scholarly journals Probing biomolecular interactions using surface enhanced Raman spectroscopy: label-free protein detection using a G-quadruplex DNA aptamer

2010 ◽  
Vol 46 (16) ◽  
pp. 2799 ◽  
Author(s):  
Michael A. Ochsenkühn ◽  
Colin J. Campbell
Keyword(s):  
Raman Spectroscopy ◽  
Protein Detection ◽  
Surface Enhanced Raman Spectroscopy ◽  
Dna Aptamer ◽  
Label Free ◽  
Quadruplex Dna ◽  
Free Protein ◽  
Surface Enhanced ◽  
G Quadruplex ◽  
Surface Enhanced Raman

scholarly journals Label-Free Detection of Human Serum Using Surface-Enhanced Raman Spectroscopy Based on Highly Branched Gold Nanoparticle Substrates for Discrimination of Non-Small Cell Lung Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Xiaowei Cao ◽  
Zhenyu Wang ◽  
Liyan Bi ◽  
Jie Zheng
Keyword(s):  
Lung Cancer ◽  
Raman Spectroscopy ◽  
Cell Lung Cancer ◽  
Surface Enhanced Raman Spectroscopy ◽  
Small Cell ◽  
Label Free ◽  
Small Cell Lung ◽  
Surface Enhanced ◽  
Different Types ◽  
Surface Enhanced Raman

Surface-enhanced Raman spectroscopy (SERS) is a good candidate for the development of fast and easy-to-use diagnostic tools, possibly used on serum in screening tests. In this study, a potential label-free serum test based on SERS spectroscopy was developed to analyze human serum for the diagnosis of the non-small cell lung cancer (NSCLC). We firstly synthesized novel highly branched gold nanoparticles (HGNPs) at high yield through a one-step reduction of HAuCl4 with dopamine hydrochloride at 60°C. Then, HGNP substrates with good reproducibility, uniformity, and high SERS effect were fabricated by the electrostatically assisted (3-aminopropyl) triethoxysilane-(APTES-) functionalized silicon wafer surface-sedimentary self-assembly method. Using as-prepared HGNP substrates as a high-performance sensing platform, SERS spectral data of serum obtained from healthy subjects, lung adenocarcinoma patients, lung squamous carcinoma patients, and large cell lung cancer patients were collected. The difference spectra among different types of NSCLC were compared, and analysis result revealed their intrinsic difference in types and contents of nucleic acids, proteins, carbohydrates, amino acids, and lipids. SERS spectra were analyzed by principal component analysis (PCA), which was able to distinguish different types of NSCLC. Considering its time efficiency, being label-free, and sensitivity, SERS based on HGNP substrates is very promising for mass screening NSCLC and plays an important role in the detection and prevention of other diseases.

Label-Free Quantification of Anticancer Drug Imatinib in Human Plasma with Surface Enhanced Raman Spectroscopy

2018 ◽  
Vol 90 (21) ◽  
pp. 12670-12677 ◽  
Author(s):  
Stefano Fornasaro ◽  
Alois Bonifacio ◽  
Elena Marangon ◽  
Mauro Buzzo ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Human Plasma ◽  
Anticancer Drug ◽  
Surface Enhanced Raman Spectroscopy ◽  
Label Free ◽  
Label Free Quantification ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Free Quantification

scholarly journals Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy

Nanomedicine ◽  
2021 ◽  
Vol 16 (24) ◽  
pp. 2175-2188
Author(s):  
Stacy Grieve ◽  
Nagaprasad Puvvada ◽  
Angkoon Phinyomark ◽  
Kevin Russell ◽  
Alli Murugesan ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Hematological Malignancies ◽  
Residual Disease ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Clinical Tool ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
A Cell

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.

scholarly journals Surface Enhanced Raman Spectroscopy and Intracellular Components

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 14
Author(s):  
Mario D’Acunto
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Biological Agents ◽  
Small Sample ◽  
Gold Nanoshells ◽  
Label Free ◽  
Sample Sizes ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Small Sample Sizes

In the last decade, surface-enhanced Raman spectroscopy (SERS) met increasing interest in the detection of chemical and biological agents due to its rapid performance and ultra-sensitive features. SERS is a combination of Raman spectroscopy and nanotechnology; it includes the advantages of Raman spectroscopy, providing rapid spectra collection, small sample sizes, and characteristic spectral fingerprints for specific analytes. In this paper, we detected label-free SERS signals for arbitrarily configurations of dimers, trimers, etc., composed of gold nanoshells (AuNSs) and applied to the mapping of osteosarcoma intracellular components.

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