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.

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.

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.

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.

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.

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: .

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.

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.

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.

Renal Graft Fibrosis and Inflammation Quantification by an Automated Fourier-Transform Infrared Imaging Technique.

Renal interstitial fibrosis and interstitial active inflammation are the main histologic features of renal allograft biopsy specimens. Fibrosis is currently assessed by semiquantitative subjective analysis, and color image analysis has been developed to improve the reliability and repeatability of this evaluation. However, these techniques fail to distinguish fibrosis from constitutive collagen or active inflammation. We developed an automatic, reproducible Fourier-transform infrared (FTIR) imaging-based technique for simultaneous quantification of fibrosis and inflammation in renal allograft biopsy specimens. We generated and validated a classification model using 49 renal biopsy specimens and subsequently tested the robustness of this classification algorithm on 166 renal grafts. Finally, we explored the clinical relevance of fibrosis quantification using FTIR imaging by comparing results with renal function at 3 months after transplantation (M3) and the variation of renal function between M3 and M12. We showed excellent robustness for fibrosis and inflammation classification, with >90% of renal biopsy specimens adequately classified by FTIR imaging. Finally, fibrosis quantification by FTIR imaging correlated with renal function at M3, and the variation in fibrosis between M3 and M12 correlated well with the variation in renal function over the same period. This study shows that FTIR-based analysis of renal graft biopsy specimens is a reproducible and reliable label-free technique for quantifying fibrosis and active inflammation. This technique seems to be more relevant than digital image analysis and promising for both research studies and routine clinical practice.

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.

Probing in vitro ribose induced DNA-glycation using Raman microspectroscopy.

To identify and characterize glycation, induced modifications of DNA are crucial toward understanding their functional significance due to their significant role in the long term control of aging and age-related diseases. In this study, we present the ability of Raman microspectroscopy as a novel analytical technique for a rapid and reliable identification of glycated DNA in a reagent-free manner. We have demonstrated that this technique has potential to provide very small conformational modifications. The combination of principal component analysis (PCA) and two-dimensional (2D) correlation spectroscopy has assisted us to explore in vitro DNA-glycation and provide more insights into the dynamics of the DNA-glycation process in an easier fashion. PCA analysis of Raman spectra shows a clear discrimination between native and glycated DNA samples. On the other hand, 2D correlation Raman analysis provides sequential order of the mechanism of the DNA-glycation process, and most likely, it occurs in the following sequence: Structural modifications of individual nucleobases (G > A > C) → DNA backbone modifications → partial transition of DNA conformations (A to B form). Our observations clearly suggest that the structure of DNA is altered, i.e., a partial transition of DNA backbone conformation from A to B form when glycated, but does not induce any final transition in DNA double helix conformation, and eventually, DNA presents in an intermediate A-B form, more toward the B form.

Rapid screening of classic galactosemia patients: a proof-of-concept study using high-throughput FTIR analysis of plasma.

Classic galactosemia is an autosomal recessive metabolic disease involving the galactose pathway, caused by the deficiency of galactose-1-phosphate uridyltransferase. Galactose accumulation induces in newborns many symptoms, such as liver disease, cataracts, and sepsis leading to death if untreated. Neonatal screening is developed and applied in many countries using several methods to detect galactose or its derived product accumulation in blood or urine. High-throughput FTIR spectroscopy was investigated as a potential tool in the current screening methods. IR spectra were obtained from blood plasma of healthy, diabetic, and galactosemic patients. The major spectral differences were in the carbohydrate region, which was first analysed in an exploratory manner using principal component analysis (PCA). PCA score plots showed a clear discrimination between diabetic and galactosemic patients and this was more marked as a function of the glucose and galactose increased concentration in these patients' plasma respectively. Then, a support vector machine leave-one-out cross-validation (SVM-LOOCV) classifier was built with the PCA scores as the input and the model was tested on median, mean and all spectra from the three population groups. This classifier was able to discriminate healthy/diabetic, healthy/galactosemic, and diabetic/galactosemic patients with sensitivity and specificity rates ranging from 80% to 94%. The total accuracy rate ranged from 87% to 96%. High-throughput FTIR spectroscopy combined with the SVM-LOOCV classification procedure appears to be a promising tool in the screening of galactosemia patients, with good sensitivity and specificity. Furthermore, this approach presents the advantages of being cost-effective, fast, and straightforward in the screening of galactosemic patients.

Development of a hierarchical double application of crisp cluster validity indices: a proof-of-concept study for automated FTIR spectral histology.

Fourier-transform infrared (FTIR) spectral imaging is currently used as a non-destructive and label-free method for analyzing biological specimens. However, to highlight the different tissue regions, unsupervised clustering methods are commonly used leading to a subjective choice of the number of clusters. Here, we develop a hierarchical double application of 9 selected crisp cluster validity indices (CCVIs) using K-Means clustering. This approach when tested first on an artificial dataset showed that the indices Pakhira-Bandyopadhyay-Maulik (PBM) and Sym-Index (SI) perfectly estimated the expected 9 sub-clusters. Then, the concept was applied to a real dataset consisting of FTIR spectral images of normal human colon tissue samples originating from 5 patients. PBM and SI were revealed to be the most efficient indices that correctly identified the different colon histological components including crypts, lamina propria, muscularis mucosae, submucosa, and lymphoid aggregates. In conclusion, these results strongly suggest that the hierarchical double CCVI application is a promising method for automated and informative spectral histology.

Diagnosis approach of chronic lymphocytic leukemia on unstained blood smears using Raman microspectroscopy and supervised classification.

We have investigated the potential of Raman microspectroscopy combined with supervised classification algorithms to diagnose a blood lymphoproliferative disease, namely chronic lymphocytic leukemia (CLL). This study was conducted directly on human blood smears (27 volunteers and 49 CLL patients) spread on standard glass slides according to a cytological protocol before the staining step. Visible excitation at 532 nm was chosen, instead of near infrared, in order to minimize the glass contribution in the Raman spectra. After Raman measurements, blood smears were stained using the May-Grünwald Giemsa procedure to correlate spectroscopic data classifications with cytological analysis. A first prediction model was built using support vector machines to discriminate between the two main leukocyte subpopulations (lymphocytes and polymorphonuclears) with sensitivity and specificity over 98.5%. The spectral differences between these two classes were associated to higher nucleic acid content in lymphocytes compared to polymorphonuclears. Then, we developed a classification model to discriminate between neoplastic and healthy lymphocyte spectra, with a mean sensitivity and specificity of 88% and 91% respectively. The main molecular differences between healthy and CLL cells were associated with DNA and protein changes. These spectroscopic markers could lead, in the future, to the development of a helpful medical tool for CLL diagnosis.

Infrared spectral histopathology for cancer diagnosis: a novel approach for automated pattern recognition of colon adenocarcinoma.

Histopathology remains the gold standard method for colon cancer diagnosis. Novel complementary approaches for molecular level diagnosis of the disease are need of the hour. Infrared (IR) imaging could be a promising candidate method as it probes the intrinsic chemical bonds present in a tissue, and provides a "spectral fingerprint" of the biochemical composition. To this end, IR spectral histopathology, which combines IR imaging and data processing techniques, was employed on seventy seven paraffinized colon tissue samples (48 tumoral and 29 non-tumoral) in the form of tissue arrays. To avoid chemical deparaffinization, a digital neutralization of the spectral interference of paraffin was implemented. Clustering analysis was used to partition the spectra and construct pseudo-colored images, for assigning spectral clusters to various tissue structures (normal epithelium, malignant epithelium, connective tissue etc.). Based on the clustering results, linear discriminant analysis was then used to construct a stringent prediction model which was applied on samples without a priori histopathological information. The predicted spectral images not only revealed common features representative of the colonic tissue biochemical make-up, but also highlighted additional features like tumor budding and tumor-stroma association in a label-free manner. This novel approach of IR spectral imaging on paraffinized tissues showed 100% sensitivity and allowed detection and differentiation of normal and malignant colonic features based purely on their intrinsic biochemical features. This non-destructive methodology combined with multivariate statistical image analysis appears as a promising tool for colon cancer diagnosis and opens up the way to the concept of numerical spectral histopathology.