High-Content Image-Based Screening and Deep Learning for the Detection of Anti-Inflammatory Drug Leads.

We developed a high-content image-based screen that utilizes the pro-inflammatory stimulus lipopolysaccharide (LPS) and murine macrophages (RAW264.7) with the goal of enabling the identification of novel anti-inflammatory lead compounds. We screened 2,259 bioactive compounds with annotated mechanisms of action (MOA) to identify compounds that block the LPS-induced phenotype in macrophages. We utilized a set of seven fluorescence microscopy probes to generate images that were used to train and optimize a deep neural network classifier to distinguish between unstimulated and LPS-stimulated macrophages. The top hits from the deep learning classifier were validated using a linear classifier trained on individual cells and subsequently investigated in a multiplexed cytokine secretion assay. All 12 hits significantly modulated the expression of at least one cytokine upon LPS stimulation. Seven of these were allosteric inhibitors of the mitogen-activated protein kinase kinase (MEK1/2) and showed similar effects on cytokine expression. This deep learning morphological assay identified compounds that modulate the innate immune response to LPS and may aid in identifying new anti-inflammatory drug leads.

Three-dimensional models of natural environments and the mapping of navigational information.

Much evidence has accumulated in recent years, demonstrating that the degree to which navigating insects rely on path integration or landmark guidance when displaced depends on the navigational information content of their specific habitat. There is thus a need to quantify this information content. Here we present one way of achieving this by constructing 3D models of natural environments using a laser scanner and purely camera-based methods that allow us to render panoramic views at any location. We provide (1) ground-truthing of such reconstructed views against panoramic images recorded at the same locations; (2) evidence of their potential to map the navigational information content of natural habitats; (3) methods to register these models with GPS or with stereo camera recordings and (4) examples of their use in reconstructing the visual information available to walking and flying insects. We discuss the current limitations of 3D modelling, including the lack of spectral and polarisation information, but also the opportunities such models offer to map the navigational information content of natural habitats and to test visual navigation algorithms under 'real-life' conditions.