Aronow, B. | bioRxiv Science

bioRxiv2020

Ontology-guided segmentation and object identification for developmental mouse lung immunofluorescent images

BackgroundImmunofluorescent confocal microscopy uses labeled antibodies as probes against specific macromolecules to discriminate between multiple cell types. For images of the developmental mouse lung, these cells are themselves organized into densely packed higher-level anatomical structures. These types of images can be challenging to segment automatically for several reasons, including the relevance of biomedical context, dependence on the specific set of probes used, prohibitive cost of generating labeled training data, as well as the complexity and dense packing of anatomical structures in the image. The use of an application ontology surmounts these challenges by combining image data with its metadata to provide a meaningful biological context, and hence constraining and simplifying the process of segmentation and object identification. ResultsWe propose an innovative approach for the automated analysis of complex and densely packed anatomical structures from immunofluorescent images that utilizes an application ontology to provide a simplified context for image segmentation and object identification. We describe how the logical organization of biological facts in the form of an ontology can provide useful constraints that enhance automatic processing of complex images. We demonstrate the results of ontology-guided segmentation and object identification in mouse developmental lung images from the Bioinformatics REsource ATlas for the Healthy lung (BREATH) database of the Molecular Atlas of Lung Development (LungMAP1) program. ConclusionThe microscopy analysis pipeline library (micap) is available at https://github.com/duke-lungmap-team/microscopy-analysis-pipeline. Code to reproduce our analysis of LungMAP images is also available at https://github.com/duke-lungmap-team/lungmap-pipeline. Finally, the application ontology is available at https://github.com/duke-lungmap-team/lung_ontology and includes example SPARQL queries. ContactAnna Maria Masci email: annamaria.masci@duke.edu

bioinformatics↗

bioRxiv2020

Confronting the COVID-19 Pandemic with Systems Biology

Using a Systems Biology approach, we integrated genomic, transcriptomic, proteomic, and molecular structure information to provide a holistic understanding of the COVID-19 pandemic. The expression data analysis of the Renin Angiotensin System indicates mild nasal, oral or throat infections are likely and that the gastrointestinal tissues are a common primary target of SARS-CoV-2. Extreme symptoms in the lower respiratory system likely result from a secondary-infection possibly by a comorbidity-driven upregulation of ACE2 in the lung. The remarkable differences in expression of other RAS elements, the elimination of macrophages and the activation of cytokines in COVID-19 bronchoalveolar samples suggest that a functional immune deficiency is a critical outcome of COVID-19. We posit that using a non-respiratory system as a major pathway of infection is likely determining the unprecedented global spread of this coronavirus. One Sentence SummaryA Systems Approach Indicates Non-respiratory Pathways of Infection as Key for the COVID-19 Pandemic

systems biology↗