In Situ Nucleic Acid Amplification and Ultrasensitive Colorimetric Readout in a Paper-Based Analytical Device Using Silver Nanoplates.
Akkapol Suea-NgamIlada ChooparaShangkun LiMathias SchmelcherNaraporn SomboonnaPhilip D HowesAndrew J deMelloPublished in: Advanced healthcare materials (2020)
A rapid, highly sensitive, and quantitative colorimetric paper-based analytical device (PAD) based on silver nanoplates (AgNPls) and loop-mediated isothermal amplification (LAMP) is presented. It is shown that cauliflower-like concatemer LAMP products can mediate crystal etching of AgNPls, with a threefold signal enhancement versus linear dsDNA. Methicillin-resistant Staphylococcus aureus (MRSA), an antimicrobial resistant bacterium that poses a formidable risk with persistently high mortality, is used as a model pathogen. Due to the excellent color contrast provided by AgNPls, the PAD allows qualitative analysis by the naked eye and quantitative analysis using a smartphone camera, with detection limits down to a single copy in just 30 min, and a linear response from 1 to 104 copies (R2 = 0.994). The entire assay runs in situ on the paper surface, which drastically simplifies operation of the device. This is the first demonstration of single copy detection using a colorimetric readout, and the developed PAD shows great promise for translation into an ultrasensitive gene-based point-of-care test for any infectious disease target, via modification of the LAMP primer set.
Keyphrases
- loop mediated isothermal amplification
- gold nanoparticles
- methicillin resistant staphylococcus aureus
- nucleic acid
- sensitive detection
- staphylococcus aureus
- quantum dots
- infectious diseases
- label free
- magnetic resonance
- high resolution
- liquid chromatography
- fluorescent probe
- high throughput
- cardiovascular events
- magnetic resonance imaging
- risk factors
- hydrogen peroxide
- living cells
- copy number
- coronary artery disease
- type diabetes
- gene expression
- neural network
- nitric oxide
- convolutional neural network
- deep learning
- single cell