Array-based Generation of Response Patterns with Common Fluorescent Dyes for Identification of Proteins and Cells

Mimura, Masahiro; Tomita, Shunsuke; Kurita, Ryoji; Shiraki, Kentaro

doi:10.2116/analsci.18sdn01

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…We recently reported that a sensor array can be constructed using only readily available materials, i.e., commercially available fluorescent dyes, including fluorescein, rhodamine 6G, and acridine orange. 68 Each fluorescent dye exhibits a different affinity against cell surfaces due to their structural differences (e.g., charge and hydrophobicity). Thus, the array of common fluorescent dyes generates unique response patterns to cells.…”

Section: •3 Tips For the Design Of Easy-to-prepare Sensor Arraysmentioning

confidence: 99%

“…Advance Publication by J-STAGE Received January 15, 2020; Accepted March 30, 2020; Published online on April 3, 2020 DOI: 10.2116/analsci.20R002 from a single probe is one of the easiest ways to increase the number of dimensions of sensing. 50, [64][65][66]68 For a greater response, additional components can be further added to the reaction vessels or wells of the array. An innovative approach to efficiently double the pattern information using additives has been proposed by Rotello and co-workers.…”

Section: Analytical Sciencesmentioning

confidence: 99%

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Pattern-recognition-based Sensor Arrays for Cell Characterization: From Materials and Data Analyses to Biomedical Applications

2020

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To capture a broader scope of complex biological phenomena, alternatives to conventional sensing based on specificity for cell detection and characterization are needed. Patternrecognition-based sensing is an analytical method designed to mimic mammalian sensory systems for analyte identification based on pattern recognition of multivariate data, which are generated using an array of multiple probes that cross-reactively interact with analytes. This sensing approach is significantly different from conventional specific cell sensing based on highly specific probes, including antibodies against biomarkers. Encouraged by the advantages of this technique, such as the simplicity, rapidity, and tunability of the systems without requiring a priori knowledge of biomarkers, numerous sensor arrays have been developed over the past decade and used in a variety of cell sensing applications; these include disease diagnosis, drug discovery, and fundamental research. This review summarizes recent progress in pattern-recognition-based cell sensing, with a particular focus on guidelines for designing materials and arrays, techniques for analyzing response patterns, and applications of sensor systems that are focused primarily for the biomedical field.

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Section: •3 Tips For the Design Of Easy-to-prepare Sensor Arraysmentioning

confidence: 99%

Section: Analytical Sciencesmentioning

confidence: 99%

Pattern-recognition-based Sensor Arrays for Cell Characterization: From Materials and Data Analyses to Biomedical Applications

2020

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“…In the field of analytical chemistry, discriminant analysis based on not only conventional LDA but also the advanced algorithms have been reported in many kinds of samples, e.g., DNA, 26 phospholipids, 118 cancer cells, 119 etc.…”

Section: Classificationmentioning

confidence: 99%

Chemometrics and Related Fields in Python

Morita

2019

ANAL. SCI.

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The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

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Array-based Generation of Response Patterns with Common Fluorescent Dyes for Identification of Proteins and Cells

Cited by 2 publications

References 38 publications

Pattern-recognition-based Sensor Arrays for Cell Characterization: From Materials and Data Analyses to Biomedical Applications

Pattern-recognition-based Sensor Arrays for Cell Characterization: From Materials and Data Analyses to Biomedical Applications

Chemometrics and Related Fields in Python

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