MoS2-Based Sensor Array for Accurate Identification of Cancer Cells with Ensemble-Modified Aptamers

In this work, we present an array-based chemical nose sensor that utilizes a set of ensemble-modified aptamer (EMAmer) probes to sense subtle physicochemical changes on the cell surface for cancer cell identification. The EMAmer probes are engineered by domain-selective incorporation of different types and/or copies of positively charged functional groups into DNA scaffolds, and their differential interactions with cancer cells can be transduced through competitive adsorption of fluorophore-labeled EMAmer probes loaded on MoS2 nanosheets. We demonstrate that this MoS2-EMAmer-based sensor array enables rapid and effective discrimination among six types of cancer cells and their mixtures with a concentration of 104 cells within 60 min, achieving a 94.4% accuracy in identifying blinded unknown cell samples. The established MoS2-EMAmer sensing platform is anticipated to show significant promise in the advancement of cancer diagnostics.

Gold nanoparticles-mediated fluorescent chemical nose sensor for pathogenic diagnosis and phenotype

Pathogens are one of the important factors affecting national economic construction. An ideal detection system for pathogen control with excellent sensitivity, high specificity, and time-saving is needed. Here, we reported a method for bacterial detection using gold nanoparticles-mediated fluorescent "chemical nose" sensors (GFCEs). The technique consists of gold nanoparticles-coated magnetic particle using benzaldehyde, octyl aldehyde, and pyrimidine-4-formaldehyde modified, respectively. And these positively charged nanocompound interacting with three different fluorescent proteins (FPs) to form three kinds of GFCEs, respectively, named GFCE1, GFCE2, and GFCE3. Upon binding with pathogenic cells, functionalized gold nanoparticles could identify patches on hydrophobic/functional surfaces of microorganisms, and self-assemble with living bacteria by complementary electrostatic interactions. The binding ability between GFCEs and bacteria determines the change of fluorescence response of three FPs from GFCEs. These feature fluorescent level are pathogen-specific, highly repeatable, and can be analyzed by Linear Discriminant Analysis (LDA). The combination of GFCE1 and GFCE2 has the best performance when detecting pathogens with concentrations of 10⁶  cfu mL^-1 . The first discriminant within 15 minutes is 93.8%, which could be used for subsequent identification of unknown samples. The commonly applicable system provides a simple way for the rapid bacterial detection without preprocessing procedures.

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

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

Ratiometric Fluorescent Sensor Array as a Versatile Tool for Bacterial Pathogen Identification and Analysis

Rapid and reliable identification of pathogenic microorganisms is of great importance for human and animal health. Most conventional approaches are time-consuming and require expensive reagents, sophisticated equipment, trained personnel, and special storage and handling conditions. Sensor arrays based on small molecules offer a chemically stable and cost-effective alternative. Here we present a ratiometric fluorescent sensor array based on the derivatives of 2-(4'- N, N-dimethylamino)-3-hydroxyflavone and investigate its ability to provide a dual-channel ratiometric response. We demonstrate that, by using discriminant analysis of the sensor array responses, it is possible to effectively distinguish between eight bacterial species and recognize their Gram status. Thus, multiple parameters can be derived from the same data set. Moreover, the predictive potential of this sensor array is discussed, and its ability to analyze unknown samples beyond the list of species used for the training matrix is demonstrated. The proposed sensor array and analysis strategies open new avenues for the development of advanced ratiometric sensors for multiparametric analysis.

Cancer Cell Discrimination Using Host-Guest "Doubled" Arrays

We report a nanosensor that uses cell lysates to rapidly profile the tumorigenicity of cancer cells. This sensing platform uses host-guest interactions between cucurbit[7]uril and the cationic headgroup of a gold nanoparticle to non-covalently modify the binding of three fluorescent proteins of a multi-channel sensor in situ. This approach doubles the number of output channels to six, providing single-well identification of cell lysates with 100% accuracy. Significantly, this classification could be extended beyond the training set, determining the invasiveness of novel cell lines. The unique fingerprint of these cell lysates required minimal sample quantity (200 ng, ∼1000 cells), making the methodology compatible with microbiopsy technology.