scverse x single-cell proteomics 2026

With recent advances in mass spectrometry, MS based single-cell proteomics is now able to identify thousands of proteins in one cell. However, there are still challenges: lack of good benchmarking data sets, missing standards for normalization and analysis strategies. This hackathon wants to bring together the MS-based single-cell proteomics community and scverse to address these challenges. We are co-organizing this hackathon with the 7th European Symposium on Single-Cell Proteomics (ESCP) conference in mind. In a two day event at the Vienna BioCenter we want to discuss and work on three main topics: curated datasets, data processing, and downstream analysis.

For this purpose, we explicitly welcome scientists with experience in MS based low input / single cell proteomics, alongside software developers, bioinformaticians and computational biologists. Since it is the first event for the MS-based sc community the scope will be very open. Let’s come together to identify challenges and opportunities. As a rough direction you can check the workstreams. Our main goal is to build a community that joins forces also beyond this event to select and provide curated datasets that can be used to benchmark workflows and methods, discussing analysis strategies and also providing functionality in form of software solutions.

For MS-based single-cell proteomics (SCP) practitioners: Active participation in this community will foster collaborative problem-solving and the establishment of community-curated, standardized datasets. This framework will streamline the testing and validation of analyses, significantly reducing methodological overhead. Ultimately, this increased rigor and efficiency will empower the community to dedicate greater time and resources to deeper biological interpretation and scientific breakthrough.

For computational biologists and developers: The stage of MS-based SCP currently transfers analytical solutions from single-cell transcriptomics. However, the distinct characteristics of protein data compared to transcript data; especially regarding data sparsity, dynamic range, and technical variability; demand purpose-built methodologies. This challenge represents a critical opportunity to apply your expertise in developing scalable computational approaches that can manage complex datasets, ensuring method reproducibility and establishing new gold standards for data analysis in the field.