These observations suggest that maintenance and loss of engraftment potential may be controlled by an orchestrated sequence of gene expression profiles that oscillate HSC between engrafting and non-engrafting potential. It would be interesting to closely PD 0332991 inquirer examine whether these gene expression profiles are modulated by the progression of cells through different phases of cell cycle. Such associations between cell cycle status and a genetic fingerprint that promotes engraftment may explain why position of HSC in cell cycle is an important parameter in determining their engraftment potential. Alternating status of engraftment potential of HSC has been previously reported by Quesenberry and colleagues. It is important to stress here that restriction of engraftment to cells in G0 may not be applicable to unseparated BM cells. However, the necessity to use purified cells for genomic and proteomic analyses, preclude the use of unseparated cells for these studies. We compared our 484 target genes identified by microarray CT99021 side effects analysis to the published stem cell database where Ivanova et al. mapped mRNA expression profiles of hematopoietic stem cells of various phenotypically defined hierarchical levels or clusters including LT-HSC, ST-HSC, and early-intermediate-late progenitors. Among the functionally annotated 341 genes identified in our set, 57, which mapped to all clusters of hematopoietic cells examined, were present in the database of Ivanova et al. Hypothetical expression patterns used by Ivanova et al., for cluster assignment may be one of the reasons for our target list matching to all clusters. It is important to note that when we compared our target gene list to common genes expressed by different types of stem cells, 14 genes were common to both lists and interestingly, all of them mapped to higher clusters of HSC developmental hierarchy. Using mass spectrometry based proteomic analysis we successfully identified 646 proteins from the same 18 groups of cells that were subjected in parallel to microarray analysis. Analysis strategies similar to those used with our microarray data revealed that only 4 common proteins were differentially expressed in both BM and MPB, and not changed in UCB. We consider this a huge constraint of our proteomic analysis since compared to data from microarrays. We were limited to the identification of a rather small number of expressed proteins using currently available techniques. Given the logistical difficulties involving the flow cytometric cell sorting of highly purified phenotypically defined groups of cells, and the decision not to mix samples, we were only able to use a rather small number of sorted cells for the proteomic analysis of each sample. This may have contributed significantly to why only few proteins were identified in our analysis. It is also important to note that another possible reason for our inability to identify a larger number of proteins is that cells in our samples are mostly metabolically inactive thus limiting the proteome abundance. The use of independent pooled samples with significantly higher numbers of cells may have generated a more robust set of proteomic data and showed a higher synergy between the transcriptome and microarray analyses of these cells. We found a poor correlation between microarray and proteomic results. For instance, among the 62 differentially expressed proteins between BM G0 and G1 cells, only 10 matched to differentially expressed genes by ID matching. Such discrepancies between proteomic and microarray data were previously reported. For example, Gygi et al. detected a 20 fold-increase in protein expression for some genes that were unaltered at the mRNA level by microarray analysis.