@article {335, title = {Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning.}, journal = {Neuro Oncol}, year = {2022}, month = {2022 Jun 18}, abstract = {

BACKGROUND: Lower grade gliomas (LGG) are heterogenous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.

METHODS: Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5- year overall survival (OS) of LGG patients. Tumor mutational burden (TMB), and immune cell infiltration between subtypes were analyzed using the Mann-Whitney test. The double-blinded validation for important immunotherapy-related biomarkers were executed using immunohistochemistry (IHC).

RESULTS: We developed a machine learning pipeline to extract CMBs from whole slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multi-center cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by immunohistochemical staining. In addition, the subtypes learned from LGG demonstrates translational impact on glioblastoma (GBM).

CONCLUSIONS: We developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune micro-environment, prognosis, and treatment response.

}, issn = {1523-5866}, doi = {10.1093/neuonc/noac154}, author = {Liu, Xiao-Ping and Jin, Xiaoqing and Ahmadian, Saman Seyed and Yang, Xu and Tian, Su-Fang and Cai, Yu-Xiang and Chawla, Kuldeep and Snijders, Antoine M and Xia, Yankai and van Diest, Paul J and Weiss, William A and Mao, Jian-Hua and Li, Zhi-Qiang and Vogel, Hannes and Chang, Hang} } @article {332, title = {Prospective Study Reveals Host Microbial Determinants of Clinical Response to Fecal Microbiota Transplant Therapy in Type 2 Diabetes Patients.}, journal = {Front Cell Infect Microbiol}, volume = {12}, year = {2022}, month = {2022}, pages = {820367}, abstract = {

Background: Increasing evidence shows that alterations in gut microbiome (GM) contribute to the development of type 2 diabetes mellitus (T2DM), and fecal microbiota transplantation (FMT) successfully treats various human diseases. However, the benefits of FMT therapy to T2DM patients remain unknown.

Methods: We enrolled 17 patients with T2DM for nonblinded, one-armed intervention trial of FMT. A total of 20 healthy individuals were recruited as the baseline control. HbA1c\% and metabolic parameter change were evaluated in 17 T2DM patients 12 weeks after they received FMT from healthy donors. The GM composition was characterized by 16S rRNA gene amplicon sequencing from fecal samples prior to and 12 weeks after FMT treatment.

Results: We found that the GM of T2DM patients was reconstituted by FMT. We observed a statistically significant decrease in HbA1c\% (from 7.565 {\textpm} 0.148 to 7.190 {\textpm} 0.210, p\<0.01), blood glucose (from 8.483 {\textpm} 0.497 to 7.286 {\textpm} 0.454 mmol/L, p\<0.01), and uric acid (from 309.4 {\textpm} 21.5 to 259.1 {\textpm} 15.8 {\textmu}mol/L, p\<0.01) while a significant increase in postprandial C-peptide (from 4.503 {\textpm} 0.600 to 5.471 {\textpm} 0.728 ng/ml, p\<0.01) at 12 weeks after FMT. Closely evaluating the changes in these assays, we found individual variability in response to FMT treatment. Out of 17 T2DM patients, 11 were found to significantly improve T2DM symptoms. The FMT responders have significantly higher levels of the family and the genus (family ) in their pretreated fecal in comparison to nonresponders, which could predict the clinical response with an area under the curve of 0.83.

Conclusion: Our findings suggest that certain T2DM patients can potentially benefit from FMT, and the pretreated abundance of and in the fecal of patients may serve as potential biomarkers for selecting T2DM patients to receive FMT.

}, keywords = {Diabetes Mellitus, Type 2, Fecal Microbiota Transplantation, Feces, Glycated Hemoglobin A, Humans, Prospective Studies, RNA, Ribosomal, 16S, Treatment Outcome}, issn = {2235-2988}, doi = {10.3389/fcimb.2022.820367}, author = {Ding, Dafa and Yong, Huijuan and You, Na and Lu, Wei and Yang, Xu and Ye, Xiaolong and Wang, Yayun and Cai, Tingting and Zheng, Xiaoling and Chen, Hui and Cui, Bota and Zhang, Faming and Liu, Xingyin and Mao, Jian-Hua and Lu, Yibing and Chang, Hang} } @article {327, title = {Development and Validation of an Unsupervised Feature Learning System for Leukocyte Characterization and Classification: A Multi-Hospital Study}, volume = {129}, year = {2021}, month = {2021/06/01}, pages = {1837 - 1856}, abstract = {

The characterization and classification of white blood cells (WBC) are critical for the diagnosis of anemia, leukemia, and many other hematologic diseases. We developed WBC-Profiler, an unsupervised feature learning system for quantitative analysis of leukocytes. We demonstrate, through independent validation, that WBC-Profiler enables automatic extraction of complex and robust signatures from microscopic images without human-intervention and, thereafter, effective construction of interpretable leukocyte profiles, which decouples large scale complex leukocyte characterization from limitations in both human-based feature engineering/optimization and the end-to-end solutions provided by many modern deep neural networks. Further evaluation in a real-world clinical setting confirms that, compared with 23 clinicians from 8 hospitals (class-average-sensitivity, 0.798; class-average-specificity, 0.963; cell-average-timecost: 3.158\  s), WBC-Profiler performs with significantly improved accuracy and speed (class-average-sensitivity, 0.890; class-average-specificity, 0.980; cell-average-timecost: 0.375\  s). Our findings suggest that WBC-Profiler has the potential clinical implications.

}, isbn = {1573-1405}, url = {https://doi.org/10.1007/s11263-021-01449-9}, author = {Yan, Hong and Mao, Xuanyu and Yang, Xu and Xia, Yongquan and Wang, Chengbin and Wang, Junjun and Xia, Rui and Xu, Xuejing and Wang, Zhiqiang and Li, Zhiyang and Zhao, Xie and Li, Yan and Liu, Guoye and He, Li and Wang, Zhongyu and Wang, Zhiqiong and Li, Zhiqiang and Cai, Weidong and Shen, Han and Chang, Hang} } @article {323, title = {From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer.}, journal = {Front Oncol}, volume = {11}, year = {2021}, month = {2021}, pages = {819565}, abstract = {

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H\&E) micrographs of orthotopic -null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan-Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H\&E preparations can provide unbiased biological stratification to inform patient care.

}, issn = {2234-943X}, doi = {10.3389/fonc.2021.819565}, author = {Chang, Hang and Yang, Xu and Moore, Jade and Liu, Xiao-Ping and Jen, Kuang-Yu and Snijders, Antoine M and Ma, Lin and Chou, William and Corchado-Cobos, Roberto and Garc{\'\i}a-Sancha, Natalia and Mendiburu-Eli{\c c}abe, Marina and Perez-Losada, Jesus and Barcellos-Hoff, Mary Helen and Mao, Jian-Hua} } @article {325, title = {Host genetics and gut microbiota cooperatively contribute to azoxymethane-induced acute toxicity in Collaborative Cross mice.}, journal = {Arch Toxicol}, volume = {95}, year = {2021}, month = {2021 03}, pages = {949-958}, abstract = {

Azoxymethane (AOM) is a widely used carcinogen to study chemical-induced colorectal carcinogenesis and is an agent for studying fulminant hepatic failure. The inter-strain susceptibility to acute toxicity by AOM has been reported, but its association with host genetics or gut microbiota remains largely unexplored. Here a cohort of genetically diverse Collaborative Cross (CC) mice was used to assess the contribution of host genetics and the gut microbiome to AOM-induced acute toxicity. We observed variation in AOM-induced acute liver failure across CC strains. Quantitative trait loci (QTL) analysis revealed three chromosome regions significantly associated with AOM toxicity. Genes located within these QTL, including peroxisome proliferator-activated receptor alpha (Ppara), were enriched for enzyme activator and nucleoside-triphosphatase regulator activity. We further demonstrated that the protein level of PPARα in liver tissues from sensitive strains was remarkably lower compared to levels in resistant strains, consistent with protective role of PPAR family in liver injury. We discovered that the abundance levels of gut microbial families Anaeroplasmataceae, Ruminococcaceae, Lactobacillaceae, Akkermansiaceae and Clostridiaceae were significantly higher in the sensitive strains\ compared to the resistant strains. Using a random forest classifier method, we determined that the relative abundance levels of these microbial families predicted AOM toxicity with the area under the receiver-operating curve (AUC) of 0.75. Combining the three genetic loci and five microbial families increased the predictive accuracy of AOM toxicity (AUC of 0.99). Moreover, we found that Ruminococcaceae and Lactobacillaceae acted as mediators between host genetics and AOM toxicity. In conclusion, this study shows that host genetics and specific microbiome members play a critical role in AOM-induced acute toxicity, which provides a framework for analysis of the health effects from environmental toxicants.

}, keywords = {Animals, Azoxymethane, Carcinogens, Chemical and Drug Induced Liver Injury, Collaborative Cross Mice, Gastrointestinal Microbiome, Liver Failure, Acute, Male, Mice, Quantitative Trait Loci, Species Specificity}, issn = {1432-0738}, doi = {10.1007/s00204-021-02972-x}, author = {Zhong, Chenhan and He, Li and Lee, Sun-Young and Chang, Hang and Zhang, Yuqing and Threadgill, David W and Yuan, Ying and Zhou, Fuling and Celniker, Susan E and Xia, Yankai and Snijders, Antoine M and Mao, Jian-Hua} } @article {309, title = {Genetic and metabolic links between the murine microbiome and memory.}, journal = {Microbiome}, volume = {8}, year = {2020}, month = {2020 04 17}, pages = {53}, abstract = {

BACKGROUND: Recent evidence has linked the gut microbiome to host behavior via the gut-brain axis [1-3]; however, the underlying mechanisms remain unexplored. Here, we determined the links between host genetics, the gut microbiome and memory using the genetically defined Collaborative Cross (CC) mouse cohort, complemented with microbiome and metabolomic analyses in conventional and germ-free (GF) mice.

RESULTS: A genome-wide association analysis (GWAS) identified 715 of 76,080 single-nucleotide polymorphisms (SNPs) that were significantly associated with short-term memory using the passive avoidance model. The identified SNPs were enriched in genes known to be involved in learning and memory functions. By 16S rRNA gene sequencing of the gut microbial community in the same CC cohort, we identified specific microorganisms that were significantly correlated with longer latencies in our retention test, including a positive correlation with Lactobacillus. Inoculation of GF mice with individual species of Lactobacillus (L. reuteri F275, L. plantarum BDGP2 or L. brevis BDGP6) resulted in significantly improved memory compared to uninoculated or E. coli DH10B inoculated controls. Untargeted metabolomics analysis revealed significantly higher levels of several metabolites, including lactate, in the stools of Lactobacillus-colonized mice, when compared to GF control mice. Moreover, we demonstrate that dietary lactate treatment alone boosted memory in conventional mice. Mechanistically, we show that both inoculation with Lactobacillus or lactate treatment significantly increased the levels of the neurotransmitter, gamma-aminobutyric acid (GABA), in the hippocampus of the mice.

CONCLUSION: Together, this study provides new evidence for a link between Lactobacillus and memory and our results open possible new avenues for treating memory impairment disorders using specific gut microbial inoculants and/or metabolites. Video Abstract.

}, issn = {2049-2618}, doi = {10.1186/s40168-020-00817-w}, author = {Mao, Jian-Hua and Kim, Young-Mo and Zhou, Yan-Xia and Hu, Dehong and Zhong, Chenhan and Hang Chang and Brislawn, Colin J and Fansler, Sarah and Langley, Sasha and Wang, Yunshan and Peisl, B Y Loulou and Celniker, Susan E and Threadgill, David W and Wilmes, Paul and Orr, Galya and Metz, Thomas O and Jansson, Janet K and Snijders, Antoine M} } @article {310, title = {Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation}, journal = {Symmetry}, volume = {12}, year = {2020}, pages = {1230}, abstract = {

Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net, a novel, symmetric encoder{\textendash} decoder architecture with dynamic receptive field for medical image segmentation. We modify the Selective Kernel convolution (a module proposed in Selective Kernel Networks) by inserting a newly proposed Match operation, which makes similar features in different convolution branches have corresponding positions, and then we replace the U-Net{\textquoteright}s convolution with the redesigned Selective Kernel convolution. This network is a combination of U-Net and improved Selective Kernel convolution. It inherits the advantages of simple structure and low parameter complexity of U-Net, and enhances the efficiency of dynamic receptive field in Selective Kernel convolution, making it an ideal model for medical image segmentation tasks which often have small training data and large changes in targets size. Compared with state-of-the-art segmentation methods, the number of parameters in Match Feature U-Net (2.65 M) is 34\% of U-Net (7.76 M), 29\% of UNet++ (9.04 M), and 9.1\% of CE-Net (29 M). We evaluated the proposed architecture in four medical image segmentation tasks: nuclei segmentation in microscopy images, breast cancer cell segmentation, gland segmentation in colon histology images, and disc/cup segmentation. Our experimental results show that Match Feature U-Net achieves an average Mean Intersection over Union (MIoU) gain of 1.8, 1.45, and 2.82 points over U-Net, UNet++, and CE-Net, respectively.

}, issn = {2073-8994}, doi = {10.3390/sym12081230}, url = {https://www.mdpi.com/2073-8994/12/8/1230}, author = {Qin, Xiaofei and Wu, Chengzi and Hang Chang and Lu, Hao and Zhang, Xuedian} } @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 {300, title = {High-Dimensional Phenotyping Identifies Age-Emergent Cells in Human Mammary Epithelia.}, journal = {Cell Rep}, volume = {23}, year = {2018}, month = {2018 Apr 24}, pages = {1205-1219}, abstract = {

Aging is associated with tissue-level changes in\ cellular composition that are correlated with increased susceptibility to disease. Aging human mammary tissue shows skewed progenitor cell potency, resulting in diminished tumor-suppressive cell types and the accumulation of defective epithelial progenitors. Quantitative characterization of these age-emergent human cell subpopulations is lacking, impeding our understanding of the relationship between age and cancer susceptibility. We conducted single-cell resolution proteomic phenotyping of healthy breast epithelia from 57 women, aged 16-91 years, using mass cytometry. Remarkable heterogeneity was quantified within the two mammary epithelial lineages. Population partitioning identified a subset of aberrant basal-like luminal cells that accumulate with age and originate from age-altered progenitors. Quantification of age-emergent phenotypes\ enabled robust classification of breast tissues by age in healthy women. This high-resolution mapping highlighted specific epithelial subpopulations that\ change with age in a manner consistent with increased susceptibility to breast cancer.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2018.03.114}, author = {Pelissier Vatter, Fanny A and Schapiro, Denis and Hang Chang and Borowsky, Alexander D and Lee, Jonathan K and Parvin, Bahram and Stampfer, Martha R and LaBarge, Mark A and Bodenmiller, Bernd and Lorens, James B} } @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, Cell Transformation, Coculture Techniques, Epithelial Cells, Experimental, Extracellular Matrix, Extracellular Signal-Regulated MAP Kinases, Female, Fibroblasts, Humans, Lung Neoplasms, Mammary Neoplasms, Neoplasm Metastasis, Neoplastic, Phenotype, Proto-Oncogene Proteins c-jun, rho GTP-Binding Proteins, Signal Transduction, Transforming Growth Factor beta, Tumor}, 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} } @article {205, title = {FAM83D promotes cell proliferation and motility by downregulating tumor suppressor gene FBXW7.}, journal = {Oncotarget}, volume = {4}, year = {2013}, month = {2013 Dec}, pages = {2476-86}, abstract = {

Amplification of chromosome 20q is frequently found in various types of human cancers, including breast cancer. The list of candidate oncogenes in 20q has expanded over the past decade. Here, we investigate whether FAM83D (family with sequence similarity 83, member D) on chromosome 20q plays any role in breast cancer development. The expression level of FAM83D is significantly elevated in breast cancer cell lines and primary human breast cancers. High expression levels of FAM83D are significantly associated with poor clinical outcome and distant metastasis in breast cancer patients. We show that ectopic expression of FAM83D in human mammary epithelial cells promotes cell proliferation, migration and invasion along with epithelial-mesenchymal transition (EMT). Ablation of FAM83D in breast cancer cells induces apoptosis and consequently inhibits cell proliferation and colony formation. Mechanistic studies reveal that overexpression of FAM83D downregulates FBXW7 expression levels through a physical interaction, which results in elevated protein levels of oncogenic substrates downstream to FBXW7, such as mTOR, whose inhibition by rapamycin can suppress FAM83D-induced cell migration and invasion. The results demonstrate that FAM83D has prognostic value for breast cancer patients and is a novel oncogene in breast cancer development that at least in part acts through mTOR hyper-activation by inhibiting FBXW7.

}, keywords = {Apoptosis, Breast Neoplasms, Cell Cycle Proteins, Cell Growth Processes, Cell Line, Chromosomal Proteins, Down-Regulation, Epithelial-Mesenchymal Transition, F-Box Proteins, Genes, Humans, Microtubule-Associated Proteins, Non-Histone, Prognosis, Transfection, Tumor, Tumor Suppressor, Ubiquitin-Protein Ligases}, issn = {1949-2553}, doi = {10.18632/oncotarget.1581}, author = {Wang, Zeran and Liu, Yueyong and Zhang, Pengju and Zhang, Weiguo and Wang, Weijing and Curr, Kenneth and Wei, Guangwei and Jiang-Hua Mao} } @article {204, title = {Hematein, a casein kinase II inhibitor, inhibits lung cancer tumor growth in a murine xenograft model.}, journal = {Int J Oncol}, volume = {43}, year = {2013}, month = {2013 Nov}, pages = {1517-22}, abstract = {

Casein kinase II (CK2) inhibitors suppress cancer cell growth. In this study, we examined the inhibitory effects of a novel CK2 inhibitor, hematein, on tumor growth in a murine xenograft model. We found that in lung cancer cells, hematein inhibited cancer cell growth, Akt/PKB Ser129 phosphorylation, the Wnt/TCF pathway and increased apoptosis. In a murine xenograft model of lung cancer, hematein inhibited tumor growth without significant toxicity to the mice tested. Molecular docking showed that hematein binds to CK2α in durable binding sites. Collectively, our results suggest that hematein is an allosteric inhibitor of protein kinase CK2 and has antitumor activity to lung cancer.

}, keywords = {Animals, Apoptosis, Blotting, Casein Kinase II, Cultured, Enzyme Inhibitors, Female, Hematoxylin, Humans, Immunoenzyme Techniques, Inbred BALB C, Lung Neoplasms, Mice, Phosphorylation, Proto-Oncogene Proteins c-akt, T Cell Transcription Factor 1, Tumor Cells, Western, Wnt Proteins, Xenograft Model Antitumor Assays}, issn = {1791-2423}, doi = {10.3892/ijo.2013.2087}, author = {Hung, Ming-Szu and Xu, Zhidong and Chen, Yu and Smith, Emmanuel and Jiang-Hua Mao and Hsieh, David and Lin, Yu-Ching and Yang, Cheng-Ta and Jablons, David M and You, Liang} } @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} } @article {194, title = {Mass spectrometry imaging for in situ kinetic histochemistry.}, journal = {Sci Rep}, volume = {3}, year = {2013}, month = {2013}, pages = {1656}, abstract = {

Tissues are composed of diverse cell subpopulations each with distinct metabolic characteristics that influence overall behavior. Unfortunately, traditional histopathology imaging techniques are {\textquoteright}blind{\textquoteright} to the spatially ordered metabolic dynamics within tissue. While mass spectrometry imaging enables spatial mapping of molecular composition, resulting images are only a static snapshot in time of molecules involved in highly dynamic processes; kinetic information of flux through metabolic pathways is lacking. To address this limitation, we developed kinetic mass spectrometry imaging (kMSI), a novel technique integrating soft desorption/ionization mass spectrometry with clinically accepted in vivo metabolic labeling of tissue with deuterium to generate images of kinetic information of biological processes. Applied to a tumor, kMSI revealed heterogeneous spatial distributions of newly synthesized versus pre-existing lipids, with altered lipid synthesis patterns distinguishing region-specific intratumor subpopulations. Images also enabled identification and correlation of metabolic activity of specific lipids found in tumor regions of varying grade.

}, keywords = {Cell Line, Equipment Design, Equipment Failure Analysis, Experimental, Humans, Lipids, Mass Spectrometry, Molecular Imaging, Neoplasms, Tumor}, issn = {2045-2322}, doi = {10.1038/srep01656}, author = {Louie, Katherine B and Bowen, Benjamin P and McAlhany, Stephanie and Huang, Yurong and Price, John C and Jiang-Hua Mao and Hellerstein, Marc and Northen, Trent R} } @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} } @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} } @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 {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 {87, title = {Molecular predictors of 3D morphogenesis by breast cancer cell lines in 3D culture}, journal = {PLoS Comput Biol}, volume = {6}, year = {2010}, month = {2010 Feb}, pages = {e1000684}, abstract = {

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associations with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPARgamma has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPARgamma has been validated through two supporting biological assays.

}, keywords = {Biological, Biomarkers, Breast Neoplasms, Cell Culture Techniques, Cell Line, Computer-Assisted, ErbB-2, Female, Gene Expression Profiling, Histocytochemistry, Humans, Image Processing, Models, Phenotype, PPAR gamma, Receptor, Reproducibility of Results, Tumor}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000684}, author = {Han, Ju and Hang Chang and Giricz, Orsi and Lee, Genee Y and Baehner, Frederick L and Gray, Joe W and Bissell, Mina J and Kenny, Paraic A and Parvin, Bahram} } @article {296, title = {Active mask segmentation of fluorescence microscope images.}, journal = {IEEE Trans Image Process}, volume = {18}, year = {2009}, month = {2009 Aug}, pages = {1817-29}, abstract = {

We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.

}, keywords = {Algorithms, Automated, Computer-Assisted, Cytological Techniques, Fluorescence, HeLa Cells, Humans, Image Processing, Microscopy, Pattern Recognition}, issn = {1057-7149}, doi = {10.1109/TIP.2009.2021081}, author = {Srinivasa, Gowri and Fickus, Matthew C and Yusong Guo and Linstedt, Adam D and Kovacevi{\'c}, Jelena} }