Information processing relies on precise patterns of synapses between neurons. The cellular recognition mechanisms regulating this specificity are poorly understood. In the medulla of the Drosophila visual system, different neurons form synaptic connections in different layers. Here, we sought to identify candidate cell recognition molecules underlying this specificity. Using RNA sequencing (RNA-seq), we show that neurons with different synaptic specificities express unique combinations of mRNAs encoding hundreds of cell surface and secreted proteins. Using RNA-seq and protein tagging, we demonstrate that 21 paralogs of the Dpr family, a subclass of immunoglobulin (Ig)-domain containing proteins, are expressed in unique combinations in homologous neurons with different layer-specific synaptic connections. Dpr interacting proteins (DIPs), comprising nine paralogs of another subclass of Ig-containing proteins, are expressed in a complementary layer-specific fashion in a subset of synaptic partners. We propose that pairs of Dpr/DIP paralogs contribute to layer-specific patterns of synaptic connectivity.

In primary neurons, the oncofetal RNA-binding protein IGF2BP1 (IGF2 mRNA-binding protein 1) controls spatially restricted β-actin (ACTB) mRNA translation and modulates growth cone guidance. In cultured tumor-derived cells, IGF2BP1 was shown to regulate the formation of lamellipodia and invadopodia. However, how and via which target mRNAs IGF2BP1 controls the motility of tumor-derived cells has remained elusive. In this study, we reveal that IGF2BP1 promotes the velocity and directionality of tumor-derived cell migration by determining the cytoplasmic fate of two novel target mRNAs: MAPK4 and PTEN. Inhibition of MAPK4 mRNA translation by IGF2BP1 antagonizes MK5 activation and prevents phosphorylation of HSP27, which sequesters actin monomers available for F-actin polymerization. Consequently, HSP27-ACTB association is reduced, mobilizing cellular G-actin for polymerization in order to promote the velocity of cell migration. At the same time, stabilization of the PTEN mRNA by IGF2BP1 enhances PTEN expression and antagonizes PIP(3)-directed signaling. This enforces the directionality of cell migration in a RAC1-dependent manner by preventing additional lamellipodia from forming and sustaining cell polarization intrinsically. IGF2BP1 thus promotes the velocity and persistence of tumor cell migration by controlling the expression of signaling proteins. This fine-tunes and connects intracellular signaling networks in order to enhance actin dynamics and cell polarization.

ST2 is an IL-1 receptor family member with transmembrane (ST2L) and soluble (sST2) isoforms. sST2 is a mechanically induced cardiomyocyte protein, and serum sST2 levels predict outcome in patients with acute myocardial infarction or chronic heart failure. Recently, IL-33 was identified as a functional ligand of ST2L, allowing exploration of the role of ST2 in myocardium. We found that IL-33 was a biomechanically induced protein predominantly synthesized by cardiac fibroblasts. IL-33 markedly antagonized angiotensin II- and phenylephrine-induced cardiomyocyte hypertrophy. Although IL-33 activated NF-kappaB, it inhibited angiotensin II- and phenylephrine-induced phosphorylation of inhibitor of NF-kappa B alpha (I kappa B alpha) and NF-kappaB nuclear binding activity. sST2 blocked antihypertrophic effects of IL-33, indicating that sST2 functions in myocardium as a soluble decoy receptor. Following pressure overload by transverse aortic constriction (TAC), ST2(-/-) mice had more left ventricular hypertrophy, more chamber dilation, reduced fractional shortening, more fibrosis, and impaired survival compared with WT littermates. Furthermore, recombinant IL-33 treatment reduced hypertrophy and fibrosis and improved survival after TAC in WT mice, but not in ST2(-/-) littermates. Thus, IL-33/ST2 signaling is a mechanically activated, cardioprotective fibroblast-cardiomyocyte paracrine system, which we believe to be novel. IL-33 may have therapeutic potential for beneficially regulating the myocardial response to overload.

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

Context plays a foundational role in determining how to interpret potentially fear-producing stimuli, yet the precise neurobiological substrates of context are poorly understood. In this issue of Cell, Xu et al. elegantly show that parallel neuronal circuits are necessary for two distinct roles of context in fear conditioning.

The olfactory system encodes information about molecules by spatiotemporal patterns of activity across distributed populations of neurons and extracts information from these patterns to control specific behaviors. Recent studies used in vivo recordings, optogenetics, and other methods to analyze the mechanisms by which odor information is encoded and processed in the olfactory system, the functional connectivity within and between olfactory brain areas, and the impact of spatiotemporal patterning of neuronal activity on higher-order neurons and behavioral outputs. The results give rise to a faceted picture of olfactory processing and provide insights into fundamental mechanisms underlying neuronal computations. This review focuses on some of this work presented in a Mini-Symposium at the Annual Meeting of the Society for Neuroscience in 2012.

Fluorescence image co-localization analysis is widely utilized to suggest biomolecular interaction. However, there exists some confusion as to its correct implementation and interpretation. In reality, co-localization analysis consists of at least two distinct sets of methods, termed co-occurrence and correlation. Each approach has inherent and often contrasting strengths and weaknesses. Yet, neither one can be considered to always be preferable for any given application. Rather, each method is most appropriate for answering different types of biological question. This Review discusses the main factors affecting multicolor image co-occurrence and correlation analysis, while giving insight into the types of biological behavior that are better suited to one approach or the other. Further, the limits of pixel-based co-localization analysis are discussed in the context of increasingly popular super-resolution imaging techniques.

Image diffusion can smooth away noise and small-scale structures while retaining important features, thereby enhancing the performances of many image processing algorithms such as image compression, segmentation and recognition. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries in the input images. The boundary saliency is estimated through a saliency measure which is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. The connection between filtering kernels and perceptual saliency makes it possible to remove small-scale structures and preserves significant boundaries adaptively. The effectiveness of the proposed approach is validated by experiments on various medical images including the color Chinese Visible Human data set and gray MRI brain images.

Light sheet microscopy is a powerful technique for visualizing dynamic biological processes in 3D. Studying large specimens or recording time series with high spatial and temporal resolution generates large datasets, often exceeding terabytes and potentially reaching petabytes in size. Handling these massive datasets is challenging for conventional data processing tools with their memory and performance limitations. To overcome these issues, we developed LLSM5DTools, a software solution specifically designed for the efficient management of petabyte-scale light sheet microscopy data. This toolkit, optimized for memory and per-formance, features fast image readers and writers, efficient geometric transformations, high-performance Richardson-Lucy deconvolution, and scalable Zarr-based stitching. These advancements enable LLSM5DTools to perform over ten times faster than current state-of-the-art methods, facilitating real-time processing of large datasets and opening new avenues for biological discoveries in large-scale imaging experiments.

MOTIVATION: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.

RESULTS: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems.

AVAILABILITY AND IMPLEMENTATION: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldlab/section-sort, http://github.com/saalfeldlab/z-spacing and http://github.com/saalfeldlab/z-spacing-spark CONTACT: : saalfelds@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.