Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis

Background: The distribution of the chromatin-associatedproteins plays a key role in directing nuclear function. Previously, wedeveloped an image-based method to quantify the nuclear distributions ofproteins and showed that these distributions depended on the phenotype ofhuman mammary epithelial cells. Here we describe a method that creates ahierarchical tree of the given cell phenotypes and calculates thestatistical significance between them, based on the clustering analysisof nuclear protein distributions. Results: Nuclear distributions ofnuclear mitotic apparatus protein were previously obtained fornon-neoplastic S1 and malignant T4-2 human mammary epithelial cellscultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 andthe number of days in cultured. A probabilistic ensemble approach wasused to define a set of consensus clusters from the results of multipletraditional cluster analysis techniques applied to the nucleardistribution data. Cluster histograms were constructed to show how cellsin any one phenotype were distributed across the consensus clusters.Grouping various phenotypes allowed us to build phenotype trees andcalculate the statistical difference between each group. The resultsshowed that non-neoplastic S1 cells could be distinguished from malignantT4-2 cells with 94.19 percent accuracy; that proliferating S1 cells couldbe distinguished from differentiated S1 cells with 92.86 percentaccuracy; and showed no significant difference between the variousphenotypes of T4-2 cells corresponding to increasing tumor sizes.Conclusion: This work presents a cluster analysis method that canidentify significant cell phenotypes, based on the nuclear distributionof specific proteins, with high accuracy

A 3D digital atlas of C. elegans and its application to single-cell analyses,”

We built a digital nuclear atlas of the newly hatched, first larval stage (l1) of the wild-type hermaphrodite of Caenorhabditis elegans at single-cell resolution from confocal image stacks of 15 individual worms. the atlas quantifies the stereotypy of nuclear locations and provides other statistics on the spatial patterns of the 357 nuclei that could be faithfully segmented and annotated out of the 558 present at this developmental stage. We then developed an automated approach to assign cell names to each nucleus in a threedimensional image of an l1 worm. We achieved 86% accuracy in identifying the 357 nuclei automatically. this computational method will allow high-throughput single-cell analyses of the post-embryonic worm, such as gene expression analysis, or ablation or stimulation of cells under computer control in a high-throughput functional screen. Despite the detailed knowledge of the anatomy of the nematode C. elegans 1 as well as its determined cell lineage 2,3 , the mapped connectivity of its nervous system 4,5 and its sequenced genome 6,7 , we still lack a three-dimensional (3D) digital atlas of positions of nuclei in any postembryonic stage. Such an atlas has several potential applications. First, it provides us with previously unavailable quantitative knowledge about the degree of stereotypy of the positions of nuclei and the specific spatial relationships between different cells. Second, the atlas can serve as a standard template; we can compare any 3D image of a wild-type C. elegans to the atlas and extract the identities of individual nuclei using an automated approach. This is essential for high-throughput analysis of cellular information such as gene expression at single-cell resolution. Such an analysis provides much richer information than does analysis of expression data from a DNA microarray experiment 8,9 as DNA microarrays reveal average expression from tissue or from the entire individual but not the expression in an individual cell. Prior to this study, the anatomy of C. elegans has been described qualitatively by images with a text description or two-dimensional sketches 10 . Early efforts using electron microscopy analyses have resulted in detailed views of the anatomy 10 and even a connectivity graph of the nervous system 4,5 , but to date, manual or automated segmentation of the fine structure of such an ultrahigh-resolution image stack has not been demonstrated. Whereas one might contemplate carrying out such a manual segmentation for a single worm, doing so for enough worms to deliver statistical information on the location of nuclei is effectively impractical. Our method for automatically analyzing individual cells in postembryonic worms complements the similar capability developed previously for the embryo results Building a 3d digital atlas We used DAPI (4,6-diamidino-2-phenylindole) to stain the nuclei of all 558 cells. We used a myo-3:GFP transgene to label the nuclei of the 81 body wall muscle cells and 1 depressor muscle cell. These nuclei were fiducial markers, used by our manual and automated approach to annotate cells. We used a gene encoding monomeric Cherry protein (mCherry) driven by a promoter from a gene of interest to reveal expression in a set of target cells. We collected 3D images of C. elegans at the L1 stage using a Leica confocal microscope To build a standard digital atlas, we first computationally straightened the curved worm body in the 3D image into a rod shape 1

Atlas-builder software and the eNeuro atlas: resources for developmental biology and neuroscience

A major limitation in understanding embryonic development is the lack of cell type-specific markers. Existing gene expression and marker atlases provide valuable tools, but they typically have one or more limitations: a lack of single-cell resolution; an inability to register multiple expression patterns to determine their precise relationship; an inability to be upgraded by users; an inability to compare novel patterns with the database patterns; and a lack of three-dimensional images. Here, we develop new ‘atlas-builder’ software that overcomes each of these limitations. A newly generated atlas is three-dimensional, allows the precise registration of an infinite number of cell type-specific markers, is searchable and is open-ended. Our software can be used to create an atlas of any tissue in any organism that contains stereotyped cell positions. We used the software to generate an ‘eNeuro’ atlas of the Drosophila embryonic CNS containing eight transcription factors that mark the major CNS cell types (motor neurons, glia, neurosecretory cells and interneurons). We found neuronal, but not glial, nuclei occupied stereotyped locations. We added 75 new Gal4 markers to the atlas to identify over 50% of all interneurons in the ventral CNS, and these lines allowed functional access to those interneurons for the first time. We expect the atlas-builder software to benefit a large proportion of the developmental biology community, and the eNeuro atlas to serve as a publicly accessible hub for integrating neuronal attributes – cell lineage, gene expression patterns, axon/dendrite projections, neurotransmitters – and linking them to individual neurons

Visualization and Analysis of 3D Microscopic Images

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain

Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets

Feature selection based on mutual information: Criteria of max-depe ndency, max-relevance, and min-redundancy

Abstract—Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy

Document Image Recognition Based on Template Matching of Component Block Projections

Document Image Recognition (DIR), a very useful technique in office automation and digital library applications, is to find the most similar template for any input document image in a prestored template document image data set