A Computational IMage Analysis Platform (CIMAP) for HuBMAP

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Abstract: Advancement in high-resolution microscopy has opened unprecedented opportunities to investigate cells and tissues spatially at sub-micron level, via molecular imaging of gene transcripts, proteins or metabolomes. Parallel advances in computer-based hardware technologies and AI/ machine learning (ML) also offer a vehicle to study such multi-omics data in high dimensionality. An outstanding challenge involves a fusion of such data and thorough understanding of the fused data in all possible domains, including in basic science, clinical or pre-clinical studies using model systems, clinical diagnosis, prognostication, and drug discovery. Human Bio-Molecular Atlas Project (HuBMAP) consortium is an avenue for generating high-resolution multi-omics data at single cell resolution using a multitude of spatial molecular omics technologies. Common imaging modality that connects all these data types is brightfield histology microscopy, which is inexpensive and integrates the above-mentioned multi-omics data with clinical decision making. This HIVE Tools proposal aims to develop and implement novel machine learning pipelines to predict cell types and/or states from brightfield histology images using spatial protein- and/or RNA-based technology data with concurrent brightfield histology. This will enable using these spatial omics data as a bridge to link histology with high content single cell data sets and thus create a single exploration space from histology to biomolecules in distinct cell types. As a first step, we will employ select data collected under HuBMAP or generated via this HIVE team using CODEX as well as spatial transcriptomics (ST), and develop the proposed computational pipeline. We will demonstrate mapping of cell types and cell states to brightfield histology images on the same section from which the molecular data are generated, as well as on the independent adjacent section via registration, and finally on an independent validation tissue section. We will subsequently explore application of this approach to other HuBMAP organs including lymph node, skin, liver and lung. We will also develop 3D scalable graphics of cell types being detected using our pipeline, with a goal to develop ontological framework integrating atoms to anatomy for an objective understanding of variability in reference human atlas. We will create synergies with other HIVE teams to integrate the developed pipelines, tools with HuBMAP web-cloud portal as an easy-to-use, plug-and-play end-user plugin that is openly accessible to quantify cell counts, types, features, as well as states via uploading brightfield histology tissue images to the portal. Our innovative translational science teams’ model will recruit underrepresented minority students in STEM from biology as well as from engineering disciplines to provide them a mentorship environment and scientific opportunities within our team and that of collaborators. This strategy will develop a next generation renaissance scientist, who will be able to continue investigating along the proposed direction combining knowledge from biology, imaging, and engineering in a single research project.