Spatially offset Raman spectroscopy for in vivo bone strength prediction

The Multifactorial Relationship Between Bone Tissue Water and Stiffness at the Proximal Femur

Bone mechanical function is determined by multiple factors, some of which are still being elucidated. Here, we present a multivariate analysis of the role of bone tissue composition in the proximal femur stiffness of cadaver bones (n = 12, age 44-93). Stiffness was assessed by testing under loading conditions simulating a sideways fall onto the hip. Compositional properties of cortical and trabecular tissues were quantified in femoral neck cross sections by Fourier transform infrared (FTIR) spectroscopy and near infrared (NIR) spectroscopy. In addition, cross-sectional areas and cortical thickness and tissue mineral density (TMD) were measured at the femoral neck. Pearson correlation analysis showed a significant (p < 0.05) negative relationship between bone stiffness and cortical and trabecular water content, both total (r = -0.63) and tightly bound to matrix and mineral (r = -55). Additionally, significant (p < 0.05) positive correlations were found between stiffness and bone area, both total (r = 0.67) and trabecular (r = 0.58). However, linear regression using each of these properties to predict bone stiffness resulted in weak models (R2 = 0.36-0.48). Interestingly, we found markedly stronger models (cross-validated R2 = 0.80-0.92) by using partial least squares (PLS) regression to predict stiffness based on combinations of bone properties. The models with highest R2 values were found when including bone water parameters as explanatory variables, both total and tightly bound, in cortical and trabecular. This study provides new insights by revealing a multifactorial relationship in which higher bone water content across different tissue compartments contributes to lower bone stiffness, highlighting bone water as a potential biomarker of bone quality and proximal femur mechanical function.

Determining ideal offsets of spatially offset Raman spectroscopy for transcutaneous measurements-A Monte Carlo study

Spatially offset Raman spectroscopy (SORS) is valuable for noninvasive bone assessment but requires a clearer understanding of how offset distances influence detection depth. To address this, our study devised a forward-adjoint Monte Carlo multi-layer (MCML) model to simulate photon paths in SORS, aiming to determine optimal offsets for various tissue types. We examined photon migration at offsets between 0 and 15 mm against layered phantoms of differing thicknesses and compositions to optimize the signal-to-noise ratio for bone layers. The findings highlight that optimal offsets are contingent on tissue characteristics: a metacarpal beneath 2.5 mm of tissue had an ideal offset of 6.7 mm, while a tibia with 5 mm of soft tissue required 10-11 mm. This precise calibration of SORS via MCML modeling promises substantial improvements in bone health diagnostics and potential for expansive medical applications.

Spatially Offset Raman Spectroscopy toward In Vivo Assessment of the Adipose Tissue in Cardiometabolic Pathologies

Spatially offset Raman spectroscopy (SORS) enhanced the capabilities of Raman spectroscopy for the depth-resolved analysis of biological and diffusely scattering samples. This technique offers selective probing of subsurface layers, providing molecular insights without invasive procedures. While SORS has found application in biomedical research, up to now, studies have focused mainly on the detection of mineralization of bones and tissues. Herein, for the first time, SORS is used to assess the soft, organic tissue beneath the skin's surface. In this study, we demonstrate the diagnostic utility of a hand-held SORS device for evaluating the chemical composition of the adipose tissue. We compared perigonadal white adipose tissue (gWAT) in a murine model of atherosclerosis, heart failure, and high-fat diet (HFD) induced obesity. Our results reveal distinct chemical differences in gWAT between HFD-fed and control mice, showcasing the potential of SORS for intravital adipose tissue phenotype characterization. Furthermore, our findings underscore the effectiveness of SORS as a valuable tool for noninvasive assessment of the adipose tissue composition, holding potential diagnostic significance for metabolic disorders.

Assessment of spatially offset Raman spectroscopy to detect differences in bone matrix quality

Since spatially offset Raman spectroscopy (SORS) can acquire biochemical measurements of tissue quality through light scattering materials, we investigated the feasibility of this technique to acquire Raman bands related to the fracture resistance of bone. Designed to maximize signals at different offsets, a SORS probe was used to acquire spectra from cadaveric bone with and without skin-like tissue phantoms attenuating the light. Autoclaving the lateral side of femur mid-shafts from 5 female and 5 male donors at 100 °C and again at 120 °C reduced the yield stress of cortical beams subjected to three-point bending. It did not affect the volumetric bone mineral density or porosity. Without tissue phantoms, autoclaving affected more Raman characteristics of the organic matrix when determined by peak intensity ratios, but fewer matrix properties depended on the three offsets (5 mm, 6 mm, and 7 mm) when determined by band area ratios. The cut-off in the thickness of the tissue phantom layers was ∼4 mm for most properties, irrespective of offset. Matching trends when spectra were acquired without phantom layers between bone and the probe, ν1PO43-/Amide III and ν1PO43-/(proline + OH-proline) were higher and lower in the non-treated bone than in the autoclaved bone, respectively, when the thickness of tissue phantom layers was 4 mm. The layers, however, caused a loss of sensitivity to autoclaving-related changes in ν3CO3/ν1PO43- and crystallinity. Without advanced post-processing of Raman spectra, SORS acquisition through turbid layers can detect changes in Raman properties of bone that accompany a loss in bone strength.

Detection of osteoporotic-related bone changes and prediction of distal radius strength using Raman spectra from excised human cadaver finger bones

While osteoporosis is reliably diagnosed using dual energy X-ray absorptiometry (DXA), screening rates are alarmingly low, contributing to preventable fractures. Raman spectroscopy (RS) can detect biochemical changes that occur in bones transcutaneously and can arguably be more accessible than DXA as a fracture risk assessment. A reasonable approach to translate RS is to interrogate phalangeal bones of human hands, where the soft tissues covering the bone are less likely to hamper transcutaneous measurements. To that end, we set out to first determine whether Raman spectra obtained from phalangeal bones correlate with distal radius fracture strength, which can predict subsequent osteoporotic fractures at the spine and hip. We performed RS upon diaphyseal and epiphyseal regions of exposed proximal phalanges from 12 cadaver forearms classified as healthy (n = 3), osteopenic (n = 4), or osteoporotic (n = 5) based on wrist T-scores measured by DXA. We observed a significant decrease in phosphate to matrix ratio and a significant increase in carbonate substitution in the osteoporotic phalanges relative to healthy and osteopenic phalanges. Multivariate regression models produced wrist T-score estimates with significant correlation to the DXA-measured values (r = 0.79). Furthermore, by accounting for phalangeal RS parameters, body mass index, and age, a multivariate regression significantly predicted distal radius strength measured in a simulated-fall biomechanical test (r = 0.81). These findings demonstrate the feasibility of interrogating the phalanges using RS for bone quality assessment of distant clinical sites of fragility fractures, such as the wrist. Future work will address transcutaneous measurement challenges as another requirement for scale-up and translation.

Sensitivity of the amide I band to matrix manipulation in bone: a Raman micro-spectroscopy and spatially offset Raman spectroscopy study

The fracture resistance of bone arises from the hierarchical arrangement of minerals, collagen fibrils (i.e., cross-linked triple helices of α1 and α2 collagen I chains), non-collagenous proteins, and water. Raman spectroscopy (RS) is not only sensitive to the relative fractions of these constituents, but also to the secondary structure of bone proteins. To assess the ability of RS to detect differences in the protein structure, we quantified the effect of sequentially autoclaving (AC) human cortical bone at 100 °C (∼34.47 kPa) and then at 120 °C (∼117.21 kPa) on the amide I band using a commercial Raman micro-spectroscopy (μRS) instrument and custom spatially offset RS (SORS) instrument in which rings of collection fiber optics are offset from the central excitation fiber optics within a hand-held, cylindrical probe. Being clinically viable, measurements by SORS involved collecting Raman spectra of cadaveric femur mid-shafts (5 male & 5 female donors) through layers of a tissue mimic. Otherwise, μRS and SORS measurements were acquired directly from each bone. AC-related changes in the helical status of collagen I were assessed using amide I sub-peak ratios (intensity, I, at ∼1670 cm-1 relative to intensities at ∼1610 cm-1 and ∼1640 cm-1). The autoclaving manipulation significantly decreased the selected amide I sub-peak ratios as well as shifted peaks at ∼1605 cm-1 (μRS), ∼1636 cm-1 (SORS) and ∼1667 cm-1 in both μRS and SORS. Compared to μRS, SORS detected more significant differences in the amide I sub-peak ratios when the fiber optic probe was directly applied to bone. SORS also detected AC-related decreases in I1670/I1610 and I1670/I1640 when spectra were acquired through layers of the tissue mimic with a thickness ≤2 mm by the 7 mm offset ring, but not with the 5 mm or 6 mm offset ring. Overall, the SORS instrument was more sensitive than the conventional μRS instrument to pressure- and temperature-related changes in the organic matrix that affect the fracture resistance of bone, but SORS analysis of the amide I band is limited to an overlying thickness layer of 2 mm.

In Vivo Longitudinal Monitoring of Disease Progression in Inflammatory Arthritis Animal Models Using Raman Spectroscopy

Current techniques for monitoring disease progression and testing drug efficacy in animal models of inflammatory arthritis are either destructive, time-consuming, subjective, or require ionizing radiation. To accommodate this, we have developed a non-invasive and label-free optical system based on Raman spectroscopy for monitoring tissue alterations in rodent models of arthritis at the biomolecular level. To test different sampling geometries, the system was designed to collect both transmission and reflection mode spectra. Mice with collagen antibody-induced arthritis and controls were subject to in vivo Raman spectroscopy at the tibiotarsal joint every 3 days for 14 days. Raman-derived measures of bone content correlated well with micro-computed tomography bone mineral densities. This allowed for time-resolved quantitation of bone densities, which indicated gradual bone erosion in mice with arthritis. Inflammatory pannus formation, bone erosion, and bone marrow inflammation were confirmed by histological analysis. In addition, using library-based spectral decomposition, we quantified the progression of bone and soft tissue components. In general, the tissue components followed significantly different tendencies in mice developing arthritis compared to the control group in line with the histological analysis. In total, this demonstrates Raman spectroscopy as a versatile technique for monitoring alterations to both mineralized and soft tissues simultaneously in rodent models of musculoskeletal disorders. Furthermore, the technique presented herein allows for objective repeated within-animal measurements potentially refining and reducing the use of animals in research while improving the development of novel antiarthritic therapeutics.

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids

SIGNIFICANCE

Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing.

AIM

An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise.

RESULTS

Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility.

CONCLUSIONS

A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.

Degradation of Bone Quality in a Transgenic Mouse Model of Alzheimer's Disease

Alzheimer's disease (AD) patients present with symptoms such as impairment of insulin signaling, chronic inflammation, and oxidative stress. Furthermore, there are comorbidities associated with AD progression. For example, osteoporosis is common with AD wherein patients exhibit reduced mineralization and a risk for fragility fractures. However, there is a lack of understanding on the effects of AD on bone beyond loss of bone density. To this end, we investigated the effects of AD on bone quality using the 5XFAD transgenic mouse model in which 12-month-old 5XFAD mice showed accumulation of amyloid-beta (Aβ42) compared with wild-type (WT) littermates (n = 10/group; 50% female, 50% male). Here, we observed changes in cortical bone but not in cancellous bone quality. Both bone mass and bone quality, measured in femoral samples using imaging (micro-CT, confocal Raman spectroscopy, X-ray diffraction [XRD]), mechanical (fracture tests), and chemical analyses (biochemical assays), were altered in the 5XFAD mice compared with WT. Micro-CT results showed 5XFAD mice had lower volumetric bone mineral density (BMD) and increased endocortical bone loss. XRD results showed decreased mineralization with smaller mineral crystals. Bone matrix compositional properties, from Raman, showed decreased crystallinity along with higher accumulation of glycoxidation products and glycation products, measured biochemically. 5XFAD mice also demonstrated loss of initiation and maximum toughness. We observed that carboxymethyl-lysine (CML) and mineralization correlated with initiation toughness, whereas crystal size and pentosidine (PEN) correlated with maximum toughness, suggesting bone matrix changes predominated by advanced glycation end products (AGEs) and altered/poor mineral quality explained loss of fracture toughness. Our findings highlight two pathways to skeletal fragility in AD through alteration of bone quality: (i) accumulation of AGEs; and (ii) loss of crystallinity, decreased crystal size, and loss of mineralization. We observed that the accumulation of amyloidosis in brain correlated with an increase in several AGEs, consistent with a mechanistic link between elevated Aβ42 levels in the brain and AGE accumulation in bone. © 2022 American Society for Bone and Mineral Research (ASBMR).

Calibration Technique for Suppressing Residual Etalon Artifacts in Slit-Averaged Raman Spectroscopy

Back-illuminated charged-coupled device (BI-CCD) arrays increase quantum efficiency but also amplify etaloning, a multiplicative, wavelength-dependent fixed-pattern effect. When spectral data from hundreds of BI-CCD rows are combined, the averaged spectrum will generally appear etalon-free. This can mask substantial etaloning at the row level, even if the BI-CCD has been treated to suppress the effect. This paper compares two methods of etalon correction, one with simple averaging and one with row-by-row calibration using a fluorescence standard. Two BI-CCD arrays, both roughened by the supplier to reduce etaloning, were used to acquire Raman spectra of murine bone specimens. For one array, etaloning was the dominant source of noise under the exposure conditions chosen, even for the averaged spectrum across all rows; near-infrared-excited Raman peaks were noticeably affected. In this case, row-by-row calibration improved the spectral quality of the average spectrum. The other CCD's performance was shot-noise limited and therefore received no benefit from the extra calibration. The different results highlight the importance of checking for and correcting row-level fixed pattern when measuring weak Raman signals in the presence of a large fluorescence background.

Compositional assessment of bone by Raman spectroscopy

Raman spectroscopy (RS) is used to analyze the physiochemical properties of bone because it is non-destructive and requires minimal sample preparation. With over two decades of research involving measurements of mineral-to-matrix ratio, type-B carbonate substitution, crystallinity, and other compositional characteristics of the bone matrix by RS, there are multiple methods to acquire Raman signals from bone, to process those signals, and to determine peak ratios including sub-peak ratios as well as the full-width at half maximum of the most prominent Raman peak, which is nu1 phosphate (ν1PO4). Selecting which methods to use is not always clear. Herein, we describe the components of RS instruments and how they influence the quality of Raman spectra acquired from bone because signal-to-noise of the acquisition and the accompanying background fluorescence dictate the pre-processing of the Raman spectra. We also describe common methods and challenges in preparing acquired spectra for the determination of matrix properties of bone. This article also serves to provide guidance for the analysis of bone by RS with examples of how methods for pre-processing the Raman signals and for determining properties of bone composition affect RS sensitivity to potential differences between experimental groups. Attention is also given to deconvolution methods that are used to ascertain sub-peak ratios of the amide I band as a way to assess characteristics of collagen type I. We provide suggestions and recommendations on the application of RS to bone with the goal of improving reproducibility across studies and solidify RS as a valuable technique in the field of bone research.

Determination of best Raman spectroscopy spatial offsets for transcutaneous bone quality assessments in human hands

Spatially offset Raman spectroscopy (SORS) is able to detect bone signal transcutaneously and could assist in predicting bone fracture risk. Criteria for optimal source-detector offsets for transcutaneous human measurements, however, are not well-established. Although larger offsets yield a higher percentage of bone signal, the absolute amount of bone signal decreases. Spectral unmixing into bone, adipose, and non-adipose components was employed to quantify changes in bone signal to noise ratio across a range of offsets, and optimal offsets for phalanx and metacarpal measurements were determined. The bone signal to noise ratio was maximized at offsets ranging from 4-6 mm.

Raman spectroscopy in skeletal tissue disorders and tissue engineering: present and prospective

Musculoskeletal disorders are the most common reason of chronic pain and disability representing worldwide an enormous socio-economic burden. In this review, new biomedical application fields for Raman spectroscopy (RS) technique related to skeletal tissues are discussed showing that it can provide a comprehensive profile of tissue composition in situ, in a rapid, label-free, and non-destructive manner. RS can be used as a tool to study tissue alterations associated to aging, pathologies, and disease treatments. The main advantage with respect to currently applied methods in clinics is its ability to provide specific information on molecular composition, which goes beyond other diagnostic tools. Being compatible with water, RS can be performed without pre-treatment on unfixed, hydrated tissue samples, without any labelling and chemical fixation used in histochemical methods. This review provides first the description of basic principles of RS as a biotechnology tool and introduces into the field of currently available RS based techniques, developed to enhance Raman signal. The main spectral processing statistical tools, fingerprint identification and available databases are mentioned. The recent literature has been analysed for such applications of RS as tendon and ligaments, cartilage, bone, and tissue engineered constructs for regenerative medicine. Several cases of proof-of-concept preclinical studies have been described. Finally, advantages, limitations, future perspectives, and challenges for translation of RS into clinical practice have been also discussed.

A computationally efficient Monte-Carlo model for biomedical Raman spectroscopy

Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through "isoweight," a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique well-suited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800 - 2000 cm^-1 with an approximately 100× increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation.

Applications of Vibrational Spectroscopy for Analysis of Connective Tissues

Advances in vibrational spectroscopy have propelled new insights into the molecular composition and structure of biological tissues. In this review, we discuss common modalities and techniques of vibrational spectroscopy, and present key examples to illustrate how they have been applied to enrich the assessment of connective tissues. In particular, we focus on applications of Fourier transform infrared (FTIR), near infrared (NIR) and Raman spectroscopy to assess cartilage and bone properties. We present strengths and limitations of each approach and discuss how the combination of spectrometers with microscopes (hyperspectral imaging) and fiber optic probes have greatly advanced their biomedical applications. We show how these modalities may be used to evaluate virtually any type of sample (ex vivo, in situ or in vivo) and how "spectral fingerprints" can be interpreted to quantify outcomes related to tissue composition and quality. We highlight the unparalleled advantage of vibrational spectroscopy as a label-free and often nondestructive approach to assess properties of the extracellular matrix (ECM) associated with normal, developing, aging, pathological and treated tissues. We believe this review will assist readers not only in better understanding applications of FTIR, NIR and Raman spectroscopy, but also in implementing these approaches for their own research projects.

Improved prediction of femoral fracture toughness in mice by combining standard medical imaging with Raman spectroscopy

Bone fragility and fracture risk are assessed by measuring the areal bone mineral density (aBMD) using dual-energy X-ray absorptiometry (DXA). While aBMD correlates with bone strength, it is a poor predictor of fragility fracture risk. Alternatively, fracture toughness assesses the bone's resistance to crack propagation and fracture, making it a suitable bone quality metric. Here, we explored how femoral midshaft measurements from DXA, micro-computed tomography (µCT), and Raman spectroscopy could predict fracture toughness. We hypothesized that ovariectomy (OVX) decreases aBMD and fracture toughness compared to controls and we can optimize a multivariate assessment of bone quality by combining results from X-ray and Raman spectroscopy. Female mice underwent an OVX (n = 5) or sham (n = 5) surgery at 3 months of age. Femurs were excised 3 months after ovariectomy and assessed with Raman spectroscopy, µCT, and DXA. Subsequently, a notch was created on the anterior side of the mid-diaphysis of the femurs. Three-point bending induced a controlled fracture that initiated at the notch. The OVX mice had a significantly lower aBMD, cortical thickness, and fracture toughness when compared to controls (p < 0.05). A leave one out cross-validated (LOOCV) partial least squares regression (PLSR) model based only on the combination of aBMD and cortical thickness showed no significant predictive correlations with fracture toughness, whereas a PLSR model based on principal components derived from the full Raman spectra yielded significant prediction (r² = 0.71, p < 0.05). Further, the PLSR model was improved by incorporating aBMD, cortical thickness, and principal components from Raman spectra (r² = 0.92, p < 0.001). This exploratory study demonstrates combining X-ray with Raman spectroscopy leads to a more accurate assessment of bone fracture toughness and could be a useful diagnostic tool for the assessment of fragility fracture risk.

Spatially offset Raman spectroscopy for biomedical applications

In recent years, Raman spectroscopy has undergone major advancements in its ability to probe deeply through turbid media such as biological tissues. This progress has been facilitated by the advent of a range of specialist techniques based around spatially offset Raman spectroscopy (SORS) to enable non-invasive probing of living tissue through depths of up to 5 cm. This represents an improvement in depth penetration of up to two orders of magnitude compared to what can be achieved with conventional Raman methods. In combination with the inherently high molecular specificity of Raman spectroscopy, this has therefore opened up entirely new prospects for a range of new analytical applications across multiple fields including medical diagnosis and disease monitoring. This article discusses SORS and related variants of deep Raman spectroscopy such as transmission Raman spectroscopy (TRS), micro-SORS and surface enhanced spatially offset Raman spectroscopy (SESORS), and reviews the progress made in this field during the past 5 years including advances in non-invasive cancer diagnosis, monitoring of neurotransmitters, and assessment of bone disease.

Soft-tissue spectral subtraction improves transcutaneous Raman estimates of murine bone strength in vivo

Transcutaneous determination of a bone's Raman spectrum is challenging because the type I collagen in the overlying soft tissue is spectroscopically identical to that in bone. In a previous transcutaneous study of murine tibiae, we developed a library-based model called SOLD to unmix spatially offset Raman measurements into three spectra: a bone estimate, a soft tissue estimate, and a residual. Here, we demonstrate the value of combining the bone estimate and the residual to produce a "top layer subtracted" (tls) spectrum. We report superior prediction of two standard bone metrics (volumetric bone mineralization density and maximum torque) using partial least squares regression models based upon tls spectra rather than SOLD bone estimates, implying that the spectral residuals contain useful information. Simulations reinforce experimental in vivo findings. This chemometric approach, which we denote as SOLD/TLS, could have broad applicability in situations where comprehensive spectral libraries are difficult to acquire.

Near infrared spectroscopic assessment of loosely and tightly bound cortical bone water

Water is an important component of bone and plays a key role in its mechanical and structural integrity. Water molecules in bone are present in different locations, including loosely or tightly bound to the matrix and/or mineral (biological apatite) phases. Identification of water location and interactions with matrix components impact bone function but have been challenging to assess. Here, we used near infrared (NIR) spectroscopy to identify loosely and tightly bound water present in cortical bone. In hydrated samples, NIR spectra have two primary water absorption bands at frequencies of ∼5200 and 7000 cm^-1. Using lyophilization and hydrogen-deuterium exchange assays, we showed that these absorption bands are primarily associated with loosely bound bone water. Using further demineralization assays, thermal denaturation, and comparison to standards, we found that these absorption bands have underlying components associated with water molecules tightly bound to bone. In dehydrated samples, the peak at ∼5200 cm^-1 was assigned to a combination of water tightly bound to collagen and to mineral, whereas the peak at 7000 cm^-1 was exclusively associated with tightly bound mineral water. We also found significant positive correlations between the NIR mineral absorption bands and the mineral content as determined by an established mid infrared spectroscopic parameter, phosphate/amide I. Moreover, the NIR water data showed correlation trends with tissue mineral density (TMD) in cortical bone tissues. These observations reveal the ability of NIR spectroscopy to non-destructively identify loosely and tightly bound water in bone, which could have further applications in biomineralization and biomedical studies.

Raman Spectroscopy: Guiding Light for the Extracellular Matrix

The extracellular matrix (ECM) consists of a complex mesh of proteins, glycoproteins, and glycosaminoglycans, and is essential for maintaining the integrity and function of biological tissues. Imaging and biomolecular characterization of the ECM is critical for understanding disease onset and for the development of novel, disease-modifying therapeutics. Recently, there has been a growing interest in the use of Raman spectroscopy to characterize the ECM. Raman spectroscopy is a label-free vibrational technique that offers unique insights into the structure and composition of tissues and cells at the molecular level. This technique can be applied across a broad range of ECM imaging applications, which encompass in vitro, ex vivo, and in vivo analysis. State-of-the-art confocal Raman microscopy imaging now enables label-free assessments of the ECM structure and composition in tissue sections with a remarkably high degree of biomolecular specificity. Further, novel fiber-optic instrumentation has opened up for clinical in vivo ECM diagnostic measurements across a range of tissue systems. A palette of advanced computational methods based on multivariate statistics, spectral unmixing, and machine learning can be applied to Raman data, allowing for the extraction of specific biochemical information of the ECM. Here, we review Raman spectroscopy techniques for ECM characterizations over a variety of exciting applications and tissue systems, including native tissue assessments (bone, cartilage, cardiovascular), regenerative medicine quality assessments, and diagnostics of disease states. We further discuss the challenges in the widespread adoption of Raman spectroscopy in biomedicine. The results of the latest discovery-driven Raman studies are summarized, illustrating the current and potential future applications of Raman spectroscopy in biomedicine.

Non-invasive In Vivo Imaging of Cancer Using Surface-Enhanced Spatially Offset Raman Spectroscopy (SESORS)

Rationale: The goal of imaging tumors at depth with high sensitivity and specificity represents a significant challenge in the field of biomedical optical imaging. 'Surface enhanced Raman scattering' (SERS) nanoparticles (NPs) have been employed as image contrast agents and can be used to specifically target cells in vivo. By tracking their unique "fingerprint" spectra, it becomes possible to determine their precise location. However, while the detection of SERS NPs is very sensitive and specific, conventional Raman spectroscopy imaging devices are limited in their inability to probe through tissue depths of more than a few millimetres, due to scattering and absorption of photons by biological tissues. Here, we combine the use of "Spatially Offset Raman spectroscopy" (SORS) with that of "surface-enhanced resonance Raman spectroscopy" (SERRS) in a technique known as "surface enhanced spatially offset resonance Raman spectroscopy" (SESO(R)RS) to image deep-seated glioblastoma multiforme (GBM) tumors in vivo in mice through the intact skull. Methods: A SORS imaging system was built in-house. Proof of concept SORS imaging was achieved using a PTFE-skull-tissue phantom. Imaging of GBMs in the RCAS-PDGF/N-tva transgenic mouse model was achieved through the use of gold nanostars functionalized with a resonant Raman reporter to create SERRS nanostars. These were then encapsulated in a thin silica shell and functionalized with a cyclic-RGDyK peptide to yield integrin-targeting SERRS nanostars. Non-invasive in vivo SORS image acquisition of the integrin-targeted nanostars was then performed in living mice under general anesthesia. Conventional non-SORS imaging was used as a direct comparison. Results: Using a low power density laser, GBMs were imaged via SESORRS in mice (n = 5) and confirmed using MRI and histopathology. The results demonstrate that via utilization of the SORS approach, it is possible to acquire clear and distinct Raman spectra from deep-seated GBMs in mice in vivo through the skull. SESORRS images generated using classical least squares outlined the tumors with high precision as confirmed via MRI and histology. Unlike SESORRS, conventional Raman imaging of the same areas did not provide a clear delineation of the tumor. Conclusion: To the best of our knowledge this is the first report of in vivo SESO(R)RS imaging. In a relevant brain tumor mouse model we demonstrate that this technique can overcome the limitations of conventional Raman imaging with regards to penetration depth. This work therefore represents a significant step forward in the potential clinical translation of SERRS nanoparticles for high precision cancer imaging.