We engineered a novel family of genetically-encoded fluorescent norepinephrine indicators in green and red. These indicators exhibit exceptional sensitivity, ligand-specificity, and temporal resolution, surpassing prior indicators and enabling the detection of norepinephrine in living animals.

Prioritized analysis enables proteins of interest to be consistently quantified across single-cell samples at no cost to depth of coverage. Applied to primary macrophages, prioritization facilitated the quantification of proteolytically regulated proteoforms and the study of endocytic competency.

The connectivity between individual neurons is fundamental to neuronal networks and brain function. Here, we replace the flavor of chemical neurotransmission with the color of photons emitted from presynaptic luciferases and light-gated ion channels in the postsynapse.

How to process, store and (publicly) share the large datasets acquired in many laboratories distributed across the globe? These questions were faced by the International Brain Laboratory; solutions found are likely to be of interest to all our global and open Neuroscience community.

The design of biocatalytic reaction systems is highly complex due to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modelling method. Consequently, reproducibility of enzymatic experiments and re-usability of enzymatic data are challenging. We developed the XML–based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, time course of substrate and product, kinetic parameters, and kinetic model, thus making enzymatic data findable, accessible, interoperable, and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, where data and metadata of different enzymatic reactions are collected and analysed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modelling of enzyme kinetics, publication platforms, and enzymatic reaction databases. EnzymeML is open, transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org/.