Supported lipid structures and human cells (human dermal derived keratinocyte, HaCaT) were investigated using tip-enhancedRaman spectroscopy (TERS) to use the high spatial resolution capabilities of TERS, which is assumed to be less than 10 nm, to determine specific components on the cell surface. As lipids are a main component of cellular membranes, the correlation of spectral properties of pure lipids with respect to the complex biological sample was investigated. Induced by dynamic structural changes as well as nanoscale effects, a particular spectral feature of the lipid TERS spectra is found to vary, and a similar spectral deviation appears among the TERS spectra measured on the cell. Modifications of the cell surface alone cannot cause such behaviour. In contrast to soft lipid agglomerates, the cells were fixed and therefore hampered for intrinsic structural changes. Hence, the main contribution for the cell TERS spectra variation results from nanoscale effects, determined by different spectral characteristics compared to conventional Raman spectroscopy. The present results demonstrate the capability of TERS to provide a detailed and fast insight into the composition of the cell surface, even allowing the detection of single components.

Tip‐enhanced Raman spectroscopy is used as a label‐free, nondestructive method for the direct mapping of nano­meter‐sized lipid and protein domains on the surface of a single cell (see graphic). Spectral unmixing allows the analysis and visualization of the different cellular surface components down to a spatial resolution of 10–20 nm.

A first vibrational mapping on the nanometer scale was performed on a protein (streptavidin) labelled supported phospholipid film by means of tip-enhanced Raman spectroscopy (TERS). For this purpose a TERS spectral map was measured on the biomembrane model, using a step size far below the diffraction limit. Considering the model composition, spectra were classified as either typical for lipids, proteins or both simultaneously, in a qualitative manner. Subsequently, the spectroscopic information was assigned with respect to the topographic features. Since a spatial differentiation between different compositional domains is difficult to achieve by topographic features only, the combination of morphology and spectral data enables a much more detailed characterization of biomembranes.

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.