In this study, we present a quantitative αSyn SAA with Lewy-fold specificity for CSF and brain homogenate samples. Blinded measurements demonstrated high sensitivity (97.8%) and specificity (100%) for Lewy-fold α-synucleinopathies, clearly distinguishing PD and DLB from MSA and detecting αSyn copathology in other neurodegenerative diseases such as AD and PSP. As highlighted by a recent consensus proposal for the biological definition of PD by Simuni et al. [50], there is a crucial need for quantitative biomarkers to measure disease progression and response to therapy. So far, assessments of αSyn SAAs provide only yes/no results. By counting the sum of all positive signals from a dilution series of positive samples and controls, we calculated a novel quantitative metric that we termed LFP score, which allowed us to stratify the patient cohort and to monitor disease progression at an individual level as revealed by longitudinal analyses. LFP scores showed significant correlations with clinical measures, including H&Y stage, MDS-UPDRS III, and MoCA.

The quality of the assay is further supported by the fact that CSF samples without pathological αSyn seeds remain negative throughout the measurement period (up to 6 days) as the intrinsic feature of αSyn to spontaneously aggregate is suppressed, preventing false-positive reactions in both neat samples and serial dilutions. In contrast, some laboratories terminate their fluorescence measurements earlier, e.g., after around 40 h, likely to avoid potential false-positive signals that may arise in controls. Interestingly, in our assay, the ThT signal exhibits an initial increase followed by a decline over time in positive samples. This phenomenon, also observed in other studies (e.g., Fig. 2 in Rossi et al. [41]) may reflect the natural decay of assay components.

The development of quantitative biomarkers for neurodegenerative diseases is critical for both diagnosis and therapeutic monitoring. While previous SAA approaches have primarily been used to provide binary outcomes (positive/negative) for diagnostic purposes, advancements in quantitation methodologies are beginning to expand their utility in tracking disease progression and evaluating treatment responses. Existing kinetic parameters, as maximum fluorescence (Fmax), time to threshold (TTT), time to 50% maximum (T50), second fastest TTT (TTT2), slope and area under the curve (AUC), have been explored in various studies [8,9,10, 13, 16, 33, 36, 48] but often fail to show consistent correlations with clinical severity, particularly in differentiating between stages of disease progression. A detailed overview of the available literature is presented in Supplementary Table 5. Of note, 2 publications compared different SAAs in the same study: Russo et al. [44] analyzed Fmax, AUC, TTT, T50 and endpoint dilution (SD50) across three different laboratories (AbbVie, Amprion and Caughey). AbbVie focused on the kinetic parameters Fmax, AUC and TTT. Significant correlations were found between these parameters and University of Pennsylvania Smell Identification Test (UPSIT) and MDS-UPDRS I scores. However, no consistent correlations with other clinical measures like MDS-UPDRS III or MoCA were observed. Amprion also analyzed Fmax, AUC and TTT, finding significant correlations with UPSIT scores. However, similar to the findings at Abbvie, no consistent correlations were observed with broader clinical measures. The Caughey laboratory took a more comprehensive approach, including SD50 to quantify relative amounts of seeding activity. They found positive correlations between SD50 and age (r =  + 0.36, p = 0.006), disease duration (r =  + 0.31, p = 0.02) and NfL levels (r =  + 0.51, p = 0.05). Despite these findings, no consistent correlations were observed between SD50 and clinical measures such as MDS-UPDRS III or MoCA. Kang et al. [27] focused on T50 across two different laboratories (Soto, Green). The Soto laboratory evaluated T50 values in a cohort of 100 PD patients. The study found no significant correlations between T50 values and disease characteristics such as H&Y stage (R2 = 0.0099, p = 0.3235), MDS-UPDRS III (R2 = 0.0013, p = 0.7202) and MDS-UPDRS total scores (R2 = 0.0004, p = 0.8458). The Green laboratory also evaluated T50 values in a cohort of 101 PD patients. Similar to the findings from the Soto laboratory, no significant correlations were found between T50 values and clinical measures including H&Y stage (R2 = 0.0093, p = 0.3365), MDS-UPDRS III (R2 = 0.0039, p = 0.5338) and MDS-UPDRS total scores (R2 = 0.0100, p = 0.3204). Recent advancements in SAA quantitation, such as those by Srivastava et al. [52], have demonstrated that employing endpoint dilution methods, combined with adjustments to dilution factors, replicate numbers, and analytical frameworks, can improve precision, allowing detection of smaller differences in αSyn seed concentrations. While these methodological improvements highlight the evolving nature of quantitative SAAs, the study does not report correlations with clinical measures. Our study introduces the LFP score as a robust, quantitative marker that correlates with established clinical scales such as MDS-UPDRS III and MoCA. Importantly, the LFP score can track disease progression over time, offering a more dynamic tool for monitoring αSyn pathology in PD and DLB. This feature makes it particularly valuable for clinical trials targeting αSyn aggregation, where precise monitoring of disease progression is essential for evaluating treatment efficacy.

There are several factors that may contribute to the inconsistency regarding clinical correlates of aSyn SAA parameters. One of the most important is the inter-individual variability of the CSF matrix that can speed up or slow down the reaction, shifting kinetic parameters such as the TTT and introducing noise, making TTT a variable measure. Several factors within the CSF matrix can accelerate αSyn aggregation. For instance, acidic pH enhances aggregation by exposing hydrophobic domains, while metal ions (e.g., Fe2⁺, Cu2⁺) neutralize charge repulsion and stabilize aggregation-prone structures [12]. Polyamines such as spermine, proteoglycans, nucleic acids (e.g., double-stranded DNA), poly-ADP ribose and fatty acids also promote aggregation by increasing local αSyn concentrations or facilitating membrane interactions. CSF matrix varies widely regarding lipoproteins and albumin. Albumin, the most abundant protein in blood and CSF, may inhibit the aggregation of various amyloidogenic proteins, including αSyn [1, 17, 26]. In addition, high-density lipoproteins in CSF can inhibit αSyn aggregation, indicating that the overall composition of CSF significantly impacts αSyn SAA kinetics [4]. This study by Bellomo et al. highlighted the need to consider intrinsic CSF components in interpreting kinetic parameters such as TTT, AUC, and Fmax due to donor-dependent inhibitory effects on αSyn aggregation. The TTT for example depends heavily on seed concentration and reaction efficiency, which works well with fibrils in a consistent matrix [43, 49]. Furthermore, the calculation of TTT is not straightforward (as well as of Fmax and AUC), especially when not all replicates in a quadruplicate are positive. The mean is sensitive to outliers, making the median a better measure; however, if only two or three replicates in a sample are positive and the others are negative, the TTT is infinite and a median or mean cannot be calculated reliably. Despite the possibility for LFP scores to produce imperfect data points, they provide comprehensive coverage of the positive signals across the dilution series. Using 8 dilutions (including neat) with 4 replicates each captures a dynamic range of signals, effectively covering 32 points. Employing two dilution series (e.g., 1:2, 1:4, 1:8 and 1:3, 1:10, 1:30, and 1:100) ensures better resolution in the relevant ranges and reliable results.

A further advantage of the serial dilution approach lies in the standardization of the CSF matrix as the original biosample is increasingly diluted in standardized control CSF. Noteworthy, the occurrence of positive SAA signals is more variable than expected, occasionally resulting in the reappearance of positive replicates at higher dilutions. This may be due to several factors, such as heterogeneity of seeds in patients. SAAs are inherently non-linear, and slight variations in initial conditions—such as seed concentration, seed conformation and additional proteins or small molecules—can significantly impact the aggregation kinetics or even prevent successful aggregation.

Our findings suggest that the LFP score likely detects the spread of pathology and/or the amount of αSyn aggregates in the CNS rather than the aggressiveness of the disease. By “aggressiveness”, we refer to the rate of clinical progression, defined by how rapidly symptoms such as motor impairment (e.g., MDS-UPDRS III, Hoehn and Yahr stages) or cognitive dysfunction (e.g., MoCA) worsen over time. MDS-UPDRS III, H&Y (motor dysfunction) and MoCA (cognitive dysfunction) scores correlate significantly with the LFP score (Fig. 4). These clinical measures represent how widely the pathology is distributed in the brain. However, our analysis showed (Supplementary Figure S8) that the LFP score was not significantly associated with disease duration (r = 0.17, p = 0.274), changes in MDS-UPDRS III over 12 months (r = 0.26, p = 0.095), changes in levodopa daily dose over 12 months (r = 0.075, p = 0.620), or changes in MoCA scores over 12 months (r =  0.039, p = 0.831). These findings suggest that the LFP score reflects a pathological characteristic rather than clinical progression or aggressiveness. We are the first to present an SAA that tracks the progression of αSyn pathology at an individual level in CSF, as previous studies could not consistently show changes in SD50 values or other kinetic parameters between paired baseline and follow-up samples [44]. The LFP score increased over time in all seven individuals studied (Fig. 5), likely indicating cumulative αSyn pathology progression in the CNS. This observation is consistent with Braak’s staging framework, which correlates disease progression with the spread of αSyn pathology [7]. However, the progression of the disease does not necessarily become more aggressive over time. This notion is consistent with a recently published study [5] which found that the sensitivity of SAAs correlated with the extent of Lewy body pathology, showing higher sensitivity in advanced stages of LBD. The study also highlighted that the number of positive SAA replicates correlated with the αSyn pathology burden. While no validated measures of disease-associated αSyn species in CSF are currently available for direct comparison, indirect evidence from Braak’s framework and recent studies supports the LFP score as a measure of pathology burden rather than clinical aggressiveness.

Our data, in conjunction with findings from recent studies, strongly support the hypothesis that PD, DLB, and MSA are linked to distinct strains of pathological αSyn aggregates. Recent cryo-electron microscopy studies [46, 60] provided detailed structural insights into αSyn fibrils from different α-synucleinopathies. It was shown that αSyn fibrils from MSA patients have unique structural characteristics distinct from those in PD and DLB. The αSyn filaments from PD and DLB share an identical Lewy-fold, characterized by a single protofilament structure. This Lewy-fold is markedly different from the αSyn filaments found in MSA. In MSA, there are two distinct filament types, each made up of two different protofilaments. Even though it is still unclear what pathological protein species is present in the CSF with low-n oligomers being a plausible candidate, our results as well as other publications [47] suggest that the seeds keep the molecular characteristic of their respective underlying α-synucleinopathy. These structural differences likely influence the seeding properties of the aggregates. The binding and nucleation of recombinant αSyn monomers in amplification assays are highly dependent on the molecular characteristics of the seeds. While our assay is optimized for detecting Lewy-related αSyn aggregates, specific conditions (e.g., pH, salt concentrations, and detergent composition) required for amplifying MSA strains may not be present in our setup. For example, studies suggest that MSA-specific strains may require lower pH, higher salt concentrations, or alternative shaking parameters to achieve efficient amplification [47].

Our study has several limitations. First, the results predominantly reflect a Caucasian population in Germany, which may restrict the applicability of our findings to other ethnic groups. Patients were recruited from a single tertiary center only (LMU Munich). To minimize the risk of clinical misdiagnosis, all patients were evaluated at a specialized outpatient clinic for movement disorders, and diagnoses were reconfirmed at each follow-up visit. We have not yet assessed the application of our assay in prodromal stages, such as idiopathic REM sleep behavior disorder (iRBD). Existing data suggest that SAAs can detect α-synuclein pathology in prodromal stages [24], making this an important direction for future research. While CSF sampling remains invasive, less invasive approaches, such as peripheral tissue sampling, may facilitate broader applicability in early detection and stratification frameworks.

The novel LFP score exhibits strong correlations with clinical severity measures, establishing it as a promising progression marker for Lewy-fold α-synucleinopathies such as PD and DLB and differentiating these conditions from MSA in blinded measurements. The findings underscore the significance of dilution series and the resulting LFP score in accurately capturing the extent of αSyn pathology across the central nervous system. Notably, our study introduces a novel aspect as our SAA is not only quantitative but also capable of capturing disease progression longitudinally at an individual level which sets it apart from previous approaches. This quantitative capability positions our SAA in combination with the LFP score as a potential pharmacodynamic/response tool for evaluating new disease-modifying therapies targeting αSyn, thereby offering significant opportunities in clinical trials and therapeutic monitoring. Future research should aim to validate these results in larger and longitudinal clinical cohorts to enhance their generalizability and clinical applicability.