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Rationale: Chronic inflammation plays an important role in alveolar tissue damage in emphysema, but the underlying immune alterations and cellular interactions are incompletely understood. Objectives: To explore disease-specific pulmonary immune cell alterations and cellular interactions in emphysema. Methods: We used single-cell mass cytometry (CyTOF) to compare the immune compartment in alveolar tissue from 15 patients with severe emphysema and 5 control subjects. Imaging mass cytometry (IMC) was applied to identify altered cell–cell interactions in alveolar tissue from patients with emphysema (n = 12) compared with control subjects (n = 8). Measurements and Main Results: We observed higher percentages of central memory CD4 T cells in combination with lower proportions of effector memory CD4 T cells in emphysema. In addition, proportions of cytotoxic central memory CD8 T cells and CD127+CD27+CD69− T cells were higher in emphysema, the latter potentially reflecting an influx of circulating lymphocytes into the lungs. Central memory CD8 T cells, isolated from alveolar tissue from patients with emphysema, exhibited an IFN-γ response upon anti-CD3 and anti-CD28 activation. Proportions of CD1c+ dendritic cells, expressing migratory and costimulatory markers, were higher in emphysema. Importantly, IMC enabled us to visualize increased spatial colocalization of CD1c+ dendritic cells and CD8 T cells in emphysema in situ. Conclusions: Using CyTOF, we characterized the alterations of the immune cell signature in alveolar tissue from patients with chronic obstructive pulmonary disease stage III or IV emphysema versus control lung tissue. These data contribute to a better understanding of the pathogenesis of emphysema and highlight the feasibility of interrogating the immune cell signature using CyTOF and IMC in human lung tissue. Clinical trial registered with (NCT04918706). [ABSTRACT FROM AUTHOR]

Background and Aims Fistula formation is a major complication in Crohn's disease [CD] and the role of the immune cell compartment remains to be elucidated. Thus, we compared the immune cell compartment of CD fistula to inflammatory CD colitis using imaging mass cytometry [IMC] and immunofluorescence. Methods A 36-marker panel including structural, functional, and lineage markers for use in IMC was established. This panel was applied to analyse paraffin-embedded CD fistula tract [ n  = 11], CD colitis [ n  = 10], and colon samples from non-inflamed controls [ n  = 12]. Computational methods for cell segmentation, dimensionality reduction, and cell type clustering were used to define cell populations for cell frequency, marker distribution, and spatial neighbourhood analysis. Multiplex immunofluorescence was used for higher resolution spatial analysis. Results Analysis of cell frequencies in CD fistulas compared to CD colitis and control colonic samples revealed a significant increase in neutrophils, effector cytotoxic T cells, and inflammatory macrophages in CD fistula samples, whereas regulatory T cells were decreased. Neutrophils in CD fistula expressed significantly more matrix metalloproteinase 9 [MMP9], correlating with extracellular matrix remodelling. Neighbourhood analysis revealed a strong association between MMP9+ neutrophils and effector cytotoxic T cells in both CD fistulas and colitis. Conclusions This study presents the first highly multiplexed single cell analysis of the immune cell compartment of CD fistulas and their spatial context. It links immune cell dynamics, particularly MMP9+ neutrophils, to extracellular matrix remodelling in CD fistulas, offering insights into the complex network of cellular interactions and potential therapeutic targets for CD complications. [ABSTRACT FROM AUTHOR]

Immune-related skin diseases represent a collective of dermatological disorders intricately linked to dysfunctional immune system processes. These conditions are primarily characterized by an immoderate activation of the immune system or deviant immune responses, involving diverse immune components including immune cells, antibodies, and inflammatory mediators. However, the precise molecular dysregulation underlying numerous individual cases of these diseases and unique subsets respond under disease conditions remains elusive. Comprehending the mechanisms and determinants governing the homeostasis and functionality of diseases could offer potential therapeutic opportunities for intervention. Mass cytometry enables precise and high-throughput quantitative measurement of proteins within individual cells by utilizing antibodies labeled with rare heavy metal isotopes. Imaging mass cytometry employs mass spectrometry to obtain spatial information on cell-to-cell interactions within tissue sections, simultaneously utilizing more than 40 markers. The application of single-cell mass cytometry presents a unique opportunity to conduct highly multiplexed analysis at the single-cell level, thereby revolutionizing our understanding of cell population heterogeneity and hierarchy, cellular states, multiplexed signaling pathways, proteolysis products, and mRNA transcripts specifically in the context of many autoimmune diseases. This information holds the potential to offer novel approaches for the diagnosis, prognostic assessment, and monitoring responses to treatment, thereby enriching our strategies in managing the respective conditions. This review summarizes the present-day utilization of single-cell mass cytometry in studying immune-related skin diseases, highlighting its advantages and limitations. This technique will become increasingly prevalent in conducting extensive investigations into these disorders, ultimately yielding significant contributions to their accurate diagnosis and efficacious therapeutic interventions. [ABSTRACT FROM AUTHOR]

The underlying pathogenesis of neurological sequelae in post-COVID-19 patients remains unclear. Here, we used multidimensional spatial immune phenotyping and machine learning methods on brains from initial COVID-19 survivors to identify the biological correlate associated with previous SARS-CoV-2 challenge. Compared to healthy controls, individuals with post-COVID-19 revealed a high percentage of TMEM119+P2RY12+CD68+Iba1+HLA-DR+CD11c+SCAMP2+ microglia assembled in prototypical cellular nodules. In contrast to acute SARS-CoV-2 cases, the frequency of CD8+ parenchymal T cells was reduced, suggesting an immune shift toward innate immune activation that may contribute to neurological alterations in post-COVID-19 patients. [ABSTRACT FROM AUTHOR]

We introduce a 35‐marker imaging mass cytometry (IMC) panel for a detailed examination of immune cell populations and HIV RNA in formalin fixed paraffin embedded (FFPE) human intestinal tissue. The panel has broad cell type coverage and particularly excels in delineating subsets of mononuclear phagocytes and T cells. Markers for key tissue structures are included, enabling identification of epithelium, blood vessels, lymphatics, and musculature. The described method for HIV RNA detection can be generalized to other low abundance RNA targets, whether endogenous or pathogen derived. As such, the panel presented here is useful for high parameter spatial mapping of intestinal immune cells and their interactions with pathogens such as HIV. [ABSTRACT FROM AUTHOR]

A multimodal mass spectrometry imaging (MSI) approach was used to investigate the chemotherapy drug-induced response of a Multicellular Tumour Spheroid (MCTS) 3D cell culture model of osteosarcoma (OS). The work addresses the critical demand for enhanced translatable early drug discovery approaches by demonstrating a robust spatially resolved molecular distribution analysis in tumour models following chemotherapeutic intervention. Advanced high-resolution techniques were employed, including desorption electrospray ionisation (DESI) mass spectrometry imaging (MSI), to assess the interplay between metabolic and cellular pathways in response to chemotherapeutic intervention. Endogenous metabolite distributions of the human OS tumour models were complemented with subcellularly resolved protein localisation by the detection of metal-tagged antibodies using Imaging Mass Cytometry (IMC). The first application of matrix-assisted laser desorption ionization–immunohistochemistry (MALDI-IHC) of 3D cell culture models is reported here. Protein localisation and expression following an acute dosage of the chemotherapy drug doxorubicin demonstrated novel indications for mechanisms of region-specific tumour survival and cell-cycle-specific drug-induced responses. Previously unknown doxorubicin-induced metabolite upregulation was revealed by DESI-MSI of MCTSs, which may be used to inform mechanisms of chemotherapeutic resistance. The demonstration of specific tumour survival mechanisms that are characteristic of those reported for in vivo tumours has underscored the increasing value of this approach as a tool to investigate drug resistance. [ABSTRACT FROM AUTHOR]

IntroductionPancreatic ductal adenocarcinoma (PDAC) represents the complexity of interaction between cancer and cells of the tumor microenvironment (TME). Immune cells affect tumor cell behavior, thus driving cancer progression. Cancer-associated fibroblasts (CAFs) are responsible of the desmoplastic and fibrotic reaction by regulating deposition and remodeling of extracellular matrix (ECM). As tumor-promoting cells abundant in PDAC ECM, CAFs represent promising targets for novel anticancer interventions. However, relevant clinical trials are hampered by the lack of specific markers and elusive differences among CAF subtypes. Indeed, while single-cell transcriptomic analyses have provided important information on the cellular constituents of PDACs and related molecular pathways, studies based on the identification of protein markers in tissues aimed at identifying CAF subtypes and new molecular targets result incomplete.MethodsHerein, we applied multiplexed Imaging Mass Cytometry (IMC) at single-cell resolution on 8 human PDAC tissues to depict the PDAC composing cells, and profiling immune cells, endothelial cells (ECs), as well as endocrine cells and tumor cells.ResultsWe focused on CAFs by characterizing up to 19 clusters distinguished by phenotype, spatiality, and interaction with immune and tumor cells. We report evidence that specific subtypes of CAFs (CAFs 10 and 11) predominantly are enriched at the tumor-stroma interface and closely associated with tumor cells. CAFs expressing different combinations of FAP, podoplanin and cadherin-11, were associated with a higher level of CA19-9. Moreover, we identified specific subsets of FAP+ and podoplanin+/cadherin-11+ CAFs enriched in patients with negative prognosis.DiscussionThe present study provides new general insights into the complexity of the PDAC microenvironment by defining phenotypic heterogeneities and spatial distributions of CAFs, thus suggesting different functions of their subtypes in the PDAC microenvironment.

Summary: Background: Presence of nerves in tumours, by axonogenesis and neurogenesis, is gaining increased attention for its impact on cancer initiation and development, and the new field of cancer neuroscience is emerging. A recent study in prostate cancer suggested that the tumour microenvironment may influence cancer progression by recruitment of Doublecortin (DCX)-expressing neural progenitor cells (NPCs). However, the presence of such cells in human breast tumours has not been comprehensively explored. Methods: Here, we investigate the presence of DCX-expressing cells in breast cancer stromal tissue from patients using Imaging Mass Cytometry. Single-cell analysis of 372,468 cells across histopathological images of 107 breast cancers enabled spatial resolution of neural elements in the stromal compartment in correlation with clinicopathological features of these tumours. In parallel, we established a 3D in vitro model mimicking breast cancer neural progenitor-innervation and examined the two cell types as they co-evolved in co-culture by using mass spectrometry-based global proteomics. Findings: Stromal presence of DCX + cells is associated with tumours of higher histological grade, a basal-like phenotype, and shorter patient survival in tumour tissue from patients with breast cancer. Global proteomics analysis revealed significant changes in the proteomic landscape of both breast cancer cells and neural progenitors in co-culture. Interpretation: These results support that neural involvement plays an active role in breast cancer and warrants further studies on the relevance of nerve elements for tumour progression. Funding: This work was supported by the Research Council of Norway through its Centre of Excellence funding scheme, project number 223250 (to L.A.A), the Norwegian Cancer Society (to L.A.A. and H.V.), the Regional Health Trust Western Norway (Helse Vest) (to L.A.A.), the Meltzer Research Fund (to H.V.) and the National Institutes of Health (NIH)/NIGMS grant R01 GM132129 (to J.A.P.).

Imaging mass cytometry (IMC) is a metal mass spectrometry-based method allowing highly multiplex immunophenotyping of cells within tissue samples. However, some limitations of IMC are its 1-µm resolution and its time and costs of analysis limiting respectively the detailed histopathological analysis of IMC-produced images and its application to small selected tissue regions of interest (ROI) of one to few square millimeters. Coupling on a single-tissue section, IMC and histopathological analyses could permit a better selection of the ROI for IMC analysis as well as co-analysis of immunophenotyping and histopathological data until the single-cell level. The development of this method is the aim of the present study in which we point to the feasibility of applying the IMC process to tissue sections previously Alcian blue-stained and digitalized before IMC tissue destructive analyses. This method could help to improve the process of IMC in terms of ROI selection, time of analysis, and the confrontation between histopathological and immunophenotypic data of cells. [ABSTRACT FROM AUTHOR]

Imaging Mass Cytometry (IMC) is a novel, and formidable high multiplexing imaging method emerging as a promising tool for in-depth studying of tissue architecture and intercellular communications. Several studies have reported various IMC antibody panels mainly focused on studying the immunological landscape of the tumor microenvironment (TME). With this paper, we wanted to address cancer associated fibroblasts (CAFs), a component of the TME very often underrepresented and not emphasized enough in present IMC studies. Therefore, we focused on the development of a comprehensive IMC panel that can be used for a thorough description of the CAF composition of breast cancer TME and for an in-depth study of different CAF niches in relation to both immune and breast cancer cell communication. We established and validated a 42 marker panel using a variety of control tissues and rigorous quantification methods. The final panel contained 6 CAF-associated markers (aSMA, FAP, PDGFRa, PDGFRb, YAP1, pSMAD2). Breast cancer tissues (4 cases of luminal, 5 cases of triple negative breast cancer) and a modified CELESTA pipelinewere used to demonstrate the utility of our IMCpanel for detailed profiling of different CAF, immune and cancer cell phenotypes. [ABSTRACT FROM AUTHOR]

Imaging mass cytometry （IMC） is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry/cytochemistry. With a unique high-dimensional perspective to comprehensively and accurately depict the complex phenotypes, signaling pathways, and tumor immune interactions in the tissue and tumor microenvironment, is widely used in gastrointestinal tumors. This article reviews the application of IMC in depicting the panorama of tumor microenvironment, revealing tumor spatial heterogeneity, clarifying tumor pharmacological mechanism, assisting new drug development, and evaluating the efficacy of immunotherapy dynamically in digestive system tumors. [ABSTRACT FROM AUTHOR]

Endometriosis (EM), characterized by the presence of endometrial-like tissue outside the uterus, is the leading cause of chronic pelvic pain and infertility in females of reproductive age. Despite its high prevalence, the molecular mechanisms underlying EM pathogenesis remain poorly understood. The endocannabinoid system (ECS) is known to influence several cardinal features of this complex disease including pain, vascularization, and overall lesion survival, but the exact mechanisms are not known. Utilizing CNR1 knockout (k/o), CNR2 k/o, and wild-type (WT) mouse models of EM, we reveal contributions of ECS and these receptors in disease initiation, progression, and immune modulation. Particularly, we identified EM-specific T cell dysfunction in the CNR2 k/o mouse model of EM. We also demonstrate the impact of decidualization-induced changes on ECS components, and the unique disease-associated transcriptional landscape of ECS components in EM. Imaging mass cytometry (IMC) analysis revealed distinct features of the microenvironment between CNR1, CNR2, and WT genotypes in the presence or absence of decidualization. This study, for the first time, provides an in-depth analysis of the involvement of the ECS in EM pathogenesis and lays the foundation for the development of novel therapeutic interventions to alleviate the burden of this debilitating condition.

PURPOSE: We have previously used Immuno Tomography (IT) to identify label-retaining stem cell populations in the cornea and meibomian gland. While this method provides the unique ability to quantify stem cell populations comprised of 1-4 cells, the number of antigens that can be sequentially used to characterize these unique cells is limited by antigen stability after antibody stripping and re-probing. To address this deficiency, we have evaluated the capability of Imaging Mass Cytometry™ (IMC™) to generate multiplexed images using metal-conjugated antibodies to label IT plastic sections and generate 3-dimensional IMC data sets (3D IMC). METHODS: K5-H2B-GFP mice, 56 days after doxycycline chase, were sacrificed and eyelid tissue processed for IT. A total of 400 serial, plastic sections, 2 μm thick, were then probed using metal-tagged antibodies specific for sox 9, collagen type I, E-cadherin, Ki67, GFP, αSMA, vimentin, and DNA intercalator. Multiplexed images were then generated using an Imaging Mass Cytometry system (Fluidigm®), and 3D reconstructions were assembled. RESULTS: All 8 metal-labeled tags were detected and their images were successfully assembled into 3D IMC data sets. GFP-labeled nuclei were identified within the meibomian glands in comparable numbers to those previously reported for slow-cycling meibomian gland stem cells. CONCLUSIONS: These findings demonstrate that IMC can be used on plastic sections to generate multiplexed, 3D data sets that can be reconstructed to show the spatial localization of meibomian gland stem cells. We propose that 3D IMC might prove valuable in more fully characterizing stem cell populations in different tissues.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS–TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28–marker panel for single–cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance. [ABSTRACT FROM AUTHOR]

Analysis of imaging mass cytometry (IMC) data and other low‐resolution multiplexed tissue imaging technologies is often confounded by poor single‐cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states, or spatial relationships compared to reference data from single‐cell suspension technologies. To this end we have developed the "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal‐tagged antibodies recognizing well‐characterized phenotypic and functional markers to stain the same Formalin‐Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood analysis. We found that single‐cell segmentation was improved by the use of an Ilastik‐derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z‐score normalization step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out‐performing phenograph in terms of cell type identification. We also found that neighborhood analysis was influenced by the method used for finding neighboring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image‐edge location. Importantly, OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates.FCS files from the segmentation output and allows for single‐cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists. [ABSTRACT FROM AUTHOR]

Imaging mass cytometry (IMC) is a powerful spatial technology that utilizes cytometry time of flight to acquire multiplexed image datasets with up to 40 markers, via metal‐tagged antibodies. Recent advances in IMC have led to the inclusion of RNAScope probes and multiple new analysis pipelines have led to faster analyses and better results. However, IMC still suffers from lower resolution (1 μm2 pixels) and relatively small regions of interest (ROIs) (<2 mm2) compared to other, light‐based microscope technologies. Capturing higher‐resolution images on serial sections causes great difficulty when attempting to align cells and structures across serial sections, especially when observing smaller cell types and structures. Therefore, we demonstrate the combination of H&E and multiplex immunofluorescence imaging, for much higher resolution of the structural and cellular compartments found throughout the entire tissue section, with the high‐dimensionality of IMC for specific ROIs on a single slide. Additionally, we demonstrate a simple and effective open‐source cell segmentation and IMC analysis pipeline with previously published and freely available software. [ABSTRACT FROM AUTHOR]

To decipher the interactions between various components of the tumor microenvironment (TME) and tumor cells in a preserved spatial context, a multiparametric approach is essential. In this pursuit, imaging mass cytometry (IMC) emerges as a valuable tool, capable of concurrently analyzing up to 40 parameters at subcellular resolution. In this study, a set of antibodies was selected to spatially resolve multiple cell types and TME elements, including a comprehensive panel targeted at dissecting the heterogeneity of cancer-associated fibroblasts (CAF), a pivotal TME component. This antibody panel was standardized and optimized using formalin-fixed paraffin-embedded tissue (FFPE) samples from different organs/lesions known to express the markers of interest. The final composition of the antibody panel was determined based on the performance of conjugated antibodies in both immunohistochemistry (IHC) and IMC. Tissue images were segmented employing the Steinbock framework. Unsupervised clustering of single-cell data was carried out using a bioinformatics pipeline developed in R program. This paper provides a detailed description of the staining procedure and analysis workflow. Subsequently, the panel underwent validation on clinical FFPE samples from head and neck squamous cell carcinoma (HNSCC). The panel and bioinformatics pipeline established here proved to be robust in characterizing different TME components of HNSCC while maintaining a high degree of spatial detail. The platform we describe shows promise for understanding the clinical implications of TMA heterogeneity in large patient cohorts with FFPE tissues available in diagnostic biobanks worldwide.

Imaging mass cytometry (IMC) is a metal mass spectrometry-based method allowing highly multiplex immunophenotyping of cells within tissue samples. However, some limitations of IMC are its 1-µm resolution and its time and costs of analysis limiting respectively the detailed histopathological analysis of IMC-produced images and its application to small selected tissue regions of interest (ROI) of one to few square millimeters. Coupling on a single-tissue section, IMC and histopathological analyses could permit a better selection of the ROI for IMC analysis as well as co-analysis of immunophenotyping and histopathological data until the single-cell level. The development of this method is the aim of the present study in which we point to the feasibility of applying the IMC process to tissue sections previously Alcian blue-stained and digitalized before IMC tissue destructive analyses. This method could help to improve the process of IMC in terms of ROI selection, time of analysis, and the confrontation between histopathological and immunophenotypic data of cells.

Imaging Mass Cytometry (IMC) is a novel, and formidable high multiplexing imaging method emerging as a promising tool for in-depth studying of tissue architecture and intercellular communications. Several studies have reported various IMC antibody panels mainly focused on studying the immunological landscape of the tumor microenvironment (TME). With this paper, we wanted to address cancer associated fibroblasts (CAFs), a component of the TME very often underrepresented and not emphasized enough in present IMC studies. Therefore, we focused on the development of a comprehensive IMC panel that can be used for a thorough description of the CAF composition of breast cancer TME and for an in-depth study of different CAF niches in relation to both immune and breast cancer cell communication. We established and validated a 42 marker panel using a variety of control tissues and rigorous quantification methods. The final panel contained 6 CAF-associated markers (aSMA, FAP, PDGFRa, PDGFRb, YAP1, pSMAD2). Breast cancer tissues (4 cases of luminal, 5 cases of triple negative breast cancer) and a modified CELESTA pipeline were used to demonstrate the utility of our IMC panel for detailed profiling of different CAF, immune and cancer cell phenotypes.

Summary: Background: Lung damage in severe COVID-19 is highly heterogeneous however studies with dedicated spatial distinction of discrete temporal phases of diffuse alveolar damage (DAD) and alternate lung injury patterns are lacking. Existing studies have also not accounted for progressive airspace obliteration in cellularity estimates. We used an imaging mass cytometry (IMC) analysis with an airspace correction step to more accurately identify the cellular immune response that underpins the heterogeneity of severe COVID-19 lung disease. Methods: Lung tissue was obtained at post-mortem from severe COVID-19 deaths. Pathologist-selected regions of interest (ROIs) were chosen by light microscopy representing the patho-evolutionary spectrum of DAD and alternate disease phenotypes were selected for comparison. Architecturally normal SARS-CoV-2-positive lung tissue and tissue from SARS-CoV-2-negative donors served as controls. ROIs were stained for 40 cellular protein markers and ablated using IMC before segmented cells were classified. Cell populations corrected by ROI airspace and their spatial relationships were compared across lung injury patterns. Findings: Forty patients (32M:8F, age: 22–98), 345 ROIs and >900k single cells were analysed. DAD progression was marked by airspace obliteration and significant increases in mononuclear phagocytes (MnPs), T and B lymphocytes and significant decreases in alveolar epithelial and endothelial cells. Neutrophil populations proved stable overall although several interferon-responding subsets demonstrated expansion. Spatial analysis revealed immune cell interactions occur prior to microscopically appreciable tissue injury. Interpretation: The immunopathogenesis of severe DAD in COVID-19 lung disease is characterised by sustained increases in MnPs and lymphocytes with key interactions occurring even prior to lung injury is established. Funding: UK Research and Innovation/Medical Research Council through the UK Coronavirus Immunology Consortium, Barbour Foundation, General Sir John Monash Foundation, Newcastle University, JGW Patterson Foundation, Wellcome Trust.

Objective: To develop an imaging mass cytometry method for identifying complex cell phenotypes, inter-cellular interactions, and population changes in the synovium and infrapatellar fat pad (IFP) of the mouse knee following a non-invasive compression injury. Design: Fifteen male C57BL/6 mice were fed a high-fat diet for 8 weeks prior to random assignment to sham, 0.88 ​mm, or 1.7 ​mm knee compression displacement at 24 weeks of age. 2-weeks after loading, limbs were prepared for histologic and imaging mass cytometry analysis, focusing on myeloid immune cell populations in the synovium and IFP. Results: 1.7 ​mm compression caused anterior cruciate ligament (ACL) rupture, development of post-traumatic osteoarthritis, and a 2- to 3-fold increase in cellularity of synovium and IFP tissues compared to sham or 0.88 ​mm compression. Imaging mass cytometry identified 11 myeloid cell subpopulations in synovium and 7 in IFP, of which approximately half were elevated 2 weeks after ACL injury in association with the vasculature. Notably, two monocyte/macrophage subpopulations and an MHC IIhi population were elevated 2-weeks post-injury in the synovium but not IFP. Vascular and immune cell interactions were particularly diverse in the synovium, incorporating 8 unique combinations of 5 myeloid cell populations, including a monocyte/macrophage population, an MHC IIhi population, and 3 different undefined F4/80+ myeloid populations. Conclusions: Developing an imaging mass cytometry method for the mouse enabled us to identify a diverse array of synovial and IFP vascular-associated myeloid cell subpopulations. These subpopulations were differentially elevated in synovial and IFP tissues 2-weeks post injury, providing new details on tissue-specific immune regulation.

A multimodal mass spectrometry imaging (MSI) approach was used to investigate the chemotherapy drug-induced response of a Multicellular Tumour Spheroid (MCTS) 3D cell culture model of osteosarcoma (OS). The work addresses the critical demand for enhanced translatable early drug discovery approaches by demonstrating a robust spatially resolved molecular distribution analysis in tumour models following chemotherapeutic intervention. Advanced high-resolution techniques were employed, including desorption electrospray ionisation (DESI) mass spectrometry imaging (MSI), to assess the interplay between metabolic and cellular pathways in response to chemotherapeutic intervention. Endogenous metabolite distributions of the human OS tumour models were complemented with subcellularly resolved protein localisation by the detection of metal-tagged antibodies using Imaging Mass Cytometry (IMC). The first application of matrix-assisted laser desorption ionization–immunohistochemistry (MALDI-IHC) of 3D cell culture models is reported here. Protein localisation and expression following an acute dosage of the chemotherapy drug doxorubicin demonstrated novel indications for mechanisms of region-specific tumour survival and cell-cycle-specific drug-induced responses. Previously unknown doxorubicin-induced metabolite upregulation was revealed by DESI-MSI of MCTSs, which may be used to inform mechanisms of chemotherapeutic resistance. The demonstration of specific tumour survival mechanisms that are characteristic of those reported for in vivo tumours has underscored the increasing value of this approach as a tool to investigate drug resistance.

Summary: Background: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. Methods: This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. Findings: With 1% annotations (18–114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828–11,459 annotated cells (−0.002 to −0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. Interpretation: By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. Funding: This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

Purpose: The presence and functional competence of intratumoral CD8+ T cells is often a barometer for successful immunotherapeutic responses in cancer. Despite this understanding and the extensive number of clinical-stage immunotherapies focused on potentiation (co-stimulation) or rescue (checkpoint blockade) of CD8+ T cell antitumor activity, dynamic biomarker strategies are often lacking. To help fill this gap, immuno-PET nuclear imaging has emerged as a powerful tool for in vivo molecular imaging of antibody targeting. Here, we took advantage of immuno-PET imaging using 89Zr-IAB42M1-14, anti-mouse CD8 minibody, to characterize CD8+ T-cell tumor infiltration dynamics following ICOS (inducible T-cell co-stimulator) agonist antibody treatment alone and in combination with PD-1 blocking antibody in a model of mammary carcinoma. Procedures. Female BALB/c mice with established EMT6 tumors received 10 µg, IP of either IgG control antibodies, ICOS agonist monotherapy, or ICOS/PD-1 combination therapy on days 0, 3, 5, 7, 9, 10, or 14. Imaging was performed at 24 and 48 h post IV dose of 89Zr IAB42M1-14. In addition to 89Zr-IAB42M1-14 uptake in tumor and tumor-draining lymph node (TDLN), 3D radiomic features were extracted from PET/CT images to identify treatment effects. Imaging mass cytometry (IMC) and immunohistochemistry (IHC) was performed at end of study. Results: 89Zr-IAB42M1-14 uptake in the tumor was observed by day 11 and was preceded by an increase in the TDLN as early as day 4. The spatial distribution of 89Zr-IAB42M1-14 was more uniform in the drug treated vs. control tumors, which had spatially distinct tracer uptake in the periphery relative to the core of the tumor. IMC analysis showed an increased percentage of cytotoxic T cells in the ICOS monotherapy and ICOS/PD-1 combination group compared to IgG controls. Additionally, temporal radiomics analysis demonstrated early predictiveness of imaging features. Conclusion: To our knowledge, this is the first detailed description of the use of a novel immune-PET imaging technique to assess the kinetics of CD8+ T-cell infiltration into tumor and lymphoid tissues following ICOS agonist and PD-1 blocking antibody therapy. By demonstrating the capacity for increased spatial and temporal resolution of CD8+ T-cell infiltration across tumors and lymphoid tissues, these observations underscore the widespread potential clinical utility of non-invasive PET imaging for T-cell-based immunotherapy in cancer. [ABSTRACT FROM AUTHOR]

Persistent inflammation can promote the development of tertiary lymphoid structures (TLS) within tissues resembling secondary lymphoid organs (SLO) such as lymph nodes (LN). The composition of TLS across different organs and diseases could be of pathophysiological and medical interest. In this work, we compared TLS to SLO in cancers of the digestive tract and in inflammatory bowel diseases. Colorectal and gastric tissues with different inflammatory diseases and cancers from the department of pathology of CHU Brest were analyzed based on 39 markers using imaging mass cytometry (IMC). Unsupervised and supervised clustering analyses of IMC images were used to compare SLO and TLS. Unsupervised analyses tended to group TLS per patient but not per disease. Supervised analyses of IMC images revealed that LN had a more organized structure than TLS and non-encapsulated SLO Peyer's patches. TLS followed a maturation spectrum with close correlations between germinal center (GC) markers' evolution. The correlations between organizational and functional markers made relevant the previously proposed TLS division into three stages: lymphoid-aggregates (LA) (CD20+CD21-CD23-) had neither organization nor GC functionality, non-GC TLS (CD20+CD21+CD23-) were organized but lacked GC's functionality and GC-like TLS (CD20+CD21+CD23+) had GC's organization and functionality. This architectural and functional maturation grading of TLS pointed to differences across diseases. TLS architectural and functional maturation grading is accessible with few markers allowing future diagnostic, prognostic, and predictive studies on the value of TLS grading, quantification and location within pathological tissues in cancers and inflammatory diseases. [ABSTRACT FROM AUTHOR]

The recent emergence of imaging mass cytometry technology has led to the generation of an increasing amount of high-dimensional data and, with it, the need for suitable performant bioinformatics tools dedicated to specific multiparametric studies. The first and most important step in treating the acquired images is the ability to performhighly efficient cell segmentation for subsequent analyses. In this context, we developed YOUPI (Your Powerful and Intelligent tool) software. It combines advanced segmentation techniques based on deep learning algorithms with a friendly graphical user interface for non-bioinformatics users. In this article, we present the segmentation algorithm developed for YOUPI. We have set a benchmark with mathematics-based segmentation approaches to estimate its robustness in segmenting different tissue biopsies. [ABSTRACT FROM AUTHOR]

The purpose of this 20‐target imaging mass cytometry (IMC) panel is to identify the main cell types in formalin fixed paraffin embedded (FFPE) mouse liver tissue with the Hyperion™ mass cytometer from Standard BioTools (formerly Fluidigm). The antibody panel includes markers to identify hepatocytes (E‐cadherin, HNF4α (hepatocyte nuclear factor 4 alpha), Arginase‐1), liver sinusoidal endothelial cells (LSECs; CD206), Kupffer cells (F4/80, CD206), neutrophils (Ly6G, CD11b), bone marrow derived myeloid cells (BMDMs; CD11b), cholangiocytes (E‐cadherin high), endothelial cells (CD31, α‐SMA), plasmacytoid dendritic cells (CD317), B cells (CD19), T cells (CD3e, CD4, CD8a), NK cells (CD161) as well markers of cell activation (CD44, CD74), proliferation (Ki‐67) and to aid in cell segmentation (Pan‐Actin, E‐cadherin, histone H3). The panel has been tested in other mouse tissues, namely the spleen, colon and lung, and therefore is likely to work across various mouse FFPE samples of interest. It has not been tested using human samples, frozen samples or in suspension mass cytometry because FFPE treatment profoundly changes epitope conformation. In summary, this panel is a powerful tool for pre‐clinical research to determine cellular abundance and spatial distribution within mouse tissues and serves as a scaffold, to which more targets can be added for project specific requirements. [ABSTRACT FROM AUTHOR]

Summary: Background: Identification of aggressive low-stage endometrial cancers is challenging. So far, studies have failed to pinpoint robust features or biomarkers associated with risk of recurrence for these patients. Methods: Imaging mass cytometry was used to examine single-cell expression of 23 proteins in 36 primary FIGO IB endometrial cancers, of which 17 recurred. Single-cell information was extracted for each tumor and unsupervised clustering was used to identify cellular phenotypes. Distinct phenotypes and cellular neighborhoods were compared in relation to recurrence. Cellular differences were validated in a separate gene expression dataset and the TCGA EC dataset. Vimentin protein expression was evaluated by IHC in pre-operative samples from 518 patients to validate its robustness as a prognostic marker. Findings: The abundance of epithelial, immune or stromal cell types did not associate with recurrence. Clustering of patients based on tumor single cell marker expression revealed distinct patient clusters associated with outcome. A cell population neighboring CD8+ T cells, defined by vimentin, ER, and PR expressing epithelial cells, was more prevalent in non-recurrent tumors. Importantly, lower epithelial vimentin expression and lower gene expression of VIM associated with worse recurrence-free survival. Loss and low expression of vimentin was validated by IHC as a robust marker for recurrence in FIGO I stage disease and predicted poor prognosis also when including all patients and in endometrioid patients only. Interpretation: This study reveals distinct characteristics in low-stage tumors and points to vimentin as a clinically relevant marker that may aid in identifying a here to unidentified subgroup of high-risk patients. Funding: A full list of funding that contributed to this study can be found in the Acknowledgements section.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS–TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28–marker panel for single–cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance.

Persistent inflammation can promote the development of tertiary lymphoid structures (TLS) within tissues resembling secondary lymphoid organs (SLO) such as lymph nodes (LN). The composition of TLS across different organs and diseases could be of pathophysiological and medical interest. In this work, we compared TLS to SLO in cancers of the digestive tract and in inflammatory bowel diseases. Colorectal and gastric tissues with different inflammatory diseases and cancers from the department of pathology of CHU Brest were analyzed based on 39 markers using imaging mass cytometry (IMC). Unsupervised and supervised clustering analyses of IMC images were used to compare SLO and TLS. Unsupervised analyses tended to group TLS per patient but not per disease. Supervised analyses of IMC images revealed that LN had a more organized structure than TLS and non-encapsulated SLO Peyer’s patches. TLS followed a maturation spectrum with close correlations between germinal center (GC) markers’ evolution. The correlations between organizational and functional markers made relevant the previously proposed TLS division into three stages: lymphoid-aggregates (LA) (CD20+CD21-CD23-) had neither organization nor GC functionality, non-GC TLS (CD20+CD21+CD23-) were organized but lacked GC’s functionality and GC-like TLS (CD20+CD21+CD23+) had GC’s organization and functionality. This architectural and functional maturation grading of TLS pointed to differences across diseases. TLS architectural and functional maturation grading is accessible with few markers allowing future diagnostic, prognostic, and predictive studies on the value of TLS grading, quantification and location within pathological tissues in cancers and inflammatory diseases.

The recent emergence of imaging mass cytometry technology has led to the generation of an increasing amount of high-dimensional data and, with it, the need for suitable performant bioinformatics tools dedicated to specific multiparametric studies. The first and most important step in treating the acquired images is the ability to perform highly efficient cell segmentation for subsequent analyses. In this context, we developed YOUPI (Your Powerful and Intelligent tool) software. It combines advanced segmentation techniques based on deep learning algorithms with a friendly graphical user interface for non-bioinformatics users. In this article, we present the segmentation algorithm developed for YOUPI. We have set a benchmark with mathematics-based segmentation approaches to estimate its robustness in segmenting different tissue biopsies.

Currently, the study of resistance mechanisms and disease progression in cancer relies on the capacity to analyze tumors as a complex ecosystem of healthy and malignant cells. Therefore, one of the current challenges is to decipher the intratumor heterogeneity and especially the spatial distribution and interactions of the different cellular actors within the tumor. Preclinical mouse models are widely used to extend our understanding of the tumor microenvironment (TME). Such models are becoming more sophisticated and allow investigating questions that cannot be addressed in clinical studies. Indeed, besides studying the tumor cell interactions within their environment, mouse models allow evaluating the efficacy of new drugs and delivery approaches, treatment posology, and toxicity. Spatially resolved analyses of the intra-tumor heterogeneity require global approaches to identify and localize a large number of different cell types. For this purpose, imaging mass cytometry (IMC) is a major asset in the field of human immuno-oncology. However, the paucity of validated IMC panels to study TME in pre-clinical mouse models remains a critical obstacle to translational or basic research in oncology. Here, we validated a panel of 31 markers for studying at the single-cell level the TME and the immune landscape for discovering/characterizing cells with complex phenotypes and the interactions shaping the tumor ecosystem in mouse models. [ABSTRACT FROM AUTHOR]

Solid tumors have a dynamic ecosystem in which malignant and non-malignant (endothelial, stromal, and immune) cell types constantly interact. Importantly, the abundance, localization, and functional orientation of each cell component within the tumor microenvironment vary significantly over time and in response to treatment. Such intratumoral heterogeneity influences the tumor course and its sensitivity to treatments. Recently, high-dimensional imaging mass cytometry (IMC) has been developed to explore the tumor ecosystem at the single-cell level. In the last years, several studies demonstrated that IMC is a powerful tool to decipher the tumor complexity. In this review, we summarize the potential of this technology and how it may be useful for cancer research (from preclinical to clinical studies). [ABSTRACT FROM AUTHOR]

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation. [ABSTRACT FROM AUTHOR]

Abstract Background Recent investigations of the meninges have highlighted the importance of the dura layer in central nervous system immune surveillance beyond a purely structural role. However, our understanding of the meninges largely stems from the use of pre-clinical models rather than human samples. Methods Single-cell RNA sequencing of seven non-tumor-associated human dura samples and six primary meningioma tumor samples (4 matched and 2 non-matched) was performed. Cell type identities, gene expression profiles, and T cell receptor expression were analyzed. Copy number variant (CNV) analysis was performed to identify putative tumor cells and analyze intratumoral CNV heterogeneity. Immunohistochemistry and imaging mass cytometry was performed on selected samples to validate protein expression and reveal spatial localization of select protein markers. Results In this study, we use single-cell RNA sequencing to perform the first characterization of both non-tumor-associated human dura and primary meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, we characterize a functionally diverse and heterogenous landscape of non-immune cells including endothelial cells and fibroblasts. Through imaging mass cytometry, we highlight the spatial relationship among immune cell types and vasculature in non-tumor-associated dura. Utilizing T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. Finally, we report copy number variant heterogeneity within our meningioma samples. Conclusions Our comprehensive investigation of both the immune and non-immune cellular landscapes of human dura and meningioma at single-cell resolution builds upon previously published data in murine models and provides new insight into previously uncharacterized roles of human dura.

Due to its stimulatory potential for immunomodulatory CD4+ regulatory T (Treg) cells, low-dose interleukin-2 (IL-2) immunotherapy has gained considerable attention for the treatment of autoimmune diseases. In this investigator-initiated single-arm non-placebo-controlled phase-2 clinical trial of low-dose IL-2 immunotherapy in systemic lupus erythematosus (SLE) patients, we generated a comprehensive atlas of in vivo human immune responses to low-dose IL-2. We performed an in-depth study of circulating and cutaneous immune cells by imaging mass cytometry, high-parameter flow cytometry, transcriptomics, and targeted serum proteomics. Low-dose IL-2 stimulated various circulating immune cells, including Treg cells with a skin-homing phenotype that appeared in the skin of SLE patients in close interaction with endothelial cells. Analysis of surface proteins and transcriptomes revealed different IL-2-driven Treg cell activation programs, including gut-homing CD38+, skin-homing HLA-DR+, and highly proliferative inflammation-homing CD38+ HLA-DR+ Treg cells. Collectively, these data define the distinct human Treg cell subsets that are responsive to IL-2 immunotherapy. [Display omitted] • Low-dose IL-2 immunotherapy restores Treg cells in SLE patients • Treg cell expansion is paralleled by clinical and serological response • IL-2 drives distinct Treg cell subsets that can home to the gut, skin, or inflamed sites • Skin-homing Treg cells appear in the skin in close interaction with endothelial cells Low doses of IL-2 can specifically activate regulatory T cells, making this an attractive therapeutic option for autoimmune diseases. Raeber et al. generated a comprehensive atlas of immune responses to low-dose IL-2 immunotherapy in systemic lupus erythematosus patients and identified distinct IL-2-driven regulatory T cell activation and tissue-homing programs. [ABSTRACT FROM AUTHOR]

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.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

Currently, the study of resistance mechanisms and disease progression in cancer relies on the capacity to analyze tumors as a complex ecosystem of healthy and malignant cells. Therefore, one of the current challenges is to decipher the intra-tumor heterogeneity and especially the spatial distribution and interactions of the different cellular actors within the tumor. Preclinical mouse models are widely used to extend our understanding of the tumor microenvironment (TME). Such models are becoming more sophisticated and allow investigating questions that cannot be addressed in clinical studies. Indeed, besides studying the tumor cell interactions within their environment, mouse models allow evaluating the efficacy of new drugs and delivery approaches, treatment posology, and toxicity. Spatially resolved analyses of the intra-tumor heterogeneity require global approaches to identify and localize a large number of different cell types. For this purpose, imaging mass cytometry (IMC) is a major asset in the field of human immuno-oncology. However, the paucity of validated IMC panels to study TME in pre-clinical mouse models remains a critical obstacle to translational or basic research in oncology. Here, we validated a panel of 31 markers for studying at the single-cell level the TME and the immune landscape for discovering/characterizing cells with complex phenotypes and the interactions shaping the tumor ecosystem in mouse models.

Imaging mass cytometry (IMC) is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry/cytochemistry. A unique high-dimensional perspective comprehensively and accurately depicts the complex interaction of phenotype, signalling pathway and tumour microenvironment and is widely used in solid tumours. However, the application scenarios of IMC in basic medicine and clinical research in solid tumours lack systematic introduction and classification. This paper reviews the application of IMC in depicting the panorama of the tumour microenvironment, revealing tumour spatial heterogeneity, clarifying tumour pharmacological mechanisms, assisting in new drug development, and dynamically evaluating the efficacy of immunotherapy in solid tumours. • IMC is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry. • This paper reviews the application of IMC in basic laboratory studies, preclinical studies and clinical observations in solid tumours. • It can depict the complex phenotypes in tissue and tumour microenvironment. • IMC is a powerful tool for solid tumour transformation research and precision treatment as new technology. • The existing research cases are classified and summarized, meanwhile the clinical trials using IMC are listed. [ABSTRACT FROM AUTHOR]

Prurigo nodularis (PN) is a chronic, inflammatory skin condition that disproportionately affects African Americans and features intensely pruritic, hyperkeratotic nodules on the extremities and trunk. PN is understudied compared with other inflammatory skin diseases, with the spatial organization of the cutaneous infiltrate in PN yet to be characterized. In this work, we employ spatial imaging mass cytometry to visualize PN lesional skin inflammation and architecture with single-cell resolution through an unbiased machine learning approach. PN lesional skin has increased expression of caspase 3, NF-kB, and phosphorylated signal transducer and activator of transcription 3 compared with healthy skin. Keratinocytes in lesional skin are subdivided into CD14+CD33+, CD11c+, CD63+, and caspase 3-positive innate subpopulations. CD14+ macrophage populations expressing phosphorylated extracellular signal-regulated kinase 1/2 correlate positively with patient-reported itch (P = .006). Hierarchical clustering reveals a cluster of patients with PN with greater atopy, increased NF-kB+ signal transducer and activator of transcription 3-positive phosphorylated extracellular signal-regulated kinase 1/2-positive monocyte-derived myeloid dendritic cells, and increased vimentin expression (P < .05). Neighborhood analysis finds interactions between CD14+ macrophages, CD3+ T cells, monocyte-derived myeloid dendritic cells, and keratinocytes expressing innate immune markers. These findings highlight phosphorylated extracellular signal-regulated kinase-positive CD14+ macrophages as contributors to itch and suggest an epithelial-immune axis in PN pathogenesis., (Copyright © 2024. Published by Elsevier Inc.)

Long-term survival after lung transplantation remains limited by chronic lung allograft dysfunction (CLAD), with 2 main phenotypes: bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS). We aimed to assess CLAD lung allografts using imaging mass cytometry (IMC), a high dimensional tissue imaging system allowing a multiparametric in situ exploration at a single cell level. Four BOS, 4 RAS, and 4 control lung samples were stained with 35 heavy metal-tagged antibodies selected to assess structural and immune proteins of interest. We identified 50 immune and non-immune cell clusters. CLAD lungs had significantly reduced club cells. A Ki67-high basal cell population was mostly present in RAS and in proximity to memory T cells. Memory CD8 + T cells were more frequent in CLAD lungs, regulatory T cells more prominent in RAS. IMC is a powerful technology for detailed cellular analysis within intact organ structures that may shed further light on CLAD mechanisms., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Presence of nerves in tumours, by axonogenesis and neurogenesis, is gaining increased attention for its impact on cancer initiation and development, and the new field of cancer neuroscience is emerging. A recent study in prostate cancer suggested that the tumour microenvironment may influence cancer progression by recruitment of Doublecortin (DCX)-expressing neural progenitor cells (NPCs). However, the presence of such cells in human breast tumours has not been comprehensively explored., Methods: Here, we investigate the presence of DCX-expressing cells in breast cancer stromal tissue from patients using Imaging Mass Cytometry. Single-cell analysis of 372,468 cells across histopathological images of 107 breast cancers enabled spatial resolution of neural elements in the stromal compartment in correlation with clinicopathological features of these tumours. In parallel, we established a 3D in vitro model mimicking breast cancer neural progenitor-innervation and examined the two cell types as they co-evolved in co-culture by using mass spectrometry-based global proteomics., Findings: Stromal presence of DCX + cells is associated with tumours of higher histological grade, a basal-like phenotype, and shorter patient survival in tumour tissue from patients with breast cancer. Global proteomics analysis revealed significant changes in the proteomic landscape of both breast cancer cells and neural progenitors in co-culture., Interpretation: These results support that neural involvement plays an active role in breast cancer and warrants further studies on the relevance of nerve elements for tumour progression., Funding: This work was supported by the Research Council of Norway through its Centre of Excellence funding scheme, project number 223250 (to L.A.A), the Norwegian Cancer Society (to L.A.A. and H.V.), the Regional Health Trust Western Norway (Helse Vest) (to L.A.A.), the Meltzer Research Fund (to H.V.) and the National Institutes of Health (NIH)/NIGMS grant R01 GM132129 (to J.A.P.)., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Abstract Background Imaging mass cytometry (IMC) combines the principles of flow cytometry and mass spectrometry (MS) with laser scanning spatial resolution and offers unique advantages for the analysis of tissue samples in unprecedented detail. In contrast to conventional immunohistochemistry, which is limited in its application by the number of possible fluorochrome combinations, IMC uses isoptope-coupled antibodies that allow multiplex analysis of up to 40 markers in the same tissue section simultaneously. Methods In this report we use IMC to analyze formalin-fixed, paraffin-embedded conjunctival tissue. We performed a 18-biomarkers IMC analysis of conjunctival tissue to determine and summarize the possibilities, relevance and limitations of IMC for deciphering the biology and pathology of ocular diseases. Results Without modifying the manufacturer’s protocol, we observed positive and plausible staining for 12 of 18 biomarkers. Subsequent bioinformatical single-cell analysis and phenograph clustering identified 24 different cellular clusters with distinct expression profiles with respect to the markers used. Conclusions IMC enables highly multiplexed imaging of ocular samples at subcellular resolution. IMC is an innovative and feasible method, providing new insights into ocular disease pathogenesis that will be valuable for basic research, drug discovery and clinical diagnostics.

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Co‐detection of multiplex cancer subtypes and bacteria subtypes in situ is crucial for understanding tumor microbiome interactions in tumor microenvironment. Current standard techniques such as immunohistochemical staining and immunofluorescence staining are limited for their multiplicity. Simultaneously visualizing detailed cell subtypes and bacteria distribution across the same pathological section remains a major technical challenge. Herein, we developed a rapid semi‐quantitative method for in situ imaging of bacteria and multiplex cell phenotypes on the same solid tumor tissue sections. We designed a panel of antibody probes labeled with mass tags, namely prokaryotic and eukaryotic cell hybrid probes for in situ imaging (PEHPSI). For application demonstration, PEHPSI stained two bacteria subtypes (lipopolysaccharides (LPS) for Gram‐negative bacteria and lipoteichoic acid (LTA) for Gram‐positive bacteria) simultaneously with four types of immune cells (leukocytes, CD8 + T‐cells, B‐cells and macrophages) and four breast cancer subtypes (classified by a panel of 12 human proteins) on the same tissue section. We unveiled that breast cancer cells are commonly enriched with Gram‐negative bacteria and almost absent of Gram‐positive bacteria, regardless of the cancer subtypes (triple‐negative breast cancer [TNBC], HER2+, Luminal A and Luminal B). Further analysis revealed that on the single‐cell level, Gram‐negative bacteria have a significant correlation with CD8 + T‐cells only in HER2+ breast cancer, while PKCD, ER, PR and Ki67 are correlated with Gram‐negative bacteria in the other three subtypes of breast cancers. On the cell population level, in TNBC, CD19 expression intensity is up‐regulated by approximately 25% in bacteria‐enriched cells, while for HER2+, Luminal A and Luminal B breast cancers, the intensity of biomarkers associated with the malignancy, metastasis and proliferation of cancer cells (PKCD, ISG15 and IFI6) is down‐regulated by 29%–38%. The flexible and expandable PEHPSI system permits intuitive multiplex co‐visualization of bacteria and mammalian cells, which facilitates future research on tumor microbiome and tumor pathogenesis. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitor-related pneumonitis (CIP) is a rare but well-recognized immune-related adverse event (irAE), causes 35% of irAE related deaths. However, the mechanism of CIP remains unclear and no evidence-based treatment except for glucocorticoids is available. Herein, we report the case of a patient with metastatic bladder cancer who received tislelizumab and was diagnosed with CIP. The patient underwent transbronchial cryobiopsy. The patient was treated with glucocorticoid, but CIP recurred when the glucocorticoid tapering. The paraffine-embedded lung tissue was sectioned, stained with 31 heavy-metal tagged antibodies, and analyzed using imaging mass cytometry (IMC) technology. We identified multiple immune cell subsets in the lung tissue and observed the infiltration of memory T cells and the CD4+ DC subset. The data indicated the great potential of IMC technology in the identification and characterization of irAEs. Further investigation is warranted to identify the mechanism of action of CIP. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person's lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes. The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications. [ABSTRACT FROM AUTHOR]