Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection.

Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study.

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.

Discrimination of urinary tract infection pathogens by means of their growth profiles using surface enhanced Raman scattering.

Urinary tract infection (UTI) is a widespread infection and affects millions of people around the globe. The gold standard for identification of microorganisms causing infection is urine culture. However, current methods require at least 24 h for the results. In clinical settings, identification and discrimination of bacteria with less time-consuming and cheaper methods are highly desired. In recent years, the power of surface-enhanced Raman scattering (SERS) for fast identification of bacteria and biomolecules has been demonstrated. In this study, we show discrimination of urinary tract infection causative pathogens within 1 h of incubation using principal component analysis (PCA) of SERS spectra of seven different UTI causative bacterial species. In addition, we showed differentiation of them at their different growth phases. We also analyzed origins of bacterial SERS spectra and demonstrated the highly dynamic structure of the bacteria cell wall during their growth. Graphical Abstract Collection of bacteria from urine sample, and their discrimination using their SERS spectra and multivariate analysis.

Influence of protein size on surface-enhanced Raman scattering (SERS) spectra in binary protein mixtures.

The size-dependent interactions of eight blood proteins with silver nanoparticles (AgNPs) in their binary mixtures were investigated using surface-enhanced Raman scattering (SERS). Principal component analysis (PCA) was performed on the SERS spectra of each binary mixture, and the differentiation ability of the mixtures was tested. It was found that the effect of relative concentration change on the SERS spectra of the binary mixtures of small proteins could be detected using PCA. However, this change was not observed with the binary mixtures of large proteins. This study demonstrated that the relative interactions of the smaller proteins with an average size of 50 nm AgNPs smaller than the large proteins could be monitored, and this information can be used for the detection of proteins in protein mixtures.