Damir Sudar

Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution I: data acquisition pipeline

BackgroundTo model and thoroughly understand animal transcription networks, it is essential to derive accurate spatial and temporal descriptions of developing gene expression patterns with cellular resolution.ResultsHere we describe a suite of methods that provide the first quantitative three-dimensional description of gene expression and morphology at cellular resolution in whole embryos. A database containing information derived from 1,282 embryos is released that describes the mRNA expression of 22 genes at multiple time points in the Drosophila blastoderm. We demonstrate that our methods are sufficiently accurate to detect previously undescribed features of morphology and gene expression. The cellular blastoderm is shown to have an intricate morphology of nuclear density patterns and apical/basal displacements that correlate with later well-known morphological features. Pair rule gene expression stripes, generally considered to specify patterning only along the anterior/posterior body axis, are shown to have complex changes in stripe location, stripe curvature, and expression level along the dorsal/ventral axis. Pair rule genes are also found to not always maintain the same register to each other.ConclusionThe application of these quantitative methods to other developmental systems will likely reveal many other previously unknown features and provide a more rigorous understanding of developmental regulatory networks.

Segmentation of confocal microscope images of cell nuclei in thick tissue sections

Segmentation of intact cell nuclei from three‐dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence‐stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525), a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analysts involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analysts visual judgement is correct.

EFFICIENT, INTERACTIVE, AND THREE-DIMENSIONAL SEGMENTATION OF CELL NUCLEI IN THICK TISSUE SECTIONS

Segmentation of intact cell nuclei in three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. Because automatic algorithms do not correctly segment all nuclei in tissue sections, interactive algorithms may be preferable for some applications. Existing interactive segmentation algorithms require the analyst to draw a border around the nucleus under consideration in all successive two-dimensional (2D) planes of the 3D image. The present paper describes an algorithm with two main advantages over the existing method. First, the analyst draws borders only in 2D planes that cut approximately through the center of the nucleus under consideration so that the nuclear borders generally are most distinct. Second, the analyst draws only five borders around each nucleus, and then the algorithm interpolates the entire surface. The algorithm results in segmented objects that correspond to individual, visually identifiable nuclei. The segmented surfaces, however, may not exactly represent the true nuclear surface. An optional, automatic surface optimization algorithm can be applied to reduce this error.

Data File Standard for Flow Cytometry, version FCS 3.1.

The flow cytometry data file standard provides the specifications needed to completely describe flow cytometry data sets within the confines of the file containing the experimental data. In 1984, the first Flow Cytometry Standard format for data files was adopted as FCS 1.0. This standard was modified in 1990 as FCS 2.0 and again in 1997 as FCS 3.0. We report here on the next generation flow cytometry standard data file format. FCS 3.1 is a minor revision based on suggested improvements from the community. The unchanged goal of the standard is to provide a uniform file format that allows files created by one type of acquisition hardware and software to be analyzed by any other type.

Comparative genomic hybridization.

Comparative Genomic Hybridization (CGH) is a powerful molecular cytogenetic technique that permits assessment of DNA copy number on a genome‐wide scale. Of note, this methodology uses tumor DNA as a probe for fluorescence in situ hybridization (FISH) to normal metaphase chromosomes and does not require dividing cells from the tumor specimen. This unit provides protocols for CGH, for preparation of metaphase chromosomes, tumor and normal DNAs for FISH and for the microscopy and image analysis of CGH experiments.

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

BackgroundThe distribution of chromatin-associated proteins plays a key role in directing nuclear function. Previously, we developed an image-based method to quantify the nuclear distributions of proteins and showed that these distributions depended on the phenotype of human mammary epithelial cells. Here we describe a method that creates a hierarchical tree of the given cell phenotypes and calculates the statistical significance between them, based on the clustering analysis of nuclear protein distributions.ResultsNuclear distributions of nuclear mitotic apparatus protein were previously obtained for non-neoplastic S1 and malignant T4-2 human mammary epithelial cells cultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 and the number of days in cultured. A probabilistic ensemble approach was used to define a set of consensus clusters from the results of multiple traditional cluster analysis techniques applied to the nuclear distribution data. Cluster histograms were constructed to show how cells in any one phenotype were distributed across the consensus clusters. Grouping various phenotypes allowed us to build phenotype trees and calculate the statistical difference between each group. The results showed that non-neoplastic S1 cells could be distinguished from malignant T4-2 cells with 94.19% accuracy; that proliferating S1 cells could be distinguished from differentiated S1 cells with 92.86% accuracy; and showed no significant difference between the various phenotypes of T4-2 cells corresponding to increasing tumor sizes.ConclusionThis work presents a cluster analysis method that can identify significant cell phenotypes, based on the nuclear distribution of specific proteins, with high accuracy.

Molecular cytometry of cancer

The application of molecular probes to diagnosis and prognosis of malignancies has redefined our perceptions of disease, allowing diagnosis by genotypic rather than phenotypic criteria. DNA analysis is especially useful when applied to pathological material in situ, because this allows the pathologist to combine information from both morphological and molecular observations. DNA in situ hybridization is a useful approach for the molecular pathologist, especially when combined with cytometric analysis. Potential clinical applications for in situ hybridization and the recently described technique of comparative genomic hybridization in tumor diagnosis and prognosis are described.

Registering Drosophila embryos at cellular resolution to build a quantitative 3D atlas of gene expression patterns and morphology

The Berkeley Drosophila Transcription Network Project is developing a suite of methods to convert volumetric data generated by confocal fluorescence microscopy into numerical three dimensional representations of gene expression at cellular resolution. One key difficulty is that fluorescence microscopy can only capture expression levels for a few gene products in a given animal. We report on a method for registering 3D expression data from different Drosophila embryos stained for overlapping subsets of gene products in order to build a composite atlas, ultimately containing co-expression information for thousands of genes. Our techniques have also allowed the discovery of a complex pattern of cell density across the blastula that changes over time and may play a role in gastrulation.

Visualization for Validation and Improvement of Three-dimensional Segmentation Algorithms

The Berkeley Drosophila Transcription Network Project (BDTNP) is developing a suite of methods that will allow a quantitative description and analysis of three dimensional (3D) gene expression patterns in an animal with cellular resolution. An important component of this approach are algorithms that segment 3D images of an organism into individual nuclei and cells and measure relative levels of gene expression. As part of the BDTNP, we are developing tools for interactive visualization, control, and verification of these algorithms. Here we present a volume visualization prototype system that, combined with user interaction tools, supports validation and quantitative determination of the accuracy of nuclear segmentation. Visualizations of nuclei are combined with information obtained from a nuclear segmentation mask, supporting the comparison of raw data and its segmentation. It is possible to select individual nuclei interactively in a volume rendered image and identify incorrectly segmented objects. Integration with segmentation algorithms, implemented in MATLAB, makes it possible to modify a segmentation based on visual examination and obtain additional information about incorrectly segmented objects. This work has already led to significant improvements in segmentation accuracy and opens the way to enhanced analysis of images of complex animal morphologies.

Mapping organism expression levels at cellular resolution in developing Drosophila

The development of an animal embryo is orchestrated by a network of genetically determined, temporal and spatial gene expression patterns that determine the animals final form. To understand such networks, we are developing novel quantitative optical imaging techniques to map gene expression levels at cellular and sub-cellular resolution within pregastrula Drosophila. Embryos at different stages of development are labeled for total DNA and specific gene products using different fluorophors and imaged in 3D with confocal microscopy. Innovative steps have been made which allow the DNA-image to be automatically segmented to produce a morphological mask of the individual nuclear boundaries. For each stage of development an average morphology is chosen to which images from different embryo are compared. The morphological mask is then used to quantify gene-product on a per nuclei basis. What results is an atlas of the relative amount of the specific gene product expressed within the nucleus of every cell in the embryo at the various stages of development. We are creating a quantitative database of transcription factor and target gene expression patterns in wild-type and factor mutant embryos with single cell resolution. Our goal is to uncover the rules determining how patterns of gene expression are generated.