@article {79, title = {BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models}, journal = {PLoS One}, volume = {11}, year = {2016}, month = {2016}, pages = {e0148379}, abstract = {

BioSig3D is a computational platform for high-content screening of three-dimensional (3D) cell culture models that are imaged in full 3D volume. It provides an end-to-end solution for designing high content screening assays, based on colony organization that is derived from segmentation of nuclei in each colony. BioSig3D also enables visualization of raw and processed 3D volumetric data for quality control, and integrates advanced bioinformatics analysis. The system consists of multiple computational and annotation modules that are coupled together with a strong use of controlled vocabularies to reduce ambiguities between different users. It is a web-based system that allows users to: design an experiment by defining experimental variables, upload a large set of volumetric images into the system, analyze and visualize the dataset, and either display computed indices as a heatmap, or phenotypic subtypes for heterogeneity analysis, or download computed indices for statistical analysis or integrative biology. BioSig3D has been used to profile baseline colony formations with two experiments: (i) morphogenesis of a panel of human mammary epithelial cell lines (HMEC), and (ii) heterogeneity in colony formation using an immortalized non-transformed cell line. These experiments reveal intrinsic growth properties of well-characterized cell lines that are routinely used for biological studies. BioSig3D is being released with seed datasets and video-based documentation.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0148379}, author = {Bilgin, Cemal Cagatay and Fontenay, Gerald and Cheng, Qingsu and Hang Chang and Ju Han and Parvin, Bahram} } @conference {314, title = {Integrative Analysis of Cellular Morphometric Context Reveals Clinically Relevant Signatures in Lower Grade Glioma}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - {MICCAI} 2016 - 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part {I}}, year = {2016}, doi = {10.1007/978-3-319-46720-7_9}, url = {http://dx.doi.org/10.1007/978-3-319-46720-7_9}, author = {Ju Han and Yunfu Wang and Weidong Cai and Alexander Borowsky and Bahram Parvin and Hang Chang} } @article {89, title = {Diagnostic and prognostic histopathology system using morphometric indices}, number = {13/886,213}, year = {2015}, month = {05/2015}, chapter = {9031306}, author = {Bahram Parvin and Hang Chang and Ju Han and Gerald Fontenay} } @inbook {288, title = {Quantification of the Dynamics of DNA Repair to Ionizing Radiation via Colocalization of 53BP1 and {\^I}{\textthreesuperior}H2AX}, booktitle = {Video Bioinformatics: From Live Imaging to Knowledge}, year = {2015}, publisher = {Springer}, organization = {Springer}, isbn = {978-3-319-23723-7}, doi = {10.1007/978-3-319-23724-4}, author = {Groesser, Torsten and Fontenay, Gerald V. and Ju Han and Hang Chang and Janice Pluth and Bahram Parvin}, editor = {Bir Bhanu and Prue Talbot} } @article {20, title = {Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association}, journal = {{IEEE} Trans. Med. Imaging}, volume = {32}, year = {2013}, pages = {670{\textendash}682}, doi = {10.1109/TMI.2012.2231420}, url = {http://dx.doi.org/10.1109/TMI.2012.2231420}, author = {Hang Chang and Ju Han and Alexander Borowsky and Leandro A. Loss and Joe W. Gray and Paul T. Spellman and Bahram Parvin} } @article {83, title = {Identification of fluorescent compounds with non-specific binding property via high throughput live cell microscopy}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e28802}, abstract = {

INTRODUCTION: Compounds exhibiting low non-specific intracellular binding or non-stickiness are concomitant with rapid clearing and in high demand for live-cell imaging assays because they allow for intracellular receptor localization with a high signal/noise ratio. The non-stickiness property is particularly important for imaging intracellular receptors due to the equilibria involved.

METHOD: Three mammalian cell lines with diverse genetic backgrounds were used to screen a combinatorial fluorescence library via high throughput live cell microscopy for potential ligands with high in- and out-flux properties. The binding properties of ligands identified from the first screen were subsequently validated on plant root hair. A correlative analysis was then performed between each ligand and its corresponding physiochemical and structural properties.

RESULTS: The non-stickiness property of each ligand was quantified as a function of the temporal uptake and retention on a cell-by-cell basis. Our data shows that (i) mammalian systems can serve as a pre-screening tool for complex plant species that are not amenable to high-throughput imaging; (ii) retention and spatial localization of chemical compounds vary within and between each cell line; and (iii) the structural similarities of compounds can infer their non-specific binding properties.

CONCLUSION: We have validated a protocol for identifying chemical compounds with non-specific binding properties that is testable across diverse species. Further analysis reveals an overlap between the non-stickiness property and the structural similarity of compounds. The net result is a more robust screening assay for identifying desirable ligands that can be used to monitor intracellular localization. Several new applications of the screening protocol and results are also presented.

}, keywords = {Animals, Arabidopsis, Cell Line, Cell Survival, Combinatorial Chemistry Techniques, Fluorescent Dyes, Humans, Ligands, Mice, Microscopy, Small Molecule Libraries}, issn = {1932-6203}, doi = {10.1371/journal.pone.0028802}, author = {Nath, Sangeeta and Spencer, Virginia A and Ju Han and Hang Chang and Zhang, Kai and Fontenay, Gerald V and Anderson, Charles and Hyman, Joel M and Nilsen-Hamilton, Marit and Chang, Young-Tae and Parvin, Bahram} } @conference {57, title = {Learning invariant features of tumor signature with independent subspace analysis}, booktitle = {9th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2012, May 2-5, 2012, Barcelona, Spain, Proceedings}, year = {2012}, doi = {10.1109/ISBI.2012.6235544}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235544}, author = {Quoc V. Le and Ju Han and Joe W. Gray and Paul T. Spellman and Alexander Borowsky and Bahram Parvin} } @conference {34, title = {Molecular bases of morphometric composition in Glioblastoma multiforme}, booktitle = {9th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2012, May 2-5, 2012, Barcelona, Spain, Proceedings}, year = {2012}, doi = {10.1109/ISBI.2012.6235889}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235889}, author = {Ju Han and Hang Chang and Gerald Fontenay and Paul T. Spellman and Alexander Borowsky and Bahram Parvin} } @article {21, title = {Multireference Level Set for the Characterization of Nuclear Morphology in Glioblastoma Multiforme}, journal = {{IEEE} Trans. Biomed. Engineering}, volume = {59}, year = {2012}, pages = {3460{\textendash}3467}, doi = {10.1109/TBME.2012.2218107}, url = {http://dx.doi.org/10.1109/TBME.2012.2218107}, author = {Hang Chang and Ju Han and Paul T. Spellman and Bahram Parvin} } @conference {36, title = {Comparison of sparse coding and kernel methods for histopathological classification of gliobastoma multiforme}, booktitle = {Proceedings of the 8th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2011, March 30 - April 2, 2011, Chicago, Illinois, {USA}}, year = {2011}, doi = {10.1109/ISBI.2011.5872505}, url = {http://dx.doi.org/10.1109/ISBI.2011.5872505}, author = {Ju Han and Hang Chang and Leandro A. Loss and Kai Zhang and Frederick L. Baehner and Joe W. Gray and Paul T. Spellman and Bahram Parvin} } @article {35, title = {Morphometic analysis of {TCGA} glioblastoma multiforme}, journal = {{BMC} Bioinformatics}, volume = {12}, year = {2011}, pages = {484}, doi = {10.1186/1471-2105-12-484}, url = {http://dx.doi.org/10.1186/1471-2105-12-484}, author = {Hang Chang and Gerald Fontenay and Ju Han and Ge Cong and Frederick L. Baehner and Joe W. Gray and Paul T. Spellman and Bahram Parvin} } @article {85, title = {Multiscale iterative voting for differential analysis of stress response for 2D and 3D cell culture models}, journal = {J Microsc}, volume = {241}, year = {2011}, month = {2011 Mar}, pages = {315-26}, abstract = {

Three-dimensional (2D) cell culture models have emerged as the basis for improved cell systems biology. However, there is a gap in robust computational techniques for segmentation of these model systems that are imaged through confocal or deconvolution microscopy. The main issues are the volume of data, overlapping subcellular compartments and variation in scale or size of subcompartments of interest, which lead to ambiguities for quantitative analysis on a cell-by-cell basis. We address these ambiguities through a series of geometric operations that constrain the problem through iterative voting and decomposition strategies. The main contributions of this paper are to (i) extend the previously developed 2D radial voting to an efficient 3D implementation, (ii) demonstrate application of iterative radial voting at multiple subcellular and molecular scales, and (iii) investigate application of the proposed technology to two endpoints between 2D and 3D cell culture models. These endpoints correspond to kinetics of DNA damage repair as measured by phosphorylation of γH2AX, and the loss of the membrane-bound E-cadherin protein as a result of ionizing radiation. Preliminary results indicate little difference in the kinetics of the DNA damage protein between 2D and 3D cell culture models; however, differences between membrane-bound E-cadherin are more pronounced.

}, keywords = {Cell Culture Techniques, Cell Line, Computer-Assisted, Epithelial Cells, Humans, Image Processing, Microscopy, Organ Culture Techniques, Physiological, Stress}, issn = {1365-2818}, doi = {10.1111/j.1365-2818.2010.03442.x}, author = {Ju Han and Hang Chang and Yang, Q and Fontenay, G and Groesser, Torsten and Barcellos-Hoff, M Helen and Parvin, B} } @article {88, title = {Multidimensional profiling of cell surface proteins and nuclear markers}, journal = {IEEE/ACM Trans Comput Biol Bioinform}, volume = {7}, year = {2010}, month = {2010 Jan-Mar}, pages = {80-90}, abstract = {

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.

}, keywords = {Biological, Cadherins, Cell Membrane, Cell Nucleus, Computer Simulation, DNA, Gene Expression Profiling, Membrane Proteins, Models}, issn = {1557-9964}, doi = {10.1109/TCBB.2008.134}, author = {Ju Han and Hang Chang and Andarawewa, Kumari and Yaswen, Paul and Barcellos-Hoff, Mary Helen and Parvin, Bahram} } @conference {43, title = {Morphometric Subtyping for a Panel of Breast Cancer Cell Lines}, booktitle = {Proceedings of the 2009 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009}, year = {2009}, doi = {10.1109/ISBI.2009.5193168}, url = {http://dx.doi.org/10.1109/ISBI.2009.5193168}, author = {Ju Han and Hang Chang and Gerald Fontenay and Nicholas J. Wang and Joe W. Gray and Bahram Parvin} } @conference {47, title = {Integrated profiling of cell surface protein and nuclear marker for discriminant analysis}, booktitle = {Proceedings of the 2008 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14-17, 2008}, year = {2008}, doi = {10.1109/ISBI.2008.4541253}, url = {http://dx.doi.org/10.1109/ISBI.2008.4541253}, author = {Ju Han and Hang Chang and Kumari L. Andarawewa and Paul Yaswen and Mary Helen Barcellos-Hoff and Bahram Parvin} } @article {49, title = {Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events}, journal = {{IEEE} Trans. Image Processing}, volume = {16}, year = {2007}, pages = {615{\textendash}623}, doi = {10.1109/TIP.2007.891154}, url = {http://dx.doi.org/10.1109/TIP.2007.891154}, author = {Bahram Parvin and Qing Yang and Ju Han and Hang Chang and Bjorn Rydberg and Mary Helen Barcellos-Hoff} } @conference {53, title = {Perceptual Grouping of Membrane Signals in Cell-Based Assays}, booktitle = {Proceedings of the 2007 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Washington, DC, USA, April 12-16, 2007}, year = {2007}, doi = {10.1109/ISBI.2007.356906}, url = {http://dx.doi.org/10.1109/ISBI.2007.356906}, author = {Hang Chang and Kumari L. Andarawewa and Ju Han and Mary Helen Barcellos-Hoff and Bahram Parvin} } @conference {52, title = {Segmentation of Mammosphere Structures from Volumetric Data}, booktitle = {Proceedings of the 2007 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Washington, DC, USA, April 12-16, 2007}, year = {2007}, doi = {10.1109/ISBI.2007.356904}, url = {http://dx.doi.org/10.1109/ISBI.2007.356904}, author = {Ju Han and Hang Chang and Qing Yang and Mary Helen Barcellos-Hoff and Bahram Parvin} } @conference {55, title = {3D Segmentation of Mammospheres for Localization Studies}, booktitle = {Advances in Visual Computing, Second International Symposium, {ISVC} 2006, Lake Tahoe, NV, USA, November 6-8, 2006 Proceedings, Part {I}}, year = {2006}, doi = {10.1007/11919476_52}, url = {http://dx.doi.org/10.1007/11919476_52}, author = {Ju Han and Hang Chang and Qing Yang and Mary Helen Barcellos-Hoff and Bahram Parvin} }