scholarly journals Comparison of 4-Mercaptobenzoic Acid Surface-Enhanced Raman Spectroscopy-Based Methods for pH Determination in Cells

2020 ◽  
Vol 74 (11) ◽  
pp. 1423-1432
Author(s):  
Brian T. Scarpitti ◽  
Amy M. Morrison ◽  
Marina Buyanova ◽  
Zachary D. Schultz
Keyword(s):  
Raman Spectroscopy ◽  
Frequency Shift ◽  
Intracellular Ph ◽  
Surface Enhanced Raman Spectroscopy ◽  
Therapeutic Drug ◽  
High Signal ◽  
Signal To Noise ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
In Cells

Measurements of cellular pH are used to infer information such as stage of cell cycle, presence of cancer and other diseases, as well as delivery or effect of a therapeutic drug. Surface-enhanced Raman spectroscopy (SERS) of nanoparticle-based pH probes have been used to interrogate intracellular pH, with the significant advantage of avoiding photobleaching compared to fluorescent indicators. 4-Mercaptobenzoic acid (MBA) is a commonly used pH-sensitive reporter molecule. Intracellular pH sensing by SERS requires analysis of the observed MBA spectrum and spectral interference can affect the pH determination. Background from common cell containers, imaging too few particles, signal-to-noise ratios, and degradation of reporter molecules are among the factors that may alter appropriate SERS-based pH determination in cells. Here, we have compared common methods of spectral analysis to see how different factors alter the calculated pH in Raman maps of MBA functionalized Au nanostars in SW620 cancer cells. The methods included in our comparison use the relative intensity of the ν(COO–) stretch, chemometric analysis of the ν8a mode, and analyzing the frequency shift of the ν8a mode. These methods show different sensitivity to some of these sources of error in live cell experiments. pH determination based on Raman frequency shift appears to give a more reliable pH determination, though in high signal-to-noise environments, intensity ratios may provide better sensitivity to small changes in pH for cellular imaging.

scholarly journals Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fatma Uysal Ciloglu ◽  
Abdullah Caliskan ◽  
Ayse Mine Saridag ◽  
Ibrahim Halil Kilic ◽  
Mahmut Tokmakci ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Deep Learning ◽  
Surface Enhanced Raman Spectroscopy ◽  
Resistant Bacteria ◽  
High Signal ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

AbstractOver the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Facile and sensitive measurement of GSH/GSSG in cells by surface-enhanced Raman spectroscopy

Talanta ◽  
2021 ◽  
Vol 224 ◽  
pp. 121852
Author(s):  
Yingjie Zhu ◽  
Jianfeng Wu ◽  
Kai Wang ◽  
Hua Xu ◽  
Minmin Qu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
In Cells

scholarly journals Live-Cell Surface-Enhanced Raman Spectroscopy Imaging of Intracellular pH: From Two Dimensions to Three Dimensions

ACS Sensors ◽  
2020 ◽  
Vol 5 (10) ◽  
pp. 3194-3206
Author(s):  
Yizhi Zhang ◽  
Dorleta Jimenez de Aberasturi ◽  
Malou Henriksen-Lacey ◽  
Judith Langer ◽  
Luis M. Liz-Marzán
Keyword(s):  
Raman Spectroscopy ◽  
Cell Surface ◽  
Intracellular Ph ◽  
Surface Enhanced Raman Spectroscopy ◽  
Two Dimensions ◽  
Live Cell ◽  
Three Dimensions ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Improving Sensitivity and Reproducibility of SERS Sensing in Microenvironments Using Individual, Optically Trapped Surface-Enhanced Raman Spectroscopy(SERS) Probes

2016 ◽  
Vol 71 (2) ◽  
pp. 279-287 ◽  
Author(s):  
Pietro Strobbia ◽  
Adam Mayer ◽  
Brian M Cullum
Keyword(s):  
Raman Spectroscopy ◽  
Optical Tweezers ◽  
Surface Enhanced Raman Spectroscopy ◽  
High Signal ◽  
Specific Information ◽  
Laser Fluences ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Trapped Surface ◽  
Tip Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS) sensors offer many advantages for chemical analyses, including the ability to provide chemical specific information and multiplexed detection capability at specific locations. However, to have operative SERS sensors for probing microenvironments, probes with high signal enhancement and reproducibility are necessary. To this end, dynamic enhancement of SERS (i.e., in-situ amplification of signal-to-noise and signal-to-background ratios) from individual probes has been explored. In this paper, we characterize the use of optical tweezers to amplify SERS signals as well as suppress background signals via trapping of individual SERS active probes. This amplification is achieved through a steady presence of a single “hot” particle in the focus of the excitation laser. In addition to increases in signal and concomitant decreases in non-SERS backgrounds, optical trapping results in an eightfold increase in the stability of the signal as well. This enhancement strategy was demonstrated using both single and multilayered SERS sub-micron probes, producing combined signal enhancements of 24-fold (beyond the native 106 SERS enhancement) for a three-layered geometry. The ability to dynamically control the enhancement offers the possibility to develop SERS-based sensors and probes with tailored sensitivities. In addition, since this trapping enhancement can be used to observe individual probes with low laser fluences, it could offer particular interest in probing the composition of microenvironments not amenable to tip-enhanced Raman spectroscopy or other scanning probe methods (e.g., intracellular analyses, etc.).

scholarly journals Raman spectroscopy for detection of imatinib in plasma: A proof of concept

2015 ◽  
Vol 08 (05) ◽  
pp. 1550019 ◽  
Author(s):  
Sanhita Rath ◽  
Aditi Sahu ◽  
Vikram Gota ◽  
P. G. Martínez-Torres ◽  
J. L. Pichardo-Molina ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Trough Concentration ◽  
Surface Enhanced Raman Spectroscopy ◽  
Clinical Samples ◽  
Therapeutic Drug ◽  
Treatment Optimization ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Long Read ◽  
Objective Tool

Imatinib is the standard first line treatment for chronic myeloid leukemia (CML). Owing to dose-related toxicities of Imatinib such as neutropenia, there is scope for treatment optimization through therapeutic drug monitoring (TDM). Trough concentration of 1 μg/mL is considered the therapeutic threshhold. Existing methods for the detection of Imatinib in plasma are limited by long read out time and expensive instrumentation. Hence, Raman spectroscopy was explored as a rapid and objective tool for monitoring Imatinib concentration. Three approaches: conventional Raman spectroscopy (CRS), Drop coating deposition Raman (DCDR) spectroscopy and surface-enhanced Raman spectroscopy (SERS) were employed to detect the required trough concentration of 1 μg/mL and above. Detection of therapeutically relevant concentrations (1 μg/mL) using SERS and suitable nanoparticle substrates has been demonstrated. Prospectively, rigorous validation using clinical samples is necessary to confirm the utility of this approach in routine clinical usage.

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