Disease associated pathological aggregates of alpha-synuclein (αSynD) exhibit prion-like spreading in synucleinopathies such as Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). Seed amplification assays (SAAs) such as real-time quaking-induced conversion (RT-QuIC) have shown high diagnostic sensitivity and specificity for detecting proteopathic αSynD seeds in a variety of biospecimens from PD and DLB patients. However, the extent to which relative proteopathic seed concentrations are useful as indices of a patient’s disease stage or prognosis remains unresolved. One feature of current SAAs that complicates attempts to correlate SAA results with patients’ clinical and other laboratory findings is their quantitative imprecision, which has typically been limited to discriminating large differences (e.g. 5–10 fold) in seed concentration. We used end-point dilution (ED) RT-QuIC assays to determine αSynD seed concentrations in patient biospecimens and tested the influence of various assay variables such as serial dilution factor, replicate number and data processing methods. The use of 2-fold versus 10-fold dilution factors and 12 versus 4 replicate reactions per dilution reduced ED-RT-QuIC assay error by as much as 70%. This enhanced assay format discriminated as little as 2-fold differences in αSynD seed concentration besides detecting ~2-16-fold seed reductions caused by inactivation treatments. In some scenarios, analysis of the data using Poisson and midSIN algorithms provided more consistent and statistically significant discrimination of different seed concentrations. We applied our improved assay strategies to multiple diagnostically relevant PD and DLB antemortem patient biospecimens, including cerebrospinal fluid, skin, and brushings of the olfactory mucosa. Using ED αSyn RT-QuIC as a model SAA, we show how to markedly improve the inter-assay reproducibility and quantitative accuracy. Enhanced quantitative SAA accuracy should facilitate assessments of pathological seeding activities as biomarkers in proteinopathy diagnostics and prognostics, as well as in patient cohort selection and assessments of pharmacodynamics and target engagement in drug trials.
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