Identification of stable reference genes in differentiating human pluripotent stem cells.
Gustav HolmgrenNidal GhoshehXianmin ZengYalda BogestålPeter SartipyJane SynnergrenPublished in: Physiological genomics (2015)
Reference genes, often referred to as housekeeping genes (HKGs), are frequently used to normalize gene expression data based on the assumption that they are expressed at a constant level in the cells. However, several studies have shown that there may be a large variability in the gene expression levels of HKGs in various cell types. In a previous study, employing human embryonic stem cells (hESCs) subjected to spontaneous differentiation, we observed that the expression of commonly used HKG varied to a degree that rendered them inappropriate to use as reference genes under those experimental settings. Here we present a substantially extended study of the HKG signature in human pluripotent stem cells (hPSC), including nine global gene expression datasets from both hESC and human induced pluripotent stem cells, obtained during directed differentiation toward endoderm-, mesoderm-, and ectoderm derivatives. Sets of stably expressed genes were compiled, and a handful of genes (e.g., EID2, ZNF324B, CAPN10, and RABEP2) were identified as generally applicable reference genes in hPSCs across all cell lines and experimental conditions. The stability in gene expression profiles was confirmed by reverse transcription quantitative PCR analysis. Taken together, the current results suggest that differentiating hPSCs have a distinct HKG signature, which in some aspects is different from somatic cell types, and underscore the necessity to validate the stability of reference genes under the actual experimental setup used. In addition, the novel putative HKGs identified in this study can preferentially be used for normalization of gene expression data obtained from differentiating hPSCs.
Keyphrases
- pluripotent stem cells
- gene expression
- genome wide
- induced pluripotent stem cells
- bioinformatics analysis
- genome wide identification
- dna methylation
- endothelial cells
- genome wide analysis
- transcription factor
- magnetic resonance imaging
- cell therapy
- poor prognosis
- mass spectrometry
- mesenchymal stem cells
- copy number
- magnetic resonance
- stem cells
- single cell
- big data
- oxidative stress
- electronic health record
- cell death
- contrast enhanced
- bone marrow
- rna seq