A biomarker can look compelling in early studies and still fall apart under further scrutiny. Changes in sample type, instrumentation or laboratory setting can expose weaknesses that were not initially apparent. A biomarker’s utility depends on whether the data remain consistent to inform development and therapeutic decisions.

This challenge has become more visible as research and development teams integrate increasingly complex technologies, including multi-omics and artificial intelligence. Most organizations can now generate more data than ever. The bottleneck has moved downstream, where teams must determine whether their evidence is sufficiently validated, controlled and traceable to support timely, high-confidence portfolio decisions.

Simply put, biomarker value now depends less on generating more signals and more on maintaining reliable, connected evidence from discovery through translation.

Reproducibility turns promising signals into decision-grade evidence

Before a biomarker can guide translational investment, it must perform reliably across the samples and analytical methods relevant to its intended use. That reliability begins with stable samples and specific reagents, supported by thorough assay characterization. These factors extend beyond the laboratory because they shape the risk attached to the next development decision.

Imaging illustrates the point. Small variations introduced during staining, image acquisition or signal segmentation can make studies difficult to compare, even when the underlying biology is similar. Standardized methods help preserve the meaning of the evidence after it leaves the laboratory where it was first generated.

“Multiplex imaging can now detect dozens of markers in a single section, but if illumination, optical alignment and analysis workflows are unstable, comparing signal intensities or marker co-localization between experiments becomes difficult.” — Amy E. McCann, Senior Director, Research Solutions, Abcam.

Start with the decision, then choose the modalities

Disease biology rarely fits within the boundaries of one measurement. Florian Eich, Director, Business Development, Life Science & Applied at Leica Microsystems, explains why the choice of modality has to be deliberate. “No single technology can capture the full complexity of disease biology. The value comes from choosing views that answer the same question and can be interpreted together.” Genomics defines DNA-level potential, proteomics measures expression and modulation and imaging reveals spatial context and phenotype.

Adding a modality does not automatically add insight. The datasets must still connect. Even technically impressive results may fail to form a coherent biological story when sample tracking is inconsistent or the underlying methods are not well characterized. Poor integration only compounds the problem.

A better approach is to work backward from the biological question and the decision the study must support. Teams can then select complementary measurements and define how the outputs will connect. This prevents unnecessary complexity and turns a multi-omics program from a collection of assays into an evidence strategy.

Fragmented workflows blur the line between biology and process

In a typical biomarker program, samples pass through multiple teams and technical environments. Every handoff creates an opportunity for critical context to be lost. If preparation records become separated from the resulting evidence, teams may be unable to determine whether a signal change reflects disease biology or process variability.

The consequences appear quickly as repeated work and slower go/no-go decisions. Cross-site comparisons become less reliable, and assay transfers become more difficult. Fragmentation also limits artificial intelligence and advanced analytics because models cannot compensate for missing or poorly governed context.

Connected workflows preserve control and context from sample preparation through measurement and analysis. Integrated data systems then keep the resulting evidence accessible and traceable. Paula Villaescusa-Sanchez, BioPharma Workflow Solutions Architect at Danaher, describes the operational payoff. “The aim is to reduce technical variability at each step so that teams can focus on biological variability and its implications for drug development.”

Design for transfer before the workflow becomes expensive to change

A workflow may appear robust when a single experienced team runs a modest study. Its true test comes when another laboratory must reproduce the same quantitative behavior. During assay development, teams should define how performance will be controlled and judged, along with the requirements for transfer. Making these expectations explicit early helps avoid slow, expensive remediation.

Eich reduces transfer readiness to a question the receiving laboratory can answer: “If another site follows this panel and acquisition protocol, will it see the same quantitative behavior?” If the answer is uncertain, the assay needs further development. Clear procedures and predefined thresholds help the receiving site distinguish assay performance issues from expected biological variation.

This early discipline does not slow translation. It keeps promising discoveries from becoming analytical dead ends and gives decision-makers clearer evidence at the next development gate.

Use automation to hold quality steady as studies grow

Once an assay is ready to scale, automation helps teams execute it consistently across a larger study. Standardized execution reduces operator-dependent variation, while faster processing adds value without weakening the evidence.

Automation also creates a reliable operational record. Each run can capture stable identifiers and the context needed to interpret analytical results. This traceability makes performance easier to monitor, drift easier to investigate and artificial intelligence-enabled analysis more dependable.

“When assays are expected to move into larger translational or clinical studies, researchers also need to consider infrastructure and traceability. That means planning for automation that captures process metadata alongside analytical outputs.” — Paula Villaescusa-Sanchez, BioPharma Workflow Solutions Architect, Danaher.

Build a connected evidence system, not a chain of isolated tools

The biomarker bottleneck often emerges at the interfaces between tools rather than within a single technology. Progress depends on integrating each stage of the workflow so that samples, measurements and data remain connected. Eich frames the practical value of this integration by explaining, “Rather than treating sample preparation, staining, imaging, quantitation and data analysis as separate projects, teams can plan them together using standardized protocols and interoperable technologies.”

Scientific flexibility still matters. The enabling condition is enough operational discipline to test new hypotheses without sacrificing comparability or traceability. Teams that strike that balance can advance stronger candidates with confidence while ending weak programs earlier.

The field needs more than data volume. It needs evidence that remains reliable as studies expand and decisions become more consequential. Connected, reproducible workflows make that possible.

Ready to identify where variability or disconnected data may be slowing your biomarker program? Explore the connected biomarker workflow to see how integration can improve reproducibility and create a clearer path from discovery to translation.

Perspectives from across the workflow

This article draws on the expertise of Amy E. McCann, Senior Director, Research Solutions at Abcam; Florian Eich, Director, Business Development Life Science & Applied at Leica Microsystems; and Paula Villaescusa-Sanchez, BioPharma Workflow Solutions Architect at Danaher. Their perspectives span reagent validation, spatial imaging, workflow standardization, automation and data integration.