Smart dual T1 MRI-optical imaging agent based on a rhodamine appended Fe(III)-catecholate complex.
Duraiyarasu MaheshwaranThavasilingam NagendrarajT Sekar BalajiGanesan KumaresanS Senthil KumaranRamamoorthy RamasubramanianPublished in: Dalton transactions (Cambridge, England : 2003) (2021)
A rhodamine appended Fe(iii)-catecholate complex Fe(RhoCat)3 is reported as a smart dual-modal T1 MRI-optical imaging probe. The high spin Fe(iii) coordination sphere and rhodamine unit act as MRI and optical reporters, respectively. The probe showed a r1-relaxivity of 4.37 mM-1 s-1 at 1.41 T via the interaction of second sphere water molecules to coordinated oxygen atoms. It produced an enhanced signal intensity of phantom images on the 7.0 T animal research MRI/MRS scanner at 25 °C and pH 7.3. The interaction of the probe with bovine serum albumin (BSA) significantly improved r1 relaxivity (7.09 mM-1 s-1). Moreover, the optical imaging reporter rhodamine moiety exhibited sensitivity towards biomolecule nitric oxide (NO) and acidic pH via the formation of a ring-opened tautomer of rhodamine, wherein the r1 relaxivity of the probe was enhanced to 5.19 mM-1 s-1 for NO and slightly decreased for acidic pH. Further, the probe visualized NO in adenocarcinoma gastric (AGS) cells via a turn-on fluorescence mechanism with 80% cell viability. Thus, Fe(RhoCat)3 is demonstrated as a potential dual "MRI-ON and Fluorescence-ON" molecular imaging probe to visualize the NO molecule and acidic pH in the tumour microenvironment.
Keyphrases
- living cells
- fluorescent probe
- high resolution
- contrast enhanced
- magnetic resonance imaging
- quantum dots
- diffusion weighted imaging
- nitric oxide
- single molecule
- metal organic framework
- computed tomography
- stem cells
- squamous cell carcinoma
- deep learning
- mass spectrometry
- magnetic resonance
- aqueous solution
- energy transfer
- high intensity
- image quality
- room temperature
- oxidative stress
- risk assessment
- nitric oxide synthase
- fluorescence imaging
- convolutional neural network