Masotti Maria, Osher Nathaniel, Eliason Joel, Rao Arvind, Baladandayuthapani Veerabhadran
University of Michigan Department Biostatistics.
University of Michigan Department of Computation Medicine and Bioinformatics.
bioRxiv. 2023 Jul 22:2023.07.20.548170. doi: 10.1101/2023.07.20.548170.
The tumor microenvironment (TME) is a complex ecosystem containing tumor cells, other surrounding cells, blood vessels, and extracellular matrix. Recent advances in multiplexed imaging technologies allow researchers to map several cellular markers in the TME at the single cell level while preserving their spatial locations. Evidence is mounting that cellular interactions in the TME can promote or inhibit tumor development and contribute to drug resistance. Current statistical approaches to quantify cell-cell interactions do not readily scale to the outputs of new imaging technologies which can distinguish many unique cell phenotypes in one image. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. In application of DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.
肿瘤微环境（TME）是一个复杂的生态系统，包含肿瘤细胞、其他周围细胞、血管和细胞外基质。多重成像技术的最新进展使研究人员能够在单细胞水平上绘制TME中的多种细胞标记物，同时保留它们的空间位置。越来越多的证据表明，TME中的细胞相互作用可以促进或抑制肿瘤发展，并导致耐药性。目前用于量化细胞间相互作用的统计方法不易扩展到新成像技术的输出结果，这些新成像技术可以在一张图像中区分许多独特的细胞表型。我们提出了一个可扩展的分析框架以及配套的R包DIMPLE，用于量化、可视化和建模TME中的细胞间相互作用。在将DIMPLE应用于公开可用的MI数据时，我们发现了细胞间相互作用的图像水平测量与患者水平协变量之间具有统计学意义的关联。
