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Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
Published on: October 4, 2024
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Population-scale proteome variation in human induced pluripotent stem cells.
Bogdan Andrei Mirauta1, Daniel D Seaton1, Dalila Bensaddek2
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom.
Elife
|August 11, 2020
Summary
This study provides the first comprehensive proteomic analysis of human induced pluripotent stem cells (iPSC). It identifies 654 protein quantitative trait loci (pQTLs), revealing genetic and non-genetic factors influencing protein levels and disease associations.
Area of Science:
- Genomics
- Proteomics
- Stem Cell Biology
- Human Disease Genetics
Background:
- Human disease phenotypes are linked to protein expression and function alterations.
- Understanding proteome variability and its relation to mRNA expression and disease loci is crucial but limited.
- Human induced pluripotent stem cells (iPSCs) are vital for disease modeling.
Purpose of the Study:
- To conduct the first comprehensive proteomic analysis of human iPSCs.
- To integrate proteomic data with transcriptome and genomic data from the same iPSC lines.
- To characterize determinants of proteome variation and regulatory mechanisms in iPSCs.
Main Methods:
- Proteomic analysis of 202 iPSC lines from 151 donors.
- Integration of transcriptome and genomic sequence data.
- Identification and characterization of protein quantitative trait loci (pQTLs).
Main Results:
- Identified 654 pQTLs in iPSCs.
- Characterized major genetic and non-genetic determinants of proteome variation.
- Discovered pQTLs linked to GWAS variants not detectable at the mRNA level.
Conclusions:
- Proteomic analysis of iPSCs reveals significant genetic and non-genetic influences on protein variation.
- Peptide-level resolution of pQTLs provides insights into disease-linked variants missed by mRNA analysis.
- This study enhances understanding of proteome variability and its role in human disease.
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