In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ’bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.

Genetically-encoded calcium indicators (GECIs) hold the promise of monitoring [Ca(2+)] in selected populations of neurons and in specific cellular compartments. Relating GECI fluorescence to neuronal activity requires quantitative characterization. We have characterized a promising new genetically-encoded calcium indicator-GCaMP2-in mammalian pyramidal neurons. Fluorescence changes in response to single action potentials (17+/-10% DeltaF/F [mean+/-SD]) could be detected in some, but not all, neurons. Trains of high-frequency action potentials yielded robust responses (302+/-50% for trains of 40 action potentials at 83 Hz). Responses were similar in acute brain slices from in utero electroporated mice, indicating that long-term expression did not interfere with GCaMP2 function. Membrane-targeted versions of GCaMP2 did not yield larger signals than their non-targeted counterparts. We further targeted GCaMP2 to dendritic spines to monitor Ca(2+) accumulations evoked by activation of synaptic NMDA receptors. We observed robust DeltaF/F responses (range: 37%-264%) to single spine uncaging stimuli that were correlated with NMDA receptor currents measured through a somatic patch pipette. One major drawback of GCaMP2 was its low baseline fluorescence. Our results show that GCaMP2 is improved from the previous versions of GCaMP and may be suited to detect bursts of high-frequency action potentials and synaptic currents in vivo.

Since their inception, computational models have become increasingly complex and useful counterparts to laboratory experiments within the field of neuroscience. Today several software programs exist to solve the underlying mathematical system of equations, but such programs typically solve these equations in all parts of a cell (or network of cells) simultaneously, regardless of whether or not all of the cell is active. This approach can be inefficient if only part of the cell is active and many simulations must be performed. We have previously developed a numerical method that provides a framework for spatial adaptivity by making the computations local to individual branches rather than entire cells (Rempe and Chopp, SIAM Journal on Scientific Computing, 28: 2139-2161, 2006). Once the computation is reduced to the level of branches instead of cells, spatial adaptivity is straightforward: the active regions of the cell are detected and computational effort is focused there, while saving computations in other regions of the cell that are at or near rest. Here we apply the adaptive method to four realistic neuronal simulation scenarios and demonstrate its improved efficiency over non-adaptive methods. We find that the computational cost of the method scales with the amount of activity present in the simulation, rather than the physical size of the system being simulated. For certain problems spatial adaptivity reduces the computation time by up to 80%.

Although information storage in the central nervous system is thought to be primarily mediated by various forms of synaptic plasticity, other mechanisms, such as modifications in membrane excitability, are available. Local dendritic spikes are nonlinear voltage events that are initiated within dendritic branches by spatially clustered and temporally synchronous synaptic input. That local spikes selectively respond only to appropriately correlated input allows them to function as input feature detectors and potentially as powerful information storage mechanisms. However, it is currently unknown whether any effective form of local dendritic spike plasticity exists. Here we show that the coupling between local dendritic spikes and the soma of rat hippocampal CA1 pyramidal neurons can be modified in a branch-specific manner through an N-methyl-d-aspartate receptor (NMDAR)-dependent regulation of dendritic Kv4.2 potassium channels. These data suggest that compartmentalized changes in branch excitability could store multiple complex features of synaptic input, such as their spatio-temporal correlation. We propose that this ’branch strength potentiation’ represents a previously unknown form of information storage that is distinct from that produced by changes in synaptic efficacy both at the mechanistic level and in the type of information stored.

Fluorescent proteins and their engineered variants have played an important role in the study of biology. The genetically encoded calcium-indicator protein GCaMP2 comprises a circularly permuted fluorescent protein coupled to the calcium-binding protein calmodulin and a calmodulin target peptide, M13, derived from the intracellular calmodulin target myosin light-chain kinase and has been used to image calcium transients in vivo. To aid rational efforts to engineer improved variants of GCaMP2, this protein was crystallized in the calcium-saturated form. X-ray diffraction data were collected to 2.0 A resolution. The crystals belong to space group C2, with unit-cell parameters a = 126.1.

The regulation of static allometry is a fundamental developmental process, yet little is understood of the mechanisms that ensure organs scale correctly across a range of body sizes. Recent studies have revealed the physiological and genetic mechanisms that control nutritional variation in the final body and organ size in holometabolous insects. The implications these mechanisms have for the regulation of static allometry is, however, unknown. Here, we formulate a mathematical description of the nutritional control of body and organ size in Drosophila melanogaster and use it to explore how the developmental regulators of size influence static allometry. The model suggests that the slope of nutritional static allometries, the ’allometric coefficient’, is controlled by the relative sensitivity of an organ’s growth rate to changes in nutrition, and the relative duration of development when nutrition affects an organ’s final size. The model also predicts that, in order to maintain correct scaling, sensitivity to changes in nutrition varies among organs, and within organs through time. We present experimental data that support these predictions. By revealing how specific physiological and genetic regulators of size influence allometry, the model serves to identify developmental processes upon which evolution may act to alter scaling relationships.

We performed patch-clamp recordings from morphologically identified and anatomically mapped pyramidal neurons of the ventral hippocampus to test the hypothesis that bursting neurons are distributed on a gradient from the CA2/CA1 border (proximal) through the subiculum (distal), with more bursting observed at distal locations. We find that the well-defined morphological boundaries between the hippocampal subregions CA1 and subiculum do not correspond to abrupt changes in electrophysiological properties. Rather, we observed that the percentage of bursting neurons is linearly correlated with position in the proximal-distal axis across the CA1 and the subiculum, the percentages of bursting neurons being 10% near the CA1-CA2 border, 24% at the CA1-subiculum border, and higher than 50% in the distal subiculum. The distribution of bursting neurons was paralleled by a gradient in afterdepolarization (ADP) amplitude. We also tested the hypothesis that there was an association between bursting and two previously described morphologically distinct groups of pyramidal neurons (twin and single apical dendrites) in the CA1 region. We found no difference in output mode between single and twin apical dendrite morphologies, which was consistent with the observation that the two morphologies were equally distributed across the transverse axis of the CA1 region. Taken together with the known organization of connections from CA3 to CA1 and CA1 to subiculum, our results indicate that bursting neurons are most likely to be connected to regular spiking neurons and vice versa.

Many important physiological processes operate at time and space scales far beyond those accessible to atom-realistic simulations, and yet discrete stochastic rather than continuum methods may best represent finite numbers of molecules interacting in complex cellular spaces. We describe and validate new tools and algorithms developed for a new version of the MCell simulation program (MCell3), which supports generalized Monte Carlo modeling of diffusion and chemical reaction in solution, on surfaces representing membranes, and combinations thereof. A new syntax for describing the spatial directionality of surface reactions is introduced, along with optimizations and algorithms that can substantially reduce computational costs (e.g., event scheduling, variable time and space steps). Examples for simple reactions in simple spaces are validated by comparison to analytic solutions. Thus we show how spatially realistic Monte Carlo simulations of biological systems can be far more cost-effective than often is assumed, and provide a level of accuracy and insight beyond that of continuum methods.

Understanding the principles of information processing in neural circuits requires systematic characterization of the participating cell types and their connections, and the ability to measure and perturb their activity. Genetic approaches promise to bring experimental access to complex neural systems, including genetic stalwarts such as the fly and mouse, but also to nongenetic systems such as primates. Together with anatomical and physiological methods, cell-type-specific expression of protein markers and sensors and transducers will be critical to construct circuit diagrams and to measure the activity of genetically defined neurons. Inactivation and activation of genetically defined cell types will establish causal relationships between activity in specific groups of neurons, circuit function, and animal behavior. Genetic analysis thus promises to reveal the logic of the neural circuits in complex brains that guide behaviors. Here we review progress in the genetic analysis of neural circuits and discuss directions for future research and development.

Neurons and glia are functionally organized into circuits and higher-order structures via synaptic connectivity, well-orchestrated molecular signaling, and activity-dependent refinement. Such organization allows the precise information processing required for complex behaviors. Disruption of nervous systems by genetic deficiency or events such as trauma or environmental exposure may produce a diseased state in which certain aspects of inter-neuron signaling are impaired. Optical imaging techniques allow the direct visualization of individual neurons in a circuit environment. Imaging probes specific for given biomolecules may help elucidate their contribution to proper circuit function. Genetically encoded sensors can visualize trafficking of particular molecules in defined neuronal populations, non-invasively in intact brain or reduced preparations. Sensor analysis in healthy and diseased brains may reveal important differences and shed light on the development and progression of nervous system disorders. We review the field of genetically encoded sensors for molecules and cellular events, and their potential applicability to the study of nervous system disease.