Identification of a unique intervillous cellular signature in chronic histiocytic intervillositis

Introduction: Chronic histiocytic intervillositis (CHI) is a rare histopathological lesion in the placenta characterized by an infiltrate of CD68+ cells in the intervillous space. CHI is associated with adverse pregnancy outcomes such as miscarriage, fetal growth restriction, and (late) intrauterine fetal death. The adverse pregnancy outcomes and a variable recurrence rate of 25–100% underline its clinical relevance. The pathophysiologic mechanism of CHI is unclear, but it appears to be immunologically driven. The aim of this study was to obtain a better understanding of the phenotype of the cellular infiltrate in CHI. Method: We used imaging mass cytometry to achieve in-depth visualization of the intervillous maternal immune cells and investigated their spatial orientation in situ in relation to the fetal syncytiotrophoblast. Results: We found three phenotypically distinct CD68+HLA-DR+CD38+ cell clusters that were unique for CHI. Additionally, syncytiotrophoblast cells in the vicinity of these CD68+HLA-DR+CD38+ cells showed decreased expression of the immunosuppressive enzyme CD39. Discussion: The current results provide novel insight into the phenotype of CD68+ cells in CHI. The identification of unique CD68+ cell clusters will allow more detailed analysis of their function and could result in novel therapeutic targets for CHI

Identification of a unique intervillous cellular signature in chronic histiocytic intervillositis

Introduction: Chronic histiocytic intervillositis (CHI) is a rare histopathological lesion in the placenta characterized by an infiltrate of CD68+ cells in the intervillous space. CHI is associated with adverse pregnancy outcomes such as miscarriage, fetal growth restriction, and (late) intrauterine fetal death. The adverse pregnancy outcomes and a variable recurrence rate of 25-100% underline its clinical relevance. The pathophysiologic mechanism of CHI is unclear, but it appears to be immunologically driven. The aim of this study was to obtain a better understanding of the phenotype of the cellular infiltrate in CHI.Method: We used imaging mass cytometry to achieve in-depth visualization of the intervillous maternal immune cells and investigated their spatial orientation in situ in relation to the fetal syncytiotrophoblast.Results: We found three phenotypically distinct CD68+HLA-DR+CD38+ cell clusters that were unique for CHI. Additionally, syncytiotrophoblast cells in the vicinity of these CD68+HLA-DR+CD38+ cells showed decreased expression of the immunosuppressive enzyme CD39.Discussion: The current results provide novel insight into the phenotype of CD68+ cells in CHI. The identification of unique CD68+ cell clusters will allow more detailed analysis of their function and could result in novel therapeutic targets for CHI.Research into fetal development and medicin

Identification of a unique intervillous cellular signature in chronic histiocytic intervillositis

Introduction: Chronic histiocytic intervillositis (CHI) is a rare histopathological lesion in the placenta characterized by an infiltrate of CD68+ cells in the intervillous space. CHI is associated with adverse pregnancy outcomes such as miscarriage, fetal growth restriction, and (late) intrauterine fetal death. The adverse pregnancy outcomes and a variable recurrence rate of 25–100% underline its clinical relevance. The pathophysiologic mechanism of CHI is unclear, but it appears to be immunologically driven. The aim of this study was to obtain a better understanding of the phenotype of the cellular infiltrate in CHI. Method: We used imaging mass cytometry to achieve in-depth visualization of the intervillous maternal immune cells and investigated their spatial orientation in situ in relation to the fetal syncytiotrophoblast. Results: We found three phenotypically distinct CD68+HLA-DR+CD38+ cell clusters that were unique for CHI. Additionally, syncytiotrophoblast cells in the vicinity of these CD68+HLA-DR+CD38+ cells showed decreased expression of the immunosuppressive enzyme CD39. Discussion: The current results provide novel insight into the phenotype of CD68+ cells in CHI. The identification of unique CD68+ cell clusters will allow more detailed analysis of their function and could result in novel therapeutic targets for CHI

Immunophenotype of Gastric Tumors Unveils a Pleiotropic Role of Regulatory T Cells in Tumor Development.

Gastric cancer (GC) patients display increased regulatory T cell (Tregs) numbers in peripheral blood and among tumor-infiltrating lymphocytes. Nevertheless, the role of Tregs in GC progression remains controversial. Here, we sought to explore the impact of Tregs in GCs with distinct histology, and whether Tregs can directly influence tumor cell behavior and GC development. We performed a comprehensive immunophenotyping of 82 human GC cases, through an integrated analysis of multispectral immunofluorescence detection of T cells markers and patient clinicopathological data. Moreover, we developed 3D in vitro co-cultures with Tregs and tumor cells that were followed by high-throughput and light-sheet imaging, and their biological features studied with conventional/imaging flow cytometry and Western blotting. We showed that Tregs located at the tumor nest were frequent in intestinal-type GCs but did not associate with increased levels of effector T cells. Our in vitro results suggested that Tregs preferentially infiltrated intestinal-type GC spheroids, induced the expression of IL2Rα and activation of MAPK signaling pathway in tumor cells, and promoted spheroid growth. Accumulation of Tregs in intestinal-type GCs was increased at early stages of the stomach wall invasion and in the absence of vascular and perineural invasion. In this study, we proposed a non-immunosuppressive mechanism through which Tregs might directly modulate GC cells and thereby promote tumor growth. Our findings hold insightful implications for therapeutic strategies targeting intestinal-type GCs and other tumors with similar immune context

Semi-automated background removal limits data loss and normalizes imaging mass cytometry data

Imaging mass cytometry (IMC) allows the detection of multiple antigens (approximately 40 markers) combined with spatial information, making it a unique tool for the evaluation of complex biological systems. Due to its widespread availability and retained tissue morphology, formalin-fixed, paraffin-embedded (FFPE) tissues are often a material of choice for IMC studies. However, antibody performance and signal to noise ratios can differ considerably between FFPE tissues as a consequence of variations in tissue processing, including fixation. In contrast to batch effects caused by differences in the immunodetection procedure, variations in tissue processing are difficult to control. We investigated the effect of immunodetection-related signal intensity fluctuations on IMC analysis and phenotype identification, in a cohort of 12 colorectal cancer tissues. Furthermore, we explored different normalization strategies and propose a workflow to normalize IMC data by semi-automated background removal, using publicly available tools. This workflow can be directly applied to previously acquired datasets and considerably improves the quality of IMC data, thereby supporting the analysis and comparison of multiple samples.Comp Graphics & Visualisatio

Visual cohort comparison for spatial single-cell omics-data

Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities

Cancer immunophenotyping by seven-colour multispectral imaging without tyramide signal amplification

Checkpoint blockade immunotherapies have revolutionised cancer treatment in the last decade. Nevertheless, these are only beneficial for a small proportion of cancer patients. Important prognosticators for response to immunotherapy are the mutation burden of tumours as well as the quality and quantity of tumour-infiltrating immune cells. High-throughput multiplex immunophenotyping technologies have a central role in deciphering the complexity of anti-tumour immune responses. Current techniques for the immunophenotyping of solid tumours are held back by the lack of spatial context, limitations in the number of targets that can be visualised simultaneously, and/or cumbersome protocols. We developed a tyramide signal amplification-free method for the simultaneous detection of seven cellular targets by immunofluorescence. This method overcomes limitations posed by most widespread techniques and provides a unique tool for extensive phenotyping by multispectral fluorescence microscopy. Furthermore, it can be easily implemented as a high-throughput technology for validation of discovery sets generated by RNA sequencing or mass cytometry and may serve in the future as a complementary diagnostic tool

γδ T cells are effectors of immunotherapy in cancers with HLA class I defects

DNA mismatch repair-deficient (MMR-d) cancers present an abundance of neoantigens that is thought to explain their exceptional responsiveness to immune checkpoint blockade (ICB)1,2. Here, in contrast to other cancer types3–5, we observed that 20 out of 21 (95%) MMR-d cancers with genomic inactivation of β2-microglobulin (encoded by B2M) retained responsiveness to ICB, suggesting the involvement of immune effector cells other than CD8+ T cells in this context. We next identified a strong association between B2M inactivation and increased infiltration by γδ T cells in MMR-d cancers. These γδ T cells mainly comprised the Vδ1 and Vδ3 subsets, and expressed high levels of PD-1, other activation markers, including cytotoxic molecules, and a broad repertoire of killer-cell immunoglobulin-like receptors. In vitro, PD-1+ γδ T cells that were isolated from MMR-d colon cancers exhibited enhanced reactivity to human leukocyte antigen (HLA)-class-I-negative MMR-d colon cancer cell lines and B2M-knockout patient-derived tumour organoids compared with antigen-presentation-proficient cells. By comparing paired tumour samples from patients with MMR-d colon cancer that were obtained before and after dual PD-1 and CTLA-4 blockade, we found that immune checkpoint blockade substantially increased the frequency of γδ T cells in B2M-deficient cancers. Taken together, these data indicate that γδ T cells contribute to the response to immune checkpoint blockade in patients with HLA-class-I-negative MMR-d colon cancers, and underline the potential of γδ T cells in cancer immunotherapy.</p

Visual cohort comparison for spatial single-cell omics-data

Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities.Accepted Author ManuscriptComp Graphics & Visualisatio