Cell membrane proteins play an important role in tissue architecture and cell-cell communication. We hypothesize that segmentation and multidimensional characterization of the distribution of cell membrane proteins, on a cell-by-cell basis, enable improved classification of treatment groups and identify important characteristics that can otherwise be hidden. We have developed a series of computational steps to 1) delineate cell membrane protein signals and associate them with a specific nucleus; 2) compute a coupled representation of the multiplexed DNA content with membrane proteins; 3) rank computed features associated with such a multidimensional representation; 4) visualize selected features for comparative evaluation through heatmaps; and 5) discriminate between treatment groups in an optimal fashion. The novelty of our method is in the segmentation of the membrane signal and the multidimensional representation of phenotypic signature on a cell-by-cell basis. To test the utility of this method, the proposed computational steps were applied to images of cells that have been irradiated with different radiation qualities in the presence and absence of other small molecules. These samples are labeled for their DNA content and E-cadherin membrane proteins. We demonstrate that multidimensional representations of cell-by-cell phenotypes improve predictive and visualization capabilities among different treatment groups, and identify hidden variables.

Several proteins involved in the response to DNA double strand breaks (DSB) form microscopically visible nuclear domains, or foci, after exposure to ionizing radiation. Radiation-induced foci (RIF) are believed to be located where DNA damage occurs. To test this assumption, we analyzed the spatial distribution of 53BP1, phosphorylated ATM, and gammaH2AX RIF in cells irradiated with high linear energy transfer (LET) radiation and low LET. Since energy is randomly deposited along high-LET particle paths, RIF along these paths should also be randomly distributed. The probability to induce DSB can be derived from DNA fragment data measured experimentally by pulsed-field gel electrophoresis. We used this probability in Monte Carlo simulations to predict DSB locations in synthetic nuclei geometrically described by a complete set of human chromosomes, taking into account microscope optics from real experiments. As expected, simulations produced DNA-weighted random (Poisson) distributions. In contrast, the distributions of RIF obtained as early as 5 min after exposure to high LET (1 GeV/amu Fe) were non-random. This deviation from the expected DNA-weighted random pattern can be further characterized by "relative DNA image measurements." This novel imaging approach shows that RIF were located preferentially at the interface between high and low DNA density regions, and were more frequent than predicted in regions with lower DNA density. The same preferential nuclear location was also measured for RIF induced by 1 Gy of low-LET radiation. This deviation from random behavior was evident only 5 min after irradiation for phosphorylated ATM RIF, while gammaH2AX and 53BP1 RIF showed pronounced deviations up to 30 min after exposure. These data suggest that DNA damage-induced foci are restricted to certain regions of the nucleus of human epithelial cells. It is possible that DNA lesions are collected in these nuclear sub-domains for more efficient repair.

Saliency is an important perceptual cue that occurs at different levels of resolution. Important attributes of saliency are symmetry, continuity, and closure. Detection of these attributes is often hindered by noise, variation in scale, and incomplete information. This paper introduces the iterative voting method, which uses oriented kernels for inferring saliency as it relates to symmetry. A unique aspect of the technique is the kernel topography, which is refined and reoriented iteratively. The technique can cluster and group nonconvex perceptual circular symmetries along the radial line of an object's shape. It has an excellent noise immunity and is shown to be tolerant to perturbation in scale. The application of this technique to images obtained through various modes of microscopy is demonstrated. Furthermore, as a case example, the method has been applied to quantify kinetics of nuclear foci formation that are formed by phosphorylation of histone gammaH2AX following ionizing radiation. Iterative voting has been implemented in both 2-D and 3-D for multi image analysis.

Spatial events largely determine the biology of cells, tissues, and organs. In this paper, we present a tool for the quantitative spatial analysis of heterogeneous cell populations, and we show experimental validation of this tool using both artificial and real (mammary gland tissue) data, in two and three dimensions. We present the refined relative neighborhood graph as a means to establish neighborhood between cells in an image while modeling the topology of the tissue. Then, we introduce the M function as a method to quantitatively evaluate the existence of spatial patterns within one cell population or the relationship between the spatial distributions of multiple cell populations. Finally, we show a number of examples that demonstrate the feasibility of our approach.

Organisms express their genomes in a cell-specific manner, resulting in a variety of cellular phenotypes or phenomes. Mapping cell phenomes under a variety of experimental conditions is necessary in order to understand the responses of organisms to stimuli. Representing such data requires an integrated view of experimental and informatic protocols. The proposed system, named BioSig, provides the foundation for cataloging cellular responses as a function of specific conditioning, treatment, staining, etc., for either fixed tissue or living cell studies. A data model has been developed to capture experimental variables and map them to image collections and their computed representation. This representation is hierarchical and spans across sample tissues, cells, and organelles, which are imaged with light microscopy. At each layer, content is represented with an attributed graph, which contains information about cellular morphology, protein localization, and cellular organization in tissue or cell culture. The Web-based multilayer informatics architecture uses the data model to provide guided workflow access for content exploration.

Mapping inter-cell signaling pathways requires an integrated view of experimental and informatic protocols. BioSig provides the foundation of cataloging inter-cell responses as a function of particular conditioning, treatment, staining, etc. for either in vivo or in vitro experiments. This paper outlines the system architecture, a functional data model for representing experimental protocols, algorithms for image analysis, and the required statistical analysis. The architecture provides remote shared operation of an inverted optical microscope, and couples instrument operation with images acquisition and annotation. The information is stored in an object-oriented database. The algorithms extract structural information such as morphology and organization, and map it to functional information such as inter-cellular responses. An example of usage of this system is included.

Article DeepView: a channel for distributed microscopy and informatics Share on Authors: B. Parvin Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile , J. Taylor Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile , G. Cong Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile , M. A. OKeefe Material Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Material Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile , M. H. Barcellos-Hoff Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile Authors Info & Claims SC '99: Proceedings of the 1999 ACM/IEEE conference on SupercomputingJanuary 1999 Pages 65–eshttps://doi.org/10.1145/331532.331597Online:01 January 1999Publication History 6citation214DownloadsMetricsTotal Citations6Total Downloads214Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access