An Aptamer-Based Biosensor for Direct, Label-Free Detection of Melamine in Raw Milk

Kaneko, Naoto; Horii, Katsunori; Akitomi, Joe; Kato, Shintaro; Shiratori, Ikuo; Waga, Iwao

doi:10.3390/s18103227

Cited by 22 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, specific recognition aptamers for some ions and molecules have been developed and are gaining increasing attention because of their low cost, good stability and biocompatibility [58,59]. As identification probes, aptamers were widely used in quantitative analysis of small molecules and ions by combination with UV, fluorescence and electrochemistry technology.…”

Section: Aptamer-based Sers Probes In Quantitative Analysis and Bimentioning

confidence: 99%

Development and Application of Aptamer-Based Surface-Enhanced Raman Spectroscopy Sensors in Quantitative Analysis and Biotherapy

Wang

Zhao

et al. 2019

Sensors

View full text Add to dashboard Cite

Surface-enhanced Raman scattering (SERS) is one of the most special and important Raman techniques. An apparent Raman signal can be observed when the target molecules are absorbed onto the surface of the SERS substrates, especially on the “hot spots” of the substrates. Early research focused on exploring the highly active SERS substrates and their detection applications in label-free SERS technology. However, it is a great challenge to use these label-free SERS sensors for detecting hydrophobic or non-polar molecules, especially in complex systems or at low concentrations. Therefore, antibodies, aptamers, and antimicrobial peptides have been used to effectively improve the target selectivity and meet the analysis requirements. Among these selective elements, aptamers are easy to use for synthesis and modifications, and their stability, affinity and specificity are extremely good; they have been successfully used in a variety of testing areas. The combination of SERS detection technology and aptamer recognition ability not only improved the selection accuracy of target molecules, but also improved the sensitivity of the analysis. Variations of aptamer-based SERS sensors have been developed and have achieved satisfactory results in the analysis of small molecules, pathogenic microorganism, mycotoxins, tumor marker and other functional molecules, as well as in successful photothermal therapy of tumors. Herein, we present the latest advances of the aptamer-based SERS sensors, as well as the assembling sensing platforms and the strategies for signal amplification. Furthermore, the existing problems and potential trends of the aptamer-based SERS sensors are discussed.

show abstract

Section: Aptamer-based Sers Probes In Quantitative Analysis and Bimentioning

confidence: 99%

Development and Application of Aptamer-Based Surface-Enhanced Raman Spectroscopy Sensors in Quantitative Analysis and Biotherapy

Wang

Zhao

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Nucleic acid aptamers [1], which are made from single-stranded DNA or RNA, fold into a specific three-dimensional structure and bind to the target molecules with high affinity and specificity. Aptamers have been used for different types of applications such as therapeutics [2], diagnostics [3], multi-protein quantitative measurement [4], and sensors [5] owing to a wide variety of the target molecules, e.g. proteins [6], small molecules [7], ions [8], toxins [9], and cells [10].…”

Section: Introductionmentioning

confidence: 99%

Parallel Implementation of String-Based Clustering for HT-SELEX Data

Kato¹,

Ono²,

Ito³

et al. 2021

EAI Endorsed Transactions on Bioengineering and Bioinformatics

Self Cite

View full text Add to dashboard Cite

INTRODUCTION: A clustering method for HT-SELEX is crucial for selecting different types of aptamer candidates. We have developed FSBC method for HT-SELEX data implemented in R. FSBC exhibited the highest accuracy of sequence clustering compared with conventional methods, while the processing time of FSBC is longer than AptaCluster. OBJECTIVES: The objective of this study is to improve the processing time of FSBC. METHODS: We propose pFSBC, which reduces the processing time of ORS estimation in FSBC by introducing parallel implementation. RESULTS: The processing time and clustering accuracy were evaluated with the last round of NCBI SRA data of SRR3279661 from BioProject PRJNA315881 comparing with other conventional clustering methods. We demonstrated that pFSBC exhibited the highest clustering accuracy and the shortest processing time. CONCLUSION: We expect that pFSBC will help to avoid the time-consuming clustering task, and it will provide accurate clustering results for the HT-SELEX data.

show abstract

“…Aptamers are RNA or short single-stranded DNA molecules, which fold into a three-dimensional structure and bind different types of target molecules such as proteins [3], small molecules [4], toxins [5], ions [6], and cell surfaces [7]. Owing to the wide variety of possible target molecules, aptamers are commonly used for therapeutics [8], clinical diagnostics [9], the high-throughput multi-protein measurement [10], imaging [11], and biosensors [12].…”

Section: Introductionmentioning

confidence: 99%

FSBC: fast string-based clustering for HT-SELEX data

Kato

Ono

Minagawa³

et al. 2020

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

Background: The combination of systematic evolution of ligands by exponential enrichment (SELEX) and deep sequencing is termed high-throughput (HT)-SELEX, which enables searching aptamer candidates from a massive amount of oligonucleotide sequences. A clustering method is an important procedure to identify sequence groups including aptamer candidates for evaluation with experimental analysis. In general, aptamer includes a specific target binding region, which is necessary for binding to the target molecules. The length of the target binding region varies depending on the target molecules and/or binding styles. Currently available clustering methods for HT-SELEX only estimate clusters based on the similarity of full-length sequences or limited length of motifs as target binding regions. Hence, a clustering method considering the target binding region with different lengths is required. Moreover, to handle such huge data and to save sequencing cost, a clustering method with fast calculation from a single round of HT-SELEX data, not multiple rounds, is also preferred. Results: We developed fast string-based clustering (FSBC) for HT-SELEX data. FSBC was designed to estimate clusters by searching various lengths of over-represented strings as target binding regions. FSBC was also designed for fast calculation with search space reduction from a single round, typically the final round, of HT-SELEX data considering imbalanced nucleobases of the aptamer selection process. The calculation time and clustering accuracy of FSBC were compared with those of four conventional clustering methods, FASTAptamer, AptaCluster, APTANI, and AptaTRACE, using HT-SELEX data (>15 million oligonucleotide sequences). FSBC, AptaCluster, and AptaTRACE could complete the clustering for all sequence data, and FSBC and AptaTRACE performed higher clustering accuracy. FSBC showed the highest clustering accuracy and had the second fastest calculation speed among all methods compared. Conclusion: FSBC is applicable to a large HT-SELEX dataset, which can facilitate the accurate identification of groups including aptamer candidates. Availability of data and materials: FSBC is available at http://www.aoki.ecei.tohoku.ac.jp/fsbc/.

show abstract

An Aptamer-Based Biosensor for Direct, Label-Free Detection of Melamine in Raw Milk

Cited by 22 publications

References 21 publications

Development and Application of Aptamer-Based Surface-Enhanced Raman Spectroscopy Sensors in Quantitative Analysis and Biotherapy

Development and Application of Aptamer-Based Surface-Enhanced Raman Spectroscopy Sensors in Quantitative Analysis and Biotherapy

Parallel Implementation of String-Based Clustering for HT-SELEX Data

FSBC: fast string-based clustering for HT-SELEX data

Contact Info

Product

Resources

About