No abstract

Digital PCR (dPCR) has recently attracted great interest due to its high sensitivity and accuracy. However, existing dPCR depends on multicolor fluorescence dyes and multiple fluorescent channels to achieve multiplex...

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,...

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.

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.