KULMAP: Human Kidney, urinary tract and lung mapping center

摘要

Abstract Modern medicine has entered the early phase of big data revolution; massive progress in microscopy, digital electronics, photonics, and sequencing technologies, in addition to established techniques like radiology, have enabled generation of multi-modal, multi-scale, multi-omics data in large volume from human subjects. Further, the digitization of the resulting data, coupled with the advanced state of computer hardware and software, has opened up new opportunities for computational image scientists to identify previously unknown statistical biomarkers from big-data whose discovery is otherwise intractable by manual means. Two important efforts along this direction are orchestrated by the National Institutes of Health; namely, the Human BioMolecular Atlas Program (HuBMAP) and Kidney Precision Medicine Project (KPMP). The former focuses on defining a reference anatomical atlas across biological scale for diverse tissues. The latter focuses solely on defining a structural and functional atlas of the homeostatic kidney and their disease state deviations. The consortiums mentioned above are committed to generate multi-scale, -omics data. The primary objective is to fuse the massive multi-modal data to develop a comprehensive model of the extent of tissue heterogeneity in reference and disease patients, so that clinical interpretations of tissue can be more objectified. Before approaching the lofty goal of multi-scale, multi-modal data fusion, the first step is to conduct pilot experiments using data from single modalities and single organs to validate that a statistical reference range can be adequately defined. Presuming success, this pipeline can then be scaled to diverse organ types and scales. Toward that objective, in this HubMAP supplemental application, we propose to investigate morphological structural diversity in ‘reference’ kidney tissue brightfield whole slide images from HuBMAP, KPMP, and other sources using a panoptic convolutional neural network. We will compare the resulting structural distribution heterogeneity with that obtained from equivalent chronic kidney disease cases from KPMP for benchmarking purpose to precisely establish the upper limit on the reference structural distributions. The PI is an expert in computational renal pathology, and has generated numerous results on objective quantification of renal compartments, computational classification of renal diseases, and computational prediction of clinical biometrics from renal tissue images. Further, the PI is part of the investigator team of KPMP, contributing to the development of a technical data analysis pipeline for KPMP data. The PI has significant experience in analyzing GTEx renal tissue image data, as well as building a cloud-based, web browser accessible image and omics data archival and visualization system with built-in plugins for AI analysis on large scale image and omics data, which requires minimal technical knowledge to operate by end-users. This work will provide HuBMAP a comprehensive digital pathology framework for image analysis of structures, efficiency in structural annotation, a ‘reference’ statistical tissue atlas generation, as well as assist pathologists with large volume annotations and facilitate molecular integration studies.