BACKGROUND: Genetically encoded calcium ion (Ca2+) indicators (GECIs) are indispensable tools for measuring Ca2+ dynamics and neuronal activities in vitro and in vivo. Red fluorescent protein (RFP)-based GECIs have inherent advantages relative to green fluorescent protein-based GECIs due to the longer wavelength light used for excitation. Longer wavelength light is associated with decreased phototoxicity and deeper penetration through tissue. Red GECI can also enable multicolor visualization with blue- or cyan-excitable fluorophores.

RESULTS: Here we report the development, structure, and validation of a new RFP-based GECI, K-GECO1, based on a circularly permutated RFP derived from the sea anemone Entacmaea quadricolor. We have characterized the performance of K-GECO1 in cultured HeLa cells, dissociated neurons, stem-cell-derived cardiomyocytes, organotypic brain slices, zebrafish spinal cord in vivo, and mouse brain in vivo.

CONCLUSION: K-GECO1 is the archetype of a new lineage of GECIs based on the RFP eqFP578 scaffold. It offers high sensitivity and fast kinetics, similar or better than those of current state-of-the-art indicators, with diminished lysosomal accumulation and minimal blue-light photoactivation. Further refinements of the K-GECO1 lineage could lead to further improved variants with overall performance that exceeds that of the most highly optimized red GECIs.

Hunger and pain are two competing signals that individuals must resolve to ensure survival. However, the neural processes that prioritize conflicting survival needs are poorly understood. We discovered that hunger attenuates behavioral responses and affective properties of inflammatory pain without altering acute nociceptive responses. This effect is centrally controlled, as activity in hunger-sensitive agouti-related protein (AgRP)-expressing neurons abrogates inflammatory pain. Systematic analysis of AgRP projection subpopulations revealed that the neural processing of hunger and inflammatory pain converge in the hindbrain parabrachial nucleus (PBN). Strikingly, activity in AgRP → PBN neurons blocked the behavioral response to inflammatory pain as effectively as hunger or analgesics. The anti-nociceptive effect of hunger is mediated by neuropeptide Y (NPY) signaling in the PBN. By investigating the intersection between hunger and pain, we have identified a neural circuit that mediates competing survival needs and uncovered NPY Y1 receptor signaling in the PBN as a target for pain suppression.

To support cognitive function, the CA3 region of the hippocampus performs computations involving attractor dynamics. Understanding how cellular and ensemble activities of CA3 neurons enable computation is critical for elucidating the neural correlates of cognition. Here we show that CA3 comprises not only classically described pyramid cells with thorny excrescences, but also includes previously unidentified 'athorny' pyramid cells that lack mossy-fiber input. Moreover, the two neuron types have distinct morphological and physiological phenotypes and are differentially modulated by acetylcholine. To understand the contribution of these athorny pyramid neurons to circuit function, we measured cell-type-specific firing patterns during sharp-wave synchronization events in vivo and recapitulated these dynamics with an attractor network model comprising two principal cell types. Our data and simulations reveal a key role for athorny cell bursting in the initiation of sharp waves: transient network attractor states that signify the execution of pattern completion computations vital to cognitive function.

Zika virus (ZIKV) is an emerging flavivirus that caused thousands of human infections in recent years. Compared to other human flaviviruses, ZIKV replication is not well understood. Using fluorescent, transmission electron, and focused ion beam-scanning electron microscopy, we examined ZIKV replication dynamics in Vero 76 cells and in the brains of infected laboratory mice. We observed the progressive development of a perinuclear flaviviral replication factory both in vitro and in vivo. In vitro, we illustrated the ZIKV lifecycle from particle cell entry to egress. ZIKV particles assembled and aggregated in an induced convoluted membrane structure and ZIKV strain-specific membranous vesicles. While most mature virus particles egressed via membrane budding, some particles also likely trafficked through late endosomes and egressed through membrane abscission. Interestingly, we consistently observed a novel sheet-like virus particle array consisting of a single layer of ZIKV particles. Our study further defines ZIKV replication and identifies a novel hallmark of ZIKV infection.

We describe the implementation and use of an adaptive imaging framework for optimizing spatial resolution and signal strength in a light-sheet microscope. The framework, termed AutoPilot, comprises hardware and software modules for automatically measuring and compensating for mismatches between light-sheet and detection focal planes in living specimens. Our protocol enables researchers to introduce adaptive imaging capabilities in an existing light-sheet microscope or use our SiMView microscope blueprint to set up a new adaptive multiview light-sheet microscope. The protocol describes (i) the mechano-optical implementation of the adaptive imaging hardware, including technical drawings for all custom microscope components; (ii) the algorithms and software library for automated adaptive imaging, including the pseudocode and annotated source code for all software modules; and (iii) the execution of adaptive imaging experiments, as well as the configuration and practical use of the AutoPilot framework. Setup of the adaptive imaging hardware and software takes 1-2 weeks each. Previous experience with light-sheet microscopy and some familiarity with software engineering and building of optical instruments are recommended. Successful implementation of the protocol recovers near diffraction-limited performance in many parts of typical multicellular organisms studied with light-sheet microscopy, such as fruit fly and zebrafish embryos, for which resolution and signal strength are improved two- to fivefold.

Mechanics plays a key role in the development of higher organisms. However, understanding this relationship is complicated by the difficulty of modeling the link between local forces generated at the subcellular level and deformations observed at the tissue and whole-embryo levels. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation by cytoskeletal elements enters a continuum mechanics formulation for the full system in the form of local changes in preferred curvature. This allows us to express and solve the system using only tissue strains. Locations of preferred curvature are simply related to products of gene expression. A solution, in that context, means relaxing the system’s mechanical energy to yield global morphogenetic predictions that accommodate a tendency toward the local preferred curvature, without a need to explicitly model force-generation mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a 3D spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.

In this Scientific Perspectives we first review the recent advances in our understanding of the functional architecture of basal ganglia circuits. Then we argue that these data can best be explained by a model in which basal ganglia act to control the gain of movement kinematics to shape performance based on prior experience, which we refer to as a history-dependent gain computation. Finally, we discuss how insights from the history-dependent gain model might translate from the bench to the bedside, primarily the implications for the design of adaptive deep brain stimulation. Thus, we explicate the key empirical and conceptual support for a normative, computational model with substantial explanatory power for the broad role of basal ganglia circuits in health and disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Using FIB-SEM we report the entire synaptic connectome of glomerulus VA1v of the right antennal lobe in . Within the glomerulus we densely reconstructed all neurons, including hitherto elusive local interneurons. The -positive, sexually dimorphic VA1v included >11,140 presynaptic sites with ~38,050 postsynaptic dendrites. These connected input olfactory receptor neurons (ORNs, 51 ipsilateral, 56 contralateral), output projection neurons (18 PNs), and local interneurons (56 of >150 previously reported LNs). ORNs are predominantly presynaptic and PNs predominantly postsynaptic; newly reported LN circuits are largely an equal mixture and confer extensive synaptic reciprocity, except the newly reported LN2V with input from ORNs and outputs mostly to monoglomerular PNs, however. PNs were more numerous than previously reported from genetic screens, suggesting that the latter failed to reach saturation. We report a matrix of 192 bodies each having 50 connections; these form 88% of the glomerulus' pre/postsynaptic sites.

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

Although most proteins conform to the classical one-structure/one-function paradigm, an increasing number of proteins with dual structures and functions are emerging. These fold-switching proteins remodel their secondary structures in response to cellular stimuli, fostering multi-functionality and tight cellular control. Accurate predictions of fold-switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Previously, we developed a prediction method for fold-switching proteins based on secondary structure predictions and structure-based thermodynamic calculations. Given the large number of genomic sequences without homologous experimentally characterized structures, however, we sought to predict fold-switching proteins from their sequences alone. To do this, we leveraged state-of-the-art secondary structure predictions, which require only amino acid sequences but are not currently designed to identify structural duality in proteins. Thus, we hypothesized that incorrect and inconsistent secondary structure predictions could be good initial predictors of fold-switching proteins. We found that secondary structure predictions of fold-switching proteins with solved structures are indeed less accurate than secondary structure predictions of non-fold-switching proteins with solved structures. These inaccuracies result largely from the conformations of fold-switching proteins that are underrepresented in the Protein Data Bank (PDB), and, consequently, the training sets of secondary structure predictors. Given that secondary structure predictions are homology-based, we hypothesized that decontextualizing the inaccurately-predicted regions of fold-switching proteins could weaken the homology relationships between these regions and their overpopulated structural representatives. Thus, we reran secondary structure predictions on these regions in isolation and found that they were significantly more inconsistent than in regions of non-fold-switching proteins. Thus, inconsistent secondary structure predictions can serve as a preliminary marker of fold switching. These findings have implications for genomics and the future development of secondary structure predictors.