Paper-Based Test Scans for Multiple Biomarkers in Human Serum

 

Researchers led by UCLA professor Aydogan Ozcan developed a deep learning-enabled biosensor for multiplexed, point-of-care (POC) testing of disease biomarkers. POC biosensors provide remote and resource-limited communities with an economical, practical alternative to centralized laboratory testing.
The UCLA-developed POC sensor includes a paper-based fluorescence vertical flow assay to simultaneously detect three biomarkers of acute coronary syndrome from human serum samples. The vertical flow assay is processed by a low-cost mobile reader, which quantifies the target biomarkers through trained neural networks.

According to the researchers, the competitive performance of the multiplexed computational fluorescence vertical flow assay, along with its inexpensive, paper-based design and hand-held footprint, give the POC sensor promise as a platform to expand access to diagnostics in resource-limited settings.

“Compared to a commonly used linear calibration method, our deep learning-based analysis benefits from the function approximation power of neural networks to learn nontrivial relationships between the multiplexed fluorescence signals from the paper-based sensor and the underlying analyte concentrations in serum,” researcher Artem Goncharov said. “As a result, we have accurate quantitative measurements for all three biomarkers of interest, despite the background noise present in clinical serum samples.”

Unlike lateral flow assays, which are the most common type of POC test, assays using the vertical flow of samples through stacked paper layers enable the arrangement of sensing regions in a 2D or 3D array and can achieve multiplexing with tens or even hundreds of independent testing channels represented by different affinity capture molecules. The vertical flow design of the POC sensor from the UCLA researchers has room for multiple test regions, with up to 100 individual test spots within a single disposable cartridge.

“This design essentially allows us to integrate tens of different POC sensors into a single cassette and perform multiplexed diagnostics tests in parallel with the same low-cost paper-based sensor,” Ozcan said.

The researchers used conjugated polymer nanoparticles (CPNs) — fluorescent labels with tunable emission and excitation properties and with minimally overlapping excitation and emission peaks — to design the fluorescence vertical flow assay. The CPNs have 480-nm excitation and 610-nm emission peaks, which helped the team reduce the strong autofluorescence background from the paper substrate.

The excitation energy transfer in CPNs takes place across the whole backbone, catalyzing an amplified emission that is higher than quantum dots (QDs). CPNs are also more stable on porous paper layers, with less photobleaching, and are larger than QDs, leading to improved luminescence.

Using human serum samples to quantify three cardiac biomarkers — myoglobin, creatine kinase-MB, and heart-type fatty acid binding protein — the researchers validated the fluorescence vertical flow assay platform. The assay achieved less than 0.52 ng/mL−1 limit-of-detection for all three biomarkers, with minimal cross-reactivity.

Biomarker concentration quantification, using the assay coupled to neural network-based inference, was blindly tested using 46 individually activated cartridges and human serum samples. The results showed a high correlation between the fluorescence vertical flow assay and the ground truth concentrations obtained through standard laboratory benchtop testing, with a greater than 0.9 linearity and a less than 15% coefficient of variation found for all three biomarkers.

The simple-to-operate POC sensor, the researchers said, involves only three injection steps performed through a single loading inlet. The steps can be executed by a minimally trained technician using a custom operation kit. The assay uses 50 µL of serum sample per patient and takes under 15 minutes to complete, which is on the same scale as, for example, COVID-19 rapid antigen tests that take between 15 and 30 minutes.

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Pensievision's 3D Imaging Tech Shines at Luminate Finals Competition

Pensievision, a creator of 3D imaging technology for industrial applications and medical devices, received the Company of the Year Award at the Luminate NY Finals 2025, held this week in Rochester. Along with the title, the company received a $1 million investment from New York State through the Finger Lakes Forward Upstate Revitalization Initiative.

Pensievision's solution delivers 3D imaging for demanding environments, from medical diagnostics and factory floors to orbital missions. Its technology combines a miniaturized single-lens setup, artificial intelligence, and astronomy-inspired optics to enable high-precision insights in tight or complex environments where bulky, multi-lens or laser-based systems fail.

“It’s a compact, affordable camera that does very high-accuracy 3D mapping of surfaces, and it’s compact enough that it can fit on anything from a robotic arm to an endoscope that goes inside the body,” said Pensievision CTO Joseph Carson. The technology has been demonstrated in both applications, and will soon be demonstrated in an upcoming visit to the International Space Station, Carson said.

The company's platform utilizes a Corning Varioptic liquid lens to rapidly take images at varying focuses to produce a 3D image through focus mapping. AI-driven software is then used to process the messy 3D mapping into the underlying high-resolution quantitative map. The output map is precise and accurate enough to be used in mission-critical applications.

Pensievision plans to use the follow-on funding to anchor its growth in the Rochester region by engaging with local supply chains, hiring engineering talent from universities in the area, and partnering with Rochester-based design and manufacturing firms. According to Carson, there are at least three firms in the newly wrapped cohort with which he envisions Pensievision fostering relationships.

“There’s a lot of collaborative efforts between the companies in this cohort because they’re working in similar fields with different technologies,” said Luminate’s managing director, Sujatha Ramanujan. “It’s been a nice outcome.”

amPICQ, originally from Hyderabad, India, and now located in Rochester, was awarded the Outstanding Graduate Award and $500,000 in follow-on funding. Its team is designing and developing PICs to make quantum-safe security both practical and accessible across quantum communications, datacom, and telecom industries.

For the first time, three companies tied for the Distinguished Graduate Award, with each being earning $200,000. Oblate Optics, from San Diego, produces ultra-thin lenses that keep laser beams in perfect focus — even on curved or uneven surfaces — without the need to move or refocus the optics. Münster, Germany based Pixel Photonics uses its waveguide-integrating design for superconducting nanowire single-photon detectors to support OEM integration across quantum communications, microscopy, medical diagnostics, and advanced sensing. SNOChip, from Princeton, N.J., is a developer of on-chip optical components, such as microlens arrays, computer-generated holograms, and metasurfaces, designed for seamless integration with semiconductor lasers and sensor chips.

Event attendees voted LirOptic as the Audience Choice, and the company earned $10,000 in follow-on funding.

The investments were presented after a panel of judges from the optics and photonics industry and venture capital community scored the participating companies based on their business pitches and due diligence completed during the seven-month accelerator program. The finals event marks the completion of the eighth year of the cohort-based program, which now includes more than 80 portfolio companies, carrying an estimated combined market value of $700 million. As required by the award, all winners of the competition will commit to establishing operations in the region for at least the next 18 months.

Since its inception, Luminate NY has invested $21 million in 85 startups. Collectively, they have created more than 210 jobs in New York State and spent $21.6 million on more than 140 projects with regional design, manufacturing and supply chain companies. Twenty-two international companies have relocated to New York, and 41 portfolio companies have women in the C-suite.

Applications are now being accepted for round nine, through Jan. 12, 2026. Teams will receive $100,000 in funding upon program start, with the expectation that $50,000 will be used to engage resources in the Finger Lakes region.


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Laser-Induced Protein Detection Speeds Diagnosis of Disease

Researchers at Osaka Metropolitan University have developed an optical alternative to immunoassays and other methods used for protein analysis. The alternative method provides rapid, highly sensitive detection of proteins through laser irradiation.

According to the researchers, the light-induced acceleration-based technique could improve detection limit and quantitative measurement, using a small number of biological samples and a simple process, to aid in the ultra-early diagnosis of cancer, dementia, and infectious diseases.

Conventional techniques for protein detection, such as enzyme-linked immunosorbent assay (ELISA), require several hours and involve multiple steps, in addition to being less sensitive than the recently developed light-induced method.

In experiments, the researchers showed a successful deployment of their approach using only three minutes of laser irradiation. They achieved a sensitivity and ultrafast specific detection more than 100× that obtained in comparison with conventional protein detection methods. Further, the researchers showed that the technique could enable diagnoses with only a small amount of body fluids — such as a single drop of blood.

To develop an optical method to achieve control of antigen-antibody reaction and detect trace amounts of proteins, the researchers conducted basic research on the synergistic effects of optical pressure and fluid pressure and how to circumvent the effect of heat. They used target proteins, to which they introduced probe particles containing modified antibodies that selectively bound to the proteins. They confined the proteins and probe particles to a microchannel and irradiated the channel.

The probe particles were 2-μm-diameter polymer beads with a minimal amount of heat generation, due to the absorption of infrared laser light as well as strong light scattering.

The researchers then used light-induced acceleration to trap antigen-antibody reactions of trace amounts of proteins at the interface between solid and liquid (i.e., the bottom of the channel, which contained liquid samples).

After tuning the laser irradiation area to be comparable to the confinement geometry, the researchers irradiated a few hundred milliwatts of laser light, defocused to a spot size of approximately 70 μm in the microchannel, which had a width of approximately 100 μm.

The laser-assisted optical pressure on the proteins and probe particles increased the probability of interaction and the acceleration of antigen-antibody reactions. The collisional probability of the target molecules and probe particles was enhanced through optical force and fluidic pressure.

The “scattering force,” a component of the optical force, was enhanced to ensure accumulating force without any thermal damage to the antibody-modified probe particles and target proteins.

After testing various conditions, the researchers found that the antigen-antibody reaction was efficiently accelerated by adjusting the flow rate to 100 to 200 μm/s.

A black region formed in a portion of the assembled structure obtained by laser irradiation, because the optical transmission was blocked by the multilayered structure formation. The researchers found that the area of this region was positively correlated with the protein concentration. A model calculation, in which binding by an antigen-antibody reaction was expressed using cohesive energy, confirmed theoretically that the formation of the multilayered structure was caused by optical force and pressure-driven flow.

When the researchers irradiated the microchannel with IR laser light for three minutes, they were able to detect trace amounts of proteins at a sensitivity level approximately 100× higher than that of conventional protein testing. The researchers achieved rapid measurement of trace amounts on the order of tens of attograms (ag) (ag = 10−18 g; one quintillionth of a gram). They measured target protein trace amounts as small as one twenty-quadrillionth of a gram after only three minutes of irradiation.

The researchers applied the principle of light-induced acceleration to several different types of membrane proteins. In experiments, the optical technique demonstrated ultrafast, specific detection of target proteins with a smaller sample volume and higher sensitivity than conventional techniques. For example, in one type of membrane protein, the researchers detected 47 to 750 ag of target proteins, without any pretreatment, from a 300-nL sample after just three minutes of laser irradiation.

A progressive collaborative study on cancer marker measurement using patient-derived samples is underway as part of the Future Society Creation Project of the Japan Science and Technology Agency. An initial validation of the light-induced acceleration technique in clinical practice is planned, with the aim of developing a basic system within a few years, the researchers said.

Since antigen-antibody reaction is a common biochemical reaction, the technology has the potential to be used not only in the medical field but also in various industrial fields, such as testing for allergens in food and drink, detecting biological substances in the environment, and testing for intermediate products in the pharmaceutical process.

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Team Applies Synthetic Wavelength Imaging to Skin Cancer Diagnoses, Treatment

 

Researchers at the University of Arizona will pursue the development of optical imaging technologies capable of deeper, clearer views into biological tissues, such as skin or soft tissue linings within the body. Led by Florian Willomitzer and Clara Curiel-Lewandrowski, the team is one of just four groups nationwide to receive funding through the Advancing Non-Invasive Optical Imaging Approaches for Biological Systems initiative.

The group will receive nearly $2.7 million from the National Institute of Health (NIH)’s Common Fund Venture Program. The final award amount is pending successful completion of milestones and availability of funds.

The team's noninvasive approach is based on synthetic wavelength imaging (SWI), which uses two separate illumination wavelengths to computationally generate one virtual, “synthetic” imaging wavelength. Due to the longer, synthetic wavelength, the signal is more resistant to light scattering inside tissue. At the same time, researchers can take advantage of the higher contrast information provided by the original illumination wavelengths.

“This project specifically focuses on nonmelanoma skin cancers, such as basal cell carcinoma or squamous cell carcinoma,” said principal investigator and project lead Willomitzer, an associate professor of optical sciences. “Those skin cancers can display significantly different imaging contrast properties than melanoma, which poses a unique challenge to the development of new 'deep' imaging technologies.”

Current skin cancer imaging methods, such as confocal microscopy or optical coherence tomography, use optical light with wavelengths in the visible to near-infrared spectrum. They offer superior contrast and resolution at shallow tissue depths, but their relatively short imaging wavelengths make them susceptible to light scattering deep inside biological tissue. Longer wavelength methods, like ultrasound or hybrid approaches, can image deeper layers, but they often lack resolution or sufficient contrast needed for certain cancer types.

“From a translational standpoint, this limitation is particularly important,” said Curiel-Lewandrowski, the other principal investigator, chair of the Department of Dermatology at the College of Medicine – Tucson and co-director of the Skin Cancer Institute at the University of Arizona Cancer Center. “Patients with nonmelanoma skin cancers often present with lesions that vary widely in size, depth and pattern of invasion.”

According to Curiel-Lewandrowski, imaging tools must be versatile enough to accurately assess tumor margins at the time of diagnosis, while also being robust and reliable enough to monitor how lesions respond over the course of treatment.

“To achieve this, we need tunable imaging capabilities that balance depth penetration with resolution and imaging contrast — something that current technologies cannot reliably provide,” she said.

The NIH's Common Fund Advancing Non-Invasive Optical Imaging Approaches for Biological Systems Venture Initiative seeks to overcome these and other limitations through technology development that will allow light to deeply image through tissue non-invasively at high resolution. Enhanced imaging techniques can make possible earlier detection of health conditions, more precise evaluation of cellular and tissue health, and advancements in non-invasive procedures to replace surgery. The NIH initiative seeks to produce highly detailed images that can reveal structures ranging from individual cells to larger features of living tissues. It also aims to record rapid biological processes, such as muscle contractions and pulse, with enough speed to capture them in real time.

“Synthetic wavelength imaging's resilience to scattering in deep tissue while preserving high tissue contrast at the optical carrier wavelengths is a rare combination,” Willomitzer said. “By pairing this property with advanced computational evaluation algorithms, our approach aims to break free from the conventional resolution-depth-contrast tradeoff.”

The team aims to bridge a critical gap in skin cancer care by advancing this new technology, Curiel-Lewandrowski said.

“Our goal is to translate these imaging advances into clinical practice,” she said. “If we can detect invasive lesions earlier, define tumor margins more precisely and monitor response to non-invasive treatments in real time, we can maximize the effectiveness of emerging therapeutic approaches. This will also allow us to tailor intervention length and dosing individually to each patient.”

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Computational Method Streamlines Spectral Imaging, Cuts Costs

 

The versatility and precision of hyperspectral imaging make it an indispensable tool in numerous scientific and industrial applications, from medical imaging to environmental monitoring to quality control. But traditional hyperspectral imaging systems can be costly, cumbersome, and challenging to scale.

A computational spectral imaging system from the University of Utah provides a fast, inexpensive, efficient alternative to capturing high-quality spectral data. The system, which the team tested across biomedical, food-quality, and astronomical use cases, could establish a new framework for high-speed, high-fidelity spectral imaging with broad translational potential.

The system uses a diffractive filter array to project spectral information into the spatial domain, enabling the capture of a single-channel, 2D image that contains both spatial and spectral data. This 2D image, called a diffractogram, is computationally decoded to reconstruct a spectral image cube with 25 spectral bands in the 440-800 nm range. Each of the 25 separate images that comprise the cube represents a distinct slice of the visible spectrum.

The team modeled and designed the diffractive filter array and the algorithm to reconstruct the hyperspectral images from the raw data captured by the sensor.

By encoding a scene into a single, compact 2D image rather than a massive 3D data cube, the camera makes hyperspectral imaging faster and more efficient. The fast encoding enables the system, which is small enough to fit into a cellphone, to take high-speed, high-definition video.

“One of the primary advantages of our camera is its ability to capture the spatial-spectral information in a highly compressed, two-dimensional image instead of a three-dimensional data cube, and use sophisticated computer algorithms to extract the full data cube at a later point,” professor Apratim Majumder said. “This allows for fast, highly compressed data capture.”

The current prototype camera can take images at just over one megapixel in size (1304 x 744 pixels) and break them down into 25 separate wavelengths across the spectrum. The diffractive element, placed directly over the camera’s sensor, encodes spatial and spectral information for each pixel on the sensor.

“We introduce a compact camera that captures both color and fine spectral details in a single snapshot, producing a ‘spectral fingerprint’ for every pixel,” professor Rajesh Menon said.

To demonstrate the camera’s capabilities, the researchers applied standard inferencing techniques to reconstructed spectral images across various sectors. The system demonstrated a spectral reproduction error of less than 15% across the 440-800 nm band.

The researchers used the camera to classify lung and trachea tissues in ex vivo chicken lung images, predict the freshness of strawberries, and mimic the spectral filters that are used in stellar imaging. These experiments highlighted the system’s potential in medical diagnostics, food-quality assessment, and astronomical observations.

The diffractive, computational spectral imaging system offers several advantages. It provides snapshot capability, eliminating issues with scan-and-stitch methods. The diffractogram serves as a form of optical compression, efficiently storing spatio-spectral content in a compact, information-rich 2D array. This is particularly beneficial for applications with limited storage or transmission bandwidth, such as airborne or satellite imaging.

“Satellites would have trouble beaming down full image cubes, but since we extract the cubes in post-processing, the original files are much smaller,” Majumder said.

The system also provides the flexibility to perform reconstructions offline and on-demand, after data capture, for scenarios with limited on-board computational resources. Also, since the diffractogram encodes spectral information continuously, it allows for information to be selected on an application-specific spectral basis, yielding smaller image cubes and faster, more stable reconstructions.

Compared to traditional hyperspectral imaging systems, the computational spectral imager’s streamlined approach reduces costs significantly.

“Our camera costs many times less, is very compact and captures data much faster than most available commercial hyperspectral cameras,” Majumder said. “We have also shown the ability to post-process the data as per the need of the application and implement different classifiers suited to different fields such as agriculture, astronomy, and bioimaging.”

Hyperspectral cameras have long been used in agriculture, astronomy, and medicine, where subtle differences in color can make a big difference. But these cameras have historically been bulky, expensive, and limited to producing still images.

“When we started out on this research, our intention was to demonstrate a compact, fast, megapixel-resolution hyperspectral camera, able to record highly compressed spatial-spectral information from scenes at video-rates, which did not exist,” Majumder said.

“This work demonstrates a first snapshot, megapixel, hyperspectral camera,” he said. “Next, we are developing a more improved version of the camera that will allow us to capture images at a larger image size and an increased number of wavelength channels, while also making the nanostructured diffractive element much simpler in design.”

By making hyperspectral imaging cheaper, faster, and more compact, the computational camera advances spectral imaging technology and potentially opens the way for technologies that could change the way the world and its contents are seen.

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Handheld Sensor Detects Markers for Early-Stage Alzheimer’s

        

A newly-developed, handheld optical sensor could make Alzheimer’s disease easier to detect in its early stages, when treatments for the disease are most effective. The photonic resonant sensor is the result of a collaboration among researchers at the University of York, the University of Strathclyde, and the University of São Paulo.

The team developed a sensor that can simultaneously detect two of the amyloid peptides that are indicators for Alzheimer’s, at the levels clinically required for diagnosis. The capability to simultaneously detect beta amyloid 40 and beta amyloid 42 in the blood opens a route to quantifying and analyzing their ratio, enabling the progression of the disease to be tracked. Single biomarker detection is insufficient for clinical diagnosis.

Photonic resonant sensors allow for the label-free detection of specific molecules, in addition to surface imaging and the multiplexing of different biomarkers. They are compatible with low-cost fabrication processes and can be implemented with minimal optoelectronic elements for the signal readout.

Detecting peptides, however, remains a challenge for this class of sensors, mainly due to the low molecular weight of the peptides. Amyloid peptides are small and occur at low concentrations.

To ensure a high-performing sensor that could detect peptides in the blood, the researchers integrated gold nanoparticles with a dielectric nanopillar photonic crystal structure in a dimer configuration. The gold nanoparticles amplified the optical signal used to detect Alzheimer’s disease biomarkers dramatically, compared to the team’s previous sensor design, which involved the use of parallel grooves.

“This new design has allowed us to detect the amyloid biomarkers at the ultralow, clinically relevant concentrations we need, which our previous sensor couldn’t quite reach,” researcher Steven Quinn said. “The added bonus is that the technology remains scalable, mass-producible, and we aim for it to be as simple to use as a Covid test.”

The sensor design combines high resonance Q-factor, amplitude, and sensitivity, leading to a high figure of merit. “When you compare different technologies in photonics, you use a ‘figure of merit,’ which is like a scorecard that takes into account key parameters like sensitivity and signal-to-noise ratio,” Quinn said. “Our new sensor’s scorecard outperforms competing technologies.”

The sensor can detect beta amyloid 40 and beta amyloid 42 peptides in the same channel, which is relevant for assessing disease progress, and opens a route toward multiplexing. To achieve high selectivity and specificity in the sensor, the researchers used an immunoassay design approach.

The researchers are integrating the sensor technology into a handheld device. Potentially, this device could provide an indication of disease within seconds from a simple finger-prick of blood, at a projected cost of less than £100 per test. Low-cost, point-of-care testing could broaden accessibility to early Alzheimer’s testing and diagnosis, giving more patients access to treatments that are most effective in the initial stages of the disease.

“New Alzheimer’s treatments work by specifically targeting the sticky amyloid proteins that build up in the brain,” Quinn said. “For these drugs to be effective, doctors first need to confirm that a patient has this protein build-up — a condition known as amyloid positivity. A simple, scalable blood test could be the way to facilitate widespread access to these emerging treatments.”

Current methods for diagnosing Alzheimer’s disease, such as brain scans (PET/MRI) or invasive lumbar punctures, are costly, time-consuming, and are not readily accessible. Highly accurate, lab-based blood tests are now available, but they rely on large, expensive machinery, with a single test potentially costing thousands of pounds.

The next milestone for the team will be to validate the photonic sensor using blood samples from patients with Alzheimer’s and a healthy control group. This crucial phase will determine how effectively the sensor can differentiate between the two groups.

The new sensor technology could be used to detect other biomarkers and markers for other diseases. “The same principles and protocols can be used to detect a protein called phosphorylated tau, another key Alzheimer’s biomarker, as well as alpha-synuclein in Parkinson’s disease,” Quinn said. “We believe this could become a platform technology to help differentiate between various forms of dementia, which is a major challenge for clinicians.”

Although the team still needs to demonstrate the effectiveness of the sensor in patient samples, it believes that the photonic biosensor holds significant promise as a cost-effective tool to open the door to widely available testing for Alzheimer’s and other neurodegenerative diseases.

“Our vision is a device that is user-friendly for clinicians and can be deployed in healthcare settings around the world,” Quinn said.

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Fiber Photometry Hastens Development of Alzheimer’s Disease Therapies

Use of mouse models to test new interventions for Alzheimer’s disease is a cornerstone of Alzheimer’s disease therapeutic development.

Current preclinical evaluation of Alzheimer’s disease pathology relies mostly on post-mortem analyses of animal models, which limits researchers’ ability to follow the progression of the disease or the efficacy of treatments over time.

In search of a method to observe the development of the disease and its response to therapies in real-time, researchers at the University of Strathclyde and the Italian Institute of Technology (IIT) investigated fiber photometry, an optical approach to monitoring neural activity in live animals. The researchers expanded the capabilities of in vivo fiber photometry, using it to examine the pathological features of an AD mouse model in a freely behaving condition. This approach to could help researchers uncover information about how Alzheimer's disease develops and enable more flexible testing of potential therapies.

Using a conventional, flat fiber-based photometry approach, the team confirmed in its initial experiments that amyloid plaque signals could be monitored across multiple depths in in vivo Alzheimer's disease mice models under anesthesia.

Instead of relying on genetically encoded sensors, the researchers implemented a non-genetic strategy, and injected the mice with a blood-brain-barrier-permeable fluorescent tracer, Methoxy-X04. The hydrophobic structure of this compound allows it to enter the brain, where it specifically binds to beta-sheets found within amyloid fibrils, allowing visualization of amyloid plaques in Alzheimer's disease models.

The team found that the depth profiles of the in vivo fluorescent signals correlated with the plaque density measured afterward in brain slices. A machine learning model could distinguish between the in vivo fluorescent signals of mice with and mice without amyloid plaques based on the depth profiles of their signals.

The researchers then assessed whether tapered optical fibers would allow depth-resolved photometry for plaque signals in ex vivo tissue. Upon examination of brain tissue slices, they found that the tapered fibers reliably tracked plaque distribution.

After validating the tapered fiber-based photometry approach in freely behaving mice, implantation into chronically in living mice revealed depth-specific increases in fluorescence after Methoxy-X04 injection in Alzheimer's disease model mice, but not in healthy controls. The technique showed age-dependent signal increases consistent with disease progression.

By exploiting the photonic properties of tapered fibers, the researchers establish depth-resolved photometry of amyloid plaque signals in vivo and ex vivo.

In contrast to existing methods, such as optoacoustic tomography, the optical fiber-based approach allows long-term monitoring of amyloid pathology across multiple deep brain regions in freely behaving animals. While the photometry technique cannot resolve individual plaques, it can provide a minimally invasive way to track pathological changes across time and across brain regions.

Amyloid plaques have long been recognized as a hallmark of Alzheimer's disease. Recent therapeutics targeting amyloid-β protofibrils or deposited amyloid plaques have proven effective in patients, and researchers have successfully translated early preclinical mouse data to the clinic. As such, evaluating new interventions in preclinical mouse models should continue to play an important role in accelerating future treatments for Alzheimer's disease.

The results of this research demonstrate the potential of using fiber optic photometry, which has been widely used in the neuroscience community, to monitor plaque signals in order to optimize therapeutic approaches and develop intervention strategies for Alzheimer's in a preclinical setting.

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