Surface enhanced Raman spectroscopy (SERS) is meeting the requirements in biomedical science being a highly sensitive and specific analytical tool. By employing portable Raman systems in combination with customized sample pre-treatment, point-of-care-testing (POCT) becomes feasible. Powerful SERS-active sensing surfaces with high stability and modification layers if required are available for testing and application in complex biological matrices such as body fluids, cells or tissues. This review summarizes the current state in sample collection and pretreatment in SERS detection protocols, SERS detection schemes, i.e. direct and indirect SERS as well as targeted and non-targeted SERS, and SERS-active sensing surfaces. Moreover, the recent developments and advances of SERS in biomedical application scenarios, such as infectious diseases, cancer diagnostics and therapeutic drug monitoring is given, which enables the readers to identify the sample collection and preparation protocols, SERS substrates and detection strategies that are best-suited for their specific applications in biomedicine.

Surface-Enhanced Raman Spectroscopy (SERS) is a label-free technique that enables quick monitoring of substances at low concentrations in biological matrices. These advantages make it an attractive tool for the development of point-of-care tests suitable for Therapeutic Drug Monitoring (TDM) of drugs with a narrow therapeutic window, such as chemotherapeutic drugs, immunosuppressants, and various anticonvulsants. In this article, the current applications of SERS in the field of TDM for cancer therapy are discussed in detail and illustrated according to the different strategies and substrates. In particular, future perspectives are provided and special concerns regarding the standardization of self-assembly methods and nanofabrication procedures, quality assurance, and technology readiness are critically evaluated.

Surface-enhanced Raman scattering (SERS) is a powerful and sensitive technique for the detection of fingerprint sig-nals of molecules and for the investigation of a series of surface chemical reactions. Many studies introduced quantita-tive applications of SERS in various fields and several SERS methods have been implemented for each specific applica-tion, ranging in performance characteristics, analytes used, instruments, and analytical matrices. In general, very few methods have been validated according to international guidelines. As a consequence, the application of SERS in high-ly-regulated environments is still considered risky and the perception of a poorly reproducible and insufficiently robust analytical technique has persistently retarded its routine implementation. Collaborative trials are a type of interlabora-tory study (ILS) frequently performed to ascertain the quality of a single analytical method. The idea of an ILS of quan-tification with SERS arose within the framework of Working Group 1 (WG1) of the COST Action BM1401 Ra-man4Clinics32 in an effort to overcome the problematic perception of quantitative SERS methods. Here we report the first interlaboratory SERS study ever conducted, involving 15 laboratories and 41 researchers. In this study we tried to define a methodology to assess the reproducibility and trueness of a quantitative SERS method, and to compare differ-ent methods. In our opinion, this is a first important step toward a “standardization” process of SERS protocols, not proposed by a single laboratory but by a larger community.

Raman mapping in combination with uni- and multi-variate methods of data analysis is applied to articular cartilage samples. Main differences in biochemical composition and collagen fibers orientation between superficial, middle and deep zone of the tissue are readily observed in the samples. Collagen, non-collagenous proteins, proteoglycans and nucleic acids can be distinguished on the basis of their different spectral characteristics, and their relative abundance can be mapped in the label-free tissue samples, at so high a resolution as to permit the analysis at the level of single cells. Differences between territorial and inter-territorial matrix, as well as inhomogeneities in the inter-territorial matrix, are properly identified. Multivariate methods of data analysis prove to be complementary to the univariate approach. In particular, our partial least squares regression model gives a semiquantitative mapping of the biochemical constituents in agreement with average composition found in the literature. The combination of hierarchical and fuzzy cluster analysis succeeds in detecting variations between different regions of the extra-cellular matrix. Because of its characteristics as an imaging technique, Raman mapping could be a promising tool for studying biochemical changes in cartilage occurring during aging or osteoarthritis.