Discrimination between basal cell carcinoma and hair follicles in skin tissue sections by Raman micro-spectroscopy

Characterization of Ex Vivo Nonmelanoma Skin Tissue Using Raman Spectroscopy

Raman spectroscopy has shown great potential in detecting nonmelanoma skin cancer accurately and quickly; however, little direct evidence exists on the sensitivity of measurements to the underlying anatomy. Here, we aimed to correlate Raman measurements directly to the underlying tissue anatomy. We acquired Raman spectra of ex vivo skin tissue from 25 patients undergoing Mohs surgery with a fiber probe. We utilized a previously developed biophysical model to extract key biomarkers in the skin from the Raman spectra. We then examined the correlations between the biomarkers and the major skin structures (including the dermis, sebaceous glands, hair follicles, fat, and two types of nonmelanoma skin cancer—basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)). SCC had a significantly different concentration of keratin, collagen, and nucleic acid than normal structures, while ceramide differentiated BCC from normal structures. Our findings identified the key proteins, lipids, and nucleic acids that discriminate nonmelanoma tumors and healthy skin using Raman spectroscopy. These markers may be promising surgical guidance tools for detecting tumors in resection margins.

Histopathology for Mohs micrographic surgery with photoacoustic remote sensing microscopy

Mohs micrographic surgery (MMS) is a precise oncological technique where layers of tissue are resected and examined with intraoperative histopathology to minimize the removal of normal tissue while completely excising the cancer. To achieve intraoperative pathology, the tissue is frozen, sectioned and stained over a 20- to 60-minute period, then analyzed by the MMS surgeon. Surgery is continued one layer at a time until no cancerous cells remain, meaning MMS can take several hours to complete. Ideally, it would be desirable to circumvent or augment frozen sectioning methods and directly visualize subcellular morphology on the unprocessed excised tissues. Employing photoacoustic remote sensing (PARS) microscopy, we present a non-contact label-free reflection-mode method of performing such visualizations in frozen sections of human skin. PARS leverages endogenous optical absorption contrast within cell nuclei to provide visualizations reminiscent of histochemical staining techniques. Presented here, is the first true one to one comparison between PARS microscopy and standard histopathological imaging in human tissues. We demonstrate the ability of PARS microscopy to provide large grossing scans (>1 cm2, sufficient to visualize entire MMS sections) and regional scans with subcellular lateral resolution (300 nm).

Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics

INTRODUCTION

Raman and Infrared spectroscopic techniques are being used for the analysis of different types of cancers and other biological molecules. It is possible to identify cancers from normal tissues both in fresh and fixed tissues. These techniques can be used not only for the early diagnosis of cancer but also for monitoring the progression of the disease. Furthermore, chemical pathways to the progression of the disease process can be understood and followed.

AREAS COVERED

More recently, Artificial Intelligence (AI), Neural Network (NN), and Machine Learning are being combined with spectroscopy, which is making it easier to understand the chemical structural details of cancers and biological molecules more precisely and accurately. In this report, these aspects are being outlined by using breast cancer as a specific example.

EXPERT OPINION

A pathway showing to combine vibrational spectroscopy with AI and ML has immense potential in predicting various stages of different disease processes, in particular, in cancer diagnosis, staging, and designing treatment. This will result in improved patient care pathways.

Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer

Background: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. Methods: In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. Results: Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. Conclusion: It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring.

Investigation on the Cancer Invasion and Metastasis of Skin Squamous Cell Carcinoma by Raman Spectroscopy

Raman spectroscopy facilitates accurate and minimally invasive investigation on biomedical samples to reveal their molecular-level biological information. In this work, the cancer field effects of squamous cell carcinoma (SCC) tissues were illustrated by Raman microspectroscopy. Referenced with hematoxylin and eosin (H&E) stained microscopic images, the biochemical variations during SCC progress were meticulously described by the Raman spectral features in different pathological areas of two lesion types, including the biochemical changes in collagen, lipids, DNA, and other components of SCC diffusion and metastasis. The experimental results demonstrated that the intensities of the Raman peaks representing collagen (853, 936, and 1248 cm⁻¹) were decreased, whereas the intensities of peaks corresponding to DNA (720, 1327 cm⁻¹) and lipids (1305 cm⁻¹) were increased significantly in cancerous lesions, which testified that SCC originates from the epidermis and invades the dermis gradually. The achieved results not only described the molecular mechanism of skin carcinogenesis, but also provided vital reference data for in vivo skin cancer diagnosis using Raman spectroscopy.

Biophysical basis of skin cancer margin assessment using Raman spectroscopy

Achieving adequate margins during tumor margin resection is critical to minimize the recurrence rate and maximize positive patient outcomes during skin cancer surgery. Although Mohs micrographic surgery is by far the most effective method to treat nonmelanoma skin cancer, it can be limited by its inherent required infrastructure, including time-consuming and expensive on-site histopathology. Previous studies have demonstrated that Raman spectroscopy can accurately detect basal cell carcinoma (BCC) from surrounding normal tissue; however, the biophysical basis of the detection remained unclear. Therefore, we aim to explore the relevant Raman biomarkers to guide BCC margin resection. Raman imaging was performed on skin tissue samples from 30 patients undergoing Mohs surgery. High correlations were found between the histopathology and Raman images for BCC and primary normal structures (including epidermis, dermis, inflamed dermis, hair follicle, hair shaft, sebaceous gland and fat). A previously developed model was used to extract the biochemical changes associated with malignancy. Our results showed that BCC had a significantly different concentration of nucleus, keratin, collagen, triolein and ceramide compared to normal structures. The nucleus accounted for most of the discriminant power (90% sensitivity, 92% specificity - balanced approach). Our findings suggest that Raman spectroscopy is a promising surgical guidance tool for identifying tumors in the resection margins.

Local-dependency of morphological and optical properties between breast cancer cell lines

Breast cancer is the most malignant type of cancer in women and is a global health problem, with mortality by metastasis being the main factor among others. Currently, detection and diagnosis of breast cancer is achieved through a variety of procedures, such as clinical examination, medical imaging, biopsy, and histopathological analysis. In contrast, spectroscopic analysis has a variety of advantages such as being noninvasive, not destroying biological materials, and not requiring additional histological analysis. In this study, various approaches using Raman spectroscopy, atomic force microscopy (AFM), and optical microscopy were used together to differentiate between and characterize normal breast cell lines (MCF-10A) and breast cancer cell lines (MDA-MB-231, MDA-MB-453). Raman spectra of normal breast cell and breast cancer cell lines confirmed visual differences in the concentrations of various compounds. These spectra were also analyzed using principle component analysis (PCA), and the PCA results showed reliable separation of the three cell lines and the cancer cell lines (MDA-MB-231, MDA-MB-453). With these results, optically synchronizing the AFM morphology, the Raman spectroscopy, and the visible RGB optical transmission intensity provided contrasts for not only conformational differences but also intracellular variation between the normal and cancer cell lines. We observed the inherent characteristic that there is no local difference in cancer cells regardless of morphology in a wide range of optical properties such as absorption, scattering and inelastic scattering.

Exploring the effect of laser excitation wavelength on signal recovery with deep tissue transmission Raman spectroscopy

The aim of this research was to find the optimal Raman excitation wavelength to attain the largest possible sensitivity in deep Raman spectroscopy of breast tissue.

Raman spectroscopy for physiological investigations of tissues and cells

Raman micro-spectroscopy provides a convenient non-destructive and location-specific means of probing cellular physiology and tissue physiology at sub-micron length scales. By probing the vibrational signature of molecules and molecular groups, the distribution and metabolic products of small molecules that cannot be labeled with fluorescent dyes can be analyzed. This method works well for molecular concentrations in the micro-molar range and has been demonstrated as a valuable tool for monitoring drug-cell interactions. If the small molecule of interest does not contain groups that would allow for a discrimination against cytoplasmic background signals, "labeling" of the molecule by isotope substitution or by incorporating other unique small groups, e.g. alkynes provides a stable signal even for time-lapse imaging such compounds in living cells. In this review we highlight recent progress in assessing the physiology of cells and tissue by Raman spectroscopy and imaging.

Applications of Raman spectroscopy in skin research--From skin physiology and diagnosis up to risk assessment and dermal drug delivery

In the field of skin research, confocal Raman microscopy is an upcoming analytical technique. Substantial technical progress in design and performance of the individual setup components like detectors and lasers as well as the combination with confocal microscopy enables chemically selective and non-destructive sample analysis with high spatial resolution in three dimensions. Due to these advantages, the technique bears tremendous potential for diverse skin applications ranging from the analysis of physiological component distribution in skin tissue and the diagnosis of pathological states up to biopharmaceutical investigations such as drug penetration kinetics within the different tissue layers. This review provides a comprehensive introduction about the basic principles of Raman microscopy highlighting the advantages and considering the limitations of the technique for skin applications. Subsequently, an overview about skin research studies applying Raman spectroscopy is given comprising various in vitro as well as in vivo implementations. Furthermore, the future perspective and potential of Raman microscopy in the field of skin research are discussed.

Applications and limitations of lipid nanoparticles in dermal and transdermal drug delivery via the follicular route

Lipid nanoparticles (LN) such as solid lipid nanoparticles (SLN) and nanolipid carriers (NLC) feature several claimed benefits for topical drug therapy including biocompatible ingredients, drug release modification, adhesion to the skin, and film formation with subsequent hydration of the superficial skin layers. However, penetration and permeation into and across deeper skin layers are restricted due to the barrier function of the stratum corneum (SC). As different kinds of nanoparticles provide the potential for penetration into hair follicles (HF) LN are applicable drug delivery systems (DDS) for this route in order to enhance the dermal and transdermal bioavailability of active pharmaceutical ingredients (API). Therefore, this review addresses the HF as application site, published formulations of LN which showed follicular penetration (FP), and characterization methods in order to identify and quantify the accumulation of API delivered by the LN in the HF. Since LN are based on lipids that appear in human sebum which is the predominant medium in HF an increased localization of the colloidal carriers as well as a promoted drug release may be assumed. Therefore, sebum-like lipid material and a size of less or equal 640 nm are appropriate specifications for FP of particulate formulations.

Histological skin morphology enhancement base on molecular hyperspectral imaging technology

BACKGROUND

Most traditional skin histological analysis methods are based on the light microscopy images, which can only provide limited information and low contrast results for pathology evaluation. Molecular hyperspectral imaging technology can provide both spatial and spectral information of skin sections, which is a new method for histological skin analysis.

METHODS

The molecular hyperspectral imaging system was developed by coupling an acousto-optic tunable filters adapter to microscopy and the molecular hyperspectral images were analyzed by home-written software with image processing algorithms. Then, the histological structures in skin sections were investigated in several locations to evaluate the potential application of the molecular hyperspectral imaging technique to dermatology.

RESULTS

Molecular hyperspectral images of skin sections were obtained. Single-band images, false color images, virtual 3D surface view images, and color-coded spectral clustering results were produced to highlight the skin structures for histological evaluation.

CONCLUSION

Unlike traditional histological analysis with light microscopy, the molecular hyperspectral imaging technology can enhance the visualization of skin structures using their spectral signatures and their gray values. This technology has potential for the diagnosis and histopathologic characterization of different kind of skin cells.

Line-scanning Raman imaging spectroscopy for detection of fingerprints

Fingerprints are the best form of personal identification for criminal investigation purposes. We present a line-scanning Raman imaging system and use it to detect fingerprints composed of β-carotene and fish oil on different substrates. Although the line-scanning Raman system has been used to map the distribution of materials such as polystyrene spheres and minerals within geological samples, this is the first time to our knowledge that the method is used in imaging fingerprints. Two Raman peaks of β-carotene (501.2, 510.3 nm) are detected and the results demonstrate that both peaks can generate excellent images with little difference between them. The system operates at a spectra resolution of about 0.4 nm and can detect β-carotene signals in petroleum ether solution with the limit of detection of 3.4×10(-9) mol/L. The results show that the line-scanning Raman imaging spectroscopy we have built has a high accuracy and can be used in the detection of latent fingerprints in the future.

Raman Spectroscopy for Clinical Oncology

Cancer is one of the leading causes of death throughout the world. Advancements in early and improved diagnosis could help prevent a significant number of these deaths. Raman spectroscopy is a vibrational spectroscopic technique which has received considerable attention recently with regards to applications in clinical oncology. Raman spectroscopy has the potential not only to improve diagnosis of cancer but also to advance the treatment of cancer. A number of studies have investigated Raman spectroscopy for its potential to improve diagnosis and treatment of a wide variety of cancers. In this paper the most recent advances in dispersive Raman spectroscopy, which have demonstrated promising leads to real world application for clinical oncology are reviewed. The application of Raman spectroscopy to breast, brain, skin, cervical, gastrointestinal, oral, and lung cancers is reviewed as well as a special focus on the data analysis techniques, which have been employed in the studies.