Nanoplasmonic amplification in microfluidics enables accelerated colorimetric quantification of nucleic acid biomarkers from pathogens.

Deployment of nucleic acid amplification assays for diagnosing pathogens in point-of-care settings is a challenge due to lengthy preparatory steps. We present a molecular diagnostic platform that integrates a fabless plasmonic nano-surface into an autonomous microfluidic cartridge. The plasmonic 'hot' electron injection in confined space yields a ninefold kinetic acceleration of RNA/DNA amplification at single nucleotide resolution by one-step isothermal loop-mediated and rolling circle amplification reactions. Sequential flow actuation with nanoplasmonic accelerated microfluidic colorimetry and in conjugation with machine learning-assisted analysis (using our 'QolorEX' device) offers an automated diagnostic platform for multiplexed amplification. The versatility of QolorEX is demonstrated by detecting respiratory viruses: SARS-CoV-2 and its variants at the single nucleotide polymorphism level, H1N1 influenza A, and bacteria. For COVID-19 saliva samples, with an accuracy of 95% on par with quantitative polymerase chain reaction and a sample-to-answer time of 13 minutes, QolorEX is expected to advance the monitoring and rapid diagnosis of pathogens.

MoS2-Plasmonic Nanocavities for Raman Spectra of Single Extracellular Vesicles Reveal Molecular Progression in Glioblastoma.

Extracellular vesicles (EVs) are continually released from cancer cells into biofluids, carrying actionable molecular fingerprints of the underlying disease with considerable diagnostic and therapeutic potential. The scarcity, heterogeneity and intrinsic complexity of tumor EVs present a major technological challenge in real-time monitoring of complex cancers such as glioblastoma (GBM). Surface-enhanced Raman spectroscopy (SERS) outputs a label-free spectroscopic fingerprint for EV molecular profiling. However, it has not been exploited to detect known biomarkers at the single EV level. We developed a multiplex fluidic device with embedded arrayed nanocavity microchips (MoSERS microchip) that achieves 97% confinement of single EVs in a minute amount of fluid (<10 µL) and enables molecular profiling of single EVs with SERS. The nanocavity arrays combine two featuring characteristics: (1) An embedded MoS2 monolayer that enables label-free isolation and nanoconfinement of single EVs due to physical interaction (Coulomb and van der Waals) between the MoS2 edge sites and the lipid bilayer; and (2) A layered plasmonic cavity that enables sufficient electromagnetic field enhancement inside the cavities to obtain a single EV level signal resolution for stratifying the molecular alterations. We used the GBM paradigm to demonstrate the diagnostic potential of the SERS single EV molecular profiling approach. The MoSERS multiplexing fluidic achieves parallel signal acquisition of glioma molecular variants (EGFRvIII oncogenic mutation and MGMT expression) in GBM cells. The detection limit of 1.23% was found for stratifying these key molecular variants in the wild-type population. When interfaced with a convolutional neural network (CNN), MoSERS improved diagnostic accuracy (87%) with which GBM mutations were detected in 12 patient blood samples, on par with clinical pathology tests. Thus, MoSERS demonstrates the potential for molecular stratification of cancer patients using circulating EVs.

Nanostructured-Based Optical Readouts Interfaced with Machine Learning for Identification of Extracellular Vesicles.

Extracellular vesicles (EVs) are shed from cancer cells into body fluids, enclosing molecular information about the underlying disease with the potential for being the target cancer biomarker in emerging diagnosis approaches such as liquid biopsy. Still, the study of EVs presents major challenges due to their heterogeneity, complexity, and scarcity. Recently, liquid biopsy platforms have allowed the study of tumor-derived materials, holding great promise for early-stage diagnosis and monitoring of cancer when interfaced with novel adaptations of optical readouts and advanced machine learning analysis. Here, recent advances in labeled and label-free optical techniques such as fluorescence, plasmonic, and chromogenic-based systems interfaced with nanostructured sensors like nanoparticles, nanoholes, and nanowires, and diverse machine learning analyses are reviewed. The adaptability of the different optical methods discussed is compared and insights are provided into prospective avenues for the translation of the technological approaches for cancer diagnosis. It is discussed that the inherent augmented properties of nanostructures enhance the sensitivity of the detection of EVs. It is concluded by reviewing recent integrations of nanostructured-based optical readouts with diverse machine learning models as novel analysis ventures that can potentially increase the capability of the methods to the point of translation into diagnostic applications.