Early and accurate diagnosis of neurodegenerative diseases (NDDs) like Alzheimer’s and Parkinson’s remains one of the biggest challenges in neurological healthcare. These conditions involve a gradual loss of neurons, leading to cognitive and motor impairments that often develop subtly and progress slowly, making timely detection difficult.
A recent article in npj Biosensing explores how emerging optical biosensing technologies are addressing this challenge by offering faster, more sensitive detection of key disease biomarkers.
Study: Optical biosensors for diagnosing neurodegenerative diseases. Image Credit: Orawan Pattarawimonchai/Shutterstock.com
The review presents a comprehensive assessment of current optical biosensing methods, their underlying principles, performance in real-world testing, and potential for clinical integration.
Understanding the Diagnostic Challenge
Neurodegenerative diseases are notoriously complex. Their symptoms often overlap, and early-stage clinical signs can be vague or misinterpreted. Biomarkers—such as amyloid-beta (Aβ) peptides, tau proteins, and alpha-synuclein—play a vital role in identifying disease onset, monitoring progression, and evaluating treatment responses.
However, conventional tools like enzyme-linked immunosorbent assays (ELISA) have clear limitations. They're time-consuming, require laboratory infrastructure, and can lack the sensitivity needed for detecting biomarkers present in low concentrations. These shortcomings are driving the need for more advanced diagnostic solutions.
Optical Biosensors: What They Offer
Thanks to advances in nanotechnology, materials science, and photonics, optical biosensors are gaining traction as powerful diagnostic tools. They’re capable of detecting minute concentrations of biomarkers in biological fluids such as cerebrospinal fluid (CSF), blood, saliva, and urine, making them particularly promising for non-invasive or minimally invasive testing.
The review outlines several types of optical biosensors, including surface plasmon resonance (SPR), localized SPR (LSPR), surface-enhanced Raman scattering (SERS), whispering-gallery mode (WGM) resonators, fiber-optic sensors, and fluorescence-based platforms. These technologies work by using biorecognition elements (like antibodies, aptamers, or molecular imprints) immobilized on sensor surfaces to detect specific interactions with biomarkers, producing measurable optical changes.
Improving Specificity and Sensitivity
One key focus of the review is how sensor surfaces are engineered for better specificity. Functionalization techniques, often involving nanomaterials like gold nanoparticles, quantum dots, and photonic structures,help sensors target relevant biomarkers more effectively.
To push detection limits further, researchers are also implementing tailored sample prep, substrate modification, and signal amplification strategies. As a result, many of these biosensors can detect target molecules in the femtomolar to picomolar range, far exceeding the capabilities of conventional assays.
Performance metrics such as sensitivity, response time, dynamic range, and reproducibility were examined across various platforms. The integration of biosensors into portable, point-of-care devices was also highlighted, as is the role of artificial intelligence (AI) in processing complex sensor data and enhancing diagnostic accuracy.
Progress and Potential
The reviewed studies show clear progress. SERS-based sensors, for example, have achieved highly sensitive detection of Aβ and tau proteins, supporting early-stage diagnosis. SPR and LSPR technologies offer real-time, label-free detection with impressive specificity, crucial for distinguishing between different forms of disease-associated proteins.
WGM resonators and fiber-optic sensors are particularly adept at handling complex biological environments, making them suitable for real-world clinical samples. And with innovations like optical microcavities and on-chip frequency combs enabling multiparametric analysis, these tools are becoming even more powerful.
Bridging the Lab-to-Clinic Gap
While the technology is advancing rapidly, some hurdles remain. Biological variability, interference from other compounds, and challenges in large-scale sensor fabrication must be overcome before widespread clinical adoption is possible.
To address this, researchers are focusing on robust surface coatings, multiplexed detection strategies, and integrating sensors with microfluidic platforms for automated workflows. These efforts are aimed at making diagnostics faster, more reliable, and easier to deploy—even in remote or resource-limited settings.
The potential for AI and machine learning to analyze multiplexed biosensor data is another exciting area. By interpreting complex patterns, AI could help deliver more personalized diagnostics and care strategies tailored to each patient's unique biomarker profile.
Looking Ahead
The article concludes that optical biosensors have strong potential to enhance how we diagnose and monitor neurodegenerative diseases. Their combination of speed, sensitivity, and adaptability positions them as powerful complements—or even alternatives—to current diagnostic tools.
With continued innovation and cross-disciplinary collaboration among photonics, nanotechnology, clinical science, and biotechnology experts, these platforms could play a pivotal role in shifting diagnostics toward earlier detection and more personalized care.
Journal Reference
Duy Mac K., Su J.(2025). Optical biosensors for diagnosing neurodegenerative diseases. npj Biosensing 2, 20. DOI: 10.1038/s44328-025-00040-3, https://www.nature.com/articles/s44328-025-00040-3