Fourier transform infrared spectroscopic signature of blood plasma in the progression of breast cancer with simultaneous metastasis to lungs

Adipose-derived mesenchymal stem cells' adipogenesis chemistry analyzed by FTIR and Raman metrics

The full understanding of molecular mechanisms of cell differentiation requires a holistic view. Here we combine label-free FTIR and Raman hyperspectral imaging with data mining to detect the molecular cell composition enabling noninvasive monitoring of cell differentiation and identifying biochemical heterogeneity. Mouse adipose-derived mesenchymal stem cells (AD-MSCs) undergoing adipogenesis were followed by Raman and FT-IR imaging, Oil Red, and immunofluorescence. A workflow of the data analysis (IRRSmetrics4stem) was designed to identify spectral predictors of adipogenesis and test machine-learning (ML) methods (hierarchical clustering, PCA, PLSR) for the control of the AD-MSCs differentiation degree. IRRSmetrics4stem provided insights into the chemism of adipogenesis. With single-cell tracking, we established IRRS metrics for lipids, proteins, and DNA variations during AD-MSCs differentiation. The over 90% predictive efficiency of the selected ML methods proved the high sensitivity of the IRRS metrics. Importantly, the IRRS metrics unequivocally recognize a switch from proliferation to differentiation. This study introduced a new bioassay identifying molecular markers indicating molecular transformations and delivering rapid and machine learning-based monitoring of adipogenesis that can be relevant to other differentiation processes. Thus, we introduce a novel, rapid, machine learning-based bioassay to identify molecular markers of adipogenesis. It can be relevant to identification of differentiation-related molecular processes in other cell types, and beyond the cell differentiation including progression of different cellular pathophysiologies reconstituted in vitro.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Impaired Extracellular Proteostasis in Patients with Heart Failure

BACKGROUND

Proteostasis impairment and the consequent increase of amyloid burden in the myocardium have been associated with heart failure (HF) development and poor prognosis. A better knowledge of the protein aggregation process in biofluids could assist the development and monitoring of tailored interventions.

AIM

To compare the proteostasis status and protein's secondary structures in plasma samples of patients with HF with preserved ejection fraction (HFpEF), patients with HF with reduced ejection fraction (HFrEF), and age-matched individuals.

METHODS

A total of 42 participants were enrolled in 3 groups: 14 patients with HFpEF, 14 patients with HFrEF, and 14 age-matched individuals. Proteostasis-related markers were analyzed by immunoblotting techniques. Fourier Transform Infrared (FTIR) Spectroscopy in Attenuated Total Reflectance (ATR) was applied to assess changes in the protein's conformational profile.

RESULTS

Patients with HFrEF showed an elevated concentration of oligomeric proteic species and reduced clusterin levels. ATR-FTIR spectroscopy coupled with multivariate analysis allowed the discrimination of HF patients from age-matched individuals in the protein amide I absorption region (1700-1600 cm^-1), reflecting changes in protein conformation, with a sensitivity of 73 and a specificity of 81%. Further analysis of FTIR spectra showed significantly reduced random coils levels in both HF phenotypes. Also, compared to the age-matched group, the levels of structures related to fibril formation were significantly increased in patients with HFrEF, whereas the β-turns were significantly increased in patients with HFpEF.

CONCLUSION

Both HF phenotypes showed a compromised extracellular proteostasis and different protein conformational changes, suggesting a less efficient protein quality control system.

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks

In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics-area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)-were averaged for comparison. The NN models were compared to six (6) machine learning models-logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)-for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C-OH C-OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample's spectrum using NN.

A robust metabolomics approach for the evaluation of human embryos from in vitro fertilization

The identification of the most competent embryos for transfer to the uterus constitutes the main challenge of in vitro fertilization (IVF). We established a metabolomic-based approach by applying Fourier transform infrared (FTIR) spectroscopy on 130 samples of 3-day embryo culture supernatants from 26 embryos that implanted and 104 embryos that failed. On examining the internal structure of the data by unsupervised multivariate analysis, we found that the supernatant spectra of nonimplanted embryos constituted a highly heterogeneous group. Whereas ∼40% of these supernatants were spectroscopically indistinguishable from those of successfully implanted embryos, ∼60% exhibited diverse, heterogeneous metabolic fingerprints. This observation proved to be the direct result of pregnancy's multifactorial nature, involving both intrinsic embryonic traits and external characteristics. Our data analysis strategy thus involved one-class modelling techniques employing soft independent modelling of class analogy that identified deviant fingerprints as unsuitable for implantation. From these findings, we could develop a noninvasive Fourier-transform-infrared-spectroscopy-based approach that represents a shift in the fundamental paradigm for data modelling applied in assisted-fertilization technologies.

Temporal relationship between systemic endothelial dysfunction and alterations in erythrocyte function in a murine model of chronic heart failure

Aims

Endothelial dysfunction (ED) and red blood cell distribution width (RDW) are both prognostic factors in heart failure (HF), but the relationship between them is not clear. In this study, we used a unique mouse model of chronic HF driven by cardiomyocyte-specific overexpression of activated Gαq protein (Tgαq*44 mice) to characterize the relationship between the development of peripheral ED and the occurrence of structural nanomechanical and biochemical changes in red blood cells (RBCs).

Methods and results

Systemic ED was detected in vivo in 8-month-old Tgαq*44 mice, as evidenced by impaired acetylcholine-induced vasodilation in the aorta and increased endothelial permeability in the brachiocephalic artery. ED in the aorta was associated with impaired nitric oxide (NO) production in the aorta and diminished systemic NO bioavailability. ED in the aorta was also characterized by increased superoxide and eicosanoid production. In 4- to 6-month-old Tgαq*44 mice, RBC size and membrane composition displayed alterations that did not result in significant changes in their nanomechanical and functional properties. However, 8-month-old Tgαq*44 mice presented greatly accentuated structural and size changes and increased RBC stiffness. In 12-month-old Tgαq*44 mice, the erythropathy was featured by severely altered RBC shape and elasticity, increased RDW, impaired RBC deformability, and increased oxidative stress (gluthatione (GSH)/glutathione disulfide (GSSG) ratio). Moreover, RBCs taken from 12-month-old Tgαq*44 mice, but not from 12-month-old FVB mice, coincubated with aortic rings from FVB mice, induced impaired endothelium-dependent vasodilation and this effect was partially reversed by an arginase inhibitor [2(S)-amino-6-boronohexanoic acid].

Conclusion

In the Tgαq*44 murine model of HF, systemic ED accelerates erythropathy and, conversely, erythropathy may contribute to ED. These results suggest that erythropathy may be regarded as a marker and a mediator of systemic ED in HF. RBC arginase and possibly other RBC-mediated mechanisms may represent novel therapeutic targets for systemic ED in HF.

Spectroscopy as a useful tool for the identification of changes with time in post-mortem vitreous humor for forensic toxicology purposes

Vitreous humor (VH) is an alternative biological matrix with a great advantage of longer availability for analysis due to the lack of many enzymes. The use of VH in forensic toxicology may have an added benefit, however, this application requires rapid, simple, non-destructive, and relatively portable analytical analysis methods. These requirements may be met by Fourier transform infrared spectroscopy technique (FT-IR) equipped with attenuated total reflection accessory (ATR). FT-IR spectra of vitreous humor samples, deposited on glass slides, were collected and subsequent chemometric data analysis by means of Hierarchical Cluster Analysis and Principal Component Analysis was conducted. Differences between animal and human VH samples and human VH samples stored for diverse periods of time were detected. A kinetic study of changes in the VH composition up to 2 weeks showed the distinction of FT-IR spectra collected on the 1st and 14th day of storage. In addition, data obtained for the most recent human vitreous humor samples—collected 3 and 2 years before the study, presented successful discrimination of all time points studied. The method introduced was unable to detect mephedrone addition to VH in the concentration of 10 µg/cm3.

Graphic abstract

Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring.

Spectroscopic Signature of Red Blood Cells in a D-Galactose-Induced Accelerated Aging Model

This work presents a semi-quantitative spectroscopic approach, including FTIR-ATR and Raman spectroscopies, for the biochemical analysis of red blood cells (RBCs) supported by the biochemical, morphological and rheological reference techniques. This multi-modal approach provided the description of the RBC alterations at the molecular level in a model of accelerated aging induced by administration of D-galactose (D-gal), in comparison to natural aging. Such an approach allowed to conclude that most age-related biochemical RBC membrane changes (a decrease in lipid unsaturation and the level of phospholipids, or an increase in acyl chain shortening) as well as alterations in the morphological parameters and RBC deformability are well reflected in the D-gal model of accelerated aging. Similarly, as in natural aging, a decrease in LDL level in blood plasma and no changes in the fraction of glucose, creatinine, total cholesterol, HDL, iron, or triglycerides were observed during the course of accelerated aging. Contrary to natural aging, the D-gal model led to an increase in cholesterol esters and the fraction of total esterified lipids in RBC membranes, and evoked significant changes in the secondary structure of the membrane proteins. Moreover, a significant decrease in the phosphorous level of blood plasma was specific for the D-gal model. On the other hand, natural aging induced stronger changes in the secondary structures of the proteins of the RBCs' interior. This work proves that research on the aging mechanism, especially in circulation-related diseases, should employ the D-gal model with caution. Nonetheless, the D-gal model enables to imitate age-related rheological alterations in RBCs, although they are partially derived from different changes observed in the RBC membrane at the molecular level.

A Comparison between FTIR Spectra from HUKE and SH-SY5Y Cell Lines Grown on Different Substrates

In recent years, Fourier Transform Infrared (FTIR) micro-spectroscopy has shown promising potential in medical diagnostics at the cellular level. In fact, FTIR spectra can provide information related to DNA, protein, and lipid content and how such a content changes when a pathological state arises. Most of these information is included in the so-called fingerprint region (1000–1800 cm−1), consisting of several spectral peaks related to vibrational modes occurring inside cellular components. Unfortunately, the slides commonly used in cytology (as the glass microscopy slides and coverslips) are opaque to IR radiation in the fingerprint region, whereas they are transparent for wavenumber values larger than 2000 cm−1, where few and broad spectral absorption bands, mainly due to lipids and proteins, are present. Nonetheless, here we show that FTIR spectra performed in the high wavenumber range 2750–3000 cm−1 can be used to discriminate two different types of cells, one from a normal cell line (Human Keratinocyte, HUKE) and the other from a cancer one (SH-SY5Y). The spectra are discriminated by means of their Principal Component Analysis, according to the PC1 component, and by means of ratiometric analysis, according to the ratio of the intensity of the peak at 2956 cm−1 and that of the peak at 2924 cm−1. The PC1 score values of the HUKE are statistically different from the PC1 score values of SH-SY5Y, whereas the intensity ratio results larger for SH-SY5Y than for HUKE cells. Such results occur for different substrates over which the cells have been grown, including the thick glass slides used for cytological analysis. This result is a further step toward the application of FTIR microspectroscopy in the cytological routine diagnosis.

Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans

The unabated global increase of allergic patients leads to an unmet need for rapid and inexpensive tools for the diagnosis of allergies and for monitoring the outcome of allergen-specific immunotherapy (SIT). In this proof-of-concept study, we investigated the potential of Fourier-Transform Infrared (FTIR) spectroscopy, a high-resolution and cost-efficient biophotonic method with high throughput capacities, to detect characteristic alterations in serum samples of healthy, allergic, and SIT-treated mice and humans. To this end, we used experimental models of ovalbumin (OVA)-induced allergic airway inflammation and allergen-specific tolerance induction in BALB/c mice. Serum collected before and at the end of the experiment was subjected to FTIR spectroscopy. As shown by our study, FTIR spectroscopy, combined with deep learning, can discriminate serum from healthy, allergic, and tolerized mice, which correlated with immunological data. Furthermore, to test the suitability of this biophotonic method for clinical diagnostics, serum samples from human patients were analyzed by FTIR spectroscopy. In line with the results from the mouse models, machine learning-assisted FTIR spectroscopy allowed to discriminate sera obtained from healthy, allergic, and SIT-treated humans, thereby demonstrating its potential for rapid diagnosis of allergy and clinical therapeutic monitoring of allergic patients.

Effect of hemolysis on Fourier transform infrared and Raman spectra of blood plasma

Hemolysis is a very common phenomenon and is referred as the release of intracellular components from red blood cells to the extracellular fluid. Hemolyzed samples are often rejected in clinics due to the interference of hemoglobin and intracellular components in laboratory measurements. Plasma and serum based vibrational spectroscopy studies are extensively applied to generate spectral biomarkers for various diseases. However, no studies have reported the effect of hemolysis in blood based vibrational spectroscopy studies. This study was undertaken to evaluate the effect of hemolysis on infrared and Raman spectra of blood plasma. In this study, prostate cancer plasma samples (n = 30) were divided into three groups (nonhemolyzed, mildly hemolyzed, and moderately hemolyzed) based on the degree of hemolysis and FTIR and Raman spectra were recorded using high throughput (HT)-FTIR and HT-Raman spectroscopy. Discrimination was observed between the infrared and Raman spectra of nonhemolyzed and hemolyzed plasma samples using principal component analysis. A classical least square fitting analysis showed differences in the weighting of pure components in nonhemolyzed and hemolyzed plasma samples. Therefore, it is worth to consider the changes in spectral features due to hemolysis when comparing the results within and between experiments.

Metabolomic studies of breast cancer in murine models: A review

BACKGROUND

Metabolomic strategies have been extensively used to search for biomarkers of disease, including cancer, in biological complex mixtures such as cells, tissues and biofluids. In breast cancer research, murine models are of great value and metabolomics has been increasingly applied to characterize tumor or organ tissues, or biofluids, for instance to follow-up metabolism during cancer progression or response to specific therapies.

SCOPE OF REVIEW

This review briefly introduces the different murine models used in breast cancer research and proceeds to present the metabolomic studies reported so far to describe the deviant metabolic behavior associated to breast cancer, in each type of model: xenografts (cell- or patient-derived), spontaneous (naturally-occurring or genetically engineered) and carcinogen-induced. The type of sample and strategies followed are identified, as well as the main findings from of study.

MAJOR CONCLUSIONS

Metabolomics has gradually become relevant in characterizing murine models of breast cancer, using either Nuclear Magnetic Resonance (NMR) or Mass Spectromety (MS). Both tissue and biofluids are matrixes of interest in this context, although in some type of models, reports have focused primarily on the former. The aims of tissue studies have comprised the search for mechanistic knowledge of carcinogenesis, metastasis development and response/resistance to therapies. Biofluid metabolomics has mainly aimed at finding non-invasive biomarkers for early breast cancer detection or prognosis determination.

GENERAL SIGNIFICANCE

Metabolomics provides exquisite detail on murine tumor and systemic metabolism of breast cancer. This knowledge paves the way for the discovery of new biomarkers, potentially translatable to in vivo non-invasive patient follow-up.

Tracking Extracellular Matrix Remodeling in Lungs Induced by Breast Cancer Metastasis. Fourier Transform Infrared Spectroscopic Studies

This work focused on a detailed assessment of lung tissue affected by metastasis of breast cancer. We used large-area chemical scanning implemented in Fourier transform infrared (FTIR) spectroscopic imaging supported with classical histological and morphological characterization. For the first time, we differentiated and defined biochemical changes due to metastasis observed in the lung parenchyma, atelectasis, fibrous, and muscle cells, as well as bronchi ciliate cells, in a qualitative and semi-quantitative manner based on spectral features. The results suggested that systematic extracellular matrix remodeling with the progress of the metastasis process evoked a decrease in the fraction of the total protein in atelectasis, fibrous, and muscle cells, as well as an increase of fibrillar proteins in the parenchyma. We also detected alterations in the secondary conformations of proteins in parenchyma and atelectasis and changes in the level of hydroxyproline residues and carbohydrate moieties in the parenchyma. The results indicate the usability of FTIR spectroscopy as a tool for the detection of extracellular matrix remodeling, thereby enabling the prediction of pre-metastatic niche formation.