Live cell microarrays offer a nondestructive way to monitor gene expression unlike most other high throughput methods such as RT-PCR or cDNA microarrays. However they suffer from spot-to-spot variations in signal because of differences in virus titer and transduction efficiencies. To overcome this limitation, a novel dual-promoter lentiviral vector (LVDP) carrying human PGK promoter driving the expression of DsRed serving as an internal control and the promoter of interest driving the expression of ZsGreen was designed in our laboratory. Herein, we demonstrate the application of LVDP in high throughput monitoring of real time gene expression during myogenic differentiation of human Bone Marrow derived Mesenchymal Stem Cells (hBM-MSCs). Specifically we cloned a library of 14 promoters and 18 response elements upstream of ZsGreen in the LVDP and monitored the promoter activity using continuous fluorescence imaging. We obtained the dynamic expression profiles of these promoters and response elements under growth and differentiation conditions for a period of several days (Fig. 1). The gene expression dynamics revealed that treatment with TGF-β and Heparin stimulated a 2 to 4-fold increase of many early myogenic markers such as α-SMA, SM22, and Myocardin with a t1/2 of ~24 hours while late markers such as SM-MHC and Calponin exhibited only 1.2-1.5-fold increase, suggesting partial myogenic differentiation. Interestingly, the combination of TGF-β and BMP4 showed higher expression of SM-MHC at 7 days post treatment, suggesting a synergistic effect of these two factors on myogenic differentiation. The promoter/RE activity data correlated well with mRNA expression as assessed by RT-PCR and with protein levels as demonstrated by immunostaining. Collectively our data demonstrates a novel method to capture real time gene expression dynamics during myogenic differentiation in live cells. In contrast to standard gene expression monitoring methods, our results provide rich dynamic information of gene expression over a period of several days. We are currently using this set of dynamic data in mathematical models to decipher the gene regulatory networks at work during stem cell differentiation.