Targeting small-molecule fluorescent indicators using genetically encoded protein tags yields new hybrid sensors for biological imaging. Optimization of such systems requires redesign of the synthetic indicator to allow cell-specific targeting without compromising the photophysical properties or cellular performance of the small-molecule probe. We developed a bright and sensitive Ca indicator by systematically exploring the relative configuration of dye and chelator, which can be targeted using the HaloTag self-labeling tag system. Our "isomeric tuning" approach is generalizable, yielding a far-red targetable indicator to visualize Ca fluxes in the primary cilium.

Transfer of autoantibodies targeting ionotropic N-methyl-D-aspartate receptors in autoimmune encephalitis patients into mice leads to typical disease signs. Long-term effects of the pathogenic antibodies consist of immunoglobulin G-induced crosslinking and receptor internalization. We focused on the direct and immediate impact of a specific pathogenic patient-derived monoclonal autoantibody (immunoglobulin G #003-102) on receptor function.We performed cell-attached recordings in cells transfected with the GluN1 and GluN2A subunit of the N-methyl-D-aspartate receptor. Immunoglobulin G #003-102 binds to the amino-terminal domain of the glycine-binding GluN1 subunit. It reduced simultaneous receptor openings significantly compared to controls at both low and high glutamate and glycine concentrations. Closer examination of our data in 50-second to 2-second intervals revealed, that Immunoglobulin G #003-102 rapidly decreases the number of open receptors. However, antigen-binding fragments of immunoglobulin G #003-102 did not reduce the receptor openings.In conclusion, patient-derived immunoglobulin G #003-102 inhibits N-methyl-D-aspartate receptors rapidly and directly before receptor internalization occurs and the entire immunoglobulin G is necessary for this acute inhibitory effect. This suggests an application of the antigen-binding fragment-like constructs of #003-102 as a potential new treatment strategy for shielding the pathogenic epitopes on the N-methyl-D-aspartate receptors.

We present a machine learning–based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

Janelia Farm, the new research campus of the Howard Hughes Medical Institute, is an ongoing experiment in the social engineering of research communities.

Neuronal signal transduction by the JNK MAP kinase pathway is altered by a broad array of stimuli including exposure to the widely abused drug ethanol, but the behavioral relevance and the regulation of JNK signaling is unclear. Here we demonstrate that JNK signaling functions downstream of the Sterile20 kinase family gene tao/Taok3 to regulate the behavioral effects of acute ethanol exposure in both the fruit fly Drosophila and mice. In flies tao is required in neurons to promote sensitivity to the locomotor stimulant effects of acute ethanol exposure and to establish specific brain structures. Reduced expression of key JNK pathway genes substantially rescued the structural and behavioral phenotypes of tao mutants. Decreasing and increasing JNK pathway activity resulted in increased and decreased sensitivity to the locomotor stimulant properties of acute ethanol exposure, respectively. Further, JNK expression in a limited pattern of neurons that included brain regions implicated in ethanol responses was sufficient to restore normal behavior. Mice heterozygous for a disrupted allele of the homologous Taok3 gene (Taok3Gt) were resistant to the acute sedative effects of ethanol. JNK activity was constitutively increased in brains of Taok3Gt/+ mice, and acute induction of phospho-JNK in brain tissue by ethanol was occluded in Taok3Gt/+ mice. Finally, acute administration of a JNK inhibitor conferred resistance to the sedative effects of ethanol in wild-type but not Taok3Gt/+ mice. Taken together, these data support a role of a TAO/TAOK3-JNK neuronal signaling pathway in regulating sensitivity to acute ethanol exposure in flies and in mice.

Sexual behaviors in animals are governed by inputs from multiple external sensory modalities. However, how these inputs are integrated to jointly control animal behavior is still poorly understood. Whereas visual information alone is not sufficient to induce courtship behavior in Drosophila melanogaster males, when a subset of male-specific fruitless (fru)- and doublesex (dsx)-expressing neurons that respond to chemosensory cues (P1 neurons) were artificially activated via a temperature-sensitive cation channel (dTRPA1), males followed and extended their wing toward moving objects (even a moving piece of rubber band) intensively. When stationary, these objects were not courted. Our results indicate that motion input and activation of P1 neurons are individually necessary, and under our assay conditions, jointly sufficient to elicit early courtship behaviors, and provide insights into how courtship decisions are made via sensory integration.

Large electron microscopy image datasets for connectomics are typically composed of thousands to millions of partially overlapping two-dimensional images (tiles), which must be registered into a coherent volume prior to further analysis. A common registration strategy is to find matching features between neighboring and overlapping image pairs, followed by a numerical estimation of optimal image deformation using a so-called solver program. 
Existing solvers are inadequate for large data volumes, and inefficient for small-scale image registration. 
In this work, an efficient and accurate matrix-based solver method is presented. A linear system is constructed that combines minimization of feature-pair square distances with explicit constraints in a regularization term. In absence of reliable priors for regularization, we show how to construct a rigid-model approximation to use as prior. The linear system is solved using available computer programs, whose performance on typical registration tasks we briefly compare, and to which future scale-up is delegated. Our method is applied to the joint alignment of 2.67 million images, with more than 200 million point-pairs and has been used for successfully aligning the first full adult fruit fly brain.

The correction of bias in magnetic resonance images is an important problem in medical image processing. Most previous approaches have used a maximum likelihood method to increase the likelihood of the pixels in a single image by adaptively estimating a correction to the unknown image bias field. The pixel likelihoods are defined either in terms of a pre-existing tissue model, or non-parametrically in terms of the image’s own pixel values. In both cases, the specific location of a pixel in the image is not used to calculate the likelihoods. We suggest a new approach in which we simultaneously eliminate the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different images, rather than within an image, to eliminate bias fields from all of the images simultaneously. The method builds a multi-resolution non-parametric tissue model conditioned on image location while eliminating the bias fields associated with the original image set. We present experiments on both synthetic and real MR data sets, and present comparisons with other methods.

To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing images of corresponding tissues into alignment is a critical first step in making a meaningful comparative analysis of these spatial patterns. Significant image noise and variability in the shapes make it hard to pick a canonical shape model. In this paper, we address these problems by combining segmentation and unsupervised shape learning algorithms. We first segment images to acquire structures of interest, then jointly align the shapes of these acquired structures using an unsupervised nonparametric maximum likelihood algorithm along the lines of congealing, while simultaneously learning the underlying shape model and associated transformations. The learned transformations are applied to corresponding images to bring them into alignment in one step. We demonstrate the results for images of various classes of Drosophila imaginal discs and discuss the methodology used for a quantitative analysis of spatial gene expression patterns.

Infective juveniles of the insect-parastic nematode canjump greater than 6 times their height, a striking evolved novelty in some species of this genus. Using high-speed videography, we observed the kinematics of spontaneousjumping behavior. Our analysis places a lower bound on the velocity and acceleration of these worms.