Quantitative measurements of α-synuclein seeds in CSF inform diagnosis of synucleinopathies.
Abdi IY., Sudhakaran IP., Ghanem SS., Vaikath NN., Majbour N., Goh YY., Vijiaratnam N., Girges C., Constantinides VC., Kapaki E., Paraskevas GP., Weber S., Adeli G., Vekrellis K., Erskine D., Hu M., Foltynie T., Houlden H., Parkkinen L., van de Berg WD., Mollenhauer B., Schlossmacher MG., El-Agnaf OM.
Diagnosing α-synucleinopathies and assessing target engagement in trials is hindered by the lack of reliable biomarkers. Here, we introduce a first-in-kind quantitative, highly sensitive, and disease-specific diagnostic assay, named Seeding Amplification ImmunoAssay (SAIA), developed and validated to detect synucleinopathy-linked disorders. To this end, we used wide range of specimens, including 38 brain homogenates (BH) and 559 cerebrospinal fluid (CSF) samples from subjects with diverse synucleinopathy disorders, non-synucleinopathy diseases, idiopathic REM sleep behavior disorder (iRBD), and controls. SAIA generated robust results detecting disease-related α-synuclein seeds in BH samples at attogram levels, as referenced to preformed fibrils of α-synuclein. Furthermore, we conducted side-by-side comparisons between SAIA and a traditional Seeding Amplification Assay (SAA), which revealed high concordance. Further, SAIA distinguished synucleinopathies from non-synucleinopathies and controls with sensitivities and specificities ranging between 80-100% and area under the curve values exceeding 0.9. SAIA also accurately identified 24/24 (100%) iRBD cases, considered a prodromal state of PD, with 100% sensitivity and 80% specificity. Further optimization of SAIA through timepoint analyses revealed that shorter incubation times enhanced the assay's specificity for distinguishing MSA from PD highlighting the potential for improved differentiation between specific synucleinopathies. In conclusion, SAIA represents a novel, high-throughput method to screen, diagnose, and monitor synucleinopathy disorders in living subjects, offering significant improvements over existing methods through its quantitative output, shorter incubation time, and simplified workflow, features that enhance its suitability for clinical trial applications.

