For many disease states, positive outcomes are directly linked to early diagnosis, where therapeutic intervention would be most effective. Recently, trends in disease diagnosis have focused on the development of label-free sensing techniques that are sensitive to low analyte concentrations found in the physiological environment. Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy that allows for label-free, highly sensitive, and selective detection of analytes through the amplification of localized electric fields on the surface of a plasmonic material when excited with monochromatic light. This results in enhancement of the Raman scattering signal, which allows for the detection of low concentration analytes, giving rise to the use of SERS as a diagnostic tool for disease. Here, we present a review of recent developments in the field of in vivo and in vitro SERS biosensing for a range of disease states including neurological disease, diabetes, cardiovascular disease, cancer, and viral disease.

Intraseasonal patterns of normalized difference vegetation index (NDVI), leaf area index (LAI), and phytomass were compared for four tundra vegetation types at Ivotuk, Alaska, during summer 1999. The vegetation types included moist acidic tundra (MAT), moist nonacidic tundra (MNT), mossy tussock tundra, and shrub tundra. The seasonal curves of NDVI were similar among the vegetation types but with varying magnitudes of the peak values. Peak NDVI in the shrub tundra (0.83) was significantly greater than in MAT (0.76), which was significantly greater than in MNT (0.71) and mossy tussock tundra (0.70). LAI and phytomass exhibited high temporal variability with distinct seasonality only in shrub tundra. Seasonal LAI and NDVI patterns were therefore correlated only in shrub tundra, which was attributed to the high quantity of deciduous shrub foliage present in this community and absent in the other vegetation types. Shrub tundra peak live aboveground phytomass (1256 6 123 g m À2) was significantly greater than peak live aboveground phytomass for MAT, MNT, and mossy tussock tundra (722 6 71, 773 6 53, 703 6 39 g m À2 respectively, P , 0.05). Relative abundances of deciduous shrubs, mosses, and graminoids were revealed as key components controlling differences in NDVI, LAI, and phytomass among tundra vegetation types.

The development of a sensor for the rapid and sensitive detection of neurotransmitters could provide a pathway for the diagnosis of neurological diseases, leading to the discovery of more effective treatment methods. We investigate the use of surface enhanced Raman spectroscopy (SERS) based sensors for the rapid detection of melatonin, serotonin, glutamate, dopamine, GABA, norepinephrine, and epinephrine. Previous studies have demonstrated SERS detection of neurotransmitters; however, there has been no comprehensive study on the effect of the metal used as the SERS substrate or the excitation wavelength used for detection. Here, we present the detection of 7 neurotransmitters using both silver and gold nanoparticles at excitation wavelengths of 532, 633, and 785 nm. Over the range of wavelengths investigated, the SERS enhancement on the silver and gold nanoparticles varies, with an average enhancement factor of 10-10. The maximum SERS enhancement occurs at an excitation wavelength of 785 nm for the gold nanoparticles and at 633 nm for the silver nanoparticles.

The ability to noninvasively detect neurotransmitters through the skull would aid in understanding brain function and the development of neurological diseases. Surface enhanced spatially offset Raman spectroscopy (SESORS) is a powerful technique that combines the sensitivity of surface-enhanced Raman spectroscopy (SERS) with the ability of spatially offset Raman spectroscopy (SORS) to probe subsurface layers. Here we present SERS measurements of neurotransmitters (melatonin, serotonin, and epinephrine) at various concentrations followed by the SESORS measurements of the neurotransmitters to a concentration as low as 100 μM in a brain tissue mimic through a cat skull. Principal components analysis was performed to distinguish between the surface bone layer and the subsurface layer, comprised of a brain tissue mimic modified with neurotransmitters, and to determine if each individual neurotransmitter could be accurately identified.

Detection techniques for neurotransmitters that are rapid, label-free, and non-invasive are needed to move towards earlier diagnosis of neurological disease.