Tad George is the senior vice president of biology at RareCyte, where he has spent eight years collaborating with the engineering team to develop products like the Orion technology for spatial biology.
Credit: Tad George, RareCyte.
Spatial proteomics is a powerful tool in biomedical research, enabling scientists to study the spatial distribution of proteins within tissues to gain deeper insights into cellular function and disease mechanisms. By mapping protein expression and localization in situ, spatial proteomics can reveal critical information about tumor microenvironments, immune cell interactions, and treatment responses that traditional bulk analyses cannot capture.
Despite this, integrating spatial proteomics into research workflows presents several challenges, including the need to balance high-plex detection with throughput, data quality, and cost efficiency. Drug Discovery News spoke with Tad George from RareCyte to discuss how the Orion™ platform is addressing these challenges by advancing imaging capabilities, streamlining panel design, and enhancing data processing to support both discovery research and large-scale translational studies.
What are the biggest challenges researchers face in spatial proteomics today?
Spatial proteomics, which involves analyzing multiple proteins using imaging technology, has enormous potential to advance science and medicine. This is because the spatial arrangement and functional state of heterogeneous cell types within tissues significantly impact patient health. Cell phenotype and function are largely determined by the expression of diverse proteins, making spatial proteomics a crucial tool for understanding disease mechanisms.
However, several challenges hinder its broader adoption in research and clinical settings. To deliver reliable, actionable data, spatial proteomics technologies must balance conflicting requirements: high-plex protein detection, high-resolution whole-slide imaging, and practical running costs and throughput. Additionally, reliable results require targeted detection using tissue-specific antibody panels, demanding both the flexibility to selectively choose antibodies and the capability to validate their performance across platforms and tissue types.
These challenges have limited the widespread application of spatial proteomics in research and rendered it impractical for large cohort studies, which are essential for clinical and translational research.
What major technological advancements have improved imaging tools for spatial proteomics?
While much of the recent progress in spatial proteomics has centered on chemistry and workflow optimization, significant advancements have also been made in imaging technologies. The Orion imaging approach is a great example of this, enabling the simultaneous detection of a significantly larger group of protein biomarkers in a single staining and imaging round1. This capability is crucial for achieving the high-plex throughput necessary for translational research. This was made possible due to a combination of innovations across instrumentation, software, and reagents that enable the collection of 20 data channels per imaging round.
On the reagent side, achieving high-plex staining in a single round is most effective using antibodies directly conjugated to fluorophores, minimizing the risk of cross-reactivity typically associated with secondary antibodies or amplification systems. The Orion system uses ArgoFluor® dyes specifically selected for their brightness, photostability, and optimal spectral spacing, ensuring sensitive and specific detection of each biomarker. Additionally, these dyes are designed for seamless labeling, maintaining immunofluorescence (IF) performance that is consistent with the IHC-validated results of the unlabeled antibodies.
The Orion instrument further enhances detection through high-powered laser excitation paired with proprietary, tunable narrow-band emission filters centered on ArgoFluor peak emissions. This setup maximizes sensitivity while minimizing spectral overlap. Finally, the Orion software incorporates an advanced automated extraction algorithm that isolates biomarker signals by removing spectral overlap caused by autofluorescence and fluor-derived crosstalk, effectively producing independent data channels for each biomarker.
The Orion Spatial Platform is a benchtop, high-resolution, spectral imaging instrument that integrates quantitative immunofluorescence and brightfield imaging.
CREDIT: Rarecyte
How does RareCyte’s Orion platform differ from other cyclic imaging methods?
The biggest difference is the number of channels imaged per round, providing substantial throughput advantages while maintaining tissue integrity at higher plex. A recent review in Nature highlighted that Orion is approximately 10x faster than most cyclic platforms, fundamentally enabling spatial proteomics to be applied to translational studies and accommodating significantly more users per week in core facility settings2.
Most imaging platforms can capture only 3–4 channels per round, necessitating cyclic approaches that limit sample throughput and compromise tissue integrity. Orion, on the other hand, collects 20 channels of data per round, including a dedicated nuclear channel, an autofluorescence channel, and 18 biomarker channels. Staining for all markers is performed in a single round, dramatically increasing sample throughput while preserving tissue integrity.
Additionally, the staining process is performed independently of the scanner, typically in batches and in parallel with imaging. This workflow is ideal for clinical trial settings and multi-user core facilities, optimizing operational efficiency. Unlike cyclic systems that utilize an on-slide staining apparatus, Orion’s open-slide format allows for the scanning of large or multiple specimens per slide without physical constraints.
With all that being said, cycling is still possible with the Orion system, enabling high-plex discovery applications with a minimum of rounds. This approach is useful for defining ‘discovery-level’ panels that can inform the optimal single-round panel for translational studies or for expanding panels on previously scanned samples.
Current image processing and analysis workflows in spatial biology are often fragmented, requiring multiple disconnected steps. How does the Orion platform address this challenge?
The goal of any spatial biology experiment is to extract quantitative spatial biomarkers that can provide prognostic, predictive, or mechanistic insights. Conceptually, this process is simple: the acquired image is typically segmented into cells to derive per-cell features such as size, biomarker intensity, and location. These features are then used to classify cells into populations, which can be further analyzed within specific regions or tissue compartments to derive spatial biomarkers such as, for example, the density of a cell type in a tumor lesion post-treatment.
In practice, however, each step requires different tools and computational resources. Segmentation and feature extraction are computationally intensive and often require high-powered processing, while region-of-interest identification benefits from human analysis. When these steps are handled separately, the workflow becomes cumbersome and can hinder analysis throughput.
The Orion system streamlines the analysis pipeline by executing the computationally intensive cell segmentation and feature extraction steps directly on its integrated high-performance processing unit. As a result, each Orion scan generates a comprehensive data package that includes an ome.TIFF file, a segmentation map, and a primary data table, all of which are compatible with third party image analysis software packages for downstream classification and spatial biomarker analysis activities.
Given the growing demand for integrated multiomics approaches, how do you see Orion fitting into workflows that combine spatial transcriptomics, proteomics, and other molecular analyses?
In discovery research, upstream transcriptomics often identifies potential protein biomarkers with clinical relevance. These are then validated in a large cohort translational study. With Orion, it is common for researchers to validate transcriptomics hits through IHC, then build a multiplex Orion panel to process extensive cohorts with clinical outcome data. This approach enables the derivation of clinically relevant spatial biomarkers, bridging the gap between molecular discovery and translational research.
What advice would you give to researchers looking to adopt spatial proteomics into their workflows?
This depends to some degree on whether you want to use the system more for discovery or translational applications. Ideally the platform should handle both. Regardless of the application, it’s important to maximize data quality, throughput, panel flexibility, and favorable running costs.
Prioritizing data quality is essential, as all quantitative insights depend on it. This includes maintaining sample integrity through processing and staining, using platforms with high sensitivity and validated reagents, and being prepared to validate reagents and panels for the specific platform.
Throughput and cost efficiency are closely linked. Faster, cost-effective systems can accommodate more users and larger cohort studies, enabling deeper translational insights. It’s also important to optimize the plex level based on study objectives without over-plexing, as throughput and costs scale with plex level. Platforms that support rapid sample processing, imaging, and analysis will further maximize throughput.
Given the complexity of spatial biology analysis, robust computational resources, software, and expertise are vital for managing and analyzing the resulting data effectively. Selecting a platform that balances data quality, throughput, panel flexibility, and cost efficiency provides the strongest foundation for successful spatial proteomics integration.
References:
- Lin, J.R. et al. High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers. Nat Cancer 4, 1036–1052 (2023).
- Carstens, J. L. et al. Spatial multiplexing and omics. Nat Rev Methods Primers 4, 1–19 (2024).
