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.

Benefits of Chemometric and Raman Spectroscopy Applied to the Kinetics of Setting and Early Age Hydration of Cement Paste.

The addition of water is used to past by internal post-curing of hardening cement. Hydration and curing of cementitious are widely identified by non-destructive 1H nuclear magnetic resonance (NMR) measurements of transverse relaxation time and self-diffusion. However, those non-destructive analytical methodologies do not give a truly chemical characterization of the cement matrix during the hydration and curing process. Indeed, the NMR studies only the water dynamics of hydrating cement with internal post-curing. Recent research indicated chemometrics coupled with Raman spectroscopy allows for a better understanding of chemical processes. Recent advances in computing gave industries and research centers the opportunity to generate cost effective data. In this work, an original method is presented, which uses both a data analysis and a non-invasive, non-destructive Raman monitoring of the hydration reaction of a Portland cement. Data was then analyzed by means of chemometrics methods (principal components analysis (PCA), independent components analysis (ICA), and multivariate curve resolution-alternated least-squares (MCR-ALS) with SIMPLe-to-use Interactive Self-modelling Mixture Analysi (SIMPLISMA) and Orthogonal Projection Approach (OP initialization). Results were compared to the ones obtained with thermogravimetric analysis of this cement paste. Besides the consistency of results from both analytical measurements, chemometrics coupled to Raman spectroscopy accurately revealed the details of the setting without any samples collection. The acquisition frequency allowed a proper identification of the occurrence of each of the various phases involved in the hydration and setting process.

Handle Matrix Rank Deficiency, Noise, and Interferences in 3D Emission-Excitation Matrices: Effective Truncated Singular-Value Decomposition in Chemometrics Applied to the Analysis of Polycyclic Aromatic Compounds.

The characterization of organic compounds in polluted matrices by eco-friendly three-dimensional (3D) fluorescence spectroscopy coupled with chemometric algorithms constitutes a powerful alternative to the separation techniques conventionally used. However, the systematic presence of Rayleigh and Raman scattering signals in the excitation-emission matrices (EEMs) complicates the spectral decomposition via PARAllel FACtor analysis (PARAFAC) due to the nontrilinear structure of these signals. Likewise, the specific problem of selectivity in spectroscopy for unexpected chemical components in a complex sample may render its chemical interpretation difficult at first glance. The relevant chemical information can then be complicated to extract, especially if the raw data is noisy. There are several strategies to overcome these drawbacks, but weaknesses remain. As a consequence, a new alternative method is proposed to handle these interferences, the noise, and the rank deficiencies in the data and applied for the characterization of polycyclic aromatic compound (PAC) mixtures. It is based on effective truncated singular-value decomposition (MT-SVD) that does not require any prior knowledge of the raw data. The algorithm provides a valuable estimation of the global rank to choose on complex samples where selectivity problems are observed. It is a real alternative compared to other existing methods applied to the fluorescence matrix to filter the signal from noise or light scattering effects. The first exploratory results of the proposed algorithm are promising to handle matrix rank deficiencies as well as the effects of noise and light scattering on complex PAC mixtures.

Osmotic stress and vesiculation as key mechanisms controlling bacterial sensitivity and resistance to TiO2 nanoparticles.

Toxicity mechanisms of metal oxide nanoparticles towards bacteria and underlying roles of membrane composition are still debated. Herein, the response of lipopolysaccharide-truncated Escherichia coli K12 mutants to TiO2 nanoparticles (TiO2NPs, exposure in dark) is addressed at the molecular, single cell, and population levels by transcriptomics, fluorescence assays, cell nanomechanics and electrohydrodynamics. We show that outer core-free lipopolysaccharides featuring intact inner core increase cell sensitivity to TiO2NPs. TiO2NPs operate as membrane strippers, which induce osmotic stress, inactivate cell osmoregulation and initiate lipid peroxidation, which ultimately leads to genesis of membrane vesicles. In itself, truncation of lipopolysaccharide inner core triggers membrane permeabilization/depolarization, lipid peroxidation and hypervesiculation. In turn, it favors the regulation of TiO2NP-mediated changes in cell Turgor stress and leads to efficient vesicle-facilitated release of damaged membrane components. Remarkably, vesicles further act as electrostatic baits for TiO2NPs, thereby mitigating TiO2NPs toxicity. Altogether, we highlight antagonistic lipopolysaccharide-dependent bacterial responses to nanoparticles and we show that the destabilized membrane can generate unexpected resistance phenotype.

Fast automated processing of AFM PeakForce curves to evaluate spatially resolved Young modulus and stiffness of turgescent cells.

Atomic Force Microscopy (AFM) is a powerful technique for the measurement of mechanical properties of individual cells in two (x × y) or three (x × y × time) dimensions. The instrumental progress makes it currently possible to generate a large amount of data in a relatively short time, which is particularly true for AFM operating in so-called PeakForce tapping mode (Bruker corporation). The latter corresponds to an AFM probe that periodically hits the sample surface while the pico-newton level interaction force is recorded from cantilever deflection. The method provides unprecedented high-resolution (a few tens of nm) imaging of the mechanical features of soft biological samples (e.g. bacteria, yeasts) and of hard abiotic surfaces (e.g. minerals). The rapid conversion of up to several tens of thousands spatially resolved force curves typically collected in AFM PeakForce tapping mode over a given cell surface area into comprehensive nanomechanical information requires the development of robust data analysis methodologies and dedicated numerical tools. In this work, we report an automated algorithm for (i) a rapid and unambiguous detection of the indentation regimes corresponding to non-linear and linear deformations of bacterial surfaces upon compression by the AFM probe, (ii) the subsequent evaluation of the Young modulus and cell surface stiffness, and (iii) the generation of spatial mappings of relevant nanomechanical properties at the single cell level. The procedure involves consistent evaluation of the contact point between the AFM probe and sample biosurface and that of the threshold indentation value marking the transition between non-linear and linear deformation regimes. For comparison purposes, the former regime is here analyzed on the basis of Hertz and Sneddon models corrected or not for effects of finite sample thickness. Analysis of AFM measurements performed on a selected Escherichia coli strain is detailed to demonstrate the feasibility, rapidity and robustness of the here-proposed PeakForce data treatment process. The flexibility of the algorithm allows consideration of force curve parameterizations other than that detailed here, which may be desired for investigation of e.g. eukaryotes nanomechanics. The performance of the adopted Hertz-based and Sneddon-based contact mechanics formalisms in recovering experimental data and in identifying nanomechanical heterogeneities at the bacterium scale is further thoroughly discussed.

Investigating pre-analytical requirements for serum and plasma based infrared spectro-diagnostic.

Infrared spectroscopy is a rapid, easy-to-operate, label-free and therefore cost-effective technique. Many studies performed on biofluids (eg, serum, plasma, urine, sputum, bile and cerebrospinal fluid) have demonstrated its promising application as a clinical diagnostic tool. Given all these characteristics, infrared spectroscopy appears to be an ideal candidate to be implemented into the clinics. However, before considering its translation, a clear effort is needed to standardise protocols for biofluid spectroscopic analysis. To reach this goal, careful investigations to identify and track errors that can occur during the pre-analytical phase is a crucial step. Here, we report for the first time, results of investigations into pre-analytical factors that can affect the quality of the spectral data acquired on serum and plasma, such as the impact of long-term freezing time storage of samples as well as the month-to-month reproducibility of the spectroscopic analysis. The spectral data discrimination has revealed to be majorly impacted by a residual water content variation in serum and plasma dried samples.

Pleiotropic effects of rfa-gene mutations on Escherichia coli envelope properties.

Mutations in the rfa operon leading to severely truncated lipopolysaccharide (LPS) structures are associated with pleiotropic effects on bacterial cells, which in turn generates a complex phenotype termed deep-rough. Literature reports distinct behavior of these mutants in terms of susceptibility to bacteriophages and to several antibacterial substances. There is so far a critical lack of understanding of such peculiar structure-reactivity relationships mainly due to a paucity of thorough biophysical and biochemical characterizations of the surfaces of these mutants. In the current study, the biophysicochemical features of the envelopes of Escherichia coli deep-rough mutants are identified from the molecular to the single cell and population levels using a suite of complementary techniques, namely microelectrophoresis, Atomic Force Microscopy (AFM) and Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) for quantitative proteomics. Electrokinetic, nanomechanical and proteomic analyses evidence enhanced mutant membrane destabilization/permeability, and differentiated abundances of outer membrane proteins involved in the susceptibility phenotypes of LPS-truncated mutants towards bacteriophages, antimicrobial peptides and hydrophobic antibiotics. In particular, inner-core LPS altered mutants exhibit the most pronounced heterogeneity in the spatial distribution of their Young modulus and stiffness, which is symptomatic of deep damages on cell envelope likely to mediate phage infection process and antibiotic action.

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).

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.

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.