Gene expression fluctuations in murine hematopoietic stem cells with cell cycle progression

Abstract

Evolving data suggest that marrow hematopoietic stem cells show reversible changes in homing, engraftment, and differentiation phenotype with cell cycle progression. Furthermore, marrow stem cells are a cycling population. Traditional concepts hold that the system is hierarchical, but the information on the lability of phenotype with cycle progression suggests a model in which stem cells are on a reversible continuum. Here we have investigated mRNA expression in murine lineage negative stem cell antigen-1 positive stem cells of a variety of cell surface epitopes and transcription regulators associated with stem cell identity or regulation. At isolation these stem cells expressed almost all cell surface markers, and transcription factors studied, including receptors for G-CSF, GM-CSF, and IL-7. When these stem cells were induced to transit cell cycle in vitro by exposure to interleukin-3 (IL-3), Il-6, IL-11, and steel factor some (CD34, CD45R c-kit, Gata-1, Gata-2, Ikaros, and Fog) showed stable expression over time, despite previously documented alterations in phenotype, while others showed variation of expression between and within experiments. These latter included Sca-1, Mac-1, c-fms, and c-mpl. Tal-1, endoglin, and CD4. These studies indicate that defined marrow stem cells express a wide variety of genes at isolation and with cytokine induced cell cycle transit show marked and reversible phenotype lability. Altogether, the phenotypic plasticity of gene expression for murine stem cells indicates a continuum model of stem cell regulation and extends the model to reversible expression with cell cycle transit of mRNA for cytokine receptors and stem cell markers.

Figure 1. Methods. (A) Quantitation of Gene Expression

Gene expression was calculated, using Applied Biosystem's Delta, Delta CT (ΔΔCT) method from results obtained on the ABI 7000 Sequence Detection System (Applied Biosystems, Foster City, CA). Relative quantitation (RQ) or “fold difference” is represented in relation to the 0Hour, non-cultured control. (B) Lin⁻Sca-1⁺ Histogram (sort). Cells were sorted based on Sca-1+ characteristics. FL6= Allophycocyanin; FSC=Forward Scatter. (C) Cell cycle Kinetics: Lin⁻Sca-1⁺ Propidium Iodide Treatment Through Cell Cycle in IL-3, IL-6, IL-11, SCF (Bar Graph). Cycling status of the cells was analyzed at different times in culture by flow cytometry of propidium iodide labeled cells. (D) Cell cycle Kinetics: Lin⁻Sca-1⁺ Propidium Iodide Treatment Through Cell Cycle in IL-3, IL-6, IL-11, SCF (Line Graph). Cycling status of the cells was analyzed at different times in culture by flow cytometry of propidium iodide labeled cells

Figure 2. Gene Expression at Isolation (0Hours)

Cycle threshold (CT) values at 0hour Lin-Sca-1+ isolation. CT values are corrected for endogenous control (Beta-2 microglobulin). The X-axis is inverted to demonstrate the relationship to expression. The higher the number of cycles to detect mRNA, the lower the mRNA expression which is directly related to the CT value. Thus points near zero represent the highest mRNA expression. Higher CT values indicate less expression. Expression of mRNA in this stem cell population is analyzed by looking at (A) Transcription Factors (B) Surface Proteins (C) Surface Receptors and (D) Adhesion Proteins. Error bars indicate standard error between the experiments with the n value indicating the number of experiments analyzed. Significant difference (*) was analyzed with Student's t-test ( 2-tailed) and p<.005 is indicated with these comparisons: Rock1 to Tal1 (Figure 2A); CD4 to CD3, CD45R, Endoglin & Sca1 (Figure 2B); CD45R to Endoglin & Sca1 (Figure 2B); Endoglin to Sca1 (Figure 2B); C-fms to cmpl (Figure 2C).

Figure 3. Gene Expression Through Cell Cycle: Lin-Sca-1+ in Culture with IL-3, IL-6, IL-11, SCF

Expression patterns in individual experiments are represented using quantitation methods shown in Figure 1. Fold difference is expressed in relation to 0 hour sample (calibrated to a value of 1). The 0 hour control is represented as a baseline of 1 at the X axis. (A) Mac-1 expression through cycle. (B) cfms expression through cycle.

Figure 4. Gene Expression Through Cell Cycle: Lin-Sca-1+ in Culture with IL-3, IL-6, IL-11, SCF

Expression patterns in individual experiments are represented using quantitation methods shown in Figure 1. Fold difference is expressed in relation to 0 hour sample (calibrated to a value of 1). The 0 hour control is represented as a baseline of 1 at the X axis. (A) Sca1 expression through cycle. (B) CD4 expression through cycle.

Figure 5. Gene Expression Through Cell Cycle: Lin-Sca-1+ in Culture with IL-3, IL-6, IL-11, SCF

Expression patterns in individual experiments are represented using quantitation methods shown in Figure 1. Fold difference is expressed in relation to 0 hour sample (calibrated to a value of 1). The 0 hour control is represented as a baseline of 1 at the X axis. (A) Tal1 expression through cycle. (B) Endoglin expression through cycle. (C) cmpl expression through cycle.

Figure 6. Stem Cell Continuum Model

This model shows promoter regions, interactions with chromatin, transcriptional regulation and proposed alterations and interactions with cell cycle progression and inducer exposure. In order for expression to occur, the complementing transcription complex must form with the promoter region. External factors such as chromatin, correlating to the specific phase of cell cycle can block this alignment. This is both a stochastic and deterministic model of gene expression.

Figure 7. Fluctuating Surface Phenotype of Stem Progenitor Cells with Cell Cycle Progression

This model demonstrates fluctuations in stem cell phenotype through cell cycle.