Artificial Intelligence-Aided Massively Parallel Spectroscopy of Freely Diffusing Nanoscale Entities.
Antonín HlaváčekKateřina UhrováJulie WeisováJana KřivánkováPublished in: Analytical chemistry (2023)
Massively parallel spectroscopy (MPS) of many single nanoparticles in an aqueous dispersion is reported. As a model system, bioconjugated photon-upconversion nanoparticles (UCNPs) with a near-infrared excitation are prepared. The UCNPs are doped either with Tm 3+ (emission 450 and 802 nm) or Er 3+ (emission 554 and 660 nm). These UCNPs are conjugated to biotinylated bovine serum albumin (Tm 3+ -doped) or streptavidin (Er 3+ -doped). MPS is correlated with an ensemble spectra measurement, and the limit of detection (1.6 fmol L -1 ) and the linearity range (4.8 fmol L -1 to 40 pmol L -1 ) for bioconjugated UCNPs are estimated. MPS is used for observing the bioaffinity clustering of bioconjugated UCNPs. This observation is correlated with a native electrophoresis and bioaffinity assay on a microtiter plate. A competitive MPS bioaffinity assay for biotin is developed and characterized with a limit of detection of 6.6 nmol L -1 . MPS from complex biological matrices (cell cultivation medium) is performed without increasing background. The compatibility with polydimethylsiloxane microfluidics is proven by recording MPS from a 30 μm deep microfluidic channel.
Keyphrases
- artificial intelligence
- photodynamic therapy
- quantum dots
- single cell
- high throughput
- machine learning
- big data
- solid state
- label free
- highly efficient
- metal organic framework
- single molecule
- rna seq
- stem cells
- breast cancer cells
- ionic liquid
- mass spectrometry
- endoplasmic reticulum
- sensitive detection
- bone marrow
- living cells
- convolutional neural network
- high speed
- fluorescent probe