Unraveling the Biology of Cancer

Bernd Bodenmiller on revealing new insights into tumors with Imaging Mass Cytometry


Hyperion Imaging System

The Hyperion™ Imaging System brings together imaging capability with proven CyTOF® technology to facilitate Imaging Mass Cytometry™ applications. This allows highly multiplexed immunohistochemistry enabling the simultaneous analysis of 4 to 37 protein markers. Deep interrogation of tissues or tumors at subcellular resolution can empower the identification of new prognostic and diagnostic biomarkers and provide key insights into disease progression, response to therapy or drug mechanisms in the future.

Discover more about using Imaging Mass Cytometry.

The innovative Hyperion™ Imaging System by Fluidigm was born out of a dedicated effort to advance translational research toward a more comprehensive understanding of the complex cellular relationships and tissue microenvironment. It is the first commercially available platform to empower simultaneous interrogation of 4 to 37 protein markers in spatial context at subcellular resolution. A lead developer and longtime champion of Imaging Mass Cytometry™, Bernd Bodenmiller, PhD, Assistant Professor for Quantitative Biology at the University of Zurich (UZH), shares his experiences using the Hyperion Imaging System.

In a Nature Methods paper published in 2014, Giesen and Wang et al. first reported on an approach they developed in the Bodenmiller Lab to simultaneously image 32 protein markers and protein modifications at subcellular resolution. They detailed an Imaging Mass Cytometry approach they believed would greatly advance the understanding of cancer and other disease states through improved tissue characterization. Imaging Mass Cytometry now brings game-changing technology to immuno-oncology researchers, with the realm of possibilities extending to autoimmune diseases, neuroscience, precision medicine and other disease areas.

At his systems biomedicine lab at the north end of Lake Zurich in Switzerland’s largest city, Bodenmiller sets his sights on understanding the workings of tumor ecosystems. The Bodenmiller Lab researches cell types present in solid tumors to better grasp how the cellular elements of the ecosystems influence each other through cell-to-cell communication to drive tumor development. Modeling the tumors as multidimensionally resolved cellular networks, Bodenmiller studies cancer biology in the context of precision medicine, hoping to inform future treatment approaches for cancer. His comprehensive investigational research encompasses development of experimental and bioinformatics methods for highly multiplexed, spatially resolved subcellular tissue analysis.

The Imaging Mass Cytometry workflow pairs immunohistochemical methods with proven CyTOF® technology to record cellular markers, transcripts and signaling to generate an image of the tissue. Having already published a paper demonstrating how Imaging Mass Cytometry enables the simultaneous imaging of 32 protein markers at subcellular resolution in breast cancer tissues, the team has also shown the simultaneous imaging of proteins and transcripts, expanding the possibilities for analyzing aspects of cancer biology.

Images generated using the Hyperion Imaging System could reveal how cells respond to oxygen levels, factors contributing to immune cell exhaustion, and which conditions within the tumor environment relate to clinical features. Put into the context of translational research, the Imaging Mass Cytometry workflow using the Hyperion Imaging System empowers scientists to gain comprehensive insights into solving questions relevant to clinical treatment and global health initiatives underway at the University of Zurich.

Analyzing the tumor ecosystem

While all of his group’s research is based on mass cytometry, Bodenmiller estimates that around two-thirds of their projects now rely on Imaging Mass Cytometry. “The Hyperion Imaging System is a key method in my lab,” he said. “Within the tumor ecosystem it is informative to reveal cell type identity and the functional state of individual cells. It is important to also add spatial information for those cells. This is highly relevant because then we can study how cellular environments might actually shape a cellular phenotype or influence treatment response.”

Imaging Mass Cytometry can provide translational data on the status of the immune repertoire within a tumor. Bodenmiller anticipates that targeted modulation of the tumor ecosystem will eventually empower researchers to interrupt and halt tumor growth. “Immune checkpoint blockade therapies are a nice example of targeting interactions within the tumor ecosystem,” he explained, “as it’s really the cell-to-cell interaction and modulation that are responsible for dampening and reactivating the immune response.”

Comparing the way Imaging Mass Cytometry uses metal isotopes as reporters to the way enzymatic immunohistochemistry uses enzymes to stain tissue, and immunofluorescent microscopy uses fluorophore as a reporter for antibodies, Bodenmiller said, “What makes Imaging Mass Cytometry special is that it enables us to perform multiplexed measurements.” When detecting isotopes on the Hyperion Imaging System, the proven CyTOF technology offers 135 channels to measure many metal-tagged antibodies at once in a single tissue.

“I see enormous potential for Imaging Mass Cytometry in assessing immune checkpoint inhibitor status and dereguated cellular pathways and determining best treatments for precision medicine.”
—Bernd Bodenmiller, PhD, University of Zurich

Unprecedented pathology possibilities

Imaging Mass Cytometry stands to benefit many different areas of medicine, health and life sciences. With a nod to translational and potential clinical applications, Bodenmiller pointed to Switzerland and countries with centralized state-run health care systems that store anonymous clinical and long-term patient data and tissue samples. He cited Zurich as an example housing a half-million tissue specimens that scientists could use for additional research under governance by the institutional and state ethics board.

“There are untold answers in the millions of biobank tumor samples stored in hospitals around the world—many with densely annotated disease and patient features or treatment data,” he remarked, adding that there is an unprecedented amount of information to explore from tissue samples. “If we analyze large cohorts using Imaging Mass Cytometry and apply computational tools and machine learning to improve patient classification, we could learn about marker relationships that translate to established clinical techniques in the future. The potential is enormous.”

Toward precision medicine

Knowledge about tumor biology could be translated to patients in the future, Bodenmiller observed. “I see enormous potential for Imaging Mass Cytometry in assessing immune checkpoint inhibitor status and deregulated cellular pathways and determining best treatments for precision medicine. With Imaging Mass Cytometry, we gain an unprecedented detailed view of tissue biology. We have the ability to determine T cell status and which markers they express, suggesting how to modulate them within the tumor ecosystem. Our ability to comprehensively measure tumor tissues—from single-cell pathways to cell-to-cell interactions and tissue morphology—will reveal which aspects of the tumor ecosystem to target.”

Bodenmiller envisions the development of applications on the Hyperion Imaging System that could potentially optimize clinical workflows. A comprehensive understanding of tissue biology stands to benefit the development of new treatments and decisions about the right drug for the right patient. He contends that such precision medicine is close at hand. Using an example of analyzing melanoma samples with highly activated signaling pathways, he noted that it would be possible to target those pathways with appropriate inhibitors.

Nature Methods on histoCAT

In September 2017, the Bodenmiller Lab’s Denis Schapiro, Hartland Jackson and the UZH team published a Nature Methods paper describing the computational toolbox they call histoCAT™ (histology topography cytometry analysis toolbox) and documenting the processing of Imaging Mass Cytometry data to analyze cellular phenotypes and spatial relationships of these cells with neighboring ones. Fluidigm recently announced a distribution agreement with UZH to offer histoCAT software for high-parameter tissue analysis.

Bodenmiller expects other publications using histoCAT software analysis to follow: “We plan to show how to relate these Imaging Mass Cytometry results to clinical data, and in an additional extension of histoCAT, how we can analyze three-dimensional representation of tumors.” Advancing from two-dimensional tissue representations to reconstructing three-dimensional tumor volumes will provide an even more comprehensive understanding of cell-to-cell interactions.

Challenges and successes

There are many obstacles ahead in immuno-oncology research. We can answer more questions faster with Imaging Mass Cytometry to achieve our common goals for better human health, but developing this remarkable technology calls for the power of committed and engaged researchers focusing together on that shared objective. As with other big data, data-driven medicine analyzes massive sets of medical records and images for patterns that can reveal previously hidden connections to advance learning and inform researchers’ decisions. The answers are out there, and the tissue samples are out there, Bodenmiller concludes.

Bodenmiller reiterated the value of Imaging Mass Cytometry using the powerful Hyperion Imaging System and the potential it holds for researchers studying human health and disease. “My lab is part of the Cancer Research UK (CRUK) Grand Challenge. Imaging Mass Cytometry is one of the main pillars of this whole project with regard to comprehensively measuring the tumor ecosystem,” he said. “It is a Grand Challenge because the grant aims to comprehensively map a high number of tumors on a molecular and cellular level in 3D. About 10 labs in the US, UK and Switzerland are involved, and we’re attempting to build the whole pipeline and data analysis to enable this.”

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