Nano-Graphene Oxide Based Multichannel Sensor Arrays towards Sensing of Protein Mixtures

Surface-Engineered Nanomaterials for Optical Array Based Sensing

Array based sensing governed by optical methods provides fast and economic way for detection of wide variety of analytes where the ideality of detection processes depends on the sensor element's versatile mode of interaction with multiple analytes in an unbiased manner. This can be achieved by either the receptor unit having multiple recognition moiety, or their surface property should possess tuning ability upon fabrication called surface engineering. Nanomaterials have a high surface to volume ratio, making them viable candidates for molecule recognition through surface adsorption phenomena, which makes it ideal to meet the above requirements. Most crucially, by engineering a nanomaterial's surface, one may produce cross-reactive responses for a variety of analytes while focusing solely on a single nanomaterial. Depending on the nature of receptor elements, in the last decade the array-based sensing has been considering as multimodal detection platform which operates through various pathway including single channel, multichannel, binding and indicator displacement assay, sequential ON-OFF sensing, enzyme amplified and nanozyme based sensing etc. In this review we will deliver the working principle for Array-based sensing by using various nanomaterials like nanoparticles, nanosheets, nanodots and self-assembled nanomaterials and their surface functionality for suitable molecular recognition.

Development of Optical Differential Sensing Based on Nanomaterials for Biological Analysis

The discrimination and recognition of biological targets, such as proteins, cells, and bacteria, are of utmost importance in various fields of biological research and production. These include areas like biological medicine, clinical diagnosis, and microbiology analysis. In order to efficiently and cost-effectively identify a specific target from a wide range of possibilities, researchers have developed a technique called differential sensing. Unlike traditional "lock-and-key" sensors that rely on specific interactions between receptors and analytes, differential sensing makes use of cross-reactive receptors. These sensors offer less specificity but can cross-react with a wide range of analytes to produce a large amount of data. Many pattern recognition strategies have been developed and have shown promising results in identifying complex analytes. To create advanced sensor arrays for higher analysis efficiency and larger recognizing range, various nanomaterials have been utilized as sensing probes. These nanomaterials possess distinct molecular affinities, optical/electrical properties, and biological compatibility, and are conveniently functionalized. In this review, our focus is on recently reported optical sensor arrays that utilize nanomaterials to discriminate bioanalytes, including proteins, cells, and bacteria.

A Multichannel Fluorescent Array Sensor for Discrimination of Different Types of Drug-Induced Kidney Injury

The differences in urinary proteins could provide a novel opportunity to distinguish the different types of drug-induced kidney injury (DIKI). In this research, Au nanoparticles-polyethyleneimine (AuNPs-PEI) and the three fluorophore-labeled proteins (FLPs) have been constructed as a multichannel fluorescent array sensor via electrostatic interaction, which was used to detect the subtle changes in urine collected from the pathological state of DIKI. Once the urine from different types of DIKI was introduced, the binding equilibrium between AuNPs-PEI and FLPs would be broken due to the competitive binding of urinary protein, and the corresponding fluorescence response pattern would be generated. Depending on the different fluorescence response patterns, the different types of DIKI were successfully identified by principal component analysis (PCA) and linear discriminant analysis (LDA). Accordingly, the strategy was expected to be a powerful technique for evaluating the potential unclear mechanisms of nephrotoxic drugs, which would provide a promising method for screening potential renal-protective drugs.

Carbocyanine-Based Optical Sensor Array for the Discrimination of Proteins and Rennet Samples Using Hypochlorite Oxidation

Optical sensor arrays are widely used in obtaining fingerprints of samples, allowing for solutions of recognition and identification problems. An approach to extending the functionality of the sensor arrays is using a kinetic factor by conducting indicator reactions that proceed at measurable rates. In this study, we propose a method for the discrimination of proteins based on their oxidation by sodium hypochlorite with the formation of the products, which, in turn, feature oxidation properties. As reducing agents to visualize these products, carbocyanine dyes IR-783 and Cy5.5-COOH are added to the reaction mixture at pH 5.3, and different spectral characteristics are registered every several minutes (absorbance in the visible region and fluorescence under excitation by UV (254 and 365 nm) and red light). The intensities of the photographic images of the 96-well plate are processed by principal component analysis (PCA) and linear discriminant analysis (LDA). Six model proteins (bovine and human serum albumins, γ-globulin, lysozyme, pepsin, and proteinase K) and 10 rennet samples (mixtures of chymosin and pepsin from different manufacturers) are recognized by the proposed method. The method is rapid and simple and uses only commercially available reagents.

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.

Cell-Based Chemical Safety Assessment and Therapeutic Discovery Using Array-Based Sensors

Synthetic chemicals are widely used in food, agriculture, and medicine, making chemical safety assessments necessary for environmental exposure. In addition, the rapid determination of chemical drug efficacy and safety is a key step in therapeutic discoveries. Cell-based screening methods are non-invasive as compared with animal studies. Cellular phenotypic changes can also provide more sensitive indicators of chemical effects than conventional cell viability. Array-based cell sensors can be engineered to maximize sensitivity to changes in cell phenotypes, lowering the threshold for detecting cellular responses under external stimuli. Overall, array-based sensing can provide a robust strategy for both cell-based chemical risk assessments and therapeutics discovery.

Monitoring the Viral Transmission of SARS-CoV-2 in Still Waterbodies Using a Lanthanide-Doped Carbon Nanoparticle-Based Sensor Array

The latest epidemic of extremely infectious coronavirus disease 2019 (COVID-19) has created a significant public health concern. Despite substantial efforts to contain severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within a specific location, shortcomings in the surveillance of predominantly asymptomatic infections constrain attempts to identify the epidemiological spread of the virus. Continuous surveillance of wastewater streams, including sewage, offers opportunities to track the spread of SARS-CoV-2, which is believed to be found in fecal waste. To demonstrate the feasibility of SARS-CoV-2 detection in wastewater systems, we herein present a novel facilely constructed fluorescence sensing array based on a panel of three different lanthanide-doped carbon nanoparticles (LnCNPs). The differential fluorescence response pattern due to the counterion-ligand interactions allowed us to employ powerful pattern recognition to effectively detect SARS-CoV-2 and differentiate it from other viruses or bacteria. The sensor results were benchmarked to the gold standard RT-qPCR, and the sensor showed excellent sensitivity (1.5 copies/μL) and a short sample-to-results time of 15 min. This differential response of the sensor array was also explained from the differential mode of binding of the LnCNPs with the surface proteins of the studied bacteria and viruses. Therefore, the developed sensor array provides a cost-effective, community diagnostic tool that could be potentially used as a novel epidemiologic surveillance approach to mitigate the spread of COVID-19.

Gram-Selective Antibacterial Activity of Mixed-Charge 2D-MoS2

Development of nanomaterial-based antibiotics can be the most potent alternative due to the increasing resistance against conventional antibiotics. But one of the important parameters in development of antibacterial agent is...

A Fluorescence Sensor Array Based on Zinc(II)-Carboxyamidoquinolines: Toward Quantitative Detection of ATP*

The newly prepared fluorescent carboxyamidoquinolines (1-3) and their Zn(II) complexes (Zn@1-Zn@3) were used to bind and sense various phosphate anions utilizing a relay mechanism, in which the Zn(II) ion migrates from the Zn@1-Zn@3 complexes to the phosphate, namely adenosine 5'-triphosphate (ATP) and pyrophosphate (PPi), a process accompanied by a dramatic change in fluorescence. Zn@1-Zn@3 assemblies interact with adenine nucleotide phosphates while displaying an analyte-specific response. This process was investigated using UV-vis, fluorescence, and NMR spectroscopy. It is shown that the different binding selectivity and the corresponding fluorescence response enable differentiation of adenosine 5'-triphosphate (ATP), adenosine 5'-diphosphate (ADP), pyrophosphate (PPi), and phosphate (Pi). The cross-reactive nature of the carboxyamidoquinolines-Zn(II) sensors in conjunction with linear discriminant analysis (LDA) was utilized in a simple fluorescence chemosensor array that allows for the identification of ATP, ADP, PPi, and Pi from 8 other anions including adenosine 5'-monophosphate (AMP) with 100 % correct classification. Furthermore, the support vector machine algorithm, a machine learning method, allowed for highly accurate quantitation of ATP in the range of 5-100 μM concentration in unknown samples with error <2.5 %.

Surface engineered amphiphilic carbon dots: solvatochromic behavior and applicability as a molecular probe

Carbon dots (C-dots) have attracted great attention in the fields of nanotechnology and bioengineering owing to their unique and tunable optical properties with excellent photoluminescence characteristics. Herein, we have engineered amphiphilic C-dots (AC-dots) using positional isomers of diamino benzene with citric acid under mild microwave irradiation to minimize any background reactions. The optical properties changed from excitation-dependent to excitation-independent depending on the isomer used. This unique optical property of the AC-dots was studied in the presence of various solvents and we extensively inspected the AC-dot-solvent interactions. The intensity of the emission wavelength varied with solvent polarity and showed a linear relationship. Furthermore, we extended this property to investigate the molecular environment in biomolecular systems such as proteins. Interestingly, we found that, in the presence of various proteins, the emission intensity was enhanced, quenched or remained unchanged depending on the nature of the protein surface. The mode of interaction between AC-dots and protein was determined using temperature-dependent fluorescence spectroscopy. This study could provide vital information about the surfaces of proteins and the potential application of C-Dots as a fluorescent probe to detect biological molecules and environments.

Machine Learning-Assisted Array-Based Detection of Proteins in Serum Using Functionalized MoS2 Nanosheets and Green Fluorescent Protein Conjugates

Abnormal concentrations of a specific protein or the presence of some biomarker proteins may indicate life-threatening diseases. Pattern-based detection of specific analytes using affinity-regulated receptors is one of the potential alternatives to specific antigen-antibody-based detection. In this report, we have schemed a sensor array by using various functionalized two-dimensional (2D)-MoS2 nanosheets and green fluorescent protein (GFP) as the receptor and the signal transducer, respectively. Two-dimensional MoS2 has been used as a promising candidate for recognition of the bioanalytes because of its high surface-to-volume ratio compared to those of other nanomaterials. Easy surface tunability of this material provides additional advantages to analyze the target of interest. The optimized 2D-MoS2-GFP conjugates are able to discriminate 15 different proteins at 50 nM concentration with a detection limit of 1 nM. Moreover, proteins in the binary mixture and in the presence of serum were discriminated successfully. Ten different proteins in serum media at relevant concentrations were classified successfully with 100% jackknifed classification accuracy, which proves the potentiality of the above system. We have also implemented and discussed the implication of using different machine learning models on the pattern recognition problem associated with array-based sensing.

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.

Machine Learning-Assisted Array-Based Biomolecular Sensing Using Surface-Functionalized Carbon Dots

Fluorescent array-based sensing is an emerging differential sensing platform for sensitive detection of analytes in a complex environment without involving a conventional "lock and key" type-specific interaction. These sensing techniques mainly rely on different optical pattern generation from a sensor array and their pattern recognition to differentiate analytes. Currently emerging, compelling pattern-recognition method, Machine Learning (ML), enables a machine to "learn" a pattern by training without having the recognition method explicitly programmed into it. Thus, ML has an enormous potential to analyze these sensing data better than widely used statistical pattern-recognition methods. Here, an array-based sensor using easy-to-synthesize carbon dots with varied surface functionality is reported, which can differentiate between eight different proteins at 100 nM concentration. The utility of using machine learning algorithms in pattern recognition of fluorescence signals from the array has also been demonstrated. In analyzing the array-based sensing data, Machine Learning algorithms like "Gradient-Boosted Trees" have achieved a 100% prediction efficiency compared to inferior-performing classical statistical method "Linear Discriminant Analysis".

High Yield Controlled Synthesis of Nano-Graphene Oxide by Water Electrolytic Oxidation of Glassy Carbon for Metal-Free Catalysis

The strong quantum confinement effect as well as abundant edges and oxygen functional groups enable nano-graphene oxide (NGO) a variety of intriguing applications such as catalysis, bioimaging, drug delivery and photovoltaic devices. However, the development of NGO is severely hindered because of the difficulty in controlled mass production. Here, we report the efficient synthesis of NGO with a high yield of ∼40 wt % by water electrolytic oxidation of glassy carbon (GC). The NGO shows a high oxidation degree (C/O atomic ratio, ∼1.4) and excellent dispersion stability. Moreover, its size can be easily tuned by the graphitization degree of GC, which enables the controlled synthesis of NGO with average size of 4, 8, and 13 nm and different oxygen functional groups. As metal-free catalysts, the 13 nm sized NGO is found to be beneficial for the oxidative coupling reaction of benzylamine, while the 4 nm sized NGO shows a conversion rate of 88 times higher than 13 nm sized NGO for the oxidation reaction of benzene. In addition, the water electrolytic oxidation mechanism of graphitic materials is systematically studied. It is found that sulfuric acid has a protective effect on the graphite electrode during the water electrolytic oxidation process, and 50 wt % sulfuric acid solution well balances the protection and oxidation processes, leading to the highest oxidation efficiency and production rate.