Towards proteome-wide assessment of cellular landscapes
EMSL Project ID
60122
Abstract
We will develop a novel workflow to characterize proteome-wide structural changes of cellular landscapes between different cell states (or cell types). The goal is to bridge the gap between cellular and molecular structure and ultimately link the phenotype to function and realize the Visual Proteomics vision. The proposed approach is based on cryo-electron tomogram comparison using machine learning (ML) to identify cellular domains and/or specific biomolecular assemblies showing the greatest change between different cellular states (e.g., time resolved changes, or mutants vs. wild type) then targeting these domains and biomolecular assemblies for subsequent detailed molecular (chromatography, mass spectrometry, proteomics) and structural characterization. As our development platform, we will use synchronized Chlamydomonas cultures to characterize cell cycle dynamics and demonstrate the power of the new capability.
Project Details
Start Date
2022-01-24
End Date
N/A
Status
Active
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
Team Members