Multiplexed Immunohistochemical Consecutive Staining on Single Slide (MICSSS): Multiplexed Chromogenic IHC Assay for High-Dimensional Tissue Analysis

Abstract

Disease states and cellular compartments can display a remarkable amount of heterogeneity, and truly appreciating this heterogeneity requires the ability to detect and probe each subpopulation present. A myriad of recent single-cell assays has allowed for in-depth analysis of these diverse cellular populations; however, fully understanding the interplay between each cell type requires knowledge not only of their mere presence but also of their spatial organization and their relation one to the other. Immunohistochemistry allows for the visualization of cells and tissue; however, standard techniques only allow for the use of very few probes on a single specimen, not allowing for in-depth analysis of complex cellular heterogeneity. A number of multiplex imaging techniques, such as immunofluorescence and multiplex immunohistochemistry, have been proposed to allow probing more cellular markers at once; however, many of these techniques still have their limitations. The use of fluorescent markers has an inherent limitation to the number of probes that can be simultaneously used due to spectral overlap. Moreover, other proposed multiplex IHC methods are time-consuming and require expensive reagents. Still, many of the methods rely on frozen tissue, which deviates from standards in human pathological evaluation. Here, we describe a multiplex IHC technique, staining for consecutive markers on a single slide, which utilizes similar steps and similar reagents as standard IHC, thus making it possible for any lab with standard IHC capabilities to perform this useful procedure. This method has been validated and confirmed that consecutive markers can be stained without the risk of cross-reactivity between staining cycles. Furthermore, we have validated that this technique does not lead to decreased antigenicity of subsequent epitopes probed, nor does it lead to steric hindrance.

Keywords: Biomarkers; CD20; CD3; CD66b; CD68; CD8; Cancer immunotherapy; Cell segmentation; Chromogenic immunohistochemistry; Consecutive staining; FOXP3; Histology; Image analysis; Immuno-oncology; Immunostaining; In situ markers; Machine learning; Morphology; Multiplexed immunohistochemistry; PD-1; PD-L1; Positive cell detection; Random forest; Serial staining; Single slide; Whole slide imaging.

Fig. 1

MICSSS pipeline

Fig. 2

Comparison of several markers on the same ROI of a triple negative breast cancer case

Fig. 3

The ROI selected above is used for estimating stain vectors (hematoxylin, AEC, residual). Hematoxylin and AEC channels are produced by using these stain vectors with color deconvolution

Fig. 4

Various annotation tools provided by QuPath are demonstrated here including line, circle, rectangle, wand, polygon, and brush tools

Fig. 5

Colorectal carcinoma stained with Ki-67. Cell segmentation is performed based on optical density, and positive cell detection is made by finding a cutoff value with using the mean nuclear chromogen color intensity feature

Fig. 6

ROI from a normal tonsil tissue including a germinal center which is diffusely positive stained with Ki67 as expected. Yellow circle shaped dots are used to train the machine learning based classifier for positive and negative cells. After marking 11 cells as positive and negative, this algorithm classified the rest of the cells correctly

Fig. 7

Normal tonsil tissue is annotated by “Simple Tissue Detection” feature of QuPath

Fig. 8

Automatic TMA annotation

Fig. 9

Expansion of a malignant melanoma annotation inward and outward by 100 μm. This feature can be useful to study peritumoral immune cell population located at the tumor border

Fig. 10

ROI from a normal tonsil tissue including a germinal center which is diffusely positive stained with Ki67 as expected. Measurement maps tool helps with finding a cutoff value for a selected feature to classify the cells. “Nucleus: DAB OD mean” is selected here to find a cutoff value and make a positive/negative classification