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The surface-enhanced Raman spectroscopy (SERS)-based analysis of bacteria suffers from the lack of a standard SERS detection protocol (type of substrates, excitation frequencies, and sampling methodologies) that could be employed throughout laboratories to produce repeatable and valuable spectral information. In this work, we have examined several factors influencing the spectrum and signal enhancement during SERS studies conducted on both Gram-negative and Gram-positive bacterial species: Escherichia coli and Bacillus subtilis, respectively. These factors can be grouped into those which are related to the structure and types of plasmonic systems used during SERS measurements and those that are associated with the culturing conditions, types of culture media, and method of biological sample preparation.

One of the potential applications of surface-enhanced Raman spectroscopy (SERS) is the detection of biological compounds and microorganisms. Here we demonstrate that SERS coupled with principal component analysis (PCA) serves as a perfect method for determining the taxonomic affiliation of bacteria at the strain level. We demonstrate for the first time that it is possible to distinguish different genoserogroups within a single species, Listeria monocytogenes, which is one of the most virulent foodborne pathogens and in some cases contact with which may be fatal. We also postulate that it is possible to detect additional proteins in the L. monocytogenes cell envelope, which provide resistance to benzalkonium chloride and cadmium. A better understanding of this infectious agent could help in selecting the appropriate pharmaceutical product for enhanced treatment.

SERS-active nanostructures incorporated into a microfluidic device have been developed for rapid and multiplex monitoring of selected Type 1 cytokine (interleukins: IL-6, IL-8, IL-18) levels in blood plasma. Multiple analyses have been performed by using nanoparticles, each coated with different Raman reporter molecules: 5,5′-dithio-bis(2-nitro-benzoic acid) (DTNB), fuchsin (FC), and p-mercatpobenzoic acid (p-MBA) and with specific antibodies. The multivariate statistical method, principal component analysis (PCA), was applied for segregation of three different antigen-antibody complexes encoded by three Raman reporters (FC, p-MBA, and DTNB) during simultaneous multiplexed detection approach. To the best of our knowledge, we have also presented, for the first time, a possibility for multiplexed quantification of three interleukins: IL-6, IL-8, and IL-18 in blood plasma samples using SERS technique. Our method improves the detection limit in comparison to standard ELISA methods. The low detection limits were estimated to be 2.3 pg·ml −1 , 6.5 pg·ml −1 , and 4.2 pg·ml −1 in a parallel approach, and 3.8 pg·ml −1 , 7.5 pg·ml −1 , and 5.2 pg·ml −1 in a simultaneous multiplexed method for IL-6, IL-8, and IL-18, respectively. This demonstrated the sensitivity and reproducibility desirable for analytical examinations.

The detection and monitoring of circulating tumor cells (CTCs) in blood is an important strategy for early cancer evidence, analysis, monitoring of therapeutic response, and optimization of cancer therapy treatments. In this work, tailor-made membranes (MBSP) for surface-enhanced Raman spectroscopy (SERS)-based analysis, which permitted the separation and enrichment of CTCs from blood samples, were developed. A thin layer of SERS-active metals deposited on polymer mat enhanced the Raman signals of CTCs and provided further insight into CTCs molecular and biochemical composition. The SERS spectra of all studied cells—prostate cancer (PC3), cervical carcinoma (HeLa), and leucocytes as an example of healthy (normal) cell—revealed significant differences in both the band positions and/or their relative intensities. The multivariate statistical technique based on principal component analysis (PCA) was applied to identify the most significant differences (marker bands) in SERS data among the analyzed cells and to perform quantitative analysis of SERS data. Based on a developed PCA algorithm, the studied cell types were classified with an accuracy of 95% in 2D PCA to 98% in 3D PCA. These results clearly indicate the diagnostic efficiency for the discrimination between cancer and normal cells. In our approach, we exploited the one-step technology that exceeds most of the multi-stage CTCs analysis methods used and enables simultaneous filtration, enrichment, and identification of the tumor cells from blood specimens.

In this paper, we present novel type of Surface-enhanced Raman spectroscopy (SERS) platform, based on stainless steel wire mesh (SSWM) covered with thin silver layer. The stainless steel wire mesh, typically used in chemical engineering industry, is a cheap and versatile substrate for SERS platforms. SSWM consists of multiple steel wires with diameter of tens of micrometers, which gives periodical structure and high stiffness. Moreover, stainless steel provides great resistance towards organic and inorganic solvents and provides excellent heat dissipation. It is worth mentioning that continuous irradiation of the laser beam over the SERS substrate can be a source of significant increase in the local temperature of metallic nanostructures, which can lead to thermal degradation or fragmentation of the adsorbed analyte. Decomposition or fragmentation of the analysed sample usually causea a significant decrease in the intensity of recorded SERS bands, which either leads to false SERS responses or enables the analysis of spectral data. To our knowledge, we have developed for the first time the thermally resistant SERS platform. This type of SERS substrate, termed Ag/SSWM, exhibit high sensitivity (Enhancement Factor (EF) = 106) and reproducibility (Relative Standard Deviation (RSD) of 6.4%) towards detection of p-mercaptobenzoic acid (p-MBA). Besides, Ag/SSWM allows the specific detection and differentiation between Gram-positive and Gram-negative bacterial species: Escherichia coli and Bacillus subtilis in label-free and reproducible manner. The unique properties of designed substrate overcome the limitations associated with photo- and thermal degradation of sensitive bacterial samples. Thus, a distinctive SERS analysis of all kinds of chemical and biological samples at high sensitivity and selectivity can be performed on the developed SERS-active substrate.

The authors would like to call the reader’s attention to the fact that unfortunately following information was missing in the original article: “Evelin Witkowska is supported by the Foundation of Polish Science (FNP).”

A highly efficient recognition unit based on surface-enhanced Raman spectroscopy (SERS) was developed as a promising, fast, and sensitive tool for detection of meningococcal meningitis, which is an extremely serious and often fatal disease of the nervous system (an inflammation of the lining around the brain and spinal cord). The results of this study confirmed that there were specific differences in SERS spectra between cerebrospinal fluid (CSF) samples infected by Neisseria meningitidis and the normal CSF, suggesting a potential role for neopterin in meningococcal meningitis detection and screening applications. To estimate the best performance of neopterin as a marker of bacterial infection, principal component analysis (PCA) was performed in a selected region (640–720 cm −1 ) where the most prominent SERS peak at 695 cm −1 arising from neopterin was observed. The calculated specificity of 95 % and sensitivity of 98 % clearly indicate the effective diagnostic efficiency for differentiation between infected and control samples. Additionally, the limit of detection (LOD) of neopterin in CSF clinical samples was estimated. The level of neopterin was significantly higher in CSF samples infected by N. meningitidis (48 nmol/L), compared to the normal (control) group (4.3 nmol/L). Additionally, this work presents a new type of SERS-active nanostructure, based on polymer mats, that allows simultaneous filtration, immobilization, and enhancement of the Raman signal, enabling detection of spectra from single bacterial cells of N. meningitidis present in CSF samples. This provides a new possibility for fast and easy detection of bacteria in CSF and other clinical body fluids on a time scale of seconds. This method of detection produces consistent results faster and cheaper than traditional laboratory techniques, demonstrates the powerful potential of SERS for detection of disease, and shows the viability of future development in healthcare applications.

We show that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast, reliable, and easy method for detection and identification of food-borne bacteria, namely Salmonella spp., Listeria monocytogenes , and Cronobacter spp., in different types of food matrices (salmon, eggs, powdered infant formula milk, mixed herbs, respectively). The main aim of this work was to introduce the SERS technique into three ISO (6579:2002; 11290–1:1996/A1:2004; 22964:2006) standard procedures required for detection of these bacteria in food. Our study demonstrates that the SERS technique is effective in distinguishing very closely related bacteria within a genus grown on solid and liquid media. The advantages of the proposed ISO-SERS method for bacteria identification include simplicity and reduced time of analysis, from almost 144 h required by standard methods to 48 h for the SERS-based approach. Additionally, PCA allows one to perform statistical classification of studied bacteria and to identify the spectrum of an unknown sample. Calculated first and second principal components (PC-1, PC-2) account for 96, 98, and 90% of total variance in the spectra and enable one to identify the Salmonella spp., L. monocytogenes , and Cronobacter spp., respectively. Moreover, the presented study demonstrates the excellent possibility for simultaneous detection of analyzed food-borne bacteria in one sample test (98% of PC-1 and PC-2) with a goal of splitting the data set into three separated clusters corresponding to the three studied bacteria species. The studies described in this paper suggest that SERS represents an alternative to standard microorganism diagnostic procedures. Graphical Abstract New approach of the SERS strategy for detection and identification of food-borne bacteria, namely S. enterica , L. monocytogenes , and C. sakazakii in selected food matrices