Evosep Webinar
Scalable Workflows for standardized proteomics
Available on demand
Scalable workflows are no longer a nice‑to‑have in proteomics, they’re essential.
In this webinar, we explore how standardized, automated proteomics workflows are enabling reproducible results at scale—from discovery to translational and applied research. Hear directly from experts and users as they share practical insights on building robust, high‑throughput workflows that keep pace with growing sample volumes, increasing complexity, and real‑world laboratory demands.
Join us to learn how scalable proteomics workflows are moving from promise to practice.
Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC-MS Plasma Proteomics
Talk by Salla Keskitalo, PhD, LSRI Director Viikki Proteomics Unit, University of Helsinki
Plasma is an ideal material for proteomics due to its diverse protein content reflecting physiological and pathological states, and its compatibility with minimally invasive sampling. Deep proteomic profiling of plasma is limited by high-abundant proteins that mask the detection of low-abundant proteins. To overcome this, we compared five plasma protein enrichment methods, Mag-Net, ENRICHplus, ENRICHiST, EasySep, and EXONET, against neat plasma using LC-MS proteomics. All five methods substantially increased protein identifications, with Mag-Net, ENRICHplus, EasySep, and EXONET yielding up to 4200 proteins per sample, over 7-fold more than neat plasma, using a 44-minute gradient on the Evosep One and data-independent acquisition on the timsTOF Pro 2. These methods enriched extracellular vesicle-associated proteins while effectively depleting high-abundant proteins. To further enhance performance and scalability, we optimized the Mag-Net protocol by increasing the plasma-to-bead ratio and automated the workflow, including Evotip loading, on the Biomek i5 liquid handler. The automated Mag-Net, combined with the Orbitrap Astral mass spectrometer, yielded up to 4500 proteins per sample with a throughput of 100 samples per day. The automated Mag-Net enrichment strategy enables affordable, scalable, high-throughput LC-MS plasma proteomics, supporting biomarker discovery across large cohorts.
High-Throughput Single-Cell Proteomics of In Vivo Cells
Talk by Joachim Smollich, PhD Student, Faculty of Biology, Department of Molecular Cell Biology, Weizmann institute of science
Single-cell mass spectrometry-based proteomics (SCP) can resolve cellular heterogeneity in complex biological systems and provide a system-level view of the proteome of each cell. Major advancements in SCP methodologies have been introduced in recent years, providing highly sensitive sample preparation methods and mass spectrometric technologies. However, most studies present limited throughput and mainly focus on the analysis of cultured cells. To enhance the depth, accuracy, and throughput of SCP for tumor analysis, we developed an automated, high-throughput pipeline that enables the analysis of 1536 single cells in a single experiment. This approach integrates low-volume sample preparation, automated sample purification, and LC-MS analysis with the Slice-PASEF method. Integration of these methodologies into a streamlined pipeline led to a robust and reproducible identification of more than 3000 proteins per cell. We applied this pipeline to analyze tumor macrophages in a murine lung metastasis model. We identified over 1700 proteins per cell, including key macrophage markers and more than 500 differentially expressed proteins between tumor and control macrophages. PCA analysis successfully separated these populations, revealing the utility of SCP in capturing biologically relevant signals in the tumor microenvironment. Our results demonstrate a robust and scalable pipeline poised to advance single-cell proteomics in cancer research.
