Two-photon probe excitation data are commonly presented as absorption cross section or molecular brightness (the detected fluorescence rate per molecule). We report two-photon molecular brightness spectra for a diverse set of organic and genetically encoded probes with an automated spectroscopic system based on fluorescence correlation spectroscopy. The two-photon action cross section can be extracted from molecular brightness measurements at low excitation intensities, while peak molecular brightness (the maximum molecular brightness with increasing excitation intensity) is measured at higher intensities at which probe photophysical effects become significant. The spectral shape of these two parameters was similar across all dye families tested. Peak molecular brightness spectra, which can be obtained rapidly and with reduced experimental complexity, can thus serve as a first-order approximation to cross-section spectra in determining optimal wavelengths for two-photon excitation, while providing additional information pertaining to probe photostability. The data shown should assist in probe choice and experimental design for multiphoton microscopy studies. Further, we show that, by the addition of a passive pulse splitter, nonlinear bleaching can be reduced-resulting in an enhancement of the fluorescence signal in fluorescence correlation spectroscopy by a factor of two. This increase in fluorescence signal, together with the observed resemblance of action cross section and peak brightness spectra, suggests higher-order photobleaching pathways for two-photon excitation.

Endocytic recycling of synaptic vesicles after exocytosis is critical for nervous system function. At synapses of cultured neurons that lack the two "neuronal" dynamins, dynamin 1 and 3, smaller excitatory postsynaptic currents are observed due to an impairment of the fission reaction of endocytosis that results in an accumulation of arrested clathrin-coated pits and a greatly reduced synaptic vesicle number. Surprisingly, despite a smaller readily releasable vesicle pool and fewer docked vesicles, a strong facilitation, which correlated with lower vesicle release probability, was observed upon action potential stimulation at such synapses. Furthermore, although network activity in mutant cultures was lower, Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) activity was unexpectedly increased, consistent with the previous report of an enhanced state of synapsin 1 phosphorylation at CaMKII-dependent sites in such neurons. These changes were partially reversed by overnight silencing of synaptic activity with tetrodotoxin, a treatment that allows progression of arrested endocytic pits to synaptic vesicles. Facilitation was also counteracted by CaMKII inhibition. These findings reveal a mechanism aimed at preventing synaptic transmission failure due to vesicle depletion when recycling vesicle traffic is backed up by a defect in dynamin-dependent endocytosis and provide new insight into the coupling between endocytosis and exocytosis.

Animal studies have been instrumental in providing knowledge about the molecular and neural mechanisms underlying drug addiction. Recently, the fruit fly Drosophila melanogaster has become a valuable system to model not only the acute stimulating and sedating effects of drugs but also their more complex rewarding properties. In this review, we describe the advantages of using the fly to study drug-related behavior, provide a brief overview of the behavioral assays used, and review the molecular mechanisms and neural circuits underlying drug-induced behavior in flies. Many of these mechanisms have been validated in mammals, suggesting that the fly is a useful model to understand the mechanisms underlying addiction.

Mice lacking leptin receptors are grossly obese and diabetic, in part due to dysfunction in brain circuits important for energy homeostasis. Transplantation of leptin receptor-expressing hypothalamic progenitor neurons into the brains of leptin receptor deficient mice led to integration into neural circuits, reduced obesity, and normalized circulating glucose levels.

This Meeting Review describes the proceedings and conclusions from the inaugural meeting of the Electron Microscopy Validation Task Force organized by the Unified Data Resource for 3DEM (http://www.emdatabank.org) and held at Rutgers University in New Brunswick, NJ on September 28 and 29, 2010. At the workshop, a group of scientists involved in collecting electron microscopy data, using the data to determine three-dimensional electron microscopy (3DEM) density maps, and building molecular models into the maps explored how to assess maps, models, and other data that are deposited into the Electron Microscopy Data Bank and Protein Data Bank public data archives. The specific recommendations resulting from the workshop aim to increase the impact of 3DEM in biology and medicine.

Fluorescent in situ hybridization (FISH) allows the quantification of single mRNAs in budding yeast using fluorescently labeled single-stranded DNA probes, a wide-field epifluorescence microscope and a spot-detection algorithm. Fixed yeast cells are attached to coverslips and hybridized with a mixture of FISH probes, each conjugated to several fluorescent dyes. Images of cells are acquired in 3D and maximally projected for single-molecule analysis. Diffraction-limited labeled mRNAs are observed as bright fluorescent spots and can be quantified using a spot-detection algorithm. FISH preserves the spatial distribution of cellular RNA distribution within the cell and the stochastic fluctuations in individual cells that can lead to phenotypic differences within a clonal population. This information, however, is lost if the RNA content is measured on a population of cells by using reverse transcriptase PCR, microarrays or high-throughput sequencing. The FISH procedure and image acquisition described here can be completed in 3 d.

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

The developing spinal cord is subdivided into distinct progenitor domains, each of which gives rise to different types of neurons. However, the developmental mechanisms responsible for generating neuronal diversity within a domain are not well understood. Here, we have studied zebrafish V0 neurons, those that derive from the p0 progenitor domain, to address this question. We find that all V0 neurons have commissural axons, but they can be divided into excitatory and inhibitory classes. V0 excitatory neurons (V0-e) can be further categorized into three groups based on their axonal trajectories; V0-eA (ascending), V0-eB (bifurcating), and V0-eD (descending) neurons. By using time-lapse imaging of p0 progenitors and their progeny, we show that inhibitory and excitatory neurons are produced from different progenitors. We also demonstrate that V0-eA neurons are produced from distinct progenitors, while V0-eB and V0-eD neurons are produced from common progenitors. We then use birth-date analysis to reveal that V0-eA, V0-eB, and V0-eD neurons arise in this order. By perturbing Notch signaling and accelerating neuronal differentiation, we predictably alter the generation of early born V0-e neurons at the expense of later born ones. These results suggest that multiple types of V0 neurons are produced by two distinct mechanisms; from heterogeneous p0 progenitors and from the same p0 progenitor, but in a time-dependent manner.

Recording activity from identified populations of neurons is a central goal of neuroscience. Changes in membrane depolarization, particularly action potentials, are the most important features of neural physiology to extract, although ions, neurotransmitters, neuromodulators, second messengers, and the activation state of specific proteins are also crucial. Modern fluorescence microscopy provides the basis for such activity mapping, through multi-photon imaging and other optical schemes. Probes remain the rate-limiting step for progress in this field: they should be bright and photostable, and ideally come in multiple colors. Only protein-based reagents permit chronic imaging from genetically specified cells. Here we review recent progress in the design, optimization and deployment of genetically encoded indicators for calcium ions (a proxy for action potentials), membrane potential, and neurotransmitters. We highlight seminal experiments, and present an outlook for future progress.

Electrophysiological recordings from behaving animals provide an unparalleled view into the functional role of individual neurons. Intracellular approaches can be especially revealing as they provide information about a neuron's inputs and intrinsic cellular properties, which together determine its spiking output. Recent technical developments have made intracellular recording possible during an ever-increasing range of behaviors in both head-fixed and freely moving animals. These recordings have yielded fundamental insights into the cellular and circuit mechanisms underlying neural activity during natural behaviors in such areas as sensory perception, motor sequence generation, and spatial navigation, forging a direct link between cellular and systems neuroscience.