Raman profile alterations of irradiated human nasopharyngeal cancer cells detected with laser tweezer Raman spectroscopy

Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy

We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells' Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Detection of nasopharyngeal cancer cells using the laser tweezer Raman spectroscopy technology

Nasopharyngeal cancer (NPC), which arises from the nasopharyngeal epithelial lining, is one of the common malignant otorhinolaryngological tumors in China. Due to its insidious anatomical location and highly invasive and metastatic features, it is challenging to detect NPC at early stages. In this work, a rapid laser tweezer Raman spectroscopic (LTRS) system was built and used to trap and characterize single NPC cells. Using LTRS, high-quality Raman signals of the normal nasopharyngeal cell line (NP69) and NPC cells could be successfully obtained. By analysing the Raman peaks, some unique changes were found in components, such as DNA, amide I and amide III, in NPC cells compared with normal cells. In addition, we also used a multivariate statistical algorithm to establish a diagnostic model for identifying NPC cells with an accuracy of 90.0%. These results demonstrate that LTRS in combination with the multivariate statistical analysis is a convenient and high-efficiency cell identification technology, providing a novel and rapid methodology for NPC detection at the single cell level.

Exploring the Mechanism and Suggesting Combination Therapies for HDAC Inhibitors in Androgen Receptor-Null Prostate Cancer Using Multivariate Statistical Analysis and Data Mining Techniques

Previously, we showed that novel histone deacetylase (HDAC) inhibitors, N1-hydroxy-N ⁸-(4-(pyridine-2-carbothioamido)phenyl)octanediamide (Jazz90) and [chlorido(η⁵-pentamethylcyclopentadienyl)(N1-hydroxy-N8-(4-(pyridine-2-carbothioamido-κ² N, S)phenyl)octanediamide)rhodium(III)] chloride (Jazz167), have cytostatic and anti-angiogenic effects in androgen receptor-negative prostate cancer cells and are also non-toxic in BALB/c mice. However, only univariate statistical analysis was carried out to determine the role of individual proteins. In this study, multivariate statistical analyses (MVAs) and data mining procedures were carried out with the objective of determining the molecular networks that explain the growth inhibitory potential of Jazz90 and Jazz167 in PC3 cells and to determine potential inhibitors that can be used in combination with these HDAC inhibitors. Lasso regression revealed that angiogenic factors, vascular endothelial growth factor-A (VEGF-A), and vascular endothelial growth factor receptor-2 (VEGFR-2), alongside HDAC inhibition, predicted the reduction in cell number with an adjusted R ² value of 0.99 following Jazz90 treatment, whereas VEGFR-2, acetylation of histone-3, and HDAC inhibition predicted cell number with an adjusted R ² value of 0.84 following Jazz167 treatment. These results were further followed up with ridge regression, hierarchical cluster analysis, random forest classification (RFC), and support vector machines. RFC and support vector machines also predicted the treatment groups with a 100% accuracy. MVAs also revealed that Jazz90 should be examined in combination with epithelial to mesenchymal transitioning inhibitors, such as simvastatin and olaparib, whereas Jazz167 should be examined with venetoclax or navitoclax. Future studies should also address the roles of VEGF-A and VEGFR-2 in cellular proliferation, whereas p27 function should be examined for its role in PC3 cell migration.

Surface-enhanced Raman spectroscopy combined with stable isotope probing to assess the metabolic activity of Escherichia coli cells in chicken carcass wash water

Rapid evaluation of the metabolic activity of microorganisms is crucial in the assessment of the disinfection ability of various antimicrobial agents in the food industry. In this study, surface-enhanced Raman spectroscopy combined with isotope probing was employed for the analysis of the disinfection of single bacterial cells in the chicken carcass wash water. The Raman signals from single Escherichia coli O157:H7 cells were enhanced by in situ synthesis of silver nanoparticles. The ΔCD of the cells grown in presence of 0.5% hydrogen peroxide and 50 ppm chlorine was 5.86 ± 1.86% and 5.1 ± 2.3%, respectively, which showed significant reduction compared with cells grown in the absence of disinfecting agents (19.86 ± 2.51%) after 2 h of incubation. The study proved that the proposed method had the potential to assess the metabolic activity of microorganisms in other food products and optimize the disinfection process.

Analyzing macromolecular composition of E. Coli O157:H7 using Raman-stable isotope probing

Metabolic dynamics of bacterial cells is needed for understanding the correlation between changes in environmental conditions and cell metabolic activity. In this study, Raman spectroscopy combined with deuterium labelling was used to analyze the metabolic activity of a single Escherichia coli O157:H7 cell. The incorporation of deuterium from heavy water into cellular biomolecules resulted in the formation of carbon-deuterium (CD) peaks in the Raman spectra, indicating the cell metabolic activity. The broad vibrational peaks corresponding to CD and CH peaks encompassed different specific shifts of macromolecules such as protein, lipids, and nucleic acid. The utilization of tryptophan and oleic acid by the cell as the sole carbon source led to changes in cell lipid composition, as indicated by new peaks in the second derivative spectra. Thus, the proposed method could semi-quantitatively determine total metabolic activity, macromolecule specific identification, and lipid and protein metabolism in a single cell.

Characterization of Drug Resistance in Chronic Myeloid Leukemia Cells Based on Laser Tweezers Raman Spectroscopy

Multidrug resistance is highly associated with poor prognosis of chronic myeloid leukemia. This work aims to explore whether the laser tweezers Raman spectroscopy (LTRS) could be practical in separating adriamycin-resistant chronic myeloid leukemia cells K562/adriamycin from its parental cells K562, and to explore the potential mechanisms. Detection of LTRS initially reflected the spectral differences caused by chemoresistance including bands assigned to carbohydrates, amino acid, protein, lipids, and nucleic acid. In addition, principal components analysis as well as the classification and regression trees algorithms showed that the specificity and sensitivity were above 90%. Moreover, the band data-based classification and regression tree model and receiver operating characteristic curve further determined some important bands and band intensity ratios to be reliable indexes in discriminating K562 chemoresistance status. Finally, we highlighted three metabolism pathways correlated with chemoresistance. This work demonstrates that the label-free LTRS analysis combined with multivariate statistical analyses have great potential to be a novel analytical strategy at the single-cell level for rapid evaluation of the chemoresistance status of K562 cells.

Raman and Fluorescence Profiles Modifications of Muscular and Adipose Tissues Exposed to Low Energy X-ray Beams

This work aims to investigate changes induced by low-energy radiation in adipose and muscular tissues employing autofluorescence and Raman spectroscopic techniques. X-ray beams expositions with 25 and 35 kV at 0.11, 1.1, and 2.1 Gy radiation dose levels were applied. Changes in Raman line intensities at specific bands assigned to collagen, proteins, and lipids were observed. Autofluorescent analysis exhibit variations in the collagen and nicotinamide adenine dinucleotide emission (NADH), resulting from the structural modifications, variations on the reduced/oxidized fluorophores equilibrium followed by radiation exposure. Results show that Raman and fluorescence spectroscopy are suitable techniques to evaluate radiation effects on biomolecules even at low radiation doses and energies.

Exploring subcellular responses of prostate cancer cells to clinical doses of X-rays by Raman microspectroscopy

Modern techniques of radiotherapy such as fractioned radiotherapy require applications of low doses of ionizing radiation (up to 10 Gy) for effective patient treatment. It is, therefore, crucial to understand the response mechanisms in cancer cells irradiated with low (clinical) doses. The cell's response to irradiation depends on a dose and post-irradiation time. Both factors should be considered when studying the influence of ionizing radiation on cancer cells. Thus, in the present study, PC-3 prostate cancer cells were irradiated with clinical doses of X-rays to determine dose- and time-dependent response to the irradiation. Raman spectroscopy and biological methods (MTT and comet assays) were applied for the analysis of biochemical changes in the cells induced by low doses of X-ray irradiation at 0 h and 24 h post-irradiation timepoints. Due to a limited view of the biochemical changes at the subcellular level given by single spectrum Raman measurements, Raman mapping of the whole cell area was performed. The results were compared with those obtained for cell irradiation with high doses. The analysis was based on the Partial Least Squares Regression (PLSR) method for the cytoplasmic and nuclear regions separately. Additionally, for the first time, irradiation classification was performed to confirm Raman spectroscopy as a powerful tool for studies on cancer cells treated with clinical doses of ionizing radiation.

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

Metabolic reprogramming is a common hallmark in cancer. The high complexity and heterogeneity in cancer render it challenging for scientists to study cancer metabolism. Despite the recent advances in single-cell metabolomics based on mass spectrometry, the analysis of metabolites is still a destructive process, thus limiting in vivo investigations. Being label-free and nonperturbative, Raman spectroscopy offers intrinsic information for elucidating active biochemical processes at subcellular level. This review summarizes recent applications of Raman-based techniques, including spontaneous Raman spectroscopy and imaging, coherent Raman imaging, and Raman-stable isotope probing, in contribution to the molecular understanding of the complex biological processes in the disease. In addition, this review discusses possible future directions of Raman-based technologies in cancer research.

Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging its molecular specificity, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also perform multivariate curve resolution alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.