@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 {102, title = {Phenotypic Characterization of Breast Invasive Carcinoma via Transferable Tissue Morphometric Patterns learned from Glioblastoma Multiforme.}, journal = {Proc IEEE Int Symp Biomed Imaging}, volume = {2016}, year = {2016}, month = {2016 Apr}, pages = {1025-1028}, abstract = {

Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG.

}, issn = {1945-7928}, doi = {10.1109/ISBI.2016.7493440}, author = {Han, Ju and Fontenay, Gerald V and Wang, Yunfu and Jiang-Hua Mao and Hang Chang} } @article {241, title = {Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.}, journal = {Sci Rep}, volume = {6}, year = {2016}, month = {2016}, pages = {28987}, abstract = {

The effects of the stiffness of the microenvironment on the molecular response of 3D colony organization, at the maximum level of mammographic density (MD), are investigated. Phenotypic profiling reveals that 3D colony formation is heterogeneous and increased stiffness of the microenvironment, within the range of the MD, correlates with the increased frequency of aberrant 3D colony formation. Further integrative analysis of the genome-wide transcriptome and phenotypic profiling hypothesizes overexpression of ERBB2 in the premalignant MCF10A cell lines at a stiffness value that corresponds to the collagen component at high mammographic density. Subsequently, ERBB2 overexpression has been validated in the same cell line. Similar experiments with a more genetically stable cell line of 184A1 also revealed an increased frequency of aberrant colony formation with the increased stiffness; however, 184A1 did not demonstrate overexpression of ERBB2 at the same stiffness value of the high MD. These results suggest that stiffness exacerbates premalignant cell line of MCF10A.

}, issn = {2045-2322}, doi = {10.1038/srep28987}, author = {Cheng, Qingsu and Bilgin, Cemal Cagatay and Fonteney, Gerald and Hang Chang and Henderson, Matthew and Han, Ju and Parvin, Bahram} } @article {242, title = {When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.}, journal = {Med Image Anal}, volume = {35}, year = {2016}, month = {2016 Sep 9}, pages = {530-543}, abstract = {

Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).

}, issn = {1361-8423}, doi = {10.1016/j.media.2016.08.010}, author = {Zhong, Cheng and Han, Ju and Borowsky, Alexander and Parvin, Bahram and Wang, Yunfu and Hang Chang} } @article {22, title = {Coupled segmentation of nuclear and membrane-bound macromolecules through voting and multiphase level set}, journal = {Pattern Recognition}, volume = {48}, year = {2015}, pages = {882{\textendash}893}, doi = {10.1016/j.patcog.2014.10.005}, url = {http://dx.doi.org/10.1016/j.patcog.2014.10.005}, author = {Hang Chang and Quan Wen and Bahram Parvin} } @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} } @article {90, title = {Methods for delineating cellular regions and classifying regions of histopathology and microanatomy}, number = {US 14/493,208}, year = {2015}, url = {http://www.google.com/patents/US20150110381}, author = {Parvin, B. and Hang Chang and Zhou, Y.} } @conference {24, title = {Nuclei segmentation via sparsity constrained convolutional regression}, booktitle = {12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015}, year = {2015}, doi = {10.1109/ISBI.2015.7164109}, url = {http://dx.doi.org/10.1109/ISBI.2015.7164109}, author = {Yin Zhou and Hang Chang and Kenneth E. Barner and Bahram Parvin} } @conference {23, title = {Predictive sparse morphometric context for classification of histology sections}, booktitle = {12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015}, year = {2015}, doi = {10.1109/ISBI.2015.7164040}, url = {http://dx.doi.org/10.1109/ISBI.2015.7164040}, author = {Hang Chang and Paul T. Spellman and Bahram Parvin} } @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 {19, title = {Stacked Predictive Sparse Decomposition for Classification of Histology Sections}, journal = {International Journal of Computer Vision}, volume = {113}, year = {2015}, pages = {3{\textendash}18}, doi = {10.1007/s11263-014-0790-9}, url = {http://dx.doi.org/10.1007/s11263-014-0790-9}, author = {Hang Chang and Yin Zhou and Alexander Borowsky and Kenneth E. Barner and Paul T. Spellman and Bahram Parvin} } @conference {25, title = {Classification of Histology Sections via Multispectral Convolutional Sparse Coding}, booktitle = {2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014}, year = {2014}, doi = {10.1109/CVPR.2014.394}, url = {http://dx.doi.org/10.1109/CVPR.2014.394}, author = {Yin Zhou and Hang Chang and Kenneth E. Barner and Paul T. Spellman and Bahram Parvin} } @article {76, title = {Stress signaling from human mammary epithelial cells contributes to phenotypes of mammographic density}, journal = {Cancer Res}, volume = {74}, year = {2014}, month = {2014 Sep 15}, pages = {5032-44}, abstract = {

Telomere malfunction and other types of DNA damage induce an activin A-dependent stress response in mortal nontumorigenic human mammary epithelial cells that subsequently induces desmoplastic-like phenotypes in neighboring fibroblasts. Some characteristics of this fibroblast/stromal response, such as reduced adipocytes and increased extracellular matrix content, are observed not only in tumor tissues but also in disease-free breast tissues at high risk for developing cancer, especially high mammographic density tissues. We found that these phenotypes are induced by repression of the fatty acid translocase CD36, which is seen in desmoplastic and disease-free high mammographic density tissues. In this study, we show that epithelial cells from high mammographic density tissues have more DNA damage signaling, shorter telomeres, increased activin A secretion and an altered DNA damage response compared with epithelial cells from low mammographic density tissues. Strikingly, both telomere malfunction and activin A expression in epithelial cells can repress CD36 expression in adjacent fibroblasts. These results provide new insights into how high mammographic density arises and why it is associated with breast cancer risk, with implications for the definition of novel invention targets (e.g., activin A and CD36) to prevent breast cancer.

}, keywords = {Antigens, CD36, Breast Neoplasms, DNA Damage, Epithelial Cells, Female, Humans, Mammary Glands, Human, Phenotype, Signal Transduction}, issn = {1538-7445}, doi = {10.1158/0008-5472.CAN-13-3390}, author = {DeFilippis, Rosa Anna and Fordyce, Colleen and Patten, Kelley and Hang Chang and Zhao, Jianxin and Fontenay, Gerald V and Kerlikowske, Karla and Parvin, Bahram and Tlsty, Thea D} } @article {81, title = {Breast fibroblasts modulate early dissemination, tumorigenesis, and metastasis through alteration of extracellular matrix characteristics}, journal = {Neoplasia}, volume = {15}, year = {2013}, month = {2013 Mar}, pages = {249-62}, abstract = {

A wealth of evidence has now demonstrated that the microenvironment in which a tumorigenic cell evolves is as critical to its evolution as the genetic mutations it accrues. However, there is still relatively little known about how signals from the microenvironment contribute to the early events in the progression to malignancy. To address this question, we used a premalignant mammary model to examine how fibroblasts, and the extracellular matrix (ECM) proteins they secrete, influence progression to malignancy. Their effect on metastatic malignant cells was also assessed for comparison. We found that carcinoma-associated fibroblasts, and the distinct aligned ECM they deposit, can cause both premalignant and malignant mammary epithelial cells to assume a mesenchymal morphology that is associated with increased dissemination and metastasis, while benign reduction mammoplasty fibroblasts favor the maintenance of an epithelial morphology and constrain early dissemination, tumor growth, and metastasis. Our results suggest that normalizing the organization of the ECM could be effective in limiting systemic dissemination and tumor growth.

}, keywords = {Animals, Breast, Cell Line, Tumor, Cell Transformation, Neoplastic, Coculture Techniques, Epithelial Cells, Extracellular Matrix, Extracellular Signal-Regulated MAP Kinases, Female, Fibroblasts, Humans, Lung Neoplasms, Mammary Neoplasms, Experimental, Neoplasm Metastasis, Phenotype, Proto-Oncogene Proteins c-jun, rho GTP-Binding Proteins, Signal Transduction, Transforming Growth Factor beta}, issn = {1476-5586}, author = {Dumont, Nancy and Liu, Bob and DeFilippis, Rosa Anna and Hang Chang and Rabban, Joseph T and Karnezis, Anthony N and Tjoe, Judy A and Marx, James and Parvin, Bahram and Tlsty, Thea D} } @conference {30, title = {Characterization of Tissue Histopathology via Predictive Sparse Decomposition and Spatial Pyramid Matching}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - {MICCAI} 2013 - 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part {II}}, year = {2013}, doi = {10.1007/978-3-642-40763-5_12}, url = {http://dx.doi.org/10.1007/978-3-642-40763-5_12}, author = {Hang Chang and Nandita Nayak and Paul T. Spellman and Bahram Parvin} } @conference {27, title = {Classification of Tumor Histology via Morphometric Context}, booktitle = {2013 {IEEE} Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013}, year = {2013}, doi = {10.1109/CVPR.2013.286}, url = {http://dx.doi.org/10.1109/CVPR.2013.286}, author = {Hang Chang and Alexander Borowsky and Paul T. Spellman and Bahram Parvin} } @conference {29, title = {Classification of tumor histopathology via sparse feature learning}, booktitle = {{ISBI}}, year = {2013}, doi = {10.1109/ISBI.2013.6556499}, url = {http://dx.doi.org/10.1109/ISBI.2013.6556499}, author = {Nandita Nayak and Hang Chang and Alexander Borowsky and Paul T. Spellman and Bahram Parvin} } @article {26, title = {Integrated profiling of three dimensional cell culture models and 3D microscopy}, journal = {Bioinformatics}, volume = {29}, year = {2013}, pages = {3087{\textendash}3093}, doi = {10.1093/bioinformatics/btt535}, url = {http://dx.doi.org/10.1093/bioinformatics/btt535}, author = {Cemal Cagatay Bilgin and Sun Kim and Elle Leung and Hang Chang and Bahram Parvin} } @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} } @inbook {74, title = {Molecular Correlates of Morphometric Subtypes in Glioblastoma Multiforme}, booktitle = {Computational Systems Biology: From Molecular Mechanisms to Disease: Second Edition}, year = {2013}, pages = {423{\textendash}454}, publisher = {Elsevier Inc.}, organization = {Elsevier Inc.}, keywords = {Glioblastoma multiforme, Histology classification, Molecular pathology, Morphometric subtyping, Nuclear segmentation, Tumor heterogeneity, Tumor histopathology}, isbn = {9780124059269}, doi = {10.1016/B978-0-12-405926-9.00018-6}, author = {Hang Chang and Gerald Fontenay and Cemal Cagatay Bilgin and Alexander Borowsky and Paul Spellman and Bahram Parvin} } @conference {28, title = {Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology}, booktitle = {{IEEE} International Conference on Computer Vision, {ICCV} 2013, Sydney, Australia, December 1-8, 2013}, year = {2013}, doi = {10.1109/ICCV.2013.28}, url = {http://dx.doi.org/10.1109/ICCV.2013.28}, author = {Hang Chang and Yin Zhou and Paul T. Spellman and Bahram Parvin} } @conference {31, title = {Automatic segmentation and quantification of filamentous structures in electron tomography}, booktitle = {{ACM} International Conference on Bioinformatics, Computational Biology and Biomedicine, BCB{\textquoteright} 12, Orlando, FL, {USA} - October 08 - 10, 2012}, year = {2012}, doi = {10.1145/2382936.2382958}, url = {http://doi.acm.org/10.1145/2382936.2382958}, author = {Leandro A. Loss and George Bebis and Hang Chang and Manfred Auer and Purbasha Sarkar and Bahram Parvin} } @conference {32, title = {Batch-invariant nuclear segmentation in whole mount histology sections}, 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.2012.6235683}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235683}, author = {Hang Chang and Leandro A. Loss and Paul T. Spellman and Alexander Borowsky and Bahram Parvin} } @article {82, title = {CD36 repression activates a multicellular stromal program shared by high mammographic density and tumor tissues}, journal = {Cancer Discov}, volume = {2}, year = {2012}, month = {2012 Sep}, pages = {826-39}, abstract = {

UNLABELLED: Although high mammographic density is considered one of the strongest risk factors for invasive breast cancer, the genes involved in modulating this clinical feature are unknown. Tissues of high mammographic density share key histologic features with stromal components within malignant lesions of tumor tissues, specifically low adipocyte and high extracellular matrix (ECM) content. We show that CD36, a transmembrane receptor that coordinately modulates multiple protumorigenic phenotypes, including adipocyte differentiation, angiogenesis, cell-ECM interactions, and immune signaling, is greatly repressed in multiple cell types of disease-free stroma associated with high mammographic density and tumor stroma. Using both in vitro and in vivo assays, we show that CD36 repression is necessary and sufficient to recapitulate the above-mentioned phenotypes observed in high mammographic density and tumor tissues. Consistent with a functional role for this coordinated program in tumorigenesis, we observe that clinical outcomes are strongly associated with CD36 expression.

SIGNIFICANCE: CD36 simultaneously controls adipocyte content and matrix accumulation and is coordinately repressed in multiple cell types within tumor and high mammographic density stroma, suggesting that activation of this stromal program is an early event in tumorigenesis. Levels of CD36 and extent of mammographic density are both modifiable factors that provide potential for intervention.

}, keywords = {Adipocytes, Animals, Antigens, CD36, Breast Neoplasms, Cell Differentiation, Female, Humans, Mammography, Mice, Mice, Knockout, Risk Factors, Signal Transduction, Stromal Cells}, issn = {2159-8290}, doi = {10.1158/2159-8290.CD-12-0107}, author = {DeFilippis, Rosa Anna and Hang Chang and Dumont, Nancy and Rabban, Joseph T and Chen, Yunn-Yi and Fontenay, Gerald V and Berman, Hal K and Gauthier, Mona L and Zhao, Jianxin and Hu, Donglei and Marx, James J and Tjoe, Judy A and Ziv, Elad and Febbraio, Maria and Kerlikowske, Karla and Parvin, Bahram and Tlsty, Thea D} } @conference {33, title = {Detection of 3D filamentous networks from tomographic electron microscopy}, 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.2012.6235826}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235826}, author = {Leandro A. Loss and Hang Chang and Purbasha Sarkar and Manfred Auer 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 {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, Epithelial Cells, Humans, Image Processing, Computer-Assisted, Microscopy, Organ Culture Techniques, Stress, Physiological}, 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 {84, title = {Persistence of γ-H2AX and 53BP1 foci in proliferating and non-proliferating human mammary epithelial cells after exposure to γ-rays or iron ions}, journal = {Int J Radiat Biol}, volume = {87}, year = {2011}, month = {2011 Jul}, pages = {696-710}, abstract = {

PURPOSE: To investigate γ-H2AX (phosphorylated histone H2AX) and 53BP1 (tumour protein 53 binding protein No. 1) foci formation and removal in proliferating and non-proliferating human mammary epithelial cells (HMEC) after exposure to sparsely and densely ionising radiation under different cell culture conditions.

MATERIAL AND METHODS: HMEC cells were grown either as monolayers (2D) or in extracellular matrix to allow the formation of acinar structures in vitro (3D). Foci numbers were quantified by image analysis at various time points after exposure.

RESULTS: Our results reveal that in non-proliferating cells under 2D and 3D cell culture conditions, iron-ion induced γ-H2AX foci were still present at 72 h after exposure, although 53BP1 foci returned to control levels at 48 h. In contrast in proliferating HMEC, both γ-H2AX and 53BP1 foci decreased to control levels during the 24-48 h time interval after irradiation under 2D conditions. Foci numbers decreased faster after γ-ray irradiation and returned to control levels by 12 h regardless of marker, cell proliferation status, and cell culture condition.

CONCLUSIONS: The disappearance of radiation-induced γ-H2AX and 53BP1 foci in HMEC has different dynamics that depend on radiation quality and proliferation status. Notably, the general patterns do not depend on the cell culture condition (2D versus 3D). We speculate that the persistent γ-H2AX foci in iron-ion irradiated non-proliferating cells could be due to limited availability of double-strand break (DSB) repair pathways in G0/G1-phase, or that repair of complex DSB requires replication or chromatin remodelling.

}, keywords = {Cell Line, Cell Proliferation, Gamma Rays, Heavy Ions, Histones, Humans, Iron, Mammary Glands, Human, Tumor Suppressor Protein p53}, issn = {1362-3095}, doi = {10.3109/09553002.2010.549535}, author = {Groesser, Torsten and Hang Chang and Gerald Fontenay and Chen, James and Costes, Sylvain V and Helen Barcellos-Hoff, Mary and Parvin, Bahram and Rydberg, Bjorn} } @article {38, title = {Linking Changes in Epithelial Morphogenesis to Cancer Mutations Using Computational Modeling}, journal = {PLoS Computational Biology}, volume = {6}, year = {2010}, doi = {10.1371/journal.pcbi.1000900}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000900}, author = {Katarzyna A. Rejniak and Shizhen E. Wang and Nicole S. Bryce and Hang Chang and Bahram Parvin and Jerome Jourquin and Lourdes Estrada and Joe W. Gray and Carlos L. Arteaga and Alissa M. Weaver and Vito Quaranta and Alexander R. A. Anderson} } @article {86, title = {Linking changes in epithelial morphogenesis to cancer mutations using computational modeling}, journal = {PLoS Comput Biol}, volume = {6}, year = {2010}, month = {2010}, abstract = {

Most tumors arise from epithelial tissues, such as mammary glands and lobules, and their initiation is associated with the disruption of a finely defined epithelial architecture. Progression from intraductal to invasive tumors is related to genetic mutations that occur at a subcellular level but manifest themselves as functional and morphological changes at the cellular and tissue scales, respectively. Elevated proliferation and loss of epithelial polarization are the two most noticeable changes in cell phenotypes during this process. As a result, many three-dimensional cultures of tumorigenic clones show highly aberrant morphologies when compared to regular epithelial monolayers enclosing the hollow lumen (acini). In order to shed light on phenotypic changes associated with tumor cells, we applied the bio-mechanical IBCell model of normal epithelial morphogenesis quantitatively matched to data acquired from the non-tumorigenic human mammary cell line, MCF10A. We then used a high-throughput simulation study to reveal how modifications in model parameters influence changes in the simulated architecture. Three parameters have been considered in our study, which define cell sensitivity to proliferative, apoptotic and cell-ECM adhesive cues. By mapping experimental morphologies of four MCF10A-derived cell lines carrying different oncogenic mutations onto the model parameter space, we identified changes in cellular processes potentially underlying structural modifications of these mutants. As a case study, we focused on MCF10A cells expressing an oncogenic mutant HER2-YVMA to quantitatively assess changes in cell doubling time, cell apoptotic rate, and cell sensitivity to ECM accumulation when compared to the parental non-tumorigenic cell line. By mapping in vitro mutant morphologies onto in silico ones we have generated a means of linking the morphological and molecular scales via computational modeling. Thus, IBCell in combination with 3D acini cultures can form a computational/experimental platform for suggesting the relationship between the histopathology of neoplastic lesions and their underlying molecular defects.

}, keywords = {Apoptosis, Breast Neoplasms, Cell Proliferation, Computer Simulation, Epithelium, Extracellular Matrix, Female, Humans, Mammary Glands, Human, Models, Biological, Morphogenesis, Mutation, Receptor, ErbB-2}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000900}, author = {Rejniak, Katarzyna A and Wang, Shizhen E and Bryce, Nicole S and Hang Chang and Parvin, Bahram and Jourquin, Jerome and Estrada, Lourdes and Gray, Joe W and Arteaga, Carlos L and Weaver, Alissa M and Quaranta, Vito and Anderson, Alexander R A} } @article {37, title = {Molecular Predictors of 3D Morphogenesis by Breast Cancer Cell Lines in 3D Culture}, journal = {PLoS Computational Biology}, volume = {6}, year = {2010}, doi = {10.1371/journal.pcbi.1000684}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000684}, author = {Ju Han and Hang Chang and Orsi Giricz and Genee Y. Lee and Frederick L. Baehner and Joe W. Gray and Mina J. Bissell and Paraic A. Kenny and Bahram Parvin} } @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 = {Cadherins, Cell Membrane, Cell Nucleus, Computer Simulation, DNA, Gene Expression Profiling, Membrane Proteins, Models, Biological}, 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 {40, title = {Multiphase level set for automated delineation of membrane-bound macromolecules}, booktitle = {Proceedings of the 2010 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands, 14-17 April, 2010}, year = {2010}, doi = {10.1109/ISBI.2010.5490389}, url = {http://dx.doi.org/10.1109/ISBI.2010.5490389}, author = {Hang Chang and Bahram Parvin} } @conference {42, title = {A Delaunay Triangulation Approach for Segmenting Clumps of Nuclei}, 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.2009.5192970}, url = {http://dx.doi.org/10.1109/ISBI.2009.5192970}, author = {Quan Wen and Hang Chang and Bahram Parvin} } @article {41, title = {Graphical methods for quantifying macromolecules through bright field imaging}, journal = {Bioinformatics}, volume = {25}, year = {2009}, pages = {1070{\textendash}1075}, doi = {10.1093/bioinformatics/btn426}, url = {http://dx.doi.org/10.1093/bioinformatics/btn426}, author = {Hang Chang and Rosa Anna DeFilippis and Thea D. Tlsty and Bahram Parvin} } @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 {44, title = {Structural Annotation of {EM} Images by Graph Cut}, 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.2009.5193249}, url = {http://dx.doi.org/10.1109/ISBI.2009.5193249}, author = {Hang Chang and Manfred Auer and Bahram Parvin} } @conference {45, title = {A Bayesian approach for image segmentation with shape priors}, booktitle = {2008 {IEEE} Computer Society Conference on Computer Vision and Pattern Recognition {(CVPR} 2008), 24-26 June 2008, Anchorage, Alaska, {USA}}, year = {2008}, doi = {10.1109/CVPR.2008.4587430}, url = {http://dx.doi.org/10.1109/CVPR.2008.4587430}, author = {Hang Chang and Qing Yang 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} } @conference {46, title = {Scoring histological sections through immunohistochemistry}, 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.2008.4541003}, url = {http://dx.doi.org/10.1109/ISBI.2008.4541003}, author = {Hang Chang and Rosa Anna DeFilippis and Thea D. Tlsty and Bahrain 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 {50, title = {Modeling of Front Evolution with Graph Cut Optimization}, booktitle = {Proceedings of the International Conference on Image Processing, {ICIP} 2007, September 16-19, 2007, San Antonio, Texas, {USA}}, year = {2007}, doi = {10.1109/ICIP.2007.4378936}, url = {http://dx.doi.org/10.1109/ICIP.2007.4378936}, author = {Hang Chang and Qing Yang and Manfred Auer and Bahram Parvin} } @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 {51, title = {Quantitative Representation of Three-dimensional Cell Culture Models}, 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.2007.356795}, url = {http://dx.doi.org/10.1109/ISBI.2007.356795}, author = {Hang Chang and Catherine Park and Bahram Parvin} } @article {48, title = {Segmentation of heterogeneous blob objects through voting and level set formulation}, journal = {Pattern Recognition Letters}, volume = {28}, year = {2007}, pages = {1781{\textendash}1787}, doi = {10.1016/j.patrec.2007.05.008}, url = {http://dx.doi.org/10.1016/j.patrec.2007.05.008}, author = {Hang Chang and Qing Yang 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} } @conference {54, title = {An Iterative Bayesian Approach for Digital Matting}, booktitle = {18th International Conference on Pattern Recognition {(ICPR} 2006), 20-24 August 2006, Hong Kong, China}, year = {2006}, doi = {10.1109/ICPR.2006.259}, url = {http://dx.doi.org/10.1109/ICPR.2006.259}, author = {Hang Chang and Qing Yang and Chunhong Pan} } @conference {56, title = {Segmentation of Three Dimensional Cell Culture Models from a Single Focal Plane}, booktitle = {Advances in Visual Computing, Second International Symposium, {ISVC} 2006 Lake Tahoe, NV, USA, November 6-8, 2006. Proceedings, Part {II}}, year = {2006}, doi = {10.1007/11919629_59}, url = {http://dx.doi.org/10.1007/11919629_59}, author = {Hang Chang and Bahram Parvin} }