Multiplexed Epitope-Based Tissue Imaging for Discovery and Healthcare Applications

Profiling Cell Signaling Networks at Single-cell Resolution

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy

Conventional immunohistochemistry (IHC) is a widely used diagnostic technique in tissue pathology. However, this technique is associated with a number of limitations, including high inter-observer variability and the capacity to label only one marker per tissue section. This review details various highly multiplexed techniques that have emerged to circumvent these constraints, allowing simultaneous detection of multiple markers on a single tissue section and the comprehensive study of cell composition, cellular functional and cell-cell interactions. Among these techniques, multiplex Immunohistochemistry/Immunofluorescence (mIHC/IF) has emerged to be particularly promising. mIHC/IF provides high-throughput multiplex staining and standardized quantitative analysis for highly reproducible, efficient and cost-effective tissue studies. This technique has immediate potential for translational research and clinical practice, particularly in the era of cancer immunotherapy.

Spatial proteomics: a powerful discovery tool for cell biology

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

Single-cell technologies - studying rheumatic diseases one cell at a time

Cells, the basic units of life, have striking differences at transcriptomic, proteomic and epigenomic levels across tissues, organs, organ systems and organisms. The coordination of individual immune cells is essential for the generation of effective immune responses to pathogens while immune tolerance is maintained to protect the host. In rheumatic diseases, when immune responses are dysregulated, pathologically important cells might represent only a small fraction of the immune system. Interrogation of the contributions of individual immune cells to pathogenesis and disease progression should therefore reveal important insights into the complicated aetiology of rheumatic diseases. Technological advances are enabling the high-dimensional dissection of single cells at multiple omics levels, which could facilitate the identification of dysregulated molecular mechanisms in patients with rheumatic diseases and the discovery of new therapeutic targets and biomarkers. The single-cell technologies that have been developed over the past decade and the experimental platforms that enable multi-omics integrative analyses have already made inroads into immunology-related fields of study and have potential for use in rheumatology. Layers of omics data derived from single cells are likely to fundamentally change our understanding of the molecular pathways that underpin the pathogenesis of rheumatic diseases.

Immunology in the Era of Single-Cell Technologies

Immune cells are characterized by diversity, specificity, plasticity, and adaptability-properties that enable them to contribute to homeostasis and respond specifically and dynamically to the many threats encountered by the body. Single-cell technologies, including the assessment of transcriptomics, genomics, and proteomics at the level of individual cells, are ideally suited to studying these properties of immune cells. In this review we discuss the benefits of adopting single-cell approaches in studying underappreciated qualities of immune cells and highlight examples where these technologies have been critical to advancing our understanding of the immune system in health and disease.

The single-cell pathology landscape of breast cancer

Single-cell analyses have revealed extensive heterogeneity between and within human tumours1-4, but complex single-cell phenotypes and their spatial context are not at present reflected in the histological stratification that is the foundation of many clinical decisions. Here we use imaging mass cytometry5 to simultaneously quantify 35 biomarkers, resulting in 720 high-dimensional pathology images of tumour tissue from 352 patients with breast cancer, with long-term survival data available for 281 patients. Spatially resolved, single-cell analysis identified the phenotypes of tumour and stromal single cells, their organization and their heterogeneity, and enabled the cellular architecture of breast cancer tissue to be characterized on the basis of cellular composition and tissue organization. Our analysis reveals multicellular features of the tumour microenvironment and novel subgroups of breast cancer that are associated with distinct clinical outcomes. Thus, spatially resolved, single-cell analysis can characterize intratumour phenotypic heterogeneity in a disease-relevant manner, with the potential to inform patient-specific diagnosis.

Immune monitoring using mass cytometry and related high-dimensional imaging approaches

The cellular complexity and functional diversity of the human immune system necessitate the use of high-dimensional single-cell tools to uncover its role in multifaceted diseases such as rheumatic diseases, as well as other autoimmune and inflammatory disorders. Proteomic technologies that use elemental (heavy metal) reporter ions, such as mass cytometry (also known as CyTOF) and analogous high-dimensional imaging approaches (including multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC)), have been developed from their low-dimensional counterparts, flow cytometry and immunohistochemistry, to meet this need. A growing number of studies have been published that use these technologies to identify functional biomarkers and therapeutic targets in rheumatic diseases, but the full potential of their application to rheumatic disease research has yet to be fulfilled. This Review introduces the underlying technologies for high-dimensional immune monitoring and discusses aspects necessary for their successful implementation, including study design principles, analytical tools and future developments for the field of rheumatology.

Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer

Genomic alterations shape cell phenotypes and the structure of tumor ecosystems in poorly defined ways. To investigate these relationships, we used imaging mass cytometry to quantify the expression of 37 proteins with subcellular spatial resolution in 483 tumors from the METABRIC cohort. Single-cell analysis revealed cell phenotypes spanning epithelial, stromal and immune types. Distinct combinations of cell phenotypes and cell-cell interactions were associated with genomic subtypes of breast cancer. Epithelial luminal cell phenotypes separated into those predominantly impacted by mutations and those affected by copy number aberrations. Several features of tumor ecosystems, including cellular neighborhoods, were linked to prognosis, illustrating their clinical relevance. In summary, systematic analysis of single-cell phenotypic and spatial correlates of genomic alterations in cancer revealed how genomes shape both the composition and architecture of breast tumor ecosystems and will enable greater understanding of the phenotypic impact of genomic alterations.

Objective quantification of BCL2 protein by multiplex immunofluorescence in routine biopsy samples of diffuse large B-cell lymphoma demonstrates associations with survival and BCL2 gene alterations

Up-regulation of BCL2 in cases of diffuse large B-cell lymphoma (DLBCL) can confer treatment resistance. Quantitative immunofluorescence (QIF) histology allows objective quantification of protein-based biomarkers. We investigated the utility of QIF for evaluating BCL2 as a biomarker in DLBCL by quantifying BCL2 selectively in CD20-expressing lymphoma cells in biopsy samples from 116 cases of DLBCL in two cohorts one of which consisted of relapsed/refractory cases from a clinical trial. BCL2 protein by QIF correlated with BCL2 mRNA abundance and was associated with both translocation and copy number gain of the BCL2 gene. Elevated BCL2 protein expression by QIF, but not immunohistochemistry or mRNA quantification, was associated with inferior overall and relapse-free survival in the relapsed/refractory cohort. QIF is an effective means of quantifying BCL2 protein objectively in routine cancer biopsy specimens and shows promise for identifying relapsed/refractory DLBCL patients at risk of inferior outcomes after salvage therapy.

Spatially resolved proteomics in osteoarthritis: State of the art and new perspectives

Osteoarthritis (OA) is one of the most common diseases worldwide caused by chronic degeneration of the joints. Its high prevalence and the involvement of several tissues define OA as a highly heterogeneous disease. New biological markers to evaluate the progression of the pathology and improve its prognosis are needed. Among all the different -omic strategies applied to OA, solution phase bottom-up proteomics has made an extensive contribution to the field of biomarker research. However, new technologies for protein analysis should be considered for a better understanding of the disease. This review focuses on complementary proteomic methodologies and new technologies for translational research of OA and other rheumatic pathologies, especially mass spectrometry imaging and protein imaging methods not applied by the OA community yet.

Multiplex LA-ICP-MS bio-imaging of brain tissue of a parkinsonian mouse model stained with metal-coded affinity-tagged antibodies and coated with indium-spiked commercial inks as internal standards

BACKGROUND

Immunohistochemistry techniques represent a powerful tool to detect and quantify disease related proteins. Improvements were accomplished by tagged antibodies using laser ablation and inductively coupled plasma mass spectrometry (LA-ICP-MS). However, these approaches are effected by day-to-variations due to instrumental drift.

NEW METHOD

Brain tissue from line 62, a Parkinson's disease model, and control mice were incubated with four antibodies relevant to the disease and standardized to three house-keeping proteins. In addition, a new standardization approach was developed and the results compared. This new approach consisted of coating specimens with gelatin and printing an indium-doped ink with a commercial ink jet printer. Furthermore, the method was evaluated for different ablation spot sizes with respect to resolution and signal-to-noise ratio.

RESULTS

Normalization using house-keeping proteins led to high background signals even at high resolution. Normalization using indium-doped ink improved the signal-to-noise ratio even when small laser spot sizes were used and further improved by overlaying tissue specimen with gelatin.

COMPARISON WITH EXISTING METHODS

Line 62 mice had more α-Synuclein and gliosis but decreased numbers of neurons, as found by conventional immunohistochemistry. These data are in line with the results obtained by LA-ICP-MS with indium standardization. However, differences between L62 and controls for tyrosine hydroxylase were only detected by LA-ICP-MS.

CONCLUSIONS

Internal standardisation using indium-doped inks is an effective method to overcome day-to-day variations and instrumental drifts. The new approach results in an increased signal-to-noise ratio and only under these conditions small but significant changes were detected, as seen for tyrosine hydroxylase.

Synovial biopsies in inflammatory arthritis: precision medicine in rheumatoid arthritis

Introduction: Synovial tissue (ST) is composed of a lining and sublining layer and is the target tissue involved in the inflammatory arthritides (IA), in which there is lining layer hyperplasia, inflammatory cell influx, macrophage recruitment and change in number and behavior of lining fibroblasts. Understanding synovial pathology has been critical in providing insights into pathogenetic mechanisms of disease and therapeutics. Pathobiological insights into ST have been underpinned by progress in molecular analytic methods; research in this area holds promise in individualizing treatment and optimizing response.Areas covered: We explore ST in IA and cover in-depth the utility of synovial biopsy and ST heterogeneity. We review recent advances in ST research and discuss implications with regards to therapeutic response. Finally, we provide perspectives on the identification of new drug targets and new diagnostic and prognostic markers.Expert opinion: ST holds the potential to individualize therapy by detecting biomarkers of diagnosis, therapeutic choice, and treatment modification in IA. Advances in molecular biology including high-throughput omics are likely to provide information that has hitherto remained unknown. ST analyzes pre- and post-treatment needs to be standard of care; only by routinely collecting and analyzing ST will we achieve the precision medicine outcomes described herein.

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 10⁹ or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.

Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

Tumor ecosystems are composed of multiple cell types that communicate by ligand-receptor interactions. Targeting ligand-receptor interactions (for instance, with immune checkpoint inhibitors) can provide significant benefits for patients. However, our knowledge of which interactions occur in a tumor and how these interactions affect outcome is still limited. We present an approach to characterize communication by ligand-receptor interactions across all cell types in a microenvironment using single-cell RNA sequencing. We apply this approach to identify and compare the ligand-receptor interactions present in six syngeneic mouse tumor models. To identify interactions potentially associated with outcome, we regress interactions against phenotypic measurements of tumor growth rate. In addition, we quantify ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publicly available human melanoma dataset. Overall, this approach provides a tool for studying cell-cell interactions, their variability across tumors, and their relationship to outcome.

Tumors are composed of cancer cells and many non-malignant cell types, such as immune and stromal cells. To better understand how all cell types in a tumor cooperate to facilitate malignant growth, Kumar et al. studied communication between cells via ligand and receptor interactions using single-cell data and computational modeling.

Mass Cytometry Imaging for the Study of Human Diseases-Applications and Data Analysis Strategies

High parameter imaging is an important tool in the life sciences for both discovery and healthcare applications. Imaging Mass Cytometry (IMC) and Multiplexed Ion Beam Imaging (MIBI) are two relatively recent technologies which enable clinical samples to be simultaneously analyzed for up to 40 parameters at subcellular resolution. Importantly, these "Mass Cytometry Imaging" (MCI) modalities are being rapidly adopted for studies of the immune system in both health and disease. In this review we discuss, first, the various applications of MCI to date. Second, due to the inherent challenge of analyzing high parameter spatial data, we discuss the various approaches that have been employed for the processing and analysis of data from MCI experiments.

Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging

Multiplexed tissue imaging enables precise, spatially resolved enumeration and characterization of cell types and states in human resection specimens. A growing number of methods applicable to formalin-fixed, paraffin-embedded (FFPE) tissue sections have been described, the majority of which rely on antibodies for antigen detection and mapping. This protocol provides step-by-step procedures for confirming the selectivity and specificity of antibodies used in fluorescence-based tissue imaging and for the construction and validation of antibody panels. Although the protocol is implemented using tissue-based cyclic immunofluorescence (t-CyCIF) as an imaging platform, these antibody-testing methods are broadly applicable. We demonstrate assembly of a 16-antibody panel for enumerating and localizing T cells and B cells, macrophages, and cells expressing immune checkpoint regulators. The protocol is accessible to individuals with experience in microscopy and immunofluorescence; some experience in computation is required for data analysis. A typical 30-antibody dataset for 20 FFPE slides can be generated within 2 weeks.

YAP and TAZ: a signalling hub of the tumour microenvironment

YAP and TAZ are transcriptional activators pervasively induced in several human solid tumours and their functions in cancer cells are the focus of intense investigation. These studies established that YAP and TAZ are essential to trigger numerous cell-autonomous responses, such as sustained proliferation, cell plasticity, therapy resistance and metastasis. Yet tumours are complex entities, wherein cancer cells are just one of the components of a composite "tumour tissue". The other component, the tumour stroma, is composed of an extracellular matrix with aberrant mechanical properties and other cell types, including cancer-associated fibroblasts and immune cells. The stroma entertains multiple and bidirectional interactions with tumour cells, establishing dependencies essential to unleash tumorigenesis. The molecular players of such interplay remain partially understood. Here, we review the emerging role of YAP and TAZ in choreographing tumour-stromal interactions. YAP and TAZ act within tumour cells to orchestrate responses in stromal cells. Vice versa, YAP and TAZ in stromal cells trigger effects that positively feed back on the growth of tumour cells. Recognizing YAP and TAZ as a hub of the network of signals exchanged within the tumour microenvironment provides a fresh perspective on the molecular principles of tumour self-organization, promising to unveil numerous new vulnerabilities.

The role of proteomics in assessing beta-cell dysfunction and death in type 1 diabetes

Introduction: Type 1 diabetes (T1D) is characterized by autoimmune-induced dysfunction and destruction of the pancreatic beta cells. Unfortunately, this process is poorly understood, and the current best treatment for type 1 diabetes is the administration of exogenous insulin. To better understand these mechanisms and to develop new therapies, there is an urgent need for biomarkers that can reliably predict disease stage. Areas covered: Mass spectrometry (MS)-based proteomics and complementary techniques play an important role in understanding the autoimmune response, inflammation and beta-cell death. MS is also a leading technology for the identification of biomarkers. This, and the technical difficulties and new technologies that provide opportunities to characterize small amounts of sample in great depth and to analyze large sample cohorts will be discussed in this review. Expert opinion: Understanding disease mechanisms and the discovery of disease-associated biomarkers are highly interconnected goals. Ideal biomarkers would be molecules specific to the different stages of the disease process that are released from beta cells to the bloodstream. However, such molecules are likely to be present in trace amounts in the blood due to the small number of pancreatic beta cells in the human body and the heterogeneity of the target organ and disease process.

Spatial heterogeneity in the mammalian liver

Hepatocytes operate in highly structured repeating anatomical units termed liver lobules. Blood flow along the lobule radial axis creates gradients of oxygen, nutrients and hormones, which, together with morphogenetic fields, give rise to a highly variable microenvironment. In line with this spatial variability, key liver functions are expressed non-uniformly across the lobules, a phenomenon termed zonation. Technologies based on single-cell transcriptomics have constructed a global spatial map of hepatocyte gene expression in mice revealing that ~50% of hepatocyte genes are expressed in a zonated manner. This broad spatial heterogeneity suggests that hepatocytes in different lobule zones might have not only different gene expression profiles but also distinct epigenetic features, regenerative capacities, susceptibilities to damage and other functional aspects. Here, we present genomic approaches for studying liver zonation, describe the principles of liver zonation and discuss the intrinsic and extrinsic factors that dictate zonation patterns. We also explore the challenges and solutions for obtaining zonation maps of liver non-parenchymal cells. These approaches facilitate global characterization of liver function with high spatial resolution along physiological and pathological timescales.

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Complex 3D tissues arise during development following tightly organized events in space and time. In particular, gene regulatory networks and local interactions between single cells lead to emergent properties at the tissue and organism levels. To understand the design principles of tissue organization, we need to characterize individual cells at given times, but we also need to consider the collective behavior of multiple cells across different spatial and temporal scales. In recent years, powerful single cell methods have been developed to characterize cells in tissues and to address the challenging questions of how different tissues are formed throughout development, maintained in homeostasis, and repaired after injury and disease. These approaches have led to a massive increase in data pertaining to both mRNA and protein abundances in single cells. As we review here, these new technologies, in combination with in toto live imaging, now allow us to bridge spatial and temporal information quantitatively at the single cell level and generate a mechanistic understanding of tissue development.

Development of a 2-dimensional atlas of the human kidney with imaging mass cytometry

An incomplete understanding of the biology of the human kidney, including the relative abundances of and interactions between intrinsic and immune cells, has long constrained the development of therapies for kidney disease. The small amount of tissue obtained by renal biopsy has previously limited the ability to use patient samples for discovery purposes. Imaging mass cytometry (IMC) is an ideal technology for quantitative interrogation of scarce samples, permitting concurrent analysis of more than 40 markers on a single tissue section. Using a validated panel of metal-conjugated antibodies designed to confer unique signatures on the structural and infiltrating cells comprising the human kidney, we performed simultaneous multiplexed imaging with IMC in 23 channels on 16 histopathologically normal human samples. We devised a machine-learning pipeline (Kidney-MAPPS) to perform single-cell segmentation, phenotyping, and quantification, thus creating a spatially preserved quantitative atlas of the normal human kidney. These data define selected baseline renal cell types, respective numbers, organization, and variability. We demonstrate the utility of IMC coupled to Kidney-MAPPS to qualitatively and quantitatively distinguish individual cell types and reveal expected as well as potentially novel abnormalities in diseased versus normal tissue. Our studies define a critical baseline data set for future quantitative analysis of human kidney disease.

Techniques for Studying Decoding of Single Cell Dynamics

Cells must be able to interpret signals they encounter and reliably generate an appropriate response. It has long been known that the dynamics of transcription factor and kinase activation can play a crucial role in selecting an individual cell's response. The study of cellular dynamics has expanded dramatically in the last few years, with dynamics being discovered in novel pathways, new insights being revealed about the importance of dynamics, and technological improvements increasing the throughput and capabilities of single cell measurements. In this review, we highlight the important developments in this field, with a focus on the methods used to make new discoveries. We also include a discussion on improvements in methods for engineering and measuring single cell dynamics and responses. Finally, we will briefly highlight some of the many challenges and avenues of research that are still open.

A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing β cells. A comprehensive picture of the changes during T1D development is lacking due to limited sample availability, inability to sample longitudinally, and the paucity of technologies enabling comprehensive tissue profiling. Here, we analyzed 1,581 islets from 12 human donors, including eight with T1D, using imaging mass cytometry (IMC). IMC enabled simultaneous measurement of 35 biomarkers with single-cell and spatial resolution. We performed pseudotime analysis of islets through T1D progression from snapshot data to reconstruct the evolution of β cell loss and insulitis. Our analyses revealed that β cell destruction is preceded by a β cell marker loss and by recruitment of cytotoxic and helper T cells. The approaches described herein demonstrate the value of IMC for improving our understanding of T1D pathogenesis, and our data lay the foundation for hypothesis generation and follow-on experiments.

Big science and big data in nephrology

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.

State-of-the-Art of Profiling Immune Contexture in the Era of Multiplexed Staining and Digital Analysis to Study Paraffin Tumor Tissues

Multiplexed platforms for multiple epitope detection have emerged in the last years as very powerful tools to study tumor tissues. These revolutionary technologies provide important visual techniques for tumor examination in formalin-fixed paraffin-embedded specimens to improve the understanding of the tumor microenvironment, promote new treatment discoveries, aid in cancer prevention, as well as allowing translational studies to be carried out. The aim of this review is to highlight the more recent methodologies that use multiplexed staining to study simultaneous protein identification in formalin-fixed paraffin-embedded tumor tissues for immune profiling, clinical research, and potential translational analysis. New multiplexed methodologies, which permit the identification of several proteins at the same time in one single tissue section, have been developed in recent years with the ability to study different cell populations, cells by cells, and their spatial distribution in different tumor specimens including whole sections, core needle biopsies, and tissue microarrays. Multiplexed technologies associated with image analysis software can be performed with a high-quality throughput assay to study cancer specimens and are important tools for new discoveries. The different multiplexed technologies described in this review have shown their utility in the study of cancer tissues and their advantages for translational research studies and application in cancer prevention and treatments.

New tools for pathology: a user's review of a highly multiplexed method for in situ analysis of protein and RNA expression in tissue

Tumor cell heterogeneity and tumor cell-stromal interactions are being explored as determinants of disease progression and treatment resistance in solid tumor and hematological malignancies. As such, tools simultaneously capable of highly multiplexed profiling of tissues' protein and RNA content, as well as interrogation of rare or single cells, are required to precisely characterize constituent tumor cell populations, infiltrating lymphocytes and stromal elements. Access to spatial relationships will enable more precise characterization of tumors, support patient stratification and may help to identify novel drug targets. Multiple platforms are being developed to address these critical unmet needs. The NanoString digital spatial profiling (DSP) platform enables highly multiplexed, spatial assessment of protein and/or RNA targets in tissues by detecting oligonucleotide barcodes conjugated via a photocleavable linker to primary antibodies or nucleic acid probes. Although this platform enables high-dimensional spatial interrogation of tissue protein and RNA expression, a detailed understanding of its composition, function and chemistry is advisable to guide experimental design and data interpretation. The purpose of this review is to provide an independent, comprehensive description of the DSP technology, including an overview of NanoString's capture and antibody barcode conjugation chemistries, experimental workflow, data output and analysis methods. The DSP technology will be discussed in the context of other highly multiplexed immunohistochemistry methods, including imaging mass cytometry and multiplexed ion beam imaging, to inform potential users of the advantages and limitations of each. Additional issues such as preanalytical variability, sampling and specimen adequacy will be considered with respect to the platforms to inform potential experimental design. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Tissue Imaging by Mass Spectrometry: A Practical Guide for the Medicinal Chemist

Understanding the tissue distribution of therapeutic molecules is often critical for assessing their efficacy and toxicity. Unfortunately, standard methods for monitoring localized drug distribution are resource-intensive and are typically performed late in the discovery process. As a result, early development efforts often progress without detailed information on the effect that changes in structure and/or formulation have on drug localization. Recent innovations in mass spectrometry (MS) provide new options for mapping the spatial distribution of drug in tissue and allow parallel detection of endogenous species. These advances are improving access to drug distribution data early in discovery and provide insight into local biochemical changes that are directly related to drug activity. The literature on these topics is voluminous, and the technology is advancing rapidly, offering a bewildering array of options for researchers who are new to the field. To guide medicinal chemists who wish to apply these methods in their research, this technology perspective provides our views on practical applications that are currently enabled by various MS imaging (MSI) approaches, along with recommendations for how best to implement these methods in pharmaceutical R&D.

Why geneticists stole cancer research even though cancer is primarily a signaling disease

Genetic approaches to cancer research have dramatically advanced our understanding of the pathophysiology of this disease, leading to similar genetics-based approaches for precision therapy, which have been less successful. Reconfiguring and adapting the types of technologies that underlie genetic research to dissect tumor cell signaling in clinical samples may offer an alternative road forward.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

Beyond the H&E: Advanced Technologies for in situ Tissue Biomarker Imaging

For decades, histopathology with routine hematoxylin and eosin staining has been and remains the gold standard for reaching a morphologic diagnosis in tissue samples from humans and veterinary species. However, within the past decade, there has been exponential growth in advanced techniques for in situ tissue biomarker imaging that bridge the divide between anatomic and molecular pathology. It is now possible to simultaneously observe localization and expression magnitude of multiple protein, nucleic acid, and molecular targets in tissue sections and apply machine learning to synthesize vast, image-derived datasets. As these technologies become more sophisticated and widely available, a team-science approach involving subspecialists with medical, engineering, and physics backgrounds is critical to upholding quality and validity in studies generating these data. The purpose of this manuscript is to detail the scientific premise, tools and training, quality control, and data collection and analysis considerations needed for the most prominent advanced imaging technologies currently applied in tissue sections: immunofluorescence, in situ hybridization, laser capture microdissection, matrix-assisted laser desorption ionization imaging mass spectrometry, and spectroscopic/optical methods. We conclude with a brief overview of future directions for ex vivo and in vivo imaging techniques.

The Precise Diagnosis of Cancer Invasion/Metastasis via 2D Laser Ablation Mass Mapping of Metalloproteinase in Primary Cancer Tissue

Cancer invasion and metastasis remain the major causes of over 90% of patient deaths. Molecular imaging methods such as computed tomography (CT)/magnetic resonance imaging (MRI) can precisely assess primary regional lymph node invasion and distant organ metastasis via body scanning; however, such diagnostic methods are often utilized too late for cancer therapy. To date, pathologic methods mainly provide information on differentiation/proliferation and potential drug therapy biomarkers of primary tumors rather than precisely reveal tumor regional invasion and distant metastasis in the body. We hypothesized that quantification of membrane type-1 matrix metalloproteinase (MT1-MMP) levels in primary tumor tissue will provide a precise assessment of tumor regional lymph node invasion and remote organ metastasis. In this work, we developed peptide-coated Au clusters with intrinsic red fluorescence and a specific mass signal. When these clusters labeled MT1-MMP in tumor tissue sections derived from the xenograft lung carcinoma model, human lung carcinoma and human renal carcinoma, we could directly observe MT1-MMP via optical fluorescence microscopy and quantitatively detect the MT1-MMP expression level via laser ablation inductively coupled plasma mass spectrometry 2D mapping (2D-LA-Mass Mapping). By observing and quantifying the MT1-MMP expression level in primary human lung carcinoma and human renal carcinoma tissue sections, we precisely assessed the risk of primary tumor invasion/metastasis. Importantly, the accuracy of this pathologic method was verified by CT/MRI molecular imaging of cancer patients and traditional hematoxylin and eosin (H&E) staining/immunohistochemistry (IHC)/immunofluorescence (IF) pathologic studies of primary tumor tissues.

Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology

The tumor microenvironment (TME) is a multifaceted ecosystem characterized by profound cellular heterogeneity, dynamicity, and complex intercellular cross-talk. The striking responses obtained with immune checkpoint blockers, i.e., antibodies targeting immune-cell regulators to boost antitumor immunity, have demonstrated the enormous potential of anticancer treatments that target TME components other than tumor cells. However, as checkpoint blockade is currently beneficial only to a limited fraction of patients, there is an urgent need to understand the mechanisms orchestrating the immune response in the TME to guide the rational design of more effective anticancer therapies. In this Mini Review, we give an overview of the methodologies that allow studying the heterogeneity of the TME from multi-omics data generated from bulk samples, single cells, or images of tumor-tissue slides. These include approaches for the characterization of the different cell phenotypes and for the reconstruction of their spatial organization and inter-cellular cross-talk. We discuss how this broader vision of the cellular heterogeneity and plasticity of tumors, which is emerging thanks to these methodologies, offers the opportunity to rationally design precision immuno-oncology treatments. These developments are fundamental to overcome the current limitations of targeted agents and checkpoint blockers and to bring long-term clinical benefits to a larger fraction of cancer patients.

Spatiotemporal segregation of human marginal zone and memory B cell populations in lymphoid tissue

Human memory B cells and marginal zone (MZ) B cells share common features such as the expression of CD27 and somatic mutations in their IGHV and BCL6 genes, but the relationship between them is controversial. Here, we show phenotypic progression within lymphoid tissues as MZ B cells emerge from the mature naïve B cell pool via a precursor CD27-CD45RBMEM55+ population distant from memory cells. By imaging mass cytometry, we find that MZ B cells and memory B cells occupy different microanatomical niches in organised gut lymphoid tissues. Both populations disseminate widely between distant lymphoid tissues and blood, and both diversify their IGHV repertoire in gut germinal centres (GC), but nevertheless remain largely clonally separate. MZ B cells are therefore not developmentally contiguous with or analogous to classical memory B cells despite their shared ability to transit through GC, where somatic mutations are acquired.

Multiparametric analysis of colorectal cancer immune responses

Colorectal cancer (CRC) is a heterogeneous disease, with a diverse and plastic immune cell infiltrate. These immune cells play an important role in regulating tumour growth - progression or elimination. Some populations of cells have a strong correlation with disease-free survival, making them useful prognostic markers. In particular, the infiltrate of CD3+ and CD8+ T cells into CRC tumours has been validated worldwide as a valuable indicator of patient prognosis. However, the heterogeneity of the immune response, both between patients with tumours of different molecular subtypes, and within the tumour itself, necessitates the use of multiparametric analysis in the investigation of tumour-specific immune responses. This review will outline the multiparametric analysis techniques that have been developed and applied to studying the role of immune cells in the tumour, with a focus on colorectal cancer. Because much of the data in this disease relates to T cell subsets and heterogeneity, we have used T cell populations as examples throughout. Flow and mass cytometry give a detailed representation of the cells within the tumour in a single-cell suspension on a per-cell basis. Imaging technologies, such as imaging mass cytometry, are used to investigate increasing numbers of markers whilst retaining the spatial and structural information of the tumour section and the infiltrating immune cells. Together, the analyses of multiple immune parameters can provide valuable information to guide clinical decision-making in CRC.

Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes

The architecture of normal and diseased tissues strongly influences the development and progression of disease as well as responsiveness and resistance to therapy. We describe a tissue-based cyclic immunofluorescence (t-CyCIF) method for highly multiplexed immuno-fluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides, the most widely used specimens for histopathological diagnosis of cancer and other diseases. t-CyCIF generates up to 60-plex images using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high-dimensional representation. t-CyCIF requires no specialized instruments or reagents and is compatible with super-resolution imaging; we demonstrate its application to quantifying signal transduction cascades, tumor antigens and immune markers in diverse tissues and tumors. The simplicity and adaptability of t-CyCIF makes it an effective method for pre-clinical and clinical research and a natural complement to single-cell genomics.

Single-Cell in Situ RNA Analysis With Switchable Fluorescent Oligonucleotides

Comprehensive RNA analyses in individual cells in their native spatial contexts promise to transform our understanding of normal physiology and disease pathogenesis. Here we report a single-cell in situ RNA analysis approach using switchable fluorescent oligonucleotides (SFO). In this method, transcripts are first hybridized by pre-decoding oligonucleotides. These oligonucleotides subsequently recruit SFO to stain their corresponding RNA targets. After fluorescence imaging, all the SFO in the whole specimen are simultaneously removed by DNA strand displacement reactions. Through continuous cycles of target staining, fluorescence imaging, and SFO removal, a large number of different transcripts can be identified by unique fluorophore sequences and visualized at the optical resolution. To demonstrate the feasibility of this approach, we show that the hybridized SFO can be efficiently stripped by strand displacement reactions within 30 min. We also demonstrate that this SFO removal process maintains the integrity of the RNA targets and the pre-decoding oligonucleotides, and keeps them hybridized. Applying this approach, we show that transcripts can be restained in at least eight hybridization cycles with high analysis accuracy, which theoretically would enable the whole transcriptome to be quantified at the single molecule sensitivity in individual cells. This in situ RNA analysis technology will have wide applications in systems biology, molecular diagnosis, and targeted therapies.

Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry

The advent of mass cytometry increased the number of parameters measured at the single-cell level while decreasing the extent of crosstalk between channels relative to dye-based flow cytometry. Although reduced, spillover still exists in mass cytometry data, and minimizing its effect requires considerable expert knowledge and substantial experimental effort. Here, we describe a novel bead-based compensation workflow and R-based software that estimates and corrects for interference between channels. We performed an in-depth characterization of the spillover properties in mass cytometry, including limitations defined by the linear range of the mass cytometer and the reproducibility of the spillover over time and across machines. We demonstrated the utility of our method in suspension and imaging mass cytometry. To conclude, our approach greatly simplifies the development of new antibody panels, increases flexibility for antibody-metal pairing, opens the way to using less pure isotopes, and improves overall data quality, thereby reducing the risk of reporting cell phenotype artifacts.

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Signal spillover can impact quality of mass cytometry data

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Spillover can be corrected by compensation throughout the linear range of the CyTOF

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Compensation enables signal correction and data structure preservation

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CATALYST is a new R package and a web tool to estimate and correct for spillover

Ruthenium counterstaining for imaging mass cytometry

Imaging mass cytometry is a novel imaging modality that enables simultaneous antibody-based detection of >40 epitopes and molecules in tissue sections at subcellular resolution by the use of isotopically pure metal tags. Essential for any imaging approach in which antigen detection is performed is counterstaining, which reveals the overall structure of the tissue. Counterstaining is necessary because antigens of interest are often present in only a small subset of cells, and the rest of the tissue structures are not visible. As most biological tissues are nearly transparent or non-fluorescent, chromogenic reagents such as haematoxylin (for immunohistochemistry) or fluorescent dyes such as 4',6-diamidino-2-phenylindole (which stains nuclei for epifluorescence and confocal microscopy) are utilized. Here, we describe a metal-based counterstain for imaging mass cytometry based on simple oxidation and subsequent covalent binding of the tissue components to ruthenium tetroxide (RuO₄ ). RuO₄ counterstaining reveals general tissue structure both in areas with high cell content and in stromal areas with low cellularity and fibrous or hyaline material in a manner analogous to haematoxylin in immunohistochemical counterstaining or eosin or other anionic dyes in conventional histology. Our new counterstain approach is applicable to any metal-based imaging technique, and will facilitate the adaptation of imaging mass cytometry for routine applications in clinical and research laboratories. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

CellCycleTRACER accounts for cell cycle and volume in mass cytometry data

Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course. CellCycleTRACER reveals signaling relationships and cell heterogeneity that were otherwise masked.

Mass cytometry is a powerful method of single cell analysis, but potential confounding effects of cell cycle and cell volume are not taken into account. Here the authors present a combined experimental and computational method to correct for these effects and reveal features of TNFα stimulation that are otherwise masked.