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Every cell in a living organism is constantly exposed to physical and chemical factors which damage the molecular structure of proteins, lipids, and nucleic acids. Cellular DNA lesions are the most dangerous because the genetic information, critical for the identity and function of each eukaryotic cell, is stored in the DNA. In this review, we describe spectroscopic markers of DNA damage, which can be detected by infrared, Raman, surface-enhanced Raman, and tip-enhanced Raman spectroscopies, using data acquired from DNA solutions and mammalian cells. Various physical and chemical DNA damaging factors are taken into consideration, including ionizing and non-ionizing radiation, chemicals, and chemotherapeutic compounds. All major spectral markers of DNA damage are presented in several tables, to give the reader a possibility of fast identification of the spectral signature related to a particular type of DNA damage.

Abnormal protein aggregation has been intensively studied for over 40 years and broadly discussed in the literature due to its significant role in neurodegenerative diseases etiology. Structural reorganization and conformational changes of the secondary structure upon the aggregation determine aggregation pathways and cytotoxicity of the aggregates, and therefore, numerous analytical techniques are employed for a deep investigation into the secondary structure of abnormal protein aggregates. Molecular spectroscopies, including Raman and infrared ones, are routinely applied in such studies. Recently, the nanoscale spatial resolution of tip-enhanced Raman and infrared nanospectroscopies, as well as the high sensitivity of the surface-enhanced Raman spectroscopy, have brought new insights into our knowledge of abnormal protein aggregation. In this review, we order and summarize all nano- and micro-spectroscopic marker bands related to abnormal aggregation. Each part presents the physical principles of each particular spectroscopic technique listed above and a concise description of all spectral markers detected with these techniques in the spectra of neurodegenerative proteins and their model systems. Finally, a section concerning the application of multivariate data analysis for extraction of the spectral marker bands is included.

DNA covers the genetic information in all living organisms. Numerous intrinsic and extrinsic factors may influence the local structure of the DNA molecule or compromise its integrity. Detailed understanding of structural modifications of DNA resulting from interactions with other molecules and surrounding environment is of central importance for the future development of medicine and pharmacology. In this paper, we review the recent achievements in research on DNA structure at nanoscale. In particular, we focused on the molecular structure of DNA revealed by high-resolution AFM (Atomic Force Microscopy) imaging at liquid/solid interfaces. Such detailed structural studies were driven by the technical developments made in SPM (Scanning Probe Microscopy) techniques. Therefore, we describe here the working principles of AFM modes allowing high-resolution visualization of DNA structure under native (liquid) environment. While AFM provides well-resolved structure of molecules at nanoscale, it does not reveal the chemical structure and composition of studied samples. The simultaneous information combining the structural and chemical details of studied analyte allows achieve a comprehensive picture of investigated phenomenon. Therefore, we also summarize recent molecular spectroscopy studies, including Tip-Enhanced Raman Spectroscopy (TERS), on the DNA structure and its structural rearrangements.

The lack of specific and sensitive early diagnostic options for pancreatic cancer (PC) results in patients being largely diagnosed with late-stage disease, thus inoperable and burdened with high mortality. Molecular spectroscopic methodologies, such as Raman or infrared spectroscopies, show promise in becoming a leader in screening for early-stage cancer diseases, including PC. However, should such technology be introduced, the identification of differentiating spectral features between various cancer types is required. This would not be possible without the precise extraction of spectra without the contamination by necrosis, inflammation, desmoplasia, or extracellular fluids such as mucous that surround tumor cells. Moreover, an efficient methodology for their interpretation has not been well defined. In this study, we compared different methods of spectral analysis to find the best for investigating the biomolecular composition of PC cells cytoplasm and nuclei separately. Sixteen PC tissue samples of main PC subtypes (ductal adenocarcinoma, intraductal papillary mucinous carcinoma, and ampulla of Vater carcinoma) were collected with Raman hyperspectral mapping, resulting in 191,355 Raman spectra and analyzed with comparative methodologies, specifically, hierarchical cluster analysis, non-negative matrix factorization, T-distributed stochastic neighbor embedding, principal components analysis (PCA), and convolutional neural networks (CNN). As a result, we propose an innovative approach to spectra classification by CNN, combined with PCA for molecular characterization. The CNN-based spectra classification achieved over 98% successful validation rate. Subsequent analyses of spectral features revealed differences among PC subtypes and between the cytoplasm and nuclei of their cells. Our study establishes an optimal methodology for cancer tissue spectral data classification and interpretation that allows precise and cognitive studies of cancer cells and their subcellular components, without mixing the results with cancer-surrounding tissue. As a proof of concept, we describe findings that add to the spectroscopic understanding of PC. Graphical Abstract

Heme released from damaged and senescent red blood cells (RBCs) may contribute to oxidant-mediated cell injury. One of the recently investigated physiological processes, essential in preventing the inflammatory impact of labile heme, is its uptake from the bloodstream by endothelial cells (ECs). In this study, we investigated heme uptake by ECs starting from the model studies on the in vitro cellular level, through the endothelium layer on the ex vivo murine aortic tissues. As the cellular model, Human Aortic Endothelial Cells (HAECs) were chosen, and the concentration of labile heme was adjusted so to avoid the excessive toxic effect of the labile heme. We utilized label-free Raman imaging with two different excitation wavelengths to capture the uptake process in situ and characterize the oxidation state of the iron ion in the intercalated heme. The phenomenon of heme uptake was demonstrated in both, the healthy control C57Bl/6J and FVB animals, as well as in mice with developed atherosclerosis (ApoE/LDLR −/− mice). In the presented work, we presented for the first time Raman-based evidence on the heme uptake process by endothelial cells in both, in vitro and ex vivo systems.

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 (R 2  = 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.

Even several thousands of DNA lesions are induced in one cell within one day. DNA damage may lead to mutations, formation of chromosomal aberrations, or cellular death. A particularly cytotoxic type of DNA damage is single- and double-strand breaks (SSBs and DSBs, respectively). In this work, we followed DNA conformational transitions induced by the disruption of DNA backbone. Conformational changes of chromatin in living cells were induced by a bleomycin (BLM), an anticancer drug, which generates SSBs and DSBs. Raman micro-spectroscopy enabled to observe chemical changes at the level of single cell and to collect hyperspectral images of molecular structure and composition with sub-micrometer resolution. We applied multivariate data analysis methods to extract key information from registered data, particularly to probe DNA conformational changes. Applied methodology enabled to track conformational transition from B-DNA to A-DNA upon cellular response to BLM treatment. Additionally, increased expression of proteins within the cell nucleus resulting from the activation of repair processes was demonstrated. The ongoing DNA repair process under the BLM action was also confirmed with confocal laser scanning fluorescent microscopy.

PurposeKnowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue.MethodsHere, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues.ResultsThe results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies.ConclusionsMolecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing.

Fontan-associated liver disease (FALD) is a common complication in patients with Fontan circulation. We aimed to evaluate the red blood cell distribution width-to-platelet ratio (RPR) index as a non-invasive marker for assessing FALD severity, given its reported usefulness in in conditions such as hepatitis. This retrospective, cross-sectional study included adult Fontan patients. Laboratory tests, liver elastography, and hemodynamic parameters were analyzed. Fifty-six patients (24 females, 43%; median age 24 years, interquartile range [22-28]) were enrolled. Patients were grouped by liver stiffness (LS) stage: METAVIR ≤ F2 (22 patients, 39%) and > F2 (34 patients, 61%). The RPR index was significantly higher in the > F2 group (0.08 vs. 0.12, = 0.02), along with FIB-4 (0.68 vs. 1.06) and APRI (0.36 vs. 0.57). RPR correlated positively with liver stiffness ( = 0.27), total bilirubin ( = 0.43), cystatin C ( = 0.36), and creatinine ( = 0.38) but not with NT-proBNP, pulmonary artery pressure, or maximal oxygen consumption. The ROC curve analysis for predicting LS > F2 showed an AUC of 0.74 (95% CI: 0.61-0.87), comparable to FIB-4. Moreover, in the logistic regression model that accounted for age, gender, height, body weight, and systemic chamber morphology, RPR showed a significant association with LS > F2, with an odds ratio indicating a 1.4-fold (95% CI: 1.1-1.7) increase for every 0.01-unit increase in RPR. The RPR index is a simple, helpful tool for evaluating FALD severity in Fontan patients.

Hybrid procedures encompass surgical and catheter-based interventions aimed at enhancing survival rates and provide alternative treatment options for severely ill patients. This is a retrospective study aimed at analyzing hybrid procedures in infants with complex heart defects and duct-dependent systemic circulation at a high risk of cross-clamp circulation based on a single center's experience. We included 12 infants (7 boys, 5 girls) who were admitted to our department between 2019 and 2024 and who underwent hybrid surgery due to left-sided obstructive lesions. All neonates presented progressive heart failure and multiple organ dysfunction preceding hybrid procedures. The interventions were performed at a mean age of 8.6 days, with a mean body weight of 3.3 kg. Extracorporeal membrane oxygenation was required postoperatively in 2 patients. Early and late mortality were 8.3% and 16.6%, respectively. Interstage catheterization was performed in 7 patients requiring re-dilation of pulmonary artery bands and in 2 infants with arterial duct stent stenosis. Severe tricuspid regurgitation and progressive right ventricular dysfunction due to inadequate coronary perfusion were reported in 2 patients. Hybrid procedures are alternative options for infants with complex heart defects, especially when cross-clamp circulation poses high surgical risk. Multiple reinterventions are necessary to maintain stable hemodynamic status and relieve hypoxia or cardiac compromise. Future studies should aim to refine the timing and techniques of these interventions to improve survival and quality of life.