Testing a Nanoparticle Reagent for Imaging Mass Cytometry.

Mass cytometry (MC), a powerful single-cell analysis technique, has limitations in detecting low-abundance biomarkers. Nanoparticle (NP) reagents offer the potential for enhancing sensitivity by carrying large numbers of heavy metal isotopes. Here, we report NP reporters for imaging mass cytometry (IMC) based on NaYF4:Yb3+/Er3+ NPs. A two-step ligand exchange was used to coat NP surfaces with either methoxy-PEG2K-neridronate (PEG-Ner) and/or poly(sulfobetaine methacrylate)-neridronate (PSBMA-Ner). Both modifications provided long-term colloidal stability in PBS buffer. IMC measurements on tonsil tissue showed that PSBMA-Ner or a 1:1 mixture of PSBMA-Ner + PEG-Ner effectively suppressed nonspecific binding (NSB) at 2 × 1010 NPs/mL, unlike PEG-Ner alone. However, breast cancer tissue samples showed increased NSB at titers above 2 × 1010 NPs/mL. Reduced NSB with mixed PEG-Ner and PSBMA-Ner coatings opens the door for using heterobifunctional PEGs for the development of NP conjugates with bioaffinity agents, enabling more sensitive and specific MC analyses.

A multi-biomarker micronucleus assay using imaging flow cytometry.

Genetic toxicity testing assesses the potential of compounds to cause DNA damage. There are many genetic toxicology screening assays designed to assess the DNA damaging potential of chemicals in early drug development aiding the identification of promising drugs that have low-risk potential for causing genetic damage contributing to cancer risk in humans. Despite this, in vitro tests generate a high number of misleading positives, the consequences of which can lead to unnecessary animal testing and/or the abandonment of promising drug candidates. Understanding chemical Mode of Action (MoA) is vital to identifying the true genotoxic potential of substances and, therefore, the risk translation into the clinic. Here we demonstrate a simple, robust protocol for staining fixed, human-lymphoblast p53 proficient TK6 cells with antibodies against É£H2AX, p53 and pH3S28 along with DRAQ5™ DNA staining that enables analysis of un-lysed cells via microscopy approaches such as imaging flow cytometry. Here, we used the Cytek® Amnis® ImageStream®X Mk II which provides a high-throughput acquisition platform with the sensitivity of flow cytometry and spatial morphological information associated with microscopy. Using the ImageStream manufacturer's software (IDEAS® 6.2), a masking strategy was developed to automatically detect and quantify micronucleus events (MN) and characterise biomarker populations. The gating strategy developed enables the generation of a template capable of automatically batch processing data files quantifying cell-cycle, MN, É£H2AX, p53 and pH3 populations simultaneously. In this way, we demonstrate how a multiplex system enables DNA damage assessment alongside MN identification using un-lysed cells on the imaging flow cytometry platform. As a proof-of-concept, we use the tool chemicals carbendazim and methyl methanesulphonate (MMS) to demonstrate the assay's ability to correctly identify clastogenic or aneugenic MoAs using the biomarker profiles established.

Easy-to-use imaging-cytometry assay to analyze chitin patterns in yeasts.

BACKGROUND & AIM: Pathogenic fungi are a major threat to public health, and fungal infections are becoming increasingly common and treatment resistant. Chitin, a component of the fungal cell wall, modifies host immunity and contributes to antifungal resistance. Moreover, chitin content is regulated by chitin synthases and chitinases. However, the specific roles and mechanisms remain unclear. In this study, we developed a cytometric imaging assay to quantify chitin content and identify the distribution of chitin in the yeast cell wall. METHODS: The Candida albicans SC5314 and Nakaseomyces glabratus (ex. C. glabrata) ATCC2001 reference strains, as well as 106 clinical isolates, were used. Chitin content, distribution, and morphological parameters were analysed in 12 yeast species. Moreover, machine learning statistical software was used to evaluate the ability of the cytometric imaging assay to predict yeast species using the values obtained for these parameters. RESULTS: Our imaging-cytometry assay was repeatable, reproducible, and sensitive to variations in chitin content in C. albicans mutants or after antifungal stimulation. The evaluated parameters classified the yeast species into the correct clade with an accuracy of 85 %. CONCLUSION: Our findings demonstrate that this easy-to-use assay is an effective tool for the exploration of chitin content in yeast species.

Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.

The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its use on different tissue types, tissue fixation conditions, image sizes and antibody panels. As PHLEX is an automated and containerised Nextflow pipeline, manual assessment, programming skills or pathology expertise are not essential. PHLEX offers an end-to-end solution in a growing field of highly multiplexed data and provides clinically relevant insights.

Quantitative Analysis of Phagocytosis in Whole Blood Using Double Staining and Visualization.

Phagocytosis is an essential innate immunity function in humans and animals. A decrease in the ability to phagocytize is associated with many diseases and aging of the immune system. Assessment of phagocytosis dynamics requires quantification of bacteria inside and outside the phagocyte. Although flow cytometry is the most common method for assessing phagocytosis, it does not include visualization and direct quantification of location of bacteria. Here, we used double-labeled Escherichia coli cells to evaluate phagocytosis by flow cytometry (cell sorting) and confocal microscopy, as well as employed image cytometry to provide high-throughput quantitative and spatial recognition of the double-labeled E. coli associated with the phagocytes. Retention of pathogens on the surface of myeloid and lymphoid cells without their internalization was suggested to be an auxiliary function of innate immunity in the fight against infections. The developed method of bacterial labeling significantly increased the accuracy of spatial and quantitative measurement of phagocytosis in whole blood and can be recommended as a tool for phagocytosis assessment by image cytometry.

OMIP-103: A 35-marker imaging mass cytometry panel for the co-detection of HIV and immune cell populations in human formalin fixed paraffin embedded intestinal tissue.

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.

Multiplexed Imaging Mass Cytometry Analysis Characterizes the Vascular Niche in Pancreatic Cancer.

Oncogenesis and progression of pancreatic ductal adenocarcinoma (PDAC) are driven by complex interactions between the neoplastic component and the tumor microenvironment, which includes immune, stromal, and parenchymal cells. In particular, most PDACs are characterized by a hypovascular and hypoxic environment that alters tumor cell behavior and limits the efficacy of chemotherapy and immunotherapy. Characterization of the spatial features of the vascular niche could advance our understanding of inter- and intratumoral heterogeneity in PDAC. In this study, we investigated the vascular microenvironment of PDAC by applying imaging mass cytometry using a 26-antibody panel on 35 regions of interest across 9 patients, capturing more than 140,000 single cells. The approach distinguished major cell types, including multiple populations of lymphoid and myeloid cells, endocrine cells, ductal cells, stromal cells, and endothelial cells. Evaluation of cellular neighborhoods identified 10 distinct spatial domains, including multiple immune and tumor-enriched environments as well as the vascular niche. Focused analysis revealed differential interactions between immune populations and the vasculature and identified distinct spatial domains wherein tumor cell proliferation occurs. Importantly, the vascular niche was closely associated with a population of CD44-expressing macrophages enriched for a proangiogenic gene signature. Taken together, this study provides insights into the spatial heterogeneity of PDAC and suggests a role for CD44-expressing macrophages in shaping the vascular niche. Significance: Imaging mass cytometry revealed that pancreatic ductal cancers are composed of 10 distinct cellular neighborhoods, including a vascular niche enriched for macrophages expressing high levels of CD44 and a proangiogenic gene signature.

Detection and Quantification of Cryptococcus Uptake by Phagocytic Cells Using Imaging Flow Cytometry.

Cryptococcus neoformans, the predominant etiological agent of cryptococcosis, is an encapsulated fungal pathogen found ubiquitously in the environment that causes pneumonia and life-threatening infections of the central nervous system. Following inhalation of yeasts or desiccated basidiospores into the lung alveoli, resident pulmonary phagocytic cells aid in the identification and eradication of Cryptococcus yeast through their arsenal of pattern recognition receptors (PRRs). PRRs recognize conserved pathogen-associated molecular patterns (PAMPs), such as branched mannans, ß-glucans, and chitins that are the major components of the fungal cell wall. However, the key receptors/ligand interactions required for cryptococcal recognition and eventual fungal clearance have yet to be elucidated. Here we present an imaging flow cytometer (IFC) method that offers a novel quantitative cellular imaging and population statistics tool to accurately measure phagocytosis of fungal cells. It has the capacity to measure two distinct steps of phagocytosis: association/attachment and internalization in a high-throughput and quantitative manner that is difficult to achieve with other technologies. Results from these IFC studies allow for the potential to identify PRRs required for recognition, uptake, and subsequent activation of cytokine production, as well as other effector cell responses required for fungal clearance.

Development of 42 marker panel for in-depth study of cancer associated fibroblast niches in breast cancer using imaging mass cytometry.

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.

Coupling imaging mass cytometry with Alcian blue histochemical staining for a single-slide approach.

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.

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Identification of neutrophils and eosinophils in upper airway mucosa with immunofluorescence multiplex image cytometry.

Characterization of inflammation in chronic rhinosinusitis with (CRSwNP) and without nasal polyps (CRSsNP) is an ongoing research process. To overcome limitations of current cytologic techniques, we investigated whether immunofluorescence multiplex image cytometry could quantify intact neutrophils, eosinophils, and other immune cells in solid upper airway mucosa. We used a four-channel immunofluorescence-microscopy technique for the simultaneous detection of the leukocyte marker CD45, the neutrophil marker myeloperoxidase, two eosinophil markers, i.e., major basic protein and eosinophil peroxidase, and DAPI (4',6-diamidin-2-phenylindole), in formalin-fixed paraffin-embedded upper airway tissue samples of patients with CRSwNP and CRSsNP, as well as of patients free of CRS with inferior turbinate hypertrophy (controls). Image acquisition and analysis were performed with TissueFAXS and StrataQuest (TissueGnostics, Vienna, Austria), respectively. Positive and negative immunostaining were differentiated with a specific fluorescence signal/background signal ratio. Isotype controls were used as negative controls. In six controls, nine patients with CRSsNP, and 11 patients with CRSwNP, the median area scanned and median cell count per patient were 14.2 mm2 and 34,356, respectively. In CRSwNP, the number of eosinophils was three times higher (23%) than that of neutrophils (7%). Three times more immune cells were encountered in CRSwNP (33%) compared to CRSsNP (11%). In controls, inflammation was balanced between the epithelial layer and lamina propria, in contrast to CRS (three times more pronounced inflammation in the lamina propria). The quantification of intact neutrophils, eosinophils, and other immune cells in solid tissue with undisrupted architecture seems feasible with immunofluorescence multiplex image cytometry.

A Pathologically Friendly Strategy for Determining the Organ-specific Spatial Tumor Microenvironment Topology in Lung Adenocarcinoma Through the Integration of snRandom-seq and Imaging Mass Cytometry.

Heterogeneous organ-specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single-cell profiling using formalin-fixed paraffin-embedded (FFPE) materials. In this study, single-cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ-specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single-cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ-specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ-specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM-3. This study originally deciphers the single-cell landscape of the organ-specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ-specific treatment approaches.

Imaging Mass Cytometry for In Situ Immune Profiling.

The complexities and cellular heterogeneity associated with tissues necessitate the concurrent detection of markers beyond the limitations of conventional imaging approaches in order to spatially resolve the relationships between immune cell populations and their environments. This is a necessary complement to single-cell suspension-based methods to inform a better understanding of the events that may underlie pathological conditions. Imaging mass cytometry is a high-dimensional imaging modality that allows for the concurrent detection of up to 40 protein markers of interest across tissues at subcellular resolution. Here, we present an optimized staining protocol for imaging mass cytometry with modifications that integrate RNAscope. This unique addition enables combined protein and single-molecule RNA detection, thereby expanding the utility of imaging mass cytometry to researchers investigating low abundance or noncoding targets. In general, the procedure described is broadly applicable for comprehensive immune profiling of host-pathogen interactions, tumor microenvironments and inflammatory conditions, all within the tissue contexture.

Spatial Single Cell Profiling Using Imaging Mass Cytometry: Inflammatory Versus Penetrating Crohn's Disease.

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.

Multi-omics and imaging mass cytometry characterization of human kidneys to identify pathways and phenotypes associated with impaired kidney function.

Despite the recent advances in our understanding of the role of lipids, metabolites, and related enzymes in mediating kidney injury, there is limited integrated multi-omics data identifying potential metabolic pathways driving impaired kidney function. The limited availability of kidney biopsies from living donors with acute kidney injury has remained a major constraint. Here, we validated the use of deceased transplant donor kidneys as a good model to study acute kidney injury in humans and characterized these kidneys using imaging and multi-omics approaches. We noted consistent changes in kidney injury and inflammatory markers in donors with reduced kidney function. Neighborhood and correlation analyses of imaging mass cytometry data showed that subsets of kidney cells (proximal tubular cells and fibroblasts) are associated with the expression profile of kidney immune cells, potentially linking these cells to kidney inflammation. Integrated transcriptomic and metabolomic analysis of human kidneys showed that kidney arachidonic acid metabolism and seven other metabolic pathways were upregulated following diminished kidney function. To validate the arachidonic acid pathway in impaired kidney function we demonstrated increased levels of cytosolic phospholipase A2 protein and related lipid mediators (prostaglandin E2) in the injured kidneys. Further, inhibition of cytosolic phospholipase A2 reduced injury and inflammation in human kidney proximal tubular epithelial cells in vitro. Thus, our study identified cell types and metabolic pathways that may be critical for controlling inflammation associated with impaired kidney function in humans.

Imaging mass cytometry analysis of Becker muscular dystrophy muscle samples reveals different stages of muscle degeneration.

Becker muscular dystrophy (BMD) is characterised by fiber loss and expansion of fibrotic and adipose tissue. Several cells interact locally in what is known as the degenerative niche. We analysed muscle biopsies of controls and BMD patients at early, moderate and advanced stages of progression using Hyperion imaging mass cytometry (IMC) by labelling single sections with 17 markers identifying different components of the muscle. We developed a software for analysing IMC images and studied changes in the muscle composition and spatial correlations between markers across disease progression. We found a strong correlation between collagen-I and the area of stroma, collagen-VI, adipose tissue, and M2-macrophages number. There was a negative correlation between the area of collagen-I and the number of satellite cells (SCs), fibres and blood vessels. The comparison between fibrotic and non-fibrotic areas allowed to study the disease process in detail. We found structural differences among non-fibrotic areas from control and patients, being these latter characterized by increase in CTGF and in M2-macrophages and decrease in fibers and blood vessels. IMC enables to study of changes in tissue structure along disease progression, spatio-temporal correlations and opening the door to better understand new potential pathogenic pathways in human samples.

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer.

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.

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non-Small Cell Lung Cancer.

Artificial intelligence (AI)-powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non-small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance. SIGNIFICANCE: Leveraging artificial intelligence-powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.