Surface-enhanced Raman spectroscopy and microfluidic platforms: challenges, solutions and potential applications.

The exhaustive body of literature published in the last four years on the development and application of systems based on surface-enhanced Raman spectroscopy (SERS) combined with microfluidic devices demonstrates that this research field is a current hot topic. This synergy, also referred to as lab-on-a-chip SERS (LoC-SERS) or nano/micro-optofluidics SERS, has opened the door for new opportunities where both techniques can profit. On the one hand, SERS measurements are considerably improved because the processes previously performed on a large scale in the laboratory and prone to human error can now be carried out in nanoliter volumes in an automatic and reproducible manner; on the other hand, microfluidic platforms need detection methods able to sense in small volumes and therefore, SERS is ideal for this task. The present review not only aims to provide the reader an overview of the recent developments and advancements in this field, but it also addresses the key aspects of fundamental SERS theory that influence the interpretation of SERS spectra, as well as the challenges brought about by the experimental conditions and chemometric data analysis.

Molecular self-healing mechanisms between C60-fullerene and anthracene unveiled by Raman and two-dimensional correlation spectroscopy.

The self-healing polymer P(LMA-co-MeAMMA) crosslinked with C60-fullerene has been studied by FT-Raman spectroscopy in combination with two-dimensional (2D) correlation analysis and density functional theory calculations. To unveil the molecular changes during the self-healing process mediated by the Diels-Alder equilibrium between 10-methyl-9-anthracenyl groups and C60-fullerene different anthracene-C60-fullerene adducts have been synthesized and characterized by time-, concentration- and temperature-dependent FT-Raman measurements. The self-healing process could be monitored via the C60-fullerene vibrations at 270, 432 and 1469 cm(-1). Furthermore, the detailed analysis of the concentration-dependent FT-Raman spectra point towards the formation of anthracene-C60-fullerene adducts with an unusual high amount of anthracene bound to C60-fullerene in the polymer film, while the 2D correlation analysis of the temperature-dependent Raman spectra suggests a stepwise dissociation of anthracene-C60-fullerene adducts, which are responsible for the self-healing of the polymer.

Two-dimensional Raman correlation spectroscopy reveals molecular structural changes during temperature-induced self-healing in polymers based on the Diels-Alder reaction.

The thermally healable polymer P(LMA-co-FMA-co-MIMA) has been studied by temperature-dependent FT-Raman spectroscopy, two-dimensional Raman correlation analysis and density functional theory (DFT) calculations. To the best of our knowledge this study reports for the first time on the investigation of a self-healing polymer by means of two-dimensional correlation techniques. The synchronous correlation spectrum reveals that the spectrally overlapping C[double bond, length as m-dash]C stretching vibrations at 1501, 1575, 1585 and 1600 cm(-1) are perfect marker bands to monitor the healing process which is based on a Diels-Alder reaction of furan and maleimide. The comparison between experimental and calculated Raman spectra as well as their correlation spectra showed a good agreement between experiment and theory. The data presented within this study nicely demonstrate that Raman correlation analysis combined with a band assignment based on DFT calculations presents a powerful tool to study the healing process of self-healing polymers.

Spectrometer calibration protocol for Raman spectra recorded with different excitation wavelengths.

The combination of Raman spectroscopy with chemometrics has gained significant importance within the last years to address a broad variety of biomedical and life science questions. However, the routine application of chemometric models to analyze Raman spectra recorded with Raman devices different from the device used to establish the model is extremely challenging due to Raman device specific influences on the recorded Raman spectra. Here we report on the influence of different non-resonant excitation wavelengths on Raman spectra and propose a calibration routine, which corrects for the main part of the spectral differences between Raman spectra recorded with different (non-resonant) excitation wavelengths. The calibration routine introduced within this contribution is an improvement to the known 'standard' calibration routines and is a starting point for the development of a calibration protocol to generate spectrometer independent Raman spectra. The presented routine ensures that a chemometric model utilizes only Raman information of the sample and not artifacts from small shifts in the excitation wavelength. This is crucial for the application of Raman-spectroscopy in real-world-settings, such as diagnostics of diseases or identification of bacteria.

Seamless stitching of tile scan microscope images.

For diagnostic purposes, optical imaging techniques need to obtain high-resolution images of extended biological specimens in reasonable time. The field of view of an objective lens, however, is often smaller than the sample size. To image the whole sample, laser scanning microscopes acquire tile scans that are stitched into larger mosaics. The appearance of such image mosaics is affected by visible edge artefacts that arise from various optical aberrations which manifest in grey level jumps across tile boundaries. In this contribution, a technique for stitching tiles into a seamless mosaic is presented. The stitching algorithm operates by equilibrating neighbouring edges and forcing the brightness at corners to a common value. The corrected image mosaics appear to be free from stitching artefacts and are, therefore, suited for further image analysis procedures. The contribution presents a novel method to seamlessly stitch tiles captured by a laser scanning microscope into a large mosaic. The motivation for the work is the failure of currently existing methods for stitching nonlinear, multimodal images captured by our microscopic setups. Our method eliminates the visible edge artefacts that appear between neighbouring tiles by taking into account the overall illumination differences among tiles in such mosaics. The algorithm first corrects the nonuniform brightness that exists within each of the tiles. It then compensates for grey level differences across tile boundaries by equilibrating neighbouring edges and forcing the brightness at the corners to a common value. After these artefacts have been removed further image analysis procedures can be applied on the microscopic images. Even though the solution presented here is tailored for the aforementioned specific case, it could be easily adapted to other contexts where image tiles are assembled into mosaics such as in astronomical or satellite photos.

Deeper understanding of biological tissue: quantitative correlation of MALDI-TOF and Raman imaging.

In order to achieve a comprehensive description of biological tissue, spectral information about proteins, lipids, nucleic acids, and other biochemical components need to be obtained concurrently. Different analytical techniques may be combined to record complementary information of the same sample. Established techniques, which can be utilized to elucidate the biochemistry of tissue samples are, for instance, MALDI-TOF-MS and Raman microscopic imaging. With this contribution, we combine these two techniques for the first time. The combination of both techniques allows the utilization and interpretation of complementary information (i.e., the information about the protein composition derived from the Raman spectra with data of the lipids analyzed by the MALDI-TOF measurements). Furthermore, we demonstrate how spectral information from MALDI-TOF experiments can be utilized to interpret Raman spectra.

Towards detection and identification of circulating tumour cells using Raman spectroscopy.

Body fluids are easily accessible and contain valuable indices for medical diagnosis. Fascinating tools are tumour cells circulating in the peripheral blood of cancer patients. As these cells are extremely rare, they constitute a challenge for clinical diagnostics. In this contribution we present the Raman spectroscopic-based identification of different single cells in suspension that are found in peripheral blood of cancer patients including healthy cells like leukocytes and erythrocytes, and tumour cells like leukaemic cells and cells originating from solid tumours. Leukocytes and erythrocytes were isolated from the peripheral blood of healthy donors while myeloid leukaemia cells (OCI-AML3) and breast carcinoma derived cells (MCF-7, BT-20) were obtained from cell cultures. A laser emitting 785 nm light was used for optical trapping the single cells in the laser focus and to excite the Raman spectrum. Support vector machines were applied to develop a supervised classification model with spectra of 1210 cells originating from three different donors and three independent cultivation batches. Distinguishing tumour cells from healthy cells was achieved with a sensitivity of >99.7% and a specificity of >99.5%. In addition, the correct cell types were predicted with an accuracy of approximately 92%.