Literature DB >> 21828245

Transcriptome profiling and sequencing of differentiated human hematopoietic stem cells reveal lineage-specific expression and alternative splicing of genes.

Poching Liu1, Jennifer Barb, Kimberly Woodhouse, James G Taylor, Peter J Munson, Nalini Raghavachari.   

Abstract

Hematopoietic differentiation is strictly regulated by complex network of transcription factors that are controlled by ligands binding to cell surface receptors. Disruptions of the intricate sequences of transcriptional activation and suppression of multiple genes cause hematological diseases, such as leukemias, myelodysplastic syndromes, or myeloproliferative syndromes. From a clinical standpoint, deciphering the pattern of gene expression during hematopoiesis may help unravel disease-specific mechanisms in hematopoietic malignancies. Herein, we describe a human in vitro hematopoietic model system where lineage-specific differentiation of CD34(+) cells was accomplished using specific cytokines. Microarray and RNAseq-based whole transcriptome and exome analysis was performed on the differentiated erythropoietic, granulopoietic, and megakaryopoietic cells to delineate changes in expression of whole transcripts and exons. Analysis on the Human 1.0 ST exon arrays indicated differential expression of 172 genes (P < 0.0000001) and significant alternate splicing of 86 genes during differentiation. Pathway analysis identified these genes to be involved in Rac/RhoA signaling, Wnt/B-catenin signaling and alanine/aspartate metabolism. Comparison of the microarray data to next generation RNAseq analysis during erythroid differentiation demonstrated a high degree of correlation in gene (R = 0.72) and exon (R = 0.62) expression. Our data provide a molecular portrait of events that regulate differentiation of hematopoietic cells. Knowledge of molecular processes by which the cells acquire their cell-specific fate would be beneficial in developing cell-based therapies for human diseases.

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Year:  2011        PMID: 21828245      PMCID: PMC3217327          DOI: 10.1152/physiolgenomics.00099.2011

Source DB:  PubMed          Journal:  Physiol Genomics        ISSN: 1094-8341            Impact factor:   3.107


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