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

Parlatan, Ugur; Ozen, Mehmet Ozgun; Kecoglu, Ibrahim; Koyuncu, Batuhan; Torun, Hulya; Khalafkhany, Davod; Loc, Irem; Ogut, Mehmet Giray; İnci, Fatih; Akin, Demir; Solaroğlu, İhsan; Özören, Nesrin; Ünlü, Mehmet; Demirci, Utkan

doi:10.1002/smll.202205519

Cited by 35 publications

(21 citation statements)

References 79 publications

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“…[ 154 ] More recently, Raman spectroscopy has been combined with machine learning algorithms to identify EVs from five different cell lines. [ 155 ] Thus, Raman spectroscopy might be suitable to reveal differences in EV loading. Another approach could be atomic force microscopy (AFM).…”

Section: Determining the Loading Performancementioning

confidence: 99%

Standardization Approaches for Extracellular Vesicle Loading with Oligonucleotides and Biologics

Roerig

Schulz‐Siegmund

2023

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Extracellular vesicles (EVs) are widely recognized for their potential as drug delivery systems. EVs are membranous nanoparticles shed from cells. Among their natural features are their ability to shield cargo molecules against degradation and enable their functional internalization into target cells. Especially biological or bio‐inspired large molecules (LMs), like nucleic acids, proteins, peptides, and others, may profit from encapsulation in EVs for drug delivery purposes. In the last years, a variety of loading protocols are explored for different LMs. The lack of standardization in the EV drug delivery field has impeded their comparability so far. Currently, the first reporting frameworks and workflows for EV drug loading are proposed. The aim of this review is to summarize these evolving standardization approaches and set recently developed methods into context. This will allow for enhanced comparability of future work on EV drug loading with LMs.

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Section: Determining the Loading Performancementioning

confidence: 99%

Standardization Approaches for Extracellular Vesicle Loading with Oligonucleotides and Biologics

Roerig

Schulz‐Siegmund

2023

Small

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“…103 The most commonly used machine learning methods are PCA-LDA, PC-DFA, and PCA&OPLS-DA. [104][105][106] In addition, the composition and structure of the SERS substrate have a more significant impact on the effectiveness of the detection in label-free SERS detection than in labeled SERS detection. Currently, commonly used substrates are solidphase and sol-gels composed of gold or silver NPs.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

“…However, the SERS signals of individual cells are not very distinct and characteristic fingerprint peaks are not obvious, so it is often necessary to combine machine learning methods to improve the sensitivity and accuracy of the test 103 . The most commonly used machine learning methods are PCA‐LDA, PC‐DFA, and PCA&OPLS‐DA 104–106 . In addition, the composition and structure of the SERS substrate have a more significant impact on the effectiveness of the detection in label‐free SERS detection than in labeled SERS detection.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

Advancements in SERS‐based biological detection and its application and perspectives in pancreatic cancer

Xu,

Xie,

Lin

et al. 2023

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Surface‐enhanced Raman spectroscopy (SERS) has become an essential bio‐detection technique. Due to its high sensitivity, good signal specificity, and resistance to photobleaching, SERS has been widely used in biomedical research fields such as molecular imaging, tumor diagnosis, and drug monitoring. This review focuses on the progress of SERS in biomedical applications. We first introduce the basic principle of SERS and the progress of substrate research. Then, we summarize the latest research progress on SERS in drug monitoring, cell and exosome detection, tumor imaging, and detection platforms combining microfluidic and lateral flow technologies. Subsequently, the applied research of SERS in early diagnosis of pancreatic cancer and drug efficacy monitoring is described. Finally, the future development direction and possible challenges of SERS in tumor diagnosis and treatment are proposed.

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“…Clearly, to improve the rigor and reproducibility in this growing field, a rapid, label-free, and transparent method of assessing the relative amounts of serum supplement-derived EVs within EV preparations from cell cultures would be ideal. While a few recent studies have made progress in using Raman spectroscopy-based methods to quantify the purity of EVs or determine the relative ratios of different EV or lipoprotein subpopulations, no system has presented a robust and sufficiently advanced analytical method which enables explainable interpretation [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures

Kazemzadeh

Martínez-Calderón

Otupiri

et al. 2023

Preprint

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Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.

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

Cited by 35 publications

References 79 publications

Standardization Approaches for Extracellular Vesicle Loading with Oligonucleotides and Biologics

Standardization Approaches for Extracellular Vesicle Loading with Oligonucleotides and Biologics

Advancements in SERS‐based biological detection and its application and perspectives in pancreatic cancer

Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures

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