Quantitative expression of regulatory and differentiation-related genes in the key steps of human hematopoiesis: The LeukoStage Database
Abstract
Differentiation during hematopoiesis leads to the generation of many cell types with specific functions. At various stages of maturation, the cells may change pathologically, leading to diseases including acute leukemias (ALs).
Expression levels of regulatory molecules (such as the IKZF, GATA, HOX, FOX, NOTCH and CEBP families, as well as SPI-1/PU1 and PAX5) and lineage-specific molecules (including CD2, CD14, CD79A, and BLNK) may be compared between pathological and physiological cells. Although the key steps of differentiation are known, the available databases focus mainly on fully differentiated cells as a reference.
Precursor cells may be a more appropriate reference point for diseases that evolve at immature stages. Therefore, we developed a quantitative real-time polymerase chain reaction (qPCR) array to investigate 90 genes that are characteristic of the lymphoid or myeloid lineages and/or are thought to be involved in their regulation.
Using this array, sorted cells of granulocytic, monocytic, T and B lineages were analyzed. For each of these lineages, 3-5 differentiation stages were selected (17 stages total), and cells were sorted from 3 different donors per stage.
The qPCR results were compared to similarly processed AL cells of lymphoblastic (n=18) or myeloid (n=6) origins and biphenotypic AL cells of B cell origin with myeloid involvement (n=5). Molecules characteristic of each lineage were found.
In addition, cells of a newly discovered switching lymphoblastic AL (swALL) were sorted at various phases during the supposed transdifferentiation from an immature B cell to a monocytic phenotype. As demonstrated previously, gene expression changed along with the immunophenotype.
The qPCR data are publicly available in the LeukoStage Database in which gene expression in malignant and non-malignant cells of different lineages can be explored graphically and differentially expressed genes can be identified.