Abstract
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.
Highlights
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
These sections were stained on separate days using CyCIF for 20 proteins and phosphoproteins identifying tumor, immune and stromal cells and functional states (the downloadable data sets carry 20 biomarkers and additional 17 markers shown in Supplementary Table 1 are validated in the protocol)
We demonstrate the feasibility of the proposed approach for intensity normalization of multiplexed image data for robust analysis and comparison of tissue samples during analysis of a data set comprised of CyCIF analyses of three adjacent sections cut from a tissue microarrays (TMAs) comprised of 59 cores from diverse breast cancers (TMA was composed of 75 breast cancer tissues and 59 tissues were analyzed in this study based on tumor content and overall quality of staining) and stained for 20 proteins and phosphoproteins selected to identify diverse tumor and stromal cells and functional status
Summary
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. The corrections of the unwanted intensity variations due to technical artifact and batch-effect within a group of samples are required as pre-processing steps for unsupervised clustering approaches to identify cell types. Note that in our previous study, we were able to register one section (Hematoxylin and Eosin stain) to the other section (IF imaging) based on nuclear staining (hematoxylin-stain and DAPI) where they differ by 5 μm thickness, which guarantees little variation in cell population within 5 μm difference These sections were stained on separate days using CyCIF for 20 proteins and phosphoproteins identifying tumor, immune and stromal cells and functional states (the downloadable data sets carry 20 biomarkers (except DAPI) and additional 17 markers shown in Supplementary Table 1 are validated in the protocol). In contrast to cancer cell types, the bottom region of t-SNE projection represents immune cell types showing a more uniformly distributed pattern between samples, which we expect to see in the ideal setting, i.e., if there is no intensity variation across three adjacent sections
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