A multichannel nanosensor for instantaneous readout of cancer drug mechanisms

Impact of Nanomedicine in Women's Metastatic Breast Cancer

Metastatic breast cancer is responsible for 90% of mortalities among women suffering from various types of breast cancers. Traditional cancer treatments such as chemotherapy and radiation therapy can cause significant side effects and may not be effective in many cases. However, recent advances in nanomedicine have shown great promise in the treatment of metastatic breast cancer. For example, nanomedicine demonstrated robust capacity in detection of metastatic cancers at early stages (i.e., before the metastatic cells leave the initial tumor site), which gives clinicians a timely option to change their treatment process (for example, instead of endocrine therapy they may use chemotherapy). Here recent advances in nanomedicine technology in the identification and treatment of metastatic breast cancers are reviewed.

Rapid Identification of Drug Mechanisms with Deep Learning-Based Multichannel Surface-Enhanced Raman Spectroscopy

Rapid identification of drug mechanisms is vital to the development and effective use of chemotherapeutics. Herein, we develop a multichannel surface-enhanced Raman scattering (SERS) sensor array and apply deep learning approaches to realize the rapid identification of the mechanisms of various chemotherapeutic drugs. By implementing a series of self-assembled monolayers (SAMs) with varied molecular characteristics to promote heterogeneous physicochemical interactions at the interfaces, the sensor can generate diversified SERS signatures for directly high-dimensionality fingerprinting drug-induced molecular changes in cells. We further train the convolutional neural network model on the multidimensional SAM-modulated SERS data set and achieve a discriminatory accuracy toward 99%. We expect that such a platform will contribute to expanding the toolbox for drug screening and characterization and facilitate the drug development process.

Mixed host co-assembled systems for broad-scope analyte sensing

Here we report a systems chemistry oriented approach for developing information-rich mixed host chemosensors. We show that co-assembling macrocyclic hosts from different classes, DimerDye sulfonatocalix[4]arenes and cucurbit[n]urils, effectively increases the scope of analyte binding interactions and therefore, sensory outputs. This simple dynamic strategy exploits cross-reactive noncovalent host-host complexation interactions while integrating a reporter dye, thereby producing emergent photophysical responses when an analyte interacts with either host. We first demonstrate the advantages of mixed host co-assembled chemosensors through an increased detection range of hydrophobic, cationic, neutral, and anionic drugs. We then implement mixed host sensors in an array-based platform for the differentiation of illicit drugs, including cannabinoids, benzodiazepine analogs, opiates, anesthetics, amphetamine, and common adulterating substances. Finally, the potential of this approach is applied to profiling real-world multi-component illicit street drug samples, proving to be more effective than classical sensor arrays.

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.

VIBRANT: spectral profiling for single-cell drug responses

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Antimicrobial Agent Functional Gold Nanocluster-Mediated Multichannel Sensor Array for Bacteria Sensing

Urinary tract infections (UTIs) have greatly affected human health in recent years. Accurate and rapid diagnosis of UTIs can enable a more effective treatment. Herein, we developed a multichannel sensor array for efficient identification of bacteria based on three antimicrobial agents (vancomycin, lysozyme, and bacitracin) functional gold nanoclusters (AuNCs). In this sensor, the fluorescence intensity of the three AuNCs was quenched to varying degrees by the bacterial species, providing a unique fingerprint for different bacteria. With this sensing platform, seven pathogenic bacteria, different concentrations of the same bacteria, and even bacterial mixtures were successfully differentiated. Furthermore, UTIs can be accurately identified with our sensors in ∼30 min with 100% classification accuracy. The proposed sensing systems offer a rapid, high-throughput, and reliable sensing platform for the diagnosis of UTIs.

Immobilization of azide-functionalized proteins to micro- and nanoparticles directly from cell lysate

Immobilization of proteins and enzymes on solid supports has been utilized in a variety of applications, from improved protein stability on supported catalysts in industrial processes to fabrication of biosensors, biochips, and microdevices. A critical requirement for these applications is facile yet stable covalent conjugation between the immobilized and fully active protein and the solid support to produce stable, highly bio-active conjugates. Here, we report functionalization of solid surfaces (gold nanoparticles and magnetic beads) with bio-active proteins using site-specific and biorthogonal labeling and azide-alkyne cycloaddition, a click chemistry. Specifically, we recombinantly express and selectively label calcium-dependent proteins, calmodulin and calcineurin, and cAMP-dependent protein kinase A (PKA) with N-terminal azide-tags for efficient conjugation to nanoparticles and magnetic beads. We successfully immobilized the proteins on to the solid supports directly from the cell lysate with click chemistry, forgoing the step of purification. This approach is optimized to yield low particle aggregation and high levels of protein activity post-conjugation. The entire process enables streamlined workflows for bioconjugation and highly active conjugated proteins.

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.

Adaptive Supramolecular Networks: Emergent Sensing from Complex Systems

Molecular differentiation by supramolecular sensors is typically achieved through sensor arrays, relying on the pattern recognition responses of large panels of isolated sensing elements. Here we report a new one-pot systems chemistry approach to differential sensing in biological solutions. We constructed an adaptive network of three cross-assembling sensor elements with diverse analyte-binding and photophysical properties. This robust sensing approach exploits complex interconnected sensor-sensor and sensor-analyte equilibria, producing emergent supramolecular and photophysical responses unique to each analyte. We characterize the basic mechanisms by which an adaptive network responds to analytes. The inherently data-rich responses of an adaptive network discriminate among very closely related proteins and protein mixtures without relying on designed protein recognition elements. We show that a single adaptive sensing solution provides better analyte discrimination using fewer response observations than a sensor array built from the same components. We also show the network's ability to adapt and respond to changing biological solutions over time.

Molecules that Generate Fingerprints: A New Class of Fluorescent Sensors for Chemical Biology, Medical Diagnosis, and Cryptography

ConspectusFluorescent molecular sensors, often referred to as "turn-on" or "turn-off" fluorescent probes, are synthetic agents that change their fluorescence signal in response to analyte binding. Although these sensors have become powerful analytical tools in a wide range of research fields, they are generally limited to detecting only one or a few analytes. Pattern-generating fluorescent probes, which can generate unique identification (ID) fingerprints for different analytes, have recently emerged as a new class of luminescent sensors that can address this limitation. A unique characteristic of these probes, termed ID-probes, is that they integrate the qualities of conventional small-molecule-based fluorescent sensors and cross-reactive sensor arrays (often referred to as chemical, optical, or electronic noses/tongues). On the one hand, ID-probes can discriminate between various analytes and their combinations, akin to array-based analytical devices. On the other hand, their minute size enables them to analyze small-volume samples, track dynamic changes in a single solution, and operate in the microscopic world, which the macroscopic arrays cannot access.Here, we describe the principles underlying the ID-probe technology, as well as provide an overview of different ID-probes that have been developed to date and the ways they can be applied to a wide range of research fields. We describe, for example, ID-probes that can identify combinations of protein biomarkers in biofluids and in living cells, screen for several protein inhibitors simultaneously, analyze the content of Aβ aggregates, as well as ensure the quality of small-molecule and biological drugs. These examples highlight the relevance of this technology to medical diagnosis, bioassay development, cell and chemical biology, and pharmaceutical quality assurance, among others. ID-probes that can authorize users and protect secret data are also presented and the mechanisms that enable them to hide (steganography), encrypt (cryptography), and prevent access to (password protection) information are discussed.The versatility of this technology is further demonstrated by describing two types of probes: unimolecular ID-probes and self-assembled ID-probes. Probes from the first type can operate inside living cells, be recycled, and their initial patterns can be more easily obtained in a reproducible manner. The second type of probes can be readily modified and optimized, allowing one to prepare various different probes from a much wider range of fluorescent reporters and supramolecular recognition elements. Taken together, these developments indicate that the ID-probe sensing methodology is generally applicable, and that such probes can better characterize analyte mixtures or process chemically encoded information than can the conventional fluorescent molecular sensors. We therefore hope that this review will inspire the development of new types of pattern-generating probes, which would extend the fluorescence molecular toolbox currently used in the analytical sciences.

Rapid and Accurate Identification of Cell Phenotypes of Different Drug Mechanisms by Using Single-Cell Fluorescence Images Via Deep Learning

Identification of a drug mechanism is vital for drug development. However, it often resorts to the expensive and cumbersome omics methods along with complex data analysis. Herein, we developed a methodology to analyze organelle staining images of single cells using a deep learning algorithm (TL-ResNet50) for rapid and accurate identification of different drug mechanisms. Based on the organelle-related cell morphological changes caused by drug action, the constructed deep learning model can fast predict the drug mechanism with a high accuracy of 92%. Further analysis reveals that drug combination at different ratios can enhance a certain mechanism or generate a new mechanism. This work would highly facilitate clinical medication and drug screening.

NIR as a "trigger switch" for situ distinguish superbacteria and photothermal synergistic antibacterial treatment with Ag2O particles/lignosulfonate/cationic guar gum hybrid hydrogel

The in situ identification of superbugs with the simultaneous killing of it is key to preventing human health. Here, a one-stop identification and killing platform for near-infrared (NIR) triggering was designed and constructed using lignosulfonate (LS), cationic guar gum (CG) and Ag₂O NPs hydrogels (LS/CG/Ag₂O). The hydrogel network is used as a fixed matrix for Ag₂O NPs and a nano reactor, meanwhile 3,3', 5,5'-tetramethylbenzidine (TMB) as a single probe sensor array for bacterial identification. In contrast to conventional methods, hybrid hydrogels have catalytic qualities through which TMB be catalyzed to generate oxidized TMB (oxTMB). The drug resistance of the same strain can be distinguished based on the different inhibition abilities of drug-resistant superbacteria in TMB and hydrogel reactions. Then, the employing of oxTMB photothermal characteristics, it can be efficiently killed in real time while being driven by a near-infrared laser. The proposed one-stop hydrogel platform paves a way for the rapid identification and killing of drug-resistant superbacteria.

Nanoparticle applications in food - a review

The use of nanotechnology in the food industry raises uncertainty in many respects. For years, achievements of nanotechnology have been applied mainly in biomedicine and computer science, but recently it has also been used in the food industry. Due to the extremely small (nano) scale, the properties and behavior of nanomaterials may differ from their macroscopic counterparts. They can be used as biosensors to detect reagents or microorganisms, monitor bacterial growth conditions, increase food durability e.g. when placed in food packaging, reducing the amount of certain ingredients without changing the consistency of the product (research on fat substitutes is underway), improve the taste of food, make some nutrients get better absorbed by the body, etc. There are companies on the market that are already introducing nanoparticles into the economy to improve their functionality, e.g. baby feeding bottles. This review focuses on the use of nanoparticles in the food industry, both organic (chitosan, cellulose, proteins) and inorganic (silver, iron, zinc oxide, titanium oxide, etc.). The use of nanomaterials in food production requires compliance with all legal requirements regarding the safety and quantity of nano-processed food products described in this review. In the future, new methods of testing nanoparticles should be developed that would ensure the effectiveness of compounds subjected to, for example, nano-encapsulation, i.e. whether the encapsulation process had a positive impact on the specific properties of these compounds. Nanotechnology has revolutionized our approach towards food engineering (from production to processing), food storage and the creation of new materials and products, and the search for new product applications.

Explainable Deep Learning-Assisted Photochromic Sensor for β-Lactam Antibiotic Identification

Photochromic sensors have the advantages of diverse isomers for multi-analysis, providing more sensing information and possessing more recognition units and more sensitivity to external stimulations, but they present enormous complexity with various stimulations as well. Deep learning (DL) algorithms contribute a huge advantage at analyzing nonlinear and multidimensional data, but they suffer from nontransparent inner networks, "black-boxes". In this work, we employed the explainable DL approach to process and explicate photochromic sensing. Spirooxazine metallic complexes were adopted to prepare a multi-state analysis array for β-Lactams identification and quantitation. A dataset of 2520 unduplicated fluorescence intensity images was collected for convolutional neural network (CNN) operation. The method clearly discriminated six β-Lactams with 97.98% prediction accuracy and allowed rapid quantification with a concentration range from 1 to 100 mg/L. The photochromic sensing mechanism was verified via molecular simulation and class activation mapping, which explicated how the CNN model assesses the importance of photochromic sensor states and makes a discrimination decision. The explainable DL-assisted analysis method establishes an end-to-end strategy to ascertain and verify the complicated sensing mechanism for device optimization and even new scientific discovery.

Ensemble Modified Aptamer Based Pattern Recognition for Adaptive Target Identification

The difficulty of the molecular design and chemical synthesis of artificial sensing receptors restricts their diagnostic and proteomic applications. Herein, we report a concept of "ensemble modified aptamers" (EMAmers) that exploits the collective recognition abilities of a small set of protein-like side-chain-modified nucleic acid ligands for discriminative identification of molecular or cellular targets. Different types and numbers of hydrophobic functional groups were incorporated at designated positions on nucleic acid scaffolds to mimic amino acid side chains. We successfully assayed 18 EMAmer probes with differential binding affinities to seven proteins. We constructed an EMAmer-based chemical nose sensor and demonstrated its application in blinded unknown protein identification, giving a 92.9% accuracy. Additionally, the sensor is generalizable to the detection of blinded unknown bacterial and cellular samples, which enabled identification accuracies of 96.3% and 94.8%, respectively. This sensing platform offers a discriminative means for adaptive target identification and holds great potential for diverse applications.

Direct discrimination of cell surface glycosylation signatures using a single pH-responsive boronic acid-functionalized polymer

Cell surface glycans serve fundamental roles in many biological processes, including cell-cell interaction, pathogen infection, and cancer metastasis. Cancer cell surface have alternative glycosylation to healthy cells, making these changes useful hallmarks of cancer. However, the diversity of glycan structures makes glycosylation profiling very challenging, with glycan 'fingerprints' providing an important tool for assessing cell state. In this work, we utilized the pH-responsive differential binding of boronic acid (BA) moieties with cell surface glycans to generate a high-content six-channel BA-based sensor array that uses a single polymer to distinguish mammalian cell types. This sensing platform provided efficient discrimination of cancer cells and readily discriminated between Chinese hamster ovary (CHO) glycomutants, providing evidence that discrimination is glycan-driven. The BA-functionalized polymer sensor array is readily scalable, providing access to new diagnostic and therapeutic strategies for cell surface glycosylation-associated diseases.

Accurate identification of kidney injury progression via a fluorescent biosensor array

Identifying the progress of kidney injury may aid the effective treatment and intervention. Herein, we developed a fluorescent biosensor array for instantaneous and accurate identification of the kidney injury progression via "doubled" signals. The multichannel biosensor array consisted of polydopamine-polyethyleneimine (PDA-PEI) and multicolor-labelled different length of DNAs including AAAAA-Cyanine7 (5A-Cy7), AAAAAAAAAA-Texas Red (10A-Texas Red), and AAAAAAAAAAAAAAAAAAAA-VIC (20A-VIC). Facing to the variety of protein in urine with alterable charge accompanied with different progress of kidney injury, the composition of urine replaces the DNA signal molecules, forming their special fluorescence patterns. Taking the size of protein into consideration, the original three variables induced by the protein charge were extended to six variables induced by the two factors of protein particle size and charge difference, which could provide a more accurate strategy to identify the progress of kidney injury. Notably, this strategy not only opened up new perspective for identification the progress of kidney injury via the size and charge of urine protein, but also improved the resolving power of sensor array by increasing the number of sensor elements for extending their potential application to various diseases.

Multiple-Aptamer-Integrated DNA-Origami-Based Chemical Nose Sensors for Accurate Identification of Cancer Cells

Developing simple, rapid, and accurate methods for cancer cell identification could facilitate early cancer diagnosis and tumor metastasis research. Herein, we develop a novel chemical nose sensor that employs the collective recognition abilities of a set of multiple-aptamer-integrated DNA origami (MADO) probes for discriminative identification of cancer cells. By controlling the types and/or copies of aptamers assembled on the DNA origami nanostructure, we constructed five MADO probes with differential binding affinities (ranging from 3.08 to 78.92 nM) to five types of cells (HeLa, MDA-MB-468, MCF-7, HepG2, and MCF-10A). We demonstrate the utility of the MADO-based chemical nose sensor in the identification of blinded unknown cell samples with a 95% accuracy. This sensing platform holds great potential for applications in medical diagnostics.

General Method for Pesticide Recognition Using Albumin-Based Host-Guest Ensembles

The massive use of pesticides nowadays has led to serious consequences for the environment and public health. Fluorescence analytical methods for pesticides are particularly advantageous with respect to simplicity and portability; however, currently available fluorescence methods (enzyme-based assays and indicator displacement assays) with poor universality are only able to detect few specific pesticides (e.g., organophosphorus). Making use of the multiple flexible and asymmetrical binding sites in albumin, we herein report a set of multicolor albumin-based host-guest ensembles. These ensembles exhibit a universal but distinctive fluorescent response to most of the common pesticides and allow array-based identification of pesticides with high accuracy. Furthermore, the simplicity, portability, and visualization of this method enable on-site determination of pesticides in a practical setting. This albumin host strategy largely expands the toolbox of traditional indicator displacement assays (synthetic macrocycles as hosts), and we expect it to inspire a series of sensor designs for pesticide detection.

A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions

Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.

Carbon Quantum Dots Based Chemosensor Array for Monitoring Multiple Metal Ions

The simultaneous identification of multiple metal ions in water has attracted enormous research interest in the past few decades. We herein describe a novel method for multiple metal ion detection using a carbon quantum dots (CQDs)-based chemosensor array and the CQDs are functionalized with different amino acids (glutamine, histidine, arginine, lysine and proline), which act as sensing elements in the sensor array. Eleven metal ions are successfully identified by the designed chemosensor array, with 100% classification accuracy. Importantly, the proposed method allowed the quantitative prediction of the concentration of individual metal ions in the mixture with the aid of a support vector machine (SVM). The sensor array also enables the qualitative detection of unknown metal ions under the interference of tap water and local river water. Thus, the strategy provides a novel high-throughput approach for the identification of various analytes in complex systems.

A Polymer-Based Multichannel Sensor for Rapid Cell-Based Screening of Antibiotic Mechanisms and Resistance Development

Antibiotic resistance presents a critical threat to public health, necessitating the rapid development of novel antibiotics and an appropriate choice of therapeutics to combat refractory bacterial infections. Here, we report a high-throughput polymer-based sensor platform that rapidly (30 min) profiles mechanisms of antibiotic activity. The sensor array features three fluorophore-conjugated polymers that can detect subtle antibiotic-induced phenotypic changes on bacterial surfaces, generating distinct mechanism-based fluorescence patterns. Notably, discrimination of different generations of antibiotic resistance was achieved with high efficiency. This sensor platform combines trainability, simplicity, and rapid screening into a readily translatable platform.

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 machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds

Rapid and accurate identification of the mechanisms of action (MoAs) of antibacterial compounds remains a challenge for the development of antibacterial compounds. Computational inference methods for determining the MoAs of antibacterial compounds have been developed in recent years. In particular, approaches combining machine learning technology enable precisely recognizing the MoA of antibacterial compounds. However, these methods heavily rely on the big data resulting from multiplexed experiments. As such, these approaches tend to produce minimal throughput and are not comprehensive enough to be adapted to widespread industrial applications. Here, we present a machine learning approach based on a customized array sensor for directly identifying the MoAs of antibacterial compounds. The array sensor consists of different two-dimensional nanomaterial fluorescence quenchers with different fluorescence-labeled single-stranded DNAs (ssDNAs). By mapping the subtle difference of the physicochemical properties on the bacterial surface treated with different antibacterial compound stimuli, the array sensor ensures visualizing the recognition process. Moreover, the customized array sensor produces a high volume of the MoA database, overcoming the dependence on big data. We further use the array sensor to build a chemical-response unique "fingerprint" database of MoAs. By combining a neural network-based genetic algorithm (NNGA), we rapidly discriminate the MoAs of four antibiotics with an overall accuracy of 100%. Furthermore, a new screening antibacterial peptide has been discovered and evaluated by our approach for determining the MoA with high accuracy proven by other techniques.

An Optical Probe for Real-Time Monitoring of Self-Replicator Emergence and Distinguishing between Replicators

Self-replicating systems play an important role in research on the synthesis and origin of life. Monitoring of these systems has mostly relied on techniques such as NMR or chromatography, which are limited in throughput and demanding when monitoring replication in real time. To circumvent these problems, we now developed a pattern-generating fluorescent molecular probe (an ID-probe) capable of discriminating replicators of different chemical composition and monitoring the process of replicator formation in real time, giving distinct signatures for starting materials, intermediates, and final products. Optical monitoring of replicators dramatically reduces the analysis time and sample quantities compared to most currently used methods and opens the door for future high-throughput experimentation in protocell environments.

Anti-cancer adjuvant drug screening via epithelial-mesenchymal transition-related aptamer probe

Epithelial-mesenchymal transition (EMT) is implicated in the pathological processes of cancer metastasis and drug resistance. Anti-cancer drugs may also potentially lead to EMT, resulting in their reduced therapeutic effect. Therefore, the combination of these anti-cancer drugs with anti-EMT agents has been promoted in clinic. Screening anti-EMT drugs and evaluation of EMT process are highly dependent on EMT biomarkers on cell membrane. At present, the detection of EMT biomarker is mainly by Western blot method, which is time-consuming and complicated. In this work, for effectively screening anti-EMT drugs by evaluation of the EMT process, a type of aptamer probe based on aggregation-induced emission (AIE) was designed. The aptamer SYL3C was employed to target the EMT biomarker EpCAM on cell membrane. Two fluorophores, FAM and tetraphenylethene (TPE, an AIE dye), were modified at the two ends of SYL3C, respectively. This aptamer probe (TPE-SYL3C-FAM) can monitor the EpCAM expression, which can be recovered by anti-EMT drugs. By observation of the change in TPE emission intensity, the anti-EMT effect of drugs can be evaluated. The FAM emission was used as internal reference to reduce environmental interferences. This probe can be potentially used to screen anti-EMT agents as anti-cancer adjuvant drugs with high throughput.

Comparative Study of In Situ Techniques to Enlarge Gold Nanoparticles for Highly Sensitive Lateral Flow Immunoassay of SARS-CoV-2

Three techniques were compared for lowering the limit of detection (LOD) of the lateral flow immunoassay (LFIA) of the receptor-binding domain of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) based on the post-assay in situ enlargement of Au nanoparticles (Au NPs) on a test strip. Silver enhancement (growth of a silver layer over Au NPs-Au@Ag NPs) and gold enhancement (growth of a gold layer over Au NPs) techniques and the novel technique of galvanic replacement of Ag by Au in Au@Ag NPs causing the formation of Au@Ag-Au NPs were performed. All the enhancements were performed on-site after completion of the conventional LFIA and maintained equipment-free assay. The assays demonstrated lowering of LODs in the following rows: 488 pg/mL (conventional LFIA with Au NPs), 61 pg/mL (silver enhancement), 8 pg/mL (galvanic replacement), and 1 pg/mL (gold enhancement). Using gold enhancement as the optimal technique, the maximal dilution of inactivated SARS-CoV-2-containing samples increased 500 times. The developed LFIA provided highly sensitive and rapid (8 min) point-of-need testing.

Biomolecular interactions of ultrasmall metallic nanoparticles and nanoclusters

The use of nanoparticles (NPs) in biomedicine has made a gradual transition from proof-of-concept to clinical applications, with several NP types meeting regulatory approval or undergoing clinical trials. A new type of metallic nanostructures called ultrasmall nanoparticles (usNPs) and nanoclusters (NCs), while retaining essential properties of the larger (classical) NPs, have features common to bioactive proteins. This combination expands the potential use of usNPs and NCs to areas of diagnosis and therapy traditionally reserved for small-molecule medicine. Their distinctive physicochemical properties can lead to unique in vivo behaviors, including improved renal clearance and tumor distribution. Both the beneficial and potentially deleterious outcomes (cytotoxicity, inflammation) can, in principle, be controlled through a judicious choice of the nanocore shape and size, as well as the chemical ligands attached to the surface. At present, the ability to control the behavior of usNPs is limited, partly because advances are still needed in nanoengineering and chemical synthesis to manufacture and characterize ultrasmall nanostructures and partly because our understanding of their interactions in biological environments is incomplete. This review addresses the second limitation. We review experimental and computational methods currently available to understand molecular mechanisms, with particular attention to usNP-protein complexation, and highlight areas where further progress is needed. We discuss approaches that we find most promising to provide relevant molecular-level insight for designing usNPs with specific behaviors and pave the way to translational applications.

Microplotter-Printed On-Chip Combinatorial Library of Ink-Derived Multiple Metal Oxides as an "Electronic Olfaction" Unit

Information about the surrounding atmosphere at a real timescale significantly relies on available gas sensors to be efficiently combined into multisensor arrays as electronic olfaction units. However, the array's performance is challenged by the ability to provide orthogonal responses from the employed sensors at a reasonable cost. This issue becomes more demanded when the arrays are designed under an on-chip paradigm to meet a number of emerging calls either in the internet-of-things industry or in situ noninvasive diagnostics of human breath, to name a few, for small-sized low-powered detectors. The recent advances in additive manufacturing provide a solid top-down background to develop such chip-based gas-analytical systems under low-cost technology protocols. Here, we employ hydrolytically active heteroligand complexes of metals as ink components for microplotter patterning a multioxide combinatorial library of chemiresistive type at a single chip equipped with multiple electrodes. To primarily test the performance of such a multisensor array, various semiconducting oxides of the p- and n-conductance origins based on pristine and mixed nanocrystalline MnO_x, TiO₂, ZrO₂, CeO₂, ZnO, Cr₂O₃, Co₃O₄, and SnO₂ thin films, of up to 70 nm thick, have been printed over hundred μm areas and their micronanostructure and fabrication conditions are thoroughly assessed. The developed multioxide library is shown to deliver at a range of operating temperatures, up to 400 °C, highly sensitive and highly selective vector signals to different, but chemically akin, alcohol vapors (methanol, ethanol, isopropanol, and n-butanol) as examples at low ppm concentrations when mixed with air. The suggested approach provides us a promising way to achieve cost-effective and well-performed electronic olfaction devices matured from the diverse chemiresistive responses of the printed nanocrystalline oxides.

Differentiation of Cancer Stem Cells through Nanoparticle Surface Engineering

Cancer stem cells (CSCs) are a crucial therapeutic target because of their role in resistance to chemo- and radiation therapy, metastasis, and tumor recurrence. Differentiation therapy presents a potential strategy for "defanging" CSCs. To date, only a limited number of small-molecule and nanomaterial-based differentiating agents have been identified. We report here the integrated use of nanoparticle engineering and hypothesis-free sensing to identify nanoparticles capable of efficient differentiation of CSCs into non-CSC phenotypes. Using this strategy, we identified a nanoparticle that induces CSC differentiation by increasing intracellular reactive oxygen species levels. Importantly, this unreported phenotype is more susceptible to drug treatment than either CSCs or non-CSCs, demonstrating a potentially powerful strategy for anticancer therapeutics.

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 novel acid-sensitive quantum dot sensor array for the identification of Chinese baijiu

The organic acid content plays important roles in the flavor and taste of Chinese baijiu. Developing a detection and discrimination technique for organic acids and employing it as a basis in baijiu classification has great practical significance. We employed 3 kinds of acid-sensitive quantum dots (QDs) to construct a fluorescence sensor array for the detection and identification of organic acids in baijiu. We report the first directional use of array sensing detection technology for the evaluation of organic acids in baijiu. Linear discriminant analysis (LDA) was successfully employed to evaluate the ability of the as-developed sensor array to classify organic acids. The Euclidean distance analysis was introduced to prove the provided sensor array possesses good quantitative detection. On this basis, our sensor array was successfully applied to distinguish 16 kinds of baijiu samples. The results were supported by principal component analysis (PCA), LDA, and systematic cluster analysis (HCA). Furthermore, Pearson correlation results indicated a strong correlation between the detection results and the organic acids in baijiu. This simple and accurate method shows potential for quality control and detection in baijiu factories and markets.

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.

High-content and high-throughput identification of macrophage polarization phenotypes

Macrophages are plastic cells of the innate immune system that perform a wide range of immune- and homeostasis-related functions. Due to their plasticity, macrophages can polarize into a spectrum of activated phenotypes. Rapid identification of macrophage polarization states provides valuable information for drug discovery, toxicological screening, and immunotherapy evaluation. The complexity associated with macrophage activation limits the ability of current biomarker-based methods to rapidly identify unique activation states. In this study, we demonstrate the ability of a 2-element sensor array that provides an information-rich 5-channel output to successfully determine macrophage polarization phenotypes in a matter of minutes. The simple and robust sensor generates a high dimensional data array which enables accurate macrophage evaluations in standard cell lines and primary cells after cytokine treatment, as well as following exposure to a model disease environment.

Metabolic Activity Phenotyping of Single Cells with Multiplexed Vibrational Probes

Quantitative measurements of metabolic activities of individual cells are essential to understanding questions in diverse fields in biology. To address this challenge, we present a method, termed metabolic activity phenotyping (MAP), to probe metabolic fluxes by utilizing multiplexed vibrational metabolic probes. With specifically designed single-whole-cell confocal micro-Raman spectroscopy, quantitative measurement of lipid and protein synthesis activity was achieved with high throughput (several orders of magnitude improvement over a commercial confocal system). In addition, metabolic heterogeneity upon various drug treatments was also revealed and evaluated at the single-cell level. We further demonstrated that MAP was more robust than the label-free Raman methods and was able to make the correct classification among diverse cancer types and breast cancer subtypes by exploring the dimension of metabolism. The capability of MAP to explore metabolic profiles at the single-cell level makes it a valuable tool for basic single-cell studies as well as other screening applications.

Multifunctional Supramolecular Assemblies with Aggregation-Induced Emission (AIE) for Cell Line Identification, Cell Contamination Evaluation, and Cancer Cell Discrimination

It is undoubted the important role of cells in biology and medicine, but worldwide misidentified and cross-contaminated cell lines have caused much trouble in related fields. Herein, three kinds of supramolecular AIE (aggregation-induced emission) nanoassemblies were constructed by the host-guest interaction between tetraphenylethene (TPE) derivatives and cucurbit[8]uril (CB[8]). Based on the recognized mechanism of AIE, the TPE derivatives could achieve stronger fluorescence emission and higher fluorescence quantum yield after assembling with CB[8]. Moreover, the constructed supramolecular AIE complexes obtained well-confirmed nanostructures and exhibited different sizes and shapes. Correspondingly, they generated characteristic biological properties and fluorescence enhancement of cells. Inspired by the concept of Big Data Analysis, these fluorescence signals were further transformed into a unique fingerprint of cells via linear discriminant analysis. Immediately, we realized the veracious identification between a normal cell line, two cancer cell lines, and two metastasized cancer cell lines in a qualitative analysis. More importantly, it was well used to monitor the evaluation of cross-contaminated cells and the discrimination of cancer cells. As a proper bioapplication of ideal supramolecular nanomaterials, this system was easy to learn and apply, and the whole procedure was kept to 20 min, without cell disruption, centrifugation, or washing steps.

Repurposing Old Antibodies for New Diseases by Exploiting Cross-Reactivity and Multicolored Nanoparticles

We exploit the cross-reactivity of dengue (DENV) and Zika (ZIKV) virus polyclonal antibodies for nonstructural protein 1 (NS1) to construct a selective sensor that can detect yellow fever virus (YFV) NS1 in a manner similar to chemical olfaction. DENV and ZIKV antibodies were screened for their ability to bind to DENV, ZIKV, and YFV NS1 by enzyme linked immunosorbent assay (ELISA) and in pairs in paper immunoassays. A strategic arrangement of antibodies immobilized on paper and conjugated to different colored gold NPs was used to distinguish the three biomarkers. Machine learning of test area RGB values showed that with two spots, readout accuracies of 100% and 87% were obtained for both pure NS1 and DENV/YFV mixtures, respectively. Additional image preprocessing allowed differentiation between all four DENV serotypes with 92% accuracy. The technique was extended to hack a commercial DENV test to detect YFV and ZIKV by augmentation with DENV and ZIKV polyclonal antibodies.

Rapid evaluation of gold nanoparticle-lipid membrane interactions using a lipid/polydiacetylene vesicle sensor

Surface modification of gold nanoparticles (AuNPs) has significant and complicated effects on their interactions with cell membranes. In this study, we used a lipid/polyacetylene (PDA) vesicle sensor as the lipid membrane model to evaluate AuNP-lipid membrane interactions. Based on the colorimetric response (CR) of PDA vesicles before and after incubation with AuNPs, it was found that the interaction was highly dependent on the surface charge of AuNPs. As compared to the positively charged NPs, neutral and zwitterionic NPs adsorbed much less on the lipid membrane. Negatively charged NPs did not induce any noticeable color changes even at high concentrations. A class of cationic AuNPs with different degrees of surface hydrophobicity was further selected to investigate the role of hydrophobicity in interacting with lipid/PDA vesicles, and log(EC50) was employed as the evaluation index. According to the log(EC50)-NP concentration curve, the hydrophobicity of NPs enhanced the lipid membrane affinity, but electrostatic interactions weakened this effect. Finally, different concentrations of bovine serum albumin (BSA) were used to study the effect of the protein corona on NP-lipid membrane interactions. The formation of a NP-protein corona was found to mask the electrostatic interactions, leading to the decrease of the CR values of cationic NPs, and highly hydrophobic NPs were less affected by a low concentration of BSA due to the strong hydrophobic interactions. Although the effect of NP surface properties on their interactions with cells is far more complicated, our study provides a rapid and effective method for the evaluation of the interactions between surface modified AuNPs and lipid membranes.

Surfactant and alcohol induced disaggregation of perylene probes and a novel sensing strategy for distinguishing the brand and authenticity of makeup removers

A novel sensing strategy based on four perylene probes to distinguish the brand and authenticity of makeup removers.

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.

New application of old methods: Development of colorimetric sensor array based on Tollen's reagent for the discrimination of aldehydes based on Tollen's reagent

Qualitative and quantitative testing of aldehydes is meaningful for chemical toxin detection, food inspection, and disease monitoring. Herein, we reported a simple, accurate, and selective Tollen's reagent-based colorimetric sensor array for determination and detection of aldehydes. Three kinds of negatively charged gold nanoparticles (Au NPs) with different sizes (13, 22, and 40 nm) were synthesized and characterized by transmission electron microscopy and zeta potential measurement. In the presence of aldehydes, Ag+ from Tollen's reagent was attracted by the negative charge on the surface of Au NPs. Ag+ was reduced into Ag0 in situ, forming Au@Ag core-shell nanostructure and resulting in a significant color change. Detailed morphological and dimensional changes were observed by transmission electron microscopy. ΔRGB values (the value changes in the red, green, and blue color model) of Au NPs were captured as the optical signal for further data processing. Results of pattern recognition indicated the outstanding discrimination performance of the system for identification of aldehydes. Moreover, the array possessed quantitative detection capability for formaldehyde, selectivity, and reproducibility and thus has great potential in practical detection.

A colorimetric sensor array for detection and discrimination of antioxidants based on Ag nanoshell deposition on gold nanoparticle surfaces

There is growing interest in developing a high-performance sensor array for detection and discrimination of antioxidants owing to their widespread use and essential role in the human body. The present work unveils a novel colorimetric sensor array for colorimetric discrimination of antioxidants based on the red, green, and blue alteration (ΔRGB) pattern recognition. In this sensor array, three concentrations of AgNO3 were used as sensing elements, and gold nanoparticles (AuNPs) were employed as a colorimetric probe. In the presence of antioxidants, the sensor array produces unique colorimetric response patterns for the discrimination of these antioxidants due to different reactivities between three different concentrations of AgNO3 and each antioxidant, leading to deposition of different quantities of Ag nanoshells on the surface of AuNPs, enabling an excellent discrimination of six antioxidants (catechin, epigallocatechin 3-gallate, epicatechin, epigallocatechin, epicatechin 3-gallate, and gallocatechin) at a 20 nM level, when linear discriminant analysis (LDA), hierarchical cluster analysis (HCA), centroid diagram, spidergram, and color contour profiles were smartly combined. Furthermore, different concentrations of antioxidants and binary antioxidant mixtures, even ternary mixtures, could also be discriminated with this sensor array. Finally, the sensor array was successfully used for the discrimination of antioxidants in serum samples, demonstrating its potential applications in the diagnosis of antioxidant-related diseases.

A Photochromic Sensor Microchip for High-Performance Multiplex Metal Ion Detection

Photochromic molecules can respond to external stimulations and undergo reversible conversion between different chemical structures, providing one photochromic molecule with multiple recognition states for targeting compounds. Here we design a facile sensor microchip with only one photochromic molecule (spirooxazine) to discriminate multiplex metal ions. The sensor chip performs in dark, ultraviolet, or visual stimulation, resulting in different molecular states of spirooxazine-metallic coordination and patterned fluorescent signals for analysis. By using this sensor microchip, 11 metal ions are discriminated. Furthermore, mineral water of 16 different brands and metal ions in human serum are distinguished.