Orthogonal validation of PD Biomarkers: Multi-platform proteomics profiling of CSF, Plasma, and Urine confirms DDC as a consistent candidate

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Abstract Background High throughput proteomics has enabled hypothesis free biomarker discovery. However, differences in sample sizes, biological fluid, and quantification technologies have limited replication and validation of the results, and studies on the cross-platform variability are lacking. Here, we present the first orthogonal validation across three platforms in Parkinson’s disease (PD) to understand the technical and biological challenges of proteomic studies. Methods We have leveraged publicly available proteomic data from cerebrospinal fluid (CSF), plasma, and urine within the Parkinson’s Progression Markers Initiative (PPMI) cohort, generated using SomaScan5K (CSF), mass spectrometry (MS; CSF, plasma, and urine), and Olink Explore (CSF and plasma). Across platforms, we compared 375 proteins that were consistently quantified. We performed differential abundance analysis comparing PD versus healthy controls followed by sensitivity analyses (mutation carriers, at-risk participants, longitudinal analyses) to further understand the findings. Results In CSF, we found significant correlations between effect sizes from the 375 proteins quantified by SomaScan5K and MS (ρ=0.42, p=2.60×10 □ □), as well as SomaScan5K and Olink Explore (ρ=0.15, p=3.15×10□ 3 ) while MS and Olink Explore showed no significant correlations in CSF or plasma. Orthogonal validation identified two proteins (DLK1, GSTA3) replicated between SomaScan5K and Olink Explore and seven proteins (ALCAM, CHL1, CNDP1, NCAM2, PEBP1, PTPRS, SCG2) replicated between MS and SomaScan5K. No proteins replicated between MS and Olink Explore in CSF or plasma. DDC showed consistent dysregulation across analyses. In CSF (Olink Explore), it was dysregulated in PD participants (beta=0.79, p=8.49×10 −16 ), and in at-risk individuals (beta=0.64, p=1.41×10 −7 ) including those with hyposmia (beta=0.70, p=2.13×10 −5 ) and REM Sleep Behavior Disorder (beta=0.52, p=1.00×10 −3 ). In urine, DDC was higher in at-risk individuals (beta=0.43, p=7.28×10 −5 ), driven by LRRK2 + at-risk participants (beta=0.59, p=1.74×10 −6 ), as well as in symptomatic mutation carriers, LRRK2 + (beta=0.68, p=9.08×10 −8 ), and GBA + (beta=0.28, p=0.04). Conclusions Biologically, these findings add further evidence that DDC has strong potential as a biomarker. Methodologically, our findings emphasize that platform selection can introduce more variance than that originating from disease status, which limits the reproducibility across technologies. This highlights the challenges and importance of cross-platform validation in proteomic biomarker research, and the translation of those discoveries to the clinic.

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