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

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.

Recent Progress in Sensor Arrays: From Construction Principles of Sensing Elements to Applications

The traditional sensors are designed based on the "lock-and-key" strategy with high selectivity and specificity for detecting specific analytes, which however are not suitable for detecting multiple analytes simultaneously. With the help of pattern recognition technologies, the sensor arrays excel in distinguishing subtle changes caused by multitarget analytes with similar structures in a complex system. To construct a sensor array, the multiple sensing elements are undoubtedly indispensable units that will selectively interact with targets to generate the unique "fingerprints" based on the distinct responses, enabling the identification among various analytes through pattern recognition methods. This comprehensive review mainly focuses on the construction strategies and principles of sensing elements, as well as the applications of sensor array for identification and detection of target analytes in a wide range of fields. Furthermore, the present challenges and further perspectives of sensor arrays are discussed in detail.

A susceptible coordination hybrid based terbium sensibilization coupled ESIPT effects for pattern discrimination of analogues

Multianalyte detection and analogue discrimination are extremely valuable frontier areas for their wide applications in environmental, medical, clinical and industrial analyses. Nowadays, researchers rack their brains on how to develop excellent multianalyte chemosensors that have presented huge challenges in designing high-efficient fluorescent sensing materials and constructing high-throughput detection methods. In this paper, we propose a novel strategy to utilize the dual-emission fluorescent detection platform as a lab-on-a-molecule, arising from the disalicylaldehyde-coordinated hybrid H₂Q_j3/Tb based terbium sensibilization coupled excited-state intramolecular proton transfer effects. Using the statistical analysis (PCA and HCA) for sensing signals of three fluorescence channels (431, 543 and 583 nm), we demonstrate this elaborate chemosensor with multianalyte detection of three species (solvents, anions and cations) and pattern discrimination of analogues. As a result, the H₂Q_j3/Tb shows great lab-on-a-molecule characters for each set of species, resulting in the easier identification of many critical analytes (e.g., H₂O, NO₂^- and Fe³⁺) and discrimination of analogues. In addition, it is also proven to be able to provide reliable content determination for an analyte, especially the NO₂^- (LOD = 0.37 μM), and discrimination for mixed analogues. A combination of easy-to-implement preparation procedure and data analysis technique makes this work promising for not only designing similar lanthanide-based materials but also realizing more high-efficient multianalyte sensing systems towards various potential applications.

Damage-free evaluation of cultured cells based on multivariate analysis with a single-polymer probe

We present a pattern-recognition-based sensor that targets cell-derived components in culture media and evaluates cultured cells without damaging them. An array sensor made of a single-polymer probe was employed to obtain fluorescence response patterns of the analyte media, allowing successful identification of the type and state of the cells via multivariate analysis.

A pH-Based Single-Sensor Array for Discriminating Metal Ions in Water

Human activities, such as mining and manufacturing, expose society and the natural environment to harmful levels of metal ions. Recently, optical sensor arrays for metal ion detection have become popular owing to their favourable features, such as facile sample preparation and the requirement of less expensive instrumentation compared to traditional, spectrometry‐based analysis techniques. Sensor arrays usually consist of numerous optical probes that are used in combination to generate unique analyte responses. In contrast, here we present an array that comprises a single fluorescent sensor, Coum4‐DPA, that produces unique responses to metal ions in different pH environments. With this simple sensing platform, we were able to classify 10 metal ions in different water sources and quantify Pb²⁺ in tap water using just one fluorescent sensor, a few pH buffers and two sets of spectral data. This novel approach significantly decreases time and costs associated with probe synthesis and data collection, making it highly transferrable to real‐world metal sensing applications.

A polymer-based chemical tongue for the non-invasive monitoring of osteogenic stem-cell differentiation by pattern recognition of serum-supplemented spent media

The development of non-invasive techniques to characterize cultured cells is invaluable not only to ensure the reproducibility of cell research, but also for quality assurance of industrial cell products for...

Identification of Water-Soluble Polymers through Discrimination of Multiple Optical Signals from a Single Peptide Sensor

The pollution of water environments is a worldwide concern. Not only marine pollution by plastic litter, including microplastics, but also the spillage of water-soluble synthetic polymers in wastewater have recently gained increasing attention due to their potential risks to soil and water environments. However, conventional methods to identify polymers dissolved in water are laborious and time-consuming. Here, we propose a simple approach to identify synthetic polymers dissolved in water using a peptide-based molecular sensor with a fluorophore unit. Supervised machine learning of multiple fluorescence signals from the sensor, which specifically or nonspecifically interacted with the polymers, was applied for polymer classification as a proof of principle demonstration. Aqueous solutions containing different polymers or multiple polymer species with different mixture ratios were identified successfully. We found that fluorophore-introduced biomolecular sensors have great potential to provide discriminative information regarding water-soluble polymers. Our approach based on the discrimination of multiple optical signals of water-soluble polymers from peptide-based molecular sensors through machine learning will be applicable to next-generation sensing systems for polymers in wastewater or natural environments.

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.

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.

Mitochondria-Targeted Sensor Array with Aggregation-Induced Emission Luminogens for Identification of Various Cells

Accurate discrimination of cancerous cells is a good solution for early diagnosis of tumors. The mitochondrion plays an important role in cells. Herein, the five aggregation-induced emission luminogens (AIEgens) with various double positive charges are synthesized to image mitochondria. Tetraphenylethylene (TPE) molecules are modified by methoxy groups, conjugated donor-acceptor, and different positive charges to achieve multicolor emission. The five AIEgens form the PTx-Sa (positive mitochondria-target molecular sensor array) to perform cross-fluorescence response based on the mitochondria-targeted imaging to achieve the discrimination of various cells. Principal component analysis of the cross-response fluorescence data of PTx-Sa shows that 100% accurate identification of various cells, including cancer cells and normal cells, digestive tract cancer cells, gastric cancer cells, and mixed gastric cancer cells. By support vector machine to show the predictive ability of PTx-Sa to unknown cells by using blind samples. This is the first time to apply mitochondria-targeted sensor array to identification of various cells.

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.

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.