News

  • Bernd Bodenmiller has been appointed dual professor.
    Sep 20

    Bernd Bodenmiller is now dual professor at the Department of Quantitative Biomedicine, UZH and at the Institute for Molecular Health Sciences, ETH Zurich.

  • New students in the Bodenmiller lab.
    Sep 1

    Daria Lazic is a visiting Ph.D. student, learning to apply imaging mass cytometry in the Bodenmiller lab. Luca Räss and Hangjia Zhao have joined us as Master's students.

  • Johanna received the “Annual Award 2020” of the Faculty of Sciences for her PhD Thesis
    Apr 28

    Auf Antrag der Mathematisch-naturwissenschaftlichen Fakultät verleiht die Universität Zürich einen Jahrespreis an
    Dr. Johanna Wagner-Albrecht für ihre Dissertation «Single-Cell Proteomic Characterization of the Tumor and Immune Ecosystem of Human Breast Cancer with Focus on Metastatic Potential».
    Johanna erforscht die Diversität der Zellen in humanen Brusttumoren. Sie entdeckte neue Patientinnen Untergruppen die von Immuntherapien profitieren könnten und dass aggressivere Tumore überraschenderweise von wenigen Krebszellarten dominiert werden. Ihre Arbeit ist richtungsweisend für individuell auf Patientinnen zugeschnittene Therapien.
    Congratulations Johanna!

  • Marco successfully defended his PhD!
    Jan 21

    The subject of his Thesis is: “Deciphering the Signaling Network Landscape of Breast Cancer Supports Precision Medicine”. Congratulations to Marco!

  • Vito successfully defended his PhD!
    Jan 7

    The subject of his Thesis is: “Investigation of Intra- and Intercellular Signaling through Mass Cytometry Based Single Cell Methods”. Congratulations to Vito!

  • Johanna awarded for outstanding doctoral theses
    Jan 7

    Johanna Wagner has received a distinction for outstanding dissertation by the Faculty of Science on the 13th of December 2019. That means that Johannas’ thesis was among the top 5% PhD theses awarded by the University of Zurich, based on a jury of international reviewers.

    The subject of her Thesis is: “Single-Cell Proteomic Characterization of the Tumor and Immune Ecosystem of Breast Cancer”. Congratulations to Johanna!

  • Bernd and his team have been awarded with the ERC Consolidator Grant
    Dec 16

    Tumors are highly complex entities that consist of many different cells communicating with each other. The project aims to develop new technologies and computer-aided methods that rationalize this complexity and describe tissues akin to (a)social networks. Such representations could help scientists understand the mechanistic underpinnings of cancer in the context of metastatic breast cancer and determine the most suitable therapies for breast cancer patients.
    Congratulations!

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Research

Methods development

We develop experimental and computational methods to study tumor ecosystems on the single-cell level. In such an ecosystem, many cell types, including tumor, stromal, immune and endothelial cells, interact and communicate in multicellular assemblies. We seek to understand how tumor ecosystems function and ultimately how their properties affect disease. We generate comprehensive single-cell datasets of tumors from many patients, which requires detection of dozens of markers simultaneously. We have pioneered mass cytometry-based methods for simultaneous multiplexed marker detection and analysis in both dissociated tissues and on tissue sections, and continue to develop these methods further (see technology section).

Simultaneous multiplexed imaging of mRNA and proteins. From Schulz et al, 2018.

Translational research and precision medicine

Solid tumors are multicellular ecosystems of diverse cell types that interact to manifest emergent phenotypes which ultimately determine clinical outcome. We combine suspension or imaging mass cytometry with computational techniques to systematically describe cell phenotypes in tumor ecosystems and to examine their distribution across patients and tumor types. We generate atlases of human tumors from large patient cohorts with known clinical outcome, and identify cellular and spatial phenotypes associated with disease progression. These atlases lay the foundation for improved patient stratification and provide potential drug targets for ongoing study. These data are also the foundation for follow-up studies to understand mechanisms of the disease. We are part of several large-scale, multi-center projects bringing together clinicians, research labs and pharmaceutical companies, in which we develop mass cytometry and imaging mass cytometry for precision medicince applications.

Artistic representation of an invasive breast tumor ecosystem, depicting cancer cells (irregular shapes) and immune cells (circles). Image by Johanna Wagner.

Mechanisms of cancer

Single-cell systems biology analyses of tumor samples yield a wealth of data about cancer biology. To understand the regulatory networks underlying the disease, we use algorithmic and data-driven approaches to model subpopulations of cells and their signaling network structures. Further, using data from imaging mass cytometry, we model how signaling network states spatially couple with those of other cells. As a complement to modeling and associative studies, we use in vitro patient-derived organoid and cell co-culture models to conduct small molecule screens and carry out perturbation studies aimed at understanding mechanistic aspects of tumor biology.

Perturbation time course of 3D tissues. Courtesy of Matthias Leutenegger and Vito Zanotelli.

The Bodenmiller lab is supported by these consortia:

and TumorProfiler and SNF Sinergia.

Technology

Imaging Mass Cytometry

Single-cell systems biology of cancer requires methods to measure multiple markers within tumors, quantitatively, and with spatial and single-cell resolution. Based on our earlier work on suspension mass cytometry, the Bodenmiller group has pioneered a spatial mass spectrometric approach called imaging mass cytometry (IMC) for the simultaneous and spatially-resolved quantification of approximately 50 markers on single cells. We employ IMC-based methods to study the cellular composition, spatial organization and regulation of tissue ecosystems, for insights into health and disease. 

An overview of the imaging mass cytometry workflow. From Giesen et al, 2014.

Imaging mass cytometry: measurement

In mass cytometry, we use a mass spectrometer to measure protein and/or transcript levels within cells, using antibodies or RNA probes linked to different metal isotopes. In imaging mass cytometry, we have extended this technology to solid tissue samples such as tumor biopsies, analyzing them spatially and capturing effects of the local microenvironment on tumor cells. We apply IMC in 2D and are also developing it in 3D. Mass cytometry can in principle reliably differentiate over a 100 probes.  We are continuously improving the speed, number of markers, resolution, reliability, sensitivity, biological interpretability and overall quality of high-dimensional single cell analysis.

2D imaging mass cytometry of a breast tumor sample. Image courtesy of Hart Jackson.

Imaging mass cytometry: analysis

Deriving relevant biological information from high-dimensional datasets is an ongoing challenge in systems biology. We explore the capabilities of existing statistical and image analysis tools to analyse imaging mass cytometry data in a meaningful way. We also develop software to process, visualize and analyze high dimensional IMC datasets. 

A depiction of multi-scale analysis of a tissue ecosystem. Reproduced from Schapiro et al, 2017.

Software

The Bodenmiller GitHub page has code and scripts for many projects.

histoCAT

histoCAT is an open-source visualization and analysis toolbox for exploration of rich multidimensional IMC datasets. It enables parallel visualization of images and single cell phenotypic distributions and includes methods to identify and quantify cell-cell interactions within tissue. Read the paper.

histoCAT++

histoCAT++ is a later implementation with more advanced features. Read the paper.

cytomapper

Mulitplex imaging cytometry acquires spatially-resolved single-cell expression values of selected proteins in a sample. Cytomapper can be used to visualize the multiplexed read-outs obtained with this technique. The main functions of this package allow (i) the visualization of pixel-level information across multiple channels and (ii) the display of cell-level information (expression and/or metadata) on segmentation masks.

AirLab

AirLab is a cloud-based laboratory-management tool for antibody-based research. You can use it to manage antibody stocks, antibody panels for CyTOF and Helios, and antibody-based experiments and results. Read the paper.

CellCycleTRACER

CellCycleTRACER permits correction of mass cytometry data for confounding effects of cell cycle and volume, as long as 4 measurement channels are left free for the relevant markers. Read the paper here.

Adnet

Adnet is a set of analysis scripts used for the paper “Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry” by XK Lun et al. Read the paper here.

Protocols

The Bodenmiller lab protocols page is in progress.

People

Current Members

Bernd Bodenmiller
Prof. Dr.

Single-cell systems biology of cancer requires methods to measure multiple markers within tumors, quantitatively and at single-cell resolution. To this end, the Bodenmiller group has pioneered a single-cell mass spectrometric approach called mass cytometry (CyTOFTM). This technology allows the simultaneous and high-throughput quantification of approximately 50 markers, including proteins and their modifications, on single cells. We employ mass cytometry-based methods to study the cellular composition and regulation of tissue ecosystems, for insights into health and disease.

Franziska Heinzel
Administrative Assistant
Marcel Burger, Ph.D.
Postdoctoral Fellow
Ruben Casanova, Ph.D.
Postdoctoral Fellow
Lena Cords, M.Sc.
Ph.D. Student
Haithem Dakhli, Ph.D.
Postdoctoral Fellow

Drug development in breast cancer suffers from a lack of faithful models able to recapitulate breast cancer heterogeneity at the single-cell level. To overcome this limitation, we have generated a patient-derived breast cancer organoid biobank from fresh and frozen breast cancer samples. We are using this model to study the different populations composing the tumor and their response to drug treatments. It is our conviction that the study of the different cellular populations composing breast tumors, and their interactions, will lead to a better understanding of the tumor ecosystem and to the identification of therapies targeting specific cellular populations that could trigger tumor demise.

Nicolas Damond, Ph.D.
Postdoctoral Fellow

I am combining highly multiplexed imaging, data analysis and experimental biology to characterize type 1 diabetes (T1D) progression and beta cell heterogeneity.

I did my PhD with Prof. Pedro Herrera at the University of Geneva, studying regeneration of insulin-producing beta cells by transdifferentiation of the closely related alpha cells. During my thesis, I used confocal microscopy and lineage tracing in complex transgenic mouse models and primary human pancreatic islets. This work sparked my interest into both T1D research and image analysis of single cells, two elements that are still at the center of my current projects.

In the Bodenmiller lab, I use imaging mass cytometry to analyze pancreas sections of individuals with or at risk for type 1 diabetes. The multiplexing capacity of this technology enables deep phenotyping of beta cells and of infiltrating immune cells, and mapping of their interactions. The goal of this project is to gain a better understanding of T1D development in the pancreas.

In parallel, I'm combining primary human islet culture with imaging mass cytometry to study how beta cell subpopulations respond to external stimuli relevant to T1D. The aim is to identify beta cell subsets that are more sensitive to destructive signals or, conversely, more responsive to regenerative cues. This work is supported by a JDRF postdoctoral fellowship.

Michelle Daniel, M.Sc.
Research Assistant
Nils Eling, Ph.D.
Postdoctoral Fellow

My current research focuses on understanding the molecular changes over breast cancer organoid growth. I'm integrating imaging mass cytometry (IMC) with single-cell based statistical approaches to model spatial heterogeneity in organoids. As part of handling IMC data, I develop software for image and single-cell analysis.

In the past, I have completed my PhD in the Marioni group at the European Bioinformatics Institute and the CRUK Cambridge Institute at the University of Cambridge.

Link to website: nilseling.github.io
Link to Google Scholar: https://scholar.google.com/citations?user=kBIvrFoAAAAJ&hl=de
Link to Github: https://github.com/nilseling

Tobias Hoch, M.Sc.
Research Assistant

As a research assistant and former Master's student in the lab, I am currently analyzing IMC data with a focus on the immune system and its relation to the protein family of chemokines. I am investigating whether and to what extent the spatial context of chemokine expression can contribute to understanding differences in the immune landscape of patients.

Tsuyoshi Hosogane, M.Sc.
Ph.D. Student
Laura Kütt, M.Sc.
Ph.D. Student
Daria Lazic, M.Sc
Visiting Ph.D. Student
Luca Räss, B.Sc.
Master's Student
Anton Rau, M.Sc.
Software Engineer
Sujana Sivapatham, M.Sc.
Research Assistant
Natalie de Souza, Ph.D.
Scientific Officer
Merrick Strotton, Ph.D.
Postdoctoral Fellow
Sandra Tietscher, M.Sc.
Ph.D. Student

Background: 

Molecular Biologist with an inclination towards Computational Biology

Research Interests:

Biological --> Cancer Biology, specifically the Tumor Immune Microenvironment

Methodological --> Single-cell technologies, including CyTOF/IMC and single-cell RNA sequencing

Currently working on:

Immune cell interactions in human breast cancer

Johanna Wagner, Ph.D.
Research Associate

Selected Publications

Jonas Windhager, M.Sc.
Ph.D. Student
Vito Zanotelli, Ph.D.
Research Associate

I like to develop high throughput methods together with tailored data analysis approaches to better understand how cells perceive their environment and interact.

Hangjia Zhao, B.Sc.
Master's Student

Alumni

  • Raza Ali, M.D./Ph.D.
    Group Leader, CRUK Cambridge Institute
  • Raúl Catena, Ph.D.
    Senior Software Engineer, Leica Geosystems
  • Esther Danenberg, M.Sc.
    Research Administrator, CRUK Cambridge Institute
  • Nadine Dobberstein, M.Sc.
    Technician, InterAx Biotech
  • Charlotte Giesen, Ph.D.
    Head, Quality Assurance, Roche
  • Hartland Jackson, Ph.D.
    Group Leader, Lunenfeld-Tanenbaum Institute
  • Pieter Langerhorst, M.Sc.
    Ph.D. Student, Radboud Institute
  • Xiaokang Lun, Ph.D.
    Postdoctoral Fellow, Wyss Institute
  • Constance Lyon, M.Sc.
    Current affiliation unknown
  • Markus Masek, M.Sc.
    Ph.D. Student, University of Zurich
  • Alaz Özcan, M.Sc.
    Ph.D. Student, University of Zurich
  • Serena Di Palma, Ph.D.
    Assistant Professor, Utrecht University
  • Swetha Raghuraman, M.Sc.
    Ph.D. Student, University of Muenster
  • Denis Schapiro, Ph.D.
    Postdoctoral Fellow, Broad Institute
  • Yannik Severin, M.Sc.
    Ph.D. Student, ETH Zurich
  • Marco Tognetti, Ph.D.
    Senior Scientist, Biognosys AG
  • Sophie Tritschler, M.Sc.
    Ph.D. Student, Helmholtz Z., Muenchen
  • Eleni Tselempi, M.Sc.
    Senior Research Associate, Roche Innovation Centre
  • James Wade, Ph.D.
    QSP Expert, LYO-X GmbH
  • Shuhan Xu, M.Sc.
    Ph.D. Student, MPI
  • Nevena Zivanovic, Ph.D.
    Research Scientist, Janssen Pharmaceuticals
Raza Ali, M.D./Ph.D.
Group Leader, CRUK Cambridge Institute
Raúl Catena, Ph.D.
Senior Software Engineer, Leica Geosystems
Esther Danenberg, M.Sc.
Research Administrator, CRUK Cambridge Institute
Nadine Dobberstein, M.Sc.
Technician, InterAx Biotech
-
Charlotte Giesen, Ph.D.
Head, Quality Assurance, Roche
Hartland Jackson, Ph.D.
Group Leader, Lunenfeld-Tanenbaum Institute
Pieter Langerhorst, M.Sc.
Ph.D. Student, Radboud Institute
Xiaokang Lun, Ph.D.
Postdoctoral Fellow, Wyss Institute
Constance Lyon, M.Sc.
Current affiliation unknown
Markus Masek, M.Sc.
Ph.D. Student, University of Zurich
Alaz Özcan, M.Sc.
Ph.D. Student, University of Zurich
Serena Di Palma, Ph.D.
Assistant Professor, Utrecht University
Swetha Raghuraman, M.Sc.
Ph.D. Student, University of Muenster
Denis Schapiro, Ph.D.
Postdoctoral Fellow, Broad Institute
Yannik Severin, M.Sc.
Ph.D. Student, ETH Zurich
Marco Tognetti, Ph.D.
Senior Scientist, Biognosys AG
Sophie Tritschler, M.Sc.
Ph.D. Student, Helmholtz Z., Muenchen
Eleni Tselempi, M.Sc.
Senior Research Associate, Roche Innovation Centre
James Wade, Ph.D.
QSP Expert, LYO-X GmbH
Shuhan Xu, M.Sc.
Ph.D. Student, MPI
Nevena Zivanovic, Ph.D.
Research Scientist, Janssen Pharmaceuticals

Lab Life

The BB lab on a retreat.
We scaled a (small) peak.
On the way back from a good dinner.
Something to celebrate!

Open Positions

We are always looking for excellent and motivated students/postdocs with a strong background in biological/biochemical/biomedical research or bioinformatics.

Prospective Ph.D. students can apply to our group via the Life Science Zurich Graduate School. Our group is a member of the Molecular Life Sciences, the Systems Biology and the Cancer Biology programs. For computational students, we offer shared Ph.D. positions with bioinformatics research groups.

Post-doctoral candidates should be highly motivated with a passion for science with great interest in quantitative biology, single-cell analysis and systems biomedicine. Our lab and collaborators provide an excellent and vibrant interdisciplinary environment, including systems (cancer) biology, biochemistry, analytical sciences, computer sciences and biomedical research. Candidates should have a demonstrated record of productivity during their Ph.D. studies, including publications in peer-reviewed journals (see DORA).

Applications should be directly sent to Bernd Bodenmiller, including your CV, Publication List and a short paragraph with your scientific interests and what you hope to achieve during your postdoctoral time.

All Publications

Recent Publications

Selected Publications

Teaching

In autumn 2020, the Bodenmiller lab will contribute to the following courses at UZH.

Bio111: Classical and Molecular Genetics

Bio242: Translational Cancer Research

Bio323: Modern Genetics and Genomics

LSZGS: Technologies and Systems Approaches in Biology

For more details see course descriptions at UZH and ETHZ.

Contact

Department of Quantitative Biomedicine
University of Zurich
Winterthurerstrasse 190
CH-8057 Zurich
Switzerland 

We are not easy to find, download our campus map.

Building / Room: Y32-J-14
phone: +41 44 6354825
fax: +41 44 635 68 79

Inst. f. Molecular Health Sciences
ETH Zurich
Otto-​Stern-Weg 7
CH-8093 Zürich
Switzerland



Building / Room: HPL G 32.2
phone: +41 44 633 31 97