Ensemble multivariate analysis to improve identification of articular cartilage disease in noisy Raman spectra

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.

Holistic vibrational spectromics assessment of human cartilage for osteoarthritis diagnosis

Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.

Synovial fluid research based on SERS and SERRS for enhanced detection of biomarkers in staged osteoarthritis

Surface-enhanced (resonance) Raman scattering (SER(R)S) can extremely enhance Raman intensity of samples, which is helpful for detecting synovial fluid (SF) that does not show Raman activity under normal conditions. In this study, SER(R)S spectra of SF from three different osteoarthritis (OA) stages were collected and analyzed for OA progress, finding that the content of collagen increased throughout the disease, while non-collagen proteins and polysaccharides decreased sharply at advanced OA stage accompanied by the increase of phospholipid. The spectral features and differences were enhanced by salting-out and centrifugation. Much more information on biomolecules at different OA stages was disclosed by using SERRS for the first time, these main trace components (β-carotene, collagen, hyaluronic acid, nucleotide, and phospholipid) can be used as potential biomarkers. It indicates that SERRS has a more comprehensive ability to assist SERS in seeking micro(trace) biomolecules as biomarkers and facilitating accurate and efficient diagnosis and mechanism research of OA.

Optical Biomarkers for the Diagnosis of Osteoarthritis through Raman Spectroscopy: Radiological and Biochemical Validation Using Ex Vivo Human Cartilage Samples

Osteoarthritis (OA) is the most common rheumatic disease, characterized by progressive articular cartilage degradation. Raman spectroscopy (RS) has been recently proposed as a label-free tool to detect molecular changes in musculoskeletal tissues. We used cartilage samples derived from human femoral heads to perform an ex vivo study of different Raman signals and ratios, related to major and minor molecular components of articular cartilage, hereby proposed as candidate optical biomarkers for OA. Validation was performed against the radiological Kellgren-Lawrence (K-L) grading system, as a gold standard, and cross-validated against sulfated glycosaminoglycans (sGAGs) and total collagens (Hyp) biochemical contents. Our results showed a significant decrease in sGAGs (SGAGs, A1063 cm^-1/A1004 cm^-1) and proteoglycans (PGs, A1375 cm^-1/A1004 cm^-1) and a significant increase in collagen disorganization (ColD/F, A1245 cm^-1/A1270 cm^-1), with OA severity. These were correlated with sGAGs or Hyp contents, respectively. Moreover, the SGAGs/HA ratio (A1063 cm^-1/A960 cm^-1), representing a functional matrix, rich in proteoglycans, to a mineralized matrix-hydroxyapatite (HA), was significantly lower in OA cartilage (K-L I vs. III-IV, p < 0.05), whilst the mineralized to collagenous matrix ratio (HA/Col, A960 cm^-1/A920 cm^-1) increased, being correlated with K-L. OA samples showed signs of tissue mineralization, supported by the presence of calcium crystals-related signals, such as phosphate, carbonate, and calcium pyrophosphate dihydrate (MGP, A960 cm^-1/A1004 cm^-1, MGC, A1070 cm^-1/A1004 cm^-1 and A1050 cm^-1/A1004 cm^-1). Finally, we observed an increase in lipids ratio (IL, A1450 cm^-1/A1670 cm^-1) with OA severity. As a conclusion, we have described the molecular fingerprint of hip cartilage, validating a panel of optical biomarkers and the potential of RS as a complementary diagnostic tool for OA.

Raman Spectroscopy Methods to Characterize the Mechanical Response of Soft Biomaterials

Raman spectroscopy has been used extensively to characterize the influence of mechanical deformation on microstructure changes in biomaterials. While traditional piezo-spectroscopy has been successful in assessing internal stresses of hard biomaterials by tracking prominent peak shifts, peak shifts due to applied loads are near or below the resolution limit of the spectrometer for soft biomaterials with moduli in the kilo- to mega-Pascal range. In this Review, in addition to peak shifts, other spectral features (e.g., polarized intensity and intensity ratio) that provide quantitative assessments of microstructural orientation and secondary structure in soft biomaterials and their strain dependence are discussed. We provide specific examples for each method and classify sensitive Raman characteristic bands common across natural (e.g., soft tissue) and synthetic (e.g., polymeric scaffolds) soft biomaterials upon mechanical deformation. This Review can provide guidance for researchers aiming to analyze micromechanics of soft tissues and engineered tissue constructs by Raman spectroscopy.

Molecular analysis of the destruction of articular joint tissues by Raman spectroscopy

INTRODUCTION

Osteoarthritis (OA) is a highly heterogenous disease influenced by different molecular, anatomic, and physiologic imbalances. Some of the bottlenecks for enhanced diagnosis and therapeutic assessment are the lack of validated biomarkers and early diagnosis tools. In this narrative review, we analyze the potential of Raman spectroscopy (RS) as a label-free optical tool for the characterization of articular joint tissues and its application as a diagnosis tool for OA.

AREAS COVERED

Raman spectra produce a unique 'molecular fingerprint' providing rotational and vibrational molecular information, allowing the identification and follow-up of molecular changes associated with OA pathological mechanisms. Focusing on multiple joint tissues (cartilage, synovium, bone, tendons, ligaments, and meniscus) and their contribution in disease incidence and progression, this review highlights the current knowledge on the application of RS in the characterization of organic and inorganic molecules present at these tissues and alterations that occur in the onset of OA.

EXPERT OPINION

Vibrational spectroscopy techniques, such as RS, are low cost, rapid and minimally invasive approaches that offer high specificity in the assessment of the molecular composition of complex tissues. Combined with multivariate statistical methods, RS offers great potential for optical biomarkers discovery or disease diagnosis applications, and we hereby discuss clinical translational progresses on the field.

Raman microspectroscopic analysis of the tissue-specific composition of the human osteochondral junction in osteoarthritis: A pilot study

This study investigates the influence of osteoarthritis (OA) disease severity on the bio-composition of the osteochondral junction at the human tibial plateau using Raman microspectroscopy. We specifically aim to analyze the spatial composition of mineralized osteochondral tissues, i.e., calcified cartilage (CC) and subchondral bone plate (SBP) from unfixed, hydrated specimens. We hypothesize that the mineralization of CC and SBP decreases in advanced OA. Twenty-eight cylindrical osteochondral samples (d = 4 mm) from tibial plateaus of seven cadaveric donors were harvested and sorted into three groups following histopathological grading: healthy (n = 5), early OA (n = 8), and advanced OA (n = 15). Raman spectra were subjected to multivariate cluster analyses to identify different tissues. Finally, the tissue-specific composition was analyzed, and the impact of OA was statistically evaluated with linear mixed models. Cluster analyses of Raman spectra successfully distinguished CC and SBP as well as a tidemark region and uncalcified cartilage. CC was found to be more mineralized and the mineral was more crystalline compared with SBP. Both tissues exhibited similar compositional changes as a function of histopathological OA severity. In early OA, the mineralization tends to increase, and the mineral contains fewer carbonate substitutions. Compared with early OA, mineral crystals are rich in carbonate while the overall mineralization decreases in advanced OA. This Raman spectroscopic study advances the methodology for investigating the complex osteochondral junction from native tissue. The developed methodology can be used to elucidate detailed tissue-specific changes in the chemical composition with advancing OA. STATEMENT OF SIGNIFICANCE: In this study, Raman microspectroscopy was utilized to investigate the influence of osteoarthritic degeneration on the tissue-specific biochemical composition of the human osteochondral junction. Multivariate cluster analyses allowed us to characterize subtle compositional changes in the calcified cartilage and subchondral bone plate as well as in the tidemark region. The compositional differences found between the calcified cartilage and subchondral bone plate in both organic and mineral phases will serve as critical benchmark parameters when designing biomaterials for osteochondral repair. We found tissue-specific changes in the mineralization and carbonate substitution as a function of histopathological OA severity. Our developed methodology can be used to investigate the metabolic changes in the osteochondral junction associated with osteoarthritis.

Lipid distribution, composition and uptake in bovine articular cartilage studied using Raman micro-spectrometry and confocal microscopy

The distribution and composition of endogenous lipids in articular cartilage and transport of exogenous fatty acids have been investigated on a microscopic scale in fresh bovine articular cartilage. To investigate the distribution and composition of the endogenous lipids, hyperspectral Raman maps were taken of chondrocytes and their surrounding matrix in both the deep and superficial zones. These revealed differences in both lipid distribution and composition between the two zones. Extracellular lipid was observed surrounding the cells in the superficial zone but not in the deep zone. Additionally, intracellular lipid droplets were observed that were larger and more numerous in the deep zone (P = 0.01). The extracellular lipid was primarily free saturated fatty acid, whereas the cellular lipid droplets contained triglycerides with unsaturated fatty acid chains. Fatty acid uptake and transport were investigated by incubating cartilage samples in Dulbecco's modified Eagle's medium containing fluorescently labelled palmitate for a range of times and temperatures. After incubation, the palmitate distribution was imaged using confocal microscopy. Palmitate accumulated preferentially in the territorial matrix only in the superficial zone where the concentration was up to 100‐fold greater than that in the bulk matrix (P = 0.001). Palmitate uptake by the chondrocytes in both zones showed differential temperature sensitivity (P = 0.05), which would support the idea that cells take up palmitate by both active and passive mechanisms. The study reveals large differences between chondrocytes in the superficial and deep zones in their lipid content, in their extracellular lipid environment and in their access to exogenous fatty acids.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

Non-invasive Chamber-Specific Identification of Cardiomyocytes in Differentiating Pluripotent Stem Cells

One major obstacle to the application of stem cell-derived cardiomyocytes (CMs) for disease modeling and clinical therapies is the inability to identify the developmental stage of these cells without the need for genetic manipulation or utilization of exogenous markers. In this study, we demonstrate that Raman microspectroscopy can non-invasively identify embryonic stem cell (ESC)-derived chamber-specific CMs and monitor cell maturation. Using this marker-free approach, Raman peaks were identified for atrial and ventricular CMs, ESCs were successfully discriminated from their cardiac derivatives, a distinct phenotypic spectrum for ESC-derived CMs was confirmed, and unique spectral differences between fetal versus adult CMs were detected. The real-time identification and characterization of CMs, their progenitors, and subpopulations by Raman microspectroscopy strongly correlated to the phenotypical features of these cells. Due to its high molecular resolution, Raman microspectroscopy offers distinct analytical characterization for differentiating cardiovascular cell populations.