Session: Session 3
Enhanced Plasma Proteomics: Nanoparticle Enrichment applied to a cohort of Becker muscular dystrophy patients
Becker muscular dystrophy is a genetic disorder causing progressive muscle weakness in need of reliable plasma biomarkers for early diagnosis, monitoring, and treatment evaluation.
Plasma proteome analysis is challenging due the presence of high abundance proteins. Nanoparticle (NP)-based enrichment promises to compress this dynamic range and enhance proteome coverage without compromising quantification.
We evaluate the added value of the NP-based approach in a large Becker cohort compared to our neat plasma workflow. Specifically, we assess the PGs IDs, quantification quality, and the impact of plasma collection on both neat and NP-enriched plasma samples.
Plasma from 264 Becker's cohort (100 patients collected up to three times across 7 years and 50 healthy controls) were digested using S-Trap plates (Protifi). The same samples were enriched with nanoparticles using the Proteograph XT SP100 automation (Seer). 600ng of peptides were injected into the EvosepOne LC-TimsTOF HT MS (Bruker Daltonics) using the 60SPD method, data acquired using the dia-PASEF mode and analyzed with DIANN1.8.1.
We quantified an average of 700 PG IDs in neat plasma using the recently described using EV-boost approach, and an average of 3300 PGs per samples (min1500; max 4500) in NP-enriched plasma, with CVs of 42% and 45% respectively (calculated on controls, while patients were more variable in both).
We observed that the number of PGs correlated with contamination from plasma cells and coagulation factors, but this didn’t impact the detection of low-abundance proteins (IL16, IL18). We propose a method to minimize bias from sample collection and initial treatment variability within the same cohort.
Importantly, our improved data analysis with NP-enriched samples identified clear markers of muscle damage, indicating tissue leakage, at levels as low as 100pg/mL. These markers correlated with Walton scoring for disease staging and were only detectable using NP-based enrichment.
Plasma proteome analysis is challenging due the presence of high abundance proteins. Nanoparticle (NP)-based enrichment promises to compress this dynamic range and enhance proteome coverage without compromising quantification.
We evaluate the added value of the NP-based approach in a large Becker cohort compared to our neat plasma workflow. Specifically, we assess the PGs IDs, quantification quality, and the impact of plasma collection on both neat and NP-enriched plasma samples.
Plasma from 264 Becker's cohort (100 patients collected up to three times across 7 years and 50 healthy controls) were digested using S-Trap plates (Protifi). The same samples were enriched with nanoparticles using the Proteograph XT SP100 automation (Seer). 600ng of peptides were injected into the EvosepOne LC-TimsTOF HT MS (Bruker Daltonics) using the 60SPD method, data acquired using the dia-PASEF mode and analyzed with DIANN1.8.1.
We quantified an average of 700 PG IDs in neat plasma using the recently described using EV-boost approach, and an average of 3300 PGs per samples (min1500; max 4500) in NP-enriched plasma, with CVs of 42% and 45% respectively (calculated on controls, while patients were more variable in both).
We observed that the number of PGs correlated with contamination from plasma cells and coagulation factors, but this didn’t impact the detection of low-abundance proteins (IL16, IL18). We propose a method to minimize bias from sample collection and initial treatment variability within the same cohort.
Importantly, our improved data analysis with NP-enriched samples identified clear markers of muscle damage, indicating tissue leakage, at levels as low as 100pg/mL. These markers correlated with Walton scoring for disease staging and were only detectable using NP-based enrichment.