(454e) Temporal Gene Expression Profiling In Live Cell Array: Application In Understanding Mesenchymal Stem Cell Differentiation towards Myogenic Lineage

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 t_1/2 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.