Early cardiac-chamber-specific fingerprints in heart failure with preserved ejection fraction detected by FTIR and Raman spectroscopic techniques

Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning

OBJECTIVE

The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.

METHODS

ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.

RESULTS

A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.

CONCLUSION

Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.

Cardiac multiscale bioimaging: from nano- through micro- to mesoscales

Cardiac multiscale bioimaging is an emerging field that aims to provide a comprehensive understanding of the heart and its functions at various levels, from the molecular to the entire organ. It combines both physiologically and clinically relevant dimensions: from nano- and micrometer resolution imaging based on vibrational spectroscopy and high-resolution microscopy to assess molecular processes in cardiac cells and myocardial tissue, to mesoscale structural investigations to improve the understanding of cardiac (patho)physiology. Tailored super-resolution deep microscopy with advanced proteomic methods and hands-on experience are thus strategically combined to improve the quality of cardiovascular research and support future medical decision-making by gaining additional biomolecular information for translational and diagnostic applications.

Vibrational spectroscopy methods for investigation of the animal models of glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common and devastating primary brain tumor among adults. It is highly lethal disease, as only 25% of patients survive longer than 1 year and only 5% more than 5 years from the diagnosis. To search for the new, more effective methods of treatment, the understanding of mechanisms underlying the process of tumorigenesis is needed. The new light on this problem may be shed by the analysis of biochemical anomalies of tissues affected by tumor growth. Therefore, in the present work, we applied the Fourier transform infrared (FTIR) and Raman microspectroscopy to evaluate changes in the distribution and structure of biomolecules appearing in the rat brain as a result of glioblastoma development. In turn, synchrotron X-ray fluorescence microscopy was utilized to determine the elemental anomalies appearing in the nervous tissue. To achieve the assumed goals of the study animal models of GBM were used. The rats were subjected to the intracranial implantation of glioma cells with different degree of invasiveness. For spectroscopic investigation brain slices taken from the area of cancer cells administration were used. The obtained results revealed, among others, the decrease content of lipids and compounds containing carbonyl groups, compositional and structural changes of proteins as well as abnormalities in the distribution of low atomic number elements within the region of tumor.

Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue

To demonstrate that point-of-care multimodal spectroscopy using Near-Infrared (NIR) and Raman Spectroscopy (RS) can be used to diagnose human heart tissue. We generated 105 spectroscopic scans, which comprised 4 NIR and 3 RS scans per sample to generate a "multimodal spectroscopic scan" (MSS) for each heart, done across 15 patients, 5 each from the dilated cardiomyopathy (DCM), Ischaemic Heart Disease (IHD) and Normal pathologies. Each of the MSS scans was undertaken in 3 s. Data were entered into machine learning (ML) algorithms to assess accuracy of MSS in diagnosing tissue type. The median age was 50 years (IQR 49-52) for IHD, 47 (IQR 45-50) for DCM and 36 (IQR 33-52) for healthy patients (p = 0.35), 60% of which were male. MSS identified key differences in IHD, DCM and normal heart samples in regions typically associated with fibrosis and collagen (NIR wavenumbers: 1433, 1509, 1581, 1689 and 1725 nm; RS wavelengths: 1658, 1450 and 1330 cm-1). In principal component (PC) analyses, these differences explained 99.2% of the variation in 4 PCs for NIR, 81.6% in 10 PCs for Raman, and 99.0% in 26 PCs for multimodal spectroscopic signatures. Using a stack machine learning algorithm with combined NIR and Raman data, our model had a precision of 96.9%, recall of 96.6%, specificity of 98.2% and Area Under Curve (AUC) of 0.989 (Table 1). NIR and Raman modalities alone had similar levels of precision at 94.4% and 89.8% respectively (Table 1). MSS combined with ML showed accuracy of 90% for detecting dilated cardiomyopathy, 100% for ischaemic heart disease and 100% for diagnosing healthy tissue. Multimodal spectroscopic signatures, based on NIR and Raman spectroscopy, could provide cardiac tissue scans in 3-s to aid accurate diagnoses of fibrosis in IHD, DCM and normal hearts. Table 1 Machine learning performance metrics for validation data sets of (a) Near-Infrared (NIR), (b) Raman and (c and d) multimodal data using logistic regression (LR), stochastic gradient descent (SGD) and support vector machines (SVM), with combined "stack" (LR + SGD + SVM) AUC Precision Recall Specificity (a) NIR model  Logistic regression 0.980 0.944 0.933 0.967  SGD 0.550 0.281 0.400 0.700  SVM 0.840 0.806 0.800 0.900  Stack 0.933 0.794 0.800 0.900 (b) Raman model  Logistic regression 0.985 0.940 0.929 0.960  SGD 0.892 0.869 0.857 0.932  SVM 0.992 0.940 0.929 0.960  Stack 0.954 0.869 0.857 0.932 (c) MSS: multimodal (NIR + Raman) to detect DCM vs. IHD vs. normal patients  Logistic regression 0.975 0.841 0.828 0.917  SGD 0.847 0.803 0.793 0.899  SVM 0.971 0.853 0.828 0.917  Stack 0.961 0.853 0.828 0.917 (d) MSS: multimodal (NIR + Raman) to detect pathological vs. normal patients  Logistic regression 0.961 0.969 0.966 0.984  SGD 0.944 0.967 0.966 0.923  SVM 1.000 1.000 1.000 1.000  Stack 1.000 0.944 0.931 0.969 Bold values indicate values obtained from the stack algorithm and used for analyses.

Translating genomic tools to Raman spectroscopy analysis enables high-dimensional tissue characterization on molecular resolution

Spatial transcriptomics of histological sections have revolutionized research in life sciences and enabled unprecedented insights into genetic processes involved in tissue reorganization. However, in contrast to genomic analysis, the actual biomolecular composition of the sample has fallen behind, leaving a gap of potentially highly valuable information. Raman microspectroscopy provides untargeted spatiomolecular information at high resolution, capable of filling this gap. In this study we demonstrate spatially resolved Raman "spectromics" to reveal homogeneity, heterogeneity and dynamics of cell matrix on molecular levels by repurposing state-of-the-art bioinformatic analysis tools commonly used for transcriptomic analyses. By exploring sections of murine myocardial infarction and cardiac hypertrophy, we identify myocardial subclusters when spatially approaching the pathology, and define the surrounding metabolic and cellular (immune-) landscape. Our innovative, label-free, non-invasive "spectromics" approach could therefore open perspectives for a profound characterization of histological samples, while additionally allowing the combination with consecutive downstream analyses of the very same specimen.

Vibrational spectroscopy identifies myocardial chemical modifications in heart failure with preserved ejection fraction

BACKGROUND

Vibrational spectroscopy can be a valuable tool to monitor the markers of cardiovascular diseases. In the present work, we explored the vibrational spectroscopy characteristics of the cardiac tissue in an experimental model of heart failure with preserved ejection fraction (HFpEF). The goal was to detect early cardiac chemical modifications associated with the development of HFpEF.

METHODS

We used the Fourier-transform infrared (FTIR) and Raman micro-spectroscopic techniques to provide complementary and objective tools for the histological assessment of heart tissues from an animal model of HFpEF. A new sampling technique was adopted (tissue print on a CaF2 disk) to characterize the extracellular matrix.

RESULTS

Several spectroscopic markers (lipids, carbohydrates, and glutamate bands) were recognized in the cardiac ventricles due to the comorbidities associated with the pathology, such as obesity and diabetes. Besides, abnormal collagen cross-linking and a decrease in tryptophan content were observed and related to the stiffening of ventricles and to the inflammatory state which is a favourable condition for HFpEF.

CONCLUSIONS

By the analyses of tissues and tissue prints, FTIR and Raman techniques were shown to be highly sensitive and selective in detecting changes in the chemistry of the heart in experimental HFpEF and its related comorbidities. Vibrational spectroscopy is a new approach that can identify novel biomarkers for early detection of HFpEF.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

FTIR Analysis of Renal Tissue for the Assessment of Hypertensive Organ Damage and proANP31–67 Treatment

The kidneys are one of the main end organs targeted by hypertensive disease. Although the central role of the kidneys in the regulation of high blood pressure has been long recognized, the detailed mechanisms behind the pathophysiology of renal damage in hypertension remain a matter of investigation. Early renal biochemical alterations due to salt-induced hypertension in Dahl/salt-sensitive rats were monitored by Fourier-Transform Infrared (FTIR) micro-imaging. Furthermore, FTIR was used to investigate the effects of proANP31–67, a linear fragment of pro-atrial natriuretic peptide, on the renal tissue of hypertensive rats. Different hypertension-induced alterations were detected in the renal parenchyma and blood vessels by the combination of FTIR imaging and principal component analysis on specific spectral regions. Changes in amino acids and protein contents observed in renal blood vessels were independent of altered lipid, carbohydrate, and glycoprotein contents in the renal parenchyma. FTIR micro-imaging was found to be a reliable tool for monitoring the remarkable heterogeneity of kidney tissue and its hypertension-induced alterations. In addition, FTIR detected a significant reduction in these hypertension-induced alterations in the kidneys of proANP31–67-treated rats, further indicating the high sensitivity of this cutting-edge imaging modality and the beneficial effects of this novel medication on the kidneys.

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.