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Ligand inducible proteins that enable precise and reversible control of nuclear translocation of passenger proteins have broad applications ranging from genetic studies in mammals to therapeutics that target diseases such as cancer and diabetes. One of the drawbacks of the current translocation systems is that the ligands used to control nuclear localization are either toxic or prone to crosstalk with endogenous protein cascades within live animals. We sought to take advantage of salicylic acid (SA), a small molecule that has been extensively used in humans. In plants, SA functions as a hormone that can mediate immunity and is sensed by the non-expressor of pathogenesis-related (NPR) proteins. Although it is well recognized that nuclear translocation of NPR1 is essential to promoting immunity in plants, the exact subdomain of Arabidopsis thaliana NPR1 (AtNPR1) essential for SA mediated nuclear translocation is controversial. Here, we utilized the fluorescent protein mCherry as the reporter to investigate the ability of SA to induce nuclear translocation of the full-length NPR1 protein or its C-terminal transactivation (TAD) domain using HEK293 cells as a heterologous system. HEK293 cells lack accessory plant proteins including NPR3/NPR4 and are thus ideally suited for studying the impact of SA-induced changes in NPR1. Our results obtained using a stable expression system show that the TAD of AtNPR1 is sufficient to enable the reversible SA mediated nuclear translocation of mCherry. Our studies advance a basic understanding of nuclear translocation mediated by the TAD of AtNPR1 and uncover a biotechnological tool for SA mediated nuclear localization. This article is protected by copyright. All rights reserved.

Aims To investigate the use of a computer-assisted technology for objective, cell-based quantification of molecular biomarkers in specified cell types in histopathology specimens, with the aim of advancing current visual estimation or pixel-level (rather than cell-based) quantification methods.

Summary The need to map regions of brain tissue that are much wider than the field of view of the microscope arises frequently. One common approach is to collect a series of overlapping partial views, and align them to synthesize a montage covering the entire region of interest. We present a method that advances this approach in multiple ways. Our method (1) produces a globally consistent joint registration of an unorganized collection of three-dimensional (3-D) multichannel images with or without stage micrometer data; (2) produces accurate registrations withstanding changes in scale, rotation, translation and shear by using a 3-D affine transformation model; (3) achieves complete automation, and does not require any parameter settings; (4) handles low and variable overlaps (5‐15%) between adjacent images, minimizing the number of images required to cover a tissue region; (5) has the self-diagnostic ability to recognize registration failures instead of delivering incorrect results; (6) can handle a broad range of biological images by exploiting generic alignment cues from multiple fluorescence channels without requiring segmentation and (7) is computationally efficient enough to run on desktop computers regardless of the number of images. The algorithm was tested with several tissue samples of at least 50 image tiles, involving over 5000 image pairs. It correctly registered all image pairs with an overlap greater than 7%, correctly recognized all failures, and successfully joint-registered all images for all

The movement of proteins within cells can provide dynamic indications of cell signaling and cell polarity, but methods are needed to track and quantify subcellular protein movement within tissue environments. Here we present a semiautomated approach to quantify subcellular protein location for hundreds of migrating cells within intact living tissue using retrovirally expressed fluorescent fusion proteins and time-lapse two-photon microscopy of intact thymic lobes. We have validated the method using GFP-PKCζ, a marker for cell polarity, and LAT-GFP, a marker for T-cell receptor signaling, and have related the asymmetric distribution of these proteins to the direction and speed of cell migration. These approaches could be readily adapted to other fluorescent fusion proteins, tissues and biological questions.

Background There is a need for integrative and quantitative methods to investigate the structural and functional relations among elements of complex systems, such as the neurovascular unit (NVU), that involve multiple cell types, microvasculatures, and various genomic/proteomic/ionic functional entities. Methods Vascular casting and selective labeling enabled simultaneous three-dimensional imaging of the microvasculature, cell nuclei, and cytoplasmic stains. Multidimensional segmentation was achieved by (i) bleed-through removal and attenuation correction; (ii) independent segmentation and morphometry for each corrected channel; and (iii) spatially associative feature computation across channels. The combined measurements enabled cell classification based on nuclear morphometry, cytoplasmic signals, and distance from vascular elements. Specific spatial relations among the NVU elements could be quantified. Results A software system combining nuclear and vessel segmentation codes and associative features was constructed and validated. Biological variability contributed to misidentified nuclei (9.3%), undersegmentation of nuclei (3.7%), hypersegmentation of nuclei (14%), and missed nuclei (4.7%). Microvessel segmentation errors occurred rarely, mainly due to nonuniform lumen staining. Conclusions Associative features across fluorescence channels, in combination with standard features, enable integrative structural and functional analysis of the NVU. By labeling additional structural and functional entities, this method can be scaled up to larger-scale systems biology studies that integrate spatial and molecular information. © 2005 Wiley-Liss, Inc.

Background Automated segmentation of fluorescently labeled cell nuclei in three-dimensional confocal images is essential for numerous studies, e.g., spatiotemporal fluorescence in situ hybridization quantification of immediate early gene transcription. High accuracy and automation levels are required in high-throughput and large-scale studies. Common sources of segmentation error include tight clustering and fragmentation of nuclei. Previous region-based methods are limited because they perform merging of two nuclear fragments at a time. To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed. Methods A recursive tree-based algorithm that can consider multiple object fragments simultaneously is described. Starting with oversegmented data, it searches efficiently for the optimal merging pattern guided by a quantitative scoring criterion based on object modeling. Computation is bounded by limiting the depth of the merging tree. Results The proposed method was found to perform consistently better, achieving merging accuracy in the range of 92% to 100% compared with our previous algorithm, which varied in the range of 75% to 97%, even with a modest merging tree depth of 3. The overall average accuracy improved from 90% to 96%, with roughly the same computational cost for a set of representative images drawn from the CA1, CA3, and parietal cortex regions of the rat hippocampus. Conclusion Hierarchical tree model-based algorithms significantly improve the accuracy of automated nuclear segmentation without sacrificing speed. © 2004 Wiley-Liss, Inc.

Background Automated segmentation of fluorescently-labeled cell nuclei in 3D confocal microscope images is essential to many studies involving morphological and functional analysis. A common source of segmentation error is tight clustering of nuclei. There is a compelling need to minimize these errors for constructing highly automated scoring systems. Methods A combination of two approaches is presented. First, an improved distance transform combining intensity gradients and geometric distance is used for the watershed step. Second, an explicit mathematical model for the anatomic characteristics of cell nuclei such as size and shape measures is incorporated. This model is constructed automatically from the data. Deliberate initial over-segmentation of the image data is performed, followed by statistical model-based merging. A confidence score is computed for each detected nucleus, measuring how well the nucleus fits the model. This is used in combination with the intensity gradient to control the merge decisions. Results Experimental validation on a set of rodent brain cell images showed 97% concordance with the human observer and significant improvement over prior methods. Conclusions Combining a gradient-weighted distance transform with a richer morphometric model significantly improves the accuracy of automated segmentation and FISH analysis. Cytometry Part A 56A:23–36, 2003. © 2003 Wiley-Liss, Inc.

Confocal microscopy is a three-dimensional (3D) imaging modality, but the specimen thickness that can be imaged is limited by depth-dependent signal attenuation. Both software and hardware methods have been used to correct the attenuation in reconstructed images, but previous methods do not increase the image signal-to-noise ratio (SNR) using conventional specimen preparation and imaging. We present a practical two-view method that increases the overall imaging depth, corrects signal attenuation and improves the SNR. This is achieved by a combination of slightly modified but conventional specimen preparation, image registration, montage synthesis and signal reconstruction methods. The specimen is mounted in a symmetrical manner between a pair of cover slips, rather than between a slide and a cover slip. It is imaged sequentially from both sides to generate two 3D image stacks from perspectives separated by approximately 180 degrees with respect to the optical axis. An automated image registration algorithm performs a precise 3D alignment, and a model-based minimum mean squared algorithm synthesizes a montage, combining the content of both the 3D views. Experiments with images of individual neurones contrasted with a space-filling fluorescent dye in thick brain tissue slices produced precise 3D montages that are corrected for depth-dependent signal attenuation. The SNR of the reconstructed image is maximized by the method, and it is significantly higher than in the single views after applying our attenuation model. We also compare our method with simpler two-view reconstruction methods and quantify the SNR improvement. The reconstructed images are a more faithful qualitative visualization of the specimen's structure and are quantitatively more accurate, providing a more rigorous basis for automated image analysis.

Techniques for three-dimensional (3D) light microscopy of a wide variety of biological specimens are rapidly maturing. The next advances are to improve image contrast, make image resolution as isotropic as possible, and perform quantitative analysis. This article discusses methods of automatic image analysis and blind deconvolution to compensate for the microscope's point-spread function (impulse response). Special emphasis is given to quantitative analysis based on adaptive segmentation, the results of which can be used to quantify and trace individual structures, and to montage high-resolution fields as well as the results of image analysis on these fields into a wide-area view with an associated computer database representation. We exploit the specific advantages accruing from the 3D nature of the data to achieve new capabilities not possible with 2D imaging. Fundamental and practical challenges in this area and our progress to date including automated segmentation, cell counting, neuron tracing, mosaic synthesis, “blind” deconvolution of large 3D images, and high-speed computation are presented. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 240–254, 1997

This paper presents a landmark based method for efEcient, robust, and automated computational synthesis of high-resolution, two-dimensional (2-D) or three-dimensional (3-D) wide-area images of a specimen from a series of overlapping partial views. The synthesized image is the set union of the areas or volumes covered by the partial views, and is called the “montage.” This technique is used not only to produce gray-level montages, but also to montage the results of automated image analysis, such as 3-D cell segmentation and counting, so as to generate large representations that are equivalent to processing the large wide-area image at high resolution. The method is based on computing a concise set of feature-tagged landmarks in each partial view, and establishing correspondences between the landmarks using a combinatorial point matching algorithm. This algorithm yields a spatial transformation linking the partial views that can be used to create the montage. Such processing can be a &st step towards high-resolution large-scale quantitative tissue studies. A detailed example using 3-D laser-scanning confocal microscope images of acriflavine-stained hippocampal sections of rat brain is presented to illustrate the method. 0 1996 wiley-us, hc.

The maximum-likelihood (ML) method for the quantitative analysis of electron-microscopic autoradiographs has been shown to be substantially superior to the conventional crossfire (CF) method. It can generate reliable and accurate tracer concentration estimates with far fewer micrographs and produce valid estimates even at counts low enough to preclude the use of the crossfire method while eliminating the need for special ad hoc treatment of narrow membranous structures as well as the secondary verification of the tracer concentration estimates. Despite these significant advantages, the large computational requirements of the ML method has to date hampered its widespread use. In this paper, we present a new line-integration method that allows us to reduce the computational requirements of the ML method to a point where it becomes feasible to implement it on a small computer system of the type typically available to a laboratory user of EM autoradiography. We present the complete line-integration method for the particular case of EM autoradiography with tritium, and show how it can be adapted to other isotopes. We have constructed a software package that implements the complete maximum-likelihood method on the IBM PC class of machines using our line-integration method. Features of this software package which are of particular importance to the research community are device independence, which makes it usable with a large variety of currently available laboratory equipment, and easy portability of the software and data between different computer systems.