Alison G. Tebo

Many regulatory mechanisms function by changing a molecule’s conformation such that it is switched between active and inactive states. Engineering macromolecules such as proteins to show robust conformational changes in response to arbitrary, well-defined molecular stimuli is of great interest in fields such as synthetic biology and nanoscale engineering, because it allows the development of “smart” molecular tools. We present a strategy that can convert “simple” affinity binders such as nanobodies into protein switches that undergo clear structural rearrangements in response to their innate targets, by introducing a peptide that competes for the binding site. We demonstrate proof-of-concept implementations of this “NanoBlock” design by developing genetically encoded fluorescent biosensors based on two entirely different binder frameworks, the ALFA nanobody and the PDZ domain of Erbin. We show that our design allows the construction of well-performing protein switches that can reliably detect their targets in vitro, in mammalian cells, as part of fluorescence-activated cell sorting (FACS) experiments, and that also generalizes to the creation of stimulus-responsive enzymes via the integration of a bioluminescent readout. We furthermore develop a theoretical model that rationalizes the observations and provides insight into the underlying molecular mechanisms. Overall, our strategy expands the possibilities to create “smart” protein-based molecules and effectors.

Piconewton forces borne by individual proteins within complex assemblies underlie cell adhesion, migration, and tissue morphogenesis, yet remain difficult to image in living systems. Here, we introduce WHaloForce, a chemigenetic HaloTag-based tension sensor that converts force-dependent relief of tryptophan-mediated dye quenching into a fluorescence lifetime change. Optical tweezers revealed a switch-like unquenching transition near 5 pN, and cellular measurements demonstrated dynamic responses to actomyosin activity. WHaloForce enabled quantitative tension imaging of diverse force-bearing proteins (vinculin, E-cadherin, α-catenin, and laminin) in mammalian cells, mouse tissue, and C. elegans. Bright synthetic dyes made tension measurements possible at endogenous expression levels. In C. elegans, vinculin and laminin showed opposite tension patterns between tissues, highlighting tissue-specific force transmission through adhesions and the extracellular matrix. We also identified ovulation as a source of molecular loading within the spermathecal laminin network. WHaloForce thus offers a modular platform for imaging spatiotemporal tension patterns in living systems.

Fructose-1,6-bisphosphate (FBP) is the product of the first committed step of glycolysis, and its concentration is tightly correlated with glycolytic flux. Glycolytic activity varies across tissues and cell types: some tissues, such as the brain, dynamically regulate glycolysis in response to demand, while others, such as the liver have characterized spatial heterogeneity. Here, we report HYlight2, an improved sensor for FBP developed through random whole-gene mutagenesis in E. coli lysate. After four rounds of screening, we isolated HYlight2, which retains its binding affinity while displaying a ΔR/R ∼9 in vitro , a three-fold improvement in mammalian cells, and a two-fold improvement in detecting glycolytic responses during stimulated neuronal activity. We further demonstrate its use in vivo to detect altered glycolytic activity in C. elegans neurons, zebrafish pancreatic islets, and mouse liver.

Understanding how neurons integrate signals from thousands of input synapses requires methods to monitor neurotransmission across many sites simultaneously. The fluorescent protein glutamate indicator iGluSnFR enables visualization of synaptic signaling, but the sensitivity, scale and speed of such measurements are limited by existing variants. Here we developed two highly sensitive fourth-generation iGluSnFR variants with fast activation and tailored deactivation rates: iGluSnFR4f for tracking rapid dynamics, and iGluSnFR4s for recording from large populations of synapses. These indicators detect glutamate with high spatial specificity and single-vesicle sensitivity in vivo. We used them to record natural patterns of synaptic transmission across multiple experimental contexts in mice, including two-photon imaging in cortical layers 1–4 and hippocampal CA1, and photometry in the midbrain. The iGluSnFR4 variants extend the speed, sensitivity and scalability of glutamate imaging, enabling direct observation of information flow through neural networks in the intact brain.

Genetically-encoded fluorescent biosensors have revolutionized our understanding of complex systems by permitting the in situ observation of chemical activities. However, only a comparatively small set of chemical activities can be monitored, largely due to the need to identify protein domains that undergo conformational and/or association changes in response to a stimulus. Here, we present a strategy that can convert ‘simple’ affinity binders such as nanobodies into biosensors for their innate targets by introducing a peptide sequence that competes for the binding site. We demonstrate proof-of-concept implementations of this ‘NanoBlock’ design, developing sensors based on the ALFA nanobody and on the PDZ domain of Erbin. We show that these sensors can reliably detect their targets in vitro , in mammalian cells, and as part of fluorescence-activated cell sorting (FACS) experiments. In doing so, our strategy offers a way to strongly expand the range of cellular processes that can be probed using fluorescent biosensors.

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neuron’s thousands of inputs in the intact brain. To facilitate this goal, we engineered and screened 3365 variants of the fluorescent protein glutamate indicator iGluSnFR3 in neuron culture, and selected variants in the mouse visual cortex. Two variants have high sensitivity, fast activation (< 2 ms) and deactivation times tailored for recording large populations of synapses (iGluSnFR4s, 153 ms) or rapid dynamics (iGluSnFR4f, 26 ms). By imaging action-potential evoked signals on axons and visually-evoked signals on dendritic spines, we show that iGluSnFR4s/4f primarily detect local synaptic glutamate with single-vesicle sensitivity. The indicators detect a wide range of naturalistic synaptic transmission, including in the vibrissal cortex layer 4 and in hippocampal CA1 dendrites. iGluSnFR4 increases the sensitivity and scale (4s) or speed (4f) of tracking information flow in neural networks in vivo.

Genetically encoded calcium ion (Ca²⁺) indicators (GECIs) are widely-used molecular tools for functional imaging of Ca²⁺ dynamics and neuronal activities with single-cell resolution. Here we report the design and development of two far-red fluorescent GECIs, FR-GECO1a and FR-GECO1c, based on the monomeric far-red fluorescent proteins mKelly1 and mKelly2. FR-GECOs have excitation and emission maxima at ~596 nm and ~644 nm, respectively, display large responses to Ca²⁺ in vitro (ΔF/F₀ = 6 for FR-GECO1a, 18 for FR-GECO1c), are bright under both one-photon and two-photon illumination, and have high affinities (apparent K_d = 29 nM for FR-GECO1a, 83 nM for FR-GECO1c) for Ca²⁺. FR-GECOs offer sensitive and fast detection of single action potentials in neurons, and enable in vivo all-optical manipulation and measurement of cellular activities in combination with optogenetic actuators.

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neuron’s thousands of inputs in the intact brain. To facilitate this goal, we engineered and screened 3365 variants of the fluorescent protein glutamate indicator iGluSnFR3 in neuron culture, and selected variants in the mouse visual cortex. Two variants have high sensitivity, fast activation (< 2 ms) and deactivation times tailored for recording large populations of synapses (iGluSnFR4s, 153 ms) or rapid dynamics (iGluSnFR4f, 26 ms). By imaging action-potential evoked signals on axons and visually-evoked signals on dendritic spines, we show that iGluSnFR4s/4f primarily detect local synaptic glutamate with single-vesicle sensitivity. The indicators detect a wide range of naturalistic synaptic transmission, including in the vibrissal cortex layer 4 and in hippocampal CA1 dendrites. iGluSnFR4 increases the sensitivity and scale (4s) or speed (4f) of tracking information flow in neural networks in vivo .

Near-infrared (NIR) fluorescent reporters open interesting perspectives for multiplexed imaging with higher contrast and depth using less toxic light. Here, we propose nirFAST, a small (14 kDa) chemogenetic NIR fluorescent reporter, displaying higher cellular brightness compared to top-performing NIR fluorescent proteins. nirFAST binds and stabilizes the fluorescent state of synthetic cell permeant fluorogenic chromophores (so-called fluorogens), otherwise dark when free. nirFAST displays tunable NIR, far-red or red emission through change of fluorogen. nirFAST allows imaging and spectral multiplexing in live cultured mammalian cells, chicken embryo tissues and zebrafish larvae. Its suitability for stimulated emission depletion nanoscopy enabled protein imaging with subdiffraction resolution in live cells. nirFAST enabled the design of a two-color cell cycle indicator for monitoring the different phases of the cell cycle. Finally, bisection of nirFAST allowed the design of a chemically induced dimerization technology with NIR fluorescence readout, enabling the control and visualization of protein proximity.

The growth of deep learning (DL) relies heavily on huge amounts of labelled data for tasks such as natural language processing and computer vision. Specifically, in image-to-text or image-to-image pipelines, opinion (sentiment) may be inadvertently learned by a model from human-generated image captions. Additionally, learning may be affected by the variety and diversity of the provided captions. While labelling large datasets has largely relied on crowd-sourcing or data-worker pools, evaluating the quality of such training data is crucial. This study proposes an evaluation method focused on sentiment and semantic richness. That method was applied to the COCO-MS dataset, comprising approximately 150K images with segmented objects and corresponding crowd-sourced captions. We employed pre-trained models (Twitter-RoBERTa-base and BERT-base) to extract sentiment scores and variability of semantic embeddings from captions. The relation of the sentiment score and semantic variability with object categories was examined using multiple linear regression. Results indicate that while most captions were neutral, about 6% of the captions exhibited strong sentiment influenced by specific object categories. Semantic variability of within-image captions remained low and uncorrelated with object categories. Model-generated captions showed less than 1.5% of strong sentiment which was not influenced by object categories and did not correlate with the sentiment of the respective human-generated captions. This research demonstrates an approach to assess the quality of crowd- or worker-sourced captions informed by image content.