Quantitative vibrational spectroscopy on liquid mixtures: concentration units matter.

Quantitative vibrational absorption spectroscopies rely on Beer's law relating spectroscopic intensities in a linear fashion to chemical concentrations. To address and clarify contrasting results in the literature about the difference between volume- and mass-based concentrations units used for quantitative spectroscopy on liquid solutions, we performed near-infrared, mid-infrared, and Raman spectroscopy measurements on four different binary solvent mixtures. Using classical least squares (CLS) and partial least squares (PLS) as multivariate analysis methods, we demonstrate that spectroscopic intensities are linearly related to volume-based concentration units rather than more widely used mass-based concentration units such as weight percent. The CLS results show that the difference in root mean square error of prediction (RMSEP) values between CLS models based on mass and volume fractions correlates strongly with the density difference between the two solvents in each binary mixture. This is explained by the fact that density differences are the source of non-linearity between mass and volume fractions in such mixtures. We also show that PLS calibration handles the non-linearity in mass-based models by the inclusion of additional latent variables that describe residual spectroscopic variation beyond the first latent variable (e.g., due to small peak shifts), as observed in the experimental data of all binary solvent mixtures. Using simulation studies, we have quantified the relative errors (up to 10-15%) that are made in PLS modeling when using mass fractions instead of volume fractions. Overall, our results provide conclusive evidence that concentration units based on volume should be preferred for optimal spectroscopic calibration results in academic and industrial practice.

Identification of Multiple Water-Iodide Species in Concentrated NaI Solutions Based on the Raman Bending Vibration of Water.

The influence of aqueous electrolytes on the water bending vibration was studied with Raman spectroscopy. For all salts investigated (NaI, NaBr, NaCl, and NaSCN), we observed a nonlinear intensity increase of the water bending vibration with increasing concentration. Different lasers and a tunable frequency-doubled optical parametric oscillator system were used to achieve excitation wavelengths between 785 and 374 nm. Focusing on NaI solutions, the relative enhancement of the water bending vibration was found to increase strongly with excitation photon energy, in line with a preresonance effect from the iodide-water charge-transfer transition. We used multivariate curve resolution (MCR) to decompose the measured Raman spectra of NaI solutions into three interconverting spectral components assigned to bulk water and water molecules interacting with one (X···H-O-H···O) and two (X···H-O-H···X) iodide ions (X = I(-)). The Raman spectrum of solid sodium iodide dihydrate supports the assignment of the latter. Using the MCR results, relative Raman scattering cross sections of 4.0 ± 0.6 and 14.0 ± 0.1 were calculated for the mono- and di-iodide species, respectively (compared to that of bulk water set to unity). In addition, it was found that at relatively low concentrations each iodide ion affects the Raman spectrum of roughly 22 surrounding water molecules, indicating that the influence of iodide extends beyond the first solvation shell. Our results demonstrate that the Raman bending vibration of water is a sensitive probe, providing new insights into anion solvation in aqueous environments.

Data size reduction strategy for the classification of breath and air samples using multicapillary column-ion mobility spectrometry.

Ion mobility spectrometry combined with multicapillary column separation (MCC-IMS) is a well-known technology for detecting volatile organic compounds (VOCs) in gaseous samples. Due to their large data size, processing of MCC-IMS spectra is still the main bottleneck of data analysis, and there is an increasing need for data analysis strategies in which the size of MCC-IMS data is reduced to enable further analysis. In our study, the first untargeted chemometric strategy is developed and employed in the analysis of MCC-IMS spectra from 264 breath and ambient air samples. This strategy does not comprise identification of compounds as a primary step but includes several preprocessing steps and a discriminant analysis. Data size is significantly reduced in three steps. Wavelet transform, mask construction, and sparse-partial least squares-discriminant analysis (s-PLS-DA) allow data size reduction with down to 50 variables relevant to the goal of analysis. The influence and compatibility of the data reduction tools are studied by applying different settings of the developed strategy. Loss of information after preprocessing is evaluated, e.g., by comparing the performance of classification models for different classes of samples. Finally, the interpretability of the classification models is evaluated, and regions of spectra that are related to the identification of potential analytical biomarkers are successfully determined. This work will greatly enable the standardization of analytical procedures across different instrumentation types promoting the adoption of MCC-IMS technology in a wide range of diverse application fields.

Simple and Effective Way for Data Preprocessing Selection Based on Design of Experiments.

The selection of optimal preprocessing is among the main bottlenecks in chemometric data analysis. Preprocessing currently is a burden, since a multitude of different preprocessing methods is available for, e.g., baseline correction, smoothing, and alignment, but it is not clear beforehand which method(s) should be used for which data set. The process of preprocessing selection is often limited to trial-and-error and is therefore considered somewhat subjective. In this paper, we present a novel, simple, and effective approach for preprocessing selection. The defining feature of this approach is a design of experiments. On the basis of the design, model performance of a few well-chosen preprocessing methods, and combinations thereof (called strategies) is evaluated. Interpretation of the main effects and interactions subsequently enables the selection of an optimal preprocessing strategy. The presented approach is applied to eight different spectroscopic data sets, covering both calibration and classification challenges. We show that the approach is able to select a preprocessing strategy which improves model performance by at least 50% compared to the raw data; in most cases, it leads to a strategy very close to the true optimum. Our approach makes preprocessing selection fast, insightful, and objective.

Low-cost handheld NIR spectroscopy for identification of organic solvents and low-level quantification of water contamination.

Quality control of liquid raw materials arriving on an industrial manufacturing site is typically performed in a dedicated laboratory using time- and chemicals-consuming analytical methods. Herein, we report the successful development of a handheld near-infrared spectroscopy method for the rapid, low-cost testing of organic solvents. Our methodology enables the classification of organic solvents with 100% accuracy and the quantification of water in methyl ethyl ketone with a precision of ~0.01 wt% in the 0-0.25 wt% range. The accessory that we have developed for the NIR sensor enables the development of a broad range of sensing applications on organic liquid systems.

Microbioreactors for Raman microscopy of stromal cell differentiation.

We present the development of microbioreactors with a sensitive and accurate optical coupling to a confocal Raman microspectrometer. We show that such devices enable in situ and in vitro investigation of cell cultures for tissue engineering by chemically sensitive Raman spectroscopic imaging techniques. The optical resolution of the Raman microspectrometer allows recognition and chemical analysis of subcellular features. Human bone marrow stromal cells (hBMSCs) have been followed after seeding through a phase of early proliferation until typically 21 days later, well after the cells have differentiated to osteoblasts. Long-term perfusion of cells in the dynamic culture conditions was shown to be compatible with experimental optical demands and off-line optical analysis. We show that Raman optical analysis of cells and cellular differentiation in microbioreactors is feasible down to the level of subcellular organelles during development. We conclude that microbioreactors combined with Raman microspectroscopy are a valuable tool to study hBMSC proliferation, differentiation, and development into tissues under in situ and in vitro conditions.

Refractive index sensing of green fluorescent proteins in living cells using fluorescence lifetime imaging microscopy.

We show that fluorescence lifetime imaging microscopy (FLIM) of green fluorescent protein (GFP) molecules in cells can be used to report on the local refractive index of intracellular GFP. We expressed GFP fusion constructs of Rac2 and gp91(phox), which are both subunits of the phagocyte NADPH oxidase enzyme, in human myeloid PLB-985 cells and showed by high-resolution confocal fluorescence microscopy that GFP-Rac2 and GFP-gp91(phox) are targeted to the cytosol and to membranes, respectively. Frequency-domain FLIM experiments on these PLB-985 cells resulted in average fluorescence lifetimes of 2.70 ns for cytosolic GFP-Rac2 and 2.31 ns for membrane-bound GFP-gp91(phox). By comparing these lifetimes with a calibration curve obtained by measuring GFP lifetimes in PBS/glycerol mixtures of known refractive index, we found that the local refractive indices of cytosolic GFP-Rac2 and membrane-targeted GFP-gp91(phox) are approximately 1.38 and approximately 1.46, respectively, which is in good correspondence with reported values for the cytosol and plasma membrane measured by other techniques. The ability to measure the local refractive index of proteins in living cells by FLIM may be important in revealing intracellular spatial heterogeneities within organelles such as the plasma and phagosomal membrane.

Noninvasive imaging of protein metabolic labeling in single human cells using stable isotopes and Raman microscopy.

We have combined nonresonant Raman microspectroscopy and spectral imaging with stable isotope labeling by amino acids in cell culture (SILAC) to selectively detect the incorporation of deuterium-labeled phenylalanine, tyrosine, and methionine into proteins in intact, single HeLa cells. The C-D stretching vibrational bands in these amino acids are observed in the 2100-2300 cm(-1) spectral region that is devoid of vibrational contributions from other, nondeuterated intracellular constituents. We found that incubation with deuterated amino acids for 8 h in cell culture already led to clearly detectable isotope-related signals in Raman spectra of HeLa cells. As expected, the level of isotope incorporation into proteins increased with incubation time, reaching 55% for deuterated phenylalanine after 28 h. Raman spectral imaging of HeLa cells incubated with deuterium-labeled amino acids showed similar spatial distributions for both isotope-labeled and unlabeled proteins, as evidenced by Raman ratio imaging. The SILAC-Raman methodology presented here combines the strengths of stable isotopic labeling of cells with the nondestructive and quantitative nature of Raman chemical imaging and is likely to become a powerful tool in both cell biology applications and research on tissues or whole organisms.

Single-cell optical imaging of the phagocyte NADPH oxidase.

The phagocyte NADPH oxidase is a key component of the innate immune response against invading microorganisms, because the generation of superoxide (O(2)(-)) inside the phagocytic vacuole by this enzyme is responsible for microbial killing by mechanisms that are directly or indirectly dependent on reactive oxygen species (ROS) formation. Most of what is known about the membrane-embedded and cytosolic NADPH oxidase subunits and their intricate network of interactions on assembly and activation has been derived from biochemical and biophysical studies involving subcellular fractionation or reconstituted cell-free systems. Such investigations can be complemented by single-cell microscopy on phagocytes, which may reveal spatial and/or temporal details about NADPH oxidase assembly that cannot be obtained from fractionated-cell assays. In recent years, we have investigated the NADPH oxidase in neutrophils using two complementary optical imaging techniques: Raman microscopy, a vibrational spectroscopic technique that does not require protein labeling, and live-cell fluorescence microscopy, which sheds light on the dynamics of NADPH oxidase assembly in individual cells. Here, we briefly introduce these techniques, compare their characteristics, and show their potential for studying NADPH oxidase at the single-cell level. New microscopy data are presented to illustrate the versatility of Raman and fluorescence microscopy on intact neutrophils.

Boosting model performance and interpretation by entangling preprocessing selection and variable selection.

The aim of data preprocessing is to remove data artifacts-such as a baseline, scatter effects or noise-and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames. In that approach, the focus was solely on improving the predictive performance of the chemometric model. This is, however, only one of the two relevant criteria in modeling: interpretation of the model results can be just as important. Variable selection is often used to achieve such interpretation. Data artifacts, however, may hamper proper variable selection by masking the true relevant variables. The choice of preprocessing therefore has a huge impact on the outcome of variable selection methods and may thus hamper an objective interpretation of the final model. To enhance such objective interpretation, we here integrate variable selection into the preprocessing selection approach that is based on DoE. We show that the entanglement of preprocessing selection and variable selection not only improves the interpretation, but also the predictive performance of the model. This is achieved by analyzing several experimental data sets of which the true relevant variables are available as prior knowledge. We show that a selection of variables is provided that complies more with the true informative variables compared to individual optimization of both model aspects. Importantly, the approach presented in this work is generic. Different types of models (e.g. PCR, PLS, …) can be incorporated into it, as well as different variable selection methods and different preprocessing methods, according to the taste and experience of the user. In this work, the approach is illustrated by using PLS as model and PPRV-FCAM (Predictive Property Ranked Variable using Final Complexity Adapted Models) for variable selection.

Increasing conclusiveness of clinical breath analysis by improved baseline correction of multi capillary column - ion mobility spectrometry (MCC-IMS) data.

Current challenges of clinical breath analysis include large data size and non-clinically relevant variations observed in exhaled breath measurements, which should be urgently addressed with competent scientific data tools. In this study, three different baseline correction methods are evaluated within a previously developed data size reduction strategy for multi capillary column - ion mobility spectrometry (MCC-IMS) datasets. Introduced for the first time in breath data analysis, the Top-hat method is presented as the optimum baseline correction method. A refined data size reduction strategy is employed in the analysis of a large breathomic dataset on a healthy and respiratory disease population. New insights into MCC-IMS spectra differences associated with respiratory diseases are provided, demonstrating the additional value of the refined data analysis strategy in clinical breath analysis.

Replacement of retinyl esters by polyunsaturated triacylglycerol species in lipid droplets of hepatic stellate cells during activation.

Activation of hepatic stellate cells has been recognized as one of the first steps in liver injury and repair. During activation, hepatic stellate cells transform into myofibroblasts with concomitant loss of their lipid droplets (LDs) and production of excessive extracellular matrix. Here we aimed to obtain more insight in the dynamics and mechanism of LD loss. We have investigated the LD degradation processes in rat hepatic stellate cells in vitro with a combined approach of confocal Raman microspectroscopy and mass spectrometric analysis of lipids (lipidomics). Upon activation of the hepatic stellate cells, LDs reduce in size, but increase in number during the first 7 days, but the total volume of neutral lipids did not decrease. The LDs also migrate to cellular extensions in the first 7 days, before they disappear. In individual hepatic stellate cells. all LDs have a similar Raman spectrum, suggesting a similar lipid profile. However, Raman studies also showed that the retinyl esters are degraded more rapidly than the triacylglycerols upon activation. Lipidomic analyses confirmed that after 7 days in culture hepatic stellate cells have lost most of their retinyl esters, but not their triacylglycerols and cholesterol esters. Furthermore, we specifically observed a large increase in triacylglycerol-species containing polyunsaturated fatty acids, partly caused by an enhanced incorporation of exogenous arachidonic acid. These results reveal that lipid droplet degradation in activated hepatic stellate cells is a highly dynamic and regulated process. The rapid replacement of retinyl esters by polyunsaturated fatty acids in LDs suggests a role for both lipids or their derivatives like eicosanoids during hepatic stellate cell activation.

Hybrid Raman-fluorescence microscopy on single cells using quantum dots.

Raman spectral imaging is a label-free, noninvasive optical technique to visualize the spatial distribution of biomolecules such as DNA, RNA, proteins, and lipids in cells and tissues. Although Raman imaging has been successfully used in the last 5 years on single cells, an important drawback of this technique is that it is traditionally regarded as incompatible with fluorescence microscopy. This is because fluorescence signals from fluorophore-labeled cells usually overwhelm the orders of magnitude weaker Raman signals from cellular biomolecules. However, we have recently shown that both nonresonance and resonance Raman microscopy can be combined with fluorescence microscopy on the same cells by spectrally separating fluorescence emission from Raman scattering. The fluorophores that are used in this case are semiconductor quantum dots (QDs), which have suitable properties in hybrid Raman-fluorescence experiments, in particular a large separation between absorption and emission wavelengths. We envisage that the combination of fluorescence microscopy with Raman spectroscopy or imaging on cells will lead to new application in cell biology. Here, we will describe detailed protocols for performing hybrid Raman-fluorescence experiments on single QD-labeled cells.

Hybrid confocal Raman fluorescence microscopy on single cells using semiconductor quantum dots.

We have overcome the traditional incompatibility of Raman microscopy with fluorescence microscopy by exploiting the optical properties of semiconductor fluorescent quantum dots (QDs). Here we present a hybrid Raman fluorescence spectral imaging approach for single-cell microscopy applications. We show that resonant Raman imaging of flavocytochrome b558 at 413.1 nm excitation in QD-labeled neutrophilic granulocytes or nonresonant Raman imaging of proteins and lipids at 647.1 nm excitation in QD-labeled macrophages can be integrated with linear one-photon excitation and nonlinear continuous-wave two-photon excitation fluorescence microscopy of QDs, respectively. The enhanced information content of these two hybrid Raman fluorescence methods provides new multiplexing possibilities for single-cell optical microscopy and intracellular chemical analysis.

Intracellular degradation of microspheres based on cross-linked dextran hydrogels or amphiphilic block copolymers: a comparative raman microscopy study.

Micro- and nanospheres composed of biodegradable polymers show promise as versatile devices for the controlled delivery of biopharmaceuticals. Whereas important properties such as drug release profiles, biocompatibility, and (bio)degradability have been determined for many types of biodegradable particles, information about particle degradation inside phagocytic cells is usually lacking. Here, we report the use of confocal Raman microscopy to obtain chemical information about cross-linked dextran hydrogel microspheres and amphiphilic poly(ethylene glycol)-terephthalate/poly(butylene terephthalate) (PEGT/PBT) microspheres inside RAW 264.7 macrophage phagosomes. Using quantitative Raman microspectroscopy, we show that the dextran concentration inside phagocytosed dextran microspheres decreases with cell incubation time. In contrast to dextran microspheres, we did not observe PEGT/PBT microsphere degradation after 1 week of internalization by macrophages, confirming previous studies showing that dextran microsphere degradation proceeds faster than PEGT/PBT degradation. Raman microscopy further showed the conversion of macrophages to lipid-laden foam cells upon prolonged incubation with both types of microspheres, suggesting that a cellular inflammatory response is induced by these biomaterials in cell culture. Our results exemplify the power of Raman microscopy to characterize microsphere degradation in cells and offer exciting prospects for this technique as a noninvasive, label-free optical tool in biomaterials histology and tissue engineering.

Single-cell Raman and fluorescence microscopy reveal the association of lipid bodies with phagosomes in leukocytes.

Cellular imaging techniques based on vibrational spectroscopy have become powerful tools in cell biology because the molecular composition of subcellular compartments can be visualized without the need for labeling. Using high-resolution, nonresonant confocal Raman microscopy on individual cells, we demonstrate here that lipid bodies (LBs) rich in arachidonate as revealed by their Raman spectra associate with latex bead-containing phagosomes in neutrophilic granulocytes. This finding was corroborated in macrophages and in PLB-985 cells, which can be induced to differentiate into neutrophil-like cells, by selective staining of LBs and visualization by confocal fluorescence microscopy. We further show that the accumulation of LBs near phagosomes is mediated at least in part by the flavohemoprotein gp91phox (in which "phox" is phagocyte oxidase), because different LB distributions around phagocytosed latex beads were observed in WT and gp91phox-deficient PLB-985 cells. gp91phox, which accumulates in the phagosomal membrane, is the catalytic subunit of the leukocyte NADPH oxidase, a critical enzyme in the innate immune response. Finally, time-lapse fluorescence microscopy experiments on neutrophils revealed that the LB-phagosome association is transient, similar to the "kiss-and-run" behavior displayed by endosomes involved in phagosome maturation. Because arachidonic acid (AA) has been shown to be involved in NADPH oxidase activation and phagosome maturation in neutrophils and macrophages, respectively, the findings reported here suggest that LBs may provide a reservoir of AA for local activation of these essential leukocyte functions.

Resonance Raman imaging of the NADPH oxidase subunit cytochrome b558 in single neutrophilic granulocytes.

We have employed confocal resonance Raman (RR) imaging to visualize the subcellular distribution of the NADPH oxidase subunit cytochrome b558 in both resting and phagocytosing neutrophils. Our Raman microscopic technique is a label-free, chemical (vibrational) imaging method that can be applied to individual, intact cells, thus probing cytochrome b558 in its native environment. The Raman signal from cytochrome b558 is resonantly and selectively enhanced in neutrophils by using 413 nm excitation. Experiments on resting neutrophils show a cytoplasmic distribution of cytochrome b558, with several areas of high content. Upon phagocytosis of polystyrene particles, we found that part of the cytochrome b558 is translocated toward the ingested beads. This is in accordance with immunocytochemistry studies combined with electron and fluorescence microscopy. As compared to these methods, RR microscopy requires minimal sample preparation and disturbance. Moreover, it allows the determination of the redox state of cytochrome b558 inside the cell, which reflects its NADPH oxidase activity.

Raman imaging of PLGA microsphere degradation inside macrophages.

Understanding the degradation behavior of polymeric microspheres is crucial for the successful application of such devices in controlled drug delivery. The degradation mechanism of poly(lactic-co-glycolic acid) (PLGA) microspheres inside phagocytic cells is not known, but different models for degradation in aqueous solution have been proposed. We have used confocal Raman spectroscopy and imaging to study the intracellular degradation of PLGA microspheres inside individual macrophages. Our results show that ingested microspheres degrade in a heterogeneous manner, with a more rapid degradation in the center. Comparison of Raman spectra from degrading beads with those of uningested beads reveals that ester hydrolysis occurs throughout the phagocytosed microspheres, with a selective loss of glycolic acid units. Furthermore, we show that PLGA degradation is a cell-mediated process, possibly caused by the low pH of the phagosome and/or the presence of hydrolytic enzymes. In conclusion, we have demonstrated that the chemical composition of degrading polymers inside cells can be probed by Raman spectral imaging. This technique will expand the capabilities of investigating biomaterial degradation in vivo.