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

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.

Enhanced "Electronic Tongue" for Dental Bacterial Discrimination and Elimination Based on a DNA-Encoded Nanozyme Sensor Array

Bacterial infections are the second leading cause of death around the world, especially those caused by delayed treatment and misdiagnosis. Therefore, rapid discrimination and effective elimination of multiple bacteria are of great importance for improving the survival rate in clinic. Herein, a novel colorimetric sensor array for bacterial discrimination and elimination is constructed using programmable DNA-encoded iron oxide nanoparticles (IONPs) as sensing elements. Utilizing differential interactions of bacteria on DNA-encoded IONPs, 11 kinds of dental bacteria and 6 kinds of proteins have been successfully identified by linear discriminant analysis (LDA). Moreover, the developed sensing system also performs well in the quantitative determination of individual bacteria and identification of bacterial mixtures. More importantly, the practicability of this sensing strategy is further verified by precise differentiation of blind and artificial saliva samples. Furthermore, the sensor array is used for efficiently killing multiple bacteria, demonstrating great potential in clinical prophylaxis and therapy.

Protein Recognition Based on Temperature-Stimulated Multiparameter Response Virtual Array Sensing Strategy

In the field of array sensing, researchers are committed to miniaturizing array sensing systems while ensuring the acquisition of multiple sensing information. Here, a new strategy called "stimulus responsive array sensing" was presented to obtain virtual multiple sensing without constructing multiple physical sensing units. Based on bioluminescence resonance energy transfer, where luciferase acts as the donor and temperature stimulus response polymers act as the receptors, by using only one sensing unit to output multiple stimulus responsive sensing signals in temperature dimension, an equivalent array sensing could be achieved. This strategy can distinguish and quantify a variety of proteins. More importantly, glucose responsive monomers were doped in polymers; thus, more virtual sensing units can be further increased to obtain more sensing signals, greatly increasing the accuracy of protein recognition, and it can also be used to differentiate several compositions of protein under different glucose concentrations in urine caused by different renal diseases. The results show the potential of the "stimulus responsive array sensing" for analyzing molecular compositions in complex biological systems and show a new tack in array sensing.

Identification of Proteins Using Supramolecular Gold Nanoparticle-Dye Sensor Arrays

The rapid detection of proteins is very important in the early diagnosis of diseases. Gold nanoparticles (AuNPs) can be engineered to bind biomolecules efficiently and differentially. Cross-reactive sensor arrays have high sensitivity for sensing proteins using differential interactions between sensor elements and bioanalytes. A new sensor array was fabricated using surface-charged AuNPs with dyes supramolecularly encapsulated into the AuNP monolayer. The fluorescence of dyes is partially quenched by the AuNPs and can be restored or further quenched due to the differential interactions between AuNPs with proteins. This sensing system enables the discrimination of proteins in both buffer and human serum, providing a potential tool for real-world disease diagnostics.

Cucurbit[7]uril Macrocyclic Sensors for Optical Fingerprinting: Predicting Protein Structural Changes to Identifying Disease-Specific Amyloid Assemblies

In a three-dimensional (3D) representation, each protein molecule displays a specific pattern of chemical and topological features, which are altered during its misfolding and aggregation pathway. Generating a recognizable fingerprint from such features could provide an enticing approach not only to identify these biomolecules but also to gain clues regarding their folding state and the occurrence of pathologically lethal misfolded aggregates. We report here a universal strategy to generate a fluorescent fingerprint from biomolecules by employing the pan-selective molecular recognition feature of a cucurbit[7]uril (CB[7]) macrocyclic receptor. We implemented a direct sensing strategy by covalently tethering CB[7] with a library of fluorescent reporters. When CB[7] recognizes the chemical and geometrical features of a biomolecule, it brings the tethered fluorophore into the vicinity, concomitantly reporting the nature of its binding microenvironment through a change in their optical signature. The photophysical properties of the fluorophores allow a multitude of probing modes, while their structural features provide additional binding diversity, generating a distinct fluorescence fingerprint from the biomolecule. We first used this strategy to rapidly discriminate a diverse range of protein analytes. The macrocyclic sensor was then applied to probe conformational changes in the protein structure and identify the formation of oligomeric and fibrillar species from misfolded proteins. Notably, the sensor system allowed us to differentiate between different self-assembled forms of the disease-specific amyloid-β (Aβ) aggregates and segregated them from other generic amyloid structures with a 100% identification accuracy. Ultimately, this sensor system predicted clinically relevant changes by fingerprinting serum samples from a cohort of pregnant women.

Fluorescence Analysis of Circulating Exosomes for Breast Cancer Diagnosis Using a Sensor Array and Deep Learning

Emerging liquid biopsy methods for investigating biomarkers in bodily fluids such as blood, saliva, or urine can be used to perform noninvasive cancer detection. However, the complexity and heterogeneity of exosomes require improved methods to achieve the desired sensitivity and accuracy. Herein, we report our study on developing a breast cancer liquid biopsy system, including a fluorescence sensor array and deep learning (DL) tool AggMapNet. In particular, we used a 12-unit sensor array composed of conjugated polyelectrolytes, fluorophore-labeled peptides, and monosaccharides or glycans to collect fluorescence signals from cells and exosomes. Linear discriminant analysis (LDA) processed the fluorescence spectral data of cells and cell-derived exosomes, demonstrating successful discrimination between normal and different cancerous cells and 100% accurate classification of different BC cells. For heterogeneous plasma-derived exosome analysis, CNN-based DL tool AggMapNet was applied to transform the unordered fluorescence spectra into feature maps (Fmaps), which gave a straightforward visual demonstration of the difference between healthy donors and BC patients with 100% prediction accuracy. Our work indicates that our fluorescent sensor array and DL model can be used as a promising noninvasive method for BC diagnosis.

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.

A Carbon Nanotube Sensor Array for the Label-Free Discrimination of Live and Dead Cells with Machine Learning

Developing robust cell recognition strategies is important in biochemical research, but the lack of well-defined target molecules creates a bottleneck in some applications. In this paper, a carbon nanotube sensor array was constructed for the label-free discrimination of live and dead mammalian cells. Three types of carbon nanotube field-effect transistors were fabricated, and different features were extracted from the transfer characteristic curves for model training with linear discriminant analysis (LDA) and support-vector machines (SVM). Live and dead cells were accurately classified in more than 90% of samples in each sensor group using LDA as the algorithm. The recursive feature elimination with cross-validation (RFECV) method was applied to handle the overfitting and optimize the model, and cells could be successfully classified with as few as four features and a higher validation accuracy (up to 97.9%) after model optimization. The RFECV method also revealed the crucial features in the classification, indicating the participation of different sensing mechanisms in the classification. Finally, the optimized LDA model was applied for the prediction of unknown samples with an accuracy of 87.5-93.8%, indicating that live and dead cell samples could be well-recognized with the constructed model.

An ionic liquid-assisted quantum dot-grafted covalent organic framework-based multi-dimensional sensing array for discrimination of insecticides using principal component analysis and clustered heat map

A robust multi-dimensional sensing array based on VBimBF₄B/MAA-anchored quantum dot (QD)-grafted covalent organic frameworks (COFs) [(V-M)/QD-grafted COFs] was established via one-pot strategy. The multi-dimensional sensing array has the outstanding advantages of physicochemical and thermal stability, large specific surface area, and regular pore structures. The assistance of ionic liquid VBimBF₄B enhanced the transduction efficiency, and the synergistic effect of COFs enhanced detection efficiency. The improved multi-dimensional sensing array by COFs and ionic liquid VBimBF₄B served to identify seven insecticides by non-specific interactions via hydrogen bonding, and the differences in the kinetics of the binding to the insecticides resulted in variation of the three-output channel (fluorescence, phosphorescence, and light scattering) signals, thus generating a distinct optical fingerprint. The unique fingerprint patterns of seven kinds of common insecticides at 200 μg L^-1 were successfully discriminated using principal component analysis and clustered heat map analysis. The multi-dimensional sensing array showed a response to seven insecticides based on three spectral channels over the range of 0.001-0.4 μg mL^-1 with a limit of detection of 1.08-18.68 μg L^-1. The spiked recovery of tap water was 79.86-134.22%, with RSD ranging from 0.89-14.9%. This study broadens the applications of sensing arrays technology and provides a promising building block for insecticide determination.

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.

Programmable Engineering of DNA-AuNP Encoders Integrated Multimodal Coupled Analysis for Precision Discrimination of Multiple Metal Ions

A cross-responsive strategy (CRS) based on gold nanoparticles (AuNPs) through attaching various recognition receptors on the surface of AuNPs for identifying multiple analytes is presented, and the detection throughput and overall identification accuracy are improved. However, the CRS's recognition receptor cannot get comprehensive information from the target analytes limited in number and type, which determines the overall identification accuracy. Therefore, the practicability of the CRS runs into a bottleneck. Herein, we report a programmable DNA-AuNP encoder combined with a multimodal coupled analysis algorithm for high-throughput detection and accurate analysis of multiple metal ions. The programmable DNA-AuNP encoder breaks through the limitation of the recognition receptor's quantity. Furthermore, the multimodal signals from target metal ion-induced DNA-AuNP aggregation are related to and observed in the ultraviolet absorbance spectrum, surface potential, and particle diameter. The multimodal coupled analysis algorithm can reflect comprehensive information on the target analyte more completely. Finally, this study provides a highly generic tool for the cross-responsive strategy.

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.