Monitoring GABAergic inhibition in the nervous system has been enabled by development of an intensiometric molecular sensor that directly detects GABA. However the first generation iGABASnFR exhibits low signal-to-noise and suboptimal kinetics, making in vivo experiments challenging. To improve sensor performance, we targeted several sites in the protein for near-saturation mutagenesis, and evaluated the resulting sensor variants in a high throughput screening system using evoked synaptic release in primary cultured neurons. This identified a sensor variant, iGABASnFR2, with 4.2-fold improved sensitivity and 20% faster kinetics, and binding affinity that remained in a range sensitive to changes in GABA concentration at synapses. We also identified sensors with an inverted response, decreasing fluorescence intensity upon GABA binding. We termed the best such negative-going sensor iGABASnFR2n, which can be used to corroborate observations with the positive-going sensor. These improvements yielded a qualitative enhancement of in vivo performance, enabling us to make the first measurements of direction selective GABA release in the retina and confirm a longstanding hypothesis for how sensitivity to motion arises in the visual system.

Monitoring GABAergic inhibition in the nervous system has been enabled by development of an intensiometric molecular sensor that directly detects GABA. However, the first generation iGABASnFR exhibits low signal-to-noise and suboptimal kinetics, making in vivo experiments challenging. To improve sensor performance, we targeted several sites in the protein for near-saturation mutagenesis and evaluated the resulting sensor variants in a high throughput screening system using evoked synaptic release in primary cultured neurons. This identified a sensor variant, iGABASnFR2, with 4.2-fold improved sensitivity and 20% faster kinetics, and binding affinity that remained in a range sensitive to changes in GABA concentration at synapses. We also identified sensors with an inverted response, decreasing fluorescence intensity upon GABA binding. We termed the best such negative-going sensor iGABASnFR2n, which can be used to corroborate observations with the positive-going sensor. These improvements yielded a qualitative enhancement of in vivo performance when compared directly to the original sensor. iGABASnFR2 enabled the first measurements of direction-selective GABA release in the retina. In vivo imaging in somatosensory cortex revealed that iGABASnFR2 can report volume-transmitted GABA release following whisker stimulation. Overall, the improved sensitivity and kinetics of iGABASnFR2 make it a more effective tool for imaging GABAergic transmission in intact neural circuits.

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.

BACKGROUND: Kidney epithelial cells perform complex vectorial fluid and solute transport at high volumes and rapid rates. Their structural organization both reflects and enables these sophisticated physiological functions. However, our understanding of the nanoscale spatial organization and intracellular ultrastructure that underlies these crucial cellular functions remains limited.

METHODS: To address this knowledge gap, we generated and reconstructed an extensive electron microscopic dataset of renal proximal tubule (PT) epithelial cells at isotropic resolutions down to 4nm. We employed artificial intelligence-based segmentation tools to identify, trace, and measure all major subcellular components. We complemented this analysis with immunofluorescence microscopy to connect subcellular architecture to biochemical function.

RESULTS: Our ultrastructural analysis revealed complex organization of membrane-bound compartments in proximal tubule cells. The apical endocytic system featured deep invaginations connected to an anastomosing meshwork of dense apical tubules, rather than discrete structures. The endoplasmic reticulum displayed distinct structural domains: fenestrated sheets in the basolateral region and smaller, disconnected clusters in the subapical region. We identified, quantified, and visualized membrane contact sites between endoplasmic reticulum, plasma membrane, mitochondria, and apical endocytic compartments. Immunofluorescence microscopy demonstrated distinct localization patterns for endoplasmic reticulum resident proteins at mitochondrial and plasma membrane interfaces.

CONCLUSIONS: This study provides novel insights into proximal tubule cell organization, revealing specialized compartmentalization and unexpected connections between membrane-bound organelles. We identified previously uncharacterized structures, including mitochondria-plasma membrane bridges and an interconnected endocytic meshwork, suggesting mechanisms for efficient energy distribution, cargo processing and structural support. Morphological differences between 4nm and 8nm datasets indicate subsegment-specific specializations within the proximal tubule. This comprehensive open-source dataset provides a foundation for understanding how subcellular architecture supports specialized epithelial function in health and disease.

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.