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Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Triple Negative Breast Cancer (TNBC), indicated by the absence of estrogen, progesterone and human epidermal growth factor receptor 2 (HER2), is the most aggressive form of breast cancer characterized by high rates of metastasis and low survival. Among those diagnosed with TNBC, 34% contain Inhibitor of Growth 4 (ING4) deletion that is associated with poor patient outcomes. We previously showed that ING4 negatively regulates NF-B in breast cancer. Previous studies show parthenolide, a compound found in feverfew (Tanacetum parthenium) to inhibit NF-B in cervical and gastric cancer. We hypothesized that parthenolide inhibits cytokine-induced activation of NF-B in ING4 deficient TNBC cells. To test the hypothesis, previously established vectors, v2, ING4 wildtype and v2h1, ING4-deleted were synthesized in MDA-MB 231, a TNBC cell line, using a CRISPR/Cas9 system. Inflammatory cytokines, IL-1 and TNF, were tested in ING4 wildtype or ING4 deleted cells for elicited phosphorylation of NF-B, proliferation, and migration in the presence or absence of parthenolide. The results showed that TNF or IL-1 induced translocation phosphorylation of NF-B regardless of ING4 deletion. ING4 inhibited proinflammatory cytokine induced pp65, consistent with previous studies demonstrating the negative regulation of NF-B in ING4-sufficent cell lines. We found the optimal working dose of parthenolide, 100nM, had no effect on cell proliferation in the presence or absence of IL-1. Parthenolide inhibited IL-1induced phosphorylation of NF-B regardless of ING4 deletion. Parthenolide inhibited TNF-induced phosphorylation of NF-B in ING4-deleted cell lines. Moreover, parthenolide induced migration of TNBC cells regardless of ING4 presence of absence. TNF and parthenolide treated samples in ING4-deleted cell lines were found to inhibit cell migration to basal level. These results demonstrate the difference in inhibitory mechanism of parthenolide in induced phosphorylation of NF-B through proinflammatory cytokines TNF or IL-1This is demonstrated by the exclusivity of parthenolide inhibition of TNF induced phosphorylation of NF-B in ING4-deleted TNBC cell line. In contrast, parthenolide inhibition of IL-1 induced phosphorylation of NF-B occurred regardless of ING4 deletion. These results may inhibit parthenolide as an alternative to those with ING4-deleted TNBC due to its role in inducing cancer phenotype cell migration.