Mammalian mtDNA has been found here to harbor RNA-DNA hybrids at a variety of locations throughout the genome. The R-loop, previously characterized in vitro at the leading strand replication origin (OH), is isolated as a native RNA-DNA hybrid copurifying with mtDNA. Surprisingly, other mitochondrial transcripts also form stable partial R-loops. These are abundant and affect mtDNA conformation. Current models regarding the mechanism of mammalian mtDNA replication have been expanded by recent data and discordant hypotheses. The presence of stable, nonreplicative, and partially hybridized RNA on the mtDNA template is significant for the reevaluation of replication models based on two-dimensional agarose gel analyses. In addition, the close association of a subpopulation of mtRNA with the DNA template has further implications regarding the structure, maintenance, and expression of the mitochondrial genome. These results demonstrate that variously processed and targeted mtRNAs within mammalian mitochondria likely have multiple functions in addition to their conventional roles.

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%.

Recent advances in optical microscopy have enabled biological imaging beyond the diffraction limit at nanometer resolution. A general feature of most of the techniques based on photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM) has been the use of thin biological samples in combination with total internal reflection, thus limiting the imaging depth to a fraction of an optical wavelength. However, to study whole cells or organelles that are typically up to 15 microm deep into the cell, the extension of these methods to a three-dimensional (3D) super resolution technique is required. Here, we report an advance in optical microscopy that enables imaging of protein distributions in cells with a lateral localization precision better than 50 nm at multiple imaging planes deep in biological samples. The approach is based on combining the lateral super resolution provided by PALM with two-photon temporal focusing that provides optical sectioning. We have generated super-resolution images over an axial range of approximately 10 microm in both mitochondrially labeled fixed cells, and in the membranes of living S2 Drosophila cells.

In practice, understanding the spatial relationships between the surfaces of an object, can significantly improve the performance of object recognition systems. In this paper we propose a novel framework to recognize objects in pictures taken from arbitrary viewpoints. The idea is to maintain the frontal views of the major faces of objects in a global flat map. Then an unfolding warping technique is used to change the pose of the query object in the test view so that all visible surfaces of the object can be observed from a frontal viewpoint, improving the handling of serious occlusions and large viewpoint changes. We demonstrate the effectiveness of our approach through analysis of recognition trials of complex objects with comparison to popular methods.

The activity of the ERK has complex spatial and temporal dynamics that are important for the specificity of downstream effects. However, current biochemical techniques do not allow for the measurement of ERK signaling with fine spatiotemporal resolution. We developed a genetically encoded, FRET-based sensor of ERK activity (the extracellular signal-regulated kinase activity reporter, EKAR), optimized for signal-to-noise ratio and fluorescence lifetime imaging. EKAR selectively and reversibly reported ERK activation in HEK293 cells after epidermal growth factor stimulation. EKAR signals were correlated with ERK phosphorylation, required ERK activity, and did not report the activities of JNK or p38. EKAR reported ERK activation in the dendrites and nucleus of hippocampal pyramidal neurons in brain slices after theta-burst stimuli or trains of back-propagating action potentials. EKAR therefore permits the measurement of spatiotemporal ERK signaling dynamics in living cells, including in neuronal compartments in intact tissues.

Neurobiological processes occur on spatiotemporal scales spanning many orders of magnitude. Greater understanding of these processes therefore demands improvements in the tools used in their study. Here we review recent efforts to enhance the speed and resolution of one such tool, fluorescence microscopy, with an eye toward its application to neurobiological problems. On the speed front, improvements in beam scanning technology, signal generation rates, and photodamage mediation are bringing us closer to the goal of real-time functional imaging of extended neural networks. With regard to resolution, emerging methods of adaptive optics may lead to diffraction-limited imaging or much deeper imaging in optically inhomogeneous tissues, and super-resolution techniques may prove a powerful adjunct to electron microscopic methods for nanometric neural circuit reconstruction.

Neurobiological processes occur on spatiotemporal scales spanning many orders of magnitude. Greater understanding of these processes therefore demands improvements in the tools used in their study. Here we review recent efforts to enhance the speed and resolution of one such tool, fluorescence microscopy, with an eye toward its application to neurobiological problems. On the speed front, improvements in beam scanning technology, signal generation rates, and photodamage mediation are bringing us closer to the goal of real-time functional imaging of extended neural networks. With regard to resolution, emerging methods of adaptive optics may lead to diffraction-limited imaging or much deeper imaging in optically inhomogeneous tissues, and super-resolution techniques may prove a powerful adjunct to electron microscopic methods for nanometric neural circuit reconstruction.

Commentary: A brief review of recent trends in microscopy. The section “Caveats regarding the application of superresolution microscopy” was written in an effort to inject a dose of reality and caution into the unquestioning enthusiasm in the academic community for all things superresolution, covering the topics of labeling density and specificity, sample preparation artifacts, speed vs. resolution vs. photodamage, and the implications of signal-to-background for Nyquist vs. Rayleigh definitions of resolution.

Key to understanding a protein’s biological function is the accurate determination of its spatial distribution inside a cell. Although fluorescent protein markers allow the targeting of specific proteins with molecular precision, much of this information is lost when the resultant fusion proteins are imaged with conventional, diffraction-limited optics. In response, several imaging modalities that are capable of resolution below the diffraction limit (approximately 200 nm) have emerged. Here, both single- and dual-color superresolution imaging of biological structures using photoactivated localization microscopy (PALM) are described. The examples discussed focus on adhesion complexes: dense, protein-filled assemblies that form at the interface between cells and their substrata. A particular emphasis is placed on the instrumentation and photoactivatable fluorescent protein (PA-FP) tags necessary to achieve PALM images at approximately 20 nm resolution in 5 to 30 min in fixed cells.

Commentary: A paper spearheaded by Hari which gives a thorough description of the methods and hardware needed to successfully practice PALM, including cover slip preparation, cell transfection and fixation, drift correction with fiducials, characterization of on/off contrast ratios for different photoactivted fluorescent proteins, identifying PALM-suitable cells, and mechanical and optical components of a PALM system.

The chemical senses, smell and taste, are the most poorly understood sensory modalities. In recent years, however, the field of chemosensation has benefited from new methods and technical innovations that have accelerated the rate of scientific progress. For example, enormous advances have been made in identifying olfactory and gustatory receptor genes and mapping their expression patterns. Genetic tools now permit us to monitor and control neural activity in vivo with unprecedented precision. New imaging techniques allow us to watch neural activity patterns unfold in real time. Finally, improved hardware and software enable multineuron electrophysiological recordings on an expanded scale. These innovations have enabled some fresh approaches to classic problems in chemosensation.

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.