Puumala, L. S. | bioRxiv Science

bioRxiv2025

What Could Go Wrong? Promoting Success by Planning for Failure in Label-Free Biosensor Assay Development

Label-free biosensors offer powerful platforms for detecting molecular interactions, but developing robust assays on these systems presents several challenges due to the complexity of the testing systems and intersection of disciplines. In this work, we describe a lightweight, 3-step experimental workflow supporting troubleshooting and root cause analysis that we developed during the design and optimization of silicon photonic biosensor assays in an academic research setting. Because such environments often lack the resources and formal quality-management infrastructure available in industry, our approach emphasizes practicality and ease of adoption. Drawing from our own assay failures and successes, we identify common failure modes, propose structured troubleshooting workflows, and provide case studies illustrating the application of established frameworks, including the Five Whys, the Plan-Do-Check-Act cycle, Open-Narrow-Close and the Ishikawa (fishbone) diagram. By applying this framework to our research, we increased our assay yield by a factor of 2, from ~45% to ~90%. This paper aims to support other teams engaged in label-free assay development by equipping them with practical tools and experimental best practices. HighlightsO_LIWe doubled assay yield from ~45% to ~90% by planning for failure, not just success. C_LIO_LITailored troubleshooting frameworks help pinpoint and solve various assay failures. C_LIO_LIApplying innovative problem-solving to workflows accelerates biosensor development. C_LIO_LISystematic troubleshooting improves assay development across a range of lab settings. C_LIO_LIA 3-step workflow highlighting what could go wrong supports continuous improvement. C_LI

bioengineering↗

bioRxiv2025

Resonating with replicability: factors shaping assay yield and variability in microfluidics-integrated silicon photonic biosensors

The integration of biosensors and microfluidics has facilitated the development of compact analytical devices capable of performing automated and information-rich detection of myriad targets, both in the lab and at the point of need. However, optimization of microfluidics-integrated biosensor systems and replicability challenges present roadblocks in validation and commercialization. Understanding factors contributing to yield and replicability in biosensor performance is key to the development of biosensor optimization frameworks and technology translation beyond the research setting. Hence, for the first time, we present a detailed analysis of factors affecting performance, assay yield, and intra- and inter-assay replicability in microfluidics-integrated silicon photonic (SiP) evanescent-field microring resonator biosensors. Strategies for mitigating bubbles--a major operational hurdle and contributor to instability and variability in microfluidics-integrated biosensors--are analyzed to improve assay yield. Effective bubble mitigation is demonstrated by combining microfluidic device degassing, plasma treatment, and microchannel pre-wetting with surfactant solution. Both intrinsic and analyte-detection performance metrics and their replicability are quantified for the first time for sub-wavelength grating-based SiP biosensors, highlighting a path to further optimization. Lastly, the effects of sensor functionalization on analyte detection performance and replicability are evaluated. We compare polydopamine-vs. Protein A-mediated bioreceptor immobilization chemistries and spotting-vs. flow-mediated bioreceptor patterning approaches. We find that simple polydopamine-mediated, spotting-based functionalization improves spike protein (1 {micro}g/mL) detection signal by 8.2x and 5.8x compared to polydopamine/flow and Protein A/flow approaches, respectively, and yields an inter-assay coefficient of variability below the standard 20% threshold for immunoassay validation. Overall, this work proposes a practical framework through which evanescent-field SiP biosensors, and microfluidics-integrated biosensors more generally, can be characterized, compared, and optimized to facilitate efficient biosensor development.

bioengineering↗