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Guido Sonsmann (Thermo Fisher Scientific)

Title: “The Beauty of Cake Proteomics”

Monika Pepelnjak (Senior Data Scientist at Biognosys Group (PreOmics, Biocatres, and Biognosys))

Title: From Challenge to Solution: a competitive library-free DIA workflow for Immunopeptidomics

Abstract: 

Introduction:

Immunopeptidomics plays a crucial role in understanding the immune system, paving the way for the development of targeted immunotherapies. While the proteomics field has mostly switched from DDA to DIA, DDA is still widely used for immunopeptidomics. Due to the exponentially larger size of a non-tryptic search space, library-free DIA analyses of immunopeptidomics are computationally challenging.  Here we present a novel, fully integrated library-free DIA workflow for class-1 and class-2 immunopeptidomics that is sensitive and time efficient.

Methods:

To alleviate the challenges of the unspecific search space, we developed a novel search engine, Kuiper, in Spectronaut. Kuiper is utilized to reduce the peptide search-space to only the top-most likely candidate peptides per MS2 scan. These peptides are then fully enumerated and scored using the Pulsar search-engine. To evaluate the performance of the new library-free pipeline, we compared Spectronaut 20 to Spectronaut 19 on over 25 different immunopeptidomics experiments. We further compared the library-free and library-based search on 13 different datasets.

Results:

Spectronaut 20 leads to on average 75% more peptide identifications for class 1 peptides and around 20% more for class 2 peptides, while reducing the search time by 70%. Furthermore, library-free pipeline leads to approximately 40% more identifications than library-based search. We observed a high overlap between the two searches, with library-free search identifying more unique peptides. Despite higher identifications, we validated that > 90% of identified peptides were predicted to be strong binders and showed expected peptide-length distribution. Furthermore, the peptide motifs constructed from the binders show high similarity between the searches and to the expected binding motifs. Lastly, we tested that global and local FDR were in the expected range (~1%), further confirming the validity of observations.

Conclusion:

Our new library-free DIA pipeline significantly increases identifications of biologically relevant immunopeptides without the need for prior HLA allele information.

Lutz Priebe (Olink, part of Thermo Fisher Scientific)

Title: High-Multiplex Plasma Proteomics Using Proximity Extension Assay Technology for Translational Research

Abstract: Scalable profiling of low-abundance proteins is essential for biomarker discovery and systems-level understanding of disease. Proximity Extension Assay (PEA) technology enables high-multiplex protein quantification by combining dual-recognition antibody assays with DNA-based detection. When paired antibodies bind the same target, their conjugated oligonucleotides hybridize and are extended to generate a unique sequence, enabling sensitive and specific quantification with minimal background.

The platform supports measurements of up to ~5,400 proteins from small sample volumes using next-generation sequencing readout, with demonstrated reproducibility and suitability for large clinical cohorts.

We highlight the application of high-throughput plasma proteomics within the UK Biobank, where large-scale protein profiling across tens of thousands of participants has enabled the identification of protein associations with incident disease, risk stratification, and biological pathway insights across multiple therapeutic areas. Integration with genomic and longitudinal health data illustrates the power of population-scale proteomics to inform disease mechanisms and biomarker development.

High-multiplex affinity proteomics provides a scalable framework to accelerate translational research at population scale.

Catherine Gilbert (Seer)

Title: Enabling Deep Proteomics and Biomarker Discovery with Seer’s Proteograph® Product Suite
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Title: TBA