Analyzing Tumor Cell Cycle Dynamics with Advanced Multiplexing and Spatial Analysis
Dr. Wayne Stallaert is an Assistant Professor in the Department of Computational and Systems Biology at the University of Pittsburgh and a member of the Cancer Biology research program at UPMC Hillman Cancer Center. He holds a PhD in Biochemistry from the University of Montréal and performed his postdoctoral studies at the Max Planck Institute for Molecular Physiology in Dortmund, Germany and at UNC Chapel Hill.
Dr. Stallaert's discusses how selecting the right tools can help researchers uncover key insights of cellular biology using a multiplexed and spatial profiling approach.
Q&A
- How are you scaling cell cycle mapping for use in clinical research and translational settings?
- How did Abcam’s antibodies support the development of your cell cycle mapping methodology?
- How does combining automated imaging, deep learning and spatial profiling facilitate single-cell analysis of the tumor cell cycle and reveal how cells progress through it?
- What tools do you use to analyze and visualize high-dimensional cell cycle imaging data?
1. How are you scaling cell cycle mapping for use in clinical research and translational settings?
We’re working to increase the scale and automation of our entire workflow—from sample preparation to image acquisition and analysis. The Leica Microsystems’ Cell DIVE system enables high-throughput, multiplex immunofluorescence imaging of FFPE tumor samples, which significantly expands the amount of data we can collect across large tissue microarrays. We’ve also built a robust antibody library—many in collaboration with Abcam—tailored for compatibility with our imaging platform and optimized for detecting key cell cycle and tumor microenvironment markers. With automated image alignment and deep learning-based cell segmentation, we can extract rich single-cell data from hundreds of tumors efficiently. This scalability opens new possibilities for studying cancer cell proliferation and therapeutic resistance across patient samples.
2. How did Abcam’s antibodies support the development of your cell cycle mapping methodology?
The cell cycle is regulated by this complex network of effectors that regulate the progression of cells through the cell cycle in space and time. When looking for plasticity in the cell cycle, it can be difficult to know where to focus within this vast signaling network. Instead of zooming in on a specific locus of signaling, we chose to zoom out and observe the entire cell cycle as a whole.
We developed this methodology called cell cycle mapping, which aims to track all possible paths a cell might take through the cycle, including detours due to stress, and any sojourns into different arrest states. The cell cycle mapping approach starts with fixed 2D cell cultures, FFPE tissue sections or tumor tissue microarrays.
A key part of this approach is building an extensive and validated antibody library to detect the proteins we're interested in. Working closely with Abcam, we selected antibodies sensitive enough to detect biomarkers related to the cell cycle and the tumor microenvironment.
Our Abcam antibody library consists of 56 antibodies covering several core cell cycle regulators, such as canonical cyclins and cyclin-dependent kinases (CDKs). The library also includes markers for different cell types, states and extracellular signaling proteins. Using multiplexed immunofluorescence, we can measure 50 to 100 biomarkers in the same sample and even within the same cells, giving us a detailed, high-resolution view of cell behavior across the tumor landscape.
3. How does combining automated imaging, deep learning and spatial profiling facilitate single-cell analysis of the tumor cell cycle and reveal how cells progress through it?
To measure the antibodies in our sample, we use Leica Microsystems’ Cell DIVE Multiplex Imager, a platform designed for multiplexing immunofluorescence. It automates image acquisition, post-processing and alignment steps, which are typically the most challenging and time-consuming. This automation was a major reason for our choosing to adopt this technology.
Another reason we decided to go with Leica Microsystems’ Cell DIVE Multiplex imager is its compatibility with a liquid handling robot. Integrating this directly into the workflow allows automation of sample preparation and the staining and bleaching of the labeled antibodies. Automating the entire process has increased the scale at which we can acquire data. Using this setup, we generate stacks of perfectly aligned images, measuring all our antibodies of interest. We then apply a deep learning-based cell segmentation method to identify individual cells across the entire tumor microarray and quantify every biomarker in every cell across all samples. This results in a rich single-cell dataset.
Another advantage of using an imaging-based approach to acquiring single-cell measurements is that we can measure a given protein's subcellular localization and the cell’s spatial relationship. From this, we can obtain a cell cycle signature. We then apply dimensionality reduction or manifold learning techniques to project these signatures into a 2D cell cycle map, capturing the trajectory of cell cycle progression in an interpretable and interactive way. We refine these maps through feature selection to produce our final, high-resolution cell cycle maps.
Once we identify multiple cell cycle routes and programs, we can explore their mechanistic and molecular differences to understand their biological significance. We can play with this data computationally to uncover how and why these distinct programs matter in the context of tumor biology.
4. What tools do you use to analyze and visualize high-dimensional cell cycle imaging data?
In our lab, we’ve developed custom image analysis pipelines using open-source tools to process both time-lapse and multiplexed imaging datasets. These solutions are tailored to our specific research needs but can be complex for other labs to adopt. To help address that, we’ve also collaborated with Leica Microsystems by beta testing and providing feedback on their Aivia AI Image Analysis Software. If we hadn’t already invested in building our own tools, Aivia would be a strong option—it’s user-friendly and helps streamline key steps like image alignment and segmentation. We’re proud to have contributed insights that helped refine the software during development.
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Learn more about standardizing human-relevant model culture and deep characterization with cutting-edge imaging and AI-based image analysis.
Learn more about Dr. Stallaert’s research: https://www.stallaertlab.com/
Dr. Stallaert's research focuses on the intricate relationship between cells and their environment, particularly the plasticity of the cell cycle and its adaptation to changes in the environment or genome. His lab employs quantitative single-cell microscopy, machine learning, and advanced computational approaches to study how the cell cycle changes during tumorigenesis, metastasis, and drug treatment. A key interest is understanding how the tumor microenvironment controls cancer cell proliferation. Ultimately, his goal is to predict disease outcomes and therapeutic strategies by directly looking at the cell cycle programs driving the growth of a given tumor.