Lecture
Scaling up and democratizing spatial tissue proteomics
- at -
- ICM Saal 2
- Type: Lecture
Lecture description
M. Klingeberg, Berlin/DE; C. Krisp, Bremen/DE; S. Fritzsche, Berlin/DE; J. Nimo, Berlin/DE; S. Schallenberg, Berlin/DE; D. Hornburg, Billerica/USA; F. Coscia, Berlin/DE
Spatial proteomics (SP) enables the mapping of protein abundance, localization, and interactions directly within intact tissues, providing powerful insights into cellular organization and disease biology. By preserving spatial context, SP allows the reconstruction of molecular networks within their native microenvironment and offers new
opportunities to study tissue heterogeneity in health and disease. Integrating complementary technologies further expands the potential of spatial proteomics to link cellular phenotypes with proteome-wide molecular information.
A central approach in this effort is Deep Visual Proteomics (DVP), which combines highresolution microscopy, AI-driven image analysis, laser microdissection, and mass spectrometry–based proteomics. This strategy enables phenotype-guided isolation of defined cellular regions or celltypes followed by deep proteomic profiling, allowing the investigation of protein expression and molecular pathways directly within histologically characterized tissue structures.
To support large-scale studies, we developed a scalable spatial proteomics workflow that integrates automated sample preparation using the cellenONE platform with advanced data-independent acquisition mass spectrometry. The optimized workflow minimizes sample loss during ultra-low input processing and enables efficient preparation of microdissected tissue samples. In combination with high-throughput LC-MS acquisition
strategies, this approach enables robust and reproducible spatial proteomic profiling across diverse tissue types.
In this presentation, I will discuss our integrated spatial proteomics workflows for scalable tissue analysis and describe our open-source computational pipelines designed to facilitate data analysis and enable broad accessibility of spatial proteomics technologies across the research community.
Spatial proteomics (SP) enables the mapping of protein abundance, localization, and interactions directly within intact tissues, providing powerful insights into cellular organization and disease biology. By preserving spatial context, SP allows the reconstruction of molecular networks within their native microenvironment and offers new
opportunities to study tissue heterogeneity in health and disease. Integrating complementary technologies further expands the potential of spatial proteomics to link cellular phenotypes with proteome-wide molecular information.
A central approach in this effort is Deep Visual Proteomics (DVP), which combines highresolution microscopy, AI-driven image analysis, laser microdissection, and mass spectrometry–based proteomics. This strategy enables phenotype-guided isolation of defined cellular regions or celltypes followed by deep proteomic profiling, allowing the investigation of protein expression and molecular pathways directly within histologically characterized tissue structures.
To support large-scale studies, we developed a scalable spatial proteomics workflow that integrates automated sample preparation using the cellenONE platform with advanced data-independent acquisition mass spectrometry. The optimized workflow minimizes sample loss during ultra-low input processing and enables efficient preparation of microdissected tissue samples. In combination with high-throughput LC-MS acquisition
strategies, this approach enables robust and reproducible spatial proteomic profiling across diverse tissue types.
In this presentation, I will discuss our integrated spatial proteomics workflows for scalable tissue analysis and describe our open-source computational pipelines designed to facilitate data analysis and enable broad accessibility of spatial proteomics technologies across the research community.
