Increased obesogenic action of palmitic acid during early stage of adipogenesis

The functional differences between preadipocytes and fully differentiated mature adipocytes derived from stromal vascular fraction stem cells, as well as primary adipocytes have been analysed by evaluating their response to the obesogenic factor (a saturated fatty acid) and TNF-triggered inflammation. The analysis of single adipocytes shows that the saturated fatty acid (palmitic acid) accumulation is accompanied by inflammation and considerably dependent on the stage of the adipogenesis. In particular, preadipocytes show the exceptional potential for palmitic acid uptake resulting in their hypertrophy and the elevated cellular expression of the inflammation marker (ICAM-1). Our research provides new information on the impact of obesogenic factors on preadipocytes that is important in the light of childhood obesity prevention.

Point-of-care device for the noninvasive assessment of hepatic macrosteatosis in liver donors

BACKGROUND

Quantification of macrosteatosis (MS) in the liver is important given that it has shown to directly correlate with adverse post-liver transplant (LT) outcomes. With advances in medical technology and an implicit understanding of pathology, noninvasive methods of quantitatively assessing MS are in various stages of development. Each of these methods is based on the physical principles of differences between a fat-laden hepatocyte and a normal one.

METHODS

In this regard, after a proof-of-concept study on a prototype for a simple, real-time, handheld device using the principle of diffuse reflectance spectroscopy, this study presents an upgraded point-of-care (POC) device for the noninvasive assessment of hepatic MS in liver donors.

RESULTS

The device was validated on cohort of donor livers and showed a sensitivity (0.0021 V/% fat) and highly correlated (r = 0.9868, P < .0001) with gold-standard liver biopsy. Results showed that this upgraded POC device provides a reliable method for the noninvasive assessment of hepatic MS, which is crucial for selecting suitable donor livers for LT.

CONCLUSION

The device has the potential to be an invaluable apparatus at the hands of the organ-retrieving surgeon. It is noninvasive, portable (handheld), and economic; provides real-time readings of the percentage of MS; and can be efficaciously handled by any member of the organ-retrieving team.

Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis

Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality. In this paper, we propose a method of utilizing a spectral encoder to extract effective spectral features, which can significantly enhance the reliability and precision of quantitative analysis. We built a latent encoded feature regression model; in the process of utilizing the autoencoder for reconstructing the spectrometer output, the latent feature obtained from the intermediate bottleneck layer is extracted. Then, these latent features are fed into a deep regression model for component concentration prediction. Through detailed ablation and comparative experiments, our proposed model demonstrates superior performance to common methods on single-component and multi-component mixture datasets, remarkably improving regression precision while without needing user-selected parameters and eliminating the interference of irrelevant and redundant information. Furthermore, in-depth analysis reveals that latent encoded feature possesses strong nonlinear feature representation capabilities, low computational costs, wide adaptability, and robustness against noise interference. This highlights its effectiveness in spectral regression tasks and indicates its potential in other application fields. Sufficient experimental results show that our proposed method provides a novel and effective feature extraction approach for spectral analysis, which is simple, suitable for various methods, and can meet the measurement needs of different real-world scenarios.

A Raman spectral marker for the iso-octyl chain structure of cholesterol

Raman spectroscopy provides label-free, specific analysis of biomolecular structure and interactions. It could have a greater impact with improved characterization of complex fingerprint vibrations. Many Raman peaks have been assigned to cholesterol, for example, but the molecular vibrations associated with those peaks are not known. In this report, time-dependent density functional theory calculations of the Raman spectrum of cholesterol are compared to measurements on microcrystalline powder to identify 23 peaks in the Raman spectrum. Among them, a band of six peaks is found to be sensitive to the conformational structure of cholesterol's iso-octyl chain. Calculations on 10 conformers in this spectral band are fit to experimental spectra to probe the cholesterol chain structure in purified powder and in phospholipid vesicles. In vesicles, the chain is found to bend perpendicular to the steroid rings, supporting the case that the chain is a dynamic structure that contributes to lipid condensation and other effects of cholesterol in biomembranes. Statement of Significance: Here we use density functional theory to identify a band of six peaks in cholesterol's Raman spectrum that is sensitive to the conformational structure of cholesterol's chain. Raman spectra were analyzed to show that in fluid-phase lipid membranes, about half of the cholesterol chains point perpendicular to the steroid rings. This new method of label-free structural analysis could make significant contributions to our understanding of cholesterol's critical role in biomembrane structure and function. More broadly, the results show that computational quantum chemistry Raman spectroscopy can make significant new contributions to molecular structure when spectra are interpreted with computational quantum chemistry.

Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases

Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.

Vascular lipid droplets formed in response to TNF, hypoxia or OA: biochemical composition and prostacyclin generation

Biogenesis of lipid droplets (LDs) in various cells plays an important role in various physiological and pathological processes. However, the function of LDs in endothelial physiology and pathology is not well understood. In the present work, we investigated the formation of LDs and prostacyclin (PGI₂) generation in the vascular tissue of isolated murine aortas following activation by pro-inflammatory factors: tumor necrosis factor (TNF), lipopolysaccharides (LPS), angiotensin II (AngII), hypoxic conditions, or oleic acid (OA). The abundance, size, and biochemical composition of LDs was characterized based on Raman spectroscopy and fluorescence imaging. We found that blockade of lipolysis by the adipose triglyceride lipase (ATGL) delayed LDs degradation and simultaneously blunted PGI₂ generation in aorta treated with all tested pro-inflammatory stimuli. Furthermore, the analysis of Raman spectra of LDs in the isolated vessels stimulated by TNF, LPS, AngII, or hypoxia uncovered that these LDs were all rich in highly unsaturated lipids and had a negligible content of phospholipids and cholesterols. Additionally, by comparing the Raman signature of endothelial LDs under hypoxic or OA-overload conditions in the presence or absence of ATGL inhibitor, atglistatin, we show that atglistatin does not affect the biochemical composition of LDs. Altogether, independent of whether LDs were induced by pro-inflammatory stimuli, hypoxia, or oleic acid, and of whether they were composed of highly unsaturated or less unsaturated lipids, we observed LDs formation invariably associated with ATGL-dependent PGI₂ generation. In conclusion, vascular LDs formation and ATGL-dependent PGI₂ generation represent a universal response to vascular pro-inflammatory insult.

Raman imaging of rat nonalcoholic fatty liver tissues reveals distinct biomolecular states

An essential challenge in diagnosing states of nonalcoholic fatty liver disease (NAFLD) is the early prediction of progression from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) before the disease progresses. Histological diagnoses of NAFLD rely on the appearance of anomalous tissue morphologies, and it is difficult to segment the biomolecular environment of the tissue through a conventional histopathological approach. Here, we show that hyperspectral Raman imaging provides diagnostic information on NAFLD in rats, as spectral changes among disease states can be detected before histological characteristics emerge. Our results demonstrate that Raman imaging of NAFLD can be a useful tool for histopathologists, offering biomolecular distinctions among tissue states that cannot be observed through standard histopathological means.

Real-time assessment of liver fat content using a filter-based Raman system operating under ambient light through lock-in amplification

During liver procurement, surgeons mostly rely on their subjective visual inspection of the liver to assess the degree of fatty infiltration, for which misclassification is common. We developed a Raman system, which consists of a 1064 nm laser, a handheld probe, optical filters, photodiodes, and a lock-in amplifier for real-time assessment of liver fat contents. The system performs consistently in normal and strong ambient light, and the excitation incident light penetrates at least 1 mm into duck fat phantoms and duck liver samples. The signal intensity is linearly correlated with MRI-calibrated fat contents of the phantoms and the liver samples.

Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network

In this study, convolutional neural network based on Gramian angular field (GAF-CNN) was firstly proposed. The 1-D Raman spectral data was converted into images and used for predicting the biochemical value of blood glucose. 106 sets of blood spectrums were acquired by Fourier transform (FT) Raman spectroscopy. Spectral data ranging from 800 cm^-1 to 1800 cm^-1 were selected for quantitative analysis of the blood glucose. Data augmentation was used to train neural networks and normalize the Raman spectra. And, we applied principal component analysis (PCA) for dimension reduction and information extraction. The root mean squared error of prediction (RMSEP) are 0.06570 (GADF) and 0.06774 (GASF), the determination coefficient of prediction (R²) are 0.99929 (GADF) and 0.99925 (GASF), and the residual predictive deviation of prediction (RPD) are 37.56324 (GADF) and 36.43362 (GASF). GAF-CNN model performed better for predicting of glucose concentration. The GAF-CNN model can be used to establish a calibration model to predict blood glucose concentration.

Diffuse Reflectance Spectroscopy for The Assessment of Steatosis in Liver Phantom and Liver Donors - A Pilot Study

This paper reports the application of a low-cost diagnostic modality for fat analysis in a liver phantom as well as human liver donors. The device works on the principle of diffuse reflectance spectroscopy, which absorbs and/or scatters depending upon the molecules that compose a tissue. Here, we describe the development of liver phantom of varying fat concentration using saturated fat mimicking liver steatosis. Followed by a pilot study in the human liver donor setting. Later, handheld device based on Infrared-LED and Photodetector for real-time time assessment of live donor liver and fat assessment. Clinical Relevance- This device can be used in the development of an accurate and non-invasive for quantification of liver fat in the deceased donor selection process. It has an error margin of 10% in the quantification of fat which is comparable to a standard biopsy technique.

The distinct phenotype of primary adipocytes and adipocytes derived from stem cells of white adipose tissue as assessed by Raman and fluorescence imaging

Spectroscopy-based analysis of chemical composition of cells is a tool still scarcely used in biological sciences, although it provides unique information about the cell identity accessible in vivo and in situ. Through time-lapse spectroscopic monitoring of adipogenesis in brown and white adipose tissue-derived stem cells we have demonstrated that considerable chemical and functional changes occur along with cells differentiation and maturation, yet yielding mature adipocytes with a similar chemical composition, independent of the cellular origin (white or brown adipose tissue). However, in essence, these stem cell-derived adipocytes have a markedly different chemical composition compared to mature primary adipocytes. The consequences of this different chemical (and, hence, functional) identity have great importance in the context of selecting a suitable methodology for adipogenesis studies, particularly in obesity-related research.

A shallow convolutional neural network with elastic nets for blood glucose quantitative analysis using Raman spectroscopy

In this paper, a one-dimensional shallow convolutional neural network structure combined with elastic nets (1D-SCNN-EN) was firstly proposed to predict the glucose concentration of blood by Raman spectroscopy. A total of 106 different blood glucose spectra were obtained by Fourier transform (FT) Raman spectroscopy. The one-dimensional shallow convolutional neural network, with elastic nets added to the full connected layer, was presented to capture multiple deep features and reduce the complexity of the model. The 1D-SCNN-EN model has a better performance than conventional approaches (partial least squares and support vector machine). The root mean squared error of calibration (RMSEC), the root mean squared error of prediction (RMSEP), the determination coefficient of prediction (R_P²), and the residual predictive deviation of prediction (RPD) were 0.10262, 0.11210, 0.99403, and 12.94601, respectively. The experiment results showed that the 1D-SCNN-EN model has a higher prediction accuracy and stronger robustness than the other regression models. The overall studies indicated that the 1D-SCNN-EN model looked promising for predict the glucose concentration of blood by Raman spectroscopy when the sample size is small.

The impact of HPV infection on human glycogen and lipid metabolism - a review

Reinterpretation of the Wartburg effect leads to understanding aerobic glycolysis as a process that provides considerable amount of molecular precursors for the production of lipids, nucleotides and amino acids that are necessary for continuous growth and rapid proliferation characteristic for cancer cells. Human papilloma virus (HPV) is a number one cause of cervical carcinoma with 99% of the cervical cancer patients being HPV positive. This tight link between HPV and cancer raises the question if and how HPV impact cells to reprogram their metabolism? Focusing on early phase proteins E1, E2, E5, E6 and E7 we demonstrate that HPV activates plethora of metabolic pathways and directly influences enzymes of the glycolysis pathway to promote the Warburg effect by increasing glucose uptake, activating glycolysis and pentose phosphate pathway, increasing the level of lactate dehydrogenase A synthesis and inhibiting β-oxidation. Our considerations lead to conclusion that HPV is substantially involved in metabolic cell reprogramming toward neoplastic phenotype and its metabolic activity is the fundamental reason of its oncogenicity.

Autofluorescence spectroscopy and multivariate analysis for predicting the induced damages to other organs due to liver fibrosis

When our liver does not work well, it can induce damage to other organs causing their dysfunction. With this background, we aim to study the effect of liver fibrosis on other organs such as heart, lungs, kidney and spleen by assessing the variations in the inherent emission property of the tissue, using fluorescence spectroscopy. Fluorescence emission spectra from excised organs of liver fibrosis induced rats were collected at excitation wavelengths 320 and 410 nm. Optical redox ratio derived from the spectral data supported by multivariate statistical analysis, principal component analysis followed by linear discriminant analysis (PCA-LDA) distinguished between control and fibrosis induced groups. The two different excitation wavelength provided variations in the endogenous flurophores collagen, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), lipopigments and porphyrins. Additionally, evaluation of redox ratio provided variations in tissue metabolic activity of different organs. The PCA-LDA modelling yielded a sensitivity of 85 to 97% and specificity of 80 to 96% on 320 nm excitation and a sensitivity of 72 to 100% and specificity of 59 to 100% on 410 nm excitation. Fluorescence emission spectral study along with multivariate analysis paved way to identify the biochemical alterations caused to other organs due to the development of liver fibrosis, which could lead to their damage and dysfunction.

Distinct Chemical Changes in Abdominal but Not in Thoracic Aorta upon Atherosclerosis Studied Using Fiber Optic Raman Spectroscopy

Fiber optic Raman spectroscopy and Raman microscopy were used to investigate alterations in the aorta wall and the surrounding perivascular adipose tissue (PVAT) in the murine model of atherosclerosis (Apoe^-/-/Ldlr^-/- mice). Both abdominal and thoracic parts of the aorta were studied to account for the heterogenic chemical composition of aorta and its localization-dependent response in progression of atherosclerosis. The average Raman spectra obtained for both parts of aorta cross sections revealed that the chemical composition of intima-media layers along aorta remains relatively homogeneous while the lipid content in the adventitia layer markedly increases with decreasing distance to PVAT. Moreover, our results demonstrate that the increase of the lipid to protein ratio in the aorta wall correlates directly with the increased unsaturation level of lipids in PVAT and these changes occur only in the abdominal, but not in thoracic, aorta. In summary, distinct pathophysiological response in the aortic vascular wall could be uncovered by fiber optic Raman spectroscopy based on simple parameters detecting chemical contents of lipids in PVAT.

Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease

Histopathology requires the expertise of specialists to diagnose morphological features of cells and tissues. Raman imaging can provide additional biochemical information to benefit histological disease diagnosis. Using a dietary model of nonalcoholic fatty liver disease in rats, we combine Raman imaging with machine learning and information theory to evaluate cellular-level information in liver tissue samples. After increasing signal-to-noise ratio in the Raman images through superpixel segmentation, we extract biochemically distinct regions within liver tissues, allowing for quantification of characteristic biochemical components such as vitamin A and lipids. Armed with microscopic information about the biochemical composition of the liver tissues, we group tissues having similar composition, providing a descriptor enabling inference of tissue states, contributing valuable information to histological inspection.

Bright Dots and Smart Optical Microscopy to Probe Intracellular Events in Single Cells

Probing intracellular events is a key step in developing new biomedical methodologies. Optical microscopy has been one of the best options to observe biological samples at single cell and sub-cellular resolutions. Morphological changes are readily detectable in brightfield images. When stained with fluorescent molecules, distributions of intracellular organelles, and biological molecules are made visible using fluorescence microscopes. In addition to these morphological views of cells, optical microscopy can reveal the chemical and physical status of defined intracellular spaces. This review begins with a brief overview of genetically encoded fluorescent probes and small fluorescent chemical dyes. Although these are the most common approaches, probing is also made possible by using tiny materials that are incorporated into cells. When these tiny materials emit enough photons, it is possible to draw conclusions about the environment in which the tiny material resides. Recent advances in these tiny but sufficiently bright fluorescent materials are nextly reviewed to show their applications in tracking target molecules and in temperature imaging of intracellular spots. The last section of this review addresses purely optical methods for reading intracellular status without staining with probes. These non-labeling methods are especially essential when biospecimens are thereafter required for in vivo uses, such as in regenerative medicine.