Raman spectroscopy of osteosarcoma cells

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the β-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

Matching Pursuit for Denoising Raman Spectra, Based on Genetic Algorithm and Hermite Atoms

Due to its various advantages, Raman spectroscopy has become a powerful tool in different fields of science and engineering; however, in specific applications, this technique's limiting factor is closely related to the inherent noise of the Raman spectra. To eliminate the noise of a Raman spectrum, preserving its position, intensity, and width characteristic, we propose using a genetic matching pursuit-Hermite atoms (GMP-HAs) algorithm in this work. This algorithm helps recover Raman spectra immersed in Gaussian noise with the least number of atoms. The noise-free Raman signal is reconstructed with the GMP-HAs algorithm, transforming the typical best-matching atom search into an optimization problem. Specifically, we maximize the fitness function, defined as the correlation between current residual and Hermite atoms, with the genetic algorithm MI-LXPM encoded in a real domain and avoiding local maxima, by adding a stopping criterion based on an exponential adjustment according to the algorithm's behavior in the presence of noise. Simulated and biological Raman spectra are used to evaluate the proposed algorithm and compare its performance with typically known methods for denoising, such as the Savitzky- Golay filter (SG) and basis pursuit denoising. Using the signal-to-noise ratio (S/N)metric resulted in a 0.31 dB advantage in the S/N product for the proposed algorithm with respect to SG. Additionally, it is shown that the algorithm uses only 25.3% of the number of atoms needed by the matching pursuit algorithm. The results indicate that the GMP-HAs algorithm has better denoising capabilities, and at the same time, the Raman spectra are decomposed with fewer atoms compared to known sparse algorithms.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Exploiting gold nanoparticles for diagnosis and cancer treatments

Gold nanoparticles (AuNPs) represent a relatively simple nanosystem to be synthesised and functionalized. AuNPs offer numerous advantages over different nanomaterials, primarily due to highly optimized protocols for their production with sizes in the range 1-150 nm and shapes, spherical, nanorods (AuNRs), nanocages, nanostars or nanoshells (AuNSs), just to name a few. AuNPs possess unique properties both from the optical and chemical point of view. AuNPs can absorb and scatter light with remarkable efficiency. Their outstanding interaction with light is due to the conduction electrons on the metal surface undergoing a collective oscillation when they are excited by light at specific wavelengths. This oscillation, known as a localized surface plasmon resonance, causes the absorption and scattering intensities of AuNPs to be significantly higher than identically sized non-plasmonic nanoparticles. In addition, AuNP absorption and scattering properties can be tuned by controlling the particle size, shape, and the local refractive index near the particle surface. By the chemical side, AuNPs offer the advantage of functionalization with therapeutic agents through covalent and ionic binding, which can be useful for biomedical applications, with particular emphasis on cancer treatments. Functionalized AuNPs exhibit good biocompatibility and controllable distribution patterns when delivered in cells and tissues, which make them particularly fine candidates for the basis of innovative therapies. Currently, major available AuNP-based cancer therapeutic approaches are the photothermal therapy (PTT) or photodynamic therapy (PDT). PTT and PDT rely upon irradiation of surface plasmon resonant AuNPs (previously delivered in cancer cells) by light, in particular, in the near-infrared range. Under irradiation, AuNPs surface electrons are excited and resonate intensely, and fast conversion of light into heat takes place in about 1 ps. The cancer cells are destroyed by the induced hyperthermia, i.e. the condition under which cells are subject to temperature in the range of 41 °C-47 °C for tens of minutes. The review is focused on the description of the optical and thermal properties of AuNPs that underlie their continuous and progressive exploitation for diagnosis and cancer therapy.

Four-Dimensional (Bio-)printing: A Review on Stimuli-Responsive Mechanisms and Their Biomedical Suitability

The applications of tissue engineered constructs have witnessed great advances in the last few years, as advanced fabrication techniques have enabled promising approaches to develop structures and devices for biomedical uses. (Bio-)printing, including both plain material and cell/material printing, offers remarkable advantages and versatility to produce multilateral and cell-laden tissue constructs; however, it has often revealed to be insufficient to fulfill clinical needs. Indeed, three-dimensional (3D) (bio-)printing does not provide one critical element, fundamental to mimic native live tissues, i.e., the ability to change shape/properties with time to respond to microenvironmental stimuli in a personalized manner. This capability is in charge of the so-called “smart materials”; thus, 3D (bio-)printing these biomaterials is a possible way to reach four-dimensional (4D) (bio-)printing. We present a comprehensive review on stimuli-responsive materials to produce scaffolds and constructs via additive manufacturing techniques, aiming to obtain constructs that closely mimic the dynamics of native tissues. Our work deploys the advantages and drawbacks of the mechanisms used to produce stimuli-responsive constructs, using a classification based on the target stimulus: humidity, temperature, electricity, magnetism, light, pH, among others. A deep understanding of biomaterial properties, the scaffolding technologies, and the implant site microenvironment would help the design of innovative devices suitable and valuable for many biomedical applications.

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors

In the last decade, Raman Spectroscopy has demonstrated to be a label-free and non-destructive optical spectroscopy able to improve diagnostic accuracy in cancer diagnosis. This is because Raman spectroscopic measurements can reveal a deep molecular understanding of the biochemical changes in cancer tissues in comparison with non-cancer tissues. In this pilot study, we apply Raman spectroscopy imaging to the diagnosis and grading of chondrogenic tumors, including enchondroma and chondrosarcomas of increasing histologic grades. The investigation included the analysis of areas of 50×50 μm² to approximately 200×200 μm², respectively. Multivariate statistical analysis, based on unsupervised (Principal Analysis Components) and supervised (Linear Discriminant Analysis) methods, differentiated between the various tumor samples, between cells and extracellular matrix, and between collagen and non-collagenous components. The results dealt out basic biochemical information on tumor progression giving the possibility to grade with certainty the malignant cartilaginous tumors under investigation. The basic processes revealed by Raman Spectroscopy are the progressive degrading of collagen type-II components, the formation of calcifications and the cell proliferation in tissues ranging from enchondroma to chondrosarcomas. This study highlights that Raman spectroscopy is particularly effective when cartilaginous tumors need to be subjected to histopathological analysis.

In Situ Surface-Enhanced Raman Spectroscopy of Cellular Components: Theory and Experimental Results

In the last decade, surface-enhanced Raman spectroscopy (SERS) met increasing interest in the detection of chemical and biological agents due to its rapid performance and ultra-sensitive features. Being SERS a combination of Raman spectroscopy and nanotechnology, it includes the advantages of Raman spectroscopy, providing rapid spectra collection, small sample sizes, characteristic spectral fingerprints for specific analytes. In addition, SERS overcomes low sensitivity or fluorescence interference that represents two major drawbacks of traditional Raman spectroscopy. Nanoscale roughened metal surfaces tremendously enhance the weak Raman signal due to electromagnetic field enhancement generated by localized surface plasmon resonances. In this paper, we detected label-free SERS signals for arbitrarily configurations of dimers, trimers, etc., composed of gold nanoshells (AuNSs) and applied to the mapping of osteosarcoma intracellular components. The experimental results combined to a theoretical model computation of SERS signal of specific AuNSs configurations, based on open cavity plasmonics, give the possibility to quantify SERS enhancement for overcoming spectral fluctuations. The results show that the Raman signal is locally enhanced inside the cell by AuNSs uptake and correspondent geometrical configuration generating dimers are able to enhance locally electromagnetic fields. The SERS signals inside such regions permit the unequivocal identification of cancer-specific biochemical components such as hydroxyapatite, phenylalanine, and protein denaturation due to disulfide bonds breaking between cysteine links or proline.