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Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
Published on: August 18, 2023
Detection of dendritic cell subsets in the tumor microenvironment by multiplex immunohistochemistry
Iris A E van der Hoorn1,2, Evgenia Martynova1,3, Beatriz Subtil1
1Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, the Netherlands.
Insights
This study introduces a new multiplex immunohistochemistry panel to identify dendritic cell (DC) subsets, including conventional DCs (cDC1s, cDC2s) and plasmacytoid DCs (pDCs), within the tumor microenvironment (TME). This method preserves spatial information crucial for understanding DC roles in antitumor immunity.
Area of Science:
- Immunology
- Cancer Research
- Cell Biology
Background:
- Dendritic cells (DCs) are critical for initiating antitumor immune responses.
- Accurate identification of distinct DC subsets (cDC1s, cDC2s, pDCs) within the tumor microenvironment (TME) is essential for understanding their functional roles.
- Current methods analyzing tumor-infiltrating DCs in cell suspensions often lose vital spatial context.
Purpose of the Study:
- To develop and validate a standardized multiplex immunohistochemistry (mIHC) panel for simultaneous detection of human DC subsets (cDC1s, cDC2s, pDCs) within tumor tissues.
- To enable the study of DC subsets in their native spatial context within the TME.
- To compare the performance of a machine learning pipeline (ImmuNet) for DC subset detection against conventional methods.
Main Methods:
- Development of a novel mIHC panel utilizing markers such as CD1c, CD303, and X-C motif chemokine receptor 1, alongside a tumor marker and DAPI.
- Training and application of the ImmuNet machine learning pipeline for automated detection and quantification of DC subsets.
- Validation of the panel and pipeline by comparing results with conventional cell phenotyping software.
- Quantitative analysis of DC subset frequencies within various tumor samples.
Main Results:
- Successful establishment and validation of a standardized mIHC panel for simultaneous detection of cDC1s, cDC2s, and pDCs.
- Demonstration of the ImmuNet pipeline's capability in accurately identifying and quantifying DC subsets within the tissue context.
- Comparison showing comparable or improved performance of ImmuNet versus traditional phenotyping software.
- Determination of DC subset frequencies in multiple tumor types, preserving spatial relationships.
Conclusions:
- The developed mIHC panel provides a robust method for studying DC subsets (cDC1s, cDC2s, pDCs) in the spatial context of the TME.
- This approach facilitates a deeper understanding of the specific functions and interactions of different DC subsets in antitumor immunity.
- The integration of ImmuNet enhances the efficiency and accuracy of DC subset analysis in complex tumor tissues.
Abstract:
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.
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