The pattern of gene expression in human CD34+ stem/progenitor cells

We have analyzed the pattern of gene expression in human primary CD34(+) stem/progenitor cells. We identified 42,399 unique serial analysis of gene expression (SAGE) tags among 106,021 SAGE tags collected from 2.5 x 10(6) CD34(+) cells purified from bone marrow. Of these unique SAGE tags, 21,546 matched known expressed sequences, including 3,687 known genes, and 20,854 were novel without a match. The SAGE tags that matched known sequences tended to be at higher levels, whereas the novel SAGE tags tended to be at lower levels. By using the generation of longer sequences from SAGE tags for gene identification (GLGI) method, we identified the correct gene for 385 of 440 high-copy SAGE tags that matched multiple genes and we generated 198 novel 3' expressed sequence tags from 138 high-copy novel SAGE tags. We observed that many different SAGE tags were derived from the same genes, reflecting the high heterogeneity of the 3' untranslated region in the expressed genes. We compared the quantitative relationship for genes known to be important in hematopoiesis. The qualitative identification and quantitative measure for each known gene, expressed sequence tag, and novel SAGE tag provide a base for studying normal gene expression in hematopoietic stem/progenitor cells and for studying abnormal gene expression in hematopoietic diseases.     

The pattern of gene expression in human CD15+ myeloid progenitor cells

We performed a genome-wide analysis of gene expression in primary human CD15(+) myeloid progenitor cells. By using the serial analysis of gene expression (SAGE) technique, we obtained quantitative information for the expression of 37,519 unique SAGE-tag sequences. Of these unique tags, (i) 25% were detected at high and intermediate levels, whereas 75% were present as single copies, (ii) 53% of the tags matched known expressed sequences, 34% of which were matched to more than one known expressed sequence, and (iii) 47% of the tags had no matches and represent potentially novel genes. The correct genes were confirmed by application of the generation of longer cDNA fragments from SAGE tags for gene identification (GLGI) technique for high-copy tags with multiple matches. A set of genes known to be important in myeloid differentiation were expressed at various levels and used different spliced forms. This study provides a normal baseline for comparison of gene expression in myeloid diseases. The strategy of using SAGE and GLGI techniques in this study has broad applications to the genome-wide identification of expressed genes.     

Serial analysis of gene expression: technical considerations and applications to cardiovascular biology

It has been 7 years since serial analysis of gene expression (SAGE) and microarray hybridization techniques were simultaneously introduced to allow the screening of thousands of expressed genes. Both techniques have stood up to the test of time as evidenced by their widespread use, and both have been used for studying cardiovascular diseases. SAGE has been used more extensively to study cancer cells, but it has also been used to examine gene expression in systems as divergent as rice seedlings, yeast, and Caenorhabditis elegans. In this review, a summary of the advances in SAGE technology and its unique attributes and potential applications to the cardiovascular system will be presented.     

Serial analysis of gene expression in human monocyte-derived dendritic cells.

Dendritic cells (DCs) are professional antigen-presenting cells in the immune system and can be generated in vitro from hematopoietic progenitor cells in the bone marrow, CD34(+) cord blood cells, precursor cells in the peripheral blood, and blood monocytes by culturing with granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-4, and tumor necrosis factor-alpha. We have performed serial analysis of gene expression (SAGE) in DCs derived from human blood monocytes. A total of 58,540 tag sequences from a DC complementary DNA (cDNA) library represented more than 17,000 different genes, and these data were compared with SAGE analysis of tags from monocytes (Mo) and GM-CSF-induced macrophages (M open diamond). Many of the genes that were differentially expressed in DCs were identified as genes encoding proteins related to cell structure and cell motility. Interestingly, the highly expressed genes in DCs encode chemokines such as TARC, MDC, and MCP-4, which preferentially chemoattract Th2-type lymphocytes. Although DCs have been considered to be very heterogeneous, the identification of specific genes expressed in human Mo-derived DCs should provide candidate genes to define subsets of, the function of, and the maturation stage of DCs and possibly also to diagnose diseases in which DCs play a significant role, such as autoimmune diseases and neoplasms. This study represents the first extensive gene expression analysis in any type of DCs.