Prabhakar R. Gudla

Allele-specific nuclear positioning of the monoallelically expressed astrocyte marker GFAP

Chromosomes and genes are nonrandomly arranged within the mammalian cell nucleus. However, the functional significance of nuclear positioning in gene expression is unclear. Here we directly probed the relationship between nuclear positioning and gene activity by comparing the location of the active and inactive copies of a monoallelically expressed gene in single cell nuclei. We demonstrate that the astrocyte-specific marker GFAP (glial fibrillary acidic protein) is monoallelically expressed in cortical astrocytes. Selection of the active allele occurs in a stochastic manner and is generally maintained through cell division. Taking advantage of the monoallelic expression of GFAP, we show that the functionally distinct alleles occupy differential radial positions within the cell nucleus and differentially associate with intranuclear compartments. In addition, coordinately regulated astrocyte-specific genes on distinct chromosomes spatially associate in their inactive state and dissociate upon activation. These results provide direct evidence for function-related differential positioning of individual gene alleles within the interphase nucleus.

Human T-cell leukemia virus type 1 p8 protein increases cellular conduits and virus transmission

The human T-cell leukemia virus type 1 (HTLV-1) is the cause of adult T-cell leukemia/lymphoma as well as tropical spastic paraparesis/HTLV-1–associated myelopathy. HTLV-1 is transmitted to T cells through the virological synapse and by extracellular viral assemblies. Here, we uncovered an additional mechanism of virus transmission that is regulated by the HTLV-1–encoded p8 protein. We found that the p8 protein, known to anergize T cells, is also able to increase T-cell contact through lymphocyte function-associated antigen-1 clustering. In addition, p8 augments the number and length of cellular conduits among T cells and is transferred to neighboring T cells through these conduits. p8, by establishing a T-cell network, enhances the envelope-dependent transmission of HTLV-1. Thus, the ability of p8 to simultaneously anergize and cluster T cells, together with its induction of cellular conduits, secures virus propagation while avoiding the hosts immune surveillance. This work identifies p8 as a viral target for the development of therapeutic strategies that may limit the expansion of infected cells in HTLV-1 carriers and decrease HTLV-1–associated morbidity.

A high-throughput system for segmenting nuclei using multiscale techniques.

Automatic segmentation of cell nuclei is critical in several high‐throughput cytometry applications whereas manual segmentation is laborious and irreproducible. One such emerging application is measuring the spatial organization (radial and relative distances) of fluorescence in situ hybridization (FISH) DNA sequences, where recent investigations strongly suggest a correlation between nonrandom arrangement of genes to carcinogenesis. Current automatic segmentation methods have varying performance in the presence of nonuniform illumination and clustering, and boundary accuracy is seldom assessed, which makes them suboptimal for this application. The authors propose a modular and model‐based algorithm for extracting individual nuclei. It uses multiscale edge reconstruction for contrast stretching and edge enhancement as well as a multiscale entropy‐based thresholding for handling nonuniform intensity variations. Nuclei are initially oversegmented and then merged based on area followed by automatic multistage classification into single nuclei and clustered nuclei. Estimation of input parameters and training of the classifiers is automatic. The algorithm was tested on 4,181 lymphoblast nuclei with varying degree of background nonuniformity and clustering. It extracted 3,515 individual nuclei and identified single nuclei and individual nuclei in clusters with 99.8 ± 0.3% and 95.5 ± 5.1% accuracy, respectively. Segmented boundaries of the individual nuclei were accurate when compared with manual segmentation with an average RMS deviation of 0.26 μm (∼2 pixels). The proposed segmentation method is efficient, robust, and accurate for segmenting individual nuclei from fluorescence images containing clustered and isolated nuclei. The algorithm allows complete automation and facilitates reproducible and unbiased spatial analysis of DNA sequences. Published 2008 Wiley‐Liss, Inc.

Segmentation of Whole Cells and Cell Nuclei From 3-D Optical Microscope Images Using Dynamic Programming

Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.

TCR Microclusters Pre-Exist and Contain Molecules Necessary for TCR Signal Transduction

TCR-dependent signaling events have been observed to occur in TCR microclusters. We found that some TCR microclusters are present in unstimulated murine T cells, indicating that the mechanisms leading to microcluster formation do not require ligand binding. These pre-existing microclusters increase in absolute number following engagement by low-potency ligands. This increase is accompanied by an increase in cell spreading, with the result that the density of TCR microclusters on the surface of the T cell is not a strong function of ligand potency. In characterizing their composition, we observed a constant number of TCRs in a microcluster, constitutive exclusion of the phosphatase CD45, and preassociation with the signaling adapters linker for activation of T cells and growth factor receptor-bound protein 2. The existence of TCR microclusters prior to ligand binding in a state that is conducive for the initiation of downstream signaling could explain, in part, the rapid kinetics with which TCR signal transduction occurs.

Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images.

Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100–200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large‐scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization‐labeled genes of interest. Segmentation was performed by a multistage watershed‐based algorithm and screening by an artificial neural network‐based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well‐segmented nuclei relative to a 2D dynamic programming‐based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach.

Ceramide Transfer Protein Deficiency Compromises Organelle Function and Leads to Senescence in Primary Cells

Ceramide transfer protein (CERT) transfers ceramide from the endoplasmic reticulum (ER) to the Golgi complex. Its deficiency in mouse leads to embryonic death at E11.5. CERT deficient embryos die from cardiac failure due to defective organogenesis, but not due to ceramide induced apoptotic or necrotic cell death. In the current study we examined the effect of CERT deficiency in a primary cell line, namely, mouse embryonic fibroblasts (MEFs). We show that in MEFs, unlike in mutant embryos, lack of CERT does not lead to increased ceramide but causes an accumulation of hexosylceramides. Nevertheless, the defects due to defective sphingolipid metabolism that ensue, when ceramide fails to be trafficked from ER to the Golgi complex, compromise the viability of the cell. Therefore, MEFs display an incipient ER stress. While we observe that ceramide trafficking from ER to the Golgi complex is compromised, the forward transport of VSVG-GFP protein is unhindered from ER to Golgi complex to the plasma membrane. However, retrograde trafficking of the plasma membrane-associated cholera toxin B to the Golgi complex is reduced. The dysregulated sphingolipid metabolism also leads to increased mitochondrial hexosylceramide. The mitochondrial functions are also compromised in mutant MEFs since they have reduced ATP levels, have increased reactive oxygen species, and show increased glutathione reductase activity. Live-cell imaging shows that the mutant mitochondria exhibit reduced fission and fusion events. The mitochondrial dysfunction leads to an increased mitophagy in the CERT mutant MEFs. The compromised organelle function compromise cell viability and results in premature senescence of these MEFs.

Automatic nuclei segmentation and spatial FISH analysis for cancer detection

Spatial analysis of gene localization using fluorescent in-situ hybridization (FISH) labeling is potentially a new method for early cancer detection. Current methodology relies heavily upon accurate segmentation of cell nuclei and FISH signals in tissue sections. While automatic FISH signal detection is a relatively simpler task, accurate nuclei segmentation is still a manual process which is fairly time consuming and subjective. Hence to use the methodology as a clinical application, it is necessary to automate all the steps involved in the process of spatial FISH signal analysis using fast, robust and accurate image processing techniques. In this work, we describe an intelligent framework for analyzing the FISH signals by coupling hybrid nuclei segmentation algorithm with pattern recognition algorithms to automatically identify well segmented nuclei. Automatic spatial statistical analysis of the FISH spots was carried out on the output from the image processing and pattern recognition unit. Results are encouraging and show that the method could evolve into a full fledged clinical application for cancer detection.

Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning

BackgroundCorrect segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings often lead to segmentation errors. This manuscript introduces a ranked-retrieval approach using logistic regression to automate selection of accurately segmented nuclei from a set of candidate segmentations. The methodology is validated on an application of spatial gene repositioning in breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue sections by labeling sequences with fluorescence in situ hybridization (FISH), followed by measurement of the relative position of each gene from the nuclear center to the nuclear periphery. This technique requires hundreds of well-segmented nuclei per sample to achieve statistical significance. Although the tissue samples in this study contain a surplus of available nuclei, automatic identification of the well-segmented subset remains a challenging task.ResultsLogistic regression was applied to features extracted from candidate segmented nuclei, including nuclear shape, texture, context, and gene copy number, in order to rank objects according to the likelihood of being an accurately segmented nucleus. The method was demonstrated on a tissue microarray dataset of 43 breast cancer patients, comprising approximately 40,000 imaged nuclei in which the HES5 and FRA2 genes were labeled with FISH probes. Three trained reviewers independently classified nuclei into three classes of segmentation accuracy. In man vs. machine studies, the automated method outperformed the inter-observer agreement between reviewers, as measured by area under the receiver operating characteristic (ROC) curve. Robustness of gene position measurements to boundary inaccuracies was demonstrated by comparing 1086 manually and automatically segmented nuclei. Pearson correlation coefficients between the gene position measurements were above 0.9 (p < 0.05). A preliminary experiment was conducted to validate the ranked retrieval in a test to detect cancer. Independent manual measurement of gene positions agreed with automatic results in 21 out of 26 statistical comparisons against a pooled normal (benign) gene position distribution.ConclusionsAccurate segmentation is necessary to automate quantitative image analysis for applications such as gene repositioning. However, due to heterogeneity within images and across different applications, no segmentation algorithm provides a satisfactory solution. Automated assessment of segmentations by ranked retrieval is capable of reducing or even eliminating the need to select segmented objects by hand and represents a significant improvement over binary classification. The method can be extended to other high-throughput applications requiring accurate detection of cells or nuclei across a range of biomedical applications.

3D SEGMENTATION OF WHOLE CELLS AND CELL NUCLEI IN TISSUE USING DYNAMIC PROGRAMMING

We present a highly robust, semi-automatic algorithm for segmenting individual, fluorescence-labeled, whole cells or cell nuclei (objects) from 3D confocal microscope images of tissue. The core of the algorithm is an extension of dynamic programming that finds the surface as a series of overlapping ribbons, where the mean intensity per unit area within each ribbon is a global maximum. Consequently, the segmented surface is not significantly affected by intermittent fluorescence labeling, diffuse borders or by spurious signals away from the borders. Minimal interaction by the user ensures correct segmentation of each object, yet segmenting 10 s to 100 s of objects per sample is feasible. The algorithm enables for the first time quantitative analysis and modeling of communication processes between cells in intact tissue