Recent advances in the use of genetically encodable optical tools to elicit and monitor signaling events.

Cells rely on a complex network of spatiotemporally regulated signaling activities to effectively transduce information from extracellular cues to intracellular machinery. To probe this activity architecture, researchers have developed an extensive molecular tool kit of fluorescent biosensors and optogenetic actuators capable of monitoring and manipulating various signaling activities with high spatiotemporal precision. The goal of this review is to provide readers with an overview of basic concepts and recent advances in the development and application of genetically encodable biosensors and optogenetic tools for understanding signaling activity.

3-D image pre-processing algorithms for improved automated tracing of neuronal arbors.

The accuracy and reliability of automated neurite tracing systems is ultimately limited by image quality as reflected in the signal-to-noise ratio, contrast, and image variability. This paper describes a novel combination of image processing methods that operate on images of neurites captured by confocal and widefield microscopy, and produce synthetic images that are better suited to automated tracing. The algorithms are based on the curvelet transform (for denoising curvilinear structures and local orientation estimation), perceptual grouping by scalar voting (for elimination of non-tubular structures and improvement of neurite continuity while preserving branch points), adaptive focus detection, and depth estimation (for handling widefield images without deconvolution). The proposed methods are fast, and capable of handling large images. Their ability to handle images of unlimited size derives from automated tiling of large images along the lateral dimension, and processing of 3-D images one optical slice at a time. Their speed derives in part from the fact that the core computations are formulated in terms of the Fast Fourier Transform (FFT), and in part from parallel computation on multi-core computers. The methods are simple to apply to new images since they require very few adjustable parameters, all of which are intuitive. Examples of pre-processing DIADEM Challenge images are used to illustrate improved automated tracing resulting from our pre-processing methods.

A broadly applicable 3-D neuron tracing method based on open-curve snake.

This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.

Neuronal tracing for connectomic studies.

Reconstruction of the complete wiring diagram, or connectome, of a neural circuit provides an alternative approach to conventional circuit analysis. One major obstacle of connectomics lies in segmenting and tracing neuronal processes from the vast number of images obtained with optical or electron microscopy. Here I review recent progress in automated tracing algorithms for connectomic reconstruction with fluorescence and electron microscopy, and discuss the challenges to image analysis posed by novel optical imaging techniques.

Transfection via whole-cell recording in vivo: bridging single-cell physiology, genetics and connectomics.

Single-cell genetic manipulation is expected to substantially advance the field of systems neuroscience. However, existing gene delivery techniques do not allow researchers to electrophysiologically characterize cells and to thereby establish an experimental link between physiology and genetics for understanding neuronal function. In the mouse brain in vivo, we found that neurons remained intact after 'blind' whole-cell recording, that DNA vectors could be delivered through the patch-pipette during such recordings and that these vectors drove protein expression in recorded cells for at least 7 d. To illustrate the utility of this approach, we recorded visually evoked synaptic responses in primary visual cortical cells while delivering DNA plasmids that allowed retrograde, monosynaptic tracing of each neuron's presynaptic inputs. By providing a biophysical profile of a cell before its specific genetic perturbation, this combinatorial method captures the synaptic and anatomical receptive field of a neuron.

The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions.

The comprehensive characterization of neuronal morphology requires tracing extensive axonal and dendritic arbors imaged with light microscopy into digital reconstructions. Considerable effort is ongoing to automate this greatly labor-intensive and currently rate-determining process. Experimental data in the form of manually traced digital reconstructions and corresponding image stacks play a vital role in developing increasingly more powerful reconstruction algorithms. The DIADEM challenge (short for DIgital reconstruction of Axonal and DEndritic Morphology) successfully stimulated progress in this area by utilizing six data set collections from different animal species, brain regions, neuron types, and visualization methods. The original research projects that provided these data are representative of the diverse scientific questions addressed in this field. At the same time, these data provide a benchmark for the types of demands automated software must meet to achieve the quality of manual reconstructions while minimizing human involvement. The DIADEM data underwent extensive curation, including quality control, metadata annotation, and format standardization, to focus the challenge on the most substantial technical obstacles. This data set package is now freely released ( http://diademchallenge.org ) to train, test, and aid development of automated reconstruction algorithms.