Poly-l-Lysine-Modified Graphene Field-Effect Transistor Biosensors for Ultrasensitive Breast Cancer miRNAs and SARS-CoV-2 RNA Detection.

(Mi)RNAs are important biomarkers for cancers diagnosis and pandemic diseases, which require fast, ultrasensitive, and economical detection strategies to quantitatively detect exact (mi)RNAs expression levels. The novel coronavirus disease (SARS-CoV-2) has been breaking out globally, and RNA detection is the most effective way to identify the SARS-CoV-2 virus. Here, we developed an ultrasensitive poly-l-lysine (PLL)-functionalized graphene field-effect transistor (PGFET) biosensor for breast cancer miRNAs and viral RNA detection. PLL is functionalized on the channel surface of GFET to immobilize DNA probes by the electrostatic force. The results show that PGFET biosensors can achieve a (mi)RNA detection range of five orders with a detection limit of 1 fM and an entire detection time within 20 min using 2 µL of human serum and throat swab samples, which exhibits more than 113% enhancement in terms of sensitivity compared to that of GFET biosensors. The performance enhancement mechanisms of PGFET biosensors were comprehensively studied based on an electrical biosensor theoretical model and experimental results. In addition, the PGFET biosensor was applied for the breast cancer miRNA detection in actual serum samples and SARS-CoV-2 RNA detection in throat swab samples, providing a promising approach for rapid cancer diagnosis and virus screening.

Enrichment-Detection Integrated Exosome Profiling Biosensors Promising for Early Diagnosis of Cancer.

Enrichment-detection integrated biosensors for exosome profiling have shown great potential in noninvasive diagnosis and point-of-care testing with the advantage of multifunctions. This Feature focuses on the enrichment-detection integrated exosome profiling biosensors emphasizing (i) the underlying working fundamentals of these sorts of biosensors, (ii) four advanced strategies developed for exosome analysis, and (iii) future outlook and present challenges of exosome profiling systems.

Multi-Phenotypic Exosome Secretion Profiling Microfluidic Platform for Exploring Single-Cell Heterogeneity.

Cellular phenotypic and functional heterogeneities have advanced cancer evolution and treatment resistance. Although exosome-bound proteins reflect cellular functions, single-cell exosomes are rarely profiled owing to the lack of effective platforms. Herein, the authors developed an integrated microfluidic platform consisting of a single-cell trapping chip and a spatially coded antibody barcode chip for the multiplexed outline of exosome secretion by single cells. Using this platform, five phenotypic exosomes of over 1 000 single cells are simultaneously profiled, in addition to inflammatory factor secretion from the same single cell. Also, a robust analysis workflow for single-cell secretion profiling is proposed to explore the intercellular heterogeneity, which integrated unsupervised clustering and linear clustering. When applied to the tumor cell lines of epithelial-origin and normal epithelial cell lines, the strategy identifies functionally heterogeneous subpopulations with unique secretion patterns. Notably, special functional cell subsets for unique phenotypic exosomes (HSP70+ , EPCAM+ ) are found within ovarian tumor cells. The strategy proposed offers a new analysis approach for cellular differential exosome secretion at single-cell resolution using inflammatory factors, ultimately reinforcing the understanding of cell-to-cell heterogeneity and tumor landscape, and providing a valuable universal platform for single-cell biomarker exploration in biological and clinical research.

High-Throughput, Living Single-Cell, Multiple Secreted Biomarker Profiling Using Microfluidic Chip and Machine Learning for Tumor Cell Classification.

Secreted proteins provide abundant functional information on living cells and can be used as important tumor diagnostic markers, of which profiling at the single-cell level is helpful for accurate tumor cell classification. Currently, achieving living single-cell multi-index, high-sensitivity, and quantitative secretion biomarker profiling remains a great challenge. Here, a high-throughput living single-cell multi-index secreted biomarker profiling platform is proposed, combined with machine learning, to achieve accurate tumor cell classification. A single-cell culture microfluidic chip with self-assembled graphene oxide quantum dots (GOQDs) enables high-activity single-cell culture, ensuring normal secretion of biomarkers and high-throughput single-cell separation, providing sufficient statistical data for machine learning. At the same time, the antibody barcode chip with self-assembled GOQDs performs multi-index, highly sensitive, and quantitative detection of secreted biomarkers, in which each cell culture chamber covers a whole barcode array. Importantly, by combining the K-means strategy with machine learning, thousands of single tumor cell secretion data are analyzed, enabling tumor cell classification with a recognition accuracy of 95.0%. In addition, further profiling of the grouping results reveals the unique secretion characteristics of subgroups. This work provides an intelligent platform for high-throughput living single-cell multiple secretion biomarker profiling, which has broad implications for cancer investigation and biomedical research.

A Sensitive and Portable Double-Layer Microfluidic Biochip for Harmful Algae Detection.

Harmful algal blooms (HABs) are common disastrous ecological anomalies in coastal waters. An effective algae monitoring approach is important for natural disaster warning and environmental governance. However, conducting rapid and sensitive detection of multiple algae is still challenging. Here, we designed an ultrasensitive, rapid and portable double-layer microfluidic biochip for the simultaneous quantitative detection of six species of algae. Specific DNA probes based on the 18S ribosomal DNA (18S rDNA) gene fragments of HABs were designed and labeled with the fluorescent molecule cyanine-3 (Cy3). The biochip had multiple graphene oxide (GO) nanosheets-based reaction units, in which GO nanosheets were applied to transfer target DNA to the fluorescence signal through a photoluminescence detection system. The entire detection process of multiple algae was completed within 45 min with the linear range of fluorescence recovery of 0.1 fM-100 nM, and the detection limit reached 108 aM. The proposed approach has a simple detection process and high detection performance and is feasible to conduct accurate detection with matched portable detection equipment. It will have promising applications in marine natural disaster monitoring and environmental care.