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

Abstract

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.

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

“…Fourth, the plasmonic nanohole array was formed via lift-off. The design of the microchip was optimized to be compatible with integration with most existing types of microfluidic devices. ,− Finally, the MoSERS microchips with the size of 0.1 × 0.1 cm 2 were embedded in the fluidic sample delivery device fabricated using a 3D printing technique with a 0.2 μm filter for SERS interrogation of the circulating EVs in the patient blood samples.…”

“…SERS is a label-free approach that converts the entire biochemical signature of an EV, including surface chemistry and molecular cargo, into a single spectroscopic pattern, or “fingerprint” , (see Supporting Table S1). SERS is inherently noninvasive, fast, reliable, and can be adapted for use in low-resource clinical settings, especially with the emergence of hand-held instruments and fiberoptic probes. , SERS applied at an ensemble level, coupled with machine learning methods, can already differentiate between EVs in blood samples collected from healthy and diseased donors of cancers. , The working principle of SERS is based on the adsorption of analyte molecules to a plasmonic surface that allows for a strong enhancement of the Raman signal from the analyte. Nanocavities with independently tunable harmonics achieve strong electromagnetic (EM) field enhancement at distinct wavelengths .…”

“…SERS is inherently noninvasive, fast, reliable, and can be adapted for use in low-resource clinical settings, especially with the emergence of hand-held instruments and fiberoptic probes. 33 , 34 SERS applied at an ensemble level, coupled with machine learning methods, can already differentiate between EVs in blood samples collected from healthy and diseased donors of cancers. 32 , 36 The working principle of SERS is based on the adsorption of analyte molecules to a plasmonic surface that allows for a strong enhancement of the Raman signal from the analyte.…”

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

“…A great number of reviews meant that an even greater number of original research articles had been published. If we limited the results of the Scopus search to reviews related to fluids, three major fields of application emerge that attracted research interest in applying ML methods to (a) theoretical and industrial oil and gas [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], (b) CFD simulations [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68], and (c) health and medical applications [69][70][71][72][73][74][75][76][77][78][79][80]…”

Clinical Translation of Extracellular Vesicles

“…Employing emerging technologies such as artificial intelligence (AI) and machine learning in the design and engineering of EV based therapeutics could be an approach to advance the research in the field. Recent studies have employed machine learning in different stages of development of EV based therapeutics including identification of EVs [166] and single EV analysis. [167][168][169] Use of above mentioned high throughput techniques such as microfluidics in combination with machine learning based EV analysis could accelerate the clinical translation of EV-based therapeutics.…”