Tracking infrared signatures of drugs in cancer cells by Fourier transform microspectroscopy

Aimed at developing accurate, reliable and cost-saving analytical techniques for drugs screening we evaluated the potential of Fourier Transform (FT) InfraRed (IR) microspectroscopy (microFTIR) as a quantitative pre-diagnostic approach for the rapid identification of IR signatures of drugs targeting specific molecular pathways causing Chronic Myeloid Leukemia (CML). To obtain reproducible FTIR absorbance spectra at the necessary spatial resolution we optimized sample preparation and acquisition parameters on a single channel Mercury-Cadmium-Telluride (MCT) detector in the spectral interval of frequencies from 4000 to 800 cm(-1). Single K562 cells were illuminated by Synchrotron Radiation (SR) and a number of ~15 K562 cells spread in monolayer were illuminated by a conventional IR source (Globar), respectively. Combining IR spectral data with the results of complementary biochemical investigations carried out in samples by different analytical methods we identified and cross-validated IR signatures of drugs targeting the oncogenic protein BCR/ABL and its associated abnormal tyrosine kinase activity in K562 cell line. Unsupervised pattern recognition performed by Hierarchical Cluster Analysis (HCA) clustered the spectra of single K562 cells in two distinct groups roughly corresponding to living and to apoptotic cells, respectively. The corresponding IR spectral profiles were assumed to represent drug-resistant and drug-sensitive cells. Significant variations with increasing percentages of apoptotic cells were observed after the treatment of K562 cells with drugs that directly or indirectly target BCR/ABL. In conclusion, we suggest that microFTIR associated with multivariate data analysis may be useful to assess drug compounds in ex vivo cancer cell models and possibly peripheral blast cells from CML patients.     

Intracellular SERS hybrid probes using BSA–reporter conjugates

Surface-enhanced Raman scattering (SERS) hybrid probes are characterized by the typical spectrum of a reporter molecule. In addition, they deliver information from their biological environment. Here, we report SERS hybrid probes generated by conjugating different reporter molecules to bovine serum albumin (BSA) and using gold nanoparticles as plasmonic core. Advantages of the BSA-conjugate hybrid nanoprobes over other SERS nanoprobes are a high biocompatibility, stabilization of the gold nanoparticles in the biological environment, stable reporter signals, and easy preparation. The coupling efficiencies of the BSA–reporter conjugates were determined by MALDI-TOF-MS. The conjugates’ characteristic SERS spectra differ from the spectra of unbound reporter molecules. This is a consequence of the covalent coupling, which leads to altered SERS enhancement and changes in the chemical structures of the reporter and of BSA. The application of the BSA–reporter conjugate hybrid probes in 3T3 cells, including duplex imaging, is demonstrated. Hierarchical cluster analysis and principal components analysis were applied for multivariate imaging using the SERS signatures of the incorporated SERS hybrid nanoprobes along with the spectral information from biomolecules in endosomal structures of cells. The results suggest more successful applications of the SERS hybrid probes in cellular imaging and other unordered high-density bioanalytical sensing.

Fourier-transform mid-infrared FPA imaging of a complex multicellular nematode

Here we report on the application of mid-IR imaging to a complex multicellular organism, a nematode. Despite the increase in sample complexity and sample thickness from previously measured cells and tissue sections, it is still possible, through the application of hierarchical cluster analysis, to elucidate biochemical information and spatial distribution information that can be linked to different tissue types in the nematode, namely somatic and non-striated muscle and the collagen rich cuticle. Comparison of HCA clusters from individuals from different species shows clusters can be associated specifically to a particular species, but that few clusters are shared between individuals from different species. In addition, using a synthetic fibre as a model for the nematode, we also show that the IR spectra can be affected by Mie scattering type baseline oscillations which are especially severe towards the edges of the fibre/nematode, but that these can be, to some extent, corrected for using currently available correction algorithms prior to cluster analysis.     

High-content analysis in preclinical drug discovery

High-Content Analysis (HCA) has developed into an established tool and is used in a wide range of academic laboratories and pharmaceutical research groups. HCA is now routinely proving to be effective in providing functionally relevant results. It is essential to select the appropriate HCA application with regard to the targeted compound's cellular function. The cellular impact and compound specificity as revealed by HCA analysis facilitates reaching definitive conclusions at an early stage in the drug discovery process. This technology therefore has the potential to substantially improve the efficiency of pharmaceutical research. Recent advances in fluorescent probes have significantly boosted the success of HCA. Auto-fluorescent proteins which minimally hinder the functioning of the living cell have been playing a decisive role in cell biology research. For companies the severely restricted license conditions regarding auto-fluorescent proteins hamper their general use in pharmaceutical research. This has opened the field for other solutions such as self-labeling protein technology, which could potentially replace the well established methods that utilize auto-fluorescent proteins. In addition, direct labeling techniques have improved considerably and may supersede many of the approaches based on fusion proteins. Following sample preparation, treated cells are imaged and the resulting multiple fluorescent signals are subjected to contextual and statistical analysis. The extraordinary advantage of HCA is that it enables the large-scale and simultaneous quantification and correlation of multiple phenotypic responses and physiological reactions using sophisticated software solutions that permit assay-specific image analysis. Hence, HCA once more has demonstrated its outstanding potential to significantly support establishing effective pharmaceutical research processes in order to both advance research projects and cut costs.     

Laser induced breakdown spectroscopy as a tool for discrimination of glass for forensic applications

Materials analysis and characterization can provide important information as evidence in legal proceedings. The potential of laser induced breakdown spectroscopy (LIBS) for the discrimination of glass fragments for forensic applications is presented here. The proposed method is based on the fact that glass materials can be characterized by their unique spectral fingerprint. Taking advantage of the multielement detection capability and minimal to no sample preparation of LIBS, we compared glass spectra from car windows using linear and rank correlation methods. Linear correlation combined with the use of a spectral mask, which eliminates some high-intensity emission lines from the major elements present in glass, provides effective identification and discrimination at a 95% confidence level.     

Surface modifications of bioglass immersed in TRIS-buffered solution. A multitechnical spectroscopic study

Bioglass 45S5 is used in the medical field as a bone regenerative material. In fact, when immersed in body fluid, a layer of hydroxy carbonate apatite (HCA), an analogue to the mineral phase that bones are made of, is deposited on its surface. A mechanism that would explain this process has been hypothesized and includes cation leaching from the glass to the solution and formation of both a silica-rich layer and a Ca/P-rich surface layer, prior to the actual crystallization of HCA. The present paper analyzes the dissolution of 2-mum-size particles of Bioglass in TRIS-buffered solution, focusing on the modifications occurring at the surface of the particles. Results from Transmission FT-IR, Raman, and X-ray Photoelectron Spectroscopy were compared in order to obtain this information. In all cases, precise spectral band assignments were obtained by comparing Bioglass spectra, before and after reaction, with the spectra registered on some selected reference samples. The results confirm the hypothesized mechanism of Bioglass reactivity and yield new insights on the surface modifications of the samples. In particular, the following is shown: the strength of the surface H-bonding system and of water coordination decreases during the reaction; surface carbonates, initially mainly bound to Na, are substituted by an increasing amount of Ca-bound carbonates; and the final calcium phosphate layer obtained is very similar, but not identical, to carbonated hydroxyapatite.     

Cluster analysis of infrared spectra of rabbit cortical bone samples during maturation and growth

Bone consists of an organic and an inorganic matrix. During development, bone undergoes changes in its composition and structure. In this study we apply three different cluster analysis algorithms [K-means (KM), fuzzy C-means (FCM) and hierarchical clustering (HCA)], and discriminant analysis (DA) on infrared spectroscopic data from developing cortical bone with the aim of comparing their ability to correctly classify the samples into different age groups. Cortical bone samples from the mid-diaphysis of the humerus of New Zealand white rabbits from three different maturation stages (newborn (NB), immature (11 days-1 month old), mature (3-6 months old)) were used. Three clusters were obtained by KM, FCM and HCA methods on different spectral regions (amide I, phosphate and carbonate). The newborn samples were well separated (71-100% correct classifications) from the other age groups by all bone components. The mature samples (3-6 months old) were well separated (100%) from those of other age groups by the carbonate spectral region, while by the phosphate and amide I regions some samples were assigned to another group (43-71% correct classifications). The greatest variance in the results for all algorithms was observed in the amide I region. In general, FCM clustering performed better than the other methods, and the overall error was lower. The discriminate analysis results showed that by combining the clustering results from all three spectral regions, the ability to predict the correct age group for all samples increased (from 29-86% to 77-91%). This study is the first to compare several clustering methods on infrared spectra of bone. Fuzzy C-means clustering performed best, and its ability to study the degree of memberships of samples to each cluster might be beneficial in future studies of medical diagnostics.     

XPS analysis of bio‐organic systems

A survey of the literature is made for the XPS analysis of food products (mainly spray‐dried powders, which reveal a considerable surface enrichment in lipids) and of microorganisms and related systems (extracellular polymer substances and biofilms). This survey is used as a background for discussions and recommendations regarding methodology. Sample preparation methods reviewed are freeze drying, analysis of frozen hydrated specimens and adsorption of surface‐active biocompounds on model substrates. Peak decomposition is a way to increase the wealth of information provided by the XPS spectra. It should be performed after a check that sample charge stabilization is satisfactory. Moreover, ensuring the precision needed to make comparisons within sets of samples may involve a trade‐off between imposing constraints and generating information. The examination of correlations between spectral data in the light of chemical guidelines is useful to validate or improve peak decomposition and component assignment, and may also upgrade the chemical information regarding speciation. Further upgrading may be achieved by expressing marker XPS data in terms of concentrations of compounds of interest. Different methods of computation are discussed, providing a composition in terms of ingredients, classes of biochemical compounds, or various organic and inorganic compounds. As an alternative or complement to this deterministic approach, multivariate analysis of suitable spectral windows provides spectral components, which may be used for comparing samples, and which may have a direct chemical relevance or be used to identify features of interest. Copyright © 2011 John Wiley & Sons, Ltd.     

Raman microscopy for the chemometric analysis of tumor cells

Raman spectroscopy is recognized as a tool for chemometric analysis of biological materials due to the high information content relating to specific physical and chemical qualities of the sample. Thirty cells belonging to two different prostatic cell lines, PNT1A (immortalized normal prostate cell line) and LNCaP (malignant cell line derived from prostate metastases), were mapped using Raman microscopy. A range of spectral preprocessing methods (partial least-squares discriminant analyses (PLSDAs), principal component analyses (PCAs), and adjacent band ratios (ABRs)) were compared for input into linear discriminant analysis to model and classify the two cell lines. PLSDA and ABR were able to correctly classify 100% of cells into benign and malignant groups, while PLSDA correctly classified a greater proportion of individual spectra. PCA was used to image the distribution of various biochemicals inside each cell and confirm differences in composition/distribution between benign and malignant cell lines. This study has demonstrated that PLSDAs and ABRs of Raman data can identify subtle differences between benign and malignant prostatic cells in vitro.     

Blind image analysis for the compositional and structural characterization of plant cell walls

A new image analysis strategy is introduced to determine the composition and the structural characteristics of plant cell walls by combining Raman microspectroscopy and unsupervised data mining methods. The proposed method consists of three main steps: spectral preprocessing, spatial clustering of the image and finally estimation of spectral profiles of pure components and their weights. Point spectra of Raman maps of cell walls were preprocessed to remove noise and fluorescence contributions and compressed with PCA. Processed spectra were then subjected to k-means clustering to identify spatial segregations in the images. Cell wall images were reconstructed with cluster identities and each cluster was represented by the average spectrum of all the pixels in the cluster. Pure components spectra were estimated by spectral entropy minimization criteria with simulated annealing optimization. Two pure spectral estimates that represent lignin and carbohydrates were recovered and their spatial distributions were calculated. Our approach partitioned the cell walls into many sublayers, based on their composition, thus enabling composition analysis at subcellular levels. It also overcame the well known problem that native lignin spectra in lignocellulosics have high spectral overlap with contributions from cellulose and hemicelluloses, thus opening up new avenues for microanalyses of monolignol composition of native lignin and carbohydrates without chemical or mechanical extraction of the cell wall materials.     

Spectral unmixing and clustering algorithms for assessment of single cells by Raman microscopic imaging

A detailed comparison of six multivariate algorithms is presented to analyze and generate Raman microscopic images that consist of a large number of individual spectra. This includes the segmentation algorithms for hierarchical cluster analysis, fuzzy C-means cluster analysis, and k-means cluster analysis and the spectral unmixing techniques for principal component analysis and vertex component analysis (VCA). All algorithms are reviewed and compared. Furthermore, comparisons are made to the new approach N-FINDR. In contrast to the related VCA approach, the used implementation of N-FINDR searches for the original input spectrum from the non-dimension reduced input matrix and sets it as the endmember signature. The algorithms were applied to hyperspectral data from a Raman image of a single cell. This data set was acquired by collecting individual spectra in a raster pattern using a 0.5-??m step size via a commercial Raman microspectrometer. The results were also compared with a fluorescence staining of the cell including its mitochondrial distribution. The ability of each algorithm to extract chemical and spatial information of subcellular components in the cell is discussed together with advantages and disadvantages.     

Micro spectroscopy FTIR reflectance examination of paint binders on ground chalk

Summary A restorer's questions on chemical analysis of paint binders to support his complicated work has always had an exceptional appeal for chemists. Non-destructive surface analysis of rather complex materials such as paint binders is possible. The advantages of vibrational spectroscopy related to the details about chemical structure of organic compounds have been combined with sophisticated methods of multivariate data analysis. Reflectance spectra of several paint binders on ground chalk were measured using a fast, sensitive FTIR spectrometer equipped with an infrared microscope. The initial reflectance spectra include sufficient structural information to allow successful classification by hierarchical cluster analysis (HCA). Similarities between the several binders are plotted as dendrograms which are well structured. Their explanation will be discussed in detail. Finally the postulated feature sets will be checked up by principal component analysis (PCA) as to their relevance and information content.     

Classification of biodiesel using NIR spectrometry and multivariate techniques

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm⁻¹. The data were preprocessed by selecting a spectral range of 5000-4500 cm⁻¹, and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

Studies of chemical fixation effects in human cell lines using Raman microspectroscopy

The in vitro study of cellular species using Raman spectroscopy has proven a powerful non-invasive modality for the analysis of cell constituents and processes. This work uses micro-Raman spectroscopy to study the chemical fixation mechanism in three human cell lines (normal skin, normal bronchial epithelium, and lung adenocarcinoma) employing fixatives that preferentially preserve proteins (formalin), and nucleic acids (Carnoy’s fixative and methanol–acetic acid). Spectral differences between the mean live cell spectra and fixed cell spectra together with principal components analysis (PCA), and clustering techniques were used to analyse and interpret the spectral changes. The results indicate that fixation in formalin produces spectral content that is closest to that in the live cell and by extension, best preserves the cellular integrity. Nucleic acid degradation, protein denaturation, and lipid leaching were observed with all fixatives and for all cell lines, but to varying degrees. The results presented here suggest that the mechanism of fixation for short fixation times is complex and dependent on both the cell line and fixative employed. Moreover, important spectral changes occur with all fixatives that have consequences for the interpretation of biochemical processes within fixed cells. The study further demonstrates the potential of vibrational spectroscopy in the characterization of complex biochemical processes in cells at a molecular level.     

Photolysis-assisted, long-path FT-IR detection of air pollutants in the presence of water and carbon dioxide

Seven important air pollutants have been investigated by photolysis-assisted FT-IR spectroscopy. This technique renders invisible the spectra of water and carbon dioxide, which are two of the main concerns in long-path infrared spectroscopy. A cell, equipped with a UV lamp, was used to oxidise the analyte in the air sample and the spectrum recorded was used as a new background for the original sample spectrum. The optimum UV irradiation time and correctness of the concentrations were determined for this technique and compared with those from traditional methods. The signal-to-noise (S/N) ratios of the so-called “shadow spectra” were better than, or at least comparable to, the S/N ratios in the absorbance spectra obtained by using as background an air or an evacuated cell reference and subtraction of the spectra of water and carbon dioxide from a spectral library. The detection limits for the volatile organic compounds investigated have been improved by using this new method in which an appropriate background spectrum can be obtained quickly. The limitations of the method are that it cannot be applied to non-UV reactive compounds, such as methane, and the detection limits can be appreciably degraded when bands due to ozone in the shadow spectra overlap with those of the compounds under investigation.     

Effects of Different Processing Methods Based on Different Drying Conditions on the Active Ingredients of Salvia miltiorrhiza Bunge

Compared to the traditional processing method, fresh processing can significantly enhance the preservation of biologically active ingredients and reduce processing time. This study evaluated the influences of fresh and traditional processing based on different drying conditions (sun drying, oven drying and shade drying) on the active ingredients in the roots and rhizomes of S. miltiorrhiza. High-performance liquid chromatography (HPLC) was utilized to determine the contents of six active ingredients in the roots and rhizomes of S. miltiorrhiza. The data were analyzed by fingerprint similarity evaluation, hierarchical cluster analysis (HCA) and principal component analysis (PCA). The results suggest that compared to the traditional processing method, the fresh processing method may significantly increase the preservation of biologically active ingredients. Furthermore, the findings demonstrated that among the three drying methods under fresh processing conditions, the shade-drying (21.02–26.38%) method is most beneficial for retaining the active ingredients in the roots and rhizomes of S. miltiorrhiza. Moreover, the fingerprint analysis identified 17 common peaks, and the similarity of fingerprints among samples processed by different methods ranged from 0.989 to 1.000. Collectively, these results suggest novel processing methods that may improve the yield of active ingredients for S. miltiorrhiza and may be implemented for industrial production.     

Surface-enhanced Raman scattering imaging of single living lymphocytes with multivariate evaluation

This paper is aimed to show the possibility to determine individual organic compounds introduced into single living cells with surface-enhanced Raman spectroscopy (SERS). Surface enhancement was achieved with gold colloids that were allowed to diffuse into lymphocytes. An introduced analyte, rhodamine 6G, could be imaged together with for example nucleotides and amino acids of the cell. Multivariate evaluation of surface-enhanced Raman images proved to be a powerful tool for the separation of spectral information of various intracellular components. The principal component analysis (PCA) enabled identification of spectra containing different chemical information and separation of the spectral contribution of rhodamine 6G from the complex cellular matrix.     

Identification and classification of respiratory syncytial virus (RSV) strains by surface-enhanced Raman spectroscopy and multivariate statistical techniques

There is a critical need for a rapid and sensitive means of detecting viruses. Recent reports from our laboratory have shown that surface-enhanced Raman spectroscopy (SERS) can meet these needs. In this study, SERS was used to obtain the Raman spectra of respiratory syncytial virus (RSV) strains A/Long, B1, and A2. SERS-active substrates composed of silver nanorods were fabricated using an oblique angle vapor deposition method. The SERS spectra obtained for each virus were shown to posses a high degree of reproducibility. Based on their intrinsic SERS spectra, the four virus strains were readily detected and classified using the multivariate statistical methods principal component analysis (PCA) and hierarchical cluster analysis (HCA). The chemometric results show that PCA is able to separate the three virus strains unambiguously, whereas the HCA method was able to readily distinguish an A2 strain-related G gene mutant virus (ΔG) from the A2 strain. The results described here demonstrate that SERS, in combination with multivariate statistical methods, can be utilized as a highly sensitive and rapid viral identification and classification method.     

Occurrence of residual water within disk-based solid-phase extraction and its effect on GC-MS measurement of organic extracts of environmental samples

Solid-phase extraction (SPE) is a widespread and powerful sample preparation technique in many analytical areas. Many of the used methods reduce residual water during sample preparation by drying the phase material. Despite the importance of this step, hardly any study deals specifically with the drying process, and if so, only few aspects are mentioned. The present study is the first systematic investigation of the drying process using SPE disks, including the influence of process parameters on the amount of residual water and its consequences for subsequent elution and gas chromatography-mass spectrometry (GC-MS) analysis. The following points were investigated in detail: (1) the change of pressure and volume flow during the drying process, (2) the remaining amount of water at different drying times for different SPE materials, (3) the influence of suspended particulate matter on the drying process and (4) the effects of the residual water on the elution step by using different organic solvents. The study shows that the volume of residual water in the SPE disk is affected by the fixation of the sorbent, the phase material, the amount of sorbent, the pumping settings and the duration of the drying process. Furthermore, systematic investigations demonstrate the influence of residual water on the GC-MS analysis and show analytical interferences only for a few of the investigated analytes. All results suggest that more problems in SPE GC-MS methods are caused by residual water than previously assumed.