Spatial proteomics data analysis
Spatial proteomics data analysis
to be announced
General context
Imaging-based spatial proteomics technologies are increasingly used to study tissue organization and protein expression within spatial contexts. These methods enable high-dimensional characterization of proteins across tissue sections using platforms such as MACSima (Miltenyi Biotec), COMET (Lunaphore), and PhenoCycler Fusion (Akoya Biosciences). However, handling and analyzing the resulting data can be challenging, requiring specialized workflows.
This course serves as a follow-up to the Targeted spatial transcriptomics analysis training and focuses on the Harpy pipeline for spatial proteomics analysis. This includes cell segmentation, feature extraction, and unsupervised clustering of cells or pixels to support cell-type annotation. You will learn to process, visualize, and interpret data from multiplex imaging platforms efficiently and reproducibly.
- Import and structure imaging-based spatial proteomics data from different commercial platforms
- Optimize segmentation strategies for multiplex imaging datasets
- Quantify marker expression levels in a spatial context
- Annotate cell populations using the Harpy pipeline
This course is fully hands-on, guiding participants through the HArpy pipeline to analyze imaging-based spatial proteomics data. Short theoretical introductions will be complemented by practical exercises using real datasets.
Participants must have completed the introductory course on targeted spatial transcriptomics data analysis first.
This course is part of the learning path Spatial targeted transcriptomics and/or proteomics data.
AI methods used
This course uses applied machine learning methods within the Harpy pipeline, particularly for cell segmentation, feature extraction, and unsupervised clustering of cells or pixels to support cell-type annotation in spatial proteomics data.
Trainers
Arne Defauw
Arne holds a masters degree in Mathematics and a PhD in Physics. After his PhD he worked as a machine learning engineer in the industry, where he specialized in natural language processing. Now he works at VIB Spatial Catalyst, where he contributed to SPArrOW, a python library to analyse all types of spatial transcriptomics data. As a member of the Spatial Catalyst he also provides support for spatial omics data analysis and helps optimize bioinformatic tools for spatial analysis.
Frank Vernaillen
After working as a software developer in a company on physics-based modeling and 2D and 3D computer graphics, Frank joined VIB in 2016 to work on image restauration and registration of electron microscopy images, machine learning,... Via VIB's first imaging-based spatial proteomics platform, he got involved in spatial omics data analysis, and naturally joined the Spatial Catalyst when it was formed in 2023, to continue work on data analysis and development of analysis pipelines.
Julien Mortier
Julien obtained his PhD on phenotypic heterogeneity and stress responses in bacteria. In his postdoc at SciLifeLab, he developed a machine-learning based image analysis workflow to study phenotypes of bacteria under starvation conditions and he established an end-to-end workflow for Transposon Sequencing. Now he works at VIB Spatial Catalyst, where he provides support for spatial omics data analysis and helps optimize bioinformatic tools for spatial analysis.