Publications by authors named "Bernd Bodenmiller"

77 Publications

Cutaneous and systemic hyperinflammation drives maculopapular drug exanthema in severely ill COVID-19 patients.

Allergy 2021 Jun 22. Epub 2021 Jun 22.

Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.

Background: Coronavirus disease-2019 (COVID-19) has been associated with cutaneous findings, some being the result of drug hypersensitivity reactions such as maculopapular drug rashes (MDR). The aim of this study was to investigate whether COVID-19 may impact the development of the MDR.

Methods: Blood and skin samples from COVID-19 patients (based on a positive nasopharyngeal PCR) suffering from MDR (COVID-MDR), healthy controls, non-COVID-19-related patients with drug rash with eosinophilia and systemic symptoms (DRESS), and MDR were analyzed. We utilized imaging mass cytometry (IMC) to characterize the cellular infiltrate in skin biopsies. Furthermore, RNA sequencing transcriptome of skin biopsy samples and high-throughput multiplexed proteomic profiling of serum were performed.

Results: IMC revealed by clustering analyses a more prominent, phenotypically shifted cytotoxic CD8 T cell population and highly activated monocyte/macrophage (Mo/Mac) clusters in COVID-MDR. The RNA sequencing transcriptome demonstrated a more robust cytotoxic response in COVID-MDR skin. However, severe acute respiratory syndrome coronavirus 2 was not detected in skin biopsies at the time point of MDR diagnosis. Serum proteomic profiling of COVID-MDR patients revealed upregulation of various inflammatory mediators (IL-4, IL-5, IL-6, TNF, and IFN-γ), eosinophil and Mo/Mac -attracting chemokines (MCP-2, MCP-3, MCP-4 and CCL11). Proteomics analyses demonstrated a massive systemic cytokine storm in COVID-MDR compared with the relatively milder cytokine storm observed in DRESS, while MDR did not exhibit such features.

Conclusion: A systemic cytokine storm may promote activation of Mo/Mac and cytotoxic CD8 T cells in severe COVID-19 patients, which in turn may impact the development of MDR.
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http://dx.doi.org/10.1111/all.14983DOI Listing
June 2021

Monogenic Diabetes and Integrated Stress Response Genes Display Altered Gene Expression in Type 1 Diabetes.

Diabetes 2021 May 25. Epub 2021 May 25.

Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL

Type 1 diabetes (T1D) has a multifactorial autoimmune etiology, involving environmental prompts and polygenic predisposition. We hypothesized that pancreata from individuals with and at risk for T1D would exhibit dysregulated expression of genes associated with monogenic forms of diabetes caused by nonredundant single-gene mutations. Using a "monogenetic transcriptomic strategy," we measured the expression of these genes in human T1D, autoantibody-positive (autoantibody+), and control pancreas tissues with real-time quantitative PCR in accordance with the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines. Gene and protein expression was visualized in situ with use of immunofluorescence, RNAscope, and confocal microscopy. Two dozen monogenic diabetes genes showed altered expression in human pancreata from individuals with T1D versus unaffected control subjects. Six of these genes also saw dysregulation in pancreata from autoantibody+ individuals at increased risk for T1D. As a subset of these genes are related to cellular stress responses, we measured integrated stress response (ISR) genes and identified 20 with altered expression in T1D pancreata, including three of the four eIF2α-dependent kinases. Equally intriguing, we observed significant repression of the three arms of the ISR in autoantibody+ pancreata. Collectively, these efforts suggest monogenic diabetes and ISR genes are dysregulated early in the T1D disease process and likely contribute to the disorder's pathogenesis.
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http://dx.doi.org/10.2337/db21-0070DOI Listing
May 2021

Deciphering the signaling network of breast cancer improves drug sensitivity prediction.

Cell Syst 2021 May 30;12(5):401-418.e12. Epub 2021 Apr 30.

Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland. Electronic address:

One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.
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http://dx.doi.org/10.1016/j.cels.2021.04.002DOI Listing
May 2021

Profound dysregulation of T cell homeostasis and function in patients with severe COVID-19.

Allergy 2021 Apr 21. Epub 2021 Apr 21.

Department of Immunology, University Hospital Zurich (USZ), Zurich, Switzerland.

Background: Coronavirus disease 2019 (COVID-19) is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and shows a broad clinical presentation ranging from asymptomatic infection to fatal disease. A very prominent feature associated with severe COVID-19 is T cell lymphopenia. However, homeostatic and functional properties of T cells are ill-defined in COVID-19.

Methods: We prospectively enrolled individuals with mild and severe COVID-19 into our multicenter cohort and performed a cross-sectional analysis of phenotypic and functional characteristics of T cells using 40-parameter mass cytometry, flow cytometry, targeted proteomics, and functional assays.

Results: Compared with mild disease, we observed strong perturbations of peripheral T cell homeostasis and function in severe COVID-19. Individuals with severe COVID-19 showed T cell lymphopenia and redistribution of T cell populations, including loss of naïve T cells, skewing toward CD4 T follicular helper cells and cytotoxic CD4 T cells, and expansion of activated and exhausted T cells. Extensive T cell apoptosis was particularly evident with severe disease and T cell lymphopenia, which in turn was accompanied by impaired T cell responses to several common viral antigens. Patients with severe disease showed elevated interleukin-7 and increased T cell proliferation. Furthermore, patients sampled at late time points after symptom onset had higher T cell counts and improved antiviral T cell responses.

Conclusion: Our study suggests that severe COVID-19 is characterized by extensive T cell dysfunction and T cell apoptosis, which is associated with signs of homeostatic T cell proliferation and T cell recovery.
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http://dx.doi.org/10.1111/all.14866DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251365PMC
April 2021

A distinct innate immune signature marks progression from mild to severe COVID-19.

Cell Rep Med 2021 Jan 26;2(1):100166. Epub 2020 Dec 26.

Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.

Coronavirus disease 2019 (COVID-19) manifests with a range of severities, but immune signatures of mild and severe disease are still not fully understood. Here, we use mass cytometry and targeted proteomics to profile the innate immune response of patients with mild or severe COVID-19 and of healthy individuals. Sampling at different stages allows us to reconstruct a pseudo-temporal trajectory of the innate response. A surge of CD169 monocytes associated with an IFN-γMCP-2 signature rapidly follows symptom onset. At later stages, we observe a persistent inflammatory phenotype in patients with severe disease, dominated by high CCL3 and CCL4 abundance correlating with the re-appearance of CD16 monocytes, whereas the response of mild COVID-19 patients normalizes. Our data provide insights into the dynamic nature of inflammatory responses in COVID-19 patients and identify sustained innate immune responses as a likely mechanism in severe patients, thus supporting the investigation of targeted interventions in severe COVID-19.
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http://dx.doi.org/10.1016/j.xcrm.2020.100166DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817872PMC
January 2021

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support.

Cancer Cell 2021 Mar 21;39(3):288-293. Epub 2021 Jan 21.

Roche Pharmaceutical Research and Early Development, Roche Innovation Center Zurich, Wagistrasse 10, 8952 Schlieren, Switzerland.

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.
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http://dx.doi.org/10.1016/j.ccell.2021.01.004DOI Listing
March 2021

A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids.

Mol Syst Biol 2020 12;16(12):e9798

Department of Quantitative Biomedicine, University of Zurich, Zürich, Switzerland.

Cells react to their microenvironment by integrating external stimuli into phenotypic decisions via an intracellular signaling network. To analyze the interplay of environment, local neighborhood, and internal cell state effects on phenotypic variability, we developed an experimental approach that enables multiplexed mass cytometric imaging analysis of up to 240 pooled spheroid microtissues. We quantified the contributions of environment, neighborhood, and intracellular state to marker variability in single cells of the spheroids. A linear model explained on average more than half of the variability of 34 markers across four cell lines and six growth conditions. The contributions of cell-intrinsic and environmental factors to marker variability are hierarchically interdependent, a finding that we propose has general implications for systems-level studies of single-cell phenotypic variability. By the overexpression of 51 signaling protein constructs in subsets of cells, we also identified proteins that have cell-intrinsic and cell-extrinsic effects. Our study deconvolves factors influencing cellular phenotype in a 3D tissue and provides a scalable experimental system, analytical principles, and rich multiplexed imaging datasets for future studies.
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http://dx.doi.org/10.15252/msb.20209798DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765047PMC
December 2020

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

Bioinformatics 2020 Dec 26. Epub 2020 Dec 26.

Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.

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 visualisation 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 visualisation 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.
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http://dx.doi.org/10.1093/bioinformatics/btaa1061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023672PMC
December 2020

Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition.

Front Physiol 2020 30;11:579117. Epub 2020 Nov 30.

Department of Quantitative Biomedicine, University of Zürich, Zürich, Switzerland.

Intracellular signaling pathways are at the core of cellular information processing. The states of these pathways and their inputs determine signaling dynamics and drive cell function. Within a cancerous tumor, many combinations of cell states and microenvironments can lead to dramatic variations in responses to treatment. Network rewiring has been thought to underlie these context-dependent differences in signaling; however, from a biochemical standpoint, rewiring of signaling networks should not be a prerequisite for heterogeneity in responses to stimuli. Here we address this conundrum by analyzing an model of the epithelial mesenchymal transition (EMT), a biological program implicated in increased tumor invasiveness, heterogeneity, and drug resistance. We used mass cytometry to measure EGF signaling dynamics in the ERK and AKT signaling pathways before and after induction of EMT in Py2T murine breast cancer cells. Analysis of the data with standard network inference methods suggested EMT-dependent network rewiring. In contrast, use of a modeling approach that adequately accounts for single-cell variation demonstrated that a single reaction-based pathway model with constant structure and near-constant parameters is sufficient to represent differences in EGF signaling across EMT. This result indicates that rewiring of the signaling network is not necessary for heterogeneous responses to a signal and that unifying reaction-based models should be employed for characterization of signaling in heterogeneous environments, such as cancer.
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http://dx.doi.org/10.3389/fphys.2020.579117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733964PMC
November 2020

High-Dimensional T Helper Cell Profiling Reveals a Broad Diversity of Stably Committed Effector States and Uncovers Interlineage Relationships.

Immunity 2020 09 30;53(3):597-613.e6. Epub 2020 Jul 30.

Institute of Molecular Health Sciences, ETH Zurich, 8093 Zurich, Switzerland. Electronic address:

CD4 T helper (Th) cells are fundamental players in immunity. Based on the expression of signature cytokines and transcription factors, several Th subsets have been defined. Th cells are thought to be far more heterogeneous and multifunctional than originally believed, but characterization of the full diversity has been hindered by technical limitations. Here, we employ mass cytometry to analyze the diversity of Th cell responses generated in vitro and in animal disease models, revealing a vast heterogeneity of effector states with distinct cytokine footprints. The diversities of cytokine responses established during primary antigen encounters in Th1- and Th2-cell-polarizing conditions are largely maintained after secondary challenge, regardless of the new inflammatory environment, highlighting many of the identified states as stable Th cell sublineages. We also find that Th17 cells tend to upregulate Th2-cell-associated cytokines upon challenge, indicating a closer developmental connection between Th17 and Th2 cells than previously anticipated.
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http://dx.doi.org/10.1016/j.immuni.2020.07.001DOI Listing
September 2020

Profiling Cell Signaling Networks at Single-cell Resolution.

Mol Cell Proteomics 2020 05 4;19(5):744-756. Epub 2020 Mar 4.

Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland. Electronic address:

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.
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http://dx.doi.org/10.1074/mcp.R119.001790DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196580PMC
May 2020

Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data.

Sci Rep 2020 01 27;10(1):1233. Epub 2020 Jan 27.

Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.

Inferring cell-signaling networks from high-throughput data is a challenging problem in systems biology. Recent advances in cytometric technology enable us to measure the abundance of a large number of proteins at the single-cell level across time. Traditional network reconstruction approaches usually consider each time point separately, resulting thus in inferred networks that strongly vary across time. To account for the possibly time-invariant physical couplings within the signaling network, we extend the traditional graphical lasso with an additional regularizer that penalizes network variations over time. ROC evaluation of the method on in silico data showed higher reconstruction accuracy than standard graphical lasso. We also tested our approach on single-cell mass cytometry data of IFNγ-stimulated THP1 cells with 26 phospho-proteins simultaneously measured. Our approach recapitulated known signaling relationships, such as connection within the JAK/STAT pathway, and was further validated in characterizing perturbed signaling network with PI3K, MEK1/2 and AMPK inhibitors.
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http://dx.doi.org/10.1038/s41598-019-56444-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985232PMC
January 2020

The single-cell pathology landscape of breast cancer.

Nature 2020 02 20;578(7796):615-620. Epub 2020 Jan 20.

Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.

Single-cell analyses have revealed extensive heterogeneity between and within human tumours, 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 cytometry 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.
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http://dx.doi.org/10.1038/s41586-019-1876-xDOI Listing
February 2020

Uncovering axes of variation among single-cell cancer specimens.

Nat Methods 2020 03 13;17(3):302-310. Epub 2020 Jan 13.

Department of Genetics, Yale School of Medicine, New Haven, CT, USA.

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed 'phenotypic earth mover's distance' (PhEMD). PhEMD is a general method for embedding a 'manifold of manifolds', in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.
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http://dx.doi.org/10.1038/s41592-019-0689-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339867PMC
March 2020

Hair eruption initiates and commensal skin microbiota aggravate adverse events of anti-EGFR therapy.

Sci Transl Med 2019 12;11(522)

Institute of Cancer Research, Department of Medicine I, Medical University of Vienna and Comprehensive Cancer Center, Vienna 1090, Austria.

Epidermal growth factor receptor (EGFR)-targeted anticancer therapy induces stigmatizing skin toxicities affecting patients' quality of life and therapy adherence. The lack of mechanistic details underlying these adverse events hampers their management. We found that EGFR/ERK signaling is required in LRIG1-positive stem cells during de novo hair eruption to secure barrier integrity and prevent the invasion of commensal microbiota and inflammatory skin disease. EGFR-deficient epidermis is permissive for microbiota outgrowth and displays an atopic-like T2-dominated signature. The opening of the follicular ostia during hair eruption allows invasion of commensal microbiota into the hair follicle, initiating an additional T1 and T17 response culminating in chronic folliculitis. Restoration of epidermal ERK signaling via prophylactic FGF7 treatment or transgenic SOS expression rescues the barrier defect in the absence of EGFR, highlighting a therapeutic anchor point. These data reveal that commensal skin microbiota provoke atopic-like inflammatory skin diseases by invading into the follicular opening of erupting hair.
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http://dx.doi.org/10.1126/scitranslmed.aax2693DOI Listing
December 2019

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis.

Cell Rep 2019 10;29(1):202-211.e6

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany; Division of Computational Genomics and Systems Genetics, German Cancer Research Center, 69120 Heidelberg, Germany. Electronic address:

Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression. In a breast cancer Imaging Mass Cytometry dataset, our model yields interpretable spatial variance signatures, which reveal cell-cell interactions as a major driver of protein expression heterogeneity. Applied to high-dimensional imaging-derived RNA data, SVCA identifies plausible gene families that are linked to cell-cell interactions. SVCA is available as a free software tool that can be widely applied to spatial data from different technologies.
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http://dx.doi.org/10.1016/j.celrep.2019.08.077DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899515PMC
October 2019

Analysis of the Human Kinome and Phosphatome by Mass Cytometry Reveals Overexpression-Induced Effects on Cancer-Related Signaling.

Mol Cell 2019 06 14;74(5):1086-1102.e5. Epub 2019 May 14.

Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland. Electronic address:

Kinase and phosphatase overexpression drives tumorigenesis and drug resistance. We previously developed a mass-cytometry-based single-cell proteomics approach that enables quantitative assessment of overexpression effects on cell signaling. Here, we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulated cancer-related signaling in HEK293T cells in an abundance-dependent manner. Based on these data, we expanded the functional classification of human kinases and phosphatases and showed that the overexpression effects include non-catalytic roles. We detected 208 previously unreported signaling relationships. The signaling dynamics analysis indicated that the overexpression of ERK-specific phosphatases sustains proliferative signaling. This suggests a phosphatase-driven mechanism of cancer progression. Moreover, our analysis revealed a drug-resistant mechanism through which overexpression of tyrosine kinases, including SRC, FES, YES1, and BLK, induced MEK-independent ERK activation in melanoma A375 cells. These proteins could predict drug sensitivity to BRAF-MEK concurrent inhibition in cells carrying BRAF mutations.
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http://dx.doi.org/10.1016/j.molcel.2019.04.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561723PMC
June 2019

A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer.

Cell 2019 05 11;177(5):1330-1345.e18. Epub 2019 Apr 11.

Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland. Electronic address:

Breast cancer is a heterogeneous disease. Tumor cells and associated healthy cells form ecosystems that determine disease progression and response to therapy. To characterize features of breast cancer ecosystems and their associations with clinical data, we analyzed 144 human breast tumor and 50 non-tumor tissue samples using mass cytometry. The expression of 73 proteins in 26 million cells was evaluated using tumor and immune cell-centric antibody panels. Tumors displayed individuality in tumor cell composition, including phenotypic abnormalities and phenotype dominance. Relationship analyses between tumor and immune cells revealed characteristics of ecosystems related to immunosuppression and poor prognosis. High frequencies of PD-L1 tumor-associated macrophages and exhausted T cells were found in high-grade ER and ER tumors. This large-scale, single-cell atlas deepens our understanding of breast tumor ecosystems and suggests that ecosystem-based patient classification will facilitate identification of individuals for precision medicine approaches targeting the tumor and its immunoenvironment.
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http://dx.doi.org/10.1016/j.cell.2019.03.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526772PMC
May 2019

IL-8 and CXCR1 expression is associated with cancer stem cell-like properties of clear cell renal cancer.

J Pathol 2019 07 11;248(3):377-389. Epub 2019 Apr 11.

Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland.

Recent studies suggest that clear cell renal cell carcinoma (ccRCC) possesses a rare population of cancer stem cells (CSCs) that might contribute to tumor heterogeneity, metastasis and therapeutic resistance. Nevertheless, their relevance for renal cancer is still unclear. In this study, we successfully isolated CSCs from established human ccRCC cell lines. CSCs displayed high expression of the chemokine IL-8 and its receptor CXCR1. While recombinant IL-8 significantly increased CSC number and properties in vitro, CXCR1 inhibition using an anti-CXCR1 antibody or repertaxin significantly reduced these features. After injection into immune-deficient mice, CSCs formed primary tumors that metastasized to the lung and liver. All xenografted tumors in mice expressed high levels of IL-8 and CXCR1. Furthermore, IL-8/CXCR1 expression significantly correlated with decreased overall survival in ccRCC patients. These results suggest that the IL-8/CXCR1 phenotype is associated with CSC-like properties in renal cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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http://dx.doi.org/10.1002/path.5267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6618115PMC
July 2019

In-Depth Characterization of Monocyte-Derived Macrophages using a Mass Cytometry-Based Phagocytosis Assay.

Sci Rep 2019 02 13;9(1):1925. Epub 2019 Feb 13.

Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, 8057, Zürich, Switzerland.

Phagocytosis is a process in which target cells or particles are engulfed and taken up by other cells, typically professional phagocytes; this process is crucial in many physiological processes and disease states. The detection of targets for phagocytosis is directed by a complex repertoire of cell surface receptors. Pattern recognition receptors directly detect targets for binding and uptake, while opsonic and complement receptors detect objects coated by soluble factors. However, the importance of single and combinatorial surface marker expression across different phenotypes of professional phagocytes is not known. Here we developed a novel mass cytometry-based phagocytosis assay that enables the simultaneous detection of phagocytic events in combination with up to 40 other protein markers. We applied this assay to distinct monocyte derived macrophage (MDM) populations and found that prototypic M2-like MDMs phagocytose more E. coli than M1-like MDMs. Surface markers such as CD14, CD206, and CD163 rendered macrophages phagocytosis competent, but only CD209 directly correlated with the amount of particle uptake. Similarly, M2-like MDMs also phagocytosed more cancer cells than M1-like MDMs but, unlike M1-like MDMs, were insensitive to anti-CD47 opsonization. Our approach facilitates the simultaneous study of single-cell phenotypes, phagocytic activity, signaling and transcriptional events in complex cell mixtures.
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http://dx.doi.org/10.1038/s41598-018-38127-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374473PMC
February 2019

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

Cell Metab 2019 03 31;29(3):755-768.e5. Epub 2019 Jan 31.

Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland. Electronic address:

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing β cells. A comprehensive picture of the changes during T1D development is lacking due to limited sample availability, inability to sample longitudinally, and the paucity of technologies enabling comprehensive tissue profiling. Here, we analyzed 1,581 islets from 12 human donors, including eight with T1D, using imaging mass cytometry (IMC). IMC enabled simultaneous measurement of 35 biomarkers with single-cell and spatial resolution. We performed pseudotime analysis of islets through T1D progression from snapshot data to reconstruct the evolution of β cell loss and insulitis. Our analyses revealed that β cell destruction is preceded by a β cell marker loss and by recruitment of cytotoxic and helper T cells. The approaches described herein demonstrate the value of IMC for improving our understanding of T1D pathogenesis, and our data lay the foundation for hypothesis generation and follow-on experiments.
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http://dx.doi.org/10.1016/j.cmet.2018.11.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821395PMC
March 2019

Learning time-varying information flow from single-cell epithelial to mesenchymal transition data.

PLoS One 2018 29;13(10):e0203389. Epub 2018 Oct 29.

Institute for Molecular Life Sciences, University of Zurich, Zurich, Switzerland.

Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in protein abundance and confirmation. However, typical computational approaches treat them as static interaction networks derived from a single time point. Here, we provide methods for learning the dynamic modulation of relationships between proteins from static single-cell data. We demonstrate our approach using TGFß induced epithelial-to-mesenchymal transition (EMT) in murine breast cancer cell line, profiled with mass cytometry. We take advantage of the asynchronous rate of transition to EMT in the data and derive a pseudotime EMT trajectory. We propose methods for visualizing and quantifying time-varying edge behavior over the trajectory, and a metric of edge dynamism to predict the effect of drug perturbations on EMT.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0203389PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6205587PMC
March 2019

Correction to: Limited utility of tissue micro-arrays in detecting intra-tumoral heterogeneity in stem cell characteristics and tumor progression markers in breast cancer.

J Transl Med 2018 07 2;16(1):180. Epub 2018 Jul 2.

Institute of Pathology and Molecular Pathology, University Hospital Zurich, Schmelzbergstrasse 12, 8091, Zurich, Switzerland.

Following publication of the original article [1], a typesetting mistake is reported. For Fig. 7b, a copy of Fig. 6b has been published. The correct Fig. 7b is given in this correction and the original article has been updated.
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http://dx.doi.org/10.1186/s12967-018-1553-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6027575PMC
July 2018

Limited utility of tissue micro-arrays in detecting intra-tumoral heterogeneity in stem cell characteristics and tumor progression markers in breast cancer.

J Transl Med 2018 05 8;16(1):118. Epub 2018 May 8.

Institute of Pathology and Molecular Pathology, University Hospital Zurich, Schmelzbergstrasse 12, 8091, Zurich, Switzerland.

Background: Intra-tumoral heterogeneity has been recently addressed in different types of cancer, including breast cancer. A concept describing the origin of intra-tumoral heterogeneity is the cancer stem-cell hypothesis, proposing the existence of cancer stem cells that can self-renew limitlessly and therefore lead to tumor progression. Clonal evolution in accumulated single cell genomic alterations is a further possible explanation in carcinogenesis. In this study, we addressed the question whether intra-tumoral heterogeneity can be reliably detected in tissue-micro-arrays in breast cancer by comparing expression levels of conventional predictive/prognostic tumor markers, tumor progression markers and stem cell markers between central and peripheral tumor areas.

Methods: We analyzed immunohistochemical expression and/or gene amplification status of conventional prognostic tumor markers (ER, PR, HER2, CK5/6), tumor progression markers (PTEN, PIK3CA, p53, Ki-67) and stem cell markers (mTOR, SOX2, SOX9, SOX10, SLUG, CD44, CD24, TWIST) in 372 tissue-micro-array samples from 72 breast cancer patients. Expression levels were compared between central and peripheral tumor tissue areas and were correlated to histopathological grading. 15 selected cases additionally underwent RNA sequencing for transcriptome analysis.

Results: No significant difference in any of the analyzed between central and peripheral tumor areas was seen with any of the analyzed methods/or results that showed difference. Except mTOR, PIK3CA and SOX9 (nuclear) protein expression, all markers correlated significantly (p < 0.05) with histopathological grading both in central and peripheral areas.

Conclusion: Our results suggest that intra-tumoral heterogeneity of stem-cell and tumor-progression markers cannot be reliably addressed in tissue-micro-array samples in breast cancer. However, most markers correlated strongly with histopathological grading confirming prognostic information as expression profiles were independent on the site of the biopsy was taken.
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http://dx.doi.org/10.1186/s12967-018-1495-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5941467PMC
May 2018

High-Dimensional Phenotyping Identifies Age-Emergent Cells in Human Mammary Epithelia.

Cell Rep 2018 04;23(4):1205-1219

Department of Biomedicine, University of Bergen, Bergen 5009, Norway; Centre for Cancer Biomarkers (CCBIO), University of Bergen, Bergen 5009, Norway. Electronic address:

Aging is associated with tissue-level changes in cellular composition that are correlated with increased susceptibility to disease. Aging human mammary tissue shows skewed progenitor cell potency, resulting in diminished tumor-suppressive cell types and the accumulation of defective epithelial progenitors. Quantitative characterization of these age-emergent human cell subpopulations is lacking, impeding our understanding of the relationship between age and cancer susceptibility. We conducted single-cell resolution proteomic phenotyping of healthy breast epithelia from 57 women, aged 16-91 years, using mass cytometry. Remarkable heterogeneity was quantified within the two mammary epithelial lineages. Population partitioning identified a subset of aberrant basal-like luminal cells that accumulate with age and originate from age-altered progenitors. Quantification of age-emergent phenotypes enabled robust classification of breast tissues by age in healthy women. This high-resolution mapping highlighted specific epithelial subpopulations that change with age in a manner consistent with increased susceptibility to breast cancer.
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http://dx.doi.org/10.1016/j.celrep.2018.03.114DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5946804PMC
April 2018

Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry.

Cell Syst 2018 05 28;6(5):612-620.e5. Epub 2018 Mar 28.

Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland. Electronic address:

The advent of mass cytometry increased the number of parameters measured at the single-cell level while decreasing the extent of crosstalk between channels relative to dye-based flow cytometry. Although reduced, spillover still exists in mass cytometry data, and minimizing its effect requires considerable expert knowledge and substantial experimental effort. Here, we describe a novel bead-based compensation workflow and R-based software that estimates and corrects for interference between channels. We performed an in-depth characterization of the spillover properties in mass cytometry, including limitations defined by the linear range of the mass cytometer and the reproducibility of the spillover over time and across machines. We demonstrated the utility of our method in suspension and imaging mass cytometry. To conclude, our approach greatly simplifies the development of new antibody panels, increases flexibility for antibody-metal pairing, opens the way to using less pure isotopes, and improves overall data quality, thereby reducing the risk of reporting cell phenotype artifacts.
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http://dx.doi.org/10.1016/j.cels.2018.02.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981006PMC
May 2018

CellCycleTRACER accounts for cell cycle and volume in mass cytometry data.

Nat Commun 2018 02 12;9(1):632. Epub 2018 Feb 12.

Zürich Research Lab, IBM, Säumerstrasse 4, 8803, Rüschlikon, Switzerland.

Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course. CellCycleTRACER reveals signaling relationships and cell heterogeneity that were otherwise masked.
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http://dx.doi.org/10.1038/s41467-018-03005-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809393PMC
February 2018

Ruthenium counterstaining for imaging mass cytometry.

J Pathol 2018 04 6;244(4):479-484. Epub 2018 Mar 6.

Institute of Molecular Life Sciences, University of Zürich, Zürich, Switzerland.

Imaging mass cytometry is a novel imaging modality that enables simultaneous antibody-based detection of >40 epitopes and molecules in tissue sections at subcellular resolution by the use of isotopically pure metal tags. Essential for any imaging approach in which antigen detection is performed is counterstaining, which reveals the overall structure of the tissue. Counterstaining is necessary because antigens of interest are often present in only a small subset of cells, and the rest of the tissue structures are not visible. As most biological tissues are nearly transparent or non-fluorescent, chromogenic reagents such as haematoxylin (for immunohistochemistry) or fluorescent dyes such as 4',6-diamidino-2-phenylindole (which stains nuclei for epifluorescence and confocal microscopy) are utilized. Here, we describe a metal-based counterstain for imaging mass cytometry based on simple oxidation and subsequent covalent binding of the tissue components to ruthenium tetroxide (RuO ). RuO counterstaining reveals general tissue structure both in areas with high cell content and in stromal areas with low cellularity and fibrous or hyaline material in a manner analogous to haematoxylin in immunohistochemical counterstaining or eosin or other anionic dyes in conventional histology. Our new counterstain approach is applicable to any metal-based imaging technique, and will facilitate the adaptation of imaging mass cytometry for routine applications in clinical and research laboratories. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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http://dx.doi.org/10.1002/path.5049DOI Listing
April 2018

Simultaneous Multiplexed Imaging of mRNA and Proteins with Subcellular Resolution in Breast Cancer Tissue Samples by Mass Cytometry.

Cell Syst 2018 Jan 27;6(1):25-36.e5. Epub 2017 Dec 27.

Insitute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland. Electronic address:

To build comprehensive models of cellular states and interactions in normal and diseased tissue, genetic and proteomic information must be extracted with single-cell and spatial resolution. Here, we extended imaging mass cytometry to enable multiplexed detection of mRNA and proteins in tissues. Three mRNA target species were detected by RNAscope-based metal in situ hybridization with simultaneous antibody detection of 16 proteins. Analysis of 70 breast cancer samples showed that HER2 and CK19 mRNA and protein levels are moderately correlated on the single-cell level, but that only HER2, and not CK19, has strong mRNA-to-protein correlation on the cell population level. The chemoattractant CXCL10 was expressed in stromal cell clusters, and the frequency of CXCL10-expressing cells correlated with T cell presence. Our flexible and expandable method will allow an increase in the information content retrieved from patient samples for biomedical purposes, enable detailed studies of tumor biology, and serve as a tool to bridge comprehensive genomic and proteomic tissue analysis.
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http://dx.doi.org/10.1016/j.cels.2017.12.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5791659PMC
January 2018
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