1
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Chen J, Moreno JL, Zhang W, Gibson-Elias LJ, Lian R, Najafi S, Zhang H, Zhong W, Hooley RJ. Optical discrimination of terpenes in citrus peels with a host:guest sensing array. Chem Commun (Camb) 2024; 60:5598-5601. [PMID: 38712724 DOI: 10.1039/d4cc01309c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A simple aqueous host:guest sensing array can selectively discriminate between different types of citrus varietal from peel extract samples. It can also distinguish between identical citrus samples at varying stages of ripening. The discrimination effects stem from detection of changes in the terpenoid composition of the peel extracts by the host:guest array, despite the overwhelming excess of a single component, limonene, in each sample. The hosts are insensitive to limonene but bind other monoterpenes strongly, even though they are similar in structure to the major limonene component. This work demonstrates the capability of host:guest arrays in sensing target molecules in environments with the competing agents present at high abundances in the sample matrix.
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Affiliation(s)
- Junyi Chen
- Department of Chemistry, University of California-Riverside, Riverside, CA 92521, U.S.A.
| | - Jose L Moreno
- Department of Chemistry, University of California-Riverside, Riverside, CA 92521, U.S.A.
| | - Wen Zhang
- Department of Chemistry, University of California-Riverside, Riverside, CA 92521, U.S.A.
| | - Lucas J Gibson-Elias
- Department of Chemistry, University of California-Riverside, Riverside, CA 92521, U.S.A.
| | - Ria Lian
- Department of Chemistry, University of California-Riverside, Riverside, CA 92521, U.S.A.
| | - Saba Najafi
- Department of Chemistry, University of California-Riverside, Riverside, CA 92521, U.S.A.
| | - Haofei Zhang
- Department of Chemistry, University of California-Riverside, Riverside, CA 92521, U.S.A.
| | - Wenwan Zhong
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China.
| | - Richard J Hooley
- Department of Chemistry, University of California-Riverside, Riverside, CA 92521, U.S.A.
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2
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Zhu Y, Xiang F, Su Y, Jiang X, Cang Y, Long W, Lan W, Deng G, Chen H, She Y, Fu H. Authenticity identification of high - Temperature Daqu Baijiu through multi-channel visual array sensor of organic dyes combined with smart phone App. Food Chem 2024; 438:137980. [PMID: 37979267 DOI: 10.1016/j.foodchem.2023.137980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/05/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
High - temperature Daqu Baijiu faces a challenge from illegal adulteration of high-grade Baijiu bottles with low-grade Baijiu, affecting its quality and value. This study developed a rapid identification method for high temperature Daqu Baijiu with the same aroma type using a four-channel visual array sensor and detection of color changes caused by competition coordination with Zn2+ and color-changing organic dyes. The array sensor demonstrated high stability and repeatability in targeting flavor components and achieved 97.78 % or more accuracy combined with DD-SIMCA model in detecting adulteration across the Baijiu with same aroma type. The results of GC-MS and Quantum Chemical Calculation showed that esters, acids, and pyrazines played a crucial role. The smart phone App could quickly identify the authenticity of Baijiu with accuracy achieved 93 %. This research provides a foundation for rapid and reliable assessment of Baijiu quality and authenticity, enabling the industry to combat fraudulent practices effectively.
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Affiliation(s)
- Yanmei Zhu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Fushuang Xiang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Yuanyuan Su
- Suqian Product Quality Supervision and Testing Institute of Jiangsu Province, Suqian 223800, China
| | - Xue Jiang
- Suqian Product Quality Supervision and Testing Institute of Jiangsu Province, Suqian 223800, China
| | - Yipeng Cang
- Suqian Product Quality Supervision and Testing Institute of Jiangsu Province, Suqian 223800, China
| | - Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Wei Lan
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Gaoqiong Deng
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China.
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China.
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China.
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3
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Xie X, Zheng S, Liu Y, Tang Y, Zhang Z, Wu H, Hao XQ, Huang Y, Cheng N, Li F. Visual Gustation via Regulable Elastic Photonic Crystals. ACS APPLIED MATERIALS & INTERFACES 2024; 16:14133-14143. [PMID: 38447141 DOI: 10.1021/acsami.3c18892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The unique structural sensitivity of photonic crystals (PCs) endows them with stretchable or elastic tunability for light propagation and spontaneous emission modulation. Hydrogel PCs have been demonstrated to have biocompatibility and flexibility for potential human health detection and environmental security monitoring. However, current elastic PCs still possess a fixed elastic modulus and uncontrollable structural colors based on a tunable elastic modulus, posing considerable challenges for in situ detection, particularly in wearable or portable sensing devices. In this work, we introduced a novel chemo-mechanical transduction mechanism embedded within a photonic crystal nanomatrix, leading to the creation of structural colors and giving rise to a visual gustation sensing experience. By utilizing the captivating structural colors generated by the hydrogel PC, we employ abundant optical information to identify various analytes. The finite element analysis proved the electric field distribution in the PC matrix during stretch operations. The elastic-optical behaviors with various chemical cosolvents, including cations, anions, saccharides, or organic acids, were investigated. The mechanism of the Hofmeister effect regulating the elasticity of hydrogels was demonstrated with the network nanostructure of the hydrogels. The hydrogel PC matrix demonstrates remarkable capability in efficiently distinguishing a wide range of cations, anions, saccharides, and organic acids across various concentrations, mixtures, and even real food samples, such as tastes and soups. Through comprehensive research, a precise relationship between the structural colors and the elastic modulus of hydrogel PCs has been established, contributing to the biomatching elastic-optics platform for wearable devices, a dynamic environment, and clinical or health monitoring auxiliary.
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Affiliation(s)
- Xinyuan Xie
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Suiting Zheng
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Yunyan Liu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Yongtao Tang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- Department of Cardiovascular Surgery, PLA General Hospital, Beijing 100853, P. R. China
| | - Zilu Zhang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- Department of Cardiovascular Surgery, PLA General Hospital, Beijing 100853, P. R. China
| | - Hao Wu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Xin-Qi Hao
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
| | - Yu Huang
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Material Science and Chemistry, China University of Geosciences, Wuhan 430074, P. R. China
| | - Nan Cheng
- Department of Cardiovascular Surgery, PLA General Hospital, Beijing 100853, P. R. China
| | - Fengyu Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
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4
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Yang M, Zhang M, Jia M. Optical sensor arrays for the detection and discrimination of natural products. Nat Prod Rep 2023; 40:628-645. [PMID: 36597853 DOI: 10.1039/d2np00065b] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Covering: up to the end of 2022Natural products (NPs) have found uses in medicine, food, cosmetics, materials science, environmental protection, and other fields related to our life. Their beneficial properties along with potential toxicities make the detection and discrimination of NPs crucial for their applications. Owing to the merits of low cost and simple operation, optical sensor arrays, including colorimetric and fluorometric sensor arrays, have been widely applied in the detection of small molecule NPs and discrimination of structurally similar small molecule NPs or complex mixtures of NPs. This review provides a brief introduction to the optical sensor array and focuses on its progress toward the detection and discrimination of NPs. We summarized the design principle of sensor arrays toward various NPs (i.e., saccharides and polyhydroxy compounds, organic acids, flavonoids, organic sulfur compounds, amines, amino acids, and saponins) based on their functional groups and characteristic chemical properties, along with representative examples. Moreover, the challenges and potential directions for further research of optical sensor arrays for NPs are proposed.
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Affiliation(s)
- Maohua Yang
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, PR China.
| | - Mei Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, PR China.
| | - Mingyan Jia
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, PR China.
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5
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Nakano-Baker O, Fong H, Shukla S, Lee RV, Cai L, Godin D, Hennig T, Rath S, Novosselov I, Dogan S, Sarikaya M, MacKenzie JD. Data-driven design of a multiplexed, peptide-sensitized transistor to detect breath VOC markers of COVID-19. Biosens Bioelectron 2023; 229:115237. [PMID: 36965380 PMCID: PMC10027305 DOI: 10.1016/j.bios.2023.115237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 02/25/2023] [Accepted: 03/16/2023] [Indexed: 03/25/2023]
Abstract
Exhaled human breath contains a rich mixture of volatile organic compounds (VOCs) whose concentration can vary in response to disease or other stressors. Using simulated odorant-binding proteins (OBPs) and machine learning methods, we designed a multiplex of short VOC- and carbon-binding peptide probes that detect a characteristic "VOC fingerprint". Specifically, we target VOCs associated with COVID-19 in a compact, molecular sensor array that directly transduces vapor composition into multi-channel electrical signals. Rapidly synthesizable, chimeric VOC- and solid-binding peptides were derived from selected OBPs using multi-sequence alignment with protein database structures. Selective peptide binding to targeted VOCs and sensor surfaces was validated using surface plasmon resonance spectroscopy and quartz crystal microbalance. VOC sensing was demonstrated by peptide-sensitized, exposed-channel carbon nanotube transistors. The data-to-device pipeline enables the development of novel devices for non-invasive monitoring, diagnostics of diseases, and environmental exposure assessment.
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Affiliation(s)
| | - Hanson Fong
- University of Washington Dept. of Materials Science and Engineering, USA
| | | | - Richard V Lee
- University of Washington Dept. of Materials Science and Engineering, USA
| | - Le Cai
- University of Washington Dept. of Materials Science and Engineering, USA
| | - Dennis Godin
- University of Washington Dept. of Biochemistry, USA
| | - Tatum Hennig
- University of Washington Dept. of Atmospheric Chemistry, USA
| | - Siddharth Rath
- University of Washington Dept. of Materials Science and Engineering, USA
| | - Igor Novosselov
- University of Washington Depts. of Mechanical Engineering, Occupational and Environmental Health Sciences, USA
| | - Sami Dogan
- University of Washington School of Dentistry, USA
| | - Mehmet Sarikaya
- University of Washington Depts. of Materials Science and Engineering, Chemical Engineering, Oral Health Sciences, USA
| | - J Devin MacKenzie
- University of Washington Depts. of Materials Science and Engineering, Mechanical Engineering, USA
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6
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Qin J, Wang H, Xu Y, Shi F, Yang S, Huang H, Liu J, Stewart C, Li L, Li F, Han J, Wu W. A simple array integrating machine learning for identification of flavonoids in red wines. RSC Adv 2023; 13:8882-8889. [PMID: 36936820 PMCID: PMC10019168 DOI: 10.1039/d2ra08049d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
Bioactive flavonoids, the major ingredients of red wines, have been proven to prevent atherosclerosis and cardiovascular disease due to their anti-inflammatory and anti-oxidant activity. However, flavonoids have proven challenging to identify, even when multiple approaches are combined. Hereby, a simple array was constructed to detect flavonoids by employing phenylboronic acid modified perylene diimide derivatives (PDIs). Through multiple non-specific interactions (hydrophilic, hydrophobic, charged, aromatic, hydrogen-bonded and reversible covalent interactions) with flavonoids, the fluorescence of PDIs can be modulated, and variations in intensity can be used to create fingerprints of flavonoids. This array successfully discriminated 14 flavonoids of diverse structures and concentrations with 100% accuracy, based on patterns in fluorescence intensity modulation, via optimized machine learning algorithms. As a result, this array demonstrated the parallel detection of 8 different types and origins of red wines with a high accuracy, revealing the excellent potential of the sensor array in food mixtures detection.
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Affiliation(s)
- Jiaojiao Qin
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Hao Wang
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Yu Xu
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Fangfang Shi
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Shijie Yang
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Hui Huang
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet Sweden
| | - Jun Liu
- Shandong Yuwang Ecological Food Industry Co., Ltd De Zhou 251200 China
| | - Callum Stewart
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet Sweden
| | - Linxian Li
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet Sweden
| | - Fei Li
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Jinsong Han
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Wenwen Wu
- Department of Pharmacy, Children's Hospital of Nanjing Medical University Nanjing Jiangsu Province 211109 China
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7
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Xie X, Zhang Z, Jiang Q, Zheng S, Yun Y, Wu H, Li C, Tian F, Su M, Li F. A Rainbow Structural Color by Stretchable Photonic Crystal for Saccharide Identification. ACS NANO 2022; 16:20094-20099. [PMID: 36314922 DOI: 10.1021/acsnano.2c08708] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Photonic crystals (PCs) with fascinating structural color nanomaterials present effectively spontaneous emission modulation and selectively optical signal amplification. Stretchability or elasticity could enable the feasible tunability for structural colors. Aimed at the regulation of structural colors, we endeavored to achieve the PC nanomatrix evolution and optical property during stretching. In this work, a rainbow structural color by stretchable PCs was exploited to provide abundant optical information for multianalyte recognition. The finite element analysis proved the electric field distribution in the PC matrix, which completely matched with the phenomenon of the measured PC spectra. By simply employing analysis of the multistate PC during stretching, the mono PC matrix chip can differentially enhance fluorescence signals in broad spectral regions, resulting in diverse sensing information for high-efficiency multianalysis. The stretchable PC chip can facilely discriminate 14 similar structured saccharides with a minimum concentration of 10-7 M using only one fluorescence complex. Furthermore, saccharides in different concentrations, mixtures, and real samples (beverages and sweets) also can be successfully distinguished. The exploration on fluorescent stretch dependence behavior of the photonic crystal contributes the biomatching optical platform for wearable devices, dynamic environment, clinical, or health monitoring auxiliary.
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Affiliation(s)
- Xinyuan Xie
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- CAS Key Laboratory of Bio-inspired Materials and Interfacial Science, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Zilu Zhang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Qing Jiang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Suiting Zheng
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Yang Yun
- Key Laboratory of Green Printing, CAS Research/Education Centre for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Hao Wu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Chunbao Li
- Graduate School of Medical School, Department of Orthopedics, the Fourth Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Feng Tian
- Phomera Metamaterials Inc., Guangdong 510535, China
| | - Meng Su
- Key Laboratory of Green Printing, CAS Research/Education Centre for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Fengyu Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- Key Laboratory of Green Printing, CAS Research/Education Centre for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
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8
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Tian JH, Hu XY, Hu ZY, Tian HW, Li JJ, Pan YC, Li HB, Guo DS. A facile way to construct sensor array library via supramolecular chemistry for discriminating complex systems. Nat Commun 2022; 13:4293. [PMID: 35879312 PMCID: PMC9314354 DOI: 10.1038/s41467-022-31986-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 07/13/2022] [Indexed: 12/15/2022] Open
Abstract
Differential sensing, which discriminates analytes via pattern recognition by sensor arrays, plays an important role in our understanding of many chemical and biological systems. However, it remains challenging to develop new methods to build a sensor unit library without incurring a high workload of synthesis. Herein, we propose a supramolecular approach to construct a sensor unit library by taking full advantage of recognition and assembly. Ten sensor arrays are developed by replacing the building block combinations, adjusting the ratio between system components, and changing the environment. Using proteins as model analytes, we examine the discriminative abilities of these supramolecular sensor arrays. Then the practical applicability for discriminating complex analytes is further demonstrated using honey as an example. This sensor array construction strategy is simple, tunable, and capable of developing many sensor units with as few syntheses as possible.
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Affiliation(s)
- Jia-Hong Tian
- College of Chemistry, Key Laboratory of Functional Polymer Materials (Ministry of Education), State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, China
| | - Xin-Yue Hu
- College of Chemistry, Key Laboratory of Functional Polymer Materials (Ministry of Education), State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, China
| | - Zong-Ying Hu
- College of Chemistry, Key Laboratory of Functional Polymer Materials (Ministry of Education), State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, China
| | - Han-Wen Tian
- College of Chemistry, Key Laboratory of Functional Polymer Materials (Ministry of Education), State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, China
| | - Juan-Juan Li
- College of Chemistry, Key Laboratory of Functional Polymer Materials (Ministry of Education), State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, China
| | - Yu-Chen Pan
- College of Chemistry, Key Laboratory of Functional Polymer Materials (Ministry of Education), State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, China
| | - Hua-Bin Li
- College of Chemistry, Key Laboratory of Functional Polymer Materials (Ministry of Education), State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, China
| | - Dong-Sheng Guo
- College of Chemistry, Key Laboratory of Functional Polymer Materials (Ministry of Education), State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, China.
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9
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Supramolecular optical sensor arrays for on-site analytical devices. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY C: PHOTOCHEMISTRY REVIEWS 2022. [DOI: 10.1016/j.jphotochemrev.2021.100475] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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10
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Cao L, Wu L, Li C, Tu Y, Wu H, Shen B, Meng J, Hao X, Yan B, Li F, Xia F, Huang Y. Underwater
Superoleophobic‐Oleophilic
Chips for Femtomolar Aflatoxins Identification. CHINESE J CHEM 2022. [DOI: 10.1002/cjoc.202100904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Liwei Cao
- College of Chemistry and Materials Science Jinan University Guangzhou 510632 China
| | - Lizhen Wu
- College of Chemistry and Materials Science Jinan University Guangzhou 510632 China
| | - Cheng Li
- College of Chemistry and Materials Science Jinan University Guangzhou 510632 China
| | - Yidan Tu
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano‐Geomaterials of Ministry of Education, Faculty of Material Science and Chemistry China University of Geosciences Wuhan 430074 China
| | - Hao Wu
- College of Chemistry and Materials Science Jinan University Guangzhou 510632 China
| | - Bin Shen
- College of Chemistry and Materials Science Jinan University Guangzhou 510632 China
| | - Jianxin Meng
- College of Chemistry and Materials Science Jinan University Guangzhou 510632 China
| | - Xin‐Qi Hao
- School of Materials Science and Engineering Zhengzhou University Zhengzhou 450001 China
| | - Bing Yan
- School of Environmental Studies China University of Geosciences Wuhan 430074 China
| | - Feng‐yu Li
- College of Chemistry and Materials Science Jinan University Guangzhou 510632 China
- School of Materials Science and Engineering Zhengzhou University Zhengzhou 450001 China
| | - Fan Xia
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano‐Geomaterials of Ministry of Education, Faculty of Material Science and Chemistry China University of Geosciences Wuhan 430074 China
- Zhejiang Institute China University of Geosciences Hangzhou 311305 China
| | - Yu Huang
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano‐Geomaterials of Ministry of Education, Faculty of Material Science and Chemistry China University of Geosciences Wuhan 430074 China
- Zhejiang Institute China University of Geosciences Hangzhou 311305 China
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11
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Zeng L, Kaoud TS, Zamora-Olivares D, Bohanon AL, Li Y, Pridgen JR, Ekpo YE, Zhuang DL, Nye JR, Telles M, Winkler M, Rivera S, Marini F, Dalby KN, Anslyn EV. Multiplexing the Quantitation of MAP Kinase Activities Using Differential Sensing. J Am Chem Soc 2022; 144:4017-4025. [PMID: 35195411 DOI: 10.1021/jacs.1c12757] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein kinases are therapeutic targets for many human diseases, but the lack of user-friendly quantitative assays limits the ability to follow the activities of numerous kinases at once (multiplexing). To develop such an assay, we report an array of sulfonamido-oxine (SOX)-labeled peptides showing cross-reactivity to different mitogen-activated protein kinases (MAPKs) for use in a differential sensing scheme. We first verified using linear discriminant analysis that the array could differentiate MAPK isoforms. Then, using principal component analysis, the array was optimized based on the discrimination imparted by each SOX-peptide. Next, the activity of individual MAPK families in ternary mixtures was quantified by support vector machine regression. Finally, we multiplexed the quantification of three MAPK families using partial least squares regression in A549 cell lysates, which has possible interference from other kinase classes. Thus, our method simultaneously quantifies the activity of multiple kinases. The technique could be applied to other protein kinase families and the monitoring of diseases.
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Affiliation(s)
- Lingyu Zeng
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Tamer S Kaoud
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Diana Zamora-Olivares
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States.,Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Amanda L Bohanon
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Yiru Li
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jacey R Pridgen
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Yakndara E Ekpo
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Deborah L Zhuang
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jessica R Nye
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Mitchell Telles
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michelle Winkler
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Sebastian Rivera
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Federico Marini
- Department of Chemistry, University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Kevin N Dalby
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Eric V Anslyn
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
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12
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Khan A, Ahmed S, Sun BY, Chen YC, Chuang WT, Chan YH, Gupta D, Wu PW, Lin HC. Self-healable and anti-freezing ion conducting hydrogel-based artificial bioelectronic tongue sensing toward astringent and bitter tastes. Biosens Bioelectron 2022; 198:113811. [PMID: 34823963 DOI: 10.1016/j.bios.2021.113811] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/04/2021] [Accepted: 11/13/2021] [Indexed: 01/12/2023]
Abstract
Numerous efforts have been attempted to mimic human tongue since years. However, they still have limitations because of damages, temperature effects, detection ranges etc. Herein, a self-healable hydrogel-based artificial bioelectronic tongue (E-tongue) containing mucin as a secreted protein, sodium chloride as an ion transporting electrolyte, and chitosan/poly(acrylamide-co-acrylic acid) as the main 3D structure holding hydrogel network is synthesized. This E-tongue is introduced to mimic astringent and bitter mouth feel based on cyclic voltammetry (CV) measurements subjected to target substances, which permits astringent tannic acid (TA) and bitter quinine sulfate (QS) to be detected over wide corresponding ranges of 29.3 mM-0.59 μM and 63.8 mM-6.38 μM with remarkable respective sensitivities of 0.2 and 0.12 wt%-1. Besides, the taste selectivity of this E-tongue is performed in the presence of various mixed-taste chemicals to show its high selective behavior toward bitter and astringent chemicals. The electrical self-healability is shown via CV responses to illustrate electrical recovery within a short time span. In addition, cytotoxicity tests using HeLa cells are performed, where a clear viability of ≥95% verified its biocompatibility. The anti-freezing sensing of E-tongue tastes at -5 °C also makes this work to be useful at sub-zero environments. Real time degrees of tastes are detected using beverages and fruits to confirm future potential applications in food taste detections and humanoid robots.
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Affiliation(s)
- Amir Khan
- Department of Materials Science and Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan; Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Shahzad Ahmed
- Department of Materials Science and Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan; Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Bo-Yao Sun
- Department of Materials Science and Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan; Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Yi-Chen Chen
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | | | - Yang-Hsiang Chan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Dipti Gupta
- Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400076, India
| | - Pu-Wei Wu
- Department of Materials Science and Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan; Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Hong-Cheu Lin
- Department of Materials Science and Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan; Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan; Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
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13
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SASAKI Y, LYU X, YUAN Y, MINAMI T. On-site Chemosensor Arrays for Qualitative and Quantitative Detection with Imaging Analysis. BUNSEKI KAGAKU 2021. [DOI: 10.2116/bunsekikagaku.70.691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Yui SASAKI
- Institute of Industrial Science, The University of Tokyo
| | - Xiaojun LYU
- Institute of Industrial Science, The University of Tokyo
| | - Yousi YUAN
- Institute of Industrial Science, The University of Tokyo
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14
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Turner AN, Challa AK, Cooper KM. Student Perceptions of Authoring a Publication Stemming from a Course-Based Undergraduate Research Experience (CURE). CBE LIFE SCIENCES EDUCATION 2021; 20:ar46. [PMID: 34415196 PMCID: PMC8715815 DOI: 10.1187/cbe.21-02-0051] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 07/01/2021] [Accepted: 07/08/2021] [Indexed: 05/23/2023]
Abstract
Course-based undergraduate research experiences (CUREs) engage students in authentic research experiences in a course format and can sometimes result in the publication of that research. However, little is known about student-author perceptions of CURE publications. In this study, we examined how students perceive they benefit from authoring a CURE publication and what they believe is required for authorship of a manuscript in a peer-reviewed journal. All 16 students who were enrolled in a molecular genetics CURE during their first year of college participated in semistructured interviews during their fourth year. At the time of the interviews, students had been authors of a CURE publication for a year and a half. Students reported that they benefited personally and professionally from the publication. Students had varying perceptions of what is required for authorship, but every student thought that writing the manuscript was needed, and only two mentioned needing to approve the final draft. Additionally, we identified incomplete conceptions that students had about CURE publications. This work establishes student-perceived benefits from CURE publications and highlights the need for authorship requirements to be explicitly addressed in CUREs.
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Affiliation(s)
- Ashley N. Turner
- Department of Biology, School of Science, Jacksonville State University, Jacksonville, AL 36265
| | - Anil K. Challa
- Department of Biology, College of Arts and Sciences, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Katelyn M. Cooper
- Research for Inclusive STEM Education Center, School of Life Sciences, Arizona State University, Tempe, AZ 85281
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15
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Xu Y, Bonizzoni M. Discrimination and Quantitation of Biologically Relevant Carboxylate Anions Using A [Dye•PAMAM] Complex. SENSORS 2021; 21:s21113637. [PMID: 34073712 PMCID: PMC8197244 DOI: 10.3390/s21113637] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 12/01/2022]
Abstract
Carboxylate anions are analytical targets with environmental and biological relevance, whose detection is often challenging in aqueous solutions. We describe a method for discrimination and quantitation of carboxylates in water buffered to pH 7.4 based on their differential interaction with a supramolecular fluorescent sensor, self-assembled from readily available building blocks. A fifth-generation poly(amidoamine) dendrimer (PAMAM G5), bound to organic fluorophores (calcein or pyranine) through noncovalent interactions, forms a [dye•PAMAM] complex responsive to interaction with carboxylates. The observed changes in absorbance, and in fluorescence emission and anisotropy, were interpreted through linear discriminant analysis (LDA) and principal component analysis (PCA) to differentiate 10 structurally similar carboxylates with a limit of discrimination around 100 μM. The relationship between the analytes’ chemical structures and the system’s response was also elucidated. This insight allowed us to extend the system’s capabilities to the simultaneous identification of the nature and concentration of unknown analytes, with excellent structural identification results and good concentration recovery, an uncommon feat for a pattern-based sensing system.
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Affiliation(s)
- Yifei Xu
- Department of Chemistry and Biochemistry, The University of Alabama, Tuscaloosa, AL 35487, USA;
| | - Marco Bonizzoni
- Department of Chemistry and Biochemistry, The University of Alabama, Tuscaloosa, AL 35487, USA;
- Alabama Water Institute, The University of Alabama, Tuscaloosa, AL 35487, USA
- Correspondence:
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16
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Kumar K, Giehl A, Schweiggert R, Patz CD. Network analysis on Fourier-transform infrared (FTIR) spectroscopic data sets in an Eigen space layout: Introducing a novel approach for analysing wine samples. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 251:119440. [PMID: 33477085 DOI: 10.1016/j.saa.2021.119440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 12/30/2020] [Accepted: 01/03/2021] [Indexed: 06/12/2023]
Abstract
In the present work, Eigen-directed network analysis for Fourier-transform infrared (FTIR) spectroscopic data sets of wine samples was introduced. A network can generally be viewed as a collection of nodes connected to each other through links, often also called edges. Herein, each node in the network represents a sample and the dissimilarity weight associated with the difference between the two connected nodes is described by the edge. The utility of the approach was tested by analysing a collection of 148 wine samples. The networking on FTIR data sets of these samples in the Eigen space layout was found to impart required aesthetic values as well as the chemical significance to the nodes positioning. The proposed approach successfully captured the compositional differences among the analysed wine samples and classified them in two groups. The Eigen-directed network analysis also allowed a swift assessment regarding inter- and intra-group homogeneity. Homogeneous groups were found to contain nodes with high degree of adjacency and edges with smaller lengths. In comparative study, the proposed approach was found to outperform the network analysis in force-directed layout and principal component analysis. In summary, the proposed Eigen-directed network analysis provided a simplified illustration of highly correlated spectral data sets enabling a swift and intuitive interpretation.
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Affiliation(s)
- Keshav Kumar
- Geisenheim University, Department of Beverage Research, Analysis and Technology of Plant-based Foods, Von Lade Str. 1, D-65366 Geisenheim, Germany
| | - Anja Giehl
- Geisenheim University, Department of Beverage Research, Analysis and Technology of Plant-based Foods, Von Lade Str. 1, D-65366 Geisenheim, Germany
| | - Ralf Schweiggert
- Geisenheim University, Department of Beverage Research, Analysis and Technology of Plant-based Foods, Von Lade Str. 1, D-65366 Geisenheim, Germany
| | - Claus-Dieter Patz
- Geisenheim University, Department of Beverage Research, Analysis and Technology of Plant-based Foods, Von Lade Str. 1, D-65366 Geisenheim, Germany.
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17
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Crook AA, Zamora-Olivares D, Bhinderwala F, Woods J, Winkler M, Rivera S, Shannon CE, Wagner HR, Zhuang DL, Lynch JE, Berryhill NR, Runnebaum RC, Anslyn EV, Powers R. Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage. Food Chem 2021; 354:129531. [PMID: 33756314 DOI: 10.1016/j.foodchem.2021.129531] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 01/29/2021] [Accepted: 03/02/2021] [Indexed: 12/13/2022]
Abstract
Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. 'Pinot noir' wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D 1H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.
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Affiliation(s)
- Alexandra A Crook
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 65888, United States
| | - Diana Zamora-Olivares
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States; Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States
| | - Fatema Bhinderwala
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 65888, United States; Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln NE 68588, United States; Department of Structural Biology, University of Pittsburgh, School of Medicine, 3501 Fifth Avenue, Pittsburgh, PA 15261, United States
| | - Jade Woods
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 65888, United States
| | - Michelle Winkler
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States
| | - Sebastian Rivera
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States
| | - Cassandra E Shannon
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States
| | - Holden R Wagner
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States
| | - Deborah L Zhuang
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States
| | - Jessica E Lynch
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States
| | - Nathan R Berryhill
- Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States
| | - Ron C Runnebaum
- Department of Viticulture and Enology, and Department of Chemical Engineering, University of California-Davis, Davis, CA 95616, United States.
| | - Eric V Anslyn
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States.
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 65888, United States; Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln NE 68588, United States.
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18
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Sasaki Y, Ito S, Zhang Z, Lyu X, Takizawa SY, Kubota R, Minami T. Supramolecular Sensor for Astringent Procyanidin C1: Fluorescent Artificial Tongue for Wine Components. Chemistry 2020; 26:16236-16240. [PMID: 32633434 DOI: 10.1002/chem.202002262] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Indexed: 01/15/2023]
Abstract
An artificial tongue that detects astringent components for a comprehensive evaluation of taste has not been established to date. Herein, we first propose fluorescent polythiophene (PT) derivatives (S1-S3) modified with 3-pyridinium boronic acid as supramolecular chemosensors for wine components including astringent procyanidin C1. After numerous attempts for the synthetic conditions, more than 95 mol % of the PT unit was modified with the pyridinium boronic acid moiety. To evaluate the PT derivatives as chemosensors of the artificial tongue, qualitative and quantitative analyses were performed with four types of wine components (i.e., sweet, sour, bitter, and astringent tastes) in combination with pattern recognition models. Notably, procyanidin C1 in the actual wine sample was successfully detected in a quantitative manner. In other words, we have established an authentic artificial tongue using PT based supramolecular chemosensors.
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Affiliation(s)
- Yui Sasaki
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
| | - Satoshi Ito
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan.,Nitto Denko Corporation, 1-1-2, Shimohozumi, Ibaraki, Osaka, 567-8680, Japan
| | - Zhoujie Zhang
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
| | - Xiaojun Lyu
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
| | - Shin-Ya Takizawa
- Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan
| | - Riku Kubota
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
| | - Tsuyoshi Minami
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
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19
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Abstract
A fluorescent macromolecular chemosensor, built from readily available components, performs chemical fingerprinting of carboxylate anions on low-cost, disposable paper supports.
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Affiliation(s)
- Yifei Xu
- Department of Chemistry and Biochemistry
- The University of Alabama
- Tuscaloosa
- USA
| | - Marco Bonizzoni
- Department of Chemistry and Biochemistry
- The University of Alabama
- Tuscaloosa
- USA
- Alabama Water Institute
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20
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Das Saha N, Sasmal R, Meethal SK, Vats S, Gopinathan PV, Jash O, Manjithaya R, Gagey-Eilstein N, Agasti SS. Multichannel DNA Sensor Array Fingerprints Cell States and Identifies Pharmacological Effectors of Catabolic Processes. ACS Sens 2019; 4:3124-3132. [PMID: 31763818 DOI: 10.1021/acssensors.9b01009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cells at disease onset are often associated with subtle changes in the expression level of a single or few molecular components, making traditionally used biomarker-driven clinical diagnosis a challenging task. We demonstrate here the design of a DNA nanosensor array with multichannel output that identifies the normal or pathological state of a cell based on the alteration of its global proteomic signature. Fluorophore-encoded single-stranded DNA (ssDNA) strands were coupled via supramolecular interaction with a surface-functionalized gold nanoparticle quencher to generate this integrated sensor array. In this design, ssDNA sequences exhibit dual roles, where they provide differential affinities with the receptor gold nanoparticle as well as act as transducer elements. The unique interaction mode of the analyte molecules disrupts the noncovalent supramolecular complexation, generating simultaneous multichannel fluorescence output to enable signature-based analyte identification via a linear discriminant analysis-based machine learning algorithm. Different cell types, particularly normal and cancerous cells, were effectively distinguished using their fluorescent fingerprints. Additionally, this DNA sensor array displayed excellent sensitivity to identify cellular alterations associated with chemical modulation of catabolic processes. Importantly, pharmacological effectors, which could modulate autophagic flux, have been effectively distinguished by generating responses from their global protein signatures. Taken together, these studies demonstrate that our multichannel DNA nanosensor is well suited for rapid identification of subtle changes in a complex mixture and thus can be readily expanded for point-of-care clinical diagnosis, high-throughput drug screening, or predicting the therapeutic outcome from a limited sample volume.
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Affiliation(s)
| | | | | | | | | | | | | | - Nathalie Gagey-Eilstein
- UMR-S 1139, INSERM, 3PHM, Université Paris Descartes, Faculté des Sciences Pharmaceutiques et Biologiques, Sorbonne Paris Cité, 4 avenue de l’Observatoire, 75006 Paris, France
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21
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Beatty MA, Selinger AJ, Li Y, Hof F. Parallel Synthesis and Screening of Supramolecular Chemosensors That Achieve Fluorescent Turn-on Detection of Drugs in Saliva. J Am Chem Soc 2019; 141:16763-16771. [DOI: 10.1021/jacs.9b07073] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Meagan A. Beatty
- Department of Chemistry, University of Victoria, PO Box 3065, STN CSC, Victoria, British Columbia V8W 3 V6, Canada
| | - Allison J. Selinger
- Department of Chemistry, University of Victoria, PO Box 3065, STN CSC, Victoria, British Columbia V8W 3 V6, Canada
| | - YuQi Li
- Department of Chemistry, University of Victoria, PO Box 3065, STN CSC, Victoria, British Columbia V8W 3 V6, Canada
| | - Fraser Hof
- Department of Chemistry, University of Victoria, PO Box 3065, STN CSC, Victoria, British Columbia V8W 3 V6, Canada
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22
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Liu Q, Gao J, Zheng Z, Ning D, Wang Q, Du XM, Zhao B, Ruan WJ, Li Y. Metal-organic frameworks based fluorescent sensor array for discrimination of flavonoids. Talanta 2019; 203:248-254. [DOI: 10.1016/j.talanta.2019.05.073] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/15/2019] [Accepted: 05/16/2019] [Indexed: 01/05/2023]
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23
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24
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A Photochromic Sensor Microchip for High-Performance Multiplex Metal Ion Detection. Methods Mol Biol 2019. [PMID: 31309471 DOI: 10.1007/978-1-4939-9616-2_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Photochromic molecules can respond to external stimulations and undergo reversible conversion between different chemical structures, providing one photochromic molecule with multiple recognition states for targeting compounds. Here we design a facile sensor microchip with only one photochromic molecule (spirooxazine) to discriminate multiplex metal ions. The sensor chip performs in dark, ultraviolet, or visual stimulation, resulting in different molecular states of spirooxazine-metallic coordination and patterned fluorescent signals for analysis. By using this sensor microchip, 11 metal ions are discriminated. Furthermore, mineral water of 16 different brands and metal ions in human serum are distinguished.
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25
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A visual sensor array based on an indicator displacement assay for the detection of carboxylic acids. Mikrochim Acta 2019; 186:496. [DOI: 10.1007/s00604-019-3601-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Accepted: 06/12/2019] [Indexed: 12/23/2022]
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26
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Padroni G, Eubanks CS, Hargrove AE. Differentiation and classification of RNA motifs using small molecule-based pattern recognition. Methods Enzymol 2019; 623:101-130. [PMID: 31239043 PMCID: PMC6891821 DOI: 10.1016/bs.mie.2019.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2022]
Abstract
Understanding how to design small molecules that target coding and non-coding RNA has the potential to exponentially increase the number of therapeutically-relevant druggable targets, which are currently mostly proteins. However, there is limited information on the principles at the basis of RNA recognition. In this chapter, we describe a pattern-based technique that can be used for the simultaneous elucidation of RNA motifs and small molecule features for RNA selective recognition, termed Pattern Recognition of RNA by Small Molecules (PRRSM). We provide protocols for the computational design and synthetic preparation of an RNA training set as well as how to perform the assay in plate reader format. Furthermore, we provide details on how to perform and interpret the statistical analysis and indicate possible future extensions of the technique. By combining insights into characteristics of the small molecules and of the RNA that leads to differentiation, PRRSM promises to accelerate the elucidation of the determinants at the basis of RNA recognition.
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Affiliation(s)
- Giacomo Padroni
- Department of Chemistry, Duke University, Durham, NC, United States
| | | | - Amanda E Hargrove
- Department of Chemistry, Duke University, Durham, NC, United States.
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27
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28
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Kumar K, Giehl A, Patz CD. Chemometric assisted Fourier Transform Infrared (FTIR) Spectroscopic analysis of fruit wine samples: Optimizing the initialization and convergence criteria in the non-negative factor analysis algorithm for developing a robust classification model. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 209:22-31. [PMID: 30343106 DOI: 10.1016/j.saa.2018.10.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 10/12/2018] [Accepted: 10/14/2018] [Indexed: 06/08/2023]
Abstract
The present work proposes certain optimization in the non-negative factor analysis (NNFA) algorithm to ensure an efficient analysis of the Fourier transformation infrared (FTIR) spectral data sets of the fruit wine samples. The first optimization deals with initialization of the variables in a controlled fashion that would ensure a reasonably good quality initial estimate to implement NNFA algorithm. It prevents NNFA algorithm from itinerating with random numbers that essentially have no chemical relevance. The second implemented optimization involves eliminating the alternate least square of convergence and allowing the algorithm to iterate until the iteration limit is reached. This criterion avoids the algorithm to have premature convergence and ensures that model provide the solutions which corresponds to the global minima. The application of NNFA with suggested optimizations are found to capture the subtle differences in the spectral profiles and classify the fruit wine samples that are essentially complex mixtures of several chemicals in unknown proportions. The proposed approach is also found to perform better than principal component analysis on practical grounds. In summary, the current work provides a simple, sensitive and cost-effective approach using optimized NNFA and FTIR spectroscopy for classifying the fruit wine samples.
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Affiliation(s)
- Keshav Kumar
- Hochschule Geisenheim University, Institute for Beverage Research, Von Lade Str. 1, D-65366 Geisenheim, Germany
| | - Anja Giehl
- Hochschule Geisenheim University, Institute for Beverage Research, Von Lade Str. 1, D-65366 Geisenheim, Germany
| | - Claus-Dieter Patz
- Hochschule Geisenheim University, Institute for Beverage Research, Von Lade Str. 1, D-65366 Geisenheim, Germany.
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29
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Wang K, Li Y, Li H, Yin M, Liu H, Deng Q, Wang S. Upconversion fluorescent nanoparticles based-sensor array for discrimination of the same variety red grape wines. RSC Adv 2019; 9:7349-7355. [PMID: 35519955 PMCID: PMC9061164 DOI: 10.1039/c8ra09959f] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 02/22/2019] [Indexed: 11/21/2022] Open
Abstract
A fluorescent sensor array composed of upconversion nanomaterials to distinguish the same variety of red grape wines was constructed.
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Affiliation(s)
- Kewei Wang
- Key Laboratory of Food Nutrition and Safety
- Ministry of Education
- Tianjin Key Laboratory of Food Nutrition and Safety
- College of Chemical Engineering and Materials Science
- Tianjin University of Science and Technology
| | - Yanli Li
- Key Laboratory of Food Nutrition and Safety
- Ministry of Education
- Tianjin Key Laboratory of Food Nutrition and Safety
- College of Chemical Engineering and Materials Science
- Tianjin University of Science and Technology
| | - Haijie Li
- Key Laboratory of Food Nutrition and Safety
- Ministry of Education
- Tianjin Key Laboratory of Food Nutrition and Safety
- College of Chemical Engineering and Materials Science
- Tianjin University of Science and Technology
| | - Mingyuan Yin
- Key Laboratory of Food Nutrition and Safety
- Ministry of Education
- Tianjin Key Laboratory of Food Nutrition and Safety
- College of Chemical Engineering and Materials Science
- Tianjin University of Science and Technology
| | - Huilin Liu
- Beijing Advanced Innovation Center for Food Nutrition and Human Health
- Beijing Technology and Business University
- Beijing
- China
| | - Qiliang Deng
- Key Laboratory of Food Nutrition and Safety
- Ministry of Education
- Tianjin Key Laboratory of Food Nutrition and Safety
- College of Chemical Engineering and Materials Science
- Tianjin University of Science and Technology
| | - Shuo Wang
- Key Laboratory of Food Nutrition and Safety
- Ministry of Education
- Tianjin Key Laboratory of Food Nutrition and Safety
- College of Chemical Engineering and Materials Science
- Tianjin University of Science and Technology
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30
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Eubanks CS, Hargrove AE. RNA Structural Differentiation: Opportunities with Pattern Recognition. Biochemistry 2018; 58:199-213. [PMID: 30513196 DOI: 10.1021/acs.biochem.8b01090] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Our awareness and appreciation of the many regulatory roles of RNA have dramatically increased in the past decade. This understanding, in addition to the impact of RNA in many disease states, has renewed interest in developing selective RNA-targeted small molecule probes. However, the fundamental guiding principles in RNA molecular recognition that could accelerate these efforts remain elusive. While high-resolution structural characterization can provide invaluable insight, examples of well-characterized RNA structures, not to mention small molecule:RNA complexes, remain limited. This Perspective provides an overview of the current techniques used to understand RNA molecular recognition when high-resolution structural information is unavailable. We will place particular emphasis on a new method, pattern recognition of RNA with small molecules (PRRSM), that provides rapid insight into critical components of RNA recognition and differentiation by small molecules as well as into RNA structural features.
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Affiliation(s)
- Christopher S Eubanks
- Department of Chemistry , Duke University , Durham , North Carolina 27708-0354 , United States
| | - Amanda E Hargrove
- Department of Chemistry , Duke University , Durham , North Carolina 27708-0354 , United States
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31
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Han J, Wang B, Bender M, Seehafer K, Bunz UHF. Poly(p-phenyleneethynylene)-based tongues discriminate fruit juices. Analyst 2018; 142:537-543. [PMID: 28112310 DOI: 10.1039/c6an02387h] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
We describe a simple optoelectronic tongue, consisting of a positively charged, fluorescent poly(para-phenyleneethynylene), P2, that reacts to fruit juices, when employed at three different pH-values (pH 3, 7, 13). This minimal tongue identifies and discriminates 14 different commercially available apple juices, 6 different grape juices and 5 different black currant juices from each other. A similar, negatively charged fluorescent polymer, P1, also achieved discrimination, but the analyte concentration had to be increased by a factor of 50. A mixture of black currant juice and red grape juice is identified as red grape juice, for specific combinations of grape and black currant juices. A mixture of red and green grape juice passes as red grape juice in our sensing system when it contains more than 70% of red grape juice. The data were obtained by fluorescence quenching of the conjugated polymers and processed by linear discriminant analysis of the collected data.
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Affiliation(s)
- Jinsong Han
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Benhua Wang
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Markus Bender
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Kai Seehafer
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Uwe H F Bunz
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany and CAM, Centre for Advanced Materials, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 225, 69120 Heidelberg, Germany.
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32
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Li Z, Askim JR, Suslick KS. The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays. Chem Rev 2018; 119:231-292. [DOI: 10.1021/acs.chemrev.8b00226] [Citation(s) in RCA: 476] [Impact Index Per Article: 79.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Zheng Li
- Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Jon R. Askim
- National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Kenneth S. Suslick
- Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
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33
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Choi H, Kim S, Lee S, Kim C, Ryu JH. Array-Based Protein Sensing Using an Aggregation-Induced Emission (AIE) Light-Up Probe. ACS OMEGA 2018; 3:9276-9281. [PMID: 31459059 PMCID: PMC6644794 DOI: 10.1021/acsomega.8b01269] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 08/02/2018] [Indexed: 06/10/2023]
Abstract
Protein detection and identification are important for the diagnosis of diseases; however, the development of facile sensing probes still remains challenging. Here, we present an array-based "turn on" protein-sensing platform capable of detecting and identifying proteins using aggregation-induced emission luminogens (AIEgens). The water-soluble AIEgens in which fluorescence was initially turned off showed strong fluorescence in the presence of nanomolar concentrations of proteins via restriction of the intramolecular rotation of the AIEgens. The binding affinities between the AIEgens and proteins were associated with various chemical functional groups on AIEgens, resulting in distinct fluorescent-signal outcomes for each protein. The combined fluorescence outputs provided sufficient information to detect and discriminate proteins of interest by linear discriminant analysis. Furthermore, the array-based sensor enabled classification of different concentrations of specific proteins. These results provide novel insight into the use of the AIEgens as a new type of sensing probe in array-based systems.
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Affiliation(s)
| | | | | | | | - Ja-Hyoung Ryu
- E-mail: . Tel: +82-52-2172548. Fax: +82-52-2172019 (J.-H.R.)
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34
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Fuertes A, Juanes M, Granja JR, Montenegro J. Supramolecular functional assemblies: dynamic membrane transporters and peptide nanotubular composites. Chem Commun (Camb) 2018. [PMID: 28636028 DOI: 10.1039/c7cc02997g] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The fabrication of functional molecular devices constitutes one of the most important current challenges for chemical sciences. The complex processes accomplished by living systems continuously demand the assistance of non-covalent interactions between molecular building blocks. Additionally, these building blocks (proteins, membranes, nucleotides) are also constituted by self-assembled structures. Therefore, supramolecular chemistry is the discipline required to understand the properties of the minimal self-assembled building blocks of living systems and to develop new functional smart materials. In the first part of this feature article, we highlight selected examples of the preparation of supramolecular membrane transporters with special emphasis on the application of dynamic covalent bonds. In the second section of the paper we review recent breakthroughs in the preparation of peptide nanotube hybrids with functional applications. The development of these devices constitutes an exciting process from where we can learn how to understand and manipulate supramolecular functional assemblies.
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Affiliation(s)
- Alberto Fuertes
- Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CIQUS) and Departamento de Química Orgánica, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.
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35
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Eubanks CS, Hargrove AE. Sensing the impact of environment on small molecule differentiation of RNA sequences. Chem Commun (Camb) 2018; 53:13363-13366. [PMID: 29199743 DOI: 10.1039/c7cc07157d] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Using pattern recognition of RNA with small molecules (PRRSM) with fluorescent RNA chemosensors and aminoglycosides, we reveal the impact of changing environmental conditions on the differentiation of a range of RNA structures as well as the ability to predict different sequence/size compositions of five canonical RNA motifs.
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36
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Han J, Zhang X, Li H, Hou Y, Hou J, Li Z, Yang F, Liu Y, Han T. D-A type sensor array for differentiation and identification of white wine varieties based on specific solvent effect activated by CT-LE transition. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 190:318-323. [PMID: 28941885 DOI: 10.1016/j.saa.2017.09.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 09/07/2017] [Accepted: 09/18/2017] [Indexed: 06/07/2023]
Abstract
In this work, we synthesize a series of compounds with electron donor (D) and acceptor (A) units. They show general solvent effect in aprotic solvents, suggesting a charge transfer (CT) process. While in protic solvents including water, ethanol and methanol, the spectra exert no polarity-dependence but a remarkable hypochromatic shift together with the fading of CT band. Dynamic analysis implies that intermolecular hydrogen bond will be formed between carboxylic acid and protic solvent, boosting another deactivation pathway that jumps off a bigger energy gap, in other words, favoring the locally excited (LE) state emission. The CT-LE transition involves variations in both absorption and emission spectra, and further poses competition with other mechanisms including activated/restricted intramolecular rotation (IR/RIR). Inspired by the cross-reactivity, we turn our attention to the development of sensor array, in order to identify white wine varieties. The differential spectral responses are recorded, generating multiple factors including absorption wavelength (λab), emission wavelength (λem), absorbance (Abs.) and emission intensity (Int.). These factors are processed with principal component analysis (PCA), creating a three-dimensional fingerprint data base for white wines. The data points in the coordinate system are clustered into 10 different groups, demonstrating a clear differentiation of all the white wines. More importantly, as our final test for whether the sensor array can identify the counterfeits, an adulterated liquor sample, which is provided by police officers, is fingerprinted on the three-dimensional diagram. Its canonical factors fall into an area distinct from the adulterated wine, indicating a clear identification.
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Affiliation(s)
- Jingqi Han
- Department of Chemistry, Capital Normal University, 100048, Beijing, PR China
| | - Xin Zhang
- Department of Chemistry, Capital Normal University, 100048, Beijing, PR China
| | - Hao Li
- Dongchangfu District Municipal Public Security Bureau, Liaocheng 252000, Shandong, PR China
| | - Yue Hou
- Department of Chemistry, Capital Normal University, 100048, Beijing, PR China
| | - Jingdan Hou
- Department of Chemistry, Capital Normal University, 100048, Beijing, PR China
| | - Zhongfeng Li
- Department of Chemistry, Capital Normal University, 100048, Beijing, PR China
| | - Feng Yang
- Dongchangfu District Municipal Public Security Bureau, Liaocheng 252000, Shandong, PR China
| | - Yang Liu
- Beijing Key Laboratory of Radiation Advanced Materials, Beijing Research Center for Radiation Application, 100015 Beijing, PR China.
| | - Tianyu Han
- Department of Chemistry, Capital Normal University, 100048, Beijing, PR China.
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37
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Xue A, Szymczak LC, Mrksich M, Bagheri N. Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass Spectrometry. Anal Chem 2017; 89:9039-9047. [PMID: 28719743 PMCID: PMC5588089 DOI: 10.1021/acs.analchem.7b01728] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 07/18/2017] [Indexed: 12/15/2022]
Abstract
Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal-to-noise ratios (S/N). These complex effects can lead to potentially unrepresentative signal intensities and can bias subsequent analyses. Within mass spectrometry-based peptide technologies, the relation between a peptide's amino acid sequence and S/N remains largely nonquantitative. To address this challenge, we present a method to quantify and analyze mass spectrometry S/N of two peptide arrays, and we use this analysis to portray quality of data and to design future arrays for SAMDI mass spectrometry. Our study demonstrates that S/N varies significantly across peptides within peptide arrays, and variation in S/N is attributable to differences of single amino acids. We apply supervised machine learning to predict peptide S/N based on amino acid sequence, and identify specific physical properties of the amino acids that govern variation of this metric. We find low peptide-S/N concordance between arrays, demonstrating that different arrays require individual characterization and that global peptide-S/N relationships are difficult to identify. However, with proper peptide sampling, this study illustrates how machine learning can accurately predict the S/N of a peptide in an array, allowing for the efficient design of arrays through selection of high S/N peptides.
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Affiliation(s)
- Albert
Y. Xue
- Department of Chemical & Biological Engineering, Department of Chemistry, Department of Biomedical
Engineering, Department of Cell & Molecular Biology, and Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208 United States
| | - Lindsey C. Szymczak
- Department of Chemical & Biological Engineering, Department of Chemistry, Department of Biomedical
Engineering, Department of Cell & Molecular Biology, and Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208 United States
| | - Milan Mrksich
- Department of Chemical & Biological Engineering, Department of Chemistry, Department of Biomedical
Engineering, Department of Cell & Molecular Biology, and Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208 United States
| | - Neda Bagheri
- Department of Chemical & Biological Engineering, Department of Chemistry, Department of Biomedical
Engineering, Department of Cell & Molecular Biology, and Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208 United States
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38
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Ghanem E, Afsah S, Fallah PN, Lawrence A, LeBovidge E, Raghunathan S, Rago D, Ramirez MA, Telles M, Winkler M, Schumm B, Makhnejia K, Portillo D, Vidal RC, Hall A, Yeh D, Judkins H, da Silva AA, Franco DW, Anslyn EV. Differentiation and Identification of Cachaça Wood Extracts Using Peptide-Based Receptors and Multivariate Data Analysis. ACS Sens 2017; 2:641-647. [PMID: 28723164 DOI: 10.1021/acssensors.6b00809] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
It is becoming increasingly important to differentiate complex mixtures, especially in forensics. Cachaça, the most popular alcoholic beverage in Brazil, is made from distilled and fermented sugar cane juice. It contains a mixture of naturally occurring polyphenols known as tannins, whose composition is dictated by the type of wood used to age the beverage. These tannins can be differentiated in an Indicator Displacement Assay (IDA) using peptide-based ternary sensing ensembles. This investigation demonstrates a technique for fingerprinting the identity of the woods used to age cachaças. Unknown cachaça samples were tested against a training set of Brazilian woods in addition to oaks from different countries. Results obtained from the analysis showed that 62.5% of the samples were correctly identified. Furthermore, four samples anonymously added to the pool of unknowns from the training set were identified with 100% accuracy, emphasizing both the promising results obtained from this method of differentiation and the importance of analyzing same-age samples.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Alexandre Ataide da Silva
- Departamento
de Química e Física Molecular, Instituto de Química
de São Carlos, Universidade de São Paulo (USP), Av. Trabalhador
Sancarlense 400, CP 780, CEP 13560-970, São Carlos, SP, Brazil
| | - Douglas W. Franco
- Departamento
de Química e Física Molecular, Instituto de Química
de São Carlos, Universidade de São Paulo (USP), Av. Trabalhador
Sancarlense 400, CP 780, CEP 13560-970, São Carlos, SP, Brazil
| | - Eric V. Anslyn
- Department
of Chemistry, The University of Texas at Austin, 105 E 24th Street, Mail Stop A5300, Austin, Texas 78712-1224, United States
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39
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Han J, Wang B, Bender M, Pfisterer J, Huang W, Seehafer K, Yazdani M, Rotello VM, Rotello CM, Bunz UHF. Fingerprinting antibiotics with PAE-based fluorescent sensor arrays. Polym Chem 2017; 8:2723-2732. [PMID: 29081844 DOI: 10.1039/c7py00227k] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
We outline an evolution process for tongue elements composed of poly(p-aryleneethynylene)s (PAE) and detergents, resulting in a chemical tongue (24 elements) that discerns antibiotics. Cross-breeding of this new tongue with tongue elements that consist of simple poly(p-phenyleneethynylene)s (PPE) at different pH-values leads to an enlarged sensor array, composed of 30 elements. This tongue was pruned, employing principal component analysis. We find that a filial tongue featuring three elements from each original array (i.e. a six element tongue) is superior to either of the prior tongues and the composite tongue in the discrimination of structurally different antibiotics. Such a selection process should be general and give an idea how to successfully generate powerful low-selectivity sensor elements and configure them into discriminative chemical tongues.
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Affiliation(s)
- Jinsong Han
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Benhua Wang
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Markus Bender
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Jessica Pfisterer
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Wei Huang
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Kai Seehafer
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
| | - Mahdieh Yazdani
- Department of Chemistry, University of Massachusetts Amherst, 710 North Pleasant Street, Amherst, Massachusetts 01003, USA
| | - Vincent M Rotello
- Department of Chemistry, University of Massachusetts Amherst, 710 North Pleasant Street, Amherst, Massachusetts 01003, USA
| | - Caren M Rotello
- Department of Psychology, University of Massachusetts Amherst, 710 N. Pleasant Street, Amherst, Massachusetts 01003, USA
| | - Uwe H F Bunz
- Organisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany.,CAM, Centre for Advanced Materials, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 225, 69120 Heidelberg, Germany
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40
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Eubanks CS, Forte JE, Kapral GJ, Hargrove AE. Small Molecule-Based Pattern Recognition To Classify RNA Structure. J Am Chem Soc 2017; 139:409-416. [PMID: 28004925 PMCID: PMC5465965 DOI: 10.1021/jacs.6b11087] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Three-dimensional RNA structures are notoriously difficult to determine, and the link between secondary structure and RNA conformation is only beginning to be understood. These challenges have hindered the identification of guiding principles for small molecule:RNA recognition. We herein demonstrate that the strong and differential binding ability of aminoglycosides to RNA structures can be used to classify five canonical RNA secondary structure motifs through principal component analysis (PCA). In these analyses, the aminoglycosides act as receptors, while RNA structures labeled with a benzofuranyluridine fluorophore act as analytes. Complete (100%) predictive ability for this RNA training set was achieved by incorporating two exhaustively guanidinylated aminoglycosides into the receptor library. The PCA was then externally validated using biologically relevant RNA constructs. In bulge-stem-loop constructs of HIV-1 transactivation response element (TAR) RNA, we achieved nucleotide-specific classification of two independent secondary structure motifs. Furthermore, examination of cheminformatic parameters and PCA loading factors revealed trends in aminoglycoside:RNA recognition, including the importance of shape-based discrimination, and suggested the potential for size and sequence discrimination within RNA structural motifs. These studies present a new approach to classifying RNA structure and provide direct evidence that RNA topology, in addition to sequence, is critical for the molecular recognition of RNA.
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Affiliation(s)
- Christopher S Eubanks
- Department of Chemistry, Duke University , Durham, North Carolina 27708, United States
| | - Jordan E Forte
- Department of Chemistry, Duke University , Durham, North Carolina 27708, United States
| | - Gary J Kapral
- Department of Chemistry, Duke University , Durham, North Carolina 27708, United States
| | - Amanda E Hargrove
- Department of Chemistry, Duke University , Durham, North Carolina 27708, United States
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41
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Peacor BC, Ramsay CM, Waters ML. Fluorogenic sensor platform for the histone code using receptors from dynamic combinatorial libraries. Chem Sci 2016; 8:1422-1428. [PMID: 28451282 PMCID: PMC5390788 DOI: 10.1039/c6sc03003c] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 10/20/2016] [Indexed: 12/13/2022] Open
Abstract
A sensor array has been developed that can differentiate multiple post-translational modifications in the same peptide and their relative positions in the sequence, including multiple methylations, providing a promising new tool for deciphering the histone code.
Post-translational modifications (PTMs) on histone tails act in diverse combinations in the ‘histone code’ to control gene expression, with dysregulation observed in a variety of diseases. However, detection and sensing methods are limited, expensive, and/or low-throughput, including MS and antibody based detection. We found that by combining four synthetic receptors developed by dynamic combinatorial chemistry (DCC) in an indicator displacement system, we are able to create a pattern-based sensor platform that can discriminate single PTMs such as methylation and acetylation on a representative histone peptide with 100% accuracy as well as peptides bearing both dimethyl and trimethyl lysine in the presence of arginine methylation, which has not previously been demonstrated, and can even correctly distinguish the position of lysine methylation individually or in the presence of other PTMs. To extend this approach, a full panel of thirteen analytes containing different combinations of PTMs were classified with 96 ± 1% overall accuracy in a 50% left-out analysis, demonstrating the robustness and versatility of the sensor array. Finally, the sensor platform was also used to demonstrate proof of concept for enzymatic assays by analysing the mock reaction of a threonine kinase, successfully identifying analytes representative of substrate conversion both with and without neighboring PTMs. This work provides a rapid platform for the analysis of peptides bearing complex modifications and highlights the utility of receptors discovered though DCC that display variations in binding affinity and selectivity.
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Affiliation(s)
- Brendan C Peacor
- Department of Chemistry , University of North Carolina at Chapel Hill , CB 3290 , Chapel Hill , NC 27599 , USA .
| | - Christopher M Ramsay
- Department of Chemistry , University of North Carolina at Chapel Hill , CB 3290 , Chapel Hill , NC 27599 , USA .
| | - Marcey L Waters
- Department of Chemistry , University of North Carolina at Chapel Hill , CB 3290 , Chapel Hill , NC 27599 , USA .
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42
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Li X, Zamora-Olivares D, Diehl KL, Tian W, Anslyn EV. Differential sensing of oils by conjugates of serum albumins and 9,10-distyrylanthracene probes: a cautionary tale. Supramol Chem 2016. [DOI: 10.1080/10610278.2016.1228934] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Xing Li
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
- State Key Laboratory of Supramolecular Structure and Materials, Jilin University, Changchun, P.R. China
| | | | - Katharine L. Diehl
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
| | - Wenjing Tian
- State Key Laboratory of Supramolecular Structure and Materials, Jilin University, Changchun, P.R. China
| | - Eric V. Anslyn
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
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43
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Han J, Bender M, Seehafer K, Bunz UHF. Identifikation von Weißweinen durch ionische Poly(para-phenylen- ethinylene) und ihre Komplexe. Angew Chem Int Ed Engl 2016. [DOI: 10.1002/ange.201602385] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Jinsong Han
- Organisch-Chemisches Institut; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 270 69120 Heidelberg Deutschland
| | - Markus Bender
- Organisch-Chemisches Institut; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 270 69120 Heidelberg Deutschland
| | - Kai Seehafer
- Organisch-Chemisches Institut; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 270 69120 Heidelberg Deutschland
| | - Uwe H. F. Bunz
- Organisch-Chemisches Institut; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 270 69120 Heidelberg Deutschland
- Center of Advanced Materials; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 225 69120 Heidelberg Deutschland
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44
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Han J, Bender M, Seehafer K, Bunz UHF. Identification of White Wines by using Two Oppositely Charged Poly(p-phenyleneethynylene)s Individually and in Complex. Angew Chem Int Ed Engl 2016; 55:7689-92. [DOI: 10.1002/anie.201602385] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 04/07/2016] [Indexed: 11/11/2022]
Affiliation(s)
- Jinsong Han
- Organisch-Chemisches Institut; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 270 69120 Heidelberg Germany
| | - Markus Bender
- Organisch-Chemisches Institut; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 270 69120 Heidelberg Germany
| | - Kai Seehafer
- Organisch-Chemisches Institut; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 270 69120 Heidelberg Germany
| | - Uwe H. F. Bunz
- Organisch-Chemisches Institut; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 270 69120 Heidelberg Germany
- Center of Advanced Materials; Ruprecht-Karls-Universität Heidelberg; Im Neuenheimer Feld 225 69120 Heidelberg Germany
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45
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Qin M, Huang Y, Li Y, Su M, Chen B, Sun H, Yong P, Ye C, Li F, Song Y. A Rainbow Structural-Color Chip for Multisaccharide Recognition. Angew Chem Int Ed Engl 2016; 55:6911-4. [DOI: 10.1002/anie.201602582] [Citation(s) in RCA: 117] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Indexed: 02/03/2023]
Affiliation(s)
- Meng Qin
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
- University of Chinese Academy of Sciences; Beijing 100049 P.R. China
| | - Yu Huang
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Yanan Li
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
- University of Chinese Academy of Sciences; Beijing 100049 P.R. China
| | - Meng Su
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
- University of Chinese Academy of Sciences; Beijing 100049 P.R. China
| | - Bingda Chen
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Heng Sun
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Peiyi Yong
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Changqing Ye
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Fengyu Li
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Yanlin Song
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
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Qin M, Huang Y, Li Y, Su M, Chen B, Sun H, Yong P, Ye C, Li F, Song Y. A Rainbow Structural-Color Chip for Multisaccharide Recognition. Angew Chem Int Ed Engl 2016. [DOI: 10.1002/ange.201602582] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Meng Qin
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
- University of Chinese Academy of Sciences; Beijing 100049 P.R. China
| | - Yu Huang
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Yanan Li
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
- University of Chinese Academy of Sciences; Beijing 100049 P.R. China
| | - Meng Su
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
- University of Chinese Academy of Sciences; Beijing 100049 P.R. China
| | - Bingda Chen
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Heng Sun
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Peiyi Yong
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Changqing Ye
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Fengyu Li
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
| | - Yanlin Song
- Key Laboratory of Green Printing; Institute of Chemistry; Chinese Academy of Sciences (ICCAS); Beijing Engineering Research Center of Nanomaterials for Green Printing Technology; Beijing National Laboratory for Molecular Sciences (BNLMS); Beijing 100190 P.R. China
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Anslyn EV. Art, auto-mechanics, and supramolecular chemistry. A merging of hobbies and career. Beilstein J Org Chem 2016; 12:362-76. [PMID: 26977197 PMCID: PMC4778509 DOI: 10.3762/bjoc.12.40] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Accepted: 02/12/2016] [Indexed: 12/24/2022] Open
Abstract
While the strict definition of supramolecular chemistry is "chemistry beyond the molecule", meaning having a focus on non-covalent interactions, the field is primarily associated with the creation of synthetic receptors and self-assembly. For synthetic ease, the receptors and assemblies routinely possess a high degree of symmetry, which lends them an aspect of aesthetic beauty. Pictures of electron orbitals similarly can be seen as akin to works of art. This similarity was an early draw for me to the fields of supramolecular chemistry and molecular orbital theory, because I grew up in a household filled with art. In addition to art, my childhood was filled with repairing and constructing mechanical entities, such as internal combustion motors, where many components work together to achieve a function. Analogously, the field of supramolecular chemistry creates systems of high complexity that achieve functions or perform tasks. Therefore, in retrospect a career in supramolecular chemistry appears to be simply an extension of childhood hobbies involving art and auto-mechanics.
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Affiliation(s)
- Eric V Anslyn
- Department of Chemistry, University of Texas, Austin, TX 78712, USA
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Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors. Molecules 2015; 20:9170-82. [PMID: 26007178 PMCID: PMC6272560 DOI: 10.3390/molecules20059170] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 05/09/2015] [Accepted: 05/12/2015] [Indexed: 11/16/2022] Open
Abstract
Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.
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Goodwin S, Gade AM, Byrom M, Herrera B, Spears C, Anslyn EV, Ellington AD. Next-generation sequencing as input for chemometrics in differential sensing routines. Angew Chem Int Ed Engl 2015; 54:6339-42. [PMID: 25826754 PMCID: PMC4426058 DOI: 10.1002/anie.201501822] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Indexed: 12/21/2022]
Abstract
Differential sensing (DS) methods traditionally use spatially arrayed receptors and optical signals to create score plots from multivariate data which classify individual analytes or complex mixtures. Herein, a new approach is described, in which nucleic acid sequences and sequence counts are used as the multivariate data without the necessity of a spatial array. To demonstrate this approach to DS, previously selected aptamers, identified from the literature, were used as semi-specific receptors, Next-Gen DNA sequencing was used to generate data, and cell line differentiation was the test-bed application. The study of a principal component analysis loading plot revealed cross-reactivity between the aptamers. The technique generates high-dimensionality score plots, and should be applicable to any mixture of complex and subtly different analytes for which nucleic acid-based receptors exist.
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Affiliation(s)
- Sara Goodwin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY (USA)
| | - Alexandra M Gade
- Department of Chemistry A1590, The University of Texas at Austin, Austin, TX 78712 (USA)
| | - Michelle Byrom
- Institute for Cell and Molecular Biology, The University of Texas at Austin, Austin, TX 78712 (USA)
| | - Baine Herrera
- Institute for Cell and Molecular Biology, The University of Texas at Austin, Austin, TX 78712 (USA)
| | - Camille Spears
- Institute for Cell and Molecular Biology, The University of Texas at Austin, Austin, TX 78712 (USA)
| | - Eric V Anslyn
- Department of Chemistry A1590, The University of Texas at Austin, Austin, TX 78712 (USA).
| | - Andrew D Ellington
- Institute for Cell and Molecular Biology, The University of Texas at Austin, Austin, TX 78712 (USA).
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50
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Grape and wine sensory attributes correlate with pattern-based discrimination of Cabernet Sauvignon wines by a peptidic sensor array. Tetrahedron 2015. [DOI: 10.1016/j.tet.2014.09.062] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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