Lecture
Towards spatial proteomics at single protein resolution
- at -
- ICM Saal 2
- Type: Lecture
Lecture description
E. Unterauer, R. Jungmann
The fundamental goal of life science research is to understand living systems in their full complexity. Spatial omics technologies aim to achieve this by creating
comprehensive spatial maps that localize all biomolecules within a given system. This ambitious goal requires mapping tens or even hundreds of interaction partners at molecular resolution. Current omics technologies can visualize large numbers of biomolecules across single cell and tissue scale. However, these technologies generally lack the resolution to reveal arrangements at the nanoscopic level. On the other hand, super-resolution microscopy can achieve nanometer resolution but has traditionally been limited multiplexing capabilities and acquisition time.
In this work I present a way to implement super-resolution microscopy as a tool for spatial proteomics. We developed a novel DNA-PAINT technology called SUM-PAINT, capable of providing virtually unlimited multiplexing with highest acquisition speed. With this technology at hand, we were able to map a 30-plex, 3D neuronal atlas at singleprotein resolution, uncovering the nanoscopic organization of neuronal structures and synaptic heterogeneity. Exploration of morphometric features of almost 900 synapses with machine learning-based approaches ultimately lead to the discovery of a new type of synapse marked by the juxtaposition of inhibitory scaffold (Gephyrin) and excitatory vesicle pool (VGlut1), which we termed the “mixed” synapse. This demonstration of single-protein resolved spatial proteomics will pave the way to deeper understanding of complex biological systems and eventually be able to reveal the molecular complexity of health and disease.
comprehensive spatial maps that localize all biomolecules within a given system. This ambitious goal requires mapping tens or even hundreds of interaction partners at molecular resolution. Current omics technologies can visualize large numbers of biomolecules across single cell and tissue scale. However, these technologies generally lack the resolution to reveal arrangements at the nanoscopic level. On the other hand, super-resolution microscopy can achieve nanometer resolution but has traditionally been limited multiplexing capabilities and acquisition time.
In this work I present a way to implement super-resolution microscopy as a tool for spatial proteomics. We developed a novel DNA-PAINT technology called SUM-PAINT, capable of providing virtually unlimited multiplexing with highest acquisition speed. With this technology at hand, we were able to map a 30-plex, 3D neuronal atlas at singleprotein resolution, uncovering the nanoscopic organization of neuronal structures and synaptic heterogeneity. Exploration of morphometric features of almost 900 synapses with machine learning-based approaches ultimately lead to the discovery of a new type of synapse marked by the juxtaposition of inhibitory scaffold (Gephyrin) and excitatory vesicle pool (VGlut1), which we termed the “mixed” synapse. This demonstration of single-protein resolved spatial proteomics will pave the way to deeper understanding of complex biological systems and eventually be able to reveal the molecular complexity of health and disease.
