Recent advances in multiplexed imaging technologies promise to improve the understanding of the functional states of individual cells and the interactions between the cells in tissues. This often requires compilation of results from multiple samples. However, quantitative integration of information between samples is complicated by variations in staining intensity and background fluorescence that obscure biological variations. Failure to remove these unwanted artifacts will complicate downstream analysis and diminish the value of multiplexed imaging for clinical applications. Here, to compensate for unwanted variations, we automatically identify negative control cells for each marker within the same tissue and use their expression levels to infer background signal level. The intensity profile is normalized by the inferred level of the negative control cells to remove between-sample variation. Using a tissue microarray data and a pair of longitudinal biopsy samples, we demonstrated that the proposed approach can remove unwanted variations effectively and shows robust performance.

中文翻译：

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恢复：健壮的标准化成像方法，可进行多重成像。

复用成像技术的最新进展有望改善对单个细胞功能状态以及组织中细胞之间相互作用的理解。这通常需要汇编来自多个样本的结果。然而，样品强度之间的差异会导致染色强度和背景荧光的变化，从而使样品之间信息的定量整合变得复杂。无法去除这些不需要的伪像将使下游分析复杂化，并降低用于临床应用的多路成像的价值。在这里，为了补偿不必要的变化，我们自动为同一组织内的每个标记物识别阴性对照细胞，并使用它们的表达水平来推断背景信号水平。通过推断阴性对照细胞的水平对强度分布进行归一化，以消除样品之间的差异。使用组织芯片数据和一对纵向活检样本，我们证明了所提出的方法可以有效地去除不需要的变异并显示出强大的性能。