Myers Lab

The GFP reconstitution across synaptic partners (GRASP) technique, based on functional complementation between two nonfluorescent GFP fragments, can be used to detect the location of synapses quickly, accurately and with high spatial resolution. The method has been previously applied in the nematode and the fruit fly but requires substantial modification for use in the mammalian brain. We developed mammalian GRASP (mGRASP) by optimizing transmembrane split-GFP carriers for mammalian synapses. Using in silico protein design, we engineered chimeric synaptic mGRASP fragments that were efficiently delivered to synaptic locations and reconstituted GFP fluorescence in vivo. Furthermore, by integrating molecular and cellular approaches with a computational strategy for the three-dimensional reconstruction of neurons, we applied mGRASP to both long-range circuits and local microcircuits in the mouse hippocampus and thalamocortical regions, analyzing synaptic distribution in single neurons and in dendritic compartments.

MOTIVATION: Automatic recognition of cell identities is critical for quantitative measurement, targeting, and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages, and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable. RESULTS: We have developed a new method, called SRS (for Simultaneous Recognition and Segmentation of cells), and applied it to 3D image stacks of the model organism C. elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a data set of 175 C. elegans image stacks containing 14,118 manually curated BWM cells providing the "ground-truth" for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving ±90-degree rotations. With these stacks excluded from the data set, the recognition accuracy rose to 99.1%. We also show SRS is generally applicable to other cell-types, e.g. intestinal cells. AVAILABILITY: The supplementary movies can be downloaded from our website http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d) and will be released in the V3D plugin source code repository.

Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial. We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve's smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties.

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of neuron function. We have developed a novel method capable of tracing neurons in three-dimensional microscopy data automatically. In contrast to template-based methods, the proposed approach makes no assumptions on the shape or appearance of neuron's body. Instead, an efficient seeding approach is applied to find significant pixels almost certainly within complex neuronal structures and the tracing problem is solved by computing an graph tree structure connecting these seeds. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient. Experiments on different types of data show promising results.

Linking activity in specific cell types with perception, cognition, and action, requires quantitative behavioral experiments in genetic model systems such as the mouse. In head-fixed primates, the combination of precise stimulus control, monitoring of motor output, and physiological recordings over large numbers of trials are the foundation on which many conceptually rich and quantitative studies have been built. Choice-based, quantitative behavioral paradigms for head-fixed mice have not been described previously. Here, we report a somatosensory absolute object localization task for head-fixed mice. Mice actively used their mystacial vibrissae (whiskers) to sense the location of a vertical pole presented to one side of the head and reported with licking whether the pole was in a target (go) or a distracter (no-go) location. Mice performed hundreds of trials with high performance (>90% correct) and localized to <0.95 mm (<6 degrees of azimuthal angle). Learning occurred over 1-2 weeks and was observed both within and across sessions. Mice could perform object localization with single whiskers. Silencing barrel cortex abolished performance to chance levels. We measured whisker movement and shape for thousands of trials. Mice moved their whiskers in a highly directed, asymmetric manner, focusing on the target location. Translation of the base of the whiskers along the face contributed substantially to whisker movements. Mice tended to maximize contact with the go (rewarded) stimulus while minimizing contact with the no-go stimulus. We conjecture that this may amplify differences in evoked neural activity between trial types.

The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.

MOTIVATION: Caenorhabditis elegans, a roundworm found in soil, is a widely studied model organism with about 1000 cells in the adult. Producing high-resolution fluorescence images of C.elegans to reveal biological insights is becoming routine, motivating the development of advanced computational tools for analyzing the resulting image stacks. For example, worm bodies usually curve significantly in images. Thus one must 'straighten' the worms if they are to be compared under a canonical coordinate system. RESULTS: We develop a worm straightening algorithm (WSA) that restacks cutting planes orthogonal to a 'backbone' that models the anterior-posterior axis of the worm. We formulate the backbone as a parametric cubic spline defined by a series of control points. We develop two methods for automatically determining the locations of the control points. Our experimental methods show that our approaches effectively straighten both 2D and 3D worm images.