Biology subjects

Gordon, H. B.

Publications and source records attributed to Gordon, H. B..

3 recordsLinked to original sources

bioRxiv2020

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune systems highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable "phenomics" platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-{beta}- and TNF--driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

immunology↗

bioRxiv2020

Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2

To identify potential therapeutic stop-gaps for SARS-CoV-2, we evaluated a library of 1,670 approved and reference compounds in an unbiased, cellular image-based screen for their ability to suppress the broad impacts of the SARS-CoV-2 virus on phenomic profiles of human renal cortical epithelial cells using deep learning. In our assay, remdesivir is the only antiviral tested with strong efficacy, neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and a small number of compounds not currently being pursued clinically for SARS-CoV-2 have efficacy. We observed weak but beneficial class effects of {beta}-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with {beta}-agonists.

systems biology↗

bioRxiv2019

LongAxis: a MATLAB-based program for 3D quantitative analysis of epithelial cell shape and orientation

Epithelial morphogenesis, a fundamental aspect of development, generates 3-dimensional tissue structures crucial for organ function. Underlying morphogenetic mechanisms are, in many cases, poorly understood, but mutations that perturb organ development can affect epithelial cell shape and orientation - difficult features to quantify in three dimensions. The basic structure of the eye is established via epithelial morphogenesis: in the embryonic optic cup, the retinal progenitor epithelium enwraps the lens. We previously found that loss of the extracellular matrix protein laminin-alpha1 (lama1) led to mislocalization of apical polarity markers and apparent misorientation of retinal progenitors. We sought to visualize and quantify this phenotype, and determine whether loss of the apical polarity determinant pard3 might rescue the phenotype. To this end, we developed LongAxis, a MATLAB-based program optimized for the retinal progenitor neuroepithelium. LongAxis facilitates 3-dimensional cell segmentation, visualization, and quantification of cell orientation and morphology. Using LongAxis, we find that retinal progenitors in the lama1-/- optic cup are misoriented and slightly less elongated. In the lama1;MZpard3 double mutant, cells are still misoriented, but larger. Therefore, loss of pard3 does not rescue loss of lama1, and in fact uncovers a novel cell size phenotype. LongAxis enables population-level visualization and quantification of retinal progenitor cell orientation and morphology. These results underscore the importance of visualizing and quantifying cell orientation and shape in three dimensions within the retina.

developmental biology↗