A fast and ultrasensitive ELISA based on rolling circle amplification.

A highly sensitive ELISA is critical for early diagnosis and biomarker discovery of various diseases. Although various ELISA technologies have been developed with high sensitivity, they are limited by poor repeatability, high cost, the dependence on complex equipment and/or a prolonged reaction time. To this end, we developed a fast and ultrasensitive ELISA (termed RELISA) based on rolling circle amplification (RCA) and enzymatic signal amplification. The RELISA is established on the traditional ELISA, with only one more RCA step that can be accomplished within 10 minutes. The prolonged single strand DNA (ssDNA) from RCA is able to enrich abundant horseradish peroxidase conjugate (HRP) modified detection probes. Consequently, the intensive HRP is able to catalyze TMB-H2O2 to produce significantly enhanced colorimetric signals. With CEACAM-7 as a model biomarker, the RELISA achieves the limit of detection as low as 2.82 pg mL-1, which is ∼50 times higher than that of the traditional ELISA. Therefore, we envision that the developed RELISA would be a powerful tool for the early diagnosis of various major diseases.

Polydopamine Nanoparticles-Based Three-Line Lateral Flow Immunoassay for COVID-19 Detection.

Currently, the global trend of several hundred thousand new confirmed COVID-19 patients per day has not abated significantly. Serological antibody detection has become an important tool for the self-screening of people. While the most commonly used colorimetric lateral flow immunoassay (LFIA) methods for the detection of COVID-19 antibodies are limited by low sensitivity and a lack of quantification ability. This leads to poor accuracy in the screening of early COVID-19 patients. Therefore, it is necessary to develop an accurate and sensitive autonomous antibody detection technique that will effectively reduce the COVID-19 infection rate. Here, we developed a three-line LFIA immunoassay based on polydopamine (PDA) nanoparticles for COVID-19 IgG and IgM antibodies detection to determine the degree of infection. The PDA-based three-line LFIA has a detection limit of 1.51 and 2.34 ng/mL for IgM and IgG, respectively. This assay reveals a good linearity for both IgM and IgG antibodies detection and is also able to achieve quantitative detection by measuring the optical density of test lines. In comparison, the commercial AuNP-based LFIA showed worse quantification results than the developed PDA-based LFIA for low-concentration COVID-19 antibody samples, making it difficult to distinguish between negative and positive samples. Therefore, the developed PDA-based three-line LFIA platform has the accurate quantitative capability and high sensitivity, which could be a powerful tool for the large-scale self-screening of people.

Similar color analysis based on deep learning (SCAD) for multiplex digital PCR via a single fluorescent channel.

Digital PCR (dPCR) has recently attracted great interest due to its high sensitivity and accuracy. However, the existing dPCR depends on multicolor fluorescent dyes and multiple fluorescent channels to achieve multiplex detection, resulting in increased detection cost and limited detection throughput. Here, we developed a deep learning-based similar color analysis method, namely SCAD, to achieve multiplex dPCR in a single fluorescent channel. As a demonstration, we designed a microwell chip-based diplex dPCR system for detecting two genes (blaNDM and blaVIM) with two kinds of green fluorescent probes, whose emission colors are difficult to discriminate by traditional fluorescence intensity-based methods. To verify the possibility of deep learning algorithms to distinguish the similar colors, we first applied t-distributed stochastic neighbor embedding (tSNE) to make a clustering map for the microwells with similar fluorescence. Then, we trained a Vision Transformer (ViT) model on 10 000 microwells with two similar colors and tested it with 262 202 microwells. Lastly, the trained model was proven to have highly accurate classification ability (>98% for both the training set and the test set) and precise quantification ability on both blaNDM and blaVIM (ratio difference <0.10). We envision that the developed SCAD method would significantly expand the detection throughput of dPCR without the need for other auxiliary equipment.

Artificial intelligence-assisted colorimetric lateral flow immunoassay for sensitive and quantitative detection of COVID-19 neutralizing antibody.

Currently, vaccination is the most effective medical measure to improve group immunity and prevent the rapid spread of COVID-19. Since the individual difference of vaccine effectiveness is inevitable, it is necessary to evaluate the vaccine effectiveness of every vaccinated person to ensure the appearance of herd immunity. Here, we developed an artificial intelligent (AI)-assisted colorimetric polydopamine nanoparticle (PDA)-based lateral flow immunoassay (LFIA) platform for the sensitive and accurate quantification of neutralizing antibodies produced from vaccinations. The platform integrates PDA-based LFIA and a smartphone-based reader to test the neutralizing antibodies in serum, where an AI algorithm is also developed to accurately and quantitatively analyze the results. The developed platform achieved a quantitative detection with 160 ng/mL of detection limit and 625-10000 ng/mL of detection range. Moreover, it also successfully detected totally 50 clinical serum samples, revealing a great consistency with the commercial ELISA kit. Comparing with commercial gold nanoparticle-based LFIA, our PDA-based LFIA platform showed more accurate quantification ability for the clinical serum. Therefore, we envision that the AI-assisted PDA-based LFIA platform with sensitive and accurate quantification ability is of great significance for large-scale evaluation of vaccine effectiveness and other point-of-care immunoassays.

Taqman-MGB nanoPCR for Highly Specific Detection of Single-Base Mutations.

PURPOSE: Detection of single-base mutations is important for real-time monitoring of tumor progression, therapeutic effects, and drug resistance. However, the specific detection of single-base mutations from excessive wild-type background sequences with routine PCR technology remains challenging. Our objective is to develop a simple and highly specific qPCR-based single-base mutation detection method. METHODS: Using EGRF T790M as a model, gold nanoparticles at different concentrations were separately added into the Taqman-MGB qPCR system to test specificity improvement, leading to the development of the optimal Taqman-MGB nanoPCR system. Then, these optimal conditions were used to test the range of improvement in the specificity of mutant-type and wild-type templates and the detection limit of mutation abundances in a spiked sample. RESULTS: The Taqman-MGB nanoPCR was established based on the traditional qPCR, with significantly suppressed background noise and improved specificity for single-base mutation detection. With EGFR T790M as a template, we demonstrated that our Taqman-MGB nanoPCR system could improve specificity across a wide concentration range from 10-9 µM to 10 µM and detect as low as 0.95% mutation abundance in spiked samples, which is lower than what the traditional Taqman-MGB qPCR and existing PCR methods can detect. Moreover, we also proposed an experimentally validated barrier hypothesis for the mechanism of improved specificity. CONCLUSION: The developed Taqman-MGB nanoPCR system could be a powerful tool for clinical single-base mutation detection.