This webinar will explore best practices for experimental design in LC-MS–based proteomics, with a focus on building robust, reproducible, and scalable workflows. Topics will include key considerations for study design, sample throughput, and data quality, as well as practical strategies to maximize performance across diverse proteomics applications.
The session will combine real-world experience and technical insight to help researchers design experiments that deliver confident, actionable results.
This presentation will discuss statistical principles of design and analysis of experiments, their application to LC-MS-based proteomics and chemoproteomics, and implementation in the free and open-source software ecosystem MSstats.
As LC–MS proteomics workflows become faster and more scalable, the bottleneck shifts from acquisition to experimental design, quality control, and consistent interpretation. In this talk we’ll share practical design patterns, throughput planning, and early‑warning metrics that catch drift and batch effects before they compromise a study. I’ll also highlight how high‑throughput LC (including Evosep workflows) pairs with standardised, automated reporting so teams can iterate quickly in pilots and then scale with confidence across diverse proteomics applications.
