Abstract 1930: Unsupervised sub-cellular segmentation of complex tissue and cell samples using highly multiplexed imaging-derived a priori knowledge

Abstract Spatial context of heterocellular interactions within solid tumor microenvironments (TMEs) is important for deciphering the mechanistic underpinnings of malignant TME phenotypes and leveraging that knowledge to improve personalized and precision medicine. Recent development of highly multiplexed imaging approaches, such as co-detection by indexing (CODEX), cyclic immunofluorescence (cycif), and imaging mass cytometry (IMC) has, for the first time, allowed deep interrogation of this heterocellular spatial complexity. Proper quantitation of this complexity, however, requires the ability to easily and accurately segment and localize cells and their sub-cellular compartments within the spatial context of the tumor microenvironment. Seminal approaches based on semi-supervised and supervised learning methods, including deep learning techniques, have been developed to segment cells and their nuclei. However, generalization of these methods to segmenting heterogenous and complex cell and tissue samples with varying resolution, magnification, and dynamic range, remains a persistent bottleneck. In case of deep learning, the requirement of large and accurate annotated datasets further adds to the challenge of seamless integration of systems-based methods in pathology and cancer research. Here, we demonstrate that by leveraging cell-compartment specific a priori knowledge captured by these imaging modalities, we can segment cells in complex tissue and cell samples in an unsupervised manner, without requiring model training. We specifically show that using nucleus and cell-membrane markers, we can accurately segment sub-cellular compartments of a diversity of tissue samples imaged at different resolutions, magnifications and dynamic ranges, and cell samples at varying levels of confluency. We also demonstrate that our method is fast. Given its ease of use, accuracy, robustness, and no requirement of large, annotated datasets, our unsupervised segmentation method fills a much-needed gap toward integration of spatial systems biology and cancer research within the convergence science paradigm. Citation Format: Bogdan Kochetov, Phoenix D. Bell, Rebecca Raphael, Benjamin J. Raymond, Brian J. Leibowitz, Jingshan Tong, Brenda Diergaarde, Jian Yu, Reetesh K. Pai, Robert E. Schoen, Lin Zhang, Aatur Singhi, Shikhar Uttam. Unsupervised sub-cellular segmentation of complex tissue and cell samples using highly multiplexed imaging-derived a priori knowledge [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1930.