Actin assembly and inward flow in the plane of the immunological synapse (IS) drives the centralization of T cell receptor microclusters (TCR MCs) and the integrin leukocyte functional antigen 1 (LFA-1). Using structured-illumination microscopy (SIM), we show that actin arcs populating the medial, lamella-like region of the IS arise from linear actin filaments generated by one or more formins present at the IS distal edge. After traversing the outer, Arp2/3-generated, lamellipodia-like region of the IS, these linear filaments are organized by myosin II into antiparallel concentric arcs. Three-dimensional SIM shows that active LFA-1 often aligns with arcs, whereas TCR MCs commonly reside between arcs, and total internal reflection fluorescence SIM shows TCR MCs being swept inward by arcs. Consistently, disrupting actin arc formation via formin inhibition results in less centralized TCR MCs, missegregated integrin clusters, decreased T-B cell adhesion, and diminished TCR signaling. Together, our results define the origin, organization, and functional significance of a major actomyosin contractile structure at the IS that directly propels TCR MC transport.

The endoplasmic reticulum (ER) is an expansive, membrane-enclosed organelle that plays crucial roles in numerous cellular functions. We used emerging superresolution imaging technologies to clarify the morphology and dynamics of the peripheral ER, which contacts and modulates most other intracellular organelles. Peripheral components of the ER have classically been described as comprising both tubules and flat sheets. We show that this system consists almost exclusively of tubules at varying densities, including structures that we term ER matrices. Conventional optical imaging technologies had led to misidentification of these structures as sheets because of the dense clustering of tubular junctions and a previously uncharacterized rapid form of ER motion. The existence of ER matrices explains previous confounding evidence that had indicated the occurrence of ER “sheet” proliferation after overexpression of tubular junction–forming proteins.

We rely on movement to explore the environment, for example, by palpating an object. In somatosensory cortex, activity related to movement of digits or whiskers is suppressed, which could facilitate detection of touch. Movement-related suppression is generally assumed to involve corollary discharges. Here we uncovered a thalamocortical mechanism in which cortical fast-spiking interneurons, driven by sensory input, suppress movement-related activity in layer 4 (L4) excitatory neurons. In mice locating objects with their whiskers, neurons in the ventral posteromedial nucleus (VPM) fired in response to touch and whisker movement. Cortical L4 fast-spiking interneurons inherited these responses from VPM. In contrast, L4 excitatory neurons responded mainly to touch. Optogenetic experiments revealed that fast-spiking interneurons reduced movement-related spiking in excitatory neurons, enhancing selectivity for touch-related information during active tactile sensation. These observations suggest a fundamental computation performed by the thalamocortical circuit to accentuate salient tactile information.

Naïve Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a database, ignoring class information. Instead, NN searches are performed within each class, generating a score per class. A key problem with NN techniques, including NBNN, is that they fail when the data representation does not capture perceptual (e.g. class-based) similarity. NBNN circumvents this by using independent engineered descriptors (e.g. SIFT). To extend its applicability outside of image-based domains, we propose to learn a metric which captures perceptual similarity. Similar to how Neighbourhood Components Analysis optimizes a differentiable form of KNN classification, we propose 'Class Conditional' metric learning (CCML), which optimizes a soft form of the NBNN selection rule. Typical metric learning algorithms learn either a global or local metric. However, our proposed method can be adjusted to a particular level of locality by tuning a single parameter. An empirical evaluation on classification and retrieval tasks demonstrates that our proposed method clearly outperforms existing learned distance metrics across a variety of image and non-image datasets.

Capturing dynamic processes in live samples is a nontrivial task in biological imaging. Although fluorescence provides high specificity and contrast compared to other light microscopy techniques, the photophysical principles of this method can have a harmful effect on the sample. Current advances in light sheet microscopy have created a novel imaging toolbox that allows for rapid acquisition of high-resolution fluorescent images with minimal perturbation of the processes of interest. Each unique design has its own advantages and limitations. In this review, we describe several cutting edge light sheet microscopes and their optimal applications.

The Polycomb PRC1 plays essential roles in development and disease pathogenesis. Targeting of PRC1 to chromatin is thought to be mediated by the Cbx family proteins (Cbx2/4/6/7/8) binding to histone H3 with a K27me3 modification (H3K27me3). Despite this prevailing view, the molecular mechanisms of targeting remain poorly understood. Here, by combining live-cell single-molecule tracking (SMT) and genetic engineering, we reveal that H3K27me3 contributes significantly to the targeting of Cbx7 and Cbx8 to chromatin, but less to Cbx2, Cbx4, and Cbx6. Genetic disruption of the complex formation of PRC1 facilitates the targeting of Cbx7 to chromatin. Biochemical analyses uncover that the CD and AT-hook-like (ATL) motif of Cbx7 constitute a functional DNA-binding unit. Live-cell SMT of Cbx7 mutants demonstrates that Cbx7 is targeted to chromatin by co-recognizing of H3K27me3 and DNA. Our data suggest a novel hierarchical cooperation mechanism by which histone modifications and DNA coordinate to target chromatin regulatory complexes.

An animal's ability to learn and to form memories is essential for its survival. The fruit fly has proven to be a valuable model system for studies of learning and memory. One learned behavior in fruit flies is courtship conditioning. In Drosophila courtship conditioning, male flies learn not to court females during training with an unreceptive female. He retains a memory of this training and for several hours decreases courtship when subsequently paired with any female. Courtship conditioning is a unique learning paradigm; it uses a positive-valence stimulus, a female fly, to teach a male to decrease an innate behavior, courtship of the female. As such, courtship conditioning is not clearly categorized as either appetitive or aversive conditioning. The mushroom body (MB) region in the fruit fly brain is important for several types of memory; however, the precise subsets of intrinsic and extrinsic MB neurons necessary for courtship conditioning are unknown. Here, we disrupted synaptic signaling by driving a shibirets effector in precise subsets of MB neurons, defined by a collection of split-GAL4 drivers. Out of 75 lines tested, 32 showed defects in courtship conditioning memory. Surprisingly, we did not have any hits in the γ lobe Kenyon cells, a region previously implicated in courtship conditioning memory. We did find that several γ lobe extrinsic neurons were necessary for courtship conditioning memory. Overall, our memory hits in the dopaminergic neurons (DANs) and the mushroom body output neurons were more consistent with results from appetitive memory assays than aversive memory assays. For example, protocerebral anterior medial DANs were necessary for courtship memory, similar to appetitive memory, while protocerebral posterior lateral 1 (PPL1) DANs, important for aversive memory, were not needed. Overall, our results indicate that the MB circuits necessary for courtship conditioning memory coincide with circuits necessary for appetitive memory.

Localized protein translation is critical in many biological contexts, particularly in highly polarized cells, such as neurons, to regulate gene expression in a spatiotemporal manner. The cytoplasmic polyadenylation element-binding (CPEB) family of RNA-binding proteins has emerged as a key regulator of mRNA transport and local translation required for early embryonic development, synaptic plasticity, and long-term memory (LTM). Drosophila Orb and Orb2 are single members of the CPEB1 and CPEB2 subfamilies of the CPEB proteins, respectively. At present, the identity of the mRNA targets they regulate is not fully known, and the binding specificity of the CPEB2 subfamily is a matter of debate. Using transcriptome-wide UV cross-linking and immunoprecipitation, we define the mRNA-binding sites and targets of Drosophila CPEBs. Both Orb and Orb2 bind linear cytoplasmic polyadenylation element-like sequences in the 3' UTRs of largely overlapping target mRNAs, with Orb2 potentially having a broader specificity. Both proteins use their RNA-recognition motifs but not the Zinc-finger region for RNA binding. A subset of Orb2 targets is translationally regulated in cultured S2 cells and fly head extracts. Moreover, pan-neuronal RNAi knockdown of these targets suggests that a number of these targets are involved in LTM. Our results provide a comprehensive list of mRNA targets of the two CPEB proteins in Drosophila, thus providing insights into local protein synthesis involved in various biological processes, including LTM.

We consider the problem of estimating the parameters of a linear autoregressive model with sub-Gaussian innovations from a limited sequence of consecutive observations. Assuming that the parameters are compressible, we analyze the performance of the ℓ1-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear models where the covariates are i.i.d. and independent of the observation history to autoregressive processes with highly inter-dependent covariates. We also derive sufficient conditions on the sparsity level that guarantee the minimax optimality of the ℓ1-regularized least squares estimate. Applying these techniques to simulated data as well as real-world datasets from crude oil prices and traffic speed data confirm our predicted theoretical performance gains in terms of estimation accuracy and model selection.

Transmembrane transporter proteins allow the passage of essentially all biologically important molecules across the lipid membranes of cells and organelles and are therefore of central importance to all forms of life. Current methods of transporter measurement, however, are lacking in several dimensions. Herein, a method is presented in which oscillating stimuli are presented to transporter-expressing cells, and activity is measured through imaging the corresponding oscillating responses of intracellular fluorescent sensors. This approach yields continuous temporal readouts of transporter activity and can therefore be used to measure time-dependent responses to drugs and other stimuli. Because of the periodic nature of the response, temporal Fourier transforms can be used to identify and quantify regions of interest in the xy plane and to overcome noise. This technique, called the Oscillating Stimulus Transporter Assay (OSTA), should greatly facilitate both functional characterization of transporters as well as high-throughput screening of drugs for transporters of particular pathophysiological interest.