3D SERS-based biosensor for the selective detection of circulating cancer-derived exosomes

Mannai, Amera Al; Haik, Yousef; Elmel, Abdelaziz; Qadri, Shahnaz

doi:10.1007/s42247-021-00325-z

Cited by 3 publications

(4 citation statements)

References 107 publications

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“…Molecular identification is essential since many EV characterization methods are size‐based, and there are still fundamental questions about how the size distributions of the EVs are determined by the cell and what makes the difference between healthy and diseased states. Characterizing EV preparations and comparing individually characterized EVs are essential tasks already demonstrated by others [ 23,24,27 ] and were achieved in our work. However, there are remaining challenges in the characterization of mixed preparations of EVs whose Raman spectra are similar.…”

Section: Discussionmentioning

confidence: 85%

“…Recently, there has been increasing attention to using Raman spectroscopy and multivariate analysis/machine learning methods to characterize EVs. [22][23][24][25][26][27][28][29][30] Most of these studies applied traditional spectral analysis methods or statistical data exploration/reduction methods like principal components analysis (PCA) and bayesian classification algorithms like linear discriminant analysis (LDA). [31] The emergence of machine learning algorithms has dramatically improved spectral analysis methods, and they found application in EV investigations.…”

Section: Introductionmentioning

confidence: 99%

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Label‐Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Parlatan

Ozen²,

Kecoglu

et al. 2023

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Exosomes, nano‐sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface‐enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label‐free Raman spectroscopy method's prediction ratio correlates with the ratio of HT‐1080 exosomes in the mixture. This machine learning‐assisted SERS method enables a new direction through label‐free investigation of EV preparations by differentiating cancer cell‐derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

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Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Label‐Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Parlatan

Ozen²,

Kecoglu

et al. 2023

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“…Using nanoporous gold substrate (NPG) can provide highly sensitive and specific detection by controlling the substrate's surface chemistry, size, and structure [14,15]. Gold is well-known among various metal nanostructures as one of the most biocompatible plasmonic materials due to the properties of the collective oscillating modes of hot electron generation at the metal/dielectric interfaces [16]. NPG significantly enhances fluorescence, resulting in increased sensitivity [17].…”

Section: Introductionmentioning

confidence: 99%

“…The plasmonic properties of NPG substrates have been used for enhancements of fluorescence emission. NPG typically contains a network of voids that occupy over 70% of the substrate volume [16,18]. The immobilization of exosomes derived from the cancerous cell on nanoporous gold substrates (NPG) can provide a predictive biomarker for early cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Serum Exosome Biomarkers Using 3D Nanoporous Gold and Spectrophotometry

Mannai

Al‐Ansari

Saoud

2022

Sensors

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Tumor-derived exosomes may provide biomarkers for cancer treatment. Using sputtering technology, an affinity-based device to capture exosomes was developed using nanoporous substrate (NPG)-coated silicon microscopy. Immunology-based techniques detect and purify exosomes using gold coating with a specific antigen. Inverted fluorescent microscopy was used to detect target exosomes quantitatively utilizing fluorescent nanospheres as the label. We quantified the expression of CD63 surface protein markers on exosomes from conditioned culture media of breast cancer cells. The exosomes that targeted specific proteins with controls were statistically analyzed and compared to those that targeted non-specific proteins. Results from SEM showed that the exosomes were circular, between 30 and 150 nanometers in size. The porous gold substrates captured more exosomes than the nonporous substrates. Nitric acid treatments at different times resulted in a variety of pore sizes. Despite the increase in the size of the pores, the number of exosomes found in the porous gold substrate treated for 10 min nearly doubled compared to the one treated for 5 min. In this work, a fluorescence biosensor was developed to detect breast cancer exosomes using nanoporous gold substrates (NPG). Assay and model exosomes of specific breast cancer cells showed that exosomes exhibit diagnostic surface protein markers, reflecting the protein profile of their parent cells. Furthermore, the specific binding between the exosome surface antibodies and the targets identified the CD63 biomarkers on the exosome, suggesting these markers’ diagnostic potential. This study can accelerate exosome research in determining tumor-related exosomes and develop novel cancer diagnostic methods.

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Comprehensive review on two dimensional nanomaterials for optical biosensors: Present progress and outlook

Verma,

Yadav

2024

Sustainable Materials and Technologies

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3D SERS-based biosensor for the selective detection of circulating cancer-derived exosomes

Cited by 3 publications

References 107 publications

Label‐Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Label‐Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Quantification of Serum Exosome Biomarkers Using 3D Nanoporous Gold and Spectrophotometry

Comprehensive review on two dimensional nanomaterials for optical biosensors: Present progress and outlook

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