## Bacterial Infections
### Metabolites as Biomarkers  
In comparison to proteins and nucleic acids, cellular metabolites are not yet a common biomarker for SERS-based pathogen detection [74, 77, 78, 83, 87, 90]. Metabolic processes can differ between bacteria that cause infections, therefore certain metabolites are targeted for increased specificity. Intrinsic SERS of metabolites is mostly used in direct SERS detection methods [74, 78, 87]. With the lack of work done on metabolites compared to proteins and nucleic acids, detection schemes are limited. [PERSON] et al. targeted pyocyanin (PCN), a major secondary metabolic biomarker of _Pseudomonas aeruginosa_, which can cause pneumonia and urinary tract infections [78]. They and other groups reported above 90% sensitivity that can be achieved using similar bacterial metabolites [74, 78, 87]. Extensive background knowledge of the bacterium is necessary to understand the proper sample preparation and where the largest concentration of the metabolite resides. Sample fluids for metabolite detection of a bacterial pathogen can vary, ranging from clinical sputum, cerebrospinal fluid (CSF), to blood samples [74, 78, 90]. Sample preparation for metabolites can be challenging. Device substrates such as a \"lab-on-a-chip\" SERS platforms are common for metabolite detection. [PERSON] et al. used an aggregated gold nanoparticle-covered silica substrate as a SERS substrate to detect urinary tract infection pathogens [87]. They used an in situ, two-step reduction method to synthesize the aggregated gold nanoparticles on the silica substrate (Figure 3). With this device, they directly detected the vibrational modes of nucleic acid metabolic degradation metabolites such as adenine, hypoxanthine, xanthine, guanine, uric acid, and adenosine monophosphate. Direct detection suits for this method because of the varying concentrations of abovementioned metabolites in the extracellular region of different bacterial strains creating unique, intrinsic SERS spectra. Their work displayed >95% sensitivity  
Figure 2: Direct, label-free cell membrane biomarker detection in bodily fluid using SERS. (Top) Photographs of a healthy and three types of keratoconjunctivitis, an infectious eye disease. Raman spectrum of (bottom left) a normal eye fluid and (bottom middle) fluid from the three types of eye infections. (Bottom right) PCA score plot classifying the normal and the three infected eyes based on the SERS spectra. Reproduced with permission from Reference [91] American Chemical Society (2016).  
and \(>\)99% specificity for direct SERS detection of target metabolites. With current methods achieving high sensitivities, metabolite detection does have a promising future in POC applications [74; 78; 87; 90]. While it is still in its infancy to exploit the metabolites of the pathogen in the SERS biosensing assay, in our opinion, the metabolites could be one of the best biomarker options as they facilitate simple label-free SERS detection. Simplicity in the assay development and the detection scheme could be highly favorable for POC applications. We hope that more metabolite-based SERS diagnostic assays will be seen in the future as biologists better understand the molecular level processes of the pathogens.