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RNA is one of the key components in the central dogma of biology. The protein-coding mRNAs are synthesised in the nucleus, processed to form the mature mRNA molecule, exported to the cytosol where they are translated into the functional protein, and eventually degraded. Gene expression can be regulated by adjusting, for example, mRNA synthesis or degradation rates. These mRNA metabolic rates are measured with RNA metabolic labelling experiments: Newly synthesised RNAs are tagged with modified nucleosides and can thereby be distinguished from pre-existing RNA transcripts. Yet, the analysis of metabolic labelling data is statistically challenging. The tagging of newly synthesised RNA transcripts is incomplete as only a fraction of the native nucleosides is replaced by the modified nucleoside. Also, to our knowledge, no currently available analysis tool provides a framework to investigate mRNA export. To address these challenges, I developed a dynamic model of mRNA metabolism. The model distinguishes between the nuclear and cytosolic compartment (two-compartment model) and thereby allows to analyse nuclear export in addition to cytosolic degradation. The data pre-processing steps are specifically tailored to metabolic labelling data and include the estimation of RNA labelling efficiencies in a time-dependent manner. We performed a metabolic labelling experiment on HeLa-S3 cells combined with cellular fractionation to measure nuclear and cytosolic RNA separately. We then applied my model to estimate nuclear and cytosolic RNA half lives. We find that nuclear half lives are much higher than cytosolic half lives, which leads to the conclusion that mRNA export is slower than mRNA degradation. Consequently, mRNA transcripts spend most of their lifetime in the nucleus and are more abundant in the nucleus than in the cytosol. We discover a group of outstanding genes, called ‘Supernova’ genes, which show an exceptionally fast mRNA export, arguing for a novel and distinct export mechanism.

Background Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.

Computer Aided Diagnosis (CAD) systems for renal histopathology applications aim to understand and replicate nephropathologists’ assessments of individual morphological compartments (e.g. glomeruli) to render case-level histological diagnoses. Deep neural networks (DNNs) hold great promise in addressing the poor intra- and interobserver agreement between pathologists. This being said, the generalization ability of DNNs heavily depends on the quality and quantity of training labels. Current “consensus” labeling strategies require multiple pathologists to evaluate every compartment unit over thousands of crops, resulting in enormous annotative costs. Additionally, these techniques fail to address the underlying reproducibility issues we observe across various diagnostic feature assessment tasks. To address both of these limitations, we introduce MorphSet, an end-to-end architecture inspired by Set Transformers which maps the combined encoded representations of Monte Carlo (MC) sampled glomerular compartment crops to produce Whole Slide Image (WSI) predictions on a case basis without the need for expensive fine-grained morphological feature labels. To evaluate performance, we use a kidney transplant Antibody Mediated Rejection (AMR) dataset, and show that we are able to achieve 98.9% case level accuracy, outperforming the consensus label baseline. Finally, we generate a visualization of prediction confidence derived from our MC evaluation experiments, which provides physicians with valuable feedback.