Search

Influenza (flu) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibit similar clinical symptoms, complicating the diagnosis and clinical management of these critical respiratory infections. Thus, there is an urgent need for rapid on-site detection technologies that can simultaneously detect SARS-CoV-2 and influenza A viruses. Here, we have developed the first platform that combines in situ sampling with immune swabs and multichannel surface-enhanced Raman spectroscopy (SERS) for simultaneous screening of these two respiratory viruses in a single assay. A seed-mediated growth method was used to assemble a number of silver spheres on the surface of Fe3O4@SiO2spheres, which not only creates extensive Raman hotspots but also provides numerous sites for Raman signaling molecules, enhancing the sensing sensitivity. Integrating two specific Raman signaling molecules into the nanospheres allows for the parallel detection of both viruses, improving the efficiency of SERS signal read-out. Rapid quantitative screening of both SARS-CoV-2 and H1N1 is achievable within 15 min, with detection limits of 7.76, and 8.13 pg·mL–1for their respective target proteins. The platform demonstrated excellent performance in testing and analyzing 98 clinical samples (SARS-CoV-2:50; influenza A:48), achieving sensitivities of 88.00, and 95.83% for SARS-CoV-2 and influenza A, respectively. Pearson’s correlation analysis revealed a significant correlation with the clinical CT values (P< 0.0001), underscoring the great potential of this platform for the early, rapid, and simultaneous diagnostic discrimination of multiple pathogens.

Visualizing spatial patterns of gene expression by optical microscopy at single-molecule resolution represents a long-standing challenge for imaging and molecular engineering technologies. In this study, we developed a method for visualizing mRNA with duplex capability by optical microscopy using rolling circle amplification with streptavidin-modified alkaline phosphatase (SA-ALP) to provide highly selective and sensitive RNA detection. ALP-based RNA detection provides comparable sensitivity and specificity to fluorescence-based in situassays and similar performance to the current RNAscope technique for single-molecule RNA detection, but with improved ease of operation. This versatile and relatively user-friendly method of single-molecule RNA visualization can also overcome common problems of background interference. Our findings show that the red spots generated by the Fast Red staining in situare readily quantified by image analysis, even in samples with heavy melanin deposition, supporting the clinical translation of this assay to improve diagnostic assays for recalcitrant tissues. This system was adaptable for duplex assays with multiple probes and multiple stains, which is ALP with horseradish peroxidase to produce red and brown signals to simultaneously visualize two different RNA targets. The duplex assay can be successfully applied to quantify mRNA expression from two genes in situwithin single cells and multiple cell types. With the advantages of high sensitivity and low hardware requirements, this versatile and user-friendly method of RNA visualization may enable low-resource institutions to conduct previously inaccessible diagnostic or research questions about the in situexpression and distribution of RNAs at single-molecule resolution.

Immune checkpoint therapy has provided a weapon against cancer, but its response rate has been extremely low due to the lack of effective predictors. Herein, we developed a FRET strategy based on lectin for glycan labeling and an aptamer for PD-L1 antigen recognition for visualization of PD-L1-specific glycosylation (FLAG). The FLAG strategy combines the PD-L1 aptamer, which efficiently labels the PD-L1 polyantigen with smaller steric hindrance than the PD-L1 antibody, and metabolism-free lectin labeling for glycosylation. As a result, the FLAG strategy enables in situvisualization of PD-L1-specific glycosylation on the tissue section while maintaining the spatial context and tissue architecture. Due to nonmetabolic labeling, the FLAG strategy revealed that the tissue level of PD-L1-specific glycosylation is correlated with the efficacy of PD-1/PD-L1 therapy. Overall, the FLAG strategy provides a powerful tool for revealing the significance of PD-L1 glycosylation, offering the unprecedented potential for immunophenotypic differential analysis to predict the immunotherapy response.

Parallel single-cell multimodal sequencing is the most intuitive and precise tool for cellular status research. In this study, we propose AMAR-seq to automate methylation, chromatin accessibility, and RNA expression coanalysis with single-cell precision. We validated the accuracy and robustness of AMAR-seq in comparison with standard single-omics methods. The high gene detection rate and genome coverage of AMAR-seq enabled us to establish a genome-wide gene expression regulatory atlas and triple-omics landscape with single base resolution and implement single-cell copy number variation analysis. Applying AMAR-seq to investigate the process of mouse embryonic stem cell differentiation, we revealed the dynamic coupling of the epigenome and transcriptome, which may contribute to unraveling the molecular mechanisms of early embryonic development. Collectively, we propose AMAR-seq for the in-depth and accurate establishment of single-cell multiomics regulatory patterns in a cost-effective, efficient, and automated manner, paving the way for insightful dissection of complex life processes.

Compartmentalization of aqueous samples in uniform emulsion droplets has proven to be a useful tool for many chemical, biological, and biomedical applications. Herein, we introduce an array-based emulsification method for rapid and easy generation of monodisperse agarose-in-oil droplets in a PDMS microwell array. The microwells are filled with agarose solution, and subsequent addition of hot oil results in immediate formation of agarose droplets due to the surface-tension of the liquid solution. Because droplet size is determined solely by the array unit dimensions, uniform droplets with preselectable diameters ranging from 20 to 100 μm can be produced with relative standard deviations less than 3.5%. The array-based droplet generation method was used to perform digital PCR for absolute DNA quantitation. The array-based droplet isolation and sol–gel switching property of agarose enable formation of stable beads by chilling the droplet array at −20 °C, thus, maintaining the monoclonality of each droplet and facilitating the selective retrieval of desired droplets. The monoclonality of droplets was demonstrated by DNA sequencing and FACS analysis, suggesting the robustness and flexibility of the approach for single molecule amplification and analysis. We believe our approach will lead to new possibilities for a great variety of applications, such as single-cell gene expression studies, aptamer selection, and oligonucleotide analysis.

Small extracellular vesicles (sEVs) assume pivotal roles as vital messengers in intercellular communication, boasting a plethora of biological functions and promising clinical applications. However, efficient isolation and sensitive detection of sEVs continue to present formidable challenges. In this study, we report a novel method for fast isolation and highly sensitive multicolor visual detection of sEVs using aptamer-functionalized polydopamine nanospheres (SIMPLE). In the SIMPLE strategy, aptamer-functionalized polydopamine nanospheres (Apt-PDANS) with 170 nm diameters were synthesized and exhibited a remarkable ability to selectively bind to specific proteins on the surface of sEVs. The binding between sEVs and Apt-PDANS engenders an increase in the overall size of the sEVs, allowing fast isolation of sEVs by filtration (a filter membrane with a pore size of 200 nm). The fast isolation strategy not only circumvents the interference posed by unbound proteins and excessive probes as well as the intricacies associated with conventional ultracentrifugation methods but also expedites the separation of sEVs. Concurrently, the incorporation of Fe3+-doped PDANS permits the multicolor visual detection of sEVs, enabling quantitative analysis by the discernment of visual cues. The proposed strategy achieves a detection limit of 3.2 × 104sEV mL–1within 1 h, devoid of any reliance on instrumental apparatus. Furthermore, we showcase the potential application of this methodology in epithelial–mesenchymal transition monitoring and cancer diagnosis, while also envisioning its widespread adoption as a straightforward, rapid, sensitive, and versatile platform for disease monitoring and functional exploration.

Extracellular vesicle PD-L1 (programmed death-1 ligand 1) is of greater value in tumor diagnosis, prognosis, and efficacy monitoring of anti-PD-1/PD-L1 immunotherapy. However, soluble PD-L1 interferes with the accurate detection of extracellular vesicle (EV) PD-L1. Here, we developed a microfluidic differentiation method for the detection of extracellular PD-L1, without the interference of soluble, by DNA computation with lipid probes and PD-L1 aptamer as inputs (DECLA). For the developed DECLA method, a cholesterol-DNA probe was designed that efficiently embeds into the EV membrane, and an aptamer-based PD-L1 probe was used for PD-L1 recognition. Due to the stable secondary structure of the designed connector, only cobinding of cholesterol-DNA and PD-L1 affinity probe induced biotin-labeled connector activation, while soluble PD-L1 cannot hybridize. As a result, PD-L1 EVs can be efficiently captured by streptavidin-functioned herringbone chip and quantified by anti-CD63-induced fluorescence signal. The high specificity of dual-input DNA computation allied to the high sensitivity of microfluidic-based detection was suitable for distinguishing lung cancer patients from healthy donors, highlighting its potential translation to clinical diagnosis and therapy monitoring.

Molecular recognition ligands are of great significance in many fields, but our ability to develop new recognition molecules remains to be expanded. Here, we developed a Sequential Multidimensional Analysis algoRiThm for aptamer discovery (SMART-Aptamer) from high-throughput sequencing (HTS) data of SELEX libraries based on multilevel structure analysis and unsupervised machine learning to discover nucleic acid recognition ligands with high accuracy and efficiency. We validated SMART-Aptamer with three sets of HTS data from screening pools against hESCs, EpCAM, and CSV. High affinity aptamers for all three targets were successfully obtained, and the results revealed that SMART-Aptamer is able to pick out high affinity aptamers with low false positive and negative rates. With the advantages of accuracy, efficiency, and robustness, SMART-Aptamer represents a paradigm-shift strategy for the discovery of binding ligands for a variety of biomedical applications.