A Genetically Encoded Reporter for Diffusion Weighted Magnetic Resonance Imaging
Synthetic Biology Engineering Evolution Design SEED
2016
2016 Synthetic Biology: Engineering, Evolution & Design (SEED)
Poster Session
Accepted Posters
Arnab Mukherjee
Normal
Arnab Mukherjee
2
139
2015-09-16T15:29:00Z
2016-02-22T23:12:00Z
2016-02-22T23:12:00Z
1
530
3022
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ability to image gene expression within the context of intact, living mammalian
organisms is critical for facilitating the eventual clinical translation of
engineered genetic circuits and cell‒based therapeutics. However,
most genetically encoded reporters, based on fluorescent and luminescent
proteins have limited utility in this respect due to the poor penetration of
light into deep tissues. In contrast to optical techniques, magnetic resonance
imaging (MRI) enables the acquisition of in
vivo images with excellent depth penetration and high spatial and temporal
resolution. However, existing MRI reporter genes, based primarily on
metal-binding proteins or chemical exchange saturation transfer probes, are
limited by their reliance on metal ions or relatively low sensitivity. In this
work, we introduce an entirely new class of genetically encoded reporters for
MRI that works by altering water diffusivity in cells. Specifically, we
show that overexpression of the human water channel aquaporin 1 (AQP1) produces
robust contrast in diffusion weighted MRI by increasing effective water
diffusivity without affecting cell viability. Low levels of AQP1 expression (<
1 μM), or mixed populations comprising as few as 10% AQP1-expressing cells
produce contrast that is readily observable using MRI. Finally, we demonstrate
the utility of AQP1 in vivo by
imaging gene expression in intracranial tumor xenografts. 14.0pt;mso-line-height-rule:exactly">Results: "Times New Roman",serif'> 11.0pt;font-family:"Times New Roman",serif'>To evaluate AQP1 as a genetically
encoded reporter for diffusion weighted MRI, we used lentiviral transfection to
generate several mammalian cell lines stably overexpressing this channel or
GFP. AQP1-expressing CHO, U87 and Neuro2A cells showed 187%, 82% and 95%
increases in water diffusivity, respectively, compared to GFP controls. Next,
using doxycycline-controlled AQP1 expression, we demonstrated that AQP1 can
report on varying degrees of gene expression in a dose‒dependent fashion.
Notably, AQP1 expression at submicromolar concentrations was found to be
sufficient for enhancing water diffusivity by 53%, thereby, placing AQP1 among
the most sensitive MRI reporters. Â Furthermore,
to evaluate AQP1 as a viable reporter for imaging small subsets of labeled
cells in heterogeneous populations, we developed Monte Carlo models for water
diffusion in mixed cell populations, which suggested that AQP1-based increase
in water diffusivity should be evident even in low (~ 10%) AQP1‒labeled cell
fraction scenarios. Consistent with these predictions, our experiments revealed
that as few as 10% AQP1 expressing cells is sufficient to enhance overall water
diffusivity by 21%. Finally, we demonstrated that AQP1 can produce induction‒dependent
contrast in mouse tumor xenografts, with the average intensity in AQP1 tumors
changing by 41% following doxycycline injection, compared to control tumors. margin-left:0in;text-align:justify;line-height:14.0pt;mso-line-height-rule:
exactly"> font-family:"Times New Roman",serif'>Conclusion: Our
results establish AQP1 as the first genetically encoded reporter for diffusion
weighted MRI. Importantly, as a sensitive reporter that can be readily imaged
in heterogeneous cell populations and low cell-fraction scenarios, AQP1 appears
ideally suited to fulfil the need for a sensitive, nontoxic reporter for
labeling and tracking immune and stem cell-based therapeutics. Overall, the
high performance, biocompatibility, and engineering capacity of aquaporin-based
reporters will enable a multitude of applications ranging from basic biological
studies to the noninvasive evaluation of cell-based devices and engineered gene
circuits in the context of preclinical animal models.         exactly">