This poster demonstrates the enhancement of the protein identification performance in plasma proteomics, using the Proteograph™ workflow with DIA LC-MS on the Orbitrap Exploris 480 MS with and without FAIMS Pro Interface, and how it identified ~3800 plasma protein IDs in ~3 hours of LC-MS analysis time.
This poster demonstrates the assessment of various methods used to roll peptides to protein groups for the Proteograph™ workflow with DIA LC-MS analysis and their potential impact on downstream bioinformatics analysis and biological insight.
This poster demonstrates the comparison of Seer's machine learning algorithms SeerML for Alzheimer’s disease classification, and how the SeerML, an AutoML classifier, achieved AUC of 0.80 vs. other classifiers.
This poster demonstrates the comparison of various pQTL mapping methods, and optimizing the pQTL analysis workflow for label-free DIA LC-MS generated data with DIA-NN, and the evaluation of the data analysis workflow in a 184-plasma sample, Alzheimer disease cohort study.
This poster demonstrates how the Proteograph™ workflow offers deep, broad, and rapid processing of samples to enable large and more powerful studies per unit time and resource. The 200 subject Alzheimer (AD) cohort study was processed in approximately three weeks and generated unbiased proteomics data of over 5,000 proteins as well as identified new targets as well as known targets and pathways implicated in AD. The data from both acquisition schemes was equally powerful for both classifications of disease state, identifying pQTLs with DIA to provide 2X higher sample analysis throughput.
This poster demonstrates how Liquid-Chromatography-Mass Spectrometry (LC-MS) based plasma proteomics technologies are now capable of providing deep proteome coverage with sufficient reproducibility, robustness, and throughput. This study highlights the importance of plasma proteomics in a large multi-omics biomarker discovery study of >1800 samples with >3900 protein groups IDs using the Proteograph workflow and Bruker timsTOF.
This poster demonstrates how the Proteograph™️ Product Suite, combined with DIA LC-MS on Orbitrap Exploris 480 MS, enables quantitative plasma proteomics with depth and scale. Seer’s multi-nanoparticle workflow uniquely enables deep, unbiased biomarker discovery in biofluids to offer significant opportunities for protein biomarker discovery at scale.
This poster highlights recent Seer's research (published in Advanced Materials) on the dynamics of protein corona formation around nanoparticles. By measuring the individual dynamics of unprecedented 3200 plasma proteins, this work paves the road for quantitative modeling of nano-bio interactions at the molecular level, streamlining the design of novel nanoparticles and further enhancing protein quantification. The latter is exemplified by successful application of machine learning to reconstruct absolute protein intensities in neat plasma from the NP-protein profiles.
This poster demonstrates how the Proteograph™ workflow provides deeper coverage of the proteome compared to neat plasma/serum digestion workflow, enabling differentiations of sample types with higher resolution. The study evaluated different blood-based sample types for deep blood-based proteomics analysis, highlighting the difference between plasma and serum proteomics and the importance of using a single sample type in large-scale biomarker discovery studies, or additional consideration to be made for sample-type differences.
This study evaluates Seer’s multi nanoparticles- based Proteograph workflow performance compared to neat plasma workflow in capturing glycoproteins and a conventional glycol-enrichment workflow using magnetic Fe-NTA beads. This poster demonstrates how by compressing the dynamic range and making the low abundance proteins and corresponding peptides more visible to the downstream LC-MS, nanoparticles can facilitate the detection of peptides with increased sensitivity and more efficiency, significantly enhancing the coverage of these proteins in blood plasma even without subsequent enrichment.