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- How can spatial proteomics be implemented in clinical research settings?

# How can spatial proteomics be implemented in clinical research settings?

RareCyte’s Dr. Tad George on moving spatial proteomics from discovery to the clinic, and why single-round, 20-channel Orion™ imaging is built for the cohort-scale studies clinical research needs.

Dr. Tad George, RareCyte’s Senior Vice President of Biology R&D, answers the title question directly: spatial proteomics reaches clinical research when a discovery-scale, information-rich high-plex study is distilled into a smaller, validated targeted-protein panel and run across large cohorts tied to clinical outcomes. Getting there, he explains, takes access to high-quality cohort samples, IHC-validated reagents, enough throughput at a workable run cost, a quantitative-analysis strategy that pairs pathology with computation, and an uncompromising emphasis on data quality. His argument for how that plex-and-throughput demand is met at clinical scale is Orion: single-round staining with directly conjugated dyes plus a scanner that resolves 20 channels in one pass, so high-plex panels can be carried through cohort-sized studies without the cyclic stain-and-strip bottleneck.

In this video:

- Discovery maximizes info with high-plex RNA and protein; clinical drops RNA to validate leads at protein level, since protein expression dictates phenotype and function.

- Bioinformatics on high-plex data defines a minimal biomarker set; each clone validated by gold-standard IHC and immunofluorescence, then locked on retrospective control tissue.

- Clinical implementation needs large outcome-linked cohorts, validated reagents, throughput at a workable run cost, QC infrastructure, and a pathology-plus-computation workflow.

- Recurring hurdles: shifting from data quantity to quality, from nucleic-acid to harder, costlier antibody detection, resisting over-plexing, and avoiding an analysis bottleneck.

- Multiplex immunofluorescence suits translation: subcellular antibody-fluorophore imaging resolves ~5 to 20 microenvironment markers, beyond three or four a microscope separates.

- The game-changer: single-round staining with direct conjugates plus a 20-channel scanner reaches clinical-scale throughput that cyclic stain-and-strip cannot.

Full transcript

Laura (Technology Networks): Welcome to Shaping Science, a collection of videos from the voices that are shaping science today. I’m Laura from Technology Networks, and today we’re joined by Dr. Tad George, Senior Vice President of Biology R&D at RareCyte. In this episode, we’ll explore how spatial proteomics, including multiplex imaging technologies like RareCyte’s Orion platform, are transforming translational research. So, without any further ado, let’s get into the science. Hi Tad, thanks for joining us today. It’s great to meet you.

Dr. Tad George (RareCyte): Hi Laura, you as well.

Laura (Technology Networks): So, today we’re talking about Orion, RareCyte’s high-plex, high-resolution imaging platform designed for spatial proteomics. To begin with, how does Orion fit into today’s multi-omics workflows, and what advice would you give researchers just starting out with spatial proteomics?

Dr. Tad George (RareCyte): Sure. That’s a big question, and it’s pretty relevant these days, because there has been a lot of discovery work done with spatial transcriptomics, and there has actually been some difficulty translating that to the clinic. If we back up a little and talk about spatial biology discovery activities, they’re typically focused on identifying candidate protein targets that impact health status through their influence on the density and functional activity of cells within microenvironments.

When you’re trying to move spatial biology into the clinic, discovery-level data is typically obtained from really information-rich data sets, either high-plex RNA or high-plex protein studies, executed on low-volume studies where the emphasis is on maximizing information content per sample. Sometimes people are even doing multi-omics in that space, integrating spatial transcriptomic data with more targeted proteomics data. When you transition, you typically leave RNA behind, because RNA expression reflects functional potential, but protein expression actually dictates the actual phenotype and function, which is more important when you’re doing clinical studies. So the multi-omics approaches are often focused on validating discovery-level leads revealed by RNA assays with multiplex proteins for translational applications.

Discovery work is often upstream, to reveal clinically relevant protein biomarkers that need to be validated at the protein level, and in the clinic you need to do that on a large cohort study. It’s more common for researchers to validate the transcriptomic hits by IHC, and then build a multiplex Orion panel to process large cohorts of data tied to clinical outcomes, to derive those clinically relevant spatial biomarkers.

Translating those discovery-oriented data into protein biomarkers with actual prognostic or therapeutic value involves transitioning to more targeted protein panels, in studies where clinical relevance, data quality per sample, run cost, and throughput are paramount. If you’re going stepwise into this, you first need to decide which cell types and states you want to analyze and develop a quantitative analysis strategy, and both of those things drive the selection of a smaller set of biomarkers for the panel. A common method is to use the bioinformatics on those high-plex data sets to determine a minimal set of biomarkers that adequately reveal what’s going on in the microenvironment, in order to separate the populations and states relevant to the pathology being studied.

That means you have to develop and validate the reagents used in a panel with control tissues, which first involves identifying clones for each biomarker that provide the expected staining results in a gold-standard method like IHC. Then you validate that the biomarker expression detected by immunofluorescence matches the IHC pattern. Once that’s done, you validate the multiplex panel to make sure it adequately resolves the cell types and states in the microenvironment you want, which is usually done with a small pilot study, typically with existing retrospective samples that have been archived with known clinical status and outcome relevant to the trial you expect to apply the panel to. It also involves making sure your analysis strategy correlates with the expected results. The last step is applying that validated panel to preclinical testing or clinical trials, which involves coming up with that microenvironment-level quantification in really large, well-designed studies with spatial biology results tied to clinical outcomes, using throughputs and turnaround times that can get through those levels. So that’s a long answer, but that’s what came to mind.

Laura (Technology Networks): Great. Building on that foundation, how do researchers take spatial biology discoveries from the lab to the clinic?

Dr. Tad George (RareCyte): Anytime you transition toward the clinical, you’re moving to a place where a lot more rigor and operationalization is required, and you also need access to the right samples and expertise. If I were to think of the big ones, first is access to high-quality samples that are part of large, well-designed cohorts tied to clinical outcomes. A lot of people just don’t have access to those samples or studies. These types of studies include prospective preclinical testing and clinical-trial studies, in drug trials for example, as well as retrospectives, where a lot of times there are tissue banks with known clinical outcomes and you may want to do some prognostic work. But those samples need to be available, they need to be high quality, and you need a large number of them to get statistical rigor, because you’re typically looking for Kaplan-Meier-curve-level data.

Another one is that, if you have access to the samples, you also need access to and successful deployment of the appropriate technology and expertise to get through the data. On the technology side, it’s things like throughput, run cost, access to validated reagents, and a workflow that enables operationalization of large cohort studies. On the expertise side, you typically have to integrate the pathologist’s spatial and medical knowledge with the data scientist’s computational and statistical knowledge, finding a way to make those two types of expertise flow together and interact with one another. These are things you’re not necessarily doing as much on the discovery side; you should, but it’s not required.

Another one is moving from an emphasis on data quantity, which is more on the research side, to data quality. You start stressing things like reagent and panel validation, dealing with lot-to-lot and run-to-run variation, and putting a QC infrastructure in place to make sure your tissue quality is fine and the staining and scanning runs performed well. There’s an emphasis on accuracy and reproducibility, work that’s not necessarily going to get you a paper but is really necessary to give you confidence that the results are reliable.

You also have to decide which biomarkers you’re going to put in the panel, and this is downstream of the more discovery-oriented data sets derived from higher-plex studies, because once you’re doing super-high plex, the cost and throughput go way down. Sometimes you’re moving from an RNA- or nucleic-acid-based world to protein detection. Even though they’re both difficult analytes to detect, the development of reagents for detecting linear sequences like DNA and RNA with complementary sequences is far more straightforward than what’s required for antibody-based detection of tertiary epitopes or shapes that are perturbed by fixation and antigen retrieval in sample processing. The antibody reagents, on a per-analyte basis, are also much more expensive than a sequence you can synthesize with a sequencer. So multiplex protein detection, which is typically what you’re trying to do in the clinic, requires empirical determination of what ensemble of clones reveals the expected expression pattern with a common sample-prep and antigen-retrieval protocol, and you have to be prepared to deal with that reality on price.

Oftentimes the elephant in the room is developing an effective quantitative analysis strategy, so that even if you can operationalize high-throughput staining and imaging, you don’t bottleneck the trial when you can’t turn those results into a meaningful quantitative result. Those would be the big things that are part of that obstacle course, the hurdles you have to run through if you’re trying to go into that translational clinical space.

Laura (Technology Networks): Of course, translating discoveries comes with its own challenges. What are the biggest hurdles researchers face when bringing spatial biology into clinical or translational settings?

Dr. Tad George (RareCyte): It does dovetail with those hurdles; basically the advice is how to get through them. The cornerstones for success in applying spatial proteomics in the translational or clinical setting are an emphasis on data quality, the right throughput, panel flexibility, favorable cost, and workflow convenience. A lot of these work together. Throughput and cost work together, because the faster you can run at a reasonable cost, two things happen: more users at the site have access to the technology, which is really important for a multi-user core-facility setting, so even if you’re more on the research side, if people can afford to use the system, you’ll get more and more data even on small cohort studies; and if you’re applying a larger cohort study, you need to be able to afford the reagents for the study, and you want to make sure the throughput is commensurate with the size of the study.

Throughput is also affected by plex level, ease of panel design (you don’t want a bottleneck where it takes a year to develop your panel), speed of sample processing, imaging, and analysis, and access to what I’d call workflow convenience and parallelization. When you get to high-throughput studies, you typically want a pathology-oriented workflow where the staining is done in parallel to the imaging and analysis. I might have samples coming in from a prospective trial this week, and I’m going to stain those, but I’m already going to be scanning stuff from last week. I don’t want to wait for the scanning to finish before I start my staining and imaging. So you start to have that clinical sample-testing workflow people are used to, where they’re staining in one area while scanning is happening in parallel.

Getting back to the plex level, it’s really important to optimize the plex level required for the application and not over-plex, because if you have a bunch of irrelevant biomarkers, it’s just going to bog down your throughput and your cost. On panel design, it’s typically performed first by validating individual antibody biomarker performance on the platform in comparison to IHC, so ideally you have access to reagents or predefined panels you can build from to begin with; otherwise you have to make all your own reagents, which can slow you down. We alluded to the complexity of spatial biology quantitative analysis; it’s always good, if you’re getting into the space, to either know how to do it or have access to good computational resources like computers, software, and experts. And finally, you can’t compromise on data quality, because all of your quantitative insights are built on the foundation of the underlying data quality. That’s impacted by the level of sample integrity you can maintain through the staining and imaging steps, and by the capabilities of the scanning system: is it sensitive enough, does it have the proper image quality and dynamic range. And people always forget this: the level of reagent validation is the foundation. So no matter what platform you’re looking at, be prepared to have validated reagents, and don’t skip that step.

Laura (Technology Networks): Despite these challenges, the tools themselves are incredibly powerful. Why is multiplex immunofluorescence such a powerful tool for translational research?

Dr. Tad George (RareCyte): Translational and clinical applications require that spatial resolution at the cellular level within microenvironments, so in that case you’re typically going to gravitate toward imaging-based technologies that can resolve more than one protein biomarker at a time. Immunofluorescence is a very common and powerful technique that falls into this space. What immunofluorescence means is detecting biomarkers with antibodies, which is the immuno part; they’re either directly or indirectly conjugated with fluorophores, which is the fluorescence part; and in tandem with imaging systems that can sensitively detect that fluorescence with subcellular resolution, that gives you the spatial knowledge. Multiplexing is enabled because the fluorophores have different absorption and emission profiles and the scanners are developed to resolve those. On normal microscopes you can typically do three or four different markers in a single scanning round. So it starts to push you toward the ability to do the things we’re talking about, where you need maybe 5 to 15 or 20 different biomarkers analyzed in spatial context, and multiplex immunofluorescence has that potential. That’s why it’s used in translational work.

Laura (Technology Networks): With such powerful tools, the ability to capture more information at once takes research to the next level. Orion can capture 20 channels in a single scan. Why is that such a game-changer?

Dr. Tad George (RareCyte): Following up on multiplex immunofluorescence, traditionally fluorescence overlap limits the number of biomarkers you can simultaneously detect on normal scanners to somewhere around three to five, which is far below the typical range of 10 to 20 markers really needed to adequately resolve the cell types, states, and biomarkers in the tissue microenvironment. So you have this challenge. Prior to Orion, this led most technology advances down the road of cyclic staining and imaging, where you would iteratively stain for two or three markers, image them, remove that signal, stain for the next three, and so forth. That is quite good at developing sufficient plex to get to those spatial answers, but it is a dead end for the throughput required for translational, clinical, large-cohort studies.

So the Orion imaging approach: we had to invent a device that would expand the number of markers imaged in a single round up to 20. We also made the staining a single step, without any amplification. That platform, single-round staining with direct conjugates combined with a scanner that can resolve 20 channels of fluorescence in a single scan, really unlocks the throughput required at the plex required for clinical and translational applications. That was not easy to do; it is a combination of instrumentation, software, and reagent advancements that worked with the sensitivity, specificity, and data quality required for translational applications.

On the reagent side, we had to screen multiple fluorophores to find ones that were bright, photostable, and had the spectral spacing that maximized sensitivity and specificity. They were also chosen specifically for ease of labeling, to make sure the labeling didn’t perturb the antibody’s performance, so that the IHC result could always be reproduced in immunofluorescence, and also so that you wouldn’t be fully dependent on our company for making the reagents; it’s actually easy for you to make your own. When you’re thinking about reagent infrastructure, you have to think about how people are going to have access to the validation and end products. We used the spectrum of the fluorophores we screened to source a laser box optimized for those fluorophores, so the system has multiple high-powered laser excitations on board for sensitivity, with proprietary tunable narrow-band emission filters centered on the ArgoFluor peaks. Then we had to come up with an automated extraction algorithm to remove the residual spectral overlap, which you’re not going to get away from with 20 channels in a round.

So the game-changer part is that 20 channels in a single round is the sweet spot for the plex you need, but gives you the throughput, run cost, simplicity, and data quality required for those clinical and translational applications.

Laura (Technology Networks): Beyond its capabilities, it’s also about usability. What sets Orion apart from multiplex imaging platforms, both in performance and ease of use?

Dr. Tad George (RareCyte): Why don’t we start with panel design. The reagent ecosystem that’s part of the Orion world involves a couple of building blocks. One is ArgoFluor conjugates that are pre-titrated and validated for use in the Orion system. We have over 500 products available spanning nearly 200 biomarkers in the human and mouse space that you can buy directly from us, already validated to work in multiplex using the protocol that works for the Orion system. I can’t overstate the importance of that, because there are lots of clones that work, but they all need to work together as an ensemble and as an entire portfolio. It’s not ideal if 10% of our conjugates work well at low pH and another 20% work at high pH, so that if you want to put them together in any order you’re on your own to change the antigen retrieval. All of these reagents are pre-titrated to work on the Orion with the antigen-retrieval condition we use all the time.

We also have off-the-shelf panels for both human and mouse as opening building blocks. Internally we’ve developed probably over 250 custom panels, for our services group as well as working with our customers, so it’s very easy to develop panels, and those can be used as building blocks for new panels. We sell the dye kits so you can label your own reagents for custom biomarkers that don’t exist in the catalog, and it’s very easy to do; we also provide services if you want us to do it. We have a panel design tool that lets you configure custom panels for any application. So that’s ease of use on developing the panels, which is one of the major hurdles to getting into translational work effectively.

In terms of the pathology-based workflow, it has extreme convenience: staining, imaging, and quantitative analysis are all done in parallel. It’s very common for us to stain this week’s samples while we’re scanning last week’s samples. The other thing is that the quantitative analysis is done on board; once a scan is made, it starts performing the quantitative analysis, segmentation, and feature calculation while it’s scanning the next slide. So you don’t have to move all your data to network-attached storage and then use a high-powered computer for the quantitation; it’s all done on board and in parallel. The system also has an option for continuous 24/7 operation with a 30-slide autoloader, and the software is designed not only to run multiple samples unattended but to let you add and remove slides throughout the day and week as samples come in and as samples finish scanning. So you can think of the imaging system as continually generating data while, in parallel, you’re staining new samples. All of these things are important when you’re thinking about ease of use for high-volume operation.

We also keep the pathologist in the loop, so in the analysis, the segmentation, feature calculation, and classification come with the platform, and you don’t have to use ours; you can use your own pipelines, but we provide it. Our software also has hooks for the pathologist to be in the loop, where they perform ROI-based annotation to say where we need to make the analysis and do the quantitation. That’s really important for integrating pathologists into the workflow, because when you’re doing the translational clinical work, you need their expertise. It comes with a data-management system that allows remote access to scanning, analysis, and imaging, and it has lots of built-in QC reports that are useful for core facilities, CROs, and so on. So that’s the ease-of-use type of stuff.

Laura (Technology Networks): Amazing stuff. Tad, thank you so much for joining us today. It was a pleasure talking with you.

Dr. Tad George (RareCyte): Yeah, you as well. Thanks, Laura.

Laura (Technology Networks): Thank you. To learn more about spatial proteomics, multiplex immunofluorescence, and the technologies enabling translational impact, check out the links in the description. Thank you for watching. Together, we are shaping science.

Transcript reproduced from the recorded interview’s auto-generated captions, cleaned for readability: speaker labels were added, verbal filler and false starts were removed, and obvious caption artifacts were corrected (including “Argo floor” to “ArgoFluor” and “floors” to “fluorophores” where the fluorophore is meant). The speakers’ substance, and every stated quantity, is preserved. Statements of quantities, catalog figures, and specifications are reproduced as spoken by the participant and may differ from formally published values.

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