Fourier transform IR imaging of primary tumors predicts lymph node metastasis of bladder carcinoma.

The process of metastasis is complex and often impossible to be recognized in conventional clinical diagnosis. Lymph node metastasis (LNM) of bladder carcinoma (BC) is often associated with muscle-invasive tumors. To prevent and recognize LNM, the standard treatment includes radical cystectomy with lymph node dissection and histological examination. Here, we propose infrared (IR) microscopy as a tool for the prediction of LNM. For this purpose, IR images of bladder biopsies from patients with diagnosed non-metastatic early (E BC) and advanced (A BC), as well as metastatic advanced (M BC) bladder cancer were first collected. Furthermore, this dataset was complemented with images of the secondary tumors from the lymph nodes (M LN) of the M BC patients. Unsupervised clustering was used to extract tissue structures from IR images to create a data set comprising 382 IR spectra of non-metastatic bladder tumors and 241 metastatic ones. Based on that, we next established discrimination models using PLS-DA with repeated random sampling double cross-validation, and permutation test to perform the classification. The accuracy of BC metastasis prediction from IR bladder biopsies was 83 % and 78 % for early and advanced BC, respectively, herein demonstrating a proof-of-concept IR detection of BC metastasis. The analysis of spectral profiles additionally showed molecular composition similarity between metastatic bladder and lymph node tumors. We also determined spectral biomarkers of LNM that are associated with sugar metabolism, remodeling of extracellular matrix, and morphological features of cancer cells. Our approach can improve clinical decision-making in urological oncology.

Comparing the direct assessment of steatosis in liver explants with mid- and near-infrared vibrational spectroscopy, prior to organ transplantation.

A fast and accurate assessment of liver steatosis is crucial during liver transplantation surgery as it can negatively impact its success. Recent research has shown that near-infrared (NIR) and attenuated total reflectance-Fourier transform mid-infrared (ATR-FTIR) spectroscopy could be used as real-time quantitative tools to assess steatosis during abdominal surgery. Here, in the frame of a clinical study, we explore the performance of NIR and ATR-FTIR spectroscopy for the direct assessment of steatosis in liver tissues. Results show that both NIR and ATR-FTIR spectroscopy are able to quantify the % of steatosis with cross-validation errors of 1.4 and 1.6%, respectively. Furthermore, the two portable instruments used both provided results within seconds and can be placed inside an operating room evidencing the potential of IR spectroscopy for initial characterization of grafts in liver transplantation surgery. We also evaluated the complementarity of the spectral ranges through correlation spectroscopy.

Enhancing the accuracy of mid-infrared spectroscopy-based liver steatosis quantification using digital image analysis as a reference.

The assessment of liver steatosis is crucial in both hepatology and liver transplantation (LT) surgery. Steatosis can negatively impact the success of LT. Steatosis is a factor for excluding donated organs for LT, but the increasing demand for transplantable organs has led to the use of organs from marginal donors. The current standard for evaluating steatosis is a semi-quantitative grading based on the visual examination of a hematoxylin and eosin (H&E)-stained liver biopsy, but this method is time-consuming, subjective, and lacks reproducibility. Recent research has shown that infrared (IR) spectroscopy could be used as a real-time quantitative tool to assess steatosis during abdominal surgery. However, the development of IR-based methods has been hindered by the lack of appropriate quantitative reference values. In this study, we developed and validated digital image analysis methods for the quantitation of steatosis in H&E-stained liver sections using univariate and multivariate strategies including linear discriminant analysis (LDA), quadratic DA, logistic regression, partial least squares-DA (PLS-DA), and support vector machines. The analysis of 37 tissue samples with varying grades of steatosis demonstrates that digital image analysis provides accurate and reproducible reference values that improve the performance of IR spectroscopic models for steatosis quantification. A PLS model in the 1810-1052 cm-1 region using first derivative ATR-FTIR spectra provided RMSECV = 0.99%. The gained improvement in accuracy critically enhances the applicability of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) to support an objective graft evaluation at the operation room, which might be especially relevant in cases of marginal liver donors to avoid unnecessary graft explantation.

Evaluation of grade and invasiveness of bladder urothelial carcinoma using infrared imaging and machine learning.

Urothelial bladder carcinoma (BC) is primarily diagnosed with a subjective examination of biopsies by histopathologists, but accurate diagnosis remains time-consuming and of low diagnostic accuracy, especially for low grade non-invasive BC. We propose a novel approach for high-throughput BC evaluation by combining infrared (IR) microscopy of bladder sections with machine learning (partial least squares-discriminant analysis) to provide an automated prediction of the presence of cancer, invasiveness and grade. Cystoscopic biopsies from 50 patients with clinical suspicion of BC were histologically examined to assign grades and stages. Adjacent tissue cross-sections were IR imaged to provide hyperspectral datasets and cluster analysis segregated IR images to extract the average spectra of epithelial and subepithelial tissues. Discriminant models, which were validated using repeated random sampling double cross-validation, showed sensitivities (AUROC) ca. 85% (0.85) for the identification of cancer in epithelium and subepithelium. The diagnosis of non-invasive and invasive cases showed sensitivity values around 80% (0.84-0.85) and 76% (0.73-0.80), respectively, while the identification of low and high grade BC showed higher sensitivity values 87-88% (0.91-0.92). Finally, models for the discrimination between cancers with different invasiveness and grades showed more modest AUROC values (0.67-0.72). This proves the high potential of IR imaging in the development of ancillary platforms to screen bladder biopsies.

Label-free testing strategy to evaluate packed red blood cell quality before transfusion to leukemia patients.

Patients worldwide require therapeutic transfusions of packed red blood cells (pRBCs), which is applied to the high-risk patients who need periodic transfusions due to leukemia, lymphoma, myeloma and other blood diseases or disorders. Contrary to the general hospital population where the transfusions are carried out mainly for healthy trauma patients, in case of high-risk patients the proper quality of pRBCs is crucial. This leads to an increased demand for efficient technology providing information on the pRBCs alterations deteriorating their quality. Here we present the design of an innovative, label-free, noninvasive, rapid Raman spectroscopy-based method for pRBCs quality evaluation, starting with the description of sample measurement and data analysis, through correlation of spectroscopic results with reference techniques' outcomes, and finishing with methodology verification and its application in clinical conditions. We have shown that Raman spectra collected from the pRBCs supernatant mixture with a proper chemometric analysis conducted for a minimum one ratio of integral intensities of the chosen Raman marker bands within the spectrum allow evaluation of the pRBC quality in a rapid, noninvasive, and free-label manner, without unsealing the pRBCs bag. Subsequently, spectroscopic data were compared with predefined reference values, either from pRBCs expiration or those defining the pRBCs quality, allowing to assess their utility for transfusion to patients with acute myeloid leukemia (AML) and lymphoblastic leukemia (ALL).

Plasmonic hot spots reveal local conformational transitions induced by DNA double-strand breaks.

DNA double-strand breaks (DSBs) are typical DNA lesions that can lead to cell death, translocations, and cancer-driving mutations. The repair process of DSBs is crucial to the maintenance of genomic integrity in all forms of life. However, the limitations of sensitivity and special resolution of analytical techniques make it difficult to investigate the local effects of chemotherapeutic drugs on DNA molecular structure. In this work, we exposed DNA to the anticancer antibiotic bleomycin (BLM), a damaging factor known to induce DSBs. We applied a multimodal approach combining (i) atomic force microscopy (AFM) for direct visualization of DSBs, (ii) surface-enhanced Raman spectroscopy (SERS) to monitor local conformational transitions induced by DSBs, and (iii) multivariate statistical analysis to correlate the AFM and SERS results. On the basis of SERS results, we identified that bands at 1050 cm-1 and 730 cm-1 associated with backbone and nucleobase vibrations shifted and changed their intensities, indicating conformational modifications and strand ruptures. Based on averaged SERS spectra, the PLS regressions for the number of DSBs caused by corresponding molar concentrations of bleomycin were calculated. The strong correlation (R2 = 0.92 for LV = 2) between the predicted and observed number of DSBs indicates, that the model can not only predict the number of DSBs from the spectra but also detect the spectroscopic markers of DNA damage and the associated conformational changes.

Addressing Delicate and Variable Cancer Morphology in Spectral Histopathology Using Canine Visceral Hemangiosarcoma.

Spectral histopathology has shown promise for the classification and diagnosis of tumors with defined morphology, but application in tumors with variable or diffuse morphologies is yet to be investigated. To address this gap, we evaluated the application of Fourier transform infrared (FTIR) imaging as an accessory diagnostic tool for canine hemangiosarcoma (HSA), a vascular endothelial cell cancer that is difficult to diagnose. To preserve the delicate vascular tumor tissue structure, and potential classification of single endothelial cells, paraffin removal was not performed, and a partial least square discrimination analysis (PLSDA) and Random Forest (RF) models to classify different tissue types at individual pixel level were established using a calibration set (24 FTIR images from 13 spleen specimens). Next, the prediction capability of the PLSDA model was tested with an independent test set (n = 11), resulting in 74% correct classification of different tissue types at an individual pixel level. Finally, the performance of the FTIR spectropathology and chemometric algorithm for diagnosis of HSA was established in a blinded set of tissue samples (n = 24), with sensitivity and specificity of 80 and 81%, respectively. Taken together, these results show that FTIR imaging without paraffin removal can be applied to tumors with diffuse morphology, and this technique is a promising tool to assist in canine splenic HSA differential diagnosis.

From bench to worktop: Rapid evaluation of nutritional parameters in liquid foodstuffs by IR spectroscopy.

We evaluated the use of attenuated total reflectance infrared spectroscopy for simultaneous in situ quantification of the nutritional composition of liquid food stuffs in the industrial kitchen context. Different methodologies were compared, including dry and wet acquisition along with instrument parameters and measurement times of 4 and 60 s. The most effective technique was 1-minute measurement, with prediction errors of 2.6, 0.7, 1.0, 2.2, 0.8, 2.4 g/100 mL and 150 Kcal, for carbohydrates, proteins, fat, sugars, saturated fat, water and energy values, respectively.The 4-second method resulted in larger errors but was more applicable for inline measurements. Dry measurements successfully predicted the fractions of proteins, fat, carbohydrates, and sugars, relative to total solids. An app was created to facilitate implementation in a kitchen environment. Compared with other techniques recommended by the FAO, the approach offered a simple alternative for simultaneous prediction of nutritional parameters in an industrial kitchen set-up.

Infrared Spectroscopy of Blood.

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.

Quantification and Identification of Microproteinuria Using Ultrafiltration and ATR-FTIR Spectroscopy.

The presence of low amounts of specific proteins in urine can be an indicator of diagnosis and prognosis of several diseases including renal failure and cancer. Hence, there is an urgent need for Point-of-care (PoC) methods, which can quantify microproteinuria levels (30-300 ppm) and identify the major proteins associated with the microproteinuria. In this study, we coupled ultracentrifugation with attenuated total reflectance-Fourier transform infrared (ATR-FTIR) to identify and quantify proteins in urine at low parts per million levels. The process involves the preconcentration of proteins from 500 µL of urine using an ultrafiltration device. After several washings, the isolated proteins are dried onto the ATR crystal forming a thin film. Imaging studies showed that the absorbance of the protein bands was linear with the amount of mass deposited on the crystal. The methodology was first evaluated with artificial urine spiked with 30-300 ppm of albumin. The calibration showed acceptable linearity (R2 = 0.97) and a limit of detection of 6.7 ppm. Linear relationships were also observed from urine of healthy subjects spiked with microproteinuria concentrations of albumin, immunoglobulin, and hemoglobin, giving a prediction error of the spiked concentration of 23 ppm. When multiple proteins were spiked into the real urine, multivariate analysis was able to decompose the data set into the different proteins, but the multicomponent evaluation was challenging for proteins at low levels. Although the introduction of a preprocessing step reduces the PoC capability of the method, it largely increases its performance, showing great potential as a tool for the diagnosis and prognosis of several illnesses affecting urine proteic composition.

Discriminant analysis and feature selection in mass spectrometry imaging using constrained repeated random sampling - Cross validation (CORRS-CV).

The identification of biomarkers through Mass spectrometry imaging (MSI) is gaining popularity in the clinical field. However, considering the complexity of spectral and spatial variables faced, data mining of the hyperspectral images can be troublesome. The discovery of markers generally depends on the creation of classification models which should be validated to ensure the statistical significance of the discriminants m/z detected. Internal validation using resampling methods such as cross validation (CV) are widely used for model selection, the estimation of its generalization performance and biomarker discovery when sample sizes are limited and an independent test set is not available. Here, we introduce for first time the use of Constrained Repeated Random Subsampling CV (CORRS-CV) on multi-images for the validation of classification models on MSI. Although several aspects must be taken into account (e.g. image size, CORRS-CV∂value, the similarity across spatially close pixels, the total computation time), CORRS-CV provides more accurate estimates of the model performance than k-fold CV using of biological replicates to define the data split when the number of biological replicates is scarce and holding images back for testing is a waste of valuable information. Besides, the combined use of CORRS-CV and rank products increases the robustness of the selection of discriminant features as candidate biomarkers which is an important issue due to the increased biological, environmental and technical variabilities when analysing multiple images, especially from human tissues collected in clinical studies.

Data mining Raman microspectroscopic responses of cells to drugs in vitro using multivariate curve resolution-alternating least squares.

Raman microspectroscopy is gaining popularity for the analysis of time-dependent biological processes such as drug uptake and cellular response. It is a label-free technique which acquires signals from a large variety of components, including cell biomolecules and exogenous compounds such as drugs and nanoparticles, and is commonly employed for in vitro analysis of cells and cell populations with no labelling or staining required. By monitoring the changes to the Raman spectra of the cell as a result of a perturbing agent (e.g. exposure to a drug or toxic agent), one can study the associated changes in cell biochemistry involved in both, the disruption and the subsequent cellular response. The main challenge is that the Raman spectra should be data mined in order to extract the information corresponding to the different actors involved on the process. Here, we study the application of multivariate curve resolution-alternating least squares (MCR-ALS) for extracting kinetic and biochemical information of time-dependent cellular processes. The technique allows the elucidation of the concentration profiles as well as the pure spectra of the components involved. Initially, we used Ordinary Differential Equations (ODE) to simulate drug uptake and 2 responses, which were employed to simulate perturbations to experimental control spectra, creating a dataset containing 36 simulated Raman spectra. Four different scenarios governing the drug exposure-response were evaluated: an undetectable disruption (e.g. radiation), a detectable disruption (e.g. a drug) and disruption with a signal significantly larger than the biological changes induced (e.g. a resonant drug), as well as simultaneous and asynchronous responses. Subsequently, data acquired from the exposure of a pulmonary adenocarcinoma cell line (A549) to Doxorubicin was analysed. The results indicate that MCR-ALS can independently identify and isolate both the spectra of the drug and the cell responses under the different scenarios. The predicted concentrations map out the drug uptake and cellular response curves. The technique shows great potential to investigate non-linear kinetics and modes of action. Advantages and limitations of the technique are discussed, providing guidelines for future analysis strategies.

Probing the action of a novel anti-leukaemic drug therapy at the single cell level using modern vibrational spectroscopy techniques.

Acute myeloid leukaemia (AML) is a life threatening cancer for which there is an urgent clinical need for novel therapeutic approaches. A redeployed drug combination of bezafibrate and medroxyprogesterone acetate (BaP) has shown anti-leukaemic activity in vitro and in vivo. Elucidation of the BaP mechanism of action is required in order to understand how to maximise the clinical benefit. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, Synchrotron radiation FTIR (S-FTIR) and Raman microspectroscopy are powerful complementary techniques which were employed to probe the biochemical composition of two AML cell lines in the presence and absence of BaP. Analysis was performed on single living cells along with dehydrated and fixed cells to provide a large and detailed data set. A consideration of the main spectral differences in conjunction with multivariate statistical analysis reveals a significant change to the cellular lipid composition with drug treatment; furthermore, this response is not caused by cell apoptosis. No change to the DNA of either cell line was observed suggesting this combination therapy primarily targets lipid biosynthesis or effects bioactive lipids that activate specific signalling pathways.

Monitoring the biochemical alterations in hypertension affected salivary gland tissues using Fourier transform infrared hyperspectral imaging.

Fourier transform infrared spectroscopy (FTIR) imaging has been applied to investigate biochemical differences between salivary glands from control and hypertensive rats. Male Sprague-Dawley rats were divided into two groups including a control group and another hypertension group that were treated orally, with N-nitro-l-arginine methyl ester (l-NAME) via drinking water for 3 weeks to develop hypertension. In the control group, rats were treated with only drinking water for 3 weeks. The formalin-fixed paraffin embedded tissue specimens from submandibular and sublingual glands were analysed with a FTIR focal plane array imaging spectrometer and multi-composite images of all tissue sections were analysed simultaneously using Unsupervised Hierarchical Cluster Analysis (UHCA) and the extracted spectra were further analysed using Partial Least Squares Discriminant Analysis (PLS-DA). In general, hypertension affected salivary gland tissues were characterised by higher concentrations of triglycerides as evidenced by an increase in the 1745 cm-1 band. Higher concentrations of carbohydrates and proteins were also observed in the hypertensive group along with a decrease in bands associated with nucleic acids. PLS-DA scores plots provided good differentiation in sublingual gland tissues between control (n = 3734 spectra) and hypertension (n = 4538) and also in submandibular gland tissues between control (n = 5051) and hypertension (n = 4408). We have shown that FTIR imaging can be used to differentiate the macromolecular information between physiological and pathological conditions in tissue biopsy specimens. In the next phase, we will investigate the infrared predictive markers of hypertension in biofluids including serum and saliva using attenuated total refection spectroscopy.

Comparison of transflection and transmission FTIR imaging measurements performed on differentially fixed tissue sections.

The widespread and cost-effective use of transflection substrates in Fourier transform infrared (FTIR) imaging of clinical samples is affected by the presence of artefacts including the electric field standing wave (EFSW) and contributions from light dispersion. For IR-based diagnostics, the manifestation of undesirable artifacts can distort the spectra and lead to erroneous diagnosis. Nevertheless, there is no clear consensus in the literature about the degree of influence of these effects. The aim of this work is to contribute to this discussion by comparing transflection and transmission images of the same tissue. For this purpose two adjacent sections of the same tissue (lymphoma sample) were fixed onto a CaF2 window and a transflective slide for FTIR imaging. The samples in this case had a central area where based on morphology it was presumed the fixative did not penetrate to the same extent hence providing a comparable region for the two different substrates with a distinct physical/chemical difference. Transmission and transflection spectra from adjacent hyperspectral tissue images were combined in an extended dataset. Surprisingly, unsupervised hierarchical cluster analysis clustered together transflection and transmission spectra, being classified according to differences in tissue fixation instead of the geometry employed for the image acquisition. A more detailed examination of spectra from the peripheral zone of the tissue indicated that the main differences between the transflection and transmission spectra were: (1) a small shift of the amide I, (2) a larger "noise" component in the transflection spectra requiring more averaging to obtain representative spectra of tissue types, and (3) the phosphate bands were generally higher in absorbance in the transflection measurements compared to the transmission ones. The amide I shift and the larger spectral variance was consistent with results obtained in previous studies where the EFWS was present. The findings indicate that artifacts resulting from transflection measurements were small but consistent across the tissue, and therefore the use of transflection measurements could be employed for disease diagnosis. Accordingly, we recommend a straightforward multivariate comparison of images from transmission and transflection measurements in a combined data matrix obtained from adjacent sections of the tissue as a useful preliminary study for establishing the impact of the EFWS on the samples, before considering the routine use of transflection substrates for any new tissue studied.

Improving the performance of hollow waveguide-based infrared gas sensors via tailored chemometrics.

The use of chemometrics in order to improve the molecular selectivity of infrared (IR) spectra has been evaluated using classic least squares (CLS), partial least squares (PLS), science-based calibration (SBC), and multivariate curve resolution-alternate least squares (MCR-ALS) techniques for improving the discriminatory and quantitative performance of infrared hollow waveguide gas sensors. Spectra of mixtures of isobutylene, methane, carbon dioxide, butane, and cyclopropane were recorded, analyzed, and validated for optimizing the prediction of associated concentrations. PLS, CLS, and SBC provided equivalent results in the absence of interferences. After addition of the spectral characteristics of water by humidifying the sample mixtures, CLS and SBC results were similar to those obtained by PLS only if the water spectrum was included in the calibration model. In the presence of an unknown interferant, CLS revealed errors up to six times higher than those obtained by PLS. However, SBC provided similar results compared to PLS by adding a measured noise matrix to the model. Using MCR-ALS provided an excellent estimation of the spectra of the unknown interference. Furthermore, this method also provided a qualitative and quantitative estimation of the components of an unknown set of samples. In summary, using the most suitable chemometrics approach could improve the selectivity and quality of the calibration model derived for a sensor system, and may avoid the need to analyze expensive calibration data sets. The results obtained in the present study demonstrated that (1) if all sample components of the system are known, CLS provides a sufficiently accurate solution; (2) the selection between PLS and SBC methods depends on whether it is easier to measure a calibration data set or a noise matrix; and (3) MCR-ALS appears to be the most suitable method for detecting interferences within a sample. However, the latter approach requires the most extensive calculations and may thus result in limited temporal resolution, if the concentration of a component should be continuously monitored.

Synthesis and biological evaluation of dehydroabietic acid derivatives.

A series of C18-oxygenated derivatives of dehydroabietic acid were synthesized from commercial abietic acid and evaluated for their cytotoxic, antimycotic, and antiviral activities.