Second Near-Infrared Aggregation-Induced Emission Fluorophores with Phenothiazine Derivatives as the Donor and 6,7-Diphenyl-[1,2,5]Thiadiazolo[3,4-g]Quinoxaline as the Acceptor for In Vivo Imaging

2020 ◽  
Vol 12 (18) ◽  
pp. 20281-20286 ◽  
Author(s):  
Shun Li ◽  
Changfeng Yin ◽  
Ruonan Wang ◽  
Quli Fan ◽  
Wei Wu ◽  
...  
Keyword(s):  
In Vivo Imaging ◽  
Near Infrared ◽  
Phenothiazine Derivatives ◽  
Aggregation Induced Emission

Near-infrared fluorescence amplified organic nanoparticles with aggregation-induced emission characteristics for in vivo imaging

Nanoscale ◽  
2014 ◽  
Vol 6 (2) ◽  
pp. 939-945 ◽  
Author(s):  
Junlong Geng ◽  
Zhenshu Zhu ◽  
Wei Qin ◽  
Lin Ma ◽  
Yong Hu ◽  
...  
Keyword(s):  
In Vivo Imaging ◽  
Near Infrared ◽  
Emission Characteristics ◽  
Organic Nanoparticles ◽  
Near Infrared Fluorescence ◽  
Aggregation Induced Emission

Fluorescent proteins for in vivo imaging, where's the biliverdin?

2020 ◽  
Vol 48 (6) ◽  
pp. 2657-2667
Author(s):  
Felipe Montecinos-Franjola ◽  
John Y. Lin ◽  
Erik A. Rodriguez
Keyword(s):  
Hydrogen Peroxide ◽  
In Vivo Imaging ◽  
Near Infrared ◽  
Fluorescent Protein ◽  
Fluorescent Proteins ◽  
Fluorescent Imaging ◽  
Infrared Light ◽  
Red Fluorescent Protein ◽  
Color Palette

Noninvasive fluorescent imaging requires far-red and near-infrared fluorescent proteins for deeper imaging. Near-infrared light penetrates biological tissue with blood vessels due to low absorbance, scattering, and reflection of light and has a greater signal-to-noise due to less autofluorescence. Far-red and near-infrared fluorescent proteins absorb light >600 nm to expand the color palette for imaging multiple biosensors and noninvasive in vivo imaging. The ideal fluorescent proteins are bright, photobleach minimally, express well in the desired cells, do not oligomerize, and generate or incorporate exogenous fluorophores efficiently. Coral-derived red fluorescent proteins require oxygen for fluorophore formation and release two hydrogen peroxide molecules. New fluorescent proteins based on phytochrome and phycobiliproteins use biliverdin IXα as fluorophores, do not require oxygen for maturation to image anaerobic organisms and tumor core, and do not generate hydrogen peroxide. The small Ultra-Red Fluorescent Protein (smURFP) was evolved from a cyanobacterial phycobiliprotein to covalently attach biliverdin as an exogenous fluorophore. The small Ultra-Red Fluorescent Protein is biophysically as bright as the enhanced green fluorescent protein, is exceptionally photostable, used for biosensor development, and visible in living mice. Novel applications of smURFP include in vitro protein diagnostics with attomolar (10−18 M) sensitivity, encapsulation in viral particles, and fluorescent protein nanoparticles. However, the availability of biliverdin limits the fluorescence of biliverdin-attaching fluorescent proteins; hence, extra biliverdin is needed to enhance brightness. New methods for improved biliverdin bioavailability are necessary to develop improved bright far-red and near-infrared fluorescent proteins for noninvasive imaging in vivo.

Sensitive and specific detection of peroxynitrite and in-vivo imaging of inflammation by a "simple" AIE bioprobe

2021 ◽  
Author(s):  
Huilin Xie ◽  
Jingtian Zhang ◽  
Chao Chen ◽  
Feiyi Sun ◽  
Haixiang Liu ◽  
...  
Keyword(s):  
In Vivo Imaging ◽  
Fluorescence Enhancement ◽  
Specific Detection ◽  
Aggregation Induced Emission

A luminogenic bioprobe TPE-DMAB for simple and specific detection of peroxynitrite (ONOO−) has been developed. TPE-DMAB exhibits aggregation-induced emission (AIE) characteristic and shows fluorescence enhancement (up to 100-fold) upon cleavage...

scholarly journals Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hongwei Zhao ◽  
Hasaan Hayat ◽  
Xiaohong Ma ◽  
Daguang Fan ◽  
Ping Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Ovarian Cancer ◽  
Deep Learning ◽  
Cancer Progression ◽  
In Vivo Imaging ◽  
Near Infrared ◽  
Response To Therapy ◽  
Response To Chemotherapy

Abstract Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we utilized AI for monitoring the expression of underglycosylated mucin 1 (uMUC1) tumor antigen, a biomarker for ovarian cancer progression and response to therapy, using contrast-enhanced in vivo imaging. This was done using a dual-modal (magnetic resonance and near infrared optical imaging) uMUC1-specific probe (termed MN-EPPT) consisted of iron-oxide magnetic nanoparticles (MN) conjugated to a uMUC1-specific peptide (EPPT) and labeled with a near-infrared fluorescent dye, Cy5.5. In vitro studies performed in uMUC1-expressing human ovarian cancer cell line SKOV3/Luc and control uMUC1low ES-2 cells showed preferential uptake on the probe by the high expressor (n = 3, p < .05). A decrease in MN-EPPT uptake by SKOV3/Luc cells in vitro due to uMUC1 downregulation after docetaxel therapy was paralleled by in vivo imaging studies that showed a reduction in probe accumulation in the docetaxel treated group (n = 5, p < .05). The imaging data were analyzed using deep learning-enabled segmentation and quantification of the tumor region of interest (ROI) from raw input MRI sequences by applying AI algorithms including a blend of Convolutional Neural Networks (CNN) and Fully Connected Neural Networks. We believe that the algorithms used in this study have the potential to improve studying and monitoring cancer progression, amongst other diseases.

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