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.

Cell surface-based sensing with metallic nanoparticles.

Metallic nanoparticles provide versatile scaffolds for biosensing applications. In this review, we focus on the use of metallic nanoparticles for cell surface sensings. Examples of the use of both specific recognition and array-based "chemical nose" approaches to cell surface sensing will be discussed.

A metric learning-based method for biomedical entity linking.

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here.

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

We report a polymer-based sensor that rapidly detects cancer based on changes in serum protein levels. Using three ratiometric fluorescence outputs, this simple system identifies early stage and metastatic lung cancer with a high level of accuracy exceeding many biomarker-based assays, making it an attractive strategy for point-of-care testing.

Rapid and ultrasensitive detection of endocrine disrupting chemicals using a nanosensor-enabled cell-based platform.

Endocrine disrupting chemicals (EDCs) interact with estrogen receptors (ERs), causing a broad range of adverse health effects. Current assays for EDC activity are slow and often lack sensitivity. We report here an ultra-sensitive nanosensor that can detect estrogenic cellular changes in ER(+) MCF-7 cells rapidly (minutes) at several orders of magnitude lower than the generally used assays. Notably, the sensor responses at these ultra-low EDC levels correlated with an increased synthesis phase (S-phase) cell population of EDC-treated cells. The nanosensor was also able to detect binary EDC mixture effects, with synergism observed for bisphenol A (BPA) - 17ß-estradiol (E2), and antagonism for dicyclohexylphthalate (DCHP) - E2 and benzo(a)pyrene (BaP) - E2.

Sensing by Smell: Nanoparticle-Enzyme Sensors for Rapid and Sensitive Detection of Bacteria with Olfactory Output.

We present here a highly efficient sensor for bacteria that provides an olfactory output, allowing detection without the use of instrumentation and with a modality that does not require visual identification. The sensor platform uses nanoparticles to reversibly complex and inhibits lipase. These complexes are disrupted in the presence of bacteria, restoring enzyme activity and generating scent from odorless pro-fragrance substrate molecules. This system provides rapid (15 min) sensing and very high sensitivity (102 cfu/mL) detection of bacteria using the human sense of smell as an output.

A Multichannel Biosensor for Rapid Determination of Cell Surface Glycomic Signatures.

Cell surface glycosylation serves a fundamental role in dictating cell and tissue behavior. Cell surface glycomes differ significantly, presenting viable biomarkers for identifying cell types and their states. Glycoprofiling is a challenging task, however, due to the complexity of the constituent glycans. We report here a rapid and effective sensor for surface-based cell differentiation that uses a three-channel sensor produced by noncovalent conjugation of a functionalized gold nanoparticle (AuNP) and fluorescent proteins. Wild-type and glycomutant mammalian cells were effectively stratified using fluorescence signatures obtained from a single sensor element. Blinded unknowns generated from the tested cell types were identified with high accuracy (44 out of 48 samples), validating the robustness of the multichannel sensor. Notably, this selectivity-based high-throughput sensor differentiated between cells, employing a nondestructive protocol that required only a single well of a microplate for detection.

A multichannel nanosensor for instantaneous readout of cancer drug mechanisms.

Screening methods that use traditional genomic, transcriptional, proteomic and metabonomic signatures to characterize drug mechanisms are known. However, they are time consuming and require specialized equipment. Here, we present a high-throughput multichannel sensor platform that can profile the mechanisms of various chemotherapeutic drugs in minutes. The sensor consists of a gold nanoparticle complexed with three different fluorescent proteins that can sense drug-induced physicochemical changes on cell surfaces. In the presence of cells, fluorescent proteins are rapidly displaced from the gold nanoparticle surface and fluorescence is restored. Fluorescence 'turn on' of the fluorescent proteins depends on the drug-induced cell surface changes, generating patterns that identify specific mechanisms of cell death induced by drugs. The nanosensor is generalizable to different cell types and does not require processing steps before analysis, offering an effective way to expedite research in drug discovery, toxicology and cell-based sensing.

Array-based sensing using nanoparticles: an alternative approach for cancer diagnostics.

Array-based sensing using nanoparticles (NPs) provides an attractive alternative to specific biomarker-focused strategies for cancer diagnosis. The physical and chemical properties of NPs provide both the recognition and transduction capabilities required for biosensing. Array-based sensors utilize a combined response from the interactions between sensors and analytes to generate a distinct pattern (fingerprint) for each analyte. These interactions can be the result of either the combination of multiple specific biomarker recognition (specific binding) or multiple selective binding responses, known as chemical nose sensing. The versatility of the latter array-based sensing using NPs can facilitate the development of new personalized diagnostic methodologies in cancer diagnostics, a necessary evolution in the current healthcare system to better provide personalized treatments. This review will describe the basic principle of array-based sensors, along with providing examples of both invasive and noninvasive samples used in cancer diagnosis.

The role of surface functionality in nanoparticle exocytosis.

Getting out is just as important for nano-therapeutics as getting in. Exocytosis rates determine residency time in the cell, an important determinant for therapeutic efficacy and also for eventual clearance from the cell. In this study, it is shown that exocytosis efficiency is determined by surface functionality, providing a strategy for optimizing nanocarriers.