Technologies bringing young Zebrafish from a niche field to the limelight

Fundamental life science and pharmaceutical research are continually striving to provide physiologically relevant context for their biological studies. Zebrafish present an opportunity for high-content screening (HCS) to bring a true in vivo model system to screening studies. Zebrafish embryos and young larvae are an economical, human-relevant model organism that are amenable to both genetic engineering and modification, and direct inspection via microscopy. The use of these organisms entails unique challenges that new technologies are overcoming, including artificial intelligence (AI). In this perspective article, we describe the state-of-the-art in terms of automated sample handling, imaging, and data analysis with zebrafish during early developmental stages. We highlight advances in orienting the embryos, including the use of robots, microfluidics, and creative multi-well plate solutions. Analyzing the micrographs in a fast, reliable fashion that maintains the anatomical context of the fluorescently labeled cells is a crucial step. Existing software solutions range from AI-driven commercial solutions to bespoke analysis algorithms. Deep learning appears to be a critical tool that researchers are only beginning to apply, but already facilitates many automated steps in the experimental workflow. Currently, such work has permitted the cellular quantification of multiple cell types in vivo, including stem cell responses to stress and drugs, neuronal myelination and macrophage behavior during inflammation and infection. We evaluate pro and cons of proprietary versus open-source methodologies for combining technologies into fully automated workflows of zebrafish studies. Zebrafish are poised to charge into HCS with ever-greater presence, bringing a new level of physiological context

Eph-ephrin signaling modulated by polymerization and condensation of receptors

Eph receptor signaling plays key roles in vertebrate tissue boundary formation, axonal pathfinding, and stem cell regeneration by steering cells to positions defined by its ligand ephrin. Some of the key events in Eph-ephrin signaling are understood: ephrin binding triggers the clustering of the Eph receptor, fostering transphosphorylation and signal transduction into the cell. However, a quantitative and mechanistic understanding of how the signal is processed by the recipient cell into precise and proportional responses is largely lacking. Studying Eph activation kinetics requires spatiotemporal data on the number and distribution of receptor oligomers, which is beyond the quantitative power offered by prevalent imaging methods. Here we describe an enhanced fluorescence fluctuation imaging analysis, which employs statistical resampling to measure the Eph receptor aggregation distribution within each pixel of an image. By performing this analysis over time courses extending tens of minutes, the information-rich 4D space (x, y, oligomerization, time) results were coupled to straightforward biophysical models of protein aggregation. This analysis reveals that Eph clustering can be explained by the combined contribution of polymerization of receptors into clusters, followed by their condensation into far larger aggregates. The modeling reveals that these two competing oligomerization mechanisms play distinct roles: polymerization mediates the activation of the receptor by assembling monomers into 6- to 8-mer oligomers; condensation of the preassembled oligomers into large clusters containing hundreds of monomers dampens the signaling. We propose that the polymerization–condensation dynamics creates mechanistic explanation for how cells properly respond to variable ligand concentrations and gradients

Using enhanced number and brightness to measure protein oligomerization dynamics in live cells

Protein dimerization and oligomerization are essential to most cellular functions, yet measurement of the size of these oligomers in live cells, especially when their size changes over time and space, remains a challenge. A commonly used approach for studying protein aggregates in cells is number and brightness (N&B), a fluorescence microscopy method that is capable of measuring the apparent average number of molecules and their oligomerization (brightness) in each pixel from a series of fluorescence microscopy images. We have recently expanded this approach in order to allow resampling of the raw data to resolve the statistical weighting of coexisting species within each pixel. This feature makes enhanced N&B (eN&B) optimal for capturing the temporal aspects of protein oligomerization when a distribution of oligomers shifts toward a larger central size over time. In this protocol, we demonstrate the application of eN&B by quantifying receptor clustering dynamics using electron-multiplying charge-coupled device (EMCCD)-based total internal reflection microscopy (TIRF) imaging. TIRF provides a superior signal-to-noise ratio, but we also provide guidelines for implementing eN&B in confocal microscopes. For each time point, eN&B requires the acquisition of 200 frames, and it takes a few seconds up to 2 min to complete a single time point. We provide an eN&B (and standard N&B) MATLAB software package amenable to any standard confocal or TIRF microscope. The software requires a high-RAM computer (64 Gb) to run and includes a photobleaching detrending algorithm, which allows extension of the live imaging for more than an hour

Relating influenza virus membrane fusion kinetics to stoichiometry of neutralizing antibodies at the single-particle level

The ability of antibodies binding the influenza hemagglutinin (HA) protein to neutralize viral infectivity is of key importance in the design of next-generation vaccines and for prophylactic and therapeutic use. The two antibodies CR6261 and CR8020 have recently been shown to efficiently neutralize influenza A infection by binding to and inhibiting the influenza A HA protein that is responsible for membrane fusion in the early steps of viral infection. Here, we use single-particle fluorescence microscopy to correlate the number of antibodies or antibody fragments (Fab) bound to an individual virion with the capacity of the same virus particle to undergo membrane fusion. To this end, individual, infectious virus particles bound by fluorescently labeled antibodies/Fab are visualized as they fuse to a planar, supported lipid bilayer. The fluorescence intensity arising from the virus-bound antibodies/Fab is used to determine the number of molecules attached to viral HA while a fluorescent marker in the viral membrane is used to simultaneously obtain kinetic information on the fusion process. We experimentally determine that the stoichiometry required for fusion inhibition by both antibody and Fab leaves large numbers of unbound HA epitopes on the viral surface. Kinetic measurements of the fusion process reveal that those few particles capable of fusion at high antibody/Fab coverage display significantly slower hemifusion kinetics. Overall, our results support a membrane fusion mechanism requiring the stochastic, coordinated action of multiple HA trimers and a model of fusion inhibition by stem-binding antibodies through disruption of this coordinated action

Relating influenza virus membrane fusion kinetics to stoichiometry of neutralizing antibodies at the single-particle level

The ability of antibodies binding the influenza hemagglutinin (HA) protein to neutralize viral infectivity is of key importance in the design of next-generation vaccines and for prophylactic and therapeutic use. The two antibodies CR6261 and CR8020 have recently been shown to efficiently neutralize influenza A infection by binding to and inhibiting the influenza A HA protein that is responsible for membrane fusion in the early steps of viral infection. Here, we use single-particle fluorescence microscopy to correlate the number of antibodies or antibody fragments (Fab) bound to an individual virion with the capacity of the same virus particle to undergo membrane fusion. To this end, individual, infectious virus particles bound by fluorescently labeled antibodies/Fab are visualized as they fuse to a planar, supported lipid bilayer. The fluorescence intensity arising from the virus-bound antibodies/Fab is used to determine the number of molecules attached to viral HA while a fluorescent marker in the viral membrane is used to simultaneously obtain kinetic information on the fusion process. We experimentally determine that the stoichiometry required for fusion inhibition by both antibody and Fab leaves large numbers of unbound HA epitopes on the viral surface. Kinetic measurements of the fusion process reveal that those few particles capable of fusion at high antibody/Fab coverage display significantly slower hemi-fusion kinetics. Overall, our results support a membrane fusion mechanism requiring the stochastic, coordinated action of multiple HA trimers and a model of fusion inhibition by stem-binding antibodies through disruption of this coordinated actio

Eph-ephrin signaling modulated by polymerization and condensation of receptors

Eph receptor signaling plays key roles in vertebrate tissue boundary formation, axonal pathfinding, and stem cell regeneration by steering cells to positions defined by its ligand ephrin. Some of the key events in Eph-ephrin signaling are understood: ephrin binding triggers the clustering of the Eph receptor, fostering transphosphorylation and signal transduction into the cell. However, a quantitative and mechanistic understanding of how the signal is processed by the recipient cell into precise and proportional responses is largely lacking. Studying Eph activation kinetics requires spatiotemporal data on the number and distribution of receptor oligomers, which is beyond the quantitative power offered by prevalent imaging methods. Here we describe an enhanced fluorescence fluctuation imaging analysis, which employs statistical resampling to measure the Eph receptor aggregation distribution within each pixel of an image. By performing this analysis over time courses extending tens of minutes, the information-rich 4D space (x, y, oligomerization, time) results were coupled to straightforward biophysical models of protein aggregation. This analysis reveals that Eph clustering can be explained by the combined contribution of polymerization of receptors into clusters, followed by their condensation into far larger aggregates. The modeling reveals that these two competing oligomerization mechanisms play distinct roles: polymerization mediates the activation of the receptor by assembling monomers into 6- to 8-mer oligomers; condensation of the preassembled oligomers into large clusters containing hundreds of monomers dampens the signaling. We propose that the polymerization–condensation dynamics creates mechanistic explanation for how cells properly respond to variable ligand concentrations and gradients

Eph-ephrin signaling modulated by polymerization and condensation of receptors

Eph receptor signaling plays key roles in vertebrate tissue boundary formation, axonal pathfinding, and stem cell regeneration by steering cells to positions defined by its ligand ephrin. Some of the key events in Eph-ephrin signaling are understood: ephrin binding triggers the clustering of the Eph receptor, fostering transphosphorylation and signal transduction into the cell. However, a quantitative and mechanistic understanding of how the signal is processed by the recipient cell into precise and proportional responses is largely lacking. Studying Eph activation kinetics requires spatiotemporal data on the number and distribution of receptor oligomers, which is beyond the quantitative power offered by prevalent imaging methods. Here we describe an enhanced fluorescence fluctuation imaging analysis, which employs statistical resampling to measure the Eph receptor aggregation distribution within each pixel of an image. By performing this analysis over time courses extending tens of minutes, the information-rich 4D space (x, y, oligomerization, time) results were coupled to straightforward biophysical models of protein aggregation. This analysis reveals that Eph clustering can be explained by the combined contribution of polymerization of receptors into clusters, followed by their condensation into far larger aggregates. The modeling reveals that these two competing oligomerization mechanisms play distinct roles: polymerization mediates the activation of the receptor by assembling monomers into 6- to 8-mer oligomers; condensation of the preassembled oligomers into large clusters containing hundreds of monomers dampens the signaling. We propose that the polymerization–condensation dynamics creates mechanistic explanation for how cells properly respond to variable ligand concentrations and gradients