Computational reconstruction of cell and tissue surfaces for modeling and data analysis

Laboratory of Immunology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA.
Nature Protocol (Impact Factor: 8.36). 02/2009; 4(7):1006-12. DOI: 10.1038/nprot.2009.94
Source: PubMed

ABSTRACT We present a method for the computational reconstruction of the 3-D morphology of biological objects, such as cells, cell conjugates or 3-D arrangements of tissue structures, using data from high-resolution microscopy modalities. The method is based on the iterative optimization of Voronoi representations of the spatial structures. The reconstructions of biological surfaces automatically adapt to morphological features of varying complexity with flexible degrees of resolution. We show how 3-D confocal images of single cells can be used to generate numerical representations of cellular membranes that may serve as the basis for realistic, spatially resolved computational models of membrane processes or intracellular signaling. Another example shows how the protocol can be used to reconstruct tissue boundaries from segmented two-photon image data that facilitate the quantitative analysis of lymphocyte migration behavior in relation to microanatomical structures. Processing time is of the order of minutes depending on data features and reconstruction parameters.

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    ABSTRACT: Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.
    PLoS ONE 09/2014; PLoS One. 2014 Sep 22;9(9):e107105(9). DOI:10.1371/journal.pone.0107105. · 3.53 Impact Factor
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    ABSTRACT: Introduction Recent studies in image cytometry evaluated the replacement of specific markers by morphological parameters. The aim of this study was to develop and evaluate a method to identify subtypes of leukocytes using morphometric data of the nuclei. Method The analyzed images were generated with a laser scanning cytometer. Two free programs were used for image analysis and statistical evaluation: Cellprofiler and Tanagra respectively. A sample of leukocytes with 200 sets of images (DAPI, CD45 and CD14) was analyzed. Using feature selection, the 20 best parameters were chosen to conduct cross-validation. Results The morphometric data identified the subpopulations of the analyzed leukocytes with a sensitivity and specificity of 0.95 per sample. Conclusion The present study is the first that identifies subpopulations of leukocytes by nuclear morphology.
    Hematology/ Oncology and Stem Cell Therapy 06/2014; 7(2). DOI:10.1016/j.hemonc.2013.11.005
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    ABSTRACT: Immune cells are thoroughbreds, moving farther and faster and surveying more diverse tissue space than their non-hematopoietic brethren. Intravital 2-photon microscopy has provided insights into the movements and interactions of many immune cell types in diverse tissues, but much more information is needed to link such analyses of dynamic cell behavior to function. Here we describe additional methods whose application promises to extend our vision, allowing more complete, multiscale dissection of how immune cell positioning and movement are linked to system state, host defense, and disease.
    European Journal of Immunology 06/2013; 43(6). DOI:10.1002/eji.201243119 · 4.52 Impact Factor

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May 15, 2014

Frederick Klauschen