Simple and robust polymer-based sensor for rapid cancer detection using serum

Conjugated Polyelectrolyte-Based Sensor Arrays: from Sensing Mechanisms to Artificial Sensory System Conceptualization

In the past decades, conjugated polyelectrolytes (CPEs) have become prominent in sensing applications due to their unique properties, including strong and tunable light absorption, high sensitivity, water solubility, and biocompatibility. Inspired by mammalian olfactory and gustatory systems, CPE-based sensor arrays have made significant strides in discriminating structurally similar analytes and complex mixtures for various applications. This review consolidates recent advancements in CPE-based sensor arrays, highlighting rational design, controllable fabrication, and effective data processing methods. It covers the fundamentals of CPE fluorescence sensing, emphasizing design strategies for sensor array units and data processing techniques. The broad applicability of CPE-based sensor arrays is demonstrated across diverse domains, including environmental monitoring (e.g., detecting metal ions and explosives), medical diagnostics (e.g., sensing disease markers and analyzing biological samples), and food safety (e.g., assessing the freshness, quality, and source of food products). Further, challenges and future directions in the field are discussed to inspire further research and development in this area.

Poly(p-Phenyleneethynylene)s-Based Sensor Array for Diagnosis of Clinical Diseases

Inspired by the mammalian taste and olfactory systems, array-based pattern recognition technology has demonstrated significant potential in discerning subtle differences between highly similar compounds and complex mixtures, owing to their unique parallel detection mechanism based on cross-reactive signals. While optical sensor array has been extensively employed in the field of chemical sensing, they encounter significant challenges in non-specific recognition of multiple analytes at low concentrations, particularly in rife environments with complex interferences. Poly(p-phenylene ethynylene)s (PPEs) offer substantial advantages in detecting multi-analytes at low concentrations, owing to its distinctive optical properties, including the "molecular wire" effect, fluorescence super-amplification and super-quenching. This is particularly promising for the parallel detection of ultra-low concentration multi-biomarkers in clinical diseases. As the continuous development of PPEs sensor array, more sensitive methods for rapid detection of clinical disease will be further developed. It will promote the further development of the field of early diagnosis of clinical diseases.

Bimodal Array-Based Fluorescence Sensor and Microfluidic Technology for Protein Fingerprinting and Clinical Diagnosis

Proteins play a crucial role in determining disease states in humans, making them prime targets for the development of diagnostic sensors. The developed sensor array is used to investigate global proteomic changes by fingerprinting multifactorial disease states in model urine simulating phenylketonuria and in serum from preeclamptic pregnant women. Here, we report a fluorescence-based chemical sensing array that exploits the host-guest interaction between cucurbit[7]uril (CB[7]) and fluorescent triphenylamine derivatives (TPA) to detect a range of proteins. Using linear discriminant analysis, we identify fluorescence fingerprints of 14 proteins with over 98% accuracy in buffer and human serum. The array is optimized on an automated droplet microfluidic-based platform, for high-throughput sensing with controlled composition and lower sample volumes. This sensor enables the discrimination of proteins in physiological buffer and human serum, with promising applications in disease diagnosis.

Expanding the recognition of monosaccharides and glycans: A comprehensive analytical approach using chemical-nose/tongue technology and a comparison to lectin microarrays

Chemical-nose/tongue technologies are emerging as promising analytical tools for glycan analysis. After briefly introducing the importance of glycans and their analytical methods, including the lectin microarray (LMA) as one of the gold standards, the fundamental principles underlying chemical noses/tongues are explained and various applications for monosaccharides and glycans are introduced. Then, the similarities and differences of these two approaches are discussed. While both technologies aim to comprehensively profile biospecimens based on 'interaction patterns' between multiple recognition probes and analytes, each has its own strengths. LMAs excel at specific, targeted analysis based on defined lectin-glycan interactions, whereas chemical nose/tongue offers greater flexibility and expandability in terms of system design, making it well-suited for discovering unknown glycan profiles and detecting broader differences in glycan mixtures. In the future, chemical-nose/tongue technologies may be applied to niche areas in glycan analysis and become powerful tools that complement LMA techniques.

Differential Sensing Approach as a Pattern-based Discrimination for Biological Samples

The differential sensing approach uses fingerprint patterning to distinguish uncharacterized biological samples. Inspired by natural sensory systems, an array of cross-reactive sensors generates unique response fingerprint depending on the samples. Until today, this array system has been developed using various materials, including the library of surface-charged nanoparticles and chemosensors. Many differential array systems have demonstrated accurate identification of bacterial species, viral subtypes, and cancer cells, as well as distinguishing disease states in blood or urine. This capability is particularly important for distinguishing between normal and abnormal states when specific marker molecules have not yet been identified, providing a powerful diagnostic tool. In this concept, we summarized representative outcomes of differential sensing applications for biological sample discrimination.

Multistep Array-Based Sensing of Bioanalytes Using Modified MoS2, Fluorescence Proteins, and Cucurbituril

One pot sensor by multiplexing in the array is an attractive system for rapid discrimination of multiple analytes. Multiplexing can be achieved in two ways, i.e., using multiple signal transducers or adding sequential agents to the sensor media. Herein, we have used a combination of both multichannel and sequential ON-OFF strategies for the discrimination of different bioanalytes. The sensor array was constructed by implementing positively charged MoS2 as a receptor and different fluorescent proteins possessing distinguishable emission profiles as signal transducers. The sensing setup was constructed with the interaction between oppositely charged MoS2 and the host-guest combination between a cationic headgroup of MoS2 and Cucurbit [7] uril (CB7) to alter the fluorescence of signal transducers in situ noncovalently. Electrodynamic analysis and optical assays suggest that the electrostatic interaction played a major role in the modulation of the fluorescence outcomes in the array. Both cationic and anionic proteins were discriminated at a 50 nM concentration. The detection limit of the sensor array by using β-gal protein was found to be 1 nM. The sensor array was further implemented for the discrimination of normal and diseased cell lines and lysates, which indicates the versatile detection ability of this reported sensor array.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

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.

An array-based nanosensor for detecting cellular responses in macrophages induced by femtomolar levels of pesticides

Environmental agents can induce cellular responses at concentrations far below the limits of detection for current viability and biomarker-based cell sensing platforms. Hypothesis-free cell sensor platforms can be engineered to maximize sensitivity to phenotypic changes, providing a tool for lowering the threshold for detecting cellular changes. Pesticides are one of the most prevalent sources of chemical exposure due to their use in food and agriculture fields. We report here a FRET-based nanosensor array engineered to maximize responses to changes at cell surfaces after pesticide exposure. This sensor array robustly detected macrophage responses to femtomolar concentrations of common pesticides-orders of magnitude lower concentrations than traditional toxicological and biomarker-based strategies. Significantly, this platform was able to classify these responses by pesticide class, demonstrating the ability to distinguish between changes induced by these different agents. Taken together, hypothesis-free cell surface sensing is a promising tool for detecting the effects of ultra-trace environmental chemicals on human health, as well as detecting threshold responses for use in drug discovery and diagnostics.

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...

Nanozyme Sensor Array Plus Solvent-Mediated Signal Amplification Strategy for Ultrasensitive Ratiometric Fluorescence Detection of Exosomal Proteins and Cancer Identification

Tumor exosomes with molecular marker-proteins inherited from their parent cells have emerged as a promising liquid biopsy biomarker for cancer diagnosis. However, facile, robust, and sensitive detection of exosomal proteins remains challenging. Therefore, a nanozyme sensor array is constructed by using aptamer-modified C₃N₄ nanosheets (Apt/C₃N₄ NSs) together with a solvent-mediated signal amplification strategy for ratiometric fluorescence detection of exosomal proteins. Three aptamers specific to exosomal proteins are selected to construct Apt/C₃N₄ NSs for high specific recognition of exosomal proteins. The adsorption of aptamers enhances the catalytic activity of C₃N₄ NSs as a nanozyme for oxidation of o-phenylenediamine (oPD) to 2,3-diaminophenazine (DAP). In the presence of target exosomes, the strong affinity between aptamer and exosome leads to the disintegration of Apt/C₃N₄ NSs, resulting in a decrease of catalytic activity, thereby reducing the production of DAP. The ratiometric fluorescence signal based on a photoinduced electron transfer (PET) effect between DAP and C₃N₄ NSs is dependent on the concentration of DAP generated, thus achieving highly facile and robust detection of exosomal proteins. Remarkably, the addition of organic solvent-1,4-dioxane can sensitize the luminescence of DAP without affecting the intrinsic fluorescence of C₃N₄ NSs, achieving the amplification of the aptamer-exosome recognition events. The detection limit for exosome is 2.5 × 10³ particles/mL. In addition, the accurate identification of cancer can be achieved by machine learning algorithms to analyze the difference of exosomal proteins from different patients' blood. We hope that this facile, robust, sensitive, and versatile nanozyme sensor array would become a promising tool in the field of cancer diagnosis.

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.

Integral Multielement Signals by DNA-Programmed UCNP-AuNP Nanosatellite Assemblies for Ultrasensitive ICP-MS Detection of Exosomal Proteins and Cancer Identification

Exosomes are expected to be used as cancer biomarkers because they carry a variety of cancer-related proteins inherited from parental cells. However, it is still challenging to develop a sensitive, robust, and high-throughput technique for simultaneous detection of exosomal proteins. Herein, three aptamers specific to cancer-associated proteins (CD63, EpCAM, and HER2) are selected to connect gold nanoparticles (AuNPs) as core with three different elements (Y, Eu, and Tb) doped up-conversion nanoparticles (UCNPs) as satellites, thereby forming three nanosatellite assemblies. The presence of exosomes causes specific aptamers to recognize surface proteins and release the corresponding UCNPs, which can be simultaneously detected by inductively coupled plasma-mass spectrometry (ICP-MS). It is worth noting that rare earth elements are scarcely present in living systems, which minimize the background for ICP-MS detection and exclude potential interferences from the coexisting species. Using this method, we are able to simultaneously detect three exosomal proteins within 40 min, and the limit of detection for exosome is 4.7 × 10³ particles/mL. The exosomes from seven different cell lines (L-02, HepG2, GES-1, MGC803, AGS, HeLa, and MCF-7) can be distinguished with 100% accuracy by linear discriminant analysis. In addition, this analytical strategy is successfully used to detect exosomes in clinical samples to distinguish stomach cancer patients from healthy individuals. These results suggest that this sensitive and high-throughput analytical strategy based on ICP-MS has the potential to play an important role in the detection of multiple exosomal proteins and the identification of early cancer.

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.

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.