Publications

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.

This poster demonstrates how nanoparticle coronas provide deep coverage of biological content, including the identification of low abundant cytokines and chemokines, and post-translationally modified peptides. Understanding the nanoparticle-protein interactions on the quantitative level enables the design of NP-based assays to interrogate specific physicochemical fraction of the proteome occupied by hundreds to thousands of proteoforms.

This poster demonstrates how the Proteograph™ Analysis Suite (PAS) is a comprehensive proteogenomic software application that enables user-friendly and reproducible multi-omic analyses of proteomic and genomic data at scale. The cloud-based solution, capable of completing data analysis of ~200 sample proteomics in approximately five and a half hours, allows researchers to process, analyze, and visualize proteomics data sets generated by Liquid Chromatography-Mass Spectrometry (LC-MS).

This poster highlights current challenges in large-scale DIA data analysis and evaluates the impact of various spectral library approaches. The study leverages the Proteograph workflow and Bruker timsTOF platforms to investigate ~900 patient serum samples with controls.

This poster demonstrates how the Proteograph workflow enables, deep, and rapid multi-omic analysis of plasma in a study that resulted in ~5X better protein groups coverage versus neat plasma digestion workflow with a single injection DDA LC-MS workflow with FAIMS Pro interface on the Orbitrap MS system.

This poster demonstrates the deployment of well-established community accepted LC-MS algorithms in a smart cloud infrastructure to enable large-scale proteomics data analysis not possible in desktop environments. Seer's data analysis platform cost-effectively scales up to tens of thousands of samples in a single aggregate analysis. The challenges of cross-file analysis are addressed using the distributed processing ecosystem such as Apache Spark, Databricks and Delta Lake. Here we demonstrate proof of concept using peptide-centric approaches to re-interrogate raw signals using such an ecosystem for ultra-fast signal detection of terabytes of data in minutes, extending the microservice architecture as well as employ