Virtual

Now available on-demand

Explore how the BeatBox homogenizer is transforming sample preparation across various tissue types – even challenging FFPE samples. Designed to fit in any lab and easy to use, BeatBox streamlines protein extraction for complex downstream analysis like LC-MS-based proteomics. Whether your focus is proteomics, lipidomics, or metabolomics, BeatBox supports your research with precision and efficiency.

Connect with fellow scientists, share best practices, and gain valuable insights into how BeatBox is driving advancements in your field. Don’t miss this chance to learn, exchange ideas, and push the boundaries of your research.

Welcome & Introduction to BeatBox tissue homogenization & cell lysis

Dr. Quentin Enjalbert, Head of EU Sales, PreOmics

Proteomic Memory in Human Skeletal Muscle

Dr. Salla Keskitalo, Laboratory Head, Helsinki Proteomics Center, University of Helsinki, Finland

Beatbox homogenization enables high-throughput, miniaturized, and in-depth tissue proteomics and phosphoproteomics

Sara Ceccacci, Postdoc, Proteomics platform Necker University Paris Cité, France

Large-Scale FFPE Mapping for Lymphoma Using PreOmics Technology and Fully Automated SP3

Lopamudra Chatterjee, PhD Student, Precision Proteomics Center Davos, University of Zurich

Towards FFPE proteomics in the University Hospital in Utrecht: Initial steps to combine spatial proteomics with FFPE proteomics of pancreatic neuroendocrine tumors 

Andreas Sonnen, Assistant professor, University Medical Center Utrecht

Q&A session 

Open questions to the speakers and PreOmics team 

Investigating repeated resistance training (RT) separated by a 10-week break revealed a proteomic memory of muscle growth. Thirty participants completed two 10-week RT bouts (with or without detraining). Muscle biopsies were analyzed via DIA-PASEF mass spectrometry following sample preparation with PreOmics Tissue kits. We found reproducible increases in energy metabolism proteins, largely reversed after detraining, while cytoskeletal and calcium-binding proteins (e.g., CAPN2) remained elevated. These findings support retained proteomic adaptations and muscle memory in human skeletal muscle.

Tissue homogenization is often a critical step in mass spectrometry (MS)-based proteomics workflows, significantly impacting the scalability, reproducibility, and depth of analysis. In addition to this, for long-term preservation and compatibility with molecular and histological analyses, tissues are frequently embedded in MS-incompatible materials, such as Optimal Cutting Temperature (OCT). This requires additional steps for the removal of these contaminants, resulting in even longer extraction times and lower protein recovery. Low protein yield and low extraction reproducibility can hinder comprehensive tissue phosphoproteomics studies, resulting in poor and variable recovery of phosphopeptides following digestion and enrichment steps. This is particularly true in challenging tissues such as skin, difficult to homogenize due to the high abundance of structural proteins like keratins and collagens. Here, we show the capability of Beatbox homogenization to facilitate tissue proteomics and phosphoproteomics studies in different tissues – snap-frozen biopsies from hippocampus, liver, and skin, as well as OCT-embedded skin sections.

Lymphomas are a heterogeneous group of malignancies requiring precise molecular characterization. To systematically map subtypes, we developed a fully automated workflow for FFPE samples, integrating the PreOmics BeatBox with SP3 on the Opentrons Flex. Benchmarking against fresh frozen samples and comparing curls to punch biopsies from the same individual, we observed high correlation across sample types (r > 0.9 between FFPE and fresh frozen). Sample preparations from consecutive slides from the same tissue block showed high protein yield reproducibility and low quantitative variation (CV < 12%), indicating robust intra-donor consistency.

Pancreatic neuroendocrine tumors (PanNETs) exhibit significant molecular heterogeneity, necessitating advanced analytical approaches for improved classification and prognostication. MALDI imaging provides spatially resolved insights into proteomic and glycomic alterations within tumor tissue, capturing regional differences at a molecular level. By integrating laser microdissection with mass spectrometry-based proteomics, distinct tumor subpopulations can be analyzed, identifying biomarkers linked to tumor behavior and progression. This methodology serves as a foundation for a larger study utilizing FFPE proteomics, aiming to enhance biomarker discovery, refine PanNET subtyping, and hopefully in the future inform personalized therapeutic strategies.

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