DNA Origami Plasmonic Nanoantenna for Programmable Biosensing of Multiple Cytokines in Cancer Immunotherapy.

Herein, we report a DNA origami plasmonic nanoantenna for the programmable surface-enhanced Raman scattering (SERS) detection of cytokine release syndrome (CRS)-associated cytokines (e.g., tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ)) in cancer immunotherapy. Typically, the nanoantenna was made of self-assembled DNA origami nanotubes (diameter: ∼19 nm; length: ∼90 nm) attached to a silver nanoparticle-modified silicon wafer (AgNP/Si). Each DNA origami nanotube contains one miniature gold nanorod (AuNR) inside (e.g., length: ∼35 nm; width: ∼7 nm). Intriguingly, TNF-α and IFN-γ logically regulate the opening of the nanotubes and the dissociation of the AuNRs from the origami structure upon binding to their corresponding aptamers. On this basis, we constructed a complete set of Boolean logic gates that read cytokine molecules as inputs and return changes in Raman signals as outputs. Significantly, we demonstrated that the presented system enables the quantification of TNF-α and IFN-γ in the serum of tumor-bearing mice receiving different types of immunotherapies (e.g., PD1/PD-L1 complex inhibitors and STING agonists). The sensing results are consistent with those of the ELISA. This strategy fills a gap in the use of DNA origami for the detection of multiple cytokines in real systems.

Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip.

Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace aldehydes at the ppt level. For rapid preconcentration, we demonstrate that the nozzle-like microfluidic concentrator allows the enrichment of rare gaseous analytes by five-fold in only 0.01 ms. The enriched gas is subsequently captured and detected by an integrated silicon-based SERS chip, which is made of zeolitic imidazolate framework-8 coated silver nanoparticles grown in situ on a silicon wafer. After SERS measurement, a fully connected deep neural network is built to extract faint features in the spectral dataset and discriminate volatile organic compound classes. We demonstrate that six kinds of gaseous aldehydes at 100 ppt could be detected and classified with an identification accuracy of ∼80.9% by using this platform.