Recognition of tumor cells by immuno-SERS-markers in a microfluidic chip at continuous flow.

SERS active nanoparticles were labeled with a reporter molecule and conjugated with anti-EpCAM antibodies. These immuno SERS markers were mixed with leukocytes, MCF-7 breast cancer cells and polystyrene beads, and the mixture was injected into a microfluidic quartz chip. Raman spectra were acquired at 785 nm excitation with 25 milliseconds exposure time in a continuous flow regime. Spectral unmixing by N-FINDR identified spectral signatures of SERS-labelled cells and polystyrene beads. This approach demonstrated rapid and reproducible SERS-assisted cell detection. Strategies are discussed to further increase the throughput for cell sorting.

Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues.

Hyperspectral images can provide useful biochemical information about tissue samples. Often, Fourier transform infrared (FTIR) images have been used to distinguish different tissue elements and changes caused by pathological causes. The spectral variation between tissue types and pathological states is very small and multivariate analysis methods are required to describe adequately these subtle changes. In this work, a strategy combining multivariate curve resolution-alternating least squares (MCR-ALS), a resolution (unmixing) method, which recovers distribution maps and pure spectra of image constituents, and K-means clustering, a segmentation method, which identifies groups of similar pixels in an image, is used to provide efficient information on tissue samples. First, multiset MCR-ALS analysis is performed on the set of images related to a particular pathology status to provide basic spectral signatures and distribution maps of the biological contributions needed to describe the tissues. Later on, multiset segmentation analysis is applied to the obtained MCR scores (concentration profiles), used as compressed initial information for segmentation purposes. The multiset idea is transferred to perform image segmentation of different tissue samples. Doing so, a difference can be made between clusters associated with relevant biological parts common to all images, linked to general trends of the type of samples analyzed, and sample-specific clusters, that reflect the natural biological sample-to-sample variability. The last step consists of performing separate multiset MCR-ALS analyses on the pixels of each of the relevant segmentation clusters for the pathology studied to obtain a finer description of the related tissue parts. The potential of the strategy combining multiset resolution on complete images, multiset segmentation and multiset local resolution analysis will be shown on a study focused on FTIR images of tissue sections recorded on inflamed and non-inflamed palatine tonsils.

Detection of vancomycin resistances in enterococci within 3 ½ hours.

Vancomycin resistant enterococci (VRE) constitute a challenging problem in health care institutions worldwide. Novel methods to rapidly identify resistances are highly required to ensure an early start of tailored therapy and to prevent further spread of the bacteria. Here, a spectroscopy-based rapid test is presented that reveals resistances of enterococci towards vancomycin within 3.5 hours. Without any specific knowledge on the strain, VRE can be recognized with high accuracy in two different enterococci species. By means of dielectrophoresis, bacteria are directly captured from dilute suspensions, making sample preparation very easy. Raman spectroscopic analysis of the trapped bacteria over a time span of two hours in absence and presence of antibiotics reveals characteristic differences in the molecular response of sensitive as well as resistant Enterococcus faecalis and Enterococcus faecium. Furthermore, the spectroscopic fingerprints provide an indication on the mechanisms of induced resistance in VRE.

Classification and prediction of HCC tissues by Raman imaging with identification of fatty acids as potential lipid biomarkers.

PURPOSE: Patients with hepatocellular carcinoma (HCC) can only be treated curatively at early stages and then have a favorable prognosis of this often fatal disease. For this reason, an early detection and diagnostic confirmation are crucial. Raman imaging spectroscopy is a promising technology for high-resolution visualization of the spatial distribution of molecular composition in tissue sections. The aim of this study was to investigate molecular information of liver tissue by Raman imaging for classification and diagnostic prediction. METHODS: Unstained cryosections of human hepatic tissues (23 patients) were measured by Raman spectroscope in the regions of HCC (n = 12) and fibrosis (n = 17). The acquired data set was used to generate a random forest classification model with 101 iterations of sevenfold cross-validation. The models obtained during cross-validation were also used to predict regions of tumor margin (n = 8) aside from independent testing. RESULTS: Raman spectra differed between malignant and non-malignant tissue regions. Based on these spectral data, a random forest classification model calculated a prediction accuracy of 86 % (76 % sensitivity and 93 % specificity). The ten most important variables were identified at 2895, 2856, 1439, 1298, 1080, 1063, 1023, 937, 920, and 719 cm(-1). CONCLUSIONS: In this study, Raman imaging spectroscopy was applied successfully for liver tissue to differentiate, classify, and predict with high accuracy malignant and non-malignant tissue regions. Furthermore, the most important differences were identified as the Raman signature of fatty acids. The demonstrated results highlight the enormous potential which vibrational spectroscopy techniques have for the future diagnostics and prognosis estimation of HCC.

Raman spectroscopy and imaging: promising optical diagnostic tools in pediatrics.

This review focuses on the use of Raman spectroscopy, an analytical technique based on the inelastic scattering of harmless laser light with biological tissues, as an innovative diagnostic tool in pediatrics. After a brief introduction to explain the fundamental concepts behind Raman spectroscopy and imaging, a short summary is given of the most important and common issues arising when handling spectral data with multivariate statistics. Then, the most relevant papers in which Raman spectroscopy or imaging has been applied with diagnostic purposes to pediatric patients are reviewed, and grouped according to the type of pathology: neoplastic, inflammatory, allergic, malformative as well as other kinds. Raman spectroscopy has been used both in vivo, mostly using optical fibers for tissue illumination, as well as on ex vivo tissue sections in a microscopic imaging approach defined as "spectral histopathology". According to the results reported so far, this technique showed a huge potential for mini- or non-invasive real-time, bedside and intra-operatory diagnosis, as well as for an ex vivo imaging tool in support to pathologists. Despite many studies are limited by the small sample size, this technique is extremely promising in terms of sensitivity and specificity.