**6. Raman spectroscopy for bacterial identification**

Correct and in-time identification of microorganisms is crucial in clinical diagnosis. Nowadays, despite the advances of technology and methodologies of bacterial identification, most of the hospitals use bacterial culture as a standard method [47, 48]. However, these approaches are time-consuming, and sometimes it requires more than a day until the results are available. This can have serious implications to patients, mainly to those with severe infections. In this way, there are several investigations that evaluate the potential of Raman-based approaches to bacterial identification and typing and producing fast and accurate results. Raman spectra of bacteria are like a fingerprint, since it represents the molecular composition and it is specific for each sample. The following studies are examples of the use of Raman in the field of clinical microbiology.

noninvasive diagnostic tools. Depciuch et al. acquired Raman spectra of breast biopsies and did direct spectral analysis without performing multivariate analysis [40]. They reported differences in spectral regions assigned to the principal biomolecules: lipids, sugars and proteins between normal and cancerous samples [40]. All these reports give new light in

Besides skin, oral, gastric and breast cancers, Raman spectroscopy techniques are also applied to diagnose other types of neoplasia (**Table 1**), mainly *in vitro*. For instance, in 2011, there was a study that used modulated Raman spectroscopy to detect the presence of human urothelial cells and bladder cancer cells after cell lines were incubated with urine [41]. The results achieved high sensitivity and specificity and, in the future, this approach may be applied in routine urine exams to detect bladder cancer or to monitor patients under treatment for bladder cancer.

Blood samples are widely used in the context of diagnosis. In the case of colorectal cancer, Lin et al. used gold nanoparticle-based SERS in blood serum samples of 83 patients to differentiate Raman spectra of healthy and disease samples [42]. This approach allowed to achieve sensitivity and specificity of 97.4 and 100%, respectively. Besides, the authors detected spectral differences between normal and cancer samples, mainly an increase in the relative amount of nucleic acids and a decrease in the amount of proteins in colorectal cancer patients, compared to control healthy subjects [42]. Serum blood samples can also be used to monitor the efficacy of treatments. In the specific case of leukemia, Gonzalez et al. used standard Raman spectroscopy and multivariate analysis to distinguish normal samples from leukemia samples with 100% of both sensitivity and specificity [43]. The authors detect some molecular changes between both groups of samples, mainly in the regions of lipids, phospholipids and β-carotene. Therefore, this Raman-PCA technique can be easily applied as a noninvasive tool

In the last decade, there was an increase in the use of Raman spectroscopy in the field of cancer diagnostic and monitoring. As it was possible to see in this section, the improvement in the algorithms to process Raman signals as well as the development of new SERS techniques and fiber optic probes allowed to produce results with high sensitivity and specificity and to apply Raman-based approaches in *in vivo*, *ex vivo* and *in vitro* clinical diagnosis of several

Correct and in-time identification of microorganisms is crucial in clinical diagnosis. Nowadays, despite the advances of technology and methodologies of bacterial identification, most of the hospitals use bacterial culture as a standard method [47, 48]. However, these approaches are time-consuming, and sometimes it requires more than a day until the results are available. This can have serious implications to patients, mainly to those with severe infections. In this way, there are several investigations that evaluate the potential of Raman-based approaches

understanding the molecular mechanisms involved in breast cancer.

to diagnosis and progression evaluation of leukemia.

**6. Raman spectroscopy for bacterial identification**

cancer types in different biological samples.

**5.5. Other cancers**

284 Raman Spectroscopy

In 2009, Willemse et al. used Raman spectroscopy to type methicillin-resistant and methicillinsensible *Staphylococcus aureus* and compare it with traditional DNA typing methods, which are time- and labor-consuming [49]. After spectra collection and cluster analysis, results showed that Raman spectroscopy has reproducibility and discrimination ability, and all Raman clusters were in accordance with epidemiologic data of the isolates [49]. Similar results were obtained by the same group using *Escherichia coli* and *Klebsiella pneumoniae* isolates [50]. Using SpectraCell analyzer (River Diagnostics), they obtained high reproducible spectra and a discriminatory power similar to traditional DNA typing methods [50]. However, Raman spectroscopy was not able to detect ESBL-producing *E. coli* transmission events even when coupled to High-throughput MultiLocus Sequence Typing [51]. SERS is also used in the field of microbiology to identify pathogens. For instance, malaria parasite can be detected using a SERS nanoplatform [52]. Besides, it is also possible to discriminate wild-type malaria DNA from mutant malaria DNA using this technique [52]. SERS was also used in milk samples to identify *Salmonella enterica* serotype Enteritidis [53]. In this study, SERS was used with Au nanoprobes, and the results were almost 100x more sensitive than those obtained by PCR [53].

In clinical microbiology, it would be of particular interest to apply bacterial identification approaches directly to biological samples. One of the possible drawbacks of using spectroscopy in this field is that biological fluids can have complex matrixes that may mask the specific spectral signature of a given pathogen. To verify the applicability of Raman to bacterial identification in biofluids, Harz et al. used micro-Raman to directly analyze cerebrospinal fluid (CFS) of patients with bacterial meningitis [54]. Since lethality of the disease depends on the pathogen involved, time is crucial and it is necessary to properly identify the bacteria and initiate the adequate antibiotic therapy in a short period of time. In this study, the authors showed that CFS did not affect Raman spectra of bacteria, and it was possible to identify it with accuracy [54]. This corroborates the idea that Raman can be used as a diagnostic assay. In hospitals, it is important not only to identify the pathogen but also to understand the antimicrobial susceptibility profile of the microorganism in order to choose the right antibiotic to mitigate and treat the infection. Raman spectroscopy has been recently successfully used with this purpose [55]. The authors were able to discriminate Raman spectra of 67 antibioticsusceptible strains isolated from positive blood cultures in the presence of different concentrations of antibiotic in only 5 h [55]. Further development of this technology could produce results with robustness similar to current methods used in hospitals, and therefore in the future, it can be applied to clinical diagnosis.

Raman-based approaches can also be used in the field of virology. It is possible to identify rotavirus with an accuracy above 96% using SERS fingerprinting, and the detection of the virus was possible even using a complex cellular matrix, although the results were not as sensitive as those obtained with purified samples [56]. A similar procedure was used to detect respiratory syncytial virus [57]. In this study, the authors applied SERS enzyme-catalyzed immunoassay of respiratory syncytial virus in cell lysates, and the results showed a linear correlation between the intensity of spectra and the amount of virus with a detection limit lower than traditional methods [57].

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The studies discussed in this section suggest that Raman-based approaches are a good alternative for real-time clinical usage, since they are easy to use, fast (it is possible to have results within 45 min after positive culture), reliable and can be easily applied to a wide variety of microorganisms, since specific dyes or labels are no need.
