Unlocking Topographic Biomarkers

The interactive histoCAT toolbox unifies the cytometry and imaging communities

Embarking on the launch of a landmark technology designed to reveal unprecedented biological insights and improve the future of health care, we at Fluidigm are pleased to collaborate with scientists at the birthplace of Imaging Mass Cytometry™, the University of Zurich (UZH). Computational biologist Denis Schapiro of the Bodenmiller Lab talked with us about developing the data analysis software distributed with the Hyperion™ Imaging System. Here he shares what he’s learned in recent years as a pioneer in the emerging field of Imaging Mass Cytometry, and how the technology stands to impact life sciences by integrating cellular biology with molecular and spatial analysis.

Schapiro joined the Bodenmiller Lab to build a computational pipeline for Imaging Mass Cytometry, which was first featured in a published paper in the March 2014 issue of Nature Methods. He became the main developer of the histoCAT™ (histology topography cytometry analysis toolbox) software for analyzing highly multiplexed tissue data such as that produced by the Hyperion™ Imaging System, which relies on IMC™. The software has emerged as a key component of the technology.

In his August 2017 Nature Methods paper on histoCAT design and application, Schapiro and co-first author Hartland Jackson demonstrated how they analyzed 49 breast cancer samples and identified cellular social networks associated with tumor grading. Researchers see different topographic structures in Grade 1 patients versus Grade 3. By quantifying patterns of interaction in the context of clinical outcomes, histoCAT could improve precision medicine applications across experimental cohorts for a range of diseases in the future. Doing so requires looking at both the composition of cell types and their spatial interactions.

“Now it’s possible to identify different cell types—potentially even all cell types—in a tissue and their exact location. You can then go beyond exploring which cell types are present in these tissues to ask questions about their signaling and functional states, and how the cells communicate.”
—Denis Schapiro, Bodenmiller Lab, University of Zurich

histoCAT workflow

Essentially, histoCAT combines imaging data with cytometry. The imaging community is trained to segment, annotate and analyze imaging data. It characterizes millions of cells using spatial features, but because of limited multiplexing capabilities has been unable to identity the cell lines or types in the tissue. In contrast, cytometry researchers have mastered single-cell analysis using scatter plots, tSNE (t-distributed stochastic neighbor embedding) visualizations and associated gating tools to investigate highly multiplexed CyTOF® datasets and identify cell types or phenotypes. But they have lacked spatial information.

Imaging Mass Cytometry multiplexing enables identification of many cell types in a tissue. “We know where the tumor cells and immune cells are located, and we know what cellular state they represent,” Schapiro said. “histoCAT unlocks this information so we can interactively select cells and ask questions about whether certain cell types and locations correlate with specific clinical information or outcomes. This could lead to the discovery of a new class of biomarkers: topographic biomarkers.”

“We designed histoCAT as a unique tool for combining cellular phenotypes with spatial information,” Schapiro said. “You can identify individual cells of interest on a scatter plot and visualize them directly in their spatial context. Alternatively, it’s possible to select cells or structures in their spatial context and visualize those in scatter plots and other single-cell visualizations. We call this interactive workflow between cytometry and image analysis the round-trip.

“Additionally, you can capture the tissue architecture quantitatively through permutation testing to see how the structure differs from a random cell type distribution,” he said, offering examples of using histoCAT in the context of immune infiltration and stroma-to-tumor interactions.

There are considerations, Schapiro observed: Strong and abundant membrane markers are key to successful segmentation, and thus to data analysis and interpretation. Schapiro and co-author Peter Schüffler have shown that Imaging Mass Cytometry enables improved segmentation by providing multiple membrane-associated markers through multiplexing (Schüffler, Schapiro et al., Cytometry Part A, 2015).

He offered this advice to new users: “Imaging Mass Cytometry is the right tool if you have a heterogeneous system with a lot of hidden spatial information. With histoCAT and other open source tools, you can do preliminary analysis leading to exciting results. With the right question, you can then follow up with more advanced tools and computational collaborations.”

histoCAT applications

Schapiro expects histoCAT to benefit scientists exploring a range of health and disease applications including histology, digital pathology and translational research. The capability of IMC to measure abundant information on a single cell with spatial resolution makes it possible to identify patterns in immuno-oncology and diseases on a single rather than consecutive sections. “The amazing part of developing histoCAT is that people can use it to do things I haven’t even thought of yet.”

He described how IMC and histoCAT support his own research: “What I value most about the Hyperion Imaging System technology is getting multiplexed spatial measurements. Now it’s possible to identify different cell types—potentially even all cell types—in a tissue, and their exact location. You can then go beyond exploring which cell types are present in these tissues to ask questions about their signaling and functional states and how these cells communicate.”

He plans to extend the methods he helped develop for dozens of samples to analyze hundreds or even thousands of samples for even deeper clinical research insights. He’s enthusiastic about optimizing the data analysis pipeline to answer questions such as how to analyze data efficiently with thousands or even 100,000 samples, and how to learn from this data. How do we normalize and segment the data and correct for errors? “I’m excited to focus on this cutting-edge technology,” he said. “We need to step-by-step continue to improve it, and then work with the broader community to make it even better.”

Developing histoCAT represents a significant step in advancing the potential of mass cytometry to deeply interrogate tissue samples. “That is the dream: histoCAT combines the immune system knowledge of the cytometry community with the pattern knowledge of pathologists and histologists, all in one software,” Schapiro said. “This is growing into a great community with so many new ideas. There will be lots of possibilities for collaborations.”

Supported by the Swiss National Science Foundation and a UZH BioEntrepreneur-Fellowship, Schapiro continues to work on multiplexed imaging data analysis as an Independent fellow affiliated with Harvard Medical School and the Broad Institute at MIT and Harvard. “The next step is to combine single-cell multiplexed imaging with single-cell transcriptomics.”

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