Detection of dendritic cell subsets in the tumor microenvironment by multiplex immunohistochemistry.

Dendritic cells (DCs) are essential in antitumor immunity. In humans, three main DC subsets are defined: two types of conventional DCs (cDC1s and cDC2s) and plasmacytoid DCs (pDCs). To study DC subsets in the tumor microenvironment (TME), it is important to correctly identify them in tumor tissues. Tumor-derived DCs are often analyzed in cell suspensions in which spatial information about DCs which can be important to determine their function within the TME is lost. Therefore, we developed the first standardized and optimized multiplex immunohistochemistry panel, simultaneously detecting cDC1s, cDC2s, and pDCs within their tissue context. We report on this panel's development, validation, and quantitative analysis. A multiplex immunohistochemistry panel consisting of CD1c, CD303, X-C motif chemokine receptor 1, CD14, CD19, a tumor marker, and DAPI was established. The ImmuNet machine learning pipeline was trained for the detection of DC subsets. The performance of ImmuNet was compared with conventional cell phenotyping software. Ultimately, frequencies of DC subsets within several tumors were defined. In conclusion, this panel provides a method to study cDC1s, cDC2s, and pDCs in the spatial context of the TME, which supports unraveling their specific roles in antitumor immunity.

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment.

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.