Multi-Omics Webinar: Registration Page
An Automated Sample Preparation Platform for Integrated Proteomics, Lipidomics, and Metabolomics
Wednesday, June 10, 2026
2:00 p.m. EDT
Presenters:
- Fred Foster, Applications Scientist, GERSTEL, Inc.
- Noah Smeriglio, Ph.D. Candidate, Chemistry, University of Maryland
- Gwangbin Lee, Postdoctoral Associate, Ph.D. in Chemistry, University of Maryland
Summary:
In this webinar, Fred Foster, Applications Scientist at GERSTEL, Inc., will introduce GERSTEL’s automated sample preparation systems, then transition to a multi-omics presentation by Noah Smeriglio, Ph.D. Candidate in Chemistry, and Gwangbin Lee, Ph.D. in Chemistry, Postdoctoral Associate at the University of Maryland.
This presentation will highlight an automated, integrated multi-omics workflow for extracting proteins, lipids, and metabolites from a single sample using the GERSTEL MPS platform. Validated across cell and tissue samples and coupled with LC-MS/MS analysis, the workflow enables reproducible, high-throughput multi-omics studies in complex biological applications.
You will learn how to:
- Optimize key parameters in your workflow
- Improve recovery and reproducibility of proteins, lipids, and metabolites
- Increase throughput for complex biological samples
Full Abstract
Mass-spectrometry-based omics technologies have enabled the comprehensive characterization of diverse types of molecules, such as proteins, lipids, and metabolites, across biological systems. Integration of multiple molecular classes from the same sample reduces variability and enables system-level insights into biology and disease. However, conventional sample preparation methods using Folch or MTBE extraction are labor-intensive, susceptible to variability, and difficult to scale for high-throughput applications. Here, we developed an automated and integrated multi-omics sample preparation workflow using the GERSTEL MPS platform. Key experimental parameters were systematically optimized to achieve robust and reproducible recovery of proteins, lipids, and metabolites from a single sample. The workflow was validated across both cell and tissue samples, demonstrating strong reproducibility and scalability. Coupled with LC-MS/MS analysis, this platform was used to analyze over 60 brain samples from a neurodevelopmental disorder mouse model, achieving the identification and quantification of over 10,000 proteins, 2,000 lipids, and 700 metabolites. In summary, this automated multi-omics workflow provides a robust and scalable solution for high-throughput MS-based multi-omics studies.
