Biomimicry Recognition of Proteins and Cells Using a Small Array of Block Copolymers Appended with Amino Acids and Fluorophores

Chemical tongues as multipurpose bioanalytical tools for the characterization of complex biological samples

Chemical tongues are emerging powerful bioanalytical tools that mimic the mechanism of the human taste system to recognize the comprehensive characteristics of complex biological samples. By using an array of chromogenic or fluorogenic probes that interact non-specifically with various components in the samples, this tool generates unique colorimetric or fluorescence patterns that reflect the biological composition of a sample. These patterns are then analyzed using multivariate analysis or machine learning to distinguish and classify the samples. This review focuses on our efforts to provide an overview of the fundamental principles of chemical tongues, probe design, and their applications as versatile tools for analyzing proteins, cells, and bacteria in biological samples. Compared to conventional methods that rely on specific targeting (e.g., antibodies or enzymes) or comprehensive omics analyses, chemical tongues offer advantages in terms of cost and the ability to analyze samples without the need for specific biomarkers. The complementary use of chemical tongues and conventional methods is expected to enable a more detailed understanding of biological samples and lead to the elucidation of new biological phenomena.

Nanosensor-Enabled Detection and Identification of Intracellular Bacterial Infections in Macrophages

Opportunistic bacterial pathogens can evade the immune response by residing and reproducing within host immune cells, including macrophages. These intracellular infections provide reservoirs for pathogens that enhance the progression of infections and inhibit therapeutic strategies. Current sensing strategies for intracellular infections generally use immunosensing of specific biomarkers on the cell surface or polymerase chain reaction (PCR) of the corresponding nucleic acids, making detection difficult, time-consuming, and challenging to generalize. Intracellular infections can induce changes in macrophage glycosylation, providing a potential strategy for signature-based detection of intracellular infections. We report here the detection of bacterial infection in macrophages using a boronic acid (BA)-based pH-responsive polymer sensor array engineered to distinguish mammalian cell phenotypes by their cell surface glycosylation signatures. The sensor was able to discriminate between different infecting bacteria in minutes, providing a promising tool for diagnostic and screening applications.

Peptomer substrates for quantitative pattern-recognition sensing of proteases

The utility of active proteases as biomarkers is often limited by overlapping substrate specificity. Here, this feature is leveraged to develop a quantitative pattern-recognition sensing system driven by the degradation patterns of peptide-peptoid hybrid substrates to classify proteases and estimate their concentration by multivariate data analysis.

Rapid Fluorescence Sensor Guided Detection of Urinary Tract Bacterial Infections

Introduction

Urinary tract infections (UTI) are one of the most serious human bacterial infections affecting millions of people every year. Therefore, simple and reliable identification of the urinary tract pathogenic bacteria within a few minutes would be of great significance for diagnosis and treatment of clinical patients with UTIs. In this study, the fluorescence sensor was reported to guide the detection of urinary tract bacterial infections rapidly.

Methods

The Ami-AuNPs-DNAs sensor was fabricated by the amino-modified Au nanoparticles (Ami-AuNPs) and six DNAs signal molecules, which bound to the urinary tract pathogenic bacteria and generated corresponding response signals. Further, based on the collected response signals, identification was performed by principal component analysis (PCA) and linear discriminant analysis (LDA). The Ami-AuNPs and Ami-AuNPs-DNAs were characterized by transmission electron microscopy, UV−vis absorption spectrum, Fourier transform infrared spectrum, dynamic light scattering and zeta potentials. Thereafter, the Ami-AuNPs-DNAs sensor was used to discriminate and identify five kinds of urinary tract pathogenic bacteria. Moreover, the quantitative analysis performance towards individual bacteria at different concentrations were also evaluated.

Results

The Ami-AuNPs-DNAs sensor were synthesized successfully in terms of spherical, well-dispersed and uniform in size, which could well discriminate five main urinary tract pathogenic bacteria with unique fingerprint-like patterns and was sufficiently sensitive to determine individual bacteria with a detection limit to 1×10⁷ cfu/mL. Furthermore, the sensor had also been successfully applied to identify bacteria in urine samples collected from clinical UTIs.

Conclusion

The developed fluorescence sensor could be applied to rapid and accurate discrimination of urinary tract pathogenic bacteria and holds great promise for the diagnosis of the disease caused by bacterial infection.

Machine Learning-Assisted Sensor Array Based on Poly(amidoamine) (PAMAM) Dendrimers for Diagnosing Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, and the early diagnosis of AD remains challenging. Here we have developed a fluorescent sensor array composed of three modified polyamidoamine dendrimers. Proteins of various properties were differentiated via this array with 100% accuracy, proving the rationality of the array's design. The mechanism of the fluorescence response was discussed. Furthermore, the robust three-element array enables parallel detection of multiple Aβ40/Aβ42 aggregates (0.5 μM) in diverse interferents, serum media, and cerebrospinal fluid (CSF) with high accuracy, through machine learning algorithms, demonstrating the tremendous potential of the sensor array in Alzheimer's disease diagnosis.

Polymer-based chemical-nose systems for optical-pattern recognition of gut microbiota

Gut-microbiota analysis has been recognized as crucial in health management and disease treatment. Metagenomics, a current standard examination method for the gut microbiome, is effective but requires both expertise and significant amounts of general resources. Here, we show highly accessible sensing systems based on the so-called chemical-nose strategy to transduce the characteristics of microbiota into fluorescence patterns. The fluorescence patterns, generated by twelve block copolymers with aggregation-induced emission (AIE) units, were analyzed using pattern-recognition algorithms, which identified 16 intestinal bacterial strains in a way that correlates with their genome-based taxonomic classification. Importantly, the chemical noses classified artificial models of obesity-associated gut microbiota, and further succeeded in detecting sleep disorder in mice through comparative analysis of normal and abnormal mouse gut microbiota. Our techniques thus allow analyzing complex bacterial samples far more quickly, simply, and inexpensively than common metagenome-based methods, which offers a powerful and complementary tool for the practical analysis of the gut microbiome.

Affinity Diversification of a Polymer Probe for Pattern-recognition-based Biosensing Using Chemical Additives

Pattern-recognition-based sensing has attracted attention as a promising alternative to conventional sensing methods that rely on selective recognition. Here, we report on novel strategy using chemical additives with the ability to modulate probe/analyte interactions to more easily construct pattern-recognition-based sensing systems for proteins and cells. The fluorescence of dansyl-modified cationic poly-L-lysine (PLL-Dnc) is enhanced upon binding to proteins in aqueous solution, while the addition of salts, inert polymers, or alcohols modulates the protein/PLL-Dnc interactions via a variety of mechanisms. Subsequent readout of the fluorescence changes produces response patterns that reflect the characteristics of the analytes. Multivariate analysis of the response patterns allowed for accurate identification of not only eight structurally similar albumin homologues, but also four mammalian cells. This strategy, which uses inexpensive and common additives, significantly improves the accessibility of pattern-recognition-based sensing, which will offer new opportunities for the detection of various bioanalytes.

Protein discrimination based on DNA induced perylene probe self-assembly

The development of a simple and effective method for the highly sensitive and selective discrimination of proteins is a subject of enormous interest. Herein, we report the construction of a novel fluorescence detection method based on a perylene probe for the highly efficient discrimination of multiple proteins. Single-stranded DNA (ssDNA) could induce aggregation of the perylene probe which caused quenching of probe fluorescence. After the addition of a protein, the protein could interact with the ssDNA-probe assembly complex with "turn-on" or further "turn-off" fluorescence response. A sensor array was designed based on the above phenomena which could realize the successful discrimination of proteins with 100% accuracy of cross validation. Nine representative proteins were successfully recognized. Moreover, it was observed that a protein could induce characteristic effect on the DNA-probe assembly with varying pH of assay buffer. Thus, different proteins showed unique fluorescence response towards assay buffers having different pH values. The assay buffer pH was then utilized as a sensing channel. Based on Linear Discriminant Analysis (LDA) nine proteins were successfully discriminated at the nanomolar concentration with 100% accuracy of cross validation. Furthermore, the sensor array also demonstrated differentiation of the nine proteins regardless of their concentration. The developed sensor array could also detect the proteins with great precision in human urine sample at a quite low concentration, which suggests its practical applicability for analysis of biological fluids.

Microfluidic Sensing System with a Multichannel Surface Plasmon Resonance Chip: Damage-Free Characterization of Cells by Pattern Recognition

The development of a versatile sensing strategy for the damage-free characterization of cultured cells is of great importance for both fundamental biological research and industrial applications. Here, we present a pattern-recognition-based cell-sensing approach using a multichannel surface plasmon resonance (SPR) chip. The chip, in which five cysteine derivatives with different structures are immobilized on Au films, is capable of generating five unique SPR sensorgrams for the cell-secreted molecules that are contained in cell culture media. An automatic statistical program was built to acquire kinetic parameters from the SPR sensorgrams and to select optimal parameters as "pattern information" for subsequent multivariate analysis. Our system rapidly (∼10 min) provides the complex information by merely depositing a small amount of cell culture media (∼25 μL) onto the chip, and the amount of information obtained is comparable to that furnished by a combination of conventional laborious biochemical assays. This noninvasive pattern-recognition-based cell-sensing approach could potentially be employed as a versatile tool for characterizing cells.

A Multichannel Pattern-Recognition-Based Protein Sensor with a Fluorophore-Conjugated Single-Stranded DNA Set

Recently, pattern-recognition-based protein sensing has received considerable attention because it offers unique opportunities that complement more conventional antibody-based detection methods. Here, we report a multichannel pattern-recognition-based sensor using a set of fluorophore-conjugated single-stranded DNAs (ssDNAs), which can detect various proteins. Three different fluorophore-conjugated ssDNAs were placed into a single microplate well together with a target protein, and the generated optical response pattern that corresponds to each environment-sensitive fluorophore was read via multiple detection channels. Multivariate analysis of the resulting optical response patterns allowed an accurate detection of eight different proteases, indicating that fluorescence signal acquisition from a single compartment containing a mixture of ssDNAs is an effective strategy for the characterization of the target proteins. Additionally, the sensor could identify proteins, which are potential targets for disease diagnosis, in a protease and inhibitor mixture of different composition ratios. As our sensor benefits from simple construction and measurement procedures, and uses accessible materials, it offers a rapid and simple platform for the detection of proteins.

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

To capture a broader scope of complex biological phenomena, alternatives to conventional sensing based on specificity for cell detection and characterization are needed. Pattern-recognition-based sensing is an analytical method designed to mimic mammalian sensory systems for analyte identification based on the 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.

Systematic Investigation of Molecular Recognition Ability in FET-Based Chemical Sensors Functionalized with a Mixed Self-Assembled Monolayer System

Exploring new strategies for simple and on-demand methods of manipulating the sensing ability of sensor devices functionalized with artificial receptors embedded in a molecular assembly is important to realizing high-throughput on-site sensing systems based on integrated and miniaturized devices such as field-effect transistors (FETs). Although FET-based chemical sensors can be used for rapid, quantitative, and simultaneous determination of various desired analytes, detectable targets in conventional FET sensors are currently restricted owing to the complicated processes used to prepare sensing materials. In this study, we investigated the relationship between the sensing features of FETs and the nanostructures of mixed self-assembled monolayers (mSAMs) for the detection of biomolecules. The FET devices were systematically functionalized using mixtures of benzenethiol derivatives (4-mercaptobenzoic acid and benzenethiol), which changed the nanostructure of the SAMs formed on gold sensing electrodes. The obtained cross-reactivity in the FETs modified with the mSAMs was derived from the multidimensional variations of the SAM characteristics. Our successful demonstration of continuous control of the molecular recognition ability in the FETs by applying the mSAM system could lead to the development of next-generation versatile analyzers, including chemical sensor arrays for the determination of multiple analytes anytime, anywhere.

A carbon-based polymer dot sensor for breast cancer detection using peripheral blood immunocytes

We constructed a carbon-based polymer dot (CPD) sensor to detect breast cancer based on the differences of peripheral blood cells, providing a new minimally invasive method for cancer diagnosis. This simple and extensible system exhibits clinically relevant accuracy in terms of cancer identification, making it an attractive strategy for diagnosis and prognosis.

Multichannel DNA Sensor Array Fingerprints Cell States and Identifies Pharmacological Effectors of Catabolic Processes

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.

Optical Fingerprints of Proteases and Their Inhibited Complexes Provided by Differential Cross-Reactivity of Fluorophore-Labeled Single-Stranded DNA

The detection of proteases and their complexes with inhibitor proteins is of great importance for diagnosis and medical-treatment applications. In this study, we report a fingerprint-based sensor using an array of single-stranded DNAs (ssDNAs) labeled with environment-responsive 3'-carboxytetramethylrhodamine (TAMRA) for the identification of proteases. Four TAMRA-modified ssDNAs with different sequences solubilized in two different buffer solutions were incorporated in an array that was capable of generating fluorescent fingerprints unique to the proteases through diverse cross-reactive interactions, allowing the discrimination of (i) 8 proteases and (ii) 12 different mixtures of trypsin and its inhibitor protein (α₁-antitrypsin) by multivariate analysis. Constructing an array with TAMRA-modified DNA aptamers that bind to different sites of human thrombin provides fluorescence fingerprints that reflect a reduction of the exposed surface area of thrombin upon complexation with antithrombin III, even in the presence of human serum. We finally demonstrate the potential of hybridization with complementary DNAs as an effective means to easily double the fingerprint information for proteases. Our approach based on the cross-reactive capability of ssDNAs enables high-throughput fingerprint-based sensing that can be flexibly designed and easily constructed, not only for the identification of a variety of proteins including proteases but also for the evaluation of their complexation ability.

One-Component Array Based on a Dansyl-Modified Polylysine: Generation of Differential Fluorescent Signatures for the Discrimination of Human Cells

A one-component array-based sensor consisting of a dansyl-modified polylysine (PLL-Dnc) is capable of discriminating the types and compositional ratios of human cells using varying buffer conditions. The PLL-Dnc sensor array, which affords turn-on fluorescence responses against analyte cells that depend on the pH value and the ionic strength, generates differential fluorescence signatures of cells and successfully discriminates eight types of human cell lines (2.0 × 10⁴ cells/mL) with 100% accuracy using multivariate analysis. The array also allows differentiation of the composition of the cell mixtures that contain cells with the same tissue origin but of different subtypes. The good discrimination ability and simple platform of our "one-component"-based array allows an easy and rapid sensing of cells without requiring any information on specific biomarkers.