Fluorescent Materials With Aggregation-Induced Emission Characteristics for Array-Based Sensing Assay

Abstract

Array-based sensing is a powerful tool for identifying analytes in complex environments with unknown interferences. In array-based sensing, the sensors, which transduce binding details to signal outputs, are of crucial importance for identifying analytes. Aggregation-induced emission luminogens (AIEgens) enjoy the advantages of easy synthesis and high sensitivity, which enable them to facilely form a sensor pool through structural modifications and sensitively reflect the subtle changes associated with binding events. All these features make AIEgens excellent candidates for array-based sensing, and attempts have been made by several research groups to explore their potentials in array-based sensing. In this review, we introduce the recent progresses of employing AIEgens as sensors in sensing assays and in building up sensor arrays for identification of varied biological analytes, including biomolecules and bacteria. Examples are selected to illustrate the working mechanism, probe design and selection, capability of the sensor array, and implications of these sensing methods.

Keywords: aggregation-induced emission; array-based sensing; bacteria identification; biological sensing; sensor array.

Figure 1

Fluorescence photographs of HPS in THF/water mixtures with different water contents. Adapted from Mei et al. (2015) with permission. Copyright 2015 American Chemical Society.

Figure 2

Illustration on the RIR process of TPE and the RIR and RIV processes of THBA. Reproduced from Zhu et al. (2018), https://pubs.acs.org/doi/10.1021/acsabm.8b00600, with permission. Copyright 2018 American Chemical Society.

Figure 3

(A) The working principle of employing TPE4S for detection of sialidase and molecular structures of TPE4S and TPE4A. (B) Fluorescence responses of TPE4S (20 μM) to various concentrations of sialidase. TPE4S was incubated with sialidase in PBS (pH 7.1, 6.7 mM) for 1 h before measuring the fluorescence intensity. (C) The inhibition effect of some potential inhibitors evaluated with TPE4S using a 96-well plate assay. I and I_C represent the fluorescence intensities with and without potential inhibitors, respectively, and I₀ is the fluorescence intensity of 20 μM TPE4S itself. λ_em = 510 nm. Reproduced from Liu et al. (2018) with permission. Copyright 2018 Royal Society of Chemistry.

Figure 4

(A) Illustrations of surface functionalities of the superwettable microchip. Octadecyltrichlorosilane-modification endows the substrate with superhydrophobicity with a water contact angle (CA) of around 157.5°, while irradiation destroys the OTS modification and makes microwell superhydrophilic with a water CA approaching 0°. (B) Design of miRNA detection by employing AIEgen-based superwettable microchips. (C) Fluorescence responses of AIEgen-based microchips toward different concentrations of miR-141. (D) Assessment on the specificity of the AIEgen-based microchips for the detection of miR-141. Reproduced from Chen et al. (2018) with permission. Copyright 2018 Elsevier.

Figure 5

(A) Molecular structure of the AIEgen employed. (B) Illustration of the high-throughput antibiotics screening strategy. (C) Evaluation of the inhibition effect of different antibiotics on S. epidermidis. Staphylococcus epidermidis is incubated with different concentrations of antibiotics for 4 h, followed by quantification with the AIEgen. (D) Evaluation of the susceptibility of E. coli and Kana^r E. coli toward KANA. λ_ex: 430 nm. Adapted from Zhao et al. (2015) with permission. Copyright 2015 John Wiley and Sons.

Figure 6

(A) Mechanism illustrations of the fluorescence turn on detection of proteins using an array with AIEgens as reporter. (B) Linear discriminant analysis of the fluorescence responses of synthesized AIEgens in the presence of the five proteins using two-dimensional (2D) plots with 95% ellipse confidence. (C) Linear discriminant analysis of the fluorescence responses of AIEgens in the presence of BSA and esterase at 200 nM, 500 nM, 1 μM, and 2 μM, using 2D plots with 95% ellipse confidence. Reproduced from Choi et al. (2018), https://pubs.acs.org/doi/10.1021/acsomega.8b01269, with permission. Copyright 2018 American Chemical Society.

Figure 7

(A) Procedures for preparing AIE-doped poly(ionic liquid) photonic sphere and the molecular structures of employed ionic liquid monomer, crosslinker, and AIEgen. (B) The optical and fluorescence images of the AIE-doped poly(ionic liquid) photonic spheres (OH⁻ form) to 20 natural amino acids (10 mM). (C,D) Three-dimensional PCA plot of AIE-doped poly(ionic liquid) photonic spheres of the OH⁻ form for the discrimination of 20 natural amino acids at 10 mM in (C) water and (D) urine. (E) Three-dimensional PCA plot of the semiquantitative assay of Trp, Cys, and Lys at five different concentrations by the AIE-doped PIL photonic spheres of the OH⁻ form. Reproduced from Zhang et al. (2017) with permission. Published by The Royal Society of Chemistry. Copyright 2017 Royal Society of Chemistry.

Figure 8

(A) The design principle for bacteria identification using a sensor array. A1–A5 denote five different probes, and B1–B8 represent eight bacteria species. (B) Molecular structures of the AIEgens employed. (C) Principal component analysis plot of the fluorescence responses of five AIEgens toward eight kinds of bacteria. F1, F2, and F3 are the top three rates of contribution. Reproduced from Chen et al. (2014) with permission. Copyright 2014 John Wiley and Sons.

Figure 9

(A) Molecular structures of TPE-ARs and schematic illustration of pathogen identification with fluorescence sensor array. P1–P7 are seven kinds of pathogens. (B) Fluorescence responses of seven TPE-ARs (20 × 10⁻⁶ M) toward different microbes. λ_em: 470 nm. I₀ and I are the fluorescence intensity of TPE-ARs in the absence and presence of microbes. (C) Canonical score plot for the fluorescence response patterns determined by LDA. Reproduced from Zhou et al. (2019) with permission. Copyright 2018 John Wiley and Sons.

Figure 10

(A) Schematic illustration of the two strategies for constructing sensor arrays. The left represents the direct interaction between AIEgens and biomolecules, and the right demonstrates the competitive interaction among AIEgen, biomolecules, and GO. (B) Molecular structures of the selected AIEgens. (C) Principal component analysis results of the formed patterns generated from six microbial lysates. F1 and F2 are the top two rates of contribution in PCA analysis. (D) Separation of biomolecules into a large molecule portion (molecular weight >3,000) and a small molecule portion (molecular weight <3,000) with a ultrafiltration membrane. The fluorescence images of these two portions after incubation with AIEgens. Reproduced from Shen et al. (2018) with permission. Copyright 2018 American Chemical Society.