Multiplexed (18-Plex) Measurement of Signaling Targets and Cytotoxic T Cells in Trastuzumab-Treated Patients using Imaging Mass Cytometry

Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum

While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

Chemical tagging mass spectrometry: an approach for single-cell omics

Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.

Single-cell spatial landscape of immunotherapy response reveals mechanisms of CXCL13 enhanced antitumor immunity

BACKGROUND

Immunotherapy has revolutionized clinical outcomes for patients suffering from lung cancer, yet relatively few patients sustain long-term durable responses. Recent studies have demonstrated that the tumor immune microenvironment fosters tumorous heterogeneity and mediates both disease progression and response to immune checkpoint inhibitors (ICI). As such, there is an unmet need to elucidate the spatially defined single-cell landscape of the lung cancer microenvironment to understand the mechanisms of disease progression and identify biomarkers of response to ICI.

METHODS

Here, in this study, we applied imaging mass cytometry to characterize the tumor and immunological landscape of immunotherapy response in non-small cell lung cancer by describing activated cell states, cellular interactions and neighborhoods associated with improved efficacy. We functionally validated our findings using preclinical mouse models of cancer treated with anti-programmed cell death protein-1 (PD-1) immune checkpoint blockade.

RESULTS

We resolved 114,524 single cells in 27 patients treated with ICI, enabling spatial resolution of immune lineages and activation states with distinct clinical outcomes. We demonstrated that CXCL13 expression is associated with ICI efficacy in patients, and that recombinant CXCL13 potentiates anti-PD-1 response in vivo in association with increased antigen experienced T cell subsets and reduced CCR2+ monocytes.

DISCUSSION

Our results provide a high-resolution molecular resource and illustrate the importance of major immune lineages as well as their functional substates in understanding the role of the tumor immune microenvironment in response to ICIs.

Single-cell spatial immune landscapes of primary and metastatic brain tumours

Single-cell technologies have enabled the characterization of the tumour microenvironment at unprecedented depth and have revealed vast cellular diversity among tumour cells and their niche. Anti-tumour immunity relies on cell-cell relationships within the tumour microenvironment^1,2, yet many single-cell studies lack spatial context and rely on dissociated tissues³. Here we applied imaging mass cytometry to characterize the immunological landscape of 139 high-grade glioma and 46 brain metastasis tumours from patients. Single-cell analysis of more than 1.1 million cells across 389 high-dimensional histopathology images enabled the spatial resolution of immune lineages and activation states, revealing differences in immune landscapes between primary tumours and brain metastases from diverse solid cancers. These analyses revealed cellular neighbourhoods associated with survival in patients with glioblastoma, which we leveraged to identify a unique population of myeloperoxidase (MPO)-positive macrophages associated with long-term survival. Our findings provide insight into the biology of primary and metastatic brain tumours, reinforcing the value of integrating spatial resolution to single-cell datasets to dissect the microenvironmental contexture of cancer.

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Simple Summary

Cancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person’s lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes.

Abstract

The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.

Application of High-Throughput Imaging Mass Cytometry Hyperion in Cancer Research

Imaging mass cytometry (IMC) enables the in situ analysis of in-depth-phenotyped cells in their native microenvironment within the preserved architecture of a single tissue section. To date, it permits the simultaneous analysis of up to 50 different protein- markers targeted by metal-conjugated antibodies. The application of IMC in the field of cancer research may notably help 1) to define biomarkers of prognostic and theragnostic significance for current and future treatments against well-established and novel therapeutic targets and 2) to improve our understanding of cancer progression and its resistance mechanisms to immune system and how to overcome them. In the present article, we not only provide a literature review on the use of the IMC in cancer-dedicated studies but we also present the IMC method and discuss its advantages and limitations among methods dedicated to deciphering the complexity of cancer tissue.

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. This article is protected by copyright. All rights reserved.

Advances in mass cytometry and its applicability to digital pathology in clinical-translational cancer research

The development and subsequent adaptation of mass cytometry for the histological analysis of tissue sections has allowed the simultaneous spatial characterization of multiple components. This is useful to find the correlation between the genotypic and phenotypic profile of tumor cells and their environment in clinical-translational studies. In this revision, we provide an overview of the most relevant hallmarks in the development, implementation and application of multiplexed imaging in the study of cancer and other conditions. A special focus is placed on studies based on imaging mass cytometry (IMC) and multiplexed ion beam imaging (MIBI). The purpose of this review is to help our readers become familiar with the verification techniques employed on this tool and outline the multiple applications reported in the literature. This review will also provide guidance on the use of IMC or MIBI in any field of biomedical research.

Using Imaging Mass Cytometry to Define Cell Identities and Interactions in Human Tissues

In the evolving landscape of highly multiplexed imaging techniques that can be applied to study complex cellular microenvironments, this review characterizes the use of imaging mass cytometry (IMC) to study the human kidney. We provide technical details for antibody validation, cell segmentation, and data analysis specifically tailored to human kidney samples, and elaborate on phenotyping of kidney cell types and novel insights that IMC can provide regarding pathophysiological processes in the injured or diseased kidney. This review will provide the reader with the necessary background to understand both the power and the limitations of IMC and thus support better perception of how IMC analysis can improve our understanding of human disease pathogenesis and can be integrated with other technologies such as single cell sequencing and proteomics to provide spatial context to cellular data.

Targeting HER2 protein in individual cells using ICP-MS detection and its potential as prognostic and predictive breast cancer biomarker

The human epidermal growth factor receptor 2 (HER2) is a transmembrane protein that has become one of the most specific prognostic and predictive biomarker of breast cancer. Its early detection is key for optimizing the patient clinical outcome. This work is focused on the detection of HER2 in individual cells using an antibody containing lutetium (Lu) as reporter group that is monitored by introducing the individual cells into the inductively coupled plasma mass spectrometer (ICP-MS). This Lu-containing antibody probe is used to label different breast cancer cell lines considered HER2 negative (MDA-MB-231) and positive (SKBR-3 and BT-474). Optimizations regarding the amount of the probe necessary to ensure complete labelling reactions are conducted in the different cell models. Concentrations in the range of 0.006 fg Lu/cell and 0.030 fg Lu/cell could be found in the HER2 negative and HER2 positive cells, respectively. In addition, the selectivity of the labelling reaction is tested by using two different metal-containing antibody probes for HER2 (containing Lu) and for transferrin receptor 1 (containing Nd), respectively, within the same cell population. Finally, the methodology is applied to the targeting of HER2 positive cells in complex cell mixtures containing variable amounts of BT-474 and MDA-MB-231 cells. The obtained results showed the excellent capabilities of the proposed strategy to discriminate among cell populations. This finding could help for scoring HER2 positive tumors improving existing technologies.

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers

INTRODUCTION

The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction).

AREA COVERED

In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions.

EXPERT OPINION

The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.

Spatial UMAP and Image Cytometry for Topographic Immuno-oncology Biomarker Discovery

Multiplex immunofluorescence (mIF) can detail spatial relationships and complex cell phenotypes in the tumor microenvironment (TME). However, the analysis and visualization of mIF data can be complex and time-consuming. Here, we used tumor specimens from 93 patients with metastatic melanoma to develop and validate a mIF data analysis pipeline using established flow cytometry workflows (image cytometry). Unlike flow cytometry, spatial information from the TME was conserved at single-cell resolution. A spatial uniform manifold approximation and projection (UMAP) was constructed using the image cytometry output. Spatial UMAP subtraction analysis (survivors vs. nonsurvivors at 5 years) was used to identify topographic and coexpression signatures with positive or negative prognostic impact. Cell densities and proportions identified by image cytometry showed strong correlations when compared with those obtained using gold-standard, digital pathology software (R2 > 0.8). The associated spatial UMAP highlighted "immune neighborhoods" and associated topographic immunoactive protein expression patterns. We found that PD-L1 and PD-1 expression intensity was spatially encoded-the highest PD-L1 expression intensity was observed on CD163+ cells in neighborhoods with high CD8+ cell density, and the highest PD-1 expression intensity was observed on CD8+ cells in neighborhoods with dense arrangements of tumor cells. Spatial UMAP subtraction analysis revealed numerous spatial clusters associated with clinical outcome. The variables represented in the key clusters from the unsupervised UMAP analysis were validated using established, supervised approaches. In conclusion, image cytometry and the spatial UMAPs presented herein are powerful tools for the visualization and interpretation of single-cell, spatially resolved mIF data and associated topographic biomarker development.

Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data

Simple Summary

Immune modulation is considered a hallmark of cancer initiation and progression, and has offered promising opportunities for therapeutic manipulation. Multiplex immunofluorescence (mIF) technology has enabled the tumor immune microenvironment (TIME) to be studied at an increased scale, in terms of both the number of markers and the number of samples. Another benefit of mIF technology is the ability to measure not only the abundance but also the spatial location of multiple cells types within a tissue sample simultaneously, allowing for assessment of the co-localization of different types of immune markers. Thus, the use of mIF technologies have enable researchers to characterize patient, clinical, and tumor characteristics in the hope of identifying patients whom might benefit from immunotherapy treatments. In this review we outline some of the challenges and opportunities in the statistical analyses of mIF data to study the TIME.

Abstract

Immune modulation is considered a hallmark of cancer initiation and progression. The recent development of immunotherapies has ushered in a new era of cancer treatment. These therapeutics have led to revolutionary breakthroughs; however, the efficacy of immunotherapy has been modest and is often restricted to a subset of patients. Hence, identification of which cancer patients will benefit from immunotherapy is essential. Multiplex immunofluorescence (mIF) microscopy allows for the assessment and visualization of the tumor immune microenvironment (TIME). The data output following image and machine learning analyses for cell segmenting and phenotyping consists of the following information for each tumor sample: the number of positive cells for each marker and phenotype(s) of interest, number of total cells, percent of positive cells for each marker, and spatial locations for all measured cells. There are many challenges in the analysis of mIF data, including many tissue samples with zero positive cells or “zero-inflated” data, repeated measurements from multiple TMA cores or tissue slides per subject, and spatial analyses to determine the level of clustering and co-localization between the cell types in the TIME. In this review paper, we will discuss the challenges in the statistical analysis of mIF data and opportunities for further research.

HER2 Isoforms Uniquely Program Intratumor Heterogeneity and Predetermine Breast Cancer Trajectories During the Occult Tumorigenic Phase

HER2-positive breast cancers are among the most heterogeneous breast cancer subtypes. The early amplification of HER2 and its known oncogenic isoforms provide a plausible mechanism in which distinct programs of tumor heterogeneity could be traced to the initial oncogenic event. Here a Cancer rainbow mouse simultaneously expressing fluorescently barcoded wildtype (^WTHER2), exon-16 null (^d16HER2), and N-terminally truncated (^p95HER2) HER2 isoforms is used to trace tumorigenesis from initiation to invasion. Tumorigenesis was visualized using whole-gland fluorescent lineage tracing and single-cell molecular pathology. We demonstrate that within weeks of expression, morphologic aberrations were already present and unique to each HER2 isoform. Although ^WTHER2 cells were abundant throughout the mammary ducts, detectable lesions were exceptionally rare. In contrast, ^d16HER2 and ^p95HER2 induced rapid tumor development. ^d16HER2 incited homogenous and proliferative luminal-like lesions which infrequently progressed to invasive phenotypes whereas ^p95HER2 lesions were heterogenous and invasive at the smallest detectable stage. Distinct cancer trajectories were observed for ^d16HER2 and ^p95HER2 tumors as evidenced by oncogene-dependent changes in epithelial specification and the tumor microenvironment. These data provide direct experimental evidence that intratumor heterogeneity programs begin very early and well in advance of screen or clinically detectable breast cancer. IMPLICATIONS: Although all HER2 breast cancers are treated equally, we show a mechanism by which clinically undetected HER2 isoforms program heterogenous cancer phenotypes through biased epithelial specification and adaptations within the tumor microenvironment.

Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease

Deep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. Single-cell and spatial maps confirmed that CD68+ cells were correlated with the enhanced Granzyme B expression and CD3+ cells exhibited enrichment of CD4+ phenotype in chronic tonsillitis. SpatialViz revealed morphological distributions of cellular organizations in distinct anatomical areas, spatially resolved single-cell associations across anatomical categories, and distance maps between the markers. Spatial topographic maps showed the unique organization of different tissue layers. The spatial reference framework generated network-based comparisons of multiplex data from healthy and diseased tonsils. SpatialViz is broadly applicable to multiplexed tissue biology.

Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Imaging in Biology

Elemental imaging gives insight into the fundamental chemical makeup of living organisms. Every cell on Earth is comprised of a complex and dynamic mixture of the chemical elements that define structure and function. Many disease states feature a disturbance in elemental homeostasis, and understanding how, and most importantly where, has driven the development of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) as the principal elemental imaging technique for biologists. This review provides an outline of ICP-MS technology, laser ablation cell designs, imaging workflows, and methods of quantification. Detailed examples of imaging applications including analyses of cancers, elemental uptake and accumulation, plant bioimaging, nanomaterials in the environment, and exposure science and neuroscience are presented and discussed. Recent incorporation of immunohistochemical workflows for imaging biomolecules, complementary and multimodal imaging techniques, and image processing methods is also reviewed.

Targeting Neuroinflammation in Brain Cancer: Uncovering Mechanisms, Pharmacological Targets, and Neuropharmaceutical Developments

Gliomas are one of the most lethal types of cancers accounting for ∼80% of all central nervous system (CNS) primary malignancies. Among gliomas, glioblastomas (GBM) are the most aggressive, characterized by a median patient survival of fewer than 15  months. Recent molecular characterization studies uncovered the genetic signatures and methylation status of gliomas and correlate these with clinical prognosis. The most relevant molecular characteristics for the new glioma classification are IDH mutation, chromosome 1p/19q deletion, histone mutations, and other genetic parameters such as ATRX loss, TP53, and TERT mutations, as well as DNA methylation levels. Similar to other solid tumors, glioma progression is impacted by the complex interactions between the tumor cells and immune cells within the tumor microenvironment. The immune system’s response to cancer can impact the glioma’s survival, proliferation, and invasiveness. Salient characteristics of gliomas include enhanced vascularization, stimulation of a hypoxic tumor microenvironment, increased oxidative stress, and an immune suppressive milieu. These processes promote the neuro-inflammatory tumor microenvironment which can lead to the loss of blood-brain barrier (BBB) integrity. The consequences of a compromised BBB are deleteriously exposing the brain to potentially harmful concentrations of substances from the peripheral circulation, adversely affecting neuronal signaling, and abnormal immune cell infiltration; all of which can lead to disruption of brain homeostasis. In this review, we first describe the unique features of inflammation in CNS tumors. We then discuss the mechanisms of tumor-initiating neuro-inflammatory microenvironment and its impact on tumor invasion and progression. Finally, we also discuss potential pharmacological interventions that can be used to target neuro-inflammation in gliomas.

Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry

PURPOSE

Imaging mass cytometry (IMC) is among the first tools with the capacity for multiplex analysis of more than 40 targets, which provides a novel approach to biomarker discovery. Here, we used IMC to characterize the tumor microenvironment of patients with metastatic melanoma who received immunotherapy in efforts to find indicative factors of treatment response. In spite of the new power of IMC, the image analysis aspects are still limited by the challenges of cell segmentation.

EXPERIMENTAL DESIGN

Here, rather than segment, we performed image analysis using a newly designed version of the AQUA software to measure marker intensity in molecularly defined compartments: tumor cells, stroma, T cells, B cells, and macrophages. IMC data were compared with quantitative immunofluorescence (QIF) and digital spatial profiling.

RESULTS

Validation of IMC results for immune markers was confirmed by regression with additional multiplexing methods and outcome assessment. Multivariable analyses by each compartment revealed significant associations of 12 markers for progression-free survival and seven markers for overall survival (OS). The most compelling indicative biomarker, beta2-microglobulin (B2M), was confirmed by correlation with OS by QIF in the discovery cohort and validated in an independent published cohort profiled by mRNA expression.

CONCLUSIONS

Using digital image analysis based on pixel colocalization to assess IMC data allowed us to quantitively measure 25 markers simultaneously on formalin-fixed, paraffin-embedded tissue microarray samples. In addition to showing high concordance with other multiplexing technologies, we identified a series of potentially indicative biomarkers for immunotherapy in metastatic melanoma, including B2M.

The use and limitations of single-cell mass cytometry for studying human microglia function

Microglia, the resident innate immune cells of the central nervous system (CNS), play an important role in brain development and homoeostasis, as well as in neuroinflammatory, neurodegenerative and psychiatric diseases. Studies in animal models have been used to determine the origin and development of microglia, and how these cells alter their transcriptional and phenotypic signatures during CNS pathology. However, little is known about their human counterparts. Recent studies in human brain samples have harnessed the power of multiplexed single-cell technologies such as single-cell RNA sequencing (scRNA-seq) and mass cytometry (cytometry by time-of-flight [CyTOF]) to provide a comprehensive molecular view of human microglia in healthy and diseased brains. CyTOF is a powerful tool to study high-dimensional protein expression of human microglia (huMG) at the single-cell level. This technology widens the possibilities of high-throughput quantification (of over 60 targeted molecules) at a single-cell resolution. CyTOF can be combined with scRNA-seq for comprehensive analysis, as it allows single-cell analysis of post-translational modifications of proteins, which provides insights into cell signalling dynamics in targeted cells. In addition, imaging mass cytometry (IMC) has recently become commercially available, and will be useful for analysing multiple cell types in human brain sections. IMC leverages mass spectrometry to acquire spatial data of cell-cell interactions on tissue sections, using (theoretically) over 40 markers at the same time. In this review, we summarise recent studies of huMG using CyTOF and IMC analyses. The uses and limitations as well as future directions of these technologies are discussed.

Conjugating immunoassays to mass spectrometry: Solutions to contemporary challenges in clinical diagnostics

Developments in immunoassays and mass spectrometry have independently influenced diagnostic technology. However, both techniques possess unique strengths and limitations, which define their ability to meet evolving requirements for faster, more affordable and more accurate clinical tests. In response, hybrid techniques, which combine the accessibility and ease-of-use of immunoassays with the sensitivity, high throughput and multiplexing capabilities of mass spectrometry are continually being explored. Developments in antibody conjugation methodology have expanded the role of these biomolecules to applications outside of conventional colorimetric assays and histology. Furthermore, the range of different mass spectrometry ionisation and analysis technologies has enabled its successful adaptation as a detection method for numerous clinically relevant immunological assays. Several recent examples of combined mass spectrometry-immunoassay techniques demonstrate the potential of these methods as improved diagnostic tests for several important human diseases. The present challenges are to continue technological advancements in mass spectrometry instrumentation and develop improved bioconjugation methods, which can overcome their existing limitations and demonstrate the clinical significance of these hybrid approaches.

A 34-Marker Panel for Imaging Mass Cytometric Analysis of Human Snap-Frozen Tissue

Imaging mass cytometry (IMC) is able to quantify the expression of dozens of markers at sub-cellular resolution on a single tissue section by combining a novel laser ablation system with mass cytometry. As such, it allows us to gain spatial information and antigen quantification in situ, and can be applied to both snap-frozen and formalin-fixed, paraffin-embedded (FFPE) tissue sections. Herein, we have developed and optimized the immunodetection conditions for a 34-antibody panel for use on human snap-frozen tissue sections. For this, we tested the performance of 80 antibodies. Moreover, we compared tissue drying times, fixation procedures and antibody incubation conditions. We observed that variations in the drying times of tissue sections had little impact on the quality of the images. Fixation with methanol for 5 min at -20°C or 1% paraformaldehyde (PFA) for 5 min at room temperature followed by methanol for 5 min at -20°C were superior to fixation with acetone or PFA only. Finally, we observed that antibody incubation overnight at 4°C yielded more consistent results as compared to staining at room temperature for 5 h. Finally, we used the optimized method for staining of human fetal and adult intestinal tissue samples. We present the tissue architecture and spatial distribution of the stromal cells and immune cells in these samples visualizing blood vessels, the epithelium and lamina propria based on the expression of α-smooth muscle actin (α-SMA), E-Cadherin and Vimentin, while simultaneously revealing the colocalization of T cells, innate lymphoid cells (ILCs), and various myeloid cell subsets in the lamina propria of the human fetal intestine. We expect that this work can aid the scientific community who wish to improve IMC data quality.

Molecular MRI of the Immuno-Metabolic Interplay in a Rabbit Liver Tumor Model: A Biomarker for Resistance Mechanisms in Tumor-targeted Therapy?

Background The immuno-metabolic interplay has gained interest for determining and targeting immunosuppressive tumor micro-environments that remain a barrier to current immuno-oncologic therapies in hepatocellular carcinoma. Purpose To develop molecular MRI tools to reveal resistance mechanisms to immuno-oncologic therapies caused by the immuno-metabolic interplay in a translational liver cancer model. Materials and Methods A total of 21 VX2 liver tumor-bearing New Zealand white rabbits were used between October 2018 and February 2020. Rabbits were divided into three groups. Group A (n = 3) underwent intra-arterial infusion of gadolinium 160 (¹⁶⁰Gd)-labeled anti-human leukocyte antigen-DR isotope (HLA-DR) antibodies to detect antigen-presenting immune cells. Group B (n = 3) received rhodamine-conjugated superparamagnetic iron oxide nanoparticles (SPIONs) intravenously to detect macrophages. These six rabbits underwent 3-T MRI, including T1- and T2-weighted imaging, before and 24 hours after contrast material administration. Group C (n = 15) underwent extracellular pH mapping with use of MR spectroscopy. Of those 15 rabbits, six underwent conventional transarterial chemoembolization (TACE), four underwent conventional TACE with extracellular pH-buffering bicarbonate, and five served as untreated controls. MRI signal intensity distribution was validated by using immunohistochemistry staining of HLA-DR and CD11b, Prussian blue iron staining, fluorescence microscopy of rhodamine, and imaging mass cytometry (IMC) of gadolinium. Statistical analysis included Mann-Whitney U and Kruskal-Wallis tests. Results T1-weighted MRI with ¹⁶⁰Gd-labeled antibodies revealed localized peritumoral ring enhancement, which corresponded to gadolinium distribution detected with IMC. T2-weighted MRI with SPIONs showed curvilinear signal intensity representing selective peritumoral deposition in macrophages. Extracellular pH-specific MR spectroscopy of untreated liver tumors showed acidosis (mean extracellular pH, 6.78 ± 0.09) compared with liver parenchyma (mean extracellular pH, 7.18 ± 0.03) (P = .008) and peritumoral immune cell exclusion. Normalization of tumor extracellular pH (mean, 6.96 ± 0.05; P = .02) using bicarbonate during TACE increased peri- and intratumoral immune cell infiltration (P = .002). Conclusion MRI in a rabbit liver tumor model was used to visualize resistance mechanisms mediated by the immuno-metabolic interplay that inform susceptibility and response to immuno-oncologic therapies, providing a therapeutic strategy to restore immune permissiveness in liver cancer. © RSNA, 2020 Online supplemental material is available for this article.

Identification of non-cancer cells from cancer transcriptomic data

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Multiplex Spatial Bioimaging for Combination Therapy Design

Multiplex spatial analyses dissect the heterogeneous cellular abundances and interactions in tumors. Single-cell bioimaging profiles many disease-associated protein biomarkers in patient biopsies to inform the design of cancer therapies. Guided by the mechanistic insights from spatial cellular maps, combination therapy can efficiently eliminate cancers with reduced off-targets, resistance, and relapse.

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.

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.

Multiplexed imaging of immune cells in staged multiple sclerosis lesions by mass cytometry

Multiple sclerosis (MS) is characterized by demyelinated and inflammatory lesions in the brain and spinal cord that are highly variable in terms of cellular content. Here, we used imaging mass cytometry (IMC) to enable the simultaneous imaging of 15+ proteins within staged MS lesions. To test the potential for IMC to discriminate between different types of lesions, we selected a case with severe rebound MS disease activity after natalizumab cessation. With post-acquisition analysis pipelines we were able to: (1) Discriminate demyelinating macrophages from the resident microglial pool; (2) Determine which types of lymphocytes reside closest to blood vessels; (3) Identify multiple subsets of T and B cells, and (4) Ascertain dynamics of T cell phenotypes vis-à-vis lesion type and location. We propose that IMC will enable a comprehensive analysis of single-cell phenotypes, their functional states and cell-cell interactions in relation to lesion morphometry and demyelinating activity in MS patients.

It takes an army of immune cells to defend the body against infection. But sometimes the body’s immune system mistakenly attacks its own cells and chronic inflammatory conditions develop. In multiple sclerosis – also known as “MS” – a horde of immune cells infiltrate the brain and spinal cord, forming lesions which strip nerve cells of their insultation, a protective fatty material called myelin. Nerve cells become damaged, scarred and exposed, and this interferes with messages between the brain and other parts of the body.

Advanced imaging techniques have revolutionized the diagnosis of multiple sclerosis by capturing lesions as they develop in the brain and spinal cord. Researchers have also focused their efforts on understanding how immune cells activated in the blood stream invade the central nervous system. To better understand how a mistaken immune response leads to nerve damage in multiple sclerosis, a forensic examination of which immune cells accumulate in brain tissue to form lesions is needed. Standard techniques for analyzing whole tissue samples are however limited by design, capable of detecting only a few cell markers in one section of tissue.

Ramaglia et al. have now validated a new imaging technique for looking at an array of cell types in brain tissue in a single sample. The technique – called imaging mass cytometry (or IMC for short) – was used to look at post-mortem brain tissue from a multiple sclerosis patient with an acute form of the illness. The tissue examined had multiple sclerosis lesions present. Different types of immune cells were simultaneously identified and characterized using a panel of antibodies which recognize the signature proteins each immune cell makes when active. The state of the underlying myelin content of the tissue was also characterized.

The imaging approach could distinguish between the immune cells of the brain (known as resident microglia) and a type of white blood cell summoned as part of the immune response (infiltrating macrophages). The analysis showed that, in the particular patient examined, microglia are abundant in active lesions in multiple sclerosis; also, different subsets of white blood cells were detected. Measuring how far different immune cells had migrated from nearby blood vessels added insights as to how immune cells move through the brain and which cells may have arrived first.

Altogether, Ramaglia et al. have shown that IMC can be used as a discovery tool to gain a deeper understanding of multiple sclerosis lesions and immune cells active in the inflamed brain. Further work will apply this now validated imaging approach to large cohorts of multiple sclerosis patients.

Update on the classification of T-cell lymphomas, Hodgkin lymphomas, and histiocytic/dendritic cell neoplasms

Introduction: The classification of lymphomas is based on the postulated normal counterparts of lymphoid neoplasms and currently constitutes over 100 definite or provisional entities. As this number of entities implies, lymphomas show marked pathological, genetic, and clinical heterogeneity. Recent molecular findings have significantly advanced our understanding of lymphomas. Areas covered: The World Health Organization (WHO) classification of lymphoid neoplasms was updated in 2017. The present review summarizes the new findings that have been gained in the areas of mature T-cell neoplasms, Hodgkin lymphomas, and histiocytic/dendritic cell neoplasms since the publication of the 2017 WHO classification. Expert opinion: Although formal revisions to the WHO classification are published only periodically, our understanding of the pathologic, genetic, and clinical features of lymphoid neoplasms is constantly evolving, particularly in the age of -omics technologies and targeted therapeutics. Even in the relatively short time since the publication of the 2017 WHO classification, many significant findings have been identified in the entities covered in this review.

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.