Detection of Dendritic Cell Subsets in the Tumor Microenvironment by Multiplex Immunohistochemistry.
Iris A E van der HoornEvgenia MartynovaBeatriz SubtilJelena MeekKiek VerrijpJohannes TextorGeorgina Flórez-GrauBerber PietMichel M van den HeuvelI Jolanda M de VriesMark A J GorrisPublished in: European journal of immunology (2023)
Dendritic cells (DCs) are essential in anti-tumor 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 (mIHC) panel, simultaneously detecting cDC1s, cDC2s, and pDCs within their tissue context. We report on this panel's development, validation, and quantitative analysis. An mIHC panel consisting of CD1c, CD303, X-C Motif Chemokine receptor 1 (XCR1), 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 to 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 anti-tumor immunity.
Keyphrases
- dendritic cells
- cell cycle
- regulatory t cells
- peripheral blood
- immune response
- machine learning
- real time pcr
- high throughput
- single cell
- cell therapy
- gene expression
- stem cells
- healthcare
- mesenchymal stem cells
- artificial intelligence
- cell proliferation
- social media
- health information
- high intensity
- big data
- sensitive detection