|
|
|
|
|
|
A Benzothiazole-Based Long-Wavelength Fluorescent Probe for Dual-Response to Viscosity and H2O2 |
ZHU Dan-dan1, 2, QU Peng2*, SUN Chuang2, YANG Yuan2, LIU Dao-sheng1*, SHEN Qi3, HAO Yuan-qiang2* |
1. College of Chemistry, Chemical Engineering and Environmental Engineering, Liaoning Shihua University, Fushun 113006,China
2. Henan Engineering Center of New Energy Battery Materials, Henan D&A Engineering Center of Advanced Battery Materials, Henan Key Laboratory of Biomolecular Recognition and Sensing, College of Chemistry and Chemical Engineering, Shangqiu Normal University, Shangqiu 476000,China
3. College of Chemistry and Molecular Engineering, Zhengzhou University, Zhengzhou 450001,China |
|
|
Abstract Hydrogen peroxide (H2O2) is an important biological molecule and plays vital roles in cell growth, immune responses, and cell signaling pathways. Cellular viscosity is a significant physiological parameter and also indicates the normal or abnormal functions of cells. Moreover, both abnormal levels of H2O2 and cellular viscosity are found to be highly related to some major diseases, such as Alzheimer’s disease and cancers. Therefore, the development of effective analytical tools for simultaneously detecting H2O2 and cellular viscosity is of great significance to elucidating some critical physiological and pathological mechanisms, as well as the diagnosis of some relevant diseases. In this work, we developed a dual-responsive fluorescent probe (1) for viscosity and H2O2. Probe (1) is almost non-fluorescent due to the quenching effect arisen from the twisted intramolecular charge transfer (TICT) process within the probe. While the probe exhibited strong near-infrared fluorescence (~680 nm) in solution with high viscosities, which can be attributed the restricted TICT process. The turn-on fluorescence reached 85 folds with the solution viscosity increased from 1.996 cp to 851.8 cp. Furthermore, probe (1) also can sensitively response to H2O2 with the evolution of a new emission band at about 590 nm. H2O2 can effectively react with the phenylboronic acid moiety of probe (1) and result in the conversion of pyridinium unit to pyridine, which could attenuate the ICT (intramolecular charge transfer) and TICT effects of the probe, and thus lead to a dramatic increase in the fluorescence intensity as well as a blue-shift in absorption profile (from 540 to 460 nm) with the observed color of the solution changed from purple-red to yellow. Fluorescence measurements indicated that probe (1) is highly sensitive and selective for H2O2. The fluorescence intensity of the probe assay at 590 nm was found to vary linearly with the concentration of H2O2 in the range of 0~25 μmol·L-1, the detection limit was calculated to be 0.34 μmol·L-1 (3σ). Furthermore, cellular imaging experiment confirmed that probe (1) is highly biocompatible and cell-membrane permeable, and can be utilized for monitoring H2O2 in living cells.
|
Received: 2019-05-28
Accepted: 2019-09-30
|
|
Corresponding Authors:
QU Peng, LIU Dao-sheng, HAO Yuan-qiang
E-mail: qupeng0212@163.com;dsliu05@126.com;hao0736@163.com
|
|
[1] Xu K, He L, Yang X, et al. Analyst, 2018, 143(15): 3555.
[2] Barnham K J, Masters C L, Bush A I. Nat. Rev. Drug Discov., 2004, 3(3): 205.
[3] Lin M T, Beal M F. Nature, 2006, 443(7113): 787.
[4] Kuimova M K. Phys. Chem. Chem. Phys., 2012, 14(37): 12671.
[5] Zhang J, Chai X, He X P, et al. Chem. Soc. Rev., 2019, 48(2): 683.
[6] Sedgwick A C, Wu L, Han H H, et al. Chem. Soc. Rev., 2018, 47(23): 8842.
[7] YUAN Jian-ying,WU Yu-tian,MU Lan,et al(袁剑英,吴玉田,牟 兰,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(5): 1424.
[8] Chen Y, Shi X, Lu Z, et al. Anal. Chem., 2017, 89(10): 5278.
[9] Lu H Z, Yu C W, Quan S, et al. Analyst, 2019, 144(4): 1153.
[10] Tang Y Q, Sun J G, Yin B Z. Anal. Chim. Acta, 2016, 942: 104.
[11] Wang F F, Liu Y J, Wang B B, et al. Dyes Pigments, 2018, 152: 29.
[12] Zhang B B, Liu H Y, Wu F X, et al. Sensor. Actuat B Chem., 2017, 243: 765.
[13] Ren M, Deng B, Zhou K, et al. Anal. Chem., 2017, 89(1): 552.
[14] Chan J, Dodani S C, Chang C J. Nat. Chem., 2012, 4: 973.
[15] Nguyen K H, Hao Y, Zeng K, et al. Spectrochim. Acta A, 2018, 199: 189. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
[3] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[4] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[5] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[6] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
[7] |
YI Min-na1, 2, 3, CAO Hui-min1, 2, 3*, LI Shuang-na-si1, 2, 3, ZHANG Zhu-shan-ying1, 2, 3, ZHU Chun-nan1, 2, 3. A Novel Dual Emission Carbon Point Ratio Fluorescent Probe for Rapid Detection of Lead Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3788-3793. |
[8] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[9] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[10] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[11] |
HE Yan-ping, WANG Xin, LI Hao-yang, LI Dong, CHEN Jin-quan, XU Jian-hua*. Room Temperature Synthesis of Polychromatic Tunable Luminescent Carbon Dots and Its Application in Sensitive Detection of Hemoglobin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3365-3371. |
[12] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[13] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[14] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[15] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
|
|
|
|