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.

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.

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.

Improvement of pixel classification by the simultaneous use of spectral and spatial information in the framework of spectroscopic imaging.

Hyperspectral imaging technology is developing in a very fast way. We find it today in many analytical developments using different spectroscopies for sample classification purposes. Instrumental developments allow us to acquire more and more data in shorter and shorter periods of time while improving their quality. Therefore, we are going in the right direction as far as the measure is concerned. On the other hand, we can make a more mixed assessment for the hyperspectral imaging data processing. Indeed, the data acquired in spectroscopic imaging have the particularity of encoding both spectral and spatial information. Unfortunately, in chemometrics, almost all classification approaches today only use spectral information from three-dimensional hyperspectral data arrays. To be more precise, an approach encompassing the unfolding/refolding of such arrays is often applied beforehand because the majority of algorithms for analysing these data are not capable of handling them in their original structure. Spatial information is therefore lost during the chemometric exploration. The study of the spectral part of the acquired data array alone is clearly a limitation that we propose to overcome in this work. 2-D Stationary Wavelet Transform will be used in the data preprocessing phase to ensure the joint use of spectral and spatial information. Two spectroscopic datasets will then be used to evaluate the potential of our approach in the context of supervised classification.

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.

Bone Molecular Modifications Induced by Diagenesis Followed-Up for 12 Months.

After death, diagenesis takes place. Numerous processes occur concomitantly, which makes it difficult to identify the diagenetic processes. The diagenetic processes refer to all processes (chemical or physical) that modify the skeletal remains. These processes are highly variable depending on the environmental factors (weather, temperature, age, sex, etc.), especially in the early stages. Numerous studies have evaluated bone diagenetic processes over long timescales (~millions of years), but fewer have been done over short timescales (between days and thousands of years). The objective of the study is to assess the early stages of diagenetic processes by Raman microspectroscopy over 12 months. The mineral and organic matrix modifications are monitored through physicochemical parameters. Ribs from six humans were buried in soil. The modifications of bone composition were followed by Raman spectroscopy each month. The decrease in the mineral/organic ratio and carbonate type-B content and the increase in crystallinity reveal that minerals undergo dissolution-recrystallization. The decrease in collagen cross-linking indicates that collagen hydrolysis induces the fragmentation of collagen fibres over 12 months.

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.

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.

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.

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.

Data fusion of LIBS and PIL hyperspectral imaging: Understanding the luminescence phenomenon of a complex mineral sample.

Laser-induced breakdown spectroscopy (LIBS) imaging is an innovative technique that associates the valuable atomic, ionic and molecular emission signals of the parent spectroscopy with spatial information. LIBS works using a powerful pulse laser as excitation source, to generate a plasma exhibiting emission lines of atoms, ions and molecules present in the ablated matter. The advantages of LIBS imaging are potential high sensitivity (in the order of ppm), easy sample preparation, fast acquisition rate (up to 1 kHz) and µm scale spatial resolution (weight of the ablated material in the order of ng). Despite these positive aspects, LIBS imaging easily provides datasets consisting of several million spectra, each containing several thousand spectral channels. Under these conditions, the current chemometric analyses of the raw data are still possible, but require too high computing resources. Therefore, the aim of this work is to propose a data compression strategy oriented to keep the most relevant spectral channel and pixel information to facilitate, fast and reliable signal unmixing for an exhaustive exploration of complex samples. This strategy will apply not only to the context of LIBS image analysis, but to the fusion of LIBS with other imaging technologies, a scenario where the data compression step becomes even more mandatory. The data fusion strategy will be applied to the analysis of a heterogeneous kyanite mineral sample containing several trace elements by LIBS imaging associated with plasma induced luminescence (PIL) imaging, these two signals being acquired simultaneously by the same microscope. The association of compression and spectral data fusion will allow extracting the compounds in the mineral sample associated with a fused LIBS/PIL fingerprint. This LIBS/PIL association will be essential to interpret the PIL spectral information, which is nowadays very complex due to the natural overlapped signals provided by this technique.

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.

Towards a new pseudo-quantitative approach to evaluate the ionization response of nitrogen compounds in complex matrices.

Ultra high-resolution mass spectrometry (FT-ICR MS) coupled to electrospray ionization (ESI) provides unprecedented molecular characterization of complex matrices such as petroleum products. However, ESI faces major ionization competition phenomena that prevent the absolute quantification of the compounds of interest. On the other hand, comprehensive two-dimensional gas chromatography (GC × GC) coupled to specific detectors (HRMS or NCD) is able to quantify the main families identified in these complex matrices. In this paper, this innovative dual approach has been used to evaluate the ionization response of nitrogen compounds in gas oils as a case study. To this extent, a large gas oil dataset has been analyzed by GC × GC/HRMS, GC × GC-NCD and ESI(+/-)-FT-ICR MS. Then, the concentrations obtained from GC × GC-NCD have been compared to those obtained from FT-ICR MS hence proving that strong ionization competitions are taking place and also depending on the origin of the sample. Finally, multilinear regressions (MLR) have been used to quantitatively predict nitrogen families from FT-ICR MS measurements as well as start rationalizing the ionization competition phenomena taking place between them in different types of gas oils.

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.

Multiplex coherent anti-Stokes Raman scattering microspectroscopy detection of lipid droplets in cancer cells expressing TrkB.

For many years, scientists have been looking for specific biomarkers associated with cancer cells for diagnosis purposes. These biomarkers mainly consist of proteins located at the cell surface (e.g. the TrkB receptor) whose activation is associated with specific metabolic modifications. Identification of these metabolic changes usually requires cell fixation and specific dye staining. MCARS microspectroscopy is a label-free, non-toxic, and minimally invasive method allowing to perform analyses of live cells and tissues. We used this method to follow the formation of lipid droplets in three colorectal cancer cell lines expressing TrkB. MCARS images of cells generated from signal integration of CH2 stretching modes allow to discriminate between lipid accumulation in the endoplasmic reticulum and the formation of cytoplasmic lipid droplets. We found that the number of the latter was related to the TrkB expression level. This result was confirmed thanks to the creation of a HEK cell line which over-expresses TrkB. We demonstrated that BDNF-induced TrkB activation leads to the formation of cytoplasmic lipid droplets, which can be abolished by K252a, an inhibitor of TrkB. So, MCARS microspectroscopy proved useful in characterizing cancer cells displaying an aberrant lipid metabolism.