Single-nucleus chromatin accessibility profiling identifies cell types and functional variants contributing to major depression.

Genetic variants associated with major depressive disorder (MDD) are enriched in the regulatory genome. Here, we investigate gene-regulatory mechanisms underlying MDD compared to neurotypical controls by combining single-cell chromatin accessibility with gene expression in over 200,000 cells from the dorsolateral prefrontal cortex of 84 individuals. MDD-associated alterations in chromatin accessibility were prominent in deep-layer excitatory neurons characterized by transcription factor (TF) motif accessibility and binding of NR4A2, an activity-dependent TF reactive to stress.

PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas.

Motivation: Online assessment of tumor characteristics during surgery is important and has the potential to establish an intra-operative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or conservative regarding the resection of the tumor. While there are methods to perform metabolomics-based tumor pathology prediction, their model complexity predictive performance is limited by the small dataset sizes.

Targeted metabolomics analyses for brain tumor margin assessment during surgery.

Motivation: Identification and removal of micro-scale residual tumor tissue during brain tumor surgery are key for survival in glioma patients. For this goal, High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) spectroscopy-based assessment of tumor margins during surgery has been an effective method. However, the time required for metabolite quantification and the need for human experts such as a pathologist to be present during surgery are major bottlenecks of this technique.

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy.

Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites.