@article {307, title = {Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles.}, journal = {mSphere}, volume = {5}, year = {2020}, month = {2020 01 08}, abstract = {

The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended. The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile.

}, issn = {2379-5042}, doi = {10.1128/mSphere.00763-19}, author = {Liang, Yali and Dong, Tianyu and Chen, Minjian and He, Lianping and Wang, Tingzhang and Liu, Xingyin and Hang Chang and Mao, Jian-Hua and Hang, Bo and Snijders, Antoine M and Xia, Yankai} } @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 {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} } @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, Breast Neoplasms, CD36, DNA Damage, Epithelial Cells, Female, Human, Humans, Mammary Glands, 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} } @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 {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 {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 {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 {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} }