Autofluorescence Spectroscopy Research Articles

Label-free classification of atherosclerosis plaque via OCT and ultraviolet autofluorescence spectroscopy

We proposed a label-free method for the identification and classification of atherosclerosis plaques by combining optical coherence tomography (OCT) with ultraviolet autofluorescence spectroscopy (uFLS). By aligning the OCT source and the FLS excitation beams, we were able to illuminate the same spot on plaques fixed to the integrated probe, which underwent rotational scanning. This setup enabled the detection of both OCT images and uFLS spectra of the plaques in a co-localized manner. In our approach, a 1300 nm centered swept laser source was utilized for OCT imaging, while a 355 nm laser source, along with a lensed multimode fiber, served as the fluorescence probe for uFLS. The successful acquisition of OCT-uFLS images provided complementary information regarding the tomographic internal structure and biochemical components within the vessels, allowing for comprehensive identification and classification of atherosclerosis plaques. Furthermore, we achieved quantitative measurements and analysis of fluorescence spectra from three main component channels, corresponding to collagen, elastin, and lipid. This enabled us to differentiate atherosclerosis from normal vessel walls and determine the specific types. With the implementation of this dual-modal OCT-uFLS technique, it is possible to facilitate the label-free classification of various histopathological types of atherosclerosis plaques, which holds the potential for both diagnosis and image-guided ablation therapy applications.

Raman and autofluorescence spectroscopy for in situ identification of neoplastic tissue during surgical treatment of brain tumors.

Raman spectroscopy (RS) is a promising method for brain tumor detection. Near-infrared autofluorescence (AF) acquired during RS provides additional useful information for tumor identification and was investigated in comparison with RS for delineating brain tumors in situ. Raman spectra were acquired together with AF in situ within the solid tumor and at the tumor border during routine brain tumor surgeries (218 spectra; glioma WHO II-III, n = 6; GBM, n = 10; metastases, n = 10; meningioma, n = 3). Tissue classification for tumor identification in situ was trained on ex vivo data (375 spectra; glioma/GBM patients, n = 20; metastases, n = 11; meningioma, n = 13; and epileptic hippocampi, n = 4). Both in situ and ex vivo data showed that AF intensity in brain tumors was lower than that in border regions and normal brain tissue. Moreover, a positive correlation was observed between the AF intensity and the intensity of the Raman band corresponding to lipids at 1437cm- 1, while a negative correlation was found with the intensity of the protein band at 1260cm- 1. The classification of in situ AF and RS datasets matched the surgeon's evaluation of tissue type, with correct rates of 0.83 and 0.84, respectively. Similar correct rates were achieved in comparison to histopathology of tissue biopsies resected in selected measurement positions (AF: 0.80, RS: 0.83). Spectroscopy was successfully integrated into existing neurosurgical workflows, and in situ spectroscopic data could be classified based on ex vivo data. RS confirmed its ability to detect brain tumors, while AF emerged as a competitive method for intraoperative tumor delineation.

Delineation of gastrointestinal tumors biopsies using a fluorescence lifetime imaging optical fiber probe.

Autofluorescence spectroscopy has emerged in recent years as a powerful tool to report label-free contrast between normal and diseased tissues, both in vivo and ex-vivo. We report the application of an instrument employing an optical fiber probe and capable of performing real-time autofluorescence lifetime imaging at a macroscopic scale, under bright background conditions. We validate and demonstrate the practicality of this technology to discriminate healthy against neoplastic tissue in freshly excised tumor biopsies. The capability of delineating tumor margins through processing the fluorescence decays in the phasors domain was demonstrated on four different types of cancer, highlighting the broad range of potential clinical applications for the proposed approach. The presented results suggest that our autofluorescence lifetime imaging probe, together with phasor analysis, can offer a real-time tool to observe lifetime contrast on tissues and, thus, is a suitable candidate for improving in situ tissue diagnostics during surgery.

Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy.

Measurements of macular pigment optical density (MPOD) using the autofluorescence spectroscopy yield underestimations of actual values in eyes with cataracts. Previously, we proposed a correction method for this error using deep learning (DL); however, the correction performance was validated through internal cross-validation. This cross-sectional study aimed to validate this approach using an external validation dataset. MPODs at 0.25°, 0.5°, 1°, and 2° eccentricities and macular pigment optical volume (MPOV) within 9° eccentricity were measured using SPECTRALIS (Heidelberg Engineering, Heidelberg, Germany) in 197 (training dataset inherited from our previous study) and 157 eyes (validating dataset) before and after cataract surgery. A DL model was trained to predict the corrected value from the pre-operative value using the training dataset, and we measured the discrepancy between the corrected value and the actual postoperative value. Subsequently, the prediction performance was validated using a validation dataset. Using the validation dataset, the mean absolute values of errors for MPOD and MPOV corrected using DL ranged from 8.2 to 12.4%, which were lower than values with no correction (P < 0.001, linear mixed model with Tukey's test). The error depended on the autofluorescence image quality used to calculate MPOD. The mean errors in high and moderate quality images ranged from 6.0 to 11.4%, which were lower than those of poor quality images. The usefulness of the DL correction method was validated. Deep learning reduced the error for a relatively good autofluorescence image quality. Poor-quality images were not corrected.

Discrimination of vocal folds lesions by multiclass classification using autofluorescence spectroscopy: An ex vivo study.

Autofluorescence spectroscopy is effective for noninvasive detection but underutilized in tissue with various pathology analyses. This study evaluates whether AFS can be used to discriminate between different types of laryngeal lesions in view of assisting in vocal fold surgery and preoperative investigations. A total of 1308 spectra were recorded from 29 vocal fold samples obtained from 23 patients. Multiclass analysis was performed on the spectral data, categorizing lesions into normal, benign, dysplastic, or carcinoma. Through an appropriate selection of spectral components and a cascading classification approach based on artificial neural networks, a classification rate of 97% was achieved for each lesion class, compared to 52% using autofluorescence intensity. The ex vivo study demonstrates the effectiveness of AFS combined with multivariate analysis for accurate classification of vocal fold lesions. Comprehensive analysis of spectral data significantly improves classification accuracy, such as distinguishing malignant from precancerous or benign lesions.

Label-free visualization of unfolding and crosslinking mediated protein aggregation in nonenzymatically glycated proteins.

Nonenzymatic glycation (NEG) unfolds and crosslinks proteins, resulting in aggregation. Label-free evaluation of such structural changes, without disturbing molecular integrity, would be beneficial for understanding the fundamental mechanisms of protein aggregation. The current study demonstrates the assessment of NEG-induced protein aggregation by combining autofluorescence (AF) spectroscopy and imaging. The methylglyoxal (MG) induced protein unfolding and the formation of cross-linking advanced glycation end-products (AGEs) leading to aggregation were evaluated using deep-UV-induced-autofluorescence (dUV-AF) spectroscopy in proteins with distinct structural characteristics. Since the AGEs formed on proteins are fluorescent, the study demonstrated the possibility of autofluorescence imaging of NEG-induced protein aggregates. Autofluorescence spectroscopy can potentially reveal molecular alterations such as protein unfolding and cross-linking. In contrast, AGE-based autofluorescence imaging offers a means to visually explore the structural arrangement of aggregates, regardless of whether they are amyloid or non-amyloid in nature.

Identification of common textile microplastics via autofluorescence spectroscopy coupled with k-means cluster analysis.

Microplastics are an emerging anthropogenic pollutant risk with a significant body of research dedicated to understanding the implications further. To generate the databases required to characterize the impact of microplastics on our environment, and improve recovery and recycling of current plastic materials, we need rapid, in-line characterization that can distinguish individual polymer types. Here, autofluorescence spectroscopy was investigated as an alternative characterization method to the current leading techniques based on vibrational spectroscopy. It was confirmed that the autofluorescence of seven common textile polymers (acrylic, polyester, nylon, polyethylene, polypropylene, cellulose/cotton, wool) arose due to the cluster-triggered emission phenomenon. Both simulated polymer aging via photooxidation and dyeing of the polymers were found to affect the resultant autofluorescence spectra. A total of 1485 spectra from 39 unique sample groups (polymer type, colour, and degree of photooxidation) were analysed via machine learning (k-means cluster analysis). Correct identification of the polymer type was achieved in 71% of the cases from only eight input values (normalized intensity values at three autofluorescence emission wavelengths, the total autofluorescence emission intensity, the sample RGB colour values, and the sample shape). This represents a significant step towards automated polymer identification at the sub-second time scales required for the in-line characterization of microplastics.

Effectiveness of autofluorescence spectroscopy method use in melanocytic dysplasia diagnosis

Effectiveness of autofluorescence spectroscopy method use in melanocytic dysplasia diagnosis

Autofluorescence spectroscopy in photodynamic therapy for skin rejuvenation: A theranostic approach in aesthetic medicine

BackgroundThe method of photodynamic therapy for skin rejuvenation (PDT-SR) provides an improvement in appearance with a safe and painless effect. The quality of treatment is most often assessed subjectively. The most informative morphological control methods are rarely used due to the invasiveness of the sampling procedure. AimThis study aimed to find out the possibility of using skin autofluorescence spectroscopy (SAF) for an objective assessment of changes occurring in the skin during PDT-SR. MethodsThis study included 12 volunteers (10 women, 2 men) aged 32 to 79 years. Two (n = 6) or three (n = 6) PDT sessions were performed at intervals of 13–30 days. Photosensitizer chlorin e6, exposure 20 min, energy density 18–24 J/cm2 were used. SAF spectra were recorded using a two-wavelength fiber optic spectrometer under excitation at wavelengths (λex) of 365 nm and 440 nm. Measurements were made both before and after each PDT session and up to 25–238 days from the start of treatment. For the evaluation, we used the spectra AF365(λ) and AF440(λ) averaged over 40 points corrected for diffuse reflection at λex=440 nm in the range λem= 460–700 nm, as well as the spectra of the ratios AFN365(λ) and AFN440(λ), which were obtained by dividing the intensities of the current spectra by the intensities collected before PDT-SR. ResultsPDT-SR led to changes in both the intensity and shape of the spectra. Analysis of the spectra using numerical fitting of the spectra showed that the main changes can be explained by changes in the content of advanced glycation end products (AGEs), as well as lipofuscin-like lipopigments (LPs) and porphyrins (PPs). The spectra of AGEs upon excitation at wavelengths of 365 and 440 nm differ, which may be due to the formation of two types of bonds, with collagen and elastin. By the end of the study, the vast majority of the examined volunteers showed a significant decrease of the parameters characterizing both of these types of AGEs, AGE365 (0.56–1.2) and AGE440 (0.58–1.01), relative to the beginning of the study. In most cases, a decrease was also noted for LPs and PPs. AGE365 and AGE440 were positively correlated with the age of the volunteers (r2 = 0.26–0.46 %). A steady decrease in the content of AGEs occurred approximately on the 40th day. ConclusionSAF spectroscopy makes it possible to assess changes in the content of AGEs, LPs, and PPs in the skin during PDT-SR. The method has great potential for non-invasive monitoring of the treatment process, as well as its improvement, including through its personalization. In addition, the method can be used to study the mechanisms of age-related skin changes at the molecular level and to study the processes of rejuvenation.

Classification of intracranial tumors based on optical-spectral analysis

The motivation for the present study was the need to develop methods of urgent intraoperative biopsy during surgery for removal of intracranial tumors. Based on the experience of previous joint work of GPI RAS and N.N. Burdenko National Medical Research Center of Neurosurgery to introduce fluorescence spectroscopy methods into clinical practice, an approach combining various optical-spectral techniques, such as autofluorescence spectroscopy, fluorescence of 5-ALA induced protoporphyrin IX, diffuse reflection of broadband light, which can be used to determine hemoglobin concentration in tissues and their optical density, Raman spectroscopy, which is a spectroscopic method that allows detection of various molecules in tissues by vibrations of individual characteristic molecular bonds. Such a variety of optical and spectral characteristics makes it difficult for the surgeon to analyze them directly during surgery, as it is usually realized in the case of fluorescence methods – tumor tissue can be distinguished from normal with a certain degree of certainty by fluorescence intensity exceeding a threshold value. In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithms to build a intraoperative decision support system for the surgeon. This paper presents research in this direction. Our earlier statistical analysis of the optical-spectral features allowed identifying statistically significant spectral ranges for analysis of diagnostically important tissue components. Studies of dimensionality reduction techniques of the optical-spectral feature vector and methods of clustering of the studied samples also allowed us to approach the implementation of the automatic classification method. Importantly, the classification task can be used in two applications – to differentiate between different tumors and to differentiate between different parts of the same (center, perifocal zone, normal) tumor. This paper presents the results of our research in the first direction. We investigated the combination of several methods and showed the possibility of differentiating glial and meningeal tumors based on the proposed optical-spectral analysis method.

On the Nature of Stationary and Time-Resolved Fluorescence Spectroscopy of Collagen Powder from Bovine Achilles Tendon.

This paper presents a more systematic study of steady-state and time-resolved autofluorescence spectroscopy of collagen isolated from bovine Achilles tendon. In steady-state fluorescence measurements, the excitation and emission spectra of collagen powder, recorded at different fluorescence excitation and detection wavelengths, were compared with the fluorescence excitation and emission spectra of the amino acids phenylalanine, tyrosine, and tryptophan, as well as with similar spectra for 13 autofluorescent collagen cross-links, which have been identified and described in the literature so far. In time-resolved studies, fluorescence was excited by the pulsed light of different wavelengths, and for each excitation wavelength, fluorescence decay was recorded for several detection wavelengths. Data analysis allowed recovery of the fluorescence decay times for each experimental excitation detection event. The obtained information on the decay times of the measured fluorescent signals was discussed, taking into account the available literature data from similar studies of isolated collagen and collagen-rich tissues. Based on the obtained results, it was found that the shape and position of the measured fluorescence excitation and emission spectra of collagen strongly depend on the emission and excitation wavelengths selected in the measurements. From the recorded excitation and emission bands of collagen, it can be concluded with high probability that there are additional, so far unidentified, collagen cross-links, which can be excited at longer excitation wavelengths. In addition, the collagen excitation spectra were measured at longer emission wavelengths at which the collagen cross-links emit fluorescent light. In addition to the emission spectra obtained for excitation in the deep-UV region, the results of time-resolved fluorescence studies with excitation in the deep-UV region and detection at longer wavelengths suggest that fluorescence excitation energy transfer processes occur from the amino acids to the collagen cross-links, and also between the cross-links themselves.

Stomach tissue classification using autofluorescence spectroscopy and machine learning.

Determination of stomach tumor location and invasion depth requires delineation of gastric histological structure, which has hitherto been widely accomplished by histochemical staining. In recent years, alternative histochemical evaluation methods have been pursued to accelerate intraoperative diagnosis, often by bypassing the time-consuming step of dyeing. Owing to strong endogenous signals from coenzymes, metabolites, and proteins, autofluorescence spectroscopy is a favorable candidate technique to achieve this aim. We investigated stomach tissue slices and block specimens using a fast fluorescence imaging scanner. To obtain histological information from broad and structureless fluorescence spectra, we analyzed tens of thousands of spectra with multiple machine-learning algorithms and built a tissue classification model trained with dissected gastric tissues. A machine-learning-based spectro-histological model was built based on the autofluorescence spectra measured from stomach tissue samples with delineated and validated histological structures. The scores from a principal components analysis were employed as input features, and prediction accuracy was confirmed to be 92.0%, 90.1%, and 91.4% for mucosa, submucosa, and muscularis propria, respectively. We investigated the tissue samples in both sliced and block forms using a fast fluorescence imaging scanner. We successfully demonstrated differentiation of multiple tissue layers of well-defined specimens with the guidance of a histologist. Our spectro-histology classification model is applicable to histological prediction for both tissue blocks and slices, even though only sliced samples were trained.

Depletion attraction driven formation of Spirulina emulsion gels for 3D printing

3D printing has been applied to create nutritious foods with tailored textures for the elderly. However, the edible materials that possess the elastoplastic properties and shear-thinning behavior required for direct-write 3D printing are limited. In this study, we developed a simple approach to fabricate Spirulina emulsion gels as edible 3D printable material by exploiting depletion attraction between emulsion droplets. By controlling the volume ratio of continuous and oil phases, mixing highly viscous Spirulina slurry and vegetable oil could form self-standing oil-in-water emulsion gels with elastoplastic rheological properties. Also, adding food polysaccharides into the continuous phase could act as depletants to drive the formation of emulsion gels. Auto-fluorescence spectroscopy and microscopy were employed to characterize the interfacial protein compositions of the emulsion, showing that phycobilisome (PBS) particles might play a dominant role in the formation of Spirulina emulsions. The depletion attraction could drive the adsorption of PBS particles onto the droplet interface, and the particle-armored droplets in the dispersed phase connected into a percolating droplet cluster, resulting in arrested dynamics and gelation. Overall, this novel approach can be applied by exploiting depletion attraction and opens up possibilities for using high-solids viscous food slurries to produce personalized 3D-printed foods.

Pressure treatment has a differential effect on cyanide- and ethanol-induced autofluorescence response in yeast.

Cellular processes associated with biological membranes and multimeric associations exhibit pressure sensitivity. In cellular respiration for example, pressure-regulated respiratory oxidases and cytochromes are found in piezophilic microbes, and pressure reduces oxygen consumption rates in eukaryotes. The presence of piezotolerant obligate aerobic yeasts in deep sea environments further justifies investigating pressure effects on cellular respiratory mechanisms. Ambient pressure analysis of UV-excited autofluorescence signals used for monitoring respiratory metabolism have identified multiple cellular NADH and NADPH conformations, significant because the distribution of conformational forms depends on metabolic conditions. Here, we use UV-excited autofluorescence spectroscopy with the spectral response quantified using phasor analysis to monitor cellular NADH and NADPH conformation in Saccharomyces cerevisiae (baker's yeast) suspensions after 30-minute pressure treatment at up to 11 kpsi (75.8 MPa). Specifically, we investigate the effects of pressure treatment on the autofluorescence response to the additions of cyanide and ethanol (both respiratory inhibitors at ambient pressure). The autofluorescence response to cyanide measured 30 min after pressure release shows a diminished response, while the response to ethanol shows no detectable difference when compared with the response prior to pressure treatment. Conditions for this study are similar to those previously reported to activate transcription factors in yeast due to pressure-induced stress tolerance, suggesting autofluorescence may be useful for assessing mitochondrial functional under induced stress-tolerance conditions.

Specific Identification of Breast Tumors Based on Laser-Induced Autofluorescence Spectroscopy

Specific Identification of Breast Tumors Based on Laser-Induced Autofluorescence Spectroscopy

Autofluorescence-based high-throughput isolation of nonbleaching Cyanidioschyzon merolae strains under nitrogen-depletion.

Photosynthetic organisms maintain optimum levels of photosynthetic pigments in response to environmental changes to adapt to the conditions. The identification of cyanobacteria strains that alleviate bleaching has revealed genes that regulate levels of phycobilisome, the main light-harvesting complex. In contrast, the mechanisms of pigment degradation in algae remain unclear, as no nonbleaching strains have previously been isolated. To address this issue, this study attempted to isolate nonbleaching strains of the unicellular red alga Cyanidioschyzon merolae after exposure to nitrogen (N)-depletion based on autofluorescence information. After four weeks under N-depletion, 13 cells from 500,000 cells with almost identical pre- and post-depletion chlorophyll a (Chl a) and/or phycocyanin autofluorescence intensities were identified. These nonbleaching candidate strains were sorted via a cell sorter, isolated on solid medium, and their post-N-depletion Chl a and phycocyanin levels were analyzed. Chl a levels of these nonbleaching candidate strains were lower at 1-4 weeks of N-depletion similar to the control strains, however, their phycocyanin levels were unchanged. Thus, we successfully isolated nonbleaching C. merolae strains in which phycocyanin was not degraded under N-depletion, via autofluorescence spectroscopy and cell sorting. This versatile method will help to elucidate the mechanisms regulating pigments in microalgae.

Physicochemical and functional properties of protein isolate recovered from Rana chensinensis ovum based on different drying techniques

This work was dedicated to evaluating the drying methods (freeze drying, spray drying, and vacuum drying) of food Rana chensinensis ovum protein isolate (RCOPI) based on comparison of the physicochemical and functional properties. The characterization and evaluation were conducted using scanning electron microscopy, surface hydrophobicity, SDS-PAGE, amino acid composition and nutritional parameters, Fourier transform infrared spectroscopy, and autofluorescence spectroscopy. The results showed the protein structure and conformation of RCOPI were greatly affected by drying techniques, leading to different physicochemical and functional properties. RCOPI possessed four main subunit bands distributed around 110, 90, 35 and 32 kDa. Seven essential amino acids were detected, accounting for 43.27–43.65% of total amino acids. Freeze drying RCOPI (FD-RCOPI) showed superior functional features, including solubility, water holding capacity, oil holding capacity, stabilization of Pickering emulsion and antioxidant capacity. FD-RCOPI exhibited applicability for the manufacture of viscous foods, bakery products and Pickering emulsions.

AUTOFLUORESCENCE SPECTRA OF A SPECIMEN OF NON-KERATINIZING LARYNGEAL SQUAMOUS CELL CARCINOMA IN ACCORDANCE WITH A HISTOLOGICAL STUDY

Abstract. The prevalence of oncological diseases of ear, nose and throat organs tends to increase in recent years. This paper describes a method for the early diagnosis of diseases of the pharynx and larynx, accompanied by epithelial dysplasia using autofluorescence spectroscopy. Fluorescence spectroscopy is a non-invasive, highly sensitive, versatile and relatively easy-to-use method that allows combined diagnostics and the ability to work in real time without tissue trauma. Its use in the diagnosis of various diseases is due to the fact that during the development of pathologies, biochemical changes occur in the tissue, and, consequently, the relative content of the main fluorophores and chromophores changes, the morphological and histological parameters of the tissue change and, as a result, their contribution to the overall observed fluorescence spectrum fabrics. Fluorescence was started using an LGI-505 nitrogen laser (excitation wavelength 337 nm, pulse repetition rate up to 1 kHz, pulse duration 6–10 ns). Registration of autofluorescence spectra carried out by usage a spectrum analyzer with fiber optic input LESA-7med. For the study, we used a sapecimen of the larynx obtained during laryngectomy wich previously histologically confirmed as laryngeal cancer. Biopsy performed in limited areas of the mucosa determined by the autofluorescence spectra analysis. Then a standard histological examination of biopsy samples were performed. A direct correlation was found between the results of autofluorescence spectroscopy and histological examination of identical localization areas of the object under study. Thus, squamous cell carcinoma is characterized, along with a general decrease in fluorescence intensity, by the shift of the main maximum of the spectrum in the region of 435-455 nm. The low content of NADH in tumor cells, as well as dysproteinemia developing in malignant diseases, are the main reason for the decrease in the intensity of the autofluorescence spectrum.

Autofluorescence spectroscopy for quantitative analysis of cellulose nanocrystals

The ability to determine the physicochemical properties of nanoparticles, such as cellulose nanocrystals, in suspension is critically important to maximize their potential. Currently, various techniques are required to ascertain different properties, which results in a laborious analysis procedure. Here, autofluorescence arising from the cluster-triggered emission (CTE) photoluminescence mechanism is utilized as an analytical spectroscopic tool to determine multiple properties from one data acquisition sequence. This study confirms that key properties - including the nanoparticle concentration in suspension, the critical concentration for liquid crystal formation, and the surface charge content - can be obtained simultaneously. Measured values are accurate to within 10% of conventional techniques with average residual errors of 0.4 wt% for the critical concentration, and 11 mmol kg-1 CNC for the surface charge content. This charge-coupled device (CCD) sensor-based methodology is rapid and does not require the addition of further chemicals. These results support the theory behind CTE and represent a new opportunity for quantitatively analysing non-aromatic, heteroatom-containing nanoparticles in flow based on understanding their inter- and intra-particle interactions.

Autofluorescence Spectroscopy and Imaging II: A Special Issue Aimed to Promote Optically Based Studies on Biological Substrates

The Journal Molecules, in particular the Photochemistry Section, and the younger Journal Photochem are active in promoting the advances and practical applications based on the interaction of light with the various biological substrates from both animal and vegetal systems [...]