An Immune Atlas of Clear Cell Renal Cell Carcinoma.

Immune cells in the tumor microenvironment modulate cancer progression and are attractive therapeutic targets. Macrophages and T cells are key components of the microenvironment, yet their phenotypes and relationships in this ecosystem and to clinical outcomes are ill defined. We used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls. In 3.5 million measured cells, we identified 17 tumor-associated macrophage phenotypes, 22 T cell phenotypes, and a distinct immune composition correlated with progression-free survival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disease. This study revealed potential biomarkers and targets for immunotherapy development and validated tools that can be used for immune profiling of other tumor types.

Integrated multi-omic characterization of congenital heart disease.

The heart, the first organ to develop in the embryo, undergoes complex morphogenesis that when defective results in congenital heart disease (CHD). With current therapies, more than 90% of patients with CHD survive into adulthood, but many suffer premature death from heart failure and non-cardiac causes1. Here, to gain insight into this disease progression, we performed single-nucleus RNA sequencing on 157,273 nuclei from control hearts and hearts from patients with CHD, including those with hypoplastic left heart syndrome (HLHS) and tetralogy of Fallot, two common forms of cyanotic CHD lesions, as well as dilated and hypertrophic cardiomyopathies. We observed CHD-specific cell states in cardiomyocytes, which showed evidence of insulin resistance and increased expression of genes associated with FOXO signalling and CRIM1. Cardiac fibroblasts in HLHS were enriched in a low-Hippo and high-YAP cell state characteristic of activated cardiac fibroblasts. Imaging mass cytometry uncovered a spatially resolved perivascular microenvironment consistent with an immunodeficient state in CHD. Peripheral immune cell profiling suggested deficient monocytic immunity in CHD, in agreement with the predilection in CHD to infection and cancer2. Our comprehensive phenotyping of CHD provides a roadmap towards future personalized treatments for CHD.

DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging.

Imaging mass cytometry (IMC) is a highly multiplexed, antibody-based imaging method that captures heterogeneous spatial protein expression patterns at subcellular resolution. Here we report the extension of IMC to low-abundance markers through incorporation of the DNA-based signal amplification by exchange reaction, immuno-SABER. We applied SABER-IMC to image the tumor immune microenvironment in human melanoma by simultaneous imaging of 18 markers with immuno-SABER and 20 markers without amplification. SABER-IMC enabled the identification of immune cell phenotypic markers, such as T cell co-receptors and their ligands, that are not detectable with IMC.

Mass Cytometry of the Human Mucosal Immune System Identifies Tissue- and Disease-Associated Immune Subsets.

Inflammatory intestinal diseases are characterized by abnormal immune responses and affect distinct locations of the gastrointestinal tract. Although the role of several immune subsets in driving intestinal pathology has been studied, a system-wide approach that simultaneously interrogates all major lineages on a single-cell basis is lacking. We used high-dimensional mass cytometry to generate a system-wide view of the human mucosal immune system in health and disease. We distinguished 142 immune subsets and through computational applications found distinct immune subsets in peripheral blood mononuclear cells and intestinal biopsies that distinguished patients from controls. In addition, mucosal lymphoid malignancies were readily detected as well as precursors from which these likely derived. These findings indicate that an integrated high-dimensional analysis of the entire immune system can identify immune subsets associated with the pathogenesis of complex intestinal disorders. This might have implications for diagnostic procedures, immune-monitoring, and treatment of intestinal diseases and mucosal malignancies.

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.

Exploring the single-cell immune landscape of kidney allograft inflammation using imaging mass cytometry.

Kidney allograft inflammation, mostly attributed to rejection and infection, is an important cause of graft injury and loss. Standard histopathological assessment of allograft inflammation provides limited insights into biological processes and the immune landscape. Here, using imaging mass cytometry with a panel of 28 validated biomarkers, we explored the single-cell landscape of kidney allograft inflammation in 32 kidney transplant biopsies and 247 high-dimensional histopathology images of various phenotypes of allograft inflammation (antibody-mediated rejection, T cell-mediated rejection, BK nephropathy, and chronic pyelonephritis). Using novel analytical tools, for cell segmentation, we segmented over 900 000 cells and developed a tissue-based classifier using over 3000 manually annotated kidney microstructures (glomeruli, tubules, interstitium, and arteries). Using PhenoGraph, we identified 11 immune and 9 nonimmune clusters and found a high prevalence of memory T cell and macrophage-enriched immune populations across phenotypes. Additionally, we trained a machine learning classifier to identify spatial biomarkers that could discriminate between the different allograft inflammatory phenotypes. Further validation of imaging mass cytometry in larger cohorts and with more biomarkers will likely help interrogate kidney allograft inflammation in more depth than has been possible to date.

OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.

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.

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.

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.

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.

Molecular architecture of proliferative lupus nephritis as elucidated using 50-plex imaging mass cytometry proteomics.

Due to unique advantages that allow high-dimensional tissue profiling, we postulated imaging mass cytometry (IMC) may shed novel insights on the molecular makeup of proliferative lupus nephritis (LN). This study interrogates the spatial expression profiles of 50 target proteins in LN and control kidneys. Proliferative LN glomeruli are marked by podocyte loss with immune infiltration dominated by CD45RO+, HLA-DR+ memory CD4 and CD8 T-cells, and CD163+ macrophages, with similar changes in tubulointerstitial regions. Macrophages are the predominant HLA-DR expressing antigen presenting cells with little expression elsewhere, while macrophages and T-cells predominate cellular crescents. End-stage sclerotic glomeruli are encircled by an acellular fibro-epithelial Bowman's space surrounded by immune infiltrates, all enmeshed in fibronectin. Proliferative LN also shows signs indicative of epithelial to mesenchymal plasticity of tubular cells and parietal epithelial cells. IMC enabled proteomics is a powerful tool to delineate the spatial architecture of LN at the protein level.

A novel process for H&E, immunofluorescence, and imaging mass cytometry on a single slide with a concise analytics pipeline.

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.

OMIP-088: Twenty-target imaging mass cytometry panel for major cell populations in mouse formalin fixed paraffin embedded liver.

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.

Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments.

Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organization of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarizing the information-rich data generated from these technologies.

Topological analysis of hepatocellular carcinoma tumour microenvironment based on imaging mass cytometry reveals cellular neighbourhood regulated reversely by macrophages with different ontogeny.

OBJECTIVE: Hepatocellular carcinoma (HCC) tumour microenvironment (TME) is highly complex with diverse cellular components organising into various functional units, cellular neighbourhoods (CNs). And we wanted to define CN of HCC while preserving the TME architecture, based on which, potential targets for novel immunotherapy could be identified. DESIGN: A highly multiplexed imaging mass cytometry (IMC) panel was designed to simultaneously quantify 36 biomarkers of tissues from 134 patients with HCC and 7 healthy donors to generate 562 highly multiplexed histology images at single-cell resolution. Different function units were defined by topological analysis of TME. CN relevant to the patients' prognosis was identified as specific target for HCC therapy. Transgenic mouse models were used to validate the novel immunotherapy target for HCC. RESULTS: Three major types of intratumour areas with distinct distribution patterns of tumorous, stromal and immune cells were identified. 22 cellular metaclusters and 16 CN were defined. CN composed of various types of cells formed regional function units and the regional immunity was regulated reversely by resident Kupffer cells and infiltrating macrophages with protumour and antitumour function, respectively. Depletion of Kupffer cells in mouse liver largely enhances the T cell response, reduces liver tumour growth and sensitises the tumour response to antiprogrammed cell death protein-1 treatment. CONCLUSION: Our findings reveal for the first time the various topological function units of HCC TME, which also presents the largest depository of pathological landscape for HCC. This work highlights the potential of Kupffer cell-specific targeting rather than overall myeloid cell blocking as a novel immunotherapy for HCC treatment.

TITAN: An end-to-end data analysis environment for the Hyperion™ imaging system.

Imaging Mass Cytometry (IMC) is a powerful high-throughput technique enabling resolution of up to 37 markers in a single fixed tissue section while also preserving in situ spatial relationships. Currently, IMC processing and analysis necessitates the use of multiple different software, labour-intensive pipeline development, different operating systems and knowledge of bioinformatics, all of which are a barrier to many potential users. Here we present TITAN - an open-source, single environment, end-to-end pipeline that can be utilized for image visualization, segmentation, analysis and export of IMC data. TITAN is implemented as an extension within the publicly available 3D Slicer software. We demonstrate the utility, application, reliability and comparability of TITAN using publicly available IMC data from recently-published breast cancer and COVID-19 lung injury studies. Compared with current IMC analysis methods, TITAN provides a user-friendly, efficient single environment to accurately visualize, segment, and analyze IMC data for all users.

In situ imaging for tumor microbiome interactions via imaging mass cytometry on single-cell level.

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.

Biotinylated Lipid-Coated NaLnF4 Nanoparticles: Demonstrating the Use of Lanthanide Nanoparticle-Based Reporters in Suspension and Imaging Mass Cytometry.

Lanthanide nanoparticles (LnNPs) have the potential to be used as high-sensitivity mass tag reporters in mass cytometry immunoassays. For this application, however, the LnNPs must be made colloidally stable in aqueous buffers, demonstrate minimal non-specific binding to cells, and have functional groups to attach antibodies or other targeting agents. One possible approach to address these requirements is by using lipid coating to modify the surface of the LnNPs. In this work, 39 nm diameter NaYF4:Yb, Er NPs (LnNPs) were coated with a lipid formulation consisting of egg sphingomyelin, 1,2-dioleoyl-sn-glycero-3-phosphocholine, 1,2-dioleoyl-3-trimethylammonium propane, cholesterol-(polyethylene glycol-600), and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[biotinyl(polyethylene glycol)-2000]. The resulting biotinylated lipid-coated LnNPs were characterized by dynamic light scattering to determine the hydrodynamic size and stability in phosphate buffered saline, and the composition of the lipid coatings was quantified by liquid chromatography-tandem mass spectrometry. The specific and non-specific binding of the biotinylated lipid-coated LnNPs to a model system of functionalized polystyrene microbeads were then tested by both suspension and imaging mass cytometry. We found that targeted binding with minimal non-specific binding can be achieved with the lipid-coated LnNPs and that the lipid composition of the coating has an impact on the performance of the LnNPs as mass cytometry reporters. These results additionally establish the importance of quantifying the composition of lipid-coated nanomaterials to optimize them more effectively for their desired application.

CytoPy: An autonomous cytometry analysis framework.

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.

Graph of graphs analysis for multiplexed data with application to imaging mass cytometry.

Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1µm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.