Machine-Learning-Driven Surface-Enhanced Raman Scattering Optophysiology Reveals Multiplexed Metabolite Gradients Near Cells

Lussier, Félix; Missirlis, Dimitris; Spatz, Joachim P.; Masson, Jean-François

doi:10.1021/acsnano.8b07024

Cited by 94 publications

(111 citation statements)

References 47 publications

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“…[ 17–20 ] On the other hand, efforts have been made to computationally decipher the contributions of a number of analytes to the final SERS spectra. [ 21,22 ]…”

Section: Introductionmentioning

confidence: 99%

Multiplex SERS Detection of Metabolic Alterations in Tumor Extracellular Media

Plou

Garcı́a

Charconnet

et al. 2020

Adv Funct Materials

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The composition and intercellular interactions of tumor cells in the tissues dictate the biochemical and metabolic properties of the tumor microenvironment. The metabolic rewiring has a profound impact on the properties of the microenvironment, to an extent that monitoring such perturbations could harbor diagnostic and therapeutic relevance. A growing interest in these phenomena has inspired the development of novel technologies with sufficient sensitivity and resolution to monitor metabolic alterations in the tumor microenvironment. In this context, surface-enhanced Raman scattering (SERS) can be used for the label-free detection and imaging of diverse molecules of interest among extracellular components. Herein, the application of nanostructured plasmonic substrates comprising Au nanoparticles, self-assembled as ordered superlattices, to the precise SERS detection of selected tumor metabolites, is presented. The potential of this technology is first demonstrated through the analysis of kynurenine, a secreted immunomodulatory derivative of the tumor metabolism and the related molecules tryptophan and purine derivatives. SERS facilitates the unambiguous identification of trace metabolites and allows the multiplex detection of their characteristic fingerprints under different conditions. Finally, the effective plasmonic SERS substrate is combined with a hydrogel-based three-dimensional cancer model, which recreates the tumor microenvironment, for the real-time imaging of metabolite alterations and cytotoxic effects on tumor cells.

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“…[ 17–20 ] On the other hand, efforts have been made to computationally decipher the contributions of a number of analytes to the final SERS spectra. [ 21,22 ]…”

Section: Introductionmentioning

confidence: 99%

Multiplex SERS Detection of Metabolic Alterations in Tumor Extracellular Media

Plou

Garcı́a

Charconnet

et al. 2020

Adv Funct Materials

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“…Recently, machine learning approaches [45][46][47][48][49] , especially neural networks, have been popular for SERS data analysis. There are several major differences/advantages of our SABARSI approach over machine learning approaches.…”

Section: Discussionmentioning

confidence: 99%

A Statistical Approach of Background Removal and Spectrum Identification for SERS Data

Wang

Xiao

Dai

et al. 2020

Sci Rep

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SERS (surface-enhanced Raman scattering) enhances the Raman signals, but the plasmonic effects are sensitive to the chemical environment and the coupling between nanoparticles, resulting in large and variable backgrounds, which make signal matching and analyte identification highly challenging. Removing background is essential, but existing methods either cannot fit the strong fluctuation of the SeRS spectrum or do not consider the spectra's shape change across time. Here we present a new statistical approach named SABARSI that overcomes these difficulties by combining information from multiple spectra. Further, after efficiently removing the background, we have developed the first automatic method, as a part of SABARSI, for detecting signals of molecules and matching signals corresponding to identical molecules. The superior efficiency and reproducibility of SABARSI are shown on two types of experimental datasets. Surface-enhanced Raman scattering (SERS) is increasingly used to identify and quantify biomolecules in complex samples 1 because the observed Raman spectrum provides a molecular fingerprint that can be used to identify specific molecules. Advances in SERS methodology incorporating internal standards enables quantitative analysis at low concentrations. Incorporating SERS with separation methods can provide high throughput molecularly specific detection 2-6. A significant challenge to using SERS for molecular analysis is separating the molecular signal from the large background arising from the enhancing nanostructure. The enhanced signal originates from the interaction of analytes with the enhanced electromagnetic field from the plasmonic nanostructures 7. These enhancements transform Raman scattering into an ultrasensitive technique that can detect single molecules 8,9. Despite this amazing sensitivity associated with SERS, a number of challenges exist that complicate analysis and interpretation of the signals observed. First, SERS signals contain both molecular contributions and a large continuum background that is associated with the plasmonic nanostructures 10-13. The origin of the continuum background observed in SERS spectra is not fully understood but is generally attributed to some form of plasmonic emission, which can vary with solvents, ionic strength, and changes in nanoparticle structure. At high laser intensities, molecules can photodegrade to produce broad features in the SERS spectrum, and the nanoparticles can change shape altering the emission background. Experiments that can minimize these photodegradation effects 14,15 are important and can also promote stable backgrounds. Additionally, in solution, the molecules can diffuse away from the nanostructures and can have competitive interactions with other solution species 16. These interactions can lead to short signal durations when the analyte can be detected 17. The substrate, solvent, and analytes of interest all make major contributions to a SERS spectrum 18. Typically, the contributions to the signal from the substrate and solvent a...

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“…SERS was also applied to detect and quantify three types of bacterial meningitis pathogens in the cerebral spinal fluid [256]. Masson group developed plasmonic nanosensors to detect metabolites that were secreted from cells, such as pyruvate, lactate, ATP, and urea [257,258], or neurotransmitters (dopamine and glutamate) [259] near living cell surface. SERS-detectors may provide quantitative estimation of the analyte or can give qualitative information regarding analyte presence in the studied medium.…”

Section: Sers-detectorsmentioning

confidence: 99%

Raman Scattering: From Structural Biology to Medical Applications

et al. 2020

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This is a review of relevant Raman spectroscopy (RS) techniques and their use in structural biology, biophysics, cells, and tissues imaging towards development of various medical diagnostic tools, drug design, and other medical applications. Classical and contemporary structural studies of different water-soluble and membrane proteins, DNA, RNA, and their interactions and behavior in different systems were analyzed in terms of applicability of RS techniques and their complementarity to other corresponding methods. We show that RS is a powerful method that links the fundamental structural biology and its medical applications in cancer, cardiovascular, neurodegenerative, atherosclerotic, and other diseases. In particular, the key roles of RS in modern technologies of structure-based drug design are the detection and imaging of membrane protein microcrystals with the help of coherent anti-Stokes Raman scattering (CARS), which would help to further the development of protein structural crystallography and would result in a number of novel high-resolution structures of membrane proteins—drug targets; and, structural studies of photoactive membrane proteins (rhodopsins, photoreceptors, etc.) for the development of new optogenetic tools. Physical background and biomedical applications of spontaneous, stimulated, resonant, and surface- and tip-enhanced RS are also discussed. All of these techniques have been extensively developed during recent several decades. A number of interesting applications of CARS, resonant, and surface-enhanced Raman spectroscopy methods are also discussed.

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Machine-Learning-Driven Surface-Enhanced Raman Scattering Optophysiology Reveals Multiplexed Metabolite Gradients Near Cells

Cited by 94 publications

References 47 publications

Multiplex SERS Detection of Metabolic Alterations in Tumor Extracellular Media

Multiplex SERS Detection of Metabolic Alterations in Tumor Extracellular Media

A Statistical Approach of Background Removal and Spectrum Identification for SERS Data

Raman Scattering: From Structural Biology to Medical Applications

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