Part 2 – Multimodal Diagnostics: Integrating tissue and blood biopsies

Realizing the promise of multimodal diagnostics raises important questions: How should we think about integrating these different sources of information? Where can multimodal approaches add the most value? And what will it take to translate increasingly complex biomarker data into meaningful clinical insights?

In Part 2 of this blog series, we continue the conversation with Jorge Marques Signes, PhD, VP of Accelerator & Clinical Services at Quanterix, to explore the challenges of multimodal diagnostics, the need for standardization, and the path toward clinical adoption.

New to the series? Start with Part 1, where we explore the past, present, and future of multimodal diagnostics.

Q1: What should labs consider before integrating multimodal diagnostics into clinical workflows?

JS: Normalization is an underappreciated barrier. A multimodal result is not a measurement; it is several measurements coming off instruments that do not share units, dynamic range, batch structure, or failure modes. A concentration in picograms per milliliter and a cell density per square millimeter in an operator-defined region have no common scale and no common calibrator. You have to normalize before you can integrate, and there is nothing obvious to normalize against. Most groups run into this at the point of integration, which is far too late. The fix is unglamorous: decide the normalization strategy before the first sample is run. Consider bridging samples, shared controls, a defined batch structure, and locked analysis plan. Retrofitting it does not work.

It is also worth recognizing integration as an informatics and interpretation problem as much as an assay problem. Most pathology workflows are built around single-analyte IHC. Adding multiplex immunofluorescence or ultra-sensitive plasma testing means new validation, new instrumentation and, hardest of all to find, new interpretive expertise – all of it under existing turnaround pressure.

Sitting underneath that is the translation gap between discovery and clinical use. Research platforms are built for depth and plex; a clinical assay needs robustness and reproducibility, which usually means deliberately cutting the plex back to what has demonstrated value. That reduction step is where most multi-omic programs stall.

Q2: How can advances in pre-analytics remove some of the barriers to integrating multimodal diagnostics into clinical workflows?

JS: Tube composition, time to centrifugation, aliquot volume, freeze-thaw history; all of it moves an ultra-sensitive result. And on the tissue side, fixation time and block age do the same thing. From what we see working with customers, you resolve more variability by building the handling protocol into the phlebotomy and grossing workflow, so that the right thing happens by default, than by any change to the assay itself. It is an operational discipline problem more than a scientific one, and it is solvable.

There is a more interesting version of that answer, which is to remove the variables rather than control them. Some of the most encouraging recent work in this area has gone in a completely different direction: measuring p-tau217 and other neurodegeneration markers from a few drops of capillary blood taken by finger prick and dried onto a card, then shipped at ambient temperature. Correlation with venous plasma has been good, and the cards travel without cold chain. That is most of the pre-analytical problem I just described simply deleted. No centrifugation window, no freeze-thaw history, no cold shipping.

I would not oversell where it is. Recovery from dried spots is much lower than from venous plasma, accuracy still trails a properly handled venous sample, a meaningful share of collections fail, and the indeterminate zone is wider than most venous tests produce. It is a research method today and the groups doing the work say so themselves. But patients have collected usable samples on their own, and longitudinal sampling over months is being demonstrated. For a disease where you want to test early, broadly, and repeatedly, and where the people you most want to reach are the least likely to reach a specialist center, that direction matters more than the current performance gap suggests.

Q3: Standardization remains a recurring challenge across emerging diagnostics. Which aspects of multimodal diagnostics do you think are furthest from consensus, and what needs to happen next?

JS: Consensus is weakest exactly where the modalities have to meet. Each one is standardizing on its own terms and nobody owns the join.

On the blood side, the live issue is calibration. Independent head-to-head comparisons of the commercial plasma p-tau217 assays have found real differences between them, with manufacturer-recommended cutoffs performing inconsistently when applied to cohorts the manufacturers did not develop them in. Other work in community populations has found broadly similar discrimination across assays, so I would put it carefully: several of these tests separate cases about equally well, but the absolute values and thresholds do not transfer between platforms.

That is a calibration problem, and those have known solutions. CSF amyloid went through exactly this and came out of it with certified reference materials and reference measurement procedures. Plasma p-tau does not have that infrastructure yet. Building it would be the highest-value standardization work anyone could fund right now.

On the spatial side, the gap is the reporting unit. Immune cell density, co-localization, margin infiltration; all of them depend on how the region of interest was drawn, and region selection is still substantially operator-dependent. Two laboratories running the same panel on the same block can report different values for entirely defensible reasons. That is harder than analytical imprecision, because there is no single right answer to converge on. Though I would push back on the idea that it is intractable.

The CIMAC-CIDC network under the Cancer Moonshot has shown you can get meaningful cross-site agreement on immune cell densities using harmonized clones and shared protocols. The engineering works. What is missing is agreement on which outputs to standardize in the first place.

Then there is the layer nobody owns. Suppose you standardized the plasma assay completely and the spatial assay completely. You would still have no agreed way to put them together into one interpretable result. There is no reference material spanning blood and tissue, no accepted framework for putting a concentration and a spatial density on the same footing, and no consensus on what an integrated report should even contain.

Q4: How would you prioritize the standardization gaps?

JS: My order of priorities would be reference materials and reference methods for the circulating markers first; then minimum reporting standards for spatial assays, covering how regions are defined and how the analysis version is recorded; and only then a harmonized output format. The temptation is to skip to the third and build integration on unstandardized inputs. That is how you get results that publish well and do not reproduce.

Q5: What changes are still required to support broader clinical uptake?

JS: When it comes to evidence, strong performance in a single research center is no longer enough. What laboratory directors want to see is reproducibility across institutions, across different instrument platforms, and across the types of specimens routinely found in clinical practice. That means proving performance not only on fresh, well-preserved samples, but also on archived tissue with varying fixation histories and years of storage behind it.

On regulation, the asymmetry between the two modalities is striking. Blood-based neurodegeneration markers have gone through 510(k) clearance with intended-use language that distinguishes rule-in from rule-out, and specialty care from primary care. Spatial assays have no comparable route in routine diagnostics. The precedent that exists is the tissue-based companion diagnostic developed alongside one specific therapy, which is narrow, expensive, and does not generalize.

Yet the factor that may have the greatest influence on adoption often receives the least attention: reimbursement. Blood-based Alzheimer’s testing is beginning to gain commercial payer coverage, and notably, many of the emerging coverage frameworks are based on clinical performance and use case rather than a specific branded product. That model has the potential to accelerate adoption across the field.

Spatial technologies face a different reality. Most applications remain research-focused, with testing costs largely absorbed by laboratories or supported through research funding. As a result, adoption is concentrated in academic medical centers and specialized research environments.

What breaks that is studies designed for clinical utility rather than analytical performance. Show that the result changes a decision, and that the changed decision improves an outcome. Guidelines committees and payers have been explicit that this is what they want. Comparatively few multimodal studies are built to answer it, because it is slower and more expensive than a correlation study. That choice gets made at protocol stage, and it largely determines whether an assay ever reaches patients.

Putting Multimodal Biomarker Science into Practice:

Turning the promise of multimodal diagnostics into meaningful insights requires more than combining the different types of data, it requires the right technologies, expertise, and translational capabilities. As a leader in precision biomarker science, Quanterix brings rigor, translational expertise, and innovative technology across both liquid and tissue biomarker modalities to help advance this broader view of health and disease biology.

By supporting both liquid and tissue biomarker analysis through clinically aligned workflows, we help programs advance from biomarker discovery through clinical development and implementation, while creating opportunities to generate and connect insights across modalities. Explore our liquid and tissue Accelerator Biomarker Services to learn how Quanterix can support your discovery, translational, and clinical research programs.