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DESP demixes cell-state profiles from dynamic bulk molecular measurements
Ahmed Youssef1, Indranil Paul2, Mark Crovella3
1Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA.
Cell Reports Methods
|March 15, 2024
Summary
A new algorithm, DESP, resolves cell states from bulk molecular data like proteomics. This computational framework enables deeper insights into molecular changes driving biological processes and disease.
Area of Science:
- Computational biology
- Molecular systems biology
- Proteomics and transcriptomics analysis
Background:
- Understanding molecular drivers of phenotypic changes in development and disease is crucial.
- Exploring these dynamics at the cell-state level is limited by technical constraints.
Purpose of the Study:
- To present DESP, an algorithm for resolving cell-state contributions to bulk molecular measurements.
- To enable cell-state level analysis of proteomes and transcriptomes using existing bulk workflows.
Main Methods:
- DESP algorithm leverages independent cell-state proportion estimates (e.g., from single-cell RNA sequencing).
- Applies DESP to quantitative proteomics and transcriptome data from an in vitro epithelial-to-mesenchymal transition model.
Main Results:
- DESP accurately reconstructs cell-state signatures from bulk proteomic and transcriptomic data.
- Provides insights into transient regulatory mechanisms during phenotypic transitions.
Conclusions:
- DESP offers a generalizable computational framework for linking bulk and single-cell molecular data.
- Facilitates cell-state level analysis of molecular profiles, advancing biological and pathobiological research.
