Journal
ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASE
Volume 17, Issue -, Pages 403-423Publisher
ANNUAL REVIEWS
DOI: 10.1146/annurev-pathmechdis-030321-091459
Keywords
high-dimensional multiplexed imaging; machine learning; pathology
Categories
Funding
- DOD EOH [W81XWH2110143, 1-DP5-864 OD019822, 1R01AG056287, 1R01AG057915, 865 1U24CA224309]
- Stanford Cancer Institute
- Bill and Melinda Gates Foundation
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Next-generation tools for multiplexed imaging have revolutionized our understanding of single-cell function and tissue structure. By using multiplexed ion beam imaging, we can visualize multiple antibodies tagged with metal reporters, increasing the breadth and depth of imaging data. To analyze these data effectively, we have developed tools for cell identification, cell classification, and spatial analysis. These tools have been applied in various disease contexts and hold great potential for improving diagnosis, prognosis, and therapeutic selection.
Next-generation tools for multiplexed imaging have driven a new wave of innovation in understanding how single-cell function and tissue structure are interrelated. In previous work, we developed multiplexed ion beam imaging by time of flight, a highly multiplexed platform that uses secondary ion mass spectrometry to image dozens of antibodies tagged with metal reporters. As instrument throughput has increased, the breadth and depth of imaging data have increased as well. To extract meaningful information from these data, we have developed tools for cell identification, cell classification, and spatial analysis. In this review, we discuss these tools and provide examples of their application in various contexts, including ductal carcinoma in situ, tuberculosis, and Alzheimer's disease. We hope the synergy between multiplexed imaging and automated image analysis will drive a new era in anatomic pathology and personalized medicine wherein quantitative spatial signatures are used routinely for more accurate diagnosis, prognosis, and therapeutic selection.
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