Raman spectroscopy detects melanoma and the tissue surrounding melanoma using tissue-engineered melanoma models

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

Raman microspectroscopy fingerprinting of organoid differentiation state

Background

Organoids, which are organs grown in a dish from stem or progenitor cells, model the structure and function of organs and can be used to define molecular events during organ formation, model human disease, assess drug responses, and perform grafting in vivo for regenerative medicine approaches. For therapeutic applications, there is a need for nondestructive methods to identify the differentiation state of unlabeled organoids in response to treatment with growth factors or pharmacologicals.

Methods

Using complex 3D submandibular salivary gland organoids developed from embryonic progenitor cells, which respond to EGF by proliferating and FGF2 by undergoing branching morphogenesis and proacinar differentiation, we developed Raman confocal microspectroscopy methods to define Raman signatures for each of these organoid states using both fixed and live organoids.

Results

Three separate quantitative comparisons, Raman spectral features, multivariate analysis, and machine learning, classified distinct organoid differentiation signatures and revealed that the Raman spectral signatures were predictive of organoid phenotype.

Conclusions

As the organoids were unlabeled, intact, and hydrated at the time of imaging, Raman spectral fingerprints can be used to noninvasively distinguish between different organoid phenotypes for future applications in disease modeling, drug screening, and regenerative medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11658-022-00347-3.

Salivary gland organoids have unique Raman signatures detectable with a confocal-based Raman imaging approach.

Raman signatures can be detected in unlabeled fixed or live organoids.

Raman spectral signatures effectively predict organoid phenotypes.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Supervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra

Malignant melanoma is an aggressive form of skin cancer, which develops from the genetic mutations of melanocytes - the most frequent involving BRAF and NRAS genes. The choice and the effectiveness of the therapeutic approach depend on tumour mutation; therefore, its assessment is of paramount importance. Current methods for mutation analysis are destructive and take a long time; instead, Raman spectroscopy could provide a fast, label-free and non-destructive alternative. In this study, confocal Raman microscopy has been used for examining three in vitro melanoma cell lines, harbouring different molecular profiles and, in particular, specific BRAF and NRAS driver mutations. The molecular information obtained from Raman spectra has served for developing two alternative classification algorithms based on linear discriminant analysis and artificial neural network. Both methods provide high accuracy (≥90%) in discriminating all cell types, suggesting that Raman spectroscopy may be an effective tool for detecting molecular differences between melanoma mutations.

In search of the correlation between nanomechanical and biomolecular properties of prostate cancer cells with different metastatic potential

Nanomechanical properties of living cells, as measured with atomic force microscopy (AFM), are increasingly recognized as criteria that differentiate normal and pathologically altered cells. Locally measured cell elastic properties, described by the parameter known as Young's modulus, are currently proposed as a new diagnostic parameter that can be used at the early stage of cancer detection. In this study, local mechanical properties of normal human prostate (RWPE-1) cells and a range of malignant (22Rv1) and metastatic prostate cells (LNCaP, Du145 and PC3) were investigated. It was found that non-malignant prostate cells are stiffer than cancer cells while the metastatic cells are much softer than malignant cells from the primary tumor site. Next, the biochemical properties of the cells were measured using confocal Raman (RS) and Fourier-transform infrared (FT-IR) spectroscopies to reveal these cells' biochemical composition as malignant transformation proceeds. Nanomechanical and biochemical profiles of five different prostate cell lines were subsequently analyzed using partial least squares regression (PLSR) in order to identify which spectral features of the RS and FT-IR spectra correlate with the cell's elastic properties. The PLSR-based model could predict Young's modulus values based on both RS and FT-IR spectral information. These outcomes show not only that AFM, RS and FT-IR techniques can be used for discrimination between normal and cancer cells, but also that a linear correlation between mechanical response and biomolecular composition of the cells that undergo malignant transformation can be found. This knowledge broadens our understanding of how prostate cancer cells evolve thorough the multistep process of tumor pathogenesis.

Optical clearing agent increases effectiveness of photodynamic therapy in a mouse model of cutaneous melanoma: an analysis by Raman microspectroscopy

Melanoma is the most aggressive type of skin cancer and a relevant health problem due to its poor treatment response with high morbidity and mortality rates. This study, aimed to investigate the tissue changes of an improved photodynamic therapy (PDT) response when combined with optical clearing agent (OCA) in the treatment of cutaneous melanoma in mice. Photodithazine (PDZ) was administered intraperitoneally and a solution of OCA was topically applied before PDT irradiation. Due to a resultant refractive index matching, OCA-treated tumors are more optically homogenous, improving the PDT response. Raman analysis revealed, when combined with OCA, the PDT response was more homogenous down to 725 µm-depth in thickness.

Photoacoustic/Ultrasound/Optical Coherence Tomography Evaluation of Melanoma Lesion and Healthy Skin in a Swine Model

The marked increase in the incidence of melanoma coupled with the rapid drop in the survival rate after metastasis has promoted the investigation into improved diagnostic methods for melanoma. High-frequency ultrasound (US), optical coherence tomography (OCT), and photoacoustic imaging (PAI) are three potential modalities that can assist a dermatologist by providing extra information beyond dermoscopic features. In this study, we imaged a swine model with spontaneous melanoma using these modalities and compared the images with images of nearby healthy skin. Histology images were used for validation.

MicroRNA-610 inhibits tumor growth of melanoma by targeting LRP6

Accumulating evidence showed that aberrant miRNAs expression was involved in initiation and progression of melanoma. However, the investigation of different miRNAs in melanoma remain attractive. In this research, we demonstrated that miR-610 expression was decreased in melanoma tissues and cell lines. The clinical data showed that the reduced miR-610 expression was obviously associated with adverse prognostic characteristics. Furthermore, our results suggested that miR-610 had a function of prognostic indicator for 5-year predicted-survival of melanoma patients. The ectopic overexpression of miR-610 suppressed cell proliferation, cell cycle progression and promoted apoptosis while miR-610 knockdown reversed the effect in vitro and in vivo. Additionally, miR-610 could modulate LRP6 by directly interacting to its 3’-UTR. In clinical samples of melanoma, miR-610 inversely correlated with LRP6. The biological function of miR-610 on melanoma cells was abrogated by alternation of LRP6 expression. In summary, our research indexed that miR-610 had a function of tumor suppressor in regulating the proliferation, cell cycle and apoptosis of melanoma via targeting LRP6. Hence, it may represent a novel potential therapeutic target and prognostic marker for melanoma.

Use of a Tissue Engineered Human Skin Model to Investigate the Effects of Wounding and of an Anti-Inflammatory on Melanoma Cell Invasion

An increasing number of studies suggest inflammation stimulates tumour invasion. In melanoma, despite recent advances in targeted therapy and immunomodulatory therapies, this cancer remains difficult to treat. Our previous studies show melanoma cells interact with skin cells in their invasion into tissue engineered skin and suggest inflammation stimulates invasion. The aim of this study was to investigate the use of an anti-inflammatory on melanoma invasion. To do this we developed a wounded and inflamed in vitro 3D melanoma model in which to investigate the use of an anti-inflammatory on melanoma invasion. The tissue engineered skin model was based on human de-epidermised acellular dermis to which keratinocytes, fibroblasts and three different melanoma cell lines were added in various combinations. A simple incisional wound was made in the model and TNF-α and fibrin were added to simulate conditions of inflammation. Topical ibuprofen in a hydrogel was added and the extent of melanoma invasion into the dermis was assessed under the various conditions. The results showed that penetration of two of the cell lines (HBL and A375SM) into the tissue engineered skin was exacerbated by wounding and ibuprofen significantly decreased invasion of A375SM cells and slightly reduced invasion of HBL cells. A third cell line, C8161, was aggressively invasive under all conditions to an extent that was not influenced by wounding, TNF-α or the addition of ibuprofen. In summary, the results for one these cell lines (and a trend for a second cell line) support the hypothesis that a wound environment is conducive to melanoma invasion but the local addition of an anti-inflammatory drug such as ibuprofen may attenuate invasion.