Longitudinal Study of Cirrhosis Development in STAM and carbon tetrachloride Mouse Models Using Fourier Transform Infrared Spectral Imaging.

Animal models of cirrhosis are of great interest to investigate the pathological process leading to the final stage of cirrhosis. The aim of this study was to analyze the different steps involved in the progressive development of cirrhosis using Fourier transform infrared spectral histology in 2 mouse models of cirrhosis, the STAM model of metabolic cirrhosis, and the carbon tetrachloride-induced cirrhosis model. Formalin-fixed, paraffin-embedded liver samples were obtained from 3 mice at 5 time points in each model to analyze the course of hepatic lesions up to the formation of cirrhosis. For each time point, adjacent 3-µm-thick liver sections were obtained for histologic stains and spectral histology. Fourier transform infrared acquisitions of liver sections were performed at projected pixel sizes of 25 µm × 25 µm and 6.25 µm × 6.25 µm. Spectral images were then preprocessed with an extended multiplicative signal correction and analyzed with common k-means clustering, including all stages in each model. In both models, the 2- and 4-class common k-means clustering in the 1000 to 1350 cm-1 range showed that spectral classes characterized by higher absorbance peaks of glycogen were predominant at baseline, then decreased markedly in early stages of hepatic damage, and almost disappeared in cirrhotic tissues. Concomitantly, spectral classes characterized by higher absorbance peaks of nucleic acids became progressively predominant during the course of hepatic lesions. These results were confirmed using k-means clustering on the peaks of interest identified for glycogen and nucleic acid content. Our study showed that the glycogen depletion previously described at the stage of cirrhosis is an early event in the pathological process, independently of the cause of cirrhosis. In addition, there was a progressive increase in the nucleic acid content, which may be linked to increased proliferation and polyploidy in response to cellular lesions.

Trajectory Inference for Unraveling Dynamic Biological Processes from Raman Spectral Data.

Cell heterogeneity is a crucial parameter for understanding the complexity of numerous biomedical issues. Trajectory inference-based approaches are recent tools developed for single-cell transcriptomics (scRNA-seq) data analysis. They aim to reconstruct evolving pathways from the variety of cell states that coexist simultaneously in a cell population. We propose to expand this concept to Raman spectroscopy, a label-free modality that probes the global molecular nature of a sample, by investigating the dynamics of adipocyte differentiation.

Unsupervised Feature Selection by a Genetic Algorithm for Mid-Infrared Spectral Data.

Dimensional reduction of highly multidimensional datasets such as those acquired by Fourier transform infrared spectroscopy (FTIR) is a critical step in the data analysis workflow. To achieve this goal, numerous feature selection methods have been developed and applied in a supervised context, i.e., using a priori knowledge about data usually in the form of labels for classification or quantitative values for regression. For this, genetic algorithms have been largely exploited due to their flexibility and global optimization principle. However, few applications in an unsupervised context have been reported in infrared spectroscopy. The aim of this article is to propose a new unsupervised feature selection method based on a genetic algorithm using a validity index computed from KMeans partitions as a fitness function. Evaluated on a simulated dataset and validated and tested on three real-world infrared spectroscopic datasets, our developed algorithm is able to find the spectral descriptors improving clustering accuracy and simplifying the spectral interpretation of results.

Combining Raman imaging and MCR-ALS analysis for monitoring retinol permeation in human skin.

BACKGROUND: Monitoring the transcutaneous permeation of exogenous molecules using conventional techniques generally requires long pre-analytical preparation or labelling of samples. However, Raman spectroscopy is a label-free and non-destructive method which provides spatial distribution of tracked actives in skin. The aim of our study was to prove the interest of Raman imaging coupled with multivariate curve resolution alternating least square (MCR-ALS) analysis in monitoring retinol penetration into frozen and living human skin. MATERIALS AND METHODS: After topical treatment of skin samples by free or encapsulated retinol, thin cross sections were analysed by Raman imaging (up to 100 µm depth). Mann-Whitney test was used to identify retinol spectroscopic markers in skin. MCR-ALS was used to estimate retinol contribution in Raman spectral images. Heat maps were constructed to compare the distribution of free and encapsulated retinol in skin models. RESULTS: We identified the bands at 1158, 1196 and 1591 cm-1 as specific features for monitoring retinol in skin. Moreover, our MCR-ALS results showed an improvement of retinol penetration (up to 30 µm depth) with the encapsulated form as well as storage reservoir formation in stratum corneum, for each skin model. Finally, greater retinol penetration into living skin was observed. CONCLUSION: This study shows a proof of concept for the evaluation of retinol penetration in skin using Raman imaging coupled with MCR-ALS. This concept needs to be validated on more subjects to include inter-individual variability but also other factors affecting skin permeation (age, sex, pH, etc). Our study can be extended to other actives.

Automatic Identification of Paraffin Pixels on FTIR Images Acquired on FFPE Human Samples.

The transfer of mid-infrared spectral histopathology to the clinic will be possible provided that its application in clinical practice is simple. Rapid analysis of formalin-fixed paraffin-embedded (FFPE) tissue section is thus a prerequisite. The chemical dewaxing of these samples before image acquisition used by the majority of studies is in contradiction with this principle. Fortunately, the in silico analysis of the images acquired on FFPE samples is possible using extended multiplicative signal correction (EMSC). However, the removal of pure paraffin pixels is essential to perform a relevant classification of tissue spectra. So far, this task was possible only if using manual and subjective histogram analysis. In this article, we thus propose a new automatic and multivariate methodology based on the analysis of optimized combinations of EMSC regression coefficients by validity indices and KMeans clustering to separate paraffin and tissue pixels. The validation of our method is performed using simulated infrared spectral images by measuring the Jaccard index between our partitions and the image model, with values always over 0.90 for diverse baseline complexity and signal-to-noise ratio. These encouraging results were also validated on real images by comparing our method with classical ones and by computing the Jaccard index between our partitions and the KMeans partitions obtained on the infrared image acquired on the same samples but after chemical dewaxing, with values always over 0.84.

New insights into hidradenitis suppurativa diagnosis via salivary infrared biosignatures: A pilot study.

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease which can lead to a prolonged physical disability. HS diagnosis is exclusively clinical with the absence of biomarkers. Our study aims at assessing the HS-diagnostic potential of infrared spectroscopy from saliva, as a biofluid reflecting the body's pathophysiological state. Infrared spectra from 127 patients (57 HS and 70 non-HS) were processed by multivariate methods: principal component analysis coupled with Kruskal-Wallis or Mann-Whitney tests to identify discriminant spectral wavenumbers and linear discriminant analysis to evaluate the performances of HS-diagnostic approach. Infrared features, mainly in the 1300 cm-1 -1600 cm-1 region, were identified as discriminant for HS and prediction models revealed diagnostic performances of about 80%. Tobacco and obesity, two main HS risk factors, do not seem to alter the infrared diagnosis. This pilot study shows the potential of salivary "liquid biopsy" associated to vibrational spectroscopy to develop a personalized medical approach for HS patients' management.

Analysis of Hepatic Fibrosis Characteristics in Cirrhotic Patients with and without Hepatocellular Carcinoma by FTIR Spectral Imaging.

The evolution of cirrhosis is marked by quantitative and qualitative modifications of the fibrosis tissue and an increasing risk of complications such as hepatocellular carcinoma (HCC). Our purpose was to identify by FTIR imaging the spectral characteristics of hepatic fibrosis in cirrhotic patients with and without HCC. FTIR images were collected at projected pixel sizes of 25 and 2.7 µm from paraffinized hepatic tissues of five patients with uncomplicated cirrhosis and five cirrhotic patients with HCC and analyzed by k-means clustering. When compared to the adjacent histological section, the spectral clusters corresponding to hepatic fibrosis and regeneration nodules were easily identified. The fibrosis area estimated by FTIR imaging was correlated to that evaluated by digital image analysis of histological sections and was higher in patients with HCC compared to those without complications. Qualitative differences were also observed when fibrosis areas were specifically targeted at higher resolution. The partition in two clusters of the fibrosis tissue highlighted subtle differences in the spectral characteristics of the two groups of patients. These data show that the quantitative and qualitative changes of fibrosis tissue occurring during the course of cirrhosis are detectable by FTIR imaging, suggesting the possibility of subclassifying cirrhosis into different steps of severity.

Correction: Confocal Raman microspectroscopy for skin characterization: a comparative study between human skin and pig skin.

Correction for 'Confocal Raman microspectroscopy for skin characterization: a comparative study between human skin and pig skin' by Sana Tfaili et al., Analyst, 2012, 137, 3673-3682, DOI: .

Correction: Towards normalization selection of Raman data in the context of protein glycation: application of validity indices to PCA processed spectra.

Correction for 'Towards normalization selection of Raman data in the context of protein glycation: application of validity indices to PCA processed spectra' by Fatima Alsamad et al., Analyst, 2020, DOI: 10.1039/c9an02155h.

Towards normalization selection of Raman data in the context of protein glycation: application of validity indices to PCA processed spectra.

Raman spectroscopy is a candidate technique for diagnosis applications in medicine due to its high molecular specificity. Optimizing the pre-treatment applied for Raman data is important for exploiting Raman signals and ensuring their relevance in medical diagnosis. One of the crucial steps in data pre-processing, normalization, can affect significantly the result interpretation. To select the appropriate normalization method, a strategy based on validity indices (VI) is proposed in this study. VI are based on measuring the quality of data partitioning without involving a full sequence of supervised classification. The approach was tested on Raman data acquired from control and in vitro glycated proteins (albumin and collagen). Protein glycation is a process involved in the molecular ageing of tissues that leads to the formation of products altering the functional and structural properties of proteins. Different methods of normalization were applied on the data sets: integrated intensity of the phenylalanine band, integrated intensity of the amide I band, standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) that performs simultaneously baseline correction and normalization. Following normalization, principal component analysis (PCA) was applied and VI were calculated from PCA scores resulting from each of the normalization methods mentioned. Based on VI quantitative values, our experiments permit to illustrate the effect of normalization on the data separability of control and glycated samples, and to determine the most appropriate normalization and simultaneously the most discriminant principal components to exploit vibrational information associated with glycation-induced modifications. In parallel, principal component analysis - linear discriminant analysis (PCA-LDA) was carried out for positioning the interest of VI in regard to a common chain of data processing.

New insights into spectral histopathology: infrared-based scoring of tumour aggressiveness of squamous cell lung carcinomas.

Spectral histopathology, based on infrared interrogation of tissue sections, proved a promising tool for helping pathologists in characterizing histological structures in a quantitative and automatic manner. In cancer diagnosis, the use of chemometric methods permits establishing numerical models able to detect cancer cells and to characterize their tissular environment. In this study, we focused on exploiting multivariate infrared data to score the tumour aggressiveness in preneoplastic lesions and squamous cell lung carcinomas. These lesions present a wide range of aggressive phenotypes; it is also possible to encounter cases with various degrees of aggressiveness within the same lesion. Implementing an infrared-based approach for a more precise histological determination of the tumour aggressiveness should arouse interest among pathologists with direct benefits for patient care. In this study, the methodology was developed from a set of samples including all degrees of tumour aggressiveness and by constructing a chain of data processing steps for an automated analysis of tissues currently manipulated in routine histopathology.

Shedding light on confounding factors likely to affect salivary infrared biosignatures.

Total human saliva is a biofluid which can be considered as a "mirror" reflecting the state of the body's health. The "spectral mid-infrared fingerprint" represents a snapshot of the intrinsic biomolecular composition of a saliva sample translating multiple information about the patient, and likely to be related not only to his physiopathological status but also to his behavioral habits or even current medical treatments. These different patient-related characteristics are "confounding factors," which may strongly affect the infrared data of salivary samples and disrupt the search for specific salivary biomarkers in the detection of diseases, especially in the case of complex pathologies influenced by multiple risk factors such as genetic factors and behavioral factors, and also other comorbidities. In this study, dealing with the processing of infrared saliva spectra from 56 patients, our aim was to highlight spectral features associated with some patient characteristics, namely tobacco smoking, periodontal diseases, and gender. By using multivariate statistical methods of feature selection (principal component analysis coupled with Kruskal-Wallis test, linear discriminant analysis coupled with randfeatures function), we were able to identify the discriminant vibrations associated with a specific factor and to assess the related spectral variability. Based on the methodology demonstrated here, it could be very valuable in the future to develop processing aimed at neutralizing these variabilities, in order to determine specific spectroscopic markers related to a multifactorial disease for diagnostic or follow-up purposes.

Comprehensive Characterization of the Interaction between Pulsed Electric Fields and Live Cells by Confocal Raman Microspectroscopy.

This study reports a comprehensive analysis of the effect of 100 µs electric pulses on the biochemical composition of live cells using a label-free approach, confocal Raman microspectroscopy. We investigated different regions of interest around the nucleus of the cells and the dose-effect relationship related to different electric pulse parameters. We also extended the study to another cell type. Membrane resealing was monitored by pulsing the cells in reversible or irreversible electropermeabilization condition at different temperatures. Our results confirmed a previous publication showing that proteins and lipids were highly impacted by the delivery of electric pulses. These chemical changes were similar in different locations around the cell nucleus. By sweeping the field magnitude, the number of electric pulses, or their repetition rate, the Raman signatures of live cells appeared to be related to the electropermeabilization state, verified by Yo-Pro-1 uptake. We also demonstrated that the chemical changes in the Raman signatures were cell-dependent even if common features were noticed between the two cell types used.

Lumican delays melanoma growth in mice and drives tumor molecular assembly as well as response to matrix-targeted TAX2 therapeutic peptide.

Lumican is a small leucine-rich proteoglycan (SLRP) being known as a key regulator of collagen fibrillogenesis. However, little attention has been given so far in studying its influence on tumor-associated matrix architecture. Here, we investigate the role of host lumican on tumor matrix organization as well as on disease progression considering an immunocompetent model of melanoma implanted in Lum -/- vs. wild type syngeneic mice. Conjointly, lumican impact on tumor response to matrix-targeted therapy was evaluated considering a previously validated peptide, namely TAX2, that targets matricellular thrombospondin-1. Analysis of available genomics and proteomics databases for melanoma first established a correlation between lumican expression and patient outcome. In the B16 melanoma allograft model, endogenous lumican inhibits tumor growth and modulates response to TAX2 peptide. Indeed, IHC analyses revealed that lumican deficiency impacts intratumoral distribution of matricellular proteins, growth factor and stromal cells. Besides, innovative imaging approaches helped demonstrating that lumican host expression drives biochemical heterogeneity of s.c. tumors, while modulating intratumoral collagen deposition as well as organization. Altogether, the results obtained present lumican as a strong endogenous inhibitor of tumor growth, while identifying for the first time this proteoglycan as a major driver of tumor matrix coherent assembly.

Demonstration of the Protein Involvement in Cell Electropermeabilization using Confocal Raman Microspectroscopy.

Confocal Raman microspectroscopy was used to study the interaction between pulsed electric fields and live cells from a molecular point of view in a non-invasive and label-free manner. Raman signatures of live human adipose-derived mesenchymal stem cells exposed or not to pulsed electric fields (8 pulses, 1 000 V/cm, 100 µs, 1 Hz) were acquired at two cellular locations (nucleus and cytoplasm) and two spectral bands (600-1 800 cm-1 and 2 800-3 100 cm-1). Vibrational modes of proteins (phenylalanine and amide I) and lipids were found to be modified by the electropermeabilization process with a statistically significant difference. The relative magnitude of four phenylalanine peaks decreased in the spectra of the pulsed group. On the contrary, the relative magnitude of the amide I band at 1658 cm-1 increased by 40% when comparing pulsed and control group. No difference was found between the control and the pulsed group in the high wavenumber spectral band. Our results reveal the modification of proteins in living cells exposed to pulsed electric fields by means of confocal Raman microspectroscopy.

Digital de-waxing on FTIR images.

This paper presents a procedure that digitally neutralizes the contribution of paraffin to FTIR hyperspectral images. A brief mathematical derivation of the procedure is demonstrated and applied on one normal human colon sample to exemplify the de-waxing procedure. The proposed method includes construction of a paraffin model based on PCA, EMSC normalization and application of two techniques for spectral quality control. We discuss every step in which the researcher needs to take a subjective decision during the de-waxing procedure, and we explain how to make an adequate choice of parameters involved. Application of this procedure to 71 hyperspectral images collected from 55 human colon biopsies (20 normal, 17 ulcerative colitis, and 18 adenocarcinoma) showed that paraffin was appropriately neutralized, which made the de-waxed images adequate for analysis by pattern-recognition techniques such as k-means clustering or PCA-LDA.

Raman-based detection of hydroxyethyl starch in kidney allograft biopsies as a potential marker of allograft quality in kidney transplant recipients.

In brain-dead donor resuscitation, hydroxyethyl starch (HES) use has been associated with presence of osmotic-nephrosis-like lesions in kidney transplant recipients. Our aim was to determine whether the presence of HES in protocol renal graft biopsies at three months (M3) after transplantation is associated with renal graft quality. According to the HES administered to the donor during the procurement procedure, two groups of patients were defined according graft exposition to HES: HES group, (N = 20) and control group (N = 6). Detection and relative quantification of HES was performed by Raman spectroscopy microimaging on M3 protocol renal graft biopsies. Statistical analyses were used to investigate the association between Raman data and graft characteristics. HES spectral signal was revealed negative in the control group, whereas it was positive in 40% of biopsies from the HES group. In the HES group, a stronger HES signal was associated with a lower risk of graft failure measured by the Kidney Donor Risk Index (KDRI) and was correlated with the allograft kidney function. Thus, HES accumulation in donor kidney, as probed by Raman biophotonic technique, is correlated with the quality of donor kidney and consequently the graft renal function and graft survival.

Vibrational Analysis of Lung Tumor Cell Lines: Implementation of an Invasiveness Scale Based on the Cell Infrared Signatures.

Assessing the tumor invasiveness is a paramount diagnostic step to improve the patients care. Infrared spectroscopy access the chemical composition of samples; and in combination with statistical multivariate processing, presents the capacity to highlight subtle molecular alterations associated with malignancy development. Our investigation demonstrated that infrared signatures of cell lines presenting various invasiveness phenotypes contain discriminant spectral features, which are useful informative signals to implement an objective invasiveness scale. This last development reflects the interest of vibrational approach as a candidate biophotonic label-free technique, usable in routine clinics, to characterize quantitatively tumor aggressiveness. In addition, the methodology can reveal the heterogeneity of cancer cells, opening the way to further researches in cancer science.

Development of a memetic clustering algorithm for optimal spectral histology: application to FTIR images of normal human colon.

The coupling between Fourier-transform infrared (FTIR) imaging and unsupervised classification is effective in revealing the different structures of human tissues based on their specific biomolecular IR signatures; thus the spectral histology of the studied samples is achieved. However, the most widely applied clustering methods in spectral histology are local search algorithms, which converge to a local optimum, depending on initialization. Multiple runs of the techniques estimate multiple different solutions. Here, we propose a memetic algorithm, based on a genetic algorithm and a k-means clustering refinement, to perform optimal clustering. In addition, this approach was applied to the acquired FTIR images of normal human colon tissues originating from five patients. The results show the efficiency of the proposed memetic algorithm to achieve the optimal spectral histology of these samples, contrary to k-means.

Fully unsupervised inter-individual IR spectral histology of paraffinized tissue sections of normal colon.

In label-free Fourier-transform infrared histology, spectral images are individually recorded from tissue sections, pre-processed and clustered. Each single resulting color-coded image is annotated by a pathologist to obtain the best possible match with tissue structures revealed after Hematoxylin-Eosin staining. However, the main limitations of this approach are the empirical choice of the number of clusters in unsupervised classification, and the marked color heterogeneity between the clustered spectral images. Here, using normal murine and human colon tissues, we developed an automatic multi-image spectral histology to simultaneously analyze a set of spectral images (8 images mice samples and 72 images human ones). This procedure consisted of a joint Extended Multiplicative Signal Correction (EMSC) to numerically deparaffinize the tissue sections, followed by an automated joint K-Means (KM) clustering using the hierarchical double application of Pakhira-Bandyopadhyay-Maulik (PBM) validity index. Using this procedure, the main murine and human colon histological structures were correctly identified at both the intra- and the inter-individual levels, especially the crypts, secreted mucus, lamina propria and submucosa. Here, we show that batched multi-image spectral histology procedure is insensitive to the reference spectrum but highly sensitive to the paraffin model of joint EMSC. In conclusion, combining joint EMSC and joint KM clustering by double PBM application allows to achieve objective and automated batched multi-image spectral histology.