Many spatial biology researchers rely on two-dimensional tools, which section the cellular architecture and processes occurring within 3D tissues into flat layers. Three-dimensional multiomic analysis offers a more accurate and comprehensive view, illuminating the structural and molecular context of cellular behavior. The shift to 3D is opening new avenues in disease research, drug discovery, and systems biology.
Todd Dickinson, PhD Chief Executive Officer Stellaromics
In this Innovation Spotlight, chief executive officer of Stellaromics Todd Dickinson discusses the company’s mission to enhance spatial biology research with the Pyxa™ platform. By providing a 3D view of cellular architecture in thick tissue samples, the technology reveals spatial gradients, structural layering, and long-range interactions invisible to 2D methods.
Why is 3D multiomic analysis so important for understanding biological processes?
Biological systems do not operate in two dimensions; cells form intricate networks, tissues fold into complex architectures, and signals often travel across long distances. Traditional 2D spatial biology methods, while powerful, capture only a thin slice of this complexity. That means critical information about how cells interact within their full microenvironment can be missed entirely.
Stellaromics was founded on a simple but transformative idea: biology is inherently three-dimensional, so the tools that scientists use to study biology should reflect that. Despite recent advances in multiplex spatial biology methods, researchers have been constrained to analysis of thin tissue slices and 2D data, which flatten biology’s complexity. Our vision is to unlock the next dimension of spatial biology by giving scientists the ability to study intact, thick tissue structures in 3D with true subcellular resolution and multiomic readouts.
In short, 3D multiomics allows scientists to study biology in its true native form, unlocking insights into tissue organization, disease progression, and therapeutic response that simply are not accessible with 2D methods.
How does your platform compare to existing spatial biology tools?
One of the biggest challenges with traditional 2D approaches is that reconstructing tissues from thin serial sections can distort delicate structures, lose cellular layers, and place a huge computational burden on labs trying to stitch hundreds of images into a 3D model. Those limitations are inherent to systems that were designed for 2D and then adapted to approximate 3D.
The Pyxa system was designed differently from the start. Instead of relying on reconstructions, it captures biology directly in intact, thick tissue samples, preserving the true architecture and cellular context. By integrating our proprietary STARmap chemistries1 with advanced optics and fluidics, Pyxa enables researchers to profile both gene expression and translational activity at subcellular resolution across large tissue volumes.
In practice, the two approaches work hand in hand: 2D methods map broad landscapes, while Pyxa delivers a three-dimensional, multiomic deep dive into the most biologically important regions. Together, they give researchers a more complete view of biology than either approach could achieve alone.
In this way, we see Pyxa as highly complementary to existing 2D platforms. Thin-section approaches remain powerful for whole-transcriptome discovery or for analyzing very large tissue areas at lower resolution. Pyxa builds on those insights by allowing researchers to zoom in on intact structures in 3D, adding resolution, depth, and multiomic context that 2D methods simply can’t provide.
Just as importantly, we’ve worked to make the workflow seamless and accessible. Pyxa automates tissue prep, imaging, and analysis, while the analysis software, PyxaStudio, provides an intuitive, interactive 3D environment for exploring data without the need for a dedicated bioinformatics team. The result is a platform that doesn’t just add another dimension to spatial biology, it makes 3D multiomics practical, reproducible, and scalable.
How have researchers used Pyxa to unlock insights that other platforms may have missed?
The most exciting part of my job is seeing how researchers use Pyxa to answer questions that were previously impossible to ask. One compelling example comes from the field of neuroscience. Using tissue sections up to 100 microns thick, scientists can now visualize neural circuits, which span multiple cell layers and are easily missed or destroyed when utilizing thin, 2D slices. Combining 3D spatial transcriptomics with single-cell RNA sequencing datasets has enabled the creation of a brain atlas and cellular mapping across 3D neural networks, including rare cell-to-cell connections often missed in 2D analyses.2
In immuno-oncology, 3D spatial transcriptomics has been used to detect rare cell-cell communication events that drive resistance to immunotherapy.3 By using single-cell and spatial analysis, scientists can detect subpopulations of treatment-resistant cancer cells before they become dominant in a tumor. Being able to identify these rare, resistant cells in 3D, where you can capture even more of the tumor microenvironment, could help us get ahead of treatment failure or relapse, and identify early signs of disease recurrence.
How do scientists using Pyxa analyze their data?
One of the biggest hurdles in spatial biology has always been the data analysis. With 2D methods, researchers often generate hundreds of thin tissue sections that need to be digitally stitched together into a pseudo-3D model—a process that is computationally heavy, prone to artifacts, and requires significant bioinformatics expertise just to make the data usable.
With Pyxa, we take a different approach. Because the system captures biology directly in intact 3D tissue, PyxaStudio starts with the true structure preserved, no reconstruction required. The software lets researchers interactively examine the 3D architecture of their sample, run automated pipelines for segmentation, clustering, and spatial pattern detection, and then easily export data for deeper downstream analysis.
Importantly, data from Pyxa is fully compatible with many of the open-source tools scientists already use, making it easy to fold 3D data into existing computational workflows.
Stellaromics’ platform combines STARmap chemistry, volumetric confocal imaging, and automated workflows to deliver high-resolution, 3D multiomic data from intact tissue samples.
©iStock, 3d_kot
This compatibility has been a priority for us, because accessibility and interpretability are as important as innovation. Our goal is not just to deliver high-quality 3D multiomic data, but to make sure it integrates seamlessly with the analysis pipelines researchers already know and trust.
As spatial biology techniques continue to evolve, where do you see Stellaromics playing the biggest role in shaping biomedical research and drug discovery?
Looking ahead, Stellaromics aims to be a foundational tool at the intersection of spatial biology, drug discovery, and clinical research. Our team is deeply driven to improve human health, and we believe the ability to generate high-resolution, 3D spatial multiomic data from intact tissue samples will be transformative, particularly in understanding complex diseases such as cancer, autoimmunity, and neurodegeneration. In drug discovery, for example, Pyxa can provide novel insights into drug targeting and efficacy in the context of cell and gene therapy. By enabling researchers to visualize the precise location of a drug and its effect on local gene expression with subcellular localization, our technology can help optimize therapeutic delivery and identify new targets with greater precision.
We are excited to see more meaningful integration of multiple omics layers, combining methods including epigenomics, proteomics, and metabolomics with transcriptomic data. We will even be introducing an entirely new data type—spatial translatomics—with our RIBOmap assay, which maps actively translating mRNAs. Together, these technologies provide a unified view of gene expression and protein translation within a 3D context, offering a more complete picture of cellular function and interaction than ever before.
