Surface-enhanced Raman spectroscopy (SERS) is a potentially important tool in the rapid and accurate detection of pathogenic bacteria in biological fluids. However, for diagnostic application of this technique, it is necessary to develop a highly sensitive, stable, biocompatible and reproducible SERS-active substrate. In this work, we have developed a silver-gold bimetallic SERS surface by a simple potentiostatic electrodeposition of a thin gold layer on an electrochemically roughened nanoscopic silver substrate. The resultant substrate was very stable under atmospheric conditions and exhibited the strong Raman enhancement with the high reproducibility of the recorded SERS spectra of bacteria (E. coli, S. enterica, S. epidermidis, and B. megaterium). The coating of the antibiotic over the SERS substrate selectively captured bacteria from blood samples and also increased the Raman signal in contrast to the bare surface. Finally, we have utilized the antibiotic-coated hybrid surface to selectively identify different pathogenic bacteria, namely E. coli, S. enterica and S. epidermidis from blood samples.

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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 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. Electronic supplementary material The online version of this article (10.1007/s00216-019-01609-4) contains supplementary material, which is available to authorized users.

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. Graphical abstractᅟ Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1153-0) contains supplementary material, which is available to authorized users.

Three of the most common meningitis pathogens, Neisseria meningitidis, Haemophilus influenzae, and Streptococcus pneumoniae, have been successfully detected and identified in clinical cerebrospinal fluid (CSF) samples using a new class of a surface-enhanced Raman scattering (SERS) assay. Bacterial meningitis is a disease of the nervous system that is extremely serious and often fatal (an inflammation encompasses the lining around the brain and spinal cord). The approach presented in this study challenges the current SERS-based method of microorganism detection in terms of sensitivity and, more importantly, reveals a simple, quick (on a timescale of seconds), label-free detection of multiple components from very small volumes of clinical samples. This new SERS class of assay, based on the combination of two types of Au/Ag-coated, nuclepore track-etched polycarbonate membranes, allow simultaneous filtration of CSF and immobilization of CSF components, enhancing their Raman signals and enabling detection of the spectra of a single bacteria cell present in the analyzed CSF samples. The multivariate statistical method, principal component analysis (PCA), was applied (i) to extract the biochemical information from the recorded bacterial spectra, (ii) to perform the statistical classification of analyzed microorganisms, and, finally, (iii) to identify the spectrum of an unknown sample by comparing it to the library of known bacterial spectra. The three meningitis pathogens, namely, N. meningitidis, H. influenzae, and S. pneumoniae, were detected and identified simultaneously using a label-free SERS method. This method of detection produces consistent results faster and cheaper than traditional laboratory techniques and demonstrates the powerful potential of SERS technique in medical applications. Additionally, the present study was undertaken to evaluate the CSF neopterin level in patients with diagnosed meningococcal meningitis. The results of this study confirmed that bacterial meningitis caused by N. meningitidis, H. influenzae, and S. pneumoniae is associated with elevated cerebrospinal fluid neopterin levels compared with control CSF samples. The neopterin concentration can be used to predict meningitis, but cannot be applied to qualify the species of bacteria inducing the meningitis infection.

Isolation and detection of circulating tumor cells (CTCs) from human blood plays an important role in non- invasive screening of cancer evolution and in predictive therapeutic treatment. Here, we present the novel tool utilizing: (i) the microfluidic device with (ii) incorporated photovoltaic (PV) based SERS-active platform, and (iii) shell-isolated nanoparticles (SHINs) for simultaneous separation and label-free analysis of circulating tumour cells CTCs in the blood specimens with high specificity and sensitivity. The proposed microfluidic chip enables the efficient size – based inertial separation of circulating cancer cells from the whole blood samples. The SERS-active platform incorporated into the microfluidic device permits the label-free detection and identification of isolated cells through the insight into their molecular and biochemical structure. Additionally, the silver nanoparticles coated with an ultrathin shell of silica (Ag@SiO 2 ) was used to improve the detection accuracy and sensitivity of analysed tumor cells via taking advantages of shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS). The empirical analysis of SHINERS spectra revealed that there are some differences among studied (HeLa), renal cell carcinoma (Caki-1), and blood cells. Unique SHINERS features and differences in bands intensities between healthy and cancer cells might be associated with the variations in the quantity and quality of molecules such as lipid, protein, and DNA or their structure during the metastasis cancer formation. To demonstrate the statistical efficiency of the developed method and improve the differentiation for circulating tumors cells detection the principal component analysis (PCA) has been performed for all SHINERS data. PCA method has been applied to recognize the most significant differences in SHINERS data among the three analyzed cells: Caki-1, HeLa, and blood cells. The proposed approach challenges the current multi-steps CTCs detection methods in the terms of simplicity, sensitivity, invasiveness, destructivity, time and cost of analysis, and also prevents the defragmentation/damage of tumor cells and thus leads to improving the accuracy of analysis. The results of this research work show the potential of developed SERS based tool for the separation of tumor cells from whole blood samples in a simple and minimally invasive manner, their detection and molecular characterization using one single technology.

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 AbstractNew 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 Electronic supplementary materialThe online version of this article (doi:10.1007/s00216-016-0090-z) contains supplementary material, which is available to authorized users.