Chronic intestinal inflammation underlies inflammatory bowel disease (IBD). Previous studies indicated alterations in the cellular immune system; however, it has been challenging to interrogate the role of all immune cell subsets simultaneously. Therefore, we aimed to identify immune cell types associated with inflammation in IBD using high-dimensional mass cytometry. We analyzed 188 intestinal biopsies and paired blood samples of newly-diagnosed, treatment-naive patients (n=42) and controls (n=26) in two independent cohorts. We applied mass cytometry (36-antibody panel) to resolve single cells and analyzed the data with unbiased Hierarchical-SNE. In addition, imaging-mass cytometry (IMC) was performed to reveal the spatial distribution of the immune subsets in the tissue. We identified 44 distinct immune subsets. Correlation network analysis identified a network of inflammation-associated subsets, including HLA-DR⁺CD38⁺ EM CD4⁺ T cells, T regulatory-like cells, PD1⁺ EM CD8⁺ T cells, neutrophils, CD27⁺ TCRγδ cells and NK cells. All disease-associated subsets were validated in a second cohort. This network was abundant in a subset of patients, independent of IBD subtype, severity or intestinal location. Putative disease-associated CD4⁺ T cells were detectable in blood. Finally, imaging-mass cytometry revealed the spatial colocalization of neutrophils, memory CD4⁺ T cells and myeloid cells in the inflamed intestine. Our study indicates that a cellular network of both innate and adaptive immune cells colocalizes in inflamed biopsies from a subset of patients. These results contribute to dissecting disease heterogeneity and may guide the development of targeted therapeutics in IBD.

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Background & Aims: Refractory celiac disease type II (RCDII) is a rare indolent lymphoma in the small intestine characterized by a clonally expanded intraepithelial intracellular CD3⁺surfaceCD3^-CD7⁺CD56^- aberrant cell population. However, RCDII pathogenesis is ill-defined. Here, we aimed at single-cell characterization of the innate and adaptive immune system in RCDII. Methods: Paired small intestinal and blood samples from 12 RCDII patients and 6 healthy controls were assessed by single-cell mass cytometry with a 39–cell surface marker antibody panel, designed to capture heterogeneity of the innate and adaptive immune system. A second single-cell mass cytometry panel that included transcription factors and immune checkpoints was used for analysis of paired samples from 5 RCDII patients. Single-cell RNA sequencing analysis was performed on duodenal samples from 2 RCDII patients. Finally, we developed a 40-marker imaging mass cytometry antibody panel to evaluate cell–cell interactions in duodenal biopsy specimens of RCDII patients. Results: We provide evidence for intertumoral and intratumoral cell heterogeneity within the duodenal and peripheral aberrant cell population present in RCDII. Phenotypic discrepancy was observed between peripheral and duodenal aberrant cells. In addition, we observed that part of the aberrant cell population proliferated and observed co-localization of aberrant cells with CD163⁺ antigen-presenting cells (APCs) in situ. In addition, we observed phenotypic discrepancy between peripheral and duodenal aberrant cells. Conclusions: Novel high-dimensional single-cell technologies show substantial intertumoral and intratumoral heterogeneity in the aberrant cell population in RCDII. This may underlie variability in refractory disease status between patients and responsiveness to therapy, pointing to the need for personalized therapy in RCDII based on patient-specific immune profiles.

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Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.@en

SummaryFactors that govern the complex formation of memory T cells are not completelyunderstood. A better understanding of thedevelopment of memory Tcell hetero-geneity is however required to enhance vaccination and immunotherapy ap-proaches. Here we examined the impact of pathogen- and tissue-specific cueson memory CD8+T cell heterogeneity using high-dimensional single-cell mass cy-tometry and a tailored bioinformatics pipeline. We identified distinct populationsof pathogen-specific CD8+T cells that uniquely connected to a specific pathogenor associated to multiple types of acute and persistent infections. In addition, thetissue environment shaped the memory CD8+T cell heterogeneity, albeit to alesser extent than infection. The programming of memory CD8+T cell differenti-ation during acute infection is eventually superseded by persistent infection.Thus, the plethora of distinct memory CD8+T cell subsets that arise upon infec-tion is dominantly sculpted by the pathogen-specific cues and further shaped by the tissue environment.

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Cancers are characterized by extensive heterogeneity that occurs intratumorally, between lesions, and across patients. To study cancer as a complex biological system, multidimensional analyses of the tumor microenvironment are paramount. Single-cell technologies such as flow cytometry, mass cytometry, or single-cell RNA-sequencing have revolutionized our ability to characterize individual cells in great detail and, with that, shed light on the complexity of cancer microenvironments. However, a key limitation of these single-cell technologies is the lack of information on spatial context and multicellular interactions. Investigating spatial contexts of cells requires the incorporation of tissue-based techniques such as multiparameter immunofluorescence, imaging mass cytometry, or in situ detection of transcripts. In this Review, we describe the rise of multidimensional single-cell technologies and provide an overview of their strengths and weaknesses. In addition, we discuss the integration of transcriptomic, genomic, epigenomic, proteomic, and spatially-resolved data in the context of human cancers. Lastly, we will deliberate on how the integration of multi-omics data will help to shed light on the complex role of cell types present within the human tumor microenvironment, and how such system-wide approaches may pave the way toward more effective therapies for the treatment of cancer.

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Motivation: High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions.
However, the power of CyTOF to explore the full heterogeneity of a biological sample at the singlecell level is currently limited by the number of markers measured simultaneously on a single panel.
Results: To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by
evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers
we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.@en

The human fetal immune system must protect the infant against the sudden exposure to a large variety of pathogens upon birth. While it is known that the fetal immune system develops in sequential waves, relatively little is known about the composition of the innate and adaptive immune system in the tissues. Here, we applied high-dimensional mass cytometry to profile the immune system in human fetal liver, spleen, and intestine. With Hierarchical Stochastic Neighbor Embedding (HSNE) we distinguished 177 distinct immune cell clusters, including both previously identified and novel cell clusters. PCA analysis indicated substantial differences between the compositions of the immune system in the different organs. Through dual t-SNE we identified tissue-specific cell clusters, which were found both in the innate and adaptive compartment. To determine the spatial location of tissue-specific subsets we developed a 31-antibody panel to reveal both the immune compartment and surrounding stromal elements through analysis of snap-frozen tissue samples with imaging mass cytometry. Imaging mass cytometry reconstructed the tissue architecture and allowed both the characterization and determination of the location of the various immune cell clusters within the tissue context. Moreover, it further underpinned the distinctness of the immune system in the tissues. Thus, our results provide evidence for early compartmentalization of the adaptive and innate immune compartment in fetal spleen, liver, and intestine. Together, our data provide a unique and comprehensive overview of the composition and organization of the human fetal immune system in several tissues.

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Objective: A comprehensive understanding of anticancer immune responses is paramount for the optimal application and development of cancer immunotherapies. We unravelled local and systemic immune profiles in patients with colorectal cancer (CRC) by high-dimensional analysis to provide an unbiased characterisation of the immune contexture of CRC. Design: Thirty-six immune cell markers were simultaneously assessed at the single-cell level by mass cytometry in 35 CRC tissues, 26 tumour-associated lymph nodes, 17 colorectal healthy mucosa and 19 peripheral blood samples from 31 patients with CRC. Additionally, functional, transcriptional and spatial analyses of tumour-infiltrating lymphocytes were performed by flow cytometry, single-cell RNA-sequencing and multispectral immunofluorescence. Results: We discovered that a previously unappreciated innate lymphocyte population (Lin^-CD7⁺CD127^-CD56⁺CD45RO⁺) was enriched in CRC tissues and displayed cytotoxic activity. This subset demonstrated a tissue-resident (CD103⁺CD69⁺) phenotype and was most abundant in immunogenic mismatch repair (MMR)-deficient CRCs. Their presence in tumours was correlated with the infiltration of tumour-resident cytotoxic, helper and γδT cells with highly similar activated (HLA-DR⁺CD38⁺PD-1⁺) phenotypes. Remarkably, activated γδT cells were almost exclusively found in MMR-deficient cancers. Non-activated counterparts of tumour-resident cytotoxic and γδT cells were present in CRC and healthy mucosa tissues, but not in lymph nodes, with the exception of tumour-positive lymph nodes. Conclusion: This work provides a blueprint for the understanding of the heterogeneous and intricate immune landscape of CRC, including the identification of previously unappreciated immune cell subsets. The concomitant presence of tumour-resident innate and adaptive immune cell populations suggests a multitargeted exploitation of their antitumour properties in a therapeutic setting.

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Background: The clinical benefit of immunotherapeutic approaches against cancer has been well established although complete responses are only observed in a minority of patients. Combination immunotherapy offers an attractive avenue to develop more effective cancer therapies by improving the efficacy and duration of the tumor-specific T-cell response. Here, we aimed at deciphering the mechanisms governing the response to PD-1/PD-L1 checkpoint blockade to support the rational design of combination immunotherapy. Methods: Mice bearing subcutaneous MC-38 tumors were treated with blocking PD-L1 antibodies. To establish high-dimensional immune signatures of immunotherapy-specific responses, the tumor microenvironment was analyzed by CyTOF mass cytometry using 38 cellular markers. Findings were further examined and validated by flow cytometry and by functional in vivo experiments. Immune profiling was extended to the tumor microenvironment of colorectal cancer patients. Results: PD-L1 blockade induced selectively the expansion of tumor-infiltrating CD4⁺ and CD8⁺ T-cell subsets, co-expressing both activating (ICOS) and inhibitory (LAG-3, PD-1) molecules. By therapeutically co-targeting these molecules on the T_AI cell subsets in vivo by agonistic and antagonist antibodies, we were able to enhance PD-L1 blockade therapy as evidenced by an increased number of T_AI cells within the tumor micro-environment and improved tumor protection. Moreover, T_AI cells were also found in the tumor-microenvironment of colorectal cancer patients. Conclusions: This study shows the presence of T cell subsets in the tumor micro-environment expressing both activating and inhibitory receptors. These T_AI cells can be targeted by combined immunotherapy leading to improved survival.

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Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify “new” cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data.

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