Related Experiment Video
Updated: May 26, 2026
09:34
A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
Published on: October 25, 2018
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome
Isaac S Kohane1, Vladimir I Valtchinov
1National Center for Biomedical Computing Informatics for Integrating Biology and the Bedside (i2b2), Boston, MA 02115, USA.
Bioinformatics (Oxford, England)
|January 6, 2012
Summary
The white blood cell (WBC) transcriptome generalizes well to the multiorgan transcriptome, with most genes showing minimal rank changes. This suggests WBCs can serve as a proxy for broader gene expression studies.
Area of Science:
- Genomics
- Transcriptomics
- Bioinformatics
Background:
- Investigating the generalizability of the white blood cell (WBC) transcriptome to the multiorgan transcriptome.
- Utilizing data from the NCBI's Gene Expression Omnibus (GEO) repository for comparative analysis.
- Defining two distinct datasets: WBC and Other Organ (OO).
Purpose of the Study:
- To quantify the extent to which gene expression in WBCs reflects expression across multiple organs.
- To establish a quantitative measure for predicting OO expression profiles based on WBC data.
Main Methods:
- Calculating comprehensive pair-wise correlation and expression level profiles for both WBC and OO datasets.
- Employing Gene Ontology (GO) category mapping and ranking to assess transcriptomic similarity.
- Defining trans-tissue gene lists based on expression rank changes.
Main Results:
- High correlation observed between WBC and OO gene expression profiles, with 48.8% of genes showing <10% rank change.
- Notable exceptions in GO mappings include ribosome components, cell adhesion, and immune response.
- Quantitative measure developed to predict OO expression profiles from WBC data.
Conclusions:
- The WBC transcriptome is a largely generalizable proxy for the multiorgan transcriptome.
- WBC gene expression data can inform broader biological insights, with specific exceptions requiring further investigation.
