrams for different rate constants that allow us to predict how a sensor should be designed to provide a desired spatial and temporal resolution. Our results show, for example, that imaging of neurotransmitter release requires rate constants of k = 10 M sand k = 10 s in many scenarios, which corresponds to high dissociation constants of K > 100 μM. This work predicts if a given fluorescent nanosensor array (rate constants, size, shape, geometry, density) is able to resolve fast concentration changes such as neurotransmitter release from cells. Additionally, we provide rational design principles to engineer nanosensors for chemical imaging.

Using a molecular-level equilibrium theory where proteins are described using their crystallographic structure, we have studied protein adsorption from binary and ternary mixtures of myoglobin, lysozyme, and cytochrome c to poly­(methacrylic acid) hydrogel films. The pH gradients these films induce can lead to selective protein adsorption, where the solution pH provides a sensible dial to externally control protein separation. Changing the chemical composition of the polymer network, adding either another acidic or a neutral comonomer, allows for protein localization to controlled spatial regions of the film with nanometer resolution. As pH-sensitive polymer hydrogels are promising candidates for smart, responsive biomaterials, understanding the complexity of competitive protein adsorption is essential. In this work, we highlight the decisive role of amino acid protonation in selective protein adsorption. We present conditions such that the hydrogel film will selectively incorporate the more weakly charged protein, provided that it requires less work to protonate its amino acids.

Epileptic seizures are characterized by abnormal and excessive neural activity, where cortical network dynamics seem to become unstable. However, most of the time, during seizure-free periods, cortex of epilepsy patients shows perfectly stable dynamics. This raises the question of how recurring instability can arise in the light of this stable default state. In this work, we examine two potential scenarios of seizure generation: (i) epileptic cortical areas might generally operate closer to instability, which would make epilepsy patients generally more susceptible to seizures, or (ii) epileptic cortical areas might drift systematically towards instability before seizure onset. We analyzed single-unit spike recordings from both the epileptogenic (focal) and the nonfocal cortical hemispheres of 20 epilepsy patients. We quantified the distance to instability in the framework of criticality, using a novel estimator, which enables an unbiased inference from a small set of recorded neurons. Surprisingly, we found no evidence for either scenario: Neither did focal areas generally operate closer to instability, nor were seizures preceded by a drift towards instability. In fact, our results from both pre-seizure and seizure-free intervals suggest that despite epilepsy, human cortex operates in the stable, slightly subcritical regime, just like cortex of other healthy mammalians.