ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data

Development of a high dimensional imaging mass cytometry panel to investigate spatial organization of tissue microenvironment in formalin-fixed archival clinical tissues

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

Activated HLA-DR+CD38+ Effector Th1/17 Cells Distinguish Crohn's Disease-associated Perianal Fistulas from Cryptoglandular Fistulas

BACKGROUND

Perianal fistulas are a debilitating complication of Crohn's disease (CD). Due to unknown reasons, CD-associated fistulas are in general more difficult to treat than cryptoglandular fistulas (non-CD-associated). Understanding the immune cell landscape is a first step towards the development of more effective therapies for CD-associated fistulas. In this work, we characterized the composition and spatial localization of disease-associated immune cells in both types of perianal fistulas by high-dimensional analyses.

METHODS

We applied single-cell mass cytometry (scMC), spectral flow cytometry (SFC), and imaging mass cytometry (IMC) to profile the immune compartment in CD-associated perianal fistulas and cryptoglandular fistulas. An exploratory cohort (CD fistula, n = 10; non-CD fistula, n = 5) was analyzed by scMC to unravel disease-associated immune cell types. SFC was performed on a second fistula cohort (CD, n = 10; non-CD, n = 11) to comprehensively phenotype disease-associated T helper (Th) cells. IMC was used on a third cohort (CD, n = 5) to investigate the spatial distribution/interaction of relevant immune cell subsets.

RESULTS

Our analyses revealed that activated HLA-DR+CD38+ effector CD4+ T cells with a Th1/17 phenotype were significantly enriched in CD-associated compared with cryptoglandular fistulas. These cells, displaying features of proliferation, regulation, and differentiation, were also present in blood, and colocalized with other CD4+ T cells, CCR6+ B cells, and macrophages in the fistula tracts.

CONCLUSIONS

Overall, proliferating activated HLA-DR+CD38+ effector Th1/17 cells distinguish CD-associated from cryptoglandular perianal fistulas and are a promising biomarker in blood to discriminate between these 2 fistula types. Targeting HLA-DR and CD38-expressing CD4+ T cells may offer a potential new therapeutic strategy for CD-related fistulas.

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

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

Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview

The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.

Neoadjuvant atezolizumab plus chemotherapy in gastric and gastroesophageal junction adenocarcinoma: the phase 2 PANDA trial

Gastric and gastroesophageal junction (G/GEJ) cancers carry a poor prognosis, and despite recent advancements, most patients die of their disease. Although immune checkpoint blockade became part of the standard-of-care for patients with metastatic G/GEJ cancers, its efficacy and impact on the tumor microenvironment (TME) in early disease remain largely unknown. We hypothesized higher efficacy of neoadjuvant immunotherapy plus chemotherapy in patients with nonmetastatic G/GEJ cancer. In the phase 2 PANDA trial, patients with previously untreated resectable G/GEJ tumors (n = 21) received neoadjuvant treatment with one cycle of atezolizumab monotherapy followed by four cycles of atezolizumab plus docetaxel, oxaliplatin and capecitabine. Treatment was well tolerated. There were grade 3 immune-related adverse events in two of 20 patients (10%) but no grade 4 or 5 immune-related adverse events, and all patients underwent resection without treatment-related delays, meeting the primary endpoint of safety and feasibility. Tissue was obtained at multiple time points, allowing analysis of the effects of single-agent anti-programmed cell death ligand 1 (PD-L1) and the subsequent combination with chemotherapy on the TME. Twenty of 21 patients underwent surgery and were evaluable for secondary pathologic response and survival endpoints, and 19 were evaluable for exploratory translational analyses. A major pathologic response (≤10% residual viable tumor) was observed in 14 of 20 (70%, 95% confidence interval 46-88%) patients, including 9 (45%, 95% confidence interval 23-68%) pathologic complete responses. At a median follow-up of 47 months, 13 of 14 responders were alive and disease-free, and five of six nonresponders had died as a result of recurrence. Notably, baseline anti-programmed cell death protein 1 (PD-1)+CD8+ T cell infiltration was significantly higher in responders versus nonresponders, and comparison of TME alterations following anti-PD-L1 monotherapy versus the subsequent combination with chemotherapy showed an increased immune activation on single-agent PD-1/L1 axis blockade. On the basis of these data, monotherapy anti-PD-L1 before its combination with chemotherapy warrants further exploration and validation in a larger cohort of patients with nonmetastatic G/GEJ cancer. ClinicalTrials.gov registration: NCT03448835 .

Multimodal profiling of chordoma immunity reveals distinct immune contextures

BACKGROUND

Chordomas are rare cancers from the axial skeleton which present a challenging clinical management with limited treatment options due to their anatomical location. In recent years, a few clinical trials demonstrated that chordomas can respond to immunotherapy. However, an in-depth portrayal of chordoma immunity and its association with clinical parameters is still lacking.

METHODS

We present a comprehensive characterization of immunological features of 76 chordomas through application of a multimodal approach. Transcriptomic profiling of 20 chordomas was performed to inform on the activity of immune-related genes through the immunologic constant of rejection (ICR) signature. Multidimensional immunophenotyping through imaging mass cytometry was applied to provide insights in the different immune contextures of 32 chordomas. T cell infiltration was further evaluated in all 76 patients by means of multispectral immunofluorescence and then associated with clinical parameters through univariate and multivariate Cox proportional hazard models as well as Kaplan-Meier estimates. Moreover, distinct expression patterns of human leukocyte antigen (HLA) class I were assessed by immunohistochemical staining in all 76 patients. Finally, clonal enrichment of the T cell receptor (TCR) was sought through profiling of the variable region of TCRB locus of 24 patients.

RESULTS

Chordomas generally presented an immune "hot" microenvironment in comparison to other sarcomas, as indicated by the ICR transcriptional signature. We identified two distinct groups of chordomas based on T cell infiltration which were independent from clinical parameters. The highly infiltrated group was further characterized by high dendritic cell infiltration and the presence of multicellular immune aggregates in tumors, whereas low T cell infiltration was associated with lower overall cell densities of immune and stromal cells. Interestingly, patients with higher T cell infiltration displayed a more pronounced clonal enrichment of the TCR repertoire compared with those with low T cell counts. Furthermore, we observed that the majority of chordomas maintained HLA class I expression.

CONCLUSION

Our findings shed light on the natural immunity against chordomas through the identification of distinct immune contextures. Understanding their immune landscape could guide the development and application of immunotherapies in a tailored manner, ultimately leading to an improved clinical outcome for patients with chordoma.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

BACKGROUND

Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R.

RESULTS

Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples.

CONCLUSIONS

The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .

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

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

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Comprehensive Single-Cell Immune Profiling Defines the Patient Multiple Myeloma Microenvironment Following Oncolytic Virus Therapy in a Phase Ib Trial

PURPOSE

Our preclinical studies showed that the oncolytic reovirus formulation pelareorep (PELA) has significant immunomodulatory anti-myeloma activity. We conducted an investigator-initiated clinical trial to evaluate PELA in combination with dexamethasone (Dex) and bortezomib (BZ) and define the tumor immune microenvironment (TiME) in patients with multiple myeloma treated with this regimen.

PATIENTS AND METHODS

Patients with relapsed/refractory multiple myeloma (n = 14) were enrolled in a phase Ib clinical trial (ClinicalTrials.gov: NCT02514382) of three escalating PELA doses administered on Days 1, 2, 8, 9, 15, and 16. Patients received 40 mg Dex and 1.5 mg/m2 BZ on Days 1, 8, and 15. Cycles were repeated every 28 days. Pre- and posttreatment bone marrow specimens (IHC, n = 9; imaging mass cytometry, n = 6) and peripheral blood samples were collected for analysis (flow cytometry, n = 5; T-cell receptor clonality, n = 7; cytokine assay, n = 7).

RESULTS

PELA/BZ/Dex was well-tolerated in all patients. Treatment-emergent toxicities were transient, and no dose-limiting toxicities occurred. Six (55%) of 11 response-evaluable patients showed decreased paraprotein. Treatment increased T and natural killer cell activation, inflammatory cytokine release, and programmed death-ligand 1 expression in bone marrow. Compared with nonresponders, responders had higher reovirus protein levels, increased cytotoxic T-cell infiltration posttreatment, cytotoxic T cells in significantly closer proximity to multiple myeloma cells, and larger populations of a novel immune-primed multiple myeloma phenotype (CD138+ IDO1+HLA-ABCHigh), indicating immunomodulation.

CONCLUSIONS

PELA/BZ/Dex is well-tolerated and associated with anti-multiple myeloma activity in a subset of responding patients, characterized by immune reprogramming and TiME changes, warranting further investigation of PELA as an immunomodulator.

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

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

Imaging mass cytometry reveals tissue-specific cellular immune phenotypes in the mouse knee following ACL injury

Objective

To develop an imaging mass cytometry method for identifying complex cell phenotypes, inter-cellular interactions, and population changes in the synovium and infrapatellar fat pad (IFP) of the mouse knee following a non-invasive compression injury.

Design

Fifteen male C57BL/6 mice were fed a high-fat diet for 8 weeks prior to random assignment to sham, 0.88 ​mm, or 1.7 ​mm knee compression displacement at 24 weeks of age. 2-weeks after loading, limbs were prepared for histologic and imaging mass cytometry analysis, focusing on myeloid immune cell populations in the synovium and IFP.

Results

1.7 ​mm compression caused anterior cruciate ligament (ACL) rupture, development of post-traumatic osteoarthritis, and a 2- to 3-fold increase in cellularity of synovium and IFP tissues compared to sham or 0.88 ​mm compression. Imaging mass cytometry identified 11 myeloid cell subpopulations in synovium and 7 in IFP, of which approximately half were elevated 2 weeks after ACL injury in association with the vasculature. Notably, two monocyte/macrophage subpopulations and an MHC IIhi population were elevated 2-weeks post-injury in the synovium but not IFP. Vascular and immune cell interactions were particularly diverse in the synovium, incorporating 8 unique combinations of 5 myeloid cell populations, including a monocyte/macrophage population, an MHC IIhi population, and 3 different undefined F4/80+ myeloid populations.

Conclusions

Developing an imaging mass cytometry method for the mouse enabled us to identify a diverse array of synovial and IFP vascular-associated myeloid cell subpopulations. These subpopulations were differentially elevated in synovial and IFP tissues 2-weeks post injury, providing new details on tissue-specific immune regulation.

VISTA Expression on Cancer-Associated Endothelium Selectively Prevents T-cell Extravasation

Cancers evade T-cell immunity by several mechanisms such as secretion of anti-inflammatory cytokines, down regulation of antigen presentation machinery, upregulation of immune checkpoint molecules, and exclusion of T cells from tumor tissues. The distribution and function of immune checkpoint molecules on tumor cells and tumor-infiltrating leukocytes is well established, but less is known about their impact on intratumoral endothelial cells. Here, we demonstrated that V-domain Ig suppressor of T-cell activation (VISTA), a PD-L1 homolog, was highly expressed on endothelial cells in synovial sarcoma, subsets of different carcinomas, and immune-privileged tissues. We created an ex vivo model of the human vasculature and demonstrated that expression of VISTA on endothelial cells selectively prevented T-cell transmigration over endothelial layers under physiologic flow conditions, whereas it does not affect migration of other immune cell types. Furthermore, endothelial VISTA correlated with reduced infiltration of T cells and poor prognosis in metastatic synovial sarcoma. In endothelial cells, we detected VISTA on the plasma membrane and in recycling endosomes, and its expression was upregulated by cancer cell-secreted factors in a VEGF-A-dependent manner. Our study reveals that endothelial VISTA is upregulated by cancer-secreted factors and that it regulates T-cell accessibility to cancer and healthy tissues. This newly identified mechanism should be considered when using immunotherapeutic approaches aimed at unleashing T cell-mediated cancer immunity.

An end-to-end workflow for multiplexed image processing and analysis

Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

Motivation

Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organization of cellular ecosystems remains a challenging problem.

Results

To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organization and deploys downstream analysis and visualization utilities that can be used for data-driven patient-risk stratification. Our case studies using mass cytometry imaging data from two well-annotated breast cancer cohorts re-confirmed that the spatial organization of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients' survival. Our work introduces an automated and versatile analysis tool for mass cytometry imaging data with many applications in future cancer research projects.

Availability and implementation

Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR.

Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification

We propose an interactive visual analytics tool, Vis-SPLIT, for partitioning a population of individuals into groups with similar gene signatures. Vis-SPLIT allows users to interactively explore a dataset and exploit visual separations to build a classification model for specific cancers. The visualization components reveal gene expression and correlation to assist specific partitioning decisions, while also providing overviews for the decision model and clustered genetic signatures. We demonstrate the effectiveness of our framework through a case study and evaluate its usability with domain experts. Our results show that Vis-SPLIT can classify patients based on their genetic signatures to effectively gain insights into RNA sequencing data, as compared to an existing classification system.

Multiplex imaging in immuno-oncology

Multiplex imaging has emerged as an invaluable tool for immune-oncologists and translational researchers, enabling them to examine intricate interactions among immune cells, stroma, matrix, and malignant cells within the tumor microenvironment (TME). It holds significant promise in the quest to discover improved biomarkers for treatment stratification and identify novel therapeutic targets. Nonetheless, several challenges exist in the realms of study design, experiment optimization, and data analysis. In this review, our aim is to present an overview of the utilization of multiplex imaging in immuno-oncology studies and inform novice researchers about the fundamental principles at each stage of the imaging and analysis process.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive data visualization of multiplexed imaging data is necessary for quality control and hypothesis examination. Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. The package operates on SingleCellExperiment, SpatialExperiment and CytoImageList objects and therefore integrates with the Bioconductor framework for single-cell and image analysis. Users of cytoviewer need little coding expertise, and the graphical user interface allows user-friendly navigation. We showcase the functionality of cytoviewer by analysis of an imaging mass cytometry dataset of cancer patients.

Identification of a unique intervillous cellular signature in chronic histiocytic intervillositis

INTRODUCTION

Chronic histiocytic intervillositis (CHI) is a rare histopathological lesion in the placenta characterized by an infiltrate of CD68⁺ cells in the intervillous space. CHI is associated with adverse pregnancy outcomes such as miscarriage, fetal growth restriction, and (late) intrauterine fetal death. The adverse pregnancy outcomes and a variable recurrence rate of 25-100% underline its clinical relevance. The pathophysiologic mechanism of CHI is unclear, but it appears to be immunologically driven. The aim of this study was to obtain a better understanding of the phenotype of the cellular infiltrate in CHI.

METHOD

We used imaging mass cytometry to achieve in-depth visualization of the intervillous maternal immune cells and investigated their spatial orientation in situ in relation to the fetal syncytiotrophoblast.

RESULTS

We found three phenotypically distinct CD68⁺HLA-DR⁺CD38⁺ cell clusters that were unique for CHI. Additionally, syncytiotrophoblast cells in the vicinity of these CD68⁺HLA-DR⁺CD38⁺ cells showed decreased expression of the immunosuppressive enzyme CD39.

DISCUSSION

The current results provide novel insight into the phenotype of CD68⁺ cells in CHI. The identification of unique CD68⁺ cell clusters will allow more detailed analysis of their function and could result in novel therapeutic targets for CHI.

Different approaches to Imaging Mass Cytometry data analysis

Imaging Mass Cytometry (IMC) is a novel, high multiplexing imaging platform capable of simultaneously detecting and visualizing up to 40 different protein targets. It is a strong asset available for in-depth study of histology and pathophysiology of the tissues. Bearing in mind the robustness of this technique and the high spatial context of the data it gives, it is especially valuable in studying the biology of cancer and tumor microenvironment. IMC-derived data are not classical micrographic images, and due to the characteristics of the data obtained using IMC, the image analysis approach, in this case, can diverge to a certain degree from the classical image analysis pipelines. As the number of publications based on the IMC is on the rise, this trend is also followed by an increase in the number of available methodologies designated solely to IMC-derived data analysis. This review has for an aim to give a systematic synopsis of all the available classical image analysis tools and pipelines useful to be employed for IMC data analysis and give an overview of tools intentionally developed solely for this purpose, easing the choice to researchers of selecting the most suitable methodologies for a specific type of analysis desired.

Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps3

Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in, for example, tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users' trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.

Visualization and Visual Analytics Approaches for Image and Video Datasets: A Survey

Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey paper, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization papers included in our survey based on different taxonomies used in visualization and visual analytics research. We review these papers in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.

Single-cell high-dimensional imaging mass cytometry: one step beyond in oncology

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

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

Reference-based cell-type annotation can significantly reduce time and effort in single-cell analysis by transferring labels from a previously-annotated dataset to a new dataset. However, label transfer by end-to-end computational methods is challenging due to the entanglement of technical (e.g., from different sequencing batches or techniques) and biological (e.g., from different cellular microenvironments) variations, only the first of which must be removed. To address this issue, we propose Polyphony, an interactive transfer learning (ITL) framework, to complement biologists' knowledge with advanced computational methods. Polyphony is motivated and guided by domain experts' needs for a controllable, interactive, and algorithm-assisted annotation process, identified through interviews with seven biologists. We introduce anchors, i.e., analogous cell populations across datasets, as a paradigm to explain the computational process and collect user feedback for model improvement. We further design a set of visualizations and interactions to empower users to add, delete, or modify anchors, resulting in refined cell type annotations. The effectiveness of this approach is demonstrated through quantitative experiments, two hypothetical use cases, and interviews with two biologists. The results show that our anchor-based ITL method takes advantage of both human and machine intelligence in annotating massive single-cell datasets.

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES

MOTIVATION

Recent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level.

RESULTS

NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell-cell signaling at single-cell resolution.

AVAILABILITY AND IMPLEMENTATION

NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at http://github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

γδ T cells are effectors of immunotherapy in cancers with HLA class I defects

DNA mismatch repair-deficient (MMR-d) cancers present an abundance of neoantigens that is thought to explain their exceptional responsiveness to immune checkpoint blockade (ICB)1,2. Here, in contrast to other cancer types3-5, we observed that 20 out of 21 (95%) MMR-d cancers with genomic inactivation of β2-microglobulin (encoded by B2M) retained responsiveness to ICB, suggesting the involvement of immune effector cells other than CD8+ T cells in this context. We next identified a strong association between B2M inactivation and increased infiltration by γδ T cells in MMR-d cancers. These γδ T cells mainly comprised the Vδ1 and Vδ3 subsets, and expressed high levels of PD-1, other activation markers, including cytotoxic molecules, and a broad repertoire of killer-cell immunoglobulin-like receptors. In vitro, PD-1+ γδ T cells that were isolated from MMR-d colon cancers exhibited enhanced reactivity to human leukocyte antigen (HLA)-class-I-negative MMR-d colon cancer cell lines and B2M-knockout patient-derived tumour organoids compared with antigen-presentation-proficient cells. By comparing paired tumour samples from patients with MMR-d colon cancer that were obtained before and after dual PD-1 and CTLA-4 blockade, we found that immune checkpoint blockade substantially increased the frequency of γδ T cells in B2M-deficient cancers. Taken together, these data indicate that γδ T cells contribute to the response to immune checkpoint blockade in patients with HLA-class-I-negative MMR-d colon cancers, and underline the potential of γδ T cells in cancer immunotherapy.

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.

Immune-based biomarker accurately predicts response to imiquimod immunotherapy in cervical high-grade squamous intraepithelial lesions

BACKGROUND

The complete response rate of cervical high-grade squamous intraepithelial lesion (cHSIL) patients to imiquimod immunotherapy is approximately 60%. Consequently, many patients are exposed to unnecessary adverse effects of imiquimod. On the other hand, conventional surgical large loop excision therapy is associated with increased risk of premature births in subsequent pregnancies. An in-depth analysis of the cHSIL immune microenvironment was performed in order to identify and develop a predictive biomarker for response to imiquimod, to maximize therapy efficacy and to avoid adverse effects in patients unlikely to respond.

METHODS

Biopsies of 35 cHSIL patients, before and 10 weeks on imiquimod treatment, were analyzed by two multispectral seven-color immunofluorescence panels for T cell and myeloid cell composition in relation to treatment response. Based on these results a simplified immunohistochemical detection protocol was developed. Samples were scanned with the Vectra multispectral imaging system and cells were automatically identified using machine learning.

RESULTS

The immune microenvironment of complete responders (CR) is characterized by a strong and coordinated infiltration by T helper cells (activated PD1⁺/type 1 Tbet⁺), M1-like macrophages (CD68⁺CD163^-) and dendritic cells (CD11c⁺) prior to imiquimod. The lesions of non-responders (NRs) displayed a high infiltration by CD3⁺FOXP3⁺ regulatory T cells. At 10 weeks on imiquimod, a strong influx of intraepithelial and stromal CD4⁺ T cells was observed in CR but not NR patients. A steep decrease in macrophages occurred both in CR and NR patients, leveling the pre-existing differences in myeloid cell composition between the two groups. Based on the pre-existing immune composition differences, the sum of intraepithelial CD4 T cell, macrophage and dendritic cell counts was used to develop a quantitative simplified one color immunohistochemical biomarker, the CHSIL immune biomarker for imiquimod (CIBI), which can be automatically and unbiasedly quantified and has an excellent predictive capacity (receiver operating characteristic area under the curve 0.95, p<0.0001).

CONCLUSION

The capacity of cHSIL patients to respond to imiquimod is associated with a pre-existing coordinated local immune process, fostering an imiquimod-mediated increase in local T cell infiltration. The CIBI immunohistochemical biomarker has strong potential to select cHSIL patients with a high likelihood to experience a complete response to imiquimod immunotherapy.

ShapoGraphy: A User-Friendly Web Application for Creating Bespoke and Intuitive Visualisation of Biomedical Data

Effective visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns from complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we develop ShapoGraphy, a first of its kind method accompanied by an interactive web-based application for creating customisable quantitative pictorial representations to facilitate the understanding and analysis of image datasets (www.shapography.com). ShapoGraphy enables the user to create a structure of interest as a set of shapes. Each shape can encode different variables that are mapped to the shape dimensions, colours, symbols, or outline. We illustrate the utility of ShapoGraphy using various image data, including high dimensional multiplexed data. Our results show that ShapoGraphy allows a better understanding of cellular phenotypes and relationships between variables. In conclusion, ShapoGraphy supports scientific discovery and communication by providing a rich vocabulary to create engaging and intuitive representations of diverse data types.

Imaging Mass cytometry reveals the prominent role of myeloid cells at the maternal-fetal interface

Although the immunological complexity of the maternal-fetal interface is well appreciated, the actual interaction of maternal immune cells and fetal trophoblasts is insufficiently understood. To comprehend the composition and spatial orientation of maternal immune cells and fetal extravillous trophoblasts, we applied imaging mass cytometry on decidua basalis of the three trimesters of healthy pregnancy. Within all trimesters, we observed considerably higher frequencies of myeloid cells in the decidua than is seen with single-cell suspension techniques. Moreover, they were the most pronounced cell type in the microenvironment of other decidual cells. In first trimester, HLA-DR^- macrophages represented the most abundant myeloid subcluster and these cells were frequently observed in the vicinity of trophoblasts. At term, HLA-DR⁺ macrophage subclusters were abundantly present and frequently observed in the microenvironment of T cells. Taken together, our results highlight the dynamic role of myeloid cells at the human maternal-fetal interface throughout gestation.

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Frequency of myeloid cells is underestimated after tissue digestion

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Myeloid cells could support NK cells with proper trophoblast invasion

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Myeloid cells are dynamic in their role throughout gestation

Mass Cytometric Analysis of Early-Stage Mycosis Fungoides

Mycosis fungoides (MF) is the most common subtype of cutaneous T-cell lymphoma. Early-stage disease is characterized by superficial infiltrates of small- to medium-sized atypical epidermotropic T lymphocytes that are clonal related. Nevertheless, the percentage of atypical T cells is low with many admixed reactive immune cells. Despite earlier studies, the composition and spatial characteristics of the cutaneous lymphocytic infiltrate has been incompletely characterized. Here, we applied mass cytometry to profile the immune system in skin biopsies of patients with early-stage MF and in normal skin from healthy individuals. Single-cell suspensions were prepared and labeled with a 43-antibody panel, and data were acquired on a Helios mass cytometer. Unbiased hierarchical clustering of the data identified the major immune lineages and heterogeneity therein. This revealed patient-unique cell clusters in both the CD4⁺ and myeloid cell compartments but also phenotypically distinct cell clusters that were shared by most patients. To characterize the immune compartment in the tissue context, we developed a 36-antibody panel and performed imaging mass cytometry on MF skin tissue. This visualized the structure of MF skin and the distribution of CD4⁺ T cells, regulatory T cells, CD8⁺ T cells, malignant T cells, and various myeloid cell subsets. We observed clusters of CD4⁺ T cells and multiple types of dendritic cells (DCs) identified through differential expression of CD11c, CD1a, and CD1c in the dermis. These results indicated substantial heterogeneity in the composition of the local immune infiltrate but suggest a prominent role for clustered CD4-DC interactions in disease pathogenesis. Probably, the local inhibition of such interactions may constitute an efficient treatment modality.

A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution

Multiplexed imaging technologies enable the study of biological tissues at single-cell resolution while preserving spatial information. Currently, high-dimension imaging data analysis is technology-specific and requires multiple tools, restricting analytical scalability and result reproducibility. Here we present SIMPLI (Single-cell Identification from MultiPLexed Images), a flexible and technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After raw image processing, SIMPLI performs a spatially resolved, single-cell analysis of the tissue slide as well as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for large datasets. SIMPLI produces multiple tabular and graphical outputs at each step of the analysis. Its containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. Software is available at “SIMPLI [https://github.com/ciccalab/SIMPLI]”.

Current high-dimension imaging data analysis methods are technology-specific and require multiple tools, restricting analytical scalability and result reproducibility. Here the authors present SIMPLI, a software that overcomes these limitations for single-cell and pixel analysis of multiplexed images at spatial resolution.

Tumor-specific T cells support chemokine-driven spatial organization of intratumoral immune microaggregates needed for long survival

BACKGROUND

The composition of the tumor immune microenvironment (TIME) associated with good prognosis generally also predicts the success of immunotherapy, and both entail the presence of pre-existing tumor-specific T cells. Here, the blueprint of the TIME associated with such an ongoing tumor-specific T-cell response was dissected in a unique prospective oropharyngeal squamous cell carcinoma (OPSCC) cohort, in which tumor-specific tumor-infiltrating T cells were detected (immune responsiveness (IR+)) or not (lack of immune responsiveness (IR-)).

METHODS

A comprehensive multimodal, high-dimensional strategy was applied to dissect the TIME of treatment-naive IR+ and IR- OPSCC tissue, including bulk RNA sequencing (NanoString), imaging mass cytometry (Hyperion) for phenotyping and spatial interaction analyses of immune cells, and combined single-cell gene expression profiling and T-cell receptor (TCR) sequencing (single-cell RNA sequencing (scRNAseq)) to characterize the transcriptional states of clonally expanded tumor-infiltrating T cells.

RESULTS

IR+ patients had an excellent survival during >10 years follow-up. The tumors of IR+ patients expressed higher levels of genes strongly related to interferon gamma signaling, T-cell activation, TCR signaling, and mononuclear cell differentiation, as well as genes involved in several immune signaling pathways, than IR- patients. The top differently overexpressed genes included CXCL12 and LTB, involved in ectopic lymphoid structure development. Moreover, scRNAseq not only revealed that CD4+ T cells were the main producers of LTB but also identified a subset of clonally expanded CD8+ T cells, dominantly present in IR+ tumors, which secreted the T cell and dendritic cell (DC) attracting chemokine CCL4. Indeed, immune cell infiltration in IR+ tumors is stronger, highly coordinated, and has a distinct spatial phenotypical signature characterized by intratumoral microaggregates of CD8+CD103+ and CD4+ T cells with DCs. In contrast, the IR- TIME comprised spatial interactions between lymphocytes and various immunosuppressive myeloid cell populations. The impact of these chemokines on local immunity and clinical outcome was confirmed in an independent The Cancer Genome Atlas OPSCC cohort.

CONCLUSION

The production of lymphoid cell attracting and organizing chemokines by tumor-specific T cells in IR+ tumors constitutes a positive feedback loop to sustain the formation of the DC-T-cell microaggregates and identifies patients with excellent survival after standard therapy.

Uncomplicated oocyte donation pregnancies display an elevated CD163-positive type 2 macrophage load in the decidua, which is associated with fetal-maternal HLA mismatches

PROBLEM

The embryo of an oocyte donation (OD) pregnancy is completely allogeneic to the mother, which may challenge the maternal immune system to tolerize the fetus. Decidual macrophages are essential in maintaining a healthy pregnancy, and type 2 macrophages may exhibit immune suppressive activity. We hypothesized that the composition of decidual macrophages is different between uncomplicated OD pregnancies and non-OD in vitro fertilization (IVF) pregnancies, and is related to fetal-maternal incompatibility.

METHOD OF STUDY

Women with uncomplicated pregnancy were enrolled: 25 singleton OD pregnancies and 17 non-OD IVF pregnancies. The extent of immunohistochemical staining of CD14 (pan-macrophage marker) and CD163 (type 2 macrophage marker) in both decidua basalis and parietalis was quantitated by digital image analysis. Maternal and fetal DNA was typed for human leukocyte antigen (HLA)-A, -B, C, -DRB1, and -DQB1, and fetal-maternal HLA mismatches were calculated.

RESULTS

OD pregnancies showed a higher percentage of CD163+ staining (P = .040) and higher CD163/CD14 ratio (P = .032) in the parietalis than non-OD IVF. The OD group was separated into a semi-allogeneic group (≤5 fetal maternal HLA mismatches) and a fully allogeneic group (> 5 mismatches). The HLA-fully-allogeneic OD group, but not the HLA-semi-allogeneic OD group, showed significantly elevated CD163/CD14 ratio in the parietalis compared with the non-OD IVF group (P < .05).

CONCLUSIONS

Uncomplicated OD pregnancies display a higher CD163-positive cell fraction in the total decidual macrophage population compared to autologous pregnancies, which may suggest that a local type 2 macrophage-related mechanism is needed to compensate for the higher fetal-maternal HLA mismatch load.

Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques

Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.

Semi-automated background removal limits data loss and normalizes imaging mass cytometry data

Imaging mass cytometry (IMC) allows the detection of multiple antigens (approximately 40 markers) combined with spatial information, making it a unique tool for the evaluation of complex biological systems. Due to its widespread availability and retained tissue morphology, formalin‐fixed, paraffin‐embedded (FFPE) tissues are often a material of choice for IMC studies. However, antibody performance and signal to noise ratios can differ considerably between FFPE tissues as a consequence of variations in tissue processing, including fixation. In contrast to batch effects caused by differences in the immunodetection procedure, variations in tissue processing are difficult to control. We investigated the effect of immunodetection‐related signal intensity fluctuations on IMC analysis and phenotype identification, in a cohort of 12 colorectal cancer tissues. Furthermore, we explored different normalization strategies and propose a workflow to normalize IMC data by semi‐automated background removal, using publicly available tools. This workflow can be directly applied to previously acquired datasets and considerably improves the quality of IMC data, thereby supporting the analysis and comparison of multiple samples.

High Dimensional Imaging Mass Cytometry Panel to Visualize the Tumor Immune Microenvironment Contexture

The integrative analysis of tumor immune microenvironment (TiME) components, their interactions and their microanatomical distribution is mandatory to better understand tumor progression. Imaging Mass Cytometry (IMC) is a high dimensional tissue imaging system which allows the comprehensive and multiparametric in situ exploration of tumor microenvironments at a single cell level. We describe here the design of a 39-antibody IMC panel for the staining of formalin-fixed paraffin-embedded human tumor sections. We also provide an optimized staining procedure and details of the experimental workflow. This panel deciphers the nature of immune cells, their functions and their interactions with tumor cells and cancer-associated fibroblasts as well as with other TiME structural components known to be associated with tumor progression like nerve fibers and tumor extracellular matrix proteins. This panel represents a valuable innovative and powerful tool for fundamental and clinical studies that could be used for the identification of prognostic biomarkers and mechanisms of resistance to current immunotherapies.

Visual analytics of COVID-19 dissemination in São Paulo state, Brazil

Visual analytics techniques are useful tools to support decision-making and cope with increasing data, particularly to monitor natural or artificial phenomena. When monitoring disease progression, visual analytics approaches help decision-makers to understand or even prevent dissemination paths. In this paper, we propose a new visual analytics tool for monitoring COVID-19 dissemination. We use k-nearest neighbors of cities to mimic neighboring cities and analyze COVID-19 dissemination based on comparing a city under consideration and its neighborhood. Moreover, such analysis is performed within periods, which facilitates the assessment of isolation policies. We validate our tool by analyzing the progression of COVID-19 in neighboring cities of São Paulo state, Brazil.

Iron loading is a prominent feature of activated microglia in Alzheimer's disease patients

Brain iron accumulation has been found to accelerate disease progression in amyloid-β(Aβ) positive Alzheimer patients, though the mechanism is still unknown. Microglia have been identified as key players in the disease pathogenesis, and are highly reactive cells responding to aberrations such as increased iron levels. Therefore, using histological methods, multispectral immunofluorescence and an automated in-house developed microglia segmentation and analysis pipeline, we studied the occurrence of iron-accumulating microglia and the effect on its activation state in human Alzheimer brains. We identified a subset of microglia with increased expression of the iron storage protein ferritin light chain (FTL), together with increased Iba1 expression, decreased TMEM119 and P2RY12 expression. This activated microglia subset represented iron-accumulating microglia and appeared morphologically dystrophic. Multispectral immunofluorescence allowed for spatial analysis of FTL+Iba1+-microglia, which were found to be the predominant Aβ-plaque infiltrating microglia. Finally, an increase of FTL+Iba1+-microglia was seen in patients with high Aβ load and Tau load. These findings suggest iron to be taken up by microglia and to influence the functional phenotype of these cells, especially in conjunction with Aβ.

Visual cohort comparison for spatial single-cell omics-data

Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities.

Cytomapper: an R/bioconductor package for visualisation of highly multiplexed imaging data

Summary

Highly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Here, we describe cytomapper, a computational tool written in R, that enables visualization of pixel- and cell-level information obtained by multiplexed imaging. To illustrate its utility, we analysed 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualization of selected cells in corresponding images.

Availability and implementation

The cytomapper package can be installed via https://www.bioconductor.org/packages/release/bioc/html/cytomapper.html. Code for analysis and further instructions can be found at https://github.com/BodenmillerGroup/cytomapper_publication.

Supplementary information

Supplementary data are available at Bioinformatics online.

Got your mother in a whirl: The role of maternal T cells and myeloid cells in pregnancy

Appropriate development of the placenta is required for healthy pregnancy to occur. After implantation of the fertilized blastocyst, fetal trophoblasts invade the endometrium and myometrium of the mother's uterus to establish placentation. In this process, fetal trophoblasts encounter maternal immune cells. In this review, we focus on the role of maternal T cells and myeloid cells (macrophages, dendritic cells) in pregnancy and their interaction with trophoblasts. To retain immunologic tolerization, trophoblasts evade immune recognition by T cells and produce factors that modulate their phenotype and function. On top of that, the local environment at the maternal-fetal interface favors expansion of regulatory T cells. Macrophages and dendritic cells are essential in maintaining a healthy pregnancy. They produce soluble factors and act as antigen-presenting cells, thereby interacting with T cells. Herein, M2 macrophages, immature dendritic cells, CD4⁺ Th2 cells, and regulatory T cells represent an axis that maintains a local immune tolerant environment. We consider outstanding issues concerning these cell types and their pathways, which need to be addressed in future investigations. Data from recent single-cell sequencing experiments of the placental bed, to study heterogeneity of maternal immune cells and to predict cell-cell interactions, are discussed. Novel ways for long-term culturing of primary trophoblasts allow for cell-cell interaction studies in a functional way. Future directions should include study of the functionality of currently known and newly identified decidual immune cell subsets in healthy and complicated pregnancies, and their interaction with and modulation by trophoblast cells.

Unraveling the Complexity of the Cancer Microenvironment With Multidimensional Genomic and Cytometric Technologies

Cancers are characterized by extensive heterogeneity that occurs intratumorally, between lesions, and across patients. To study cancer as a complex biological system, multidimensional analyses of the tumor microenvironment are paramount. Single-cell technologies such as flow cytometry, mass cytometry, or single-cell RNA-sequencing have revolutionized our ability to characterize individual cells in great detail and, with that, shed light on the complexity of cancer microenvironments. However, a key limitation of these single-cell technologies is the lack of information on spatial context and multicellular interactions. Investigating spatial contexts of cells requires the incorporation of tissue-based techniques such as multiparameter immunofluorescence, imaging mass cytometry, or in situ detection of transcripts. In this Review, we describe the rise of multidimensional single-cell technologies and provide an overview of their strengths and weaknesses. In addition, we discuss the integration of transcriptomic, genomic, epigenomic, proteomic, and spatially-resolved data in the context of human cancers. Lastly, we will deliberate on how the integration of multi-omics data will help to shed light on the complex role of cell types present within the human tumor microenvironment, and how such system-wide approaches may pave the way toward more effective therapies for the treatment of cancer.

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.