Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration

Abstract

Microarray-based studies of global gene expression (GE) have resulted in a large amount of data that can be mined for further insights into disease and physiology. Meta-analysis of these data is hampered by technical limitations due to many different platforms, gene annotations and probes used in different studies. We tested the feasibility of conducting a meta-analysis of GE studies to determine a transcriptional signature of hematopoietic progenitor and stem cells. Data from studies that used normal bone marrow-derived hematopoietic progenitors was integrated using both RefSeq and UniGene identifiers. We observed that in spite of variability introduced by experimental conditions and different microarray platforms, our meta-analytical approach can distinguish biologically distinct normal tissues by clustering them based on their cell of origin. When studied in terms of disease states, GE studies of leukemias and myelodysplasia progenitors tend to cluster with normal progenitors and remain distinct from other normal tissues, further validating the discriminatory power of this meta-analysis. Furthermore, analysis of 57 normal hematopoietic stem and progenitor cell GE samples was used to determine a gene expression signature characteristic of these cells. Genes that were most uniformly expressed in progenitors and at the same time differentially expressed when compared to other normal tissues were found to be involved in important biological processes such as cell cycle regulation and hematopoiesis. Validation studies using a different microarray platform demonstrated the enrichment of several genes such as SMARCE, Septin 6 and others not previously implicated in hematopoiesis. Most interestingly, alpha-integrin, the only common stemness gene discovered in a recent comparative murine analysis (Science 302(5644):393) was also enriched in our dataset, demonstrating the usefulness of this analytical approach.

Reduced SMAD7 Leads to Overactivation of TGF-β Signaling in MDS that Can Be Reversed by a Specific Inhibitor of TGF-β Receptor I Kinase

“…These data were obtained from published studies (20-25, 29), were integrated using Unigene protein IDs, subjected to interarray normalization and then used for analysis. This strategy was shown to be biologically valid strategy for analysis previously by us (30). Our analysis of this integrated dataset demonstrated that the SMAD7, a negative regulator of the TGF-β receptor kinase, was the most significantly differentially expressed gene in MDS and was markedly reduced in most cases (Fig 1, mean log2 expression 8.31 in controls vs 6.32 in mds cases, p <0.0001, Bejmanin Hochberg correction, multiple testing).…”

Impaired clearance of apoptotic cells leads to HMGB1 release in the bone marrow of patients with myelodysplastic syndromes and induces TLR4-mediated cytokine production

“…A number of genes have been identified that are differentially expressed between MDS patients and healthy controls. 32 It is difficult, however, to relate our findings to published microarray data because of the different cellular populations used in different studies. 33,34 Interestingly, deregulated cytokine and innate immune signaling due to interstitial deletion on chromosome 5 in humans and chromosome 11 and 18 in mice has led to the MDS phenotype.…”

TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources

“…However, this method may cause loss of data due to the removal of all genes whose expression values are missing for any dataset in order to obtain a fully filled data matrix, representing each sample as a column and the values for each gene as a row (for an example of this filtering see [26]). Alternatively, some Authors retain all data values in quantile normalization by placing missing values at the end of each sorted column [27].…”