Browsing by Author "Shen, Yang"
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Item Detecting heterogeneity in and between breast cancer cell lines(Springer Nature, 2020-02-03) Shen, Yang; Schmidt, B. U. Sebastian; Kubitschke, Hans; Morawetz, Erik W.; Wolf, Benjamin; Käs, Josef A.; Losert, WolfgangCellular heterogeneity in tumor cells is a well-established phenomenon. Genetic and phenotypic cell-to-cell variability have been observed in numerous studies both within the same type of cancer cells and across different types of cancers. Another known fact for metastatic tumor cells is that they tend to be softer than their normal or non-metastatic counterparts. However, the heterogeneity of mechanical properties in tumor cells are not widely studied. Here we analyzed single-cell optical stretcher data with machine learning algorithms on three different breast tumor cell lines and show that similar heterogeneity can also be seen in mechanical properties of cells both within and between breast tumor cell lines. We identified two clusters within MDA-MB-231 cells, with cells in one cluster being softer than in the other. In addition, we show that MDA-MB-231 cells and MDA-MB-436 cells which are both epithelial breast cancer cell lines with a mesenchymal-like phenotype derived from metastatic cancers are mechanically more different from each other than from non-malignant epithelial MCF-10A cells. Since stiffness of tumor cells can be an indicator of metastatic potential, this result suggests that metastatic abilities could vary within the same monoclonal tumor cell line.Item MULTI-DIMENSIONAL ANALYSIS APPROACHES FOR HETEROGENEOUS SINGLE-CELL DATA(2018) Shen, Yang; Losert, Wolfgang; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Improvements in experimental techniques have led to an explosion of information in biology research. The increasing number of measurements comes with challenges in analyzing resulting data, as well as opportunities to obtain deeper insights of biological systems. Conventional average based methods are unfit to analyze high dimensional datasets since they fail to take full advantage of such rich information. More importantly, they are not able to capture the heterogeneity that is prevalent in biological systems. Sophisticated algorithms that are able to utilize all available measurements simultaneously are hence emerging rapidly. These algorithms excel at making full use of information within datasets and revealing detailed heterogeneity. However, there are several important disadvantages of existing algorithms. First, specific knowledge in statistics or machine learning is required to appropriately interpret and tune parameters in these algorithms for future use. This may result in misusage and misinterpretation. Second, using all measurements with equal weighting runs the risk of noise contamination. In addition, information overload has become more common in biology research, with a large volume of irrelevant measurements. Third, regardless of the quality of measurements, analysis methods that simultaneously use a large number of measurements need to avoid the “curse of dimensionality”, which warns that distance estimation and nearest neighbor estimation are not meaningful in high dimensional space. However, most current sophisticated algorithms involve distance estimation and/or nearest neighbor estimation. In this dissertation, my goal is to build analysis methods that are complex enough to capture heterogeneity and at the same time output results in a format that is easy to interpret and familiar to biologists and medical researchers. I tackle the dimension reduction problem by finding not the best subspace but dividing them into multiple subspaces and examine them one by one. I demonstrate my methods with three types of datasets: image-based high-throughput screening data, flow cytometry data, and mass cytometry data. From each dataset, I was able to discover new biological insights as well as re-validate well-established findings with my methods.Item Probing the Internalization Mechanism of a Bacteriophage-encoded Endolysin that can Lyse Extracellular and Intracellular Streptococci(2013) Shen, Yang; Nelson, Daniel C; Molecular and Cell Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bacteriophage-encoded peptidoglycan hydrolases, or endolysins, have been investigated as an alternative to antimicrobials due to their ability to lyse the bacterial cell wall upon contact. However, pathogens are often able to invade epithelial cells where they can repopulate the mucosal surface after antibiotic or endolysin prophylaxis. Thus, there is growing interest in endolysins that can be engineered, or inherently possess, a capacity to internalize in eukaryotic cells such that they can target extracellular and intracellular pathogens. Previously, one streptococcal specific endolysin, PlyC, was shown to control group A Streptococcus localized on mucosal surfaces as well as infected tissues. To further evaluate the therapeutic potential of PlyC, a streptococci/human epithelial cell co-culture model was established to differentiate extracellular vs. intracellular bacteriolytic activity. We found that a single dose (50 μg/ml) of PlyC was able to decrease intracellular streptococci by 96% compared to controls, as well as prevented the host epithelial cells death. In addition, the internalization and co-localization of PlyC with intracellular streptococci was captured by confocal laser scanning microscopy. Further studies revealed the PlyC binding domain alone, termed PlyCB, with a highly positive-charged surface, was responsible for entry into epithelial cells. By applying site-directed mutagenesis, several positive residues (Lys-23, Lys-59, Arg-66 and Lys-70&71) of PlyCB were shown to mediate internalization. We then biochemically demonstrated that PlyCB directly and specifically bound to phosphatidic acid, phosphatidylserine and phosphatidylinositol through a phospholipid screening assay. Computational modeling suggests that two cationic residues, Lys-59 and Arg-66, form a pocket to help secure the interaction between PlyC and specific phospholipids. Internalization of PlyC was found to be via caveolae-mediated endocytosis in an energy-dependent process with the subsequent intracellular trafficking of PlyC regulated by the PI3K pathway. To the best of our knowledge, PlyC is the first endolysin reported that can penetrate through the eukaryotic lipid membrane and retain biological binding and lytic activity against streptococci in the intracellular niche.Item RefCell: multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’(Springer Nature, 2018-11-16) Shen, Yang; Kubben, Nard; Candia, Julián; Morozov, Alexandre V.; Misteli, Tom; Losert, WolfgangImage-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the “curse of dimensionality” and non-standardized outputs. Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these “typical cells” as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample. We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.