Using clustering as pre-processing in the framework of signal unmixing for exhaustive exploration of archaeological artefacts in Raman imaging

Analytical chemistry on archaeological material is an essential part of modern archaeological investigations and from year to year, instrumental improvement has made it possible to generate data at a high spatial and temporal frequency. In particular, Raman spectral imaging can be successfully applied in archaeological research by its simplicity of implementation to study past human societies through the analysis of their material remains. This technique makes it possible to simultaneously obtain spatial and spectral information by preserving sample integrity. However, because of the inherent complexity of the samples in Archaeology (e.g. seniority, fragility, lack or full absence of any information about its composition), chemical interpretation can be difficult at first glance. Indeed, specific problems of spectral selectivity related to unexpected chemical compounds could appear due to their state of conservation. Furthermore, detecting minor compounds becomes challenging as major components impose their contributions in the acquired spectra. Therefore, a relevant chemometric approach has been introduced in this context to characterize distinct spectral sources in a Raman imaging dataset of an archaeological specimen - a mosaic fragment. The fragment was unearthed during the Ruscino archaeological dig on the outskirts of Perpignan, France. It dates back to the oppidum period. The aim is to extract selective spectral information from pixel clustering analysis in order to enhance the initial optimisation step within the Multivariate Curve Resolution and Alternating Least-Squares (MCR-ALS) algorithm, a well-known signal unmixing technique. The underlying principle of the MCR-ALS is that the acquired spectra can be expressed as linear combinations of pure spectra of all individual components present in the chemical system under study. Sometimes it can be difficult to obtain the desired results through the algorithm, particularly if initial estimates of spectral or concentration profiles are inaccurate due to complex signals, noise or lack of selectivity, resulting in rank deficiency (i.e. a poor estimation of the total number of pure signals). For this reason, an innovative threshold-based clustering algorithm, combined with multiple Orthogonal Projection Approaches (OPA), has been developed to improve matrix rank investigation and thus the initialisation step of the MCR-ALS approach before optimisation. The effective analysis of Raman imaging data for an archaeological mosaic played a crucial role in uncovering significant chemical information about a particular biogenic material. This insight sheds light on the origins of mortar manufacture during the oppidum period.

Enhancing Diagnostic Capabilities for Occupational Lung Diseases Using LIBS Imaging on Biopsy Tissue

Laser-induced breakdown spectroscopy (LIBS) imaging continues to gain strength as an influential bioanalytical technique, showing intriguing potential in the field of clinical analysis. This is because hyperspectral LIBS imaging allows for rapid, comprehensive elemental analysis, covering elements from major to trace levels consistently year after year. In this study, we estimated the potential of a multivariate spectral data treatment approach based on a so-called convex envelope method to detect exotic elements (whether they are minor or in trace amounts) in biopsy tissues of patients with occupational exposure-related diseases. More precisely, we have developed an approach called Interesting Features Finder (IFF), which initially allowed us to identify unexpected elements without any preconceptions, considering only the set of spectra contained in a LIBS hyperspectral data cube. This task is, in fact, almost impossible with conventional chemometric tools, as it entails identifying a few exotic spectra among several hundred thousand others. Once this detection was performed, a second approach based on correlation was used to locate their distribution in the biopsies. Through this unique data analysis pipeline to processing massive LIBS spectroscopic data, it was possible to detect and locate exotic elements such as tin and rhodium in a patient's tissue section, ultimately leading to a possible reclassification of their lung condition as an occupational disease. This review will thus demonstrate the potential of this new diagnostic tool based on LIBS imaging in addressing the shortcomings of approaches developed thus far. The proposed data processing approach naturally transcends this specific framework and can be leveraged across various domains of analytical chemistry, where the detection of rare events is concealed within extensive data sets.

When Social Media Empowers Analytical Chemists to Explore Millions of Spectra Derived from a Complex Sample

Analytical chemistry has never yielded such a wealth of experimental data as it does today, and this exponential trend shows no sign of abating. We continually advance the capabilities of our instruments and conceive innovative concepts, all in a concerted effort to naturally push the boundaries of our understanding regarding intricate sample matrices. Spectroscopic imaging, in the broadest sense, is certainly the field where we observe this acceleration even more pronouncedly. Analytical chemistry swiftly grasped the significance of processing acquired data for comprehensive exploration through utilization of chemometrics or machine learning tools. One can assert today that chemometrics undeniably constitutes an integral facet in the advancement of an analytical approach. However, we are now faced with a new challenge, as the experimental data accumulated for certain analytical techniques are so vast and massive that exploring them with such tools has become unfeasible, and this is by no means a computational capacity issue. Analytical chemistry is far from being the sole field affected by this issue, and one could argue that others have grappled with it long before us, such as, for instance, social media, to name just one. The purpose of this paper is to demonstrate that such a domain, which may initially seem distant from our concerns, can offer novel tools capable of overcoming these barriers, even though we are not necessarily dealing with the same objects. More specifically, we delve into the clustering of over 10 million LIBS spectra acquired as part of an imaging experiment aimed at exploring a singular rock sample. This will serve to demonstrate that an open-source library developed by Meta (formerly known as Facebook) can enable us to conduct a comprehensive exploration of this sample, a feat deemed impossible with conventional data analysis approaches.

Comparing MS imaging of lipids by WALDI and MALDI: two technologies for evaluating a common ground truth in MS imaging

In this study, we conducted a direct comparison of water-assisted laser desorption ionization (WALDI) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging, with MALDI serving as the benchmark for label-free molecular tissue analysis in biomedical research. Specifically, we investigated the lipidomic profiles of several biological samples and calculated the similarity of detected peaks and Pearson's correlation of spectral profile intensities between the two techniques. We show that, overall, MALDI MS and WALDI MS present very close lipidomic analyses and that the highest similarity is obtained for the norharmane MALDI matrix. Indeed, for norharmane in negative ion mode, the lipidomic spectra revealed 100% similarity of detected peaks and over 0.90 intensity correlation between both technologies for five samples. The MALDI-MSI positive ion lipid spectra displayed more than 83% similarity of detected peaks compared to those of WALDI-MSI. However, we observed a lower percentage (77%) of detected peaks when comparing WALDI-MSI with MALDI-MSI due to the rich WALDI-MSI lipid spectra. Despite this difference, the global lipidomic spectra showed high consistency between the two technologies, indicating that they are governed by similar processes. Thanks to this similarity, we can increase datasets by including data from both modalities to either co-train classification models or obtain cross-interrogation.

Raman spectroscopy mapping of changes in the organization and relative quantities of cell wall polymers in bast fiber cell walls of flax plants exposed to gravitropic stress

Flax is an important fiber crop that is subject to lodging. In order to gain more information about the potential role of the bast fiber cell wall in the return to the vertical position, 6-week-old flax plants were subjected to a long-term (6 week) gravitropic stress by stem tilting in an experimental set-up that excluded autotropism. Stress induced significant morphometric changes (lumen surface, lumen diameter, and cell wall thickness and lumen surface/total fiber surface ratio) in pulling- and opposite-side fibers compared to control fibers. Changes in the relative amounts and spatial distribution of cell wall polymers in flax bast fibers were determined by Raman vibrational spectroscopy. Following spectra acquisition, datasets (control, pulling- and opposite sides) were analyzed by principal component analysis, PC score imaging, and Raman chemical cartography of significant chemical bonds. Our results show that gravitropic stress induces discrete but significant changes in the composition and/or spatial organization of cellulose, hemicelluloses and lignin within the cell walls of both pulling side and opposite side fibers.

Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis

Coherent Raman imaging has been extensively applied to live-cell imaging in the last 2 decades, allowing to probe the intracellular lipid, protein, nucleic acid, and water content with a high-acquisition rate and sensitivity. In this context, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy using sub-nanosecond laser pulses is now recognized as a mature and straightforward technology for label-free bioimaging, offering the high spectral resolution of conventional Raman spectroscopy with reduced acquisition time. Here, we introduce the combination of the MCARS imaging technique with unsupervised data analysis based on multivariate curve resolution (MCR). The MCR process is implemented under the classical signal non-negativity constraint and, even more originally, under a new spatial constraint based on cell segmentation. We thus introduce a new methodology for hyperspectral cell imaging and segmentation, based on a simple, unsupervised workflow without any spectrum-to-spectrum phase retrieval computation. We first assess the robustness of our approach by considering cells of different types, namely, from the human HEK293 and murine C2C12 lines. To evaluate its applicability over a broader range, we then study HEK293 cells in different physiological states and experimental situations. Specifically, we compare an interphasic cell with a mitotic (prophase) one. We also present a comparison between a fixed cell and a living cell, in order to visualize the potential changes induced by the fixation protocol in cellular architecture. Next, with the aim of assessing more precisely the sensitivity of our approach, we study HEK293 living cells overexpressing tropomyosin-related kinase B (TrkB), a cancer-related membrane receptor, depending on the presence of its ligand, brain-derived neurotrophic factor (BDNF). Finally, the segmentation capability of the approach is evaluated in the case of a single cell and also by considering cell clusters of various sizes.

Archaeological Mortar Characterization Using Laser-Induced Breakdown Spectroscopy (LIBS) Imaging Microscopy

Lime mortar is a complex mixture resulting from hardening of lime, water, and aggregates. Lime mortar was used from the time of the Roman Empire until the Industrial Revolution. The recipes used differ according to the period, geographical area of preparation, craftsman, or function. This is why the study of archaeological mortars is of such great importance in building archaeology. In this study, we used laser-induced breakdown spectroscopy (LIBS) to characterize the elemental composition of three lime mortar samples with a µ-LIBS instrument, allowing elemental image compilation. These samples originate from three different geographical locations: Angers (France), Dardilly (France), and Pompeii (Italy), and were taken from buildings that had different functions: cathedral, aqueduct, and house, respectively. Thanks to image processing and the creation of masks, it was possible to extract not only the lime signature and nature of the aggregate but also its granulometry and circularity. All this information is essential for cultural heritage research. This study shows the potential of the LIBS technique in archaeometric analysis of archaeological mortars.

Critical aspects of Raman spectroscopy as a tool for postmortem interval estimation

The estimation of the postmortem interval (PMI) from skeletal remains represents a challenging task in forensic science. PMI is often influenced by extrinsic factors (humidity, dryness, scavengers, etc.) and intrinsic factors (age, sex, pathology, way of life, medical treatments, etc.). Raman spectroscopy combined with multivariate data analysis represents a promising tool for forensic anthropologists. Despite all the advantages of the technique, Raman spectra of skeletal remains are influenced by these extrinsic and intrinsic factors, which impairs precision and reproducibility. Both parameters have to reach a high level of confidence when such spectroscopy is used as a way to predict PMI. As a consequence, advanced multivariate data analysis is necessary to quantify the effect of all factors to improve the estimation of the PMI. The objective of this work is to evaluate the effect of intrinsic and extrinsic factors on the Raman spectra of skeletal remains. We designed a protocol close to a real-world scenario. We used ANOVA-simultaneous component analysis (ASCA) to unmix and quantify the effect of 1 intrinsic (source body) and 1 extrinsic (burial time) factors on the Raman spectra. In our model, the burial time was found to generate the highest variability after the source body. ASCA showed that the variability due to the burial time has 2 mixed contributions. Seasonal variations are the first contribution. The second contribution is attributed to diagenesis. A decrease in the mineral bands and an increase in the organic bands are observed. The source body was also found to contribute to the variability in Raman spectra. ASCA showed that the source body induces variability related to the composition of bones. This quantification cannot be assessed by basic chemometrics methods such as PCA. The results of this study highlighted the need to use an advanced chemometric data analysis tool (like ASCA) combined with Raman spectroscopy to estimate the postmortem interval.

Raman microspectroscopy reveals unsaturation heterogeneity at the lipid droplet level and validates an in vitro model of bone marrow adipocyte subtypes

Bone marrow adipocytes (BMAds) constitute the most abundant stromal component of adult human bone marrow. Two subtypes of BMAds have been described, the more labile regulated adipocytes (rBMAds) and the more stable constitutive adipocytes (cBMAds), which develop earlier in life and are more resilient to environmental and metabolic disruptions. In vivo, rBMAds are enriched in saturated fatty acids, contain smaller lipid droplets (LDs) and more readily provide hematopoietic support than their cBMAd counterparts. Mouse models have been used for BMAds research, but isolation of primary BMAds presents many challenges, and thus in vitro models remain the current standard to study nuances of adipocyte differentiation. No in vitro model has yet been described for the study of rBMAds/cBMAds. Here, we present an in vitro model of BM adipogenesis with differential rBMAd and cBMAd-like characteristics. We used OP9 BM stromal cells derived from a (C57BL/6xC3H)F2-op/op mouse, which have been extensively characterized as feeder layer for hematopoiesis research. We observed similar canonical adipogenesis transcriptional signatures for spontaneously-differentiated (sOP9) and induced (iOP9) cultures, while fatty acid composition and desaturase expression of Scd1 and Fads2 differed at the population level. To resolve differences at the single adipocyte level we tested Raman microspectroscopy and show it constitutes a high-resolution method for studying adipogenesis in vitro in a label-free manner, with resolution to individual LDs. We found sOP9 adipocytes have lower unsaturation ratios, smaller LDs and higher hematopoietic support than iOP9 adipocytes, thus functionally resembling rBMAds, while iOP9 more closely resembled cBMAds. Validation in human primary samples confirmed a higher unsaturation ratio for lipids extracted from stable cBMAd-rich sites (femoral head upon hip-replacement surgery) versus labile rBMAds (iliac crest after chemotherapy). As a result, the 16:1/16:0 fatty acid unsaturation ratio, which was already shown to discriminate BMAd subtypes in rabbit and rat marrow, was validated to discriminate cBMAds from rBMAd in both the OP9 model in vitro system and in human samples. We expect our model will be useful for cBMAd and rBMAd studies, particularly where isolation of primary BMAds is a limiting step.

Novel four-dimensional approach for the structural characterization of neutral nitrogen compounds in vacuum gas oils using UHPLC-IM-QqToF analysis

The molecular analysis of complex matrices such as vacuum gas oils require powerful instruments such as Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS). As this technique does not allow the separation of two isomers, ion mobility coupled to mass spectrometry (IMMS) can be used to target a structural detail. However, the resolving power of ion mobility is not sufficient to resolve isomers in such a complex mixture. In this paper, ion mobility-mass spectrometry coupled to separative methods such as Flash-HPLC and UHPLC has been used to characterize the neutral nitrogen compounds found in vacuum gas oils. One vacuum gas oil feedstock as well as different hydrotreated samples have been analyzed through a heart-cutted HPLC-UHPLC-IM-QqToF analysis to target specific compounds that have been found to be problematic within hydrotreatment context thanks to ESI(-)-FT-ICR MS analyses. The extraction of the macroscopic descriptors (mobility, full-width at half-maximum) allowed highlighting first trends about the samples. Then, the chromatographic peaks obtained for a given alkylation degree have been divided into several retention time segments and the corresponding mobilograms have been obtained. Bi-modal distributions have been obtained and the observed Collision Cross Sections and MS/MS spectra suggested the presence of compact and non-compact structures. The evolution of these structures has been followed throughout hydrotreatment to evaluate both the quantity and the reactivity of the groups of isomers. Moreover, this methodology helped giving clues whether the targeted compounds are refractory to the hydrotreatment process or reaction intermediates of the hydrotreatment process.

Saturated signals in spectroscopic imaging: why and how should we deal with this regularly observed phenomenon?

We have all been confronted one day by saturated signals observed on acquired spectra, whatever the technique considered. A saturation, also known as clipping in signal processing, is a form of distortion that limits a signal once it exceeds a threshold. As a consequence, clipped or saturated bands with their characteristic plateau present numerical values that do not correspond to the analytical reality of the analyzed sample. Of course, analysts know that they cannot consider these erroneous values and therefore reconsider either sample preparation or instrument settings. Unfortunately, there are many experiments today (and this is the case in spectroscopic imaging) for which we will not be able to fight against the saturation effect that will undeniably be observed on the acquired spectra. The aim of this article is first to show why it is important to correct these saturation effects at the risk of having a biased view of the sample and more specifically in the context of multivariate data analysis. In a second step, we will look at strategies for managing saturated bands. An original concept will then be presented by considering saturated values as missing ones. A statistical imputation strategy will then be implemented in order to recover the information lost during the measurement.

Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food

Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths. Unfortunately, for some delicate applications like food authenticity, mislabeled and adulterated spectra occur both in the calibration and/or validation sets, with dramatic effects on the model development, its prediction accuracy and robustness. Motivated by these issues, the present paper proposes a robust model-based method that simultaneously performs variable selection, outliers and label noise detection. We demonstrate the effectiveness of our proposal in dealing with three agri-food spectroscopic studies, where several forms of perturbations are considered. Our approach succeeds in diminishing problem complexity, identifying anomalous spectra and attaining competitive predictive accuracy considering a very low number of selected wavelengths.

Fusing spectral and spatial information with 2-D stationary wavelet transform (SWT 2-D) for a deeper exploration of spectroscopic images

Nowadays, it is clear that there is an increasing importance in spectroscopic imaging in all fields of science. Obviously, one bulk analysis can no longer be satisfactory, as the interest focuses more on the chemical nature and the location of the compounds present within a given complex matrix. This is, evidently, due to the fact that for a more comprehensive exploration of complex samples, one single acquired hyperspectral data cube can provide both spectral and spatial information simultaneously. Although many techniques were proposed by the chemometric community in explorations of these specific datasets, unfortunately, they are almost always focusing on spectral information, even if chemical images were ultimately observed. In other words, spatial information is not well exploited, and therefore lost during the actual chemometric calculation phase. The goal of this short communication is to present a very simple and fast spectral/spatial fusion approach based on 2-D stationary wavelet transform (SWT 2-D) which is able to improve the obtainable information, compared with a classical data analysis, in which the spatial domain would not be considered nor used.

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study

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.

UDP-GLYCOSYLTRANSFERASE 72E3 Plays a Role in Lignification of Secondary Cell Walls in Arabidopsis

Lignin is present in plant secondary cell walls and is among the most abundant biological polymers on Earth. In this work we investigated the potential role of the UGT72E gene family in regulating lignification in Arabidopsis. Chemical determination of floral stem lignin contents in ugt72e1, ugt72e2, and ugt72e3 mutants revealed no significant differences compared to WT plants. In contrast, the use of a novel safranin O ratiometric imaging technique indicated a significant increase in the cell wall lignin content of both interfascicular fibers and xylem from young regions of ugt72e3 mutant floral stems. These results were globally confirmed in interfascicular fibers by Raman microspectroscopy. Subsequent investigation using a bioorthogonal triple labelling strategy suggested that the augmentation in lignification was associated with an increased capacity of mutant cell walls to incorporate H-, G-, and S-monolignol reporters. Expression analysis showed that this increase was associated with an up-regulation of LAC17 and PRX71, which play a key role in lignin polymerization. Altogether, these results suggest that UGT72E3 can influence the kinetics of lignin deposition by regulating monolignol flow to the cell wall as well as the potential of this compartment to incorporate monomers into the growing lignin polymer.

A frugal implementation of Surface Enhanced Raman Scattering for sensing Zn2+ in freshwaters - In depth investigation of the analytical performances

Surface Enhanced Raman Scattering (SERS) has been widely praised for its extreme sensitivity but has not so far been put to use in routine analytical applications, with the accessible scale of measurements a limiting factor. We report here on a frugal implementation of SERS dedicated to the quantitative detection of Zn²⁺ in water, Zn being an element that can serve as an indicator of contamination by heavy metals in aquatic bodies. The method consists in randomly aggregating simple silver colloids in the analyte solution in the presence of a complexometric indicator of Zn²⁺, recording the SERS spectrum with a portable Raman spectrometer and analysing the data using multivariate calibration models. The frugality of the sensing procedure enables us to acquire a dataset much larger than conventionally done in the field of SERS, which in turn allows for an in-depth statistical analysis of the analytical performances that matter to end-users. In pure water, the proposed sensor is sensitive and accurate in the 160-2230 nM range, with a trueness of 96% and a precision of 4%. Although its limit of detection is one order of magnitude higher than those of golden standard techniques for quantifying metals, its sensitivity range matches Zn levels that are relevant to the health of aquatic bodies. Moreover, its frugality positions it as an interesting alternative to monitor water quality. Critically, the combination of the simple procedure for sample preparation, abundant SERS material and affordable portable instrument paves the way for a realistic deployment to the water site, with each Zn reading three to five times cheaper than through conventional techniques. It could therefore complement current monitoring methods in a bid to solve the pressing needs for large scale water quality data.

Determination of the Reactivity Degree of Various Alkaline Solutions: A Chemometric Investigation

Knowledge of alkaline silicate solutions is crucial in order to optimize geopolymer properties. Geopolymers are new binders resulting from the activation of an aluminosilicate by an alkaline solution. It is well established that the solution reactivity strongly affects the geopolymerization and therefore the geopolymer working properties. As a consequence, an evaluation of the reactivity degree of alkaline silicate solutions prior synthesis is of the utmost interest. However, the determination of the solution reactivity is currently tedious, and for geopolymer commercialization, it would be necessary to find an easy way to determine it. Therefore, Raman spectroscopy, combined with chemometric techniques, is proposed as a solution to easily determine the alkaline silicate solution reactivity. To conduct this investigation, 65 silicate solutions were characterized by Raman spectroscopy, and reference values of their reactivity degree were determined. Finally, principal component analysis and partial least squares regression were performed to build a statistical model able to predict the alkaline silicate solution reactivity from Raman spectra.

Tracking hidden organic carbon in rocks using chemometrics and hyperspectral imaging

Finding traces of life or organic components of prebiotic interest in the rock record is an appealing goal for numerous fields in Earth and space sciences. However, this is often hampered by the scarceness and highly heterogeneous distribution of organic compounds within rocks. We assess here an innovative analytical strategy combining Synchrotron radiation-based Fourier-Transform Infrared microspectroscopy (S-FTIR) and multivariate analysis techniques to track and characterize organic compounds at the pore level in complex oceanic rocks. S-FTIR hyperspectral images are analysed individually or as multiple image combinations (multiset analysis) using Principal Component Analyses (PCA) and Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS). This approach allows extracting simultaneously pure organic and mineral spectral signatures and determining their spatial distributions and relationships. MCR-ALS analysis provides resolved S-FTIR signatures of 8 pure mineral and organic components showing the close association at a micrometric scale of organic compounds and secondary clays formed during rock alteration and known to catalyse organic synthesis. These results highlights the potential of the serpentinizing oceanic lithosphere to generate and preserve organic compounds of abiotic origin, in favour of the hydrothermal theory for the origin of life.

Fast epi-detected broadband multiplex CARS and SHG imaging of mouse skull cells

We present a bimodal imaging system able to obtain epi-detected mutiplex coherent anti-Stokes Raman scattering (M-CARS) and second harmonic generation (SHG) signals coming from biological samples. We studied a fragment of mouse parietal bone and could detect broadband anti-Stokes and SHG responses originating from bone cells and collagen respectively. In addition we compared two post-processing methods to retrieve the imaginary part of the third-order nonlinear susceptibility related to the spontaneous Raman scattering.

A Cell Wall Proteome and Targeted Cell Wall Analyses Provide Novel Information on Hemicellulose Metabolism in Flax

Experimentally-generated (nanoLC-MS/MS) proteomic analyses of four different flax organs/tissues (inner-stem, outer-stem, leaves and roots) enriched in proteins from 3 different sub-compartments (soluble-, membrane-, and cell wall-proteins) was combined with publically available data on flax seed and whole-stem proteins to generate a flax protein database containing 2996 nonredundant total proteins. Subsequent multiple analyses (MapMan, CAZy, WallProtDB and expert curation) of this database were then used to identify a flax cell wall proteome consisting of 456 nonredundant proteins localized in the cell wall and/or associated with cell wall biosynthesis, remodeling and other cell wall related processes. Examination of the proteins present in different flax organs/tissues provided a detailed overview of cell wall metabolism and highlighted the importance of hemicellulose and pectin remodeling in stem tissues. Phylogenetic analyses of proteins in the cell wall proteome revealed an important paralogy in the class IIIA xyloglucan endo-transglycosylase/hydrolase (XTH) family associated with xyloglucan endo-hydrolase activity.Immunolocalisation, FT-IR microspectroscopy, and enzymatic fingerprinting indicated that flax fiber primary/S1 cell walls contained xyloglucans with typical substituted side chains as well as glucuronoxylans in much lower quantities. These results suggest a likely central role of xyloglucans and endotransglucosylase/hydrolase activity in flax fiber formation and cell wall remodeling processes.

Studying radiolytic ageing of nuclear power plant electric cables with FTIR spectroscopy

Due to the willingness to extend the nuclear power plants length of life, it is of prime importance to understand long term ageing effect on all constitutive materials. For this purpose gamma-irradiation effects on insulation of instrumentation and control cables are studied. Mid-infrared spectroscopy and principal components analysis (PCA) were used to highlight molecular modifications induced by gamma-irradiation under oxidizing conditions. In order to be closer to real world conditions, a low dose rate of 11Gyh^-1 was used to irradiate insulations in full cable or alone with a dose up to 58 kGy. Spectral differences according to irradiation dose were extracted using PCA. It was then possible to observe different behaviors of the insulation constitutive compounds i.e. ethylene vinyl acetate (EVA), ethylene propylene diene monomer (EPDM) and aluminium trihydrate (ATH). Irradiation of insulations led to the oxidation of their constitutive polymers and a modification of filler-polymer ratio. Moreover all these modifications were observed for insulations alone or in full cable indicating that oxygen easily diffuses into the material. Spectral contributions were discussed considering different degradation mechanisms.

Developing global regression models for metabolite concentration prediction regardless of cell line

Following the Process Analytical Technology (PAT) of the Food and Drug Administration (FDA), drug manufacturers are encouraged to develop innovative techniques in order to monitor and understand their processes in a better way. Within this framework, it has been demonstrated that Raman spectroscopy coupled with chemometric tools allow to predict critical parameters of mammalian cell cultures in-line and in real time. However, the development of robust and predictive regression models clearly requires many batches in order to take into account inter-batch variability and enhance models accuracy. Nevertheless, this heavy procedure has to be repeated for every new line of cell culture involving many resources. This is why we propose in this paper to develop global regression models taking into account different cell lines. Such models are finally transferred to any culture of the cells involved. This article first demonstrates the feasibility of developing regression models, not only for mammalian cell lines (CHO and HeLa cell cultures), but also for insect cell lines (Sf9 cell cultures). Then global regression models are generated, based on CHO cells, HeLa cells, and Sf9 cells. Finally, these models are evaluated considering a fourth cell line(HEK cells). In addition to suitable predictions of glucose and lactate concentration of HEK cell cultures, we expose that by adding a single HEK-cell culture to the calibration set, the predictive ability of the regression models are substantially increased. In this way, we demonstrate that using global models, it is not necessary to consider many cultures of a new cell line in order to obtain accurate models. Biotechnol. Bioeng. 2017;114: 2550-2559. © 2017 Wiley Periodicals, Inc.

Topological data analysis (TDA) applied to reveal pedogenetic principles of European topsoil system

Recent developments in applied mathematics are bringing new tools that are capable to synthesize knowledge in various disciplines, and help in finding hidden relationships between variables. One such technique is topological data analysis (TDA), a fusion of classical exploration techniques such as principal component analysis (PCA), and a topological point of view applied to clustering of results. Various phenomena have already received new interpretations thanks to TDA, from the proper choice of sport teams to cancer treatments. For the first time, this technique has been applied in soil science, to show the interaction between physical and chemical soil attributes and main soil-forming factors, such as climate and land use. The topsoil data set of the Land Use/Land Cover Area Frame survey (LUCAS) was used as a comprehensive database that consists of approximately 20,000 samples, each described by 12 physical and chemical parameters. After the application of TDA, results obtained were cross-checked against known grouping parameters including five types of land cover, nine types of climate and the organic carbon content of soil. Some of the grouping characteristics observed using standard approaches were confirmed by TDA (e.g., organic carbon content) but novel subtle relationships (e.g., magnitude of anthropogenic effect in soil formation), were discovered as well. The importance of this finding is that TDA is a unique mathematical technique capable of extracting complex relations hidden in soil science data sets, giving the opportunity to see the influence of physicochemical, biotic and abiotic factors on topsoil formation through fresh eyes.

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

In recent years, as a result of the process analytical technology initiative of the US Food and Drug Administration, many different works have been carried out on direct and in situ monitoring of critical parameters for mammalian cell cultures by Raman spectroscopy and multivariate regression techniques. However, despite interesting results, it cannot be said that the proposed monitoring strategies, which will reduce errors of the regression models and thus confidence limits of the predictions, are really optimized. Hence, the aim of this article is to optimize some critical steps of spectroscopic acquisition and data treatment in order to reach a higher level of accuracy and robustness of bioprocess monitoring. In this way, we propose first an original strategy to assess the most suited Raman acquisition time for the processes involved. In a second part, we demonstrate the importance of the interbatch variability on the accuracy of the predictive models with a particular focus on the optical probes adjustment. Finally, we propose a methodology for the optimization of the spectral variables selection in order to decrease prediction errors of multivariate regressions. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:308-316, 2017.

Water quality assessment of a small peri-urban river using low and high frequency monitoring

The biogeochemical behaviors of small rivers that pass through suburban areas are difficult to understand because of the multi-origin inputs that can modify their behavior. In this context, a monitoring strategy has been designed for the Marque River, located in Lille Metropolitan area of northern France, that includes both low-frequency monitoring over a one-year period (monthly sampling) and high frequency monitoring (measurements every 10 minutes) in spring and summer. Several environmental and chemical parameters are evaluated including rainfall events, river flow, temperature, dissolved oxygen, turbidity, conductivity, nutritive salts and dissolved organic matter. Our results from the Marque River show that (i) it is impacted by both urban and agricultural inputs, and as a consequence, the concentrations of phosphate and inorganic nitrogen have degraded the water quality; (ii) the classic photosynthesis/respiration processes are disrupted by the inputs of organic matter and nutritive salts; (iii) during dry periods, the urban sewage inputs (treated or not) are more important during the day, as indicated by higher river flows and maximal concentrations of ammonium; (iv) phosphate concentrations depend on oxygen contents in the river; (v) high nutrient concentrations result in eutrophication of the Marque River with lower pH and oxygen concentrations in summer. During rainfalls, additional inputs of ammonium, biodegradable organic matter as well as sediment resuspension result in anoxic events; and finally (vi) concentrations of nitrate are approximately constant over the year, except in winter when higher inputs can be recorded. Having better identified the processes responsible for the observed water quality, a more informed remediation effort can be put forward to move this suburban river to a good status of water quality.

Topological data analysis: A promising big data exploration tool in biology, analytical chemistry and physical chemistry

An important feature of experimental science is that data of various kinds is being produced at an unprecedented rate. This is mainly due to the development of new instrumental concepts and experimental methodologies. It is also clear that the nature of acquired data is significantly different. Indeed in every areas of science, data take the form of always bigger tables, where all but a few of the columns (i.e. variables) turn out to be irrelevant to the questions of interest, and further that we do not necessary know which coordinates are the interesting ones. Big data in our lab of biology, analytical chemistry or physical chemistry is a future that might be closer than any of us suppose. It is in this sense that new tools have to be developed in order to explore and valorize such data sets. Topological data analysis (TDA) is one of these. It was developed recently by topologists who discovered that topological concept could be useful for data analysis. The main objective of this paper is to answer the question why topology is well suited for the analysis of big data set in many areas and even more efficient than conventional data analysis methods. Raman analysis of single bacteria should be providing a good opportunity to demonstrate the potential of TDA for the exploration of various spectroscopic data sets considering different experimental conditions (with high noise level, with/without spectral preprocessing, with wavelength shift, with different spectral resolution, with missing data).

In-line and real-time prediction of recombinant antibody titer by in situ Raman spectroscopy

The Food and Drug Administration's (FDA) process analytical technology (PAT) framework has been initiated to encourage drug manufacturers to develop innovative techniques in order to better understand their processes and institute high level quality control which allows action at any point in the manufacturing process. While Raman spectroscopy and chemometrics have been successfully used to predict concentration of conventional metabolites in cell cultures, it is really not the case for active substances. Thus, we propose, for the first time, an in-line and real-time prediction of recombinant antibody titer using an immersion probe link to a spectrometer without the tacking of samples. A good robustness of the method is observed on different culture batches and the contamination risk is drastically reduced which is an important issue in biotechnology manufacturing processes.

Pushing back the limits of Raman imaging by coupling super-resolution and chemometrics for aerosols characterization

The increasing interest in nanoscience in many research fields like physics, chemistry, and biology, including the environmental fate of the produced nano-objects, requires instrumental improvements to address the sub-micrometric analysis challenges. The originality of our approach is to use both the super-resolution concept and multivariate curve resolution (MCR-ALS) algorithm in confocal Raman imaging to surmount its instrumental limits and to characterize chemical components of atmospheric aerosols at the level of the individual particles. We demonstrate the possibility to go beyond the diffraction limit with this algorithmic approach. Indeed, the spatial resolution is improved by 65% to achieve 200 nm for the considered far-field spectrophotometer. A multivariate curve resolution method is then coupled with super-resolution in order to explore the heterogeneous structure of submicron particles for describing physical and chemical processes that may occur in the atmosphere. The proposed methodology provides new tools for sub-micron characterization of heterogeneous samples using far-field (i.e. conventional) Raman imaging spectrometer.

New chemometric approach MCR-ALS to unmix EPR spectroscopic data from complex mixtures

Electron paramagnetic resonance (EPR) spectra of mixtures are often difficult to interpret due to the superposition of spectral contribution of various species present in the complex materials. It is challenging to accurately identify the number of pure compounds present and to extract their pure spectra. In this study, the powerful chemometric method, multivariate curve resolution-alternating least squares (MCR-ALS), is applied to identify different paramagnetic centers. This method is used to simultaneously extract, with no prior knowledge, the pure spectra and the corresponding concentration profiles of all the compounds in the unknown and unresolved mixtures. The goal of our work is to apply, for the first time, this new chemometrics methodology, MCR-ALS, on EPR spectroscopic data in order to characterize a series of distinct but strongly overlapping spectra of various paramagnetic species.

Infrared chemical imaging: spatial resolution evaluation and super-resolution concept

Chemical imaging systems help to solve many challenges in various scientific fields. Able to deliver rapid spatial and chemical information, modern infrared spectrometers using Focal Plane Array detectors (FPA) are of great interest. Considering conventional infrared spectrometers with a single element detector, we can consider that the diffraction-limited spatial resolution is more or less equal to the wavelength of the light (i.e. 2.5-25 microm). Unfortunately, the spatial resolution of FPA spectroscopic setup is even lower due to the detector pixel size. This becomes a real constraint when micron-sized samples are analysed. New chemometrics methods are thus of great interest to overcome such resolution drawback, while keeping our far-field infrared imaging spectrometers. The aim of the present work is to evaluate the super-resolution concept in order to increase the spatial resolution of infrared imaging spectrometers using FPA detectors. The main idea of super-resolution is the fusion of several low-resolution images of the same sample to obtain a higher-resolution image. Applying the super-resolution concept on a relatively low number of FPA acquisitions, it was possible to observe a 30% decrease in spatial resolution.

The organization pattern of root border-like cells of Arabidopsis is dependent on cell wall homogalacturonan

Border-like cells are released by Arabidopsis (Arabidopsis thaliana) root tips as organized layers of several cells that remain attached to each other rather than completely detached from each other, as is usually observed in border cells of many species. Unlike border cells, cell attachment between border-like cells is maintained after their release into the external environment. To investigate the role of cell wall polysaccharides in the attachment and organization of border-like cells, we have examined their release in several well-characterized mutants defective in the biosynthesis of xyloglucan, cellulose, or pectin. Our data show that among all mutants examined, only quasimodo mutants (qua1-1 and qua2-1), which have been characterized as producing less homogalacturonan, had an altered border-like cell phenotype as compared with the wild type. Border-like cells in both lines were released as isolated cells separated from each other, with the phenotype being much more pronounced in qua1-1 than in qua2-1. Further analysis of border-like cells in the qua1-1 mutant using immunocytochemistry and a set of anti-cell wall polysaccharide antibodies showed that the loss of the wild-type phenotype was accompanied by (1) a reduction in homogalacturonan-JIM5 epitope in the cell wall of border-like cells, confirmed by Fourier transform infrared microspectrometry, and (2) the secretion of an abundant mucilage that is enriched in xylogalacturonan and arabinogalactan-protein epitopes, in which the cells are trapped in the vicinity of the root tip.

Multivariate curve resolution methods in imaging spectroscopy: influence of extraction methods and instrumental perturbations

Imaging spectroscopy is becoming a key field of analytical chemistry. In the face of more and more complex samples, we actually need accurate microscopic insight. Nowadays, the methods used to produce concentration maps of the pure compounds from spectral data sets are based on the classical univariate approach although multivariate approaches are sometimes investigated. But in any case, the analytical quality of the chemical images thus provided cannot be discussed since no reference methods are at our disposal. Thus the proposed research focuses on the application of multivariate methods such as Orthogonal Projection Approach (OPA), SIMPLE-to-use Self-modeling Mixture Analysis (SIMPLISMA), Multivariate Curve Resolution - Alterning Least Squares (MCR-ALS), and Positive Matrix Factorization (PMF) for imaging spectroscopy. A systematic and quantitative characterization of the accuracy of spectra and images extraction is investigated on mid-infrared spectral data sets. Of special interest is the influence of instrumental perturbations such as noise and spectral shift on the extraction ability to access the algorithm's robustness.