571 Physiologie und verwandte Themen
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Significant advances in fluorescence imaging techniques enable life scientists today to gain insights into biological systems at an unprecedented scale. The interpretation of image features in such bioimage datasets and their subsequent quantitative analysis is referred to as bioimage analysis. A substantial proportion of bioimage analyses is still performed manually by a human expert - a tedious process that is long known to be subjective. Particularly in tasks that require the annotation of image features with a low signal-to-noise ratio, like in fluorescence images of tissue samples, the inter-rater agreement drops. However, like any other scientific analysis, also bioimage analysis has to meet the general quality criteria of quantitative research, which are objectivity, reliability, and validity. Thus, the automation of bioimage analysis with computer-aided approaches is highly desirable. Albeit conventional hard-coded algorithms are fully unbiased, a human user has to set its respective feature extraction parameters. Thus, also these approaches can be considered subjective.
Recently, deep learning (DL) has enabled impressive advances in computer vision research. The predominant difference between DL and conventional algorithms is the capability of DL models to learn the respective task on base of an annotated training dataset, instead of following user-defined rules for feature extraction. This thesis hypothesized that DL can be used to increase the objectivity, reliability, and validity of bioimage analyses, thus going beyond mere automation. However, in absence of ground truth annotations, DL models have to be trained on manual and thus subjective annotations, which could cause the model to incorporate such a bias. Moreover, model training is stochastic and even training on the same data could result in models with divergent outputs. Consequently, both the training on subjective annotations and the model-to-model variability could impair the quality of DL-based bioimage analyses. This thesis systematically assessed the impacts of these two limitations experimentally by analyzing fluorescence signals of a protein called cFOS in mouse brain sections. Since the abundance of cFOS correlates with mouse behavior, behavioral analyses could be used for cross-validation of the bioimage analysis results. Furthermore, this thesis showed that pooling the input of multiple human experts during model training and integration of multiple trained models in a model ensemble can mitigate the impact of these limitations. In summary, the present study establishes guidelines for how DL can be used to increase the general quality of bioimage analyses.
TrkB mediates the effects of brain-derived neurotrophic factor (BDNF) in neuronal and nonnneuronal cells. Based on recent reports that TrkB can also be transactivated through epidermal growth-factor receptor (EGFR) signaling and thus regulates migration of early neurons, we investigated the role of TrkB in migration of lung tumor cells. Early metastasis remains a major challenge in the clinical management of non-small cell lung cancer (NSCLC). TrkB receptor signaling is associated with metastasis and poor patient prognosis in NSCLC. Expression of this receptor in A549 cells and in another adenocarcinoma cell line, NCI-H441, promoted enhanced migratory capacity in wound healing assays in the presence of the TrkB ligand BDNF. Furthermore, TrkB expression in A549 cells potentiated the stimulatory effect of EGF in wound healing and in Boyden chamber migration experiments. Consistent with a potential loss of cell polarity upon TrkB expression, cell dispersal and de-clustering was induced in A549 cells independently of exogeneous BDNF. Morphological transformation involved extensive cytoskeletal changes, reduced E-cadherin expression and suppression of E-cadherin expression on the cell surface in TrkB expressing tumor cells. This function depended on MEK and Akt kinase activity but was independent of Src. These data indicate that TrkB expression in lung adenoma cells is an early step in tumor cell dissemination, and thus could represent a target for therapy development.