CD83 is a maturation marker for dendritic cells. In the B cell lineage, CD83 is expressed especially on activated B cells and on light zone B cells during the germinal center (GC) reaction. The function of CD83 during GC responses is unclear. CD83^−/− mice have a strong reduction of CD4⁺ T cells, which makes it difficult to analyze a functional role of CD83 on B cells during GC responses. Therefore, in the present study we generated a B cell–specific CD83 conditional knockout (CD83 B-cKO) model. CD83 B-cKO B cells show defective upregulation of MHC class II and CD86 expression and impaired proliferation after different stimuli. Analyses of GC responses after immunization with various Ags revealed a characteristic shift in dark zone and light zone B cell numbers, with an increase of B cells in the dark zone of CD83 B-cKO mice. This effect was not accompanied by alterations in the level of IgG immune responses or by major differences in affinity maturation. However, an enhanced IgE response was observed in CD83 B-cKO mice. Additionally, we observed a strong competitive disadvantage of CD83-cKO B cells in GC responses in mixed bone marrow chimeras. Furthermore, infection of mice with Borrelia burgdorferi revealed a defect in bacterial clearance of CD83 B-cKO mice with a shift toward a Th2 response, indicated by a strong increase in IgE titers. Taken together, our results show that CD83 is important for B cell activation and modulates GC composition and IgE Ab responses in vivo.

While the bone marrow attracts tumor cells in many solid cancers leading to poor outcome in affected patients, comprehensive analyses of bone marrow metastases have not been performed on a single-cell level. We here set out to capture tumor heterogeneity and unravel microenvironmental changes in neuroblastoma, a solid cancer with bone marrow involvement. To this end, we employed a multi-omics data mining approach to define a multiplex imaging panel and developed DeepFLEX, a pipeline for subsequent multiplex image analysis, whereby we constructed a single-cell atlas of over 35,000 disseminated tumor cells (DTCs) and cells of their microenvironment in the metastatic bone marrow niche. Further, we independently profiled the transcriptome of a cohort of 38 patients with and without bone marrow metastasis. Our results revealed vast diversity among DTCs and suggest that FAIM2 can act as a complementary marker to capture DTC heterogeneity. Importantly, we demonstrate that malignant bone marrow infiltration is associated with an inflammatory response and at the same time the presence of immuno-suppressive cell types, most prominently an immature neutrophil/granulocytic myeloid-derived suppressor-like cell type. The presented findings indicate that metastatic tumor cells shape the bone marrow microenvironment, warranting deeper investigations of spatio-temporal dynamics at the single-cell level and their clinical relevance.

The evolution of cancer is characterized by the appearance of specific mutations, but these mutations are translated into proteins that must cooperate to induce malignant transformation. Using a systemic approach with the multiepitope ligand cartography (MELC) technology, we analyzed protein expression profiles (PEPs) in nevi and BRAF^V600E-positive superficial spreading melanomas (SSMs) from patient tissues to identify key transformation events. The PEPs in nevi and SSMs differed predominantly in the abundance of specific antigens, but the PEPs of nevi- and melanoma-associated keratinocytes gradually changed during the transformation process. A stepwise change in PEP with similar properties occurred in keratinocytes cocultured with melanoma cells. Analysis of the individual steps indicated that activation of the metalloproteinase ADAM10 by signal peptide peptidase–like 3 (SPPL3) triggered by mutant BRAF^V600E was a critical transformation event. SPPL3-mediated ADAM10 activation involved the translocation of SPPL3 and ADAM10 into Rab4- or Rab27-positive endosomal compartments. This endosomal translocation, and hence ADAM10 activation, was inhibited by the presence of the tumor suppressor PTEN. Our findings suggest that systematic tissue antigen analysis could complement whole-genome approaches to provide more insight into cancer development.

Keloid is an aberrant scarring process of the skin, characterized by excessive extracellular matrix synthesis and deposition. The pathogenesis of this prevalent cutaneous disorder is not fully understood; however, a persistent inflammatory process is observed. To obtain more insight into this process, we analyzed lesional, perilesional and healthy tissue using multi-antigen-analysis (MAA) in conjunction with a data mining approach. Here, we demonstrate that monocyte-derived inflammatory dendritic cells (CD1a+, CD11c+, CD14+) and activated CD4+ T lymphocytes (CD45 RO+) dominated the immune infiltration in keloids while associating with fibroblasts. In perilesional tissue, precursor immune cells were dominant in the perivascular area, suggesting that they were attracted by an immune process, potentially in the lesional area. Supporting this hypothesis, only in keloid lesions, high levels of ADAM10/17 and Neprilysin (CD10) were observed in both fibroblasts and leukocytes. The spatial proximity of these two cell types, which could be confirmed by image analysis only in lesional tissue, could be a potential factor leading to the activation of fibroblasts. Our findings provide new insight into the pathogenesis of keloid formation and reveal metalloproteinases as a target for therapeutical intervention.

Bone marrow commonly serves as a metastatic niche for disseminated tumor cells (DTCs) of solid cancers in patients with unfavorable clinical outcome. Single-cell assessment of bone marrow metastases is essential to decipher the entire spectrum of tumor heterogeneity in these cancers, however, has previously not been performed. Here we used multi-epitope-ligand cartography (MELC) to spatially profile 20 biomarkers and assess morphology in DTCs as well as hematopoietic and mesenchymal cells of eight bone marrow metastases from neuroblastoma patients. We developed DeepFLEX, a single-cell image analysis pipeline for MELC data that combines deep learning-based cell and nucleus segmentation and overcomes frequent challenges of multiplex imaging methods including autofluorescence and unspecific antibody binding. Using DeepFLEX, we built a single-cell atlas of bone marrow metastases comprising more than 35,000 single cells. Comparisons of cell type proportions between samples indicated that microenvironmental changes in the metastatic bone marrow are associated with tumor cell infiltration and therapy response. Hierarchical clustering of DTCs revealed multiple phenotypes with highly diverse expression of markers such as FAIM2, an inhibitory protein in the Fas apoptotic pathway, which we propose as a complementary marker to capture DTC heterogeneity in neuroblastoma. The presented single-cell atlas provides first insights into the heterogeneity of human bone marrow metastases and is an important step towards a deeper understanding of DTCs and their interactions with the bone marrow niche.