Parkinson's: New Biomarker Speeds Diagnosis and Paves the Way for Early Treatment

This discovery is very important because it offers a way to detect Parkinson's disease before motor symptoms appear, which would allow for earlier intervention and potentially slow the progression of the disease. In the future, this technology could be used to monitor patients' progress and test the effectiveness of new drugs in development.

Parkinson’s disease (PD) is a common degenerative neurological condition that causes serious motor problems (such as slow movement, muscle stiffness and tremors) as well as non-motor symptoms, such as sleep and mood problems. It represents a major public health challenge worldwide.

The disease is marked by the gradual loss of neurons in the brain that produce dopamine, a neurotransmitter essential for controlling movement. However, by the time the first clinical signs appear, such as tremor at rest or difficulty moving, more than half of these neurons have already been destroyed. This makes early diagnosis very difficult.

Currently, Parkinson’s is diagnosed primarily based on visible symptoms. Unlike Alzheimer’s disease, where blood biomarkers and imaging tests (such as PET scans) are more advanced, reliable laboratory tests to detect Parkinson’s early are still lacking.

As a result, researchers have been seeking methods that can identify the disease before motor symptoms become evident.

One recent effort has proposed a system called NSD-ISS, which uses biomarkers linked to defects in the alpha-synuclein (αSyn) protein, dopamine neuron dysfunction, and genetic factors. This system divides Parkinson’s into six stages, from genetic risk to the onset of motor symptoms.

In the brains of people with Parkinson’s, one of the hallmarks is the abnormal accumulation of the alpha-synuclein protein, which misfolds and clumps together into clumps called “Lewy bodies.” Both genetic factors and chemical changes in the proteins are thought to contribute to this problem.

Brain Areas Affected by Parkinson's Disease

To detect these misfolded proteins in cerebrospinal fluid (the fluid that bathes the brain and spinal cord), techniques called seed amplification assays (SAA) have been developed.

These methods, such as PMCA and RT-QuIC, "force" small amounts of the defective protein to replicate, making them detectable. They have already shown high accuracy in detecting Parkinson's, but they are complex processes and sensitive to many variations, which requires improvements to standardize results between laboratories.

A newer and more promising approach is the use of infrared sensors called iRS (infrared immunosensors). This method directly detects the way proteins are folded, without the need for additional chemical markers.

Basically, it analyzes the structure of proteins in the fluid and identifies whether they are in a healthy configuration (such as α-helical coils) or in a pathological form (such as β-sheets, typical of the disease).

This figure shows two experiments that help us understand how the immuno-infrared (iRS) sensor identifies the misfolded alpha-synuclein (αSyn) protein in cerebrospinal fluid (CSF), which is the fluid that circulates in the brain and spinal cord. In part (A), the same CSF from a person with Parkinson’s was analyzed twice using the sensor. The first time (run 1), the sensor detected a strong signal, showing that there was a lot of alpha-synuclein present. After capturing this protein, the remaining sample (the “supernatant”) was analyzed again (run 2) and almost no signal was detected, indicating that almost all of the protein had been captured in the first analysis. The graphs show this clearly: the black line (first measurement) is much higher than the red line (second measurement). In part (B), they did a comparison experiment to measure the total amount of alpha-synuclein before and after capture by the sensor. They circulated CSF on surfaces with and without antibodies (antibodies act as “hooks” that hold the protein). When the surface had antibodies, the amount of alpha-synuclein dropped by 87%, showing that almost all of the protein was captured. When the surface had no antibodies, the reduction was minimal (only 11%), which confirms that efficient capture depends on antibodies. These results show that the iRS sensor is capable of accurately capturing and detecting alpha-synuclein present in CSF, something that is essential to aid in the early diagnosis of Parkinson’s disease.

In the case of Alzheimer’s disease, this technique has already been shown to be able to predict risk many years before symptoms appear. Now, in this study, researchers from Ruhr-University Bochum, Germany, applied the same principle to detect alpha-synuclein misfolding in people with Parkinson’s.

The results showed that the iRS technology was able to differentiate with high accuracy (sensitivity of 97% and specificity of 92%) individuals with Parkinson’s from those without the disease.

Identifying the presence of Parkinson’s and other related diseases, such as multiple system atrophy (MSA), by analyzing a protein called α-synuclein (αSyn) in the fluid that bathes the brain and spinal cord (CSF). Using a special infrared sensor (called iRS), they measure how αSyn is organized. Normally, this protein has a shape called an “alpha helix”, but in people with Parkinson’s or MSA, it folds incorrectly, forming what they call a “beta sheet”. The graph on the left shows these two different shapes. In the graph on the right, each point represents a patient: those with Parkinson’s/MSA (in red) show more of this misfolding, while healthy individuals (in green) have the protein in the normal shape. A cut-off line (limit) was established to clearly separate those who have the protein alteration and those who do not, helping to diagnose these diseases early.

As the disease progresses, the distribution of protein shapes changes from healthy to pathological structures, which the sensor can monitor. In addition to distinguishing patients from healthy individuals, the test also clearly distinguished different types of Parkinson's, such as multiple system atrophy (MSA), which can be difficult to diagnose.

This discovery is very important because it offers a way to detect Parkinson’s disease before motor symptoms appear, which would allow for earlier intervention and potentially slow the progression of the disease. In the future, this technology could be used to monitor patients’ progress and test the effectiveness of new drugs in development.

READ MORE:

Alpha-synuclein misfolding as fluid biomarker for Parkinson’s disease measured with the iRS platform

Martin Schuler, Grischa Gerwert, Marvin Mann, Nathalie Woitzik, Lennart Langenhoff, Diana Hubert, Deniz Duman, Adrian Höveler, Sandy Galkowski, 

Jonas Simon, Robin Denz,Sandrina Weber, Eun-Hae Kwon, Robin Wanka, 

Carsten Kötting, Jörn Güldenhaupt, Léon Beyer, Lars Tönges, Brit Mollenhauer, and Klaus Gerwert 

EMBO Mol Med (2025)

https://doi.org/10.1038/s44321-025-00229-z

Abstract

Misfolding and aggregation of alpha-synuclein (αSyn) play a key role in the pathophysiology of Parkinson’s disease (PD). Despite considerable advances in diagnostics, an early and differential diagnosis of PD still represents a major challenge. We innovated the immuno-infrared sensor (iRS) platform for measuring αSyn misfolding. We analyzed cerebrospinal fluid (CSF) from two cohorts comprising PD cases, atypical Parkinsonian disorders, and disease controls. We obtained an AUC of 0.90 (n = 134, 95% CI 0.85–0.96) for separating PD/MSA from controls by determination of the αSyn misfolding by iRS. Using two thresholds divided individuals as unaffected/affected by misfolding with an intermediate area in between. Comparing the affected/unaffected cases, controls versus PD/MSA cases were classified with 97% sensitivity and 92% specificity. The spectral data revealed misfolding from an α-helical/random-coil αSyn in controls to β-sheet enriched αSyn in PD and MSA cases. Moreover, a first subgroup analysis implied the potential for patient stratification in clinically overlapping cases. The iRS, directly measuring all αSyn conformers, is complementary to the αSyn seed-amplification assays (SAAs), which however only amplify seeding competent conformers.