Raman needle arthroscopy for in vivo molecular assessment of cartilage

Kroupa, Kimberly R.; Wu, Man I; Zhang, Juncheng; Jensen, Magnus; Wong, Wei Chang; Engiles, Julie B.; Grinstaff, Mark W.; Snyder, Brian D.; Bergholt, Mads Sylvest; Albro, Michael B.

doi:10.1002/jor.25155

Cited by 10 publications

(14 citation statements)

References 42 publications

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“…To enable molecular specific reconstruction, we employ multivariate regression of purified components. 29 3-D image reconstruction and rendering can finally be performed using inverse Radon transform - backprojection of the regression component abundances (Figure 1B). 30…”

Section: Resultsmentioning

confidence: 99%

Label-free 3-D molecular imaging of living tissues using Raman Spectral Projection Tomography

Stepula,

Walther,

Mehrotra

et al. 2023

Preprint

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The ability to image tissues in three-dimensions (3-D) with label-free molecular contrast at mesoscale would be a valuable capability in biology and biomedicine. Here, we introduce Raman spectral projection tomography (RSPT) for live volumetric molecular imaging of tissues with sub-millimeter spatial resolution. We have developed a novel RSPT imaging instrument capable of providing 3-D molecular contrast in transparent and semi-transparent samples. A computational pipeline for multivariate reconstruction was established to extract label-free spatial molecular information from Raman projection data. We demonstrate imaging and visualization of phantoms of various complex shapes with label-free molecular contrast. Finally, we apply RSPT as a novel tool for imaging of molecular gradients and extracellular matrix heterogeneities in fixed and live tissue-engineered constructs and explanted native tissues. RSPT imaging opens new possibilities for label-free molecular monitoring of tissues.

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Section: Resultsmentioning

confidence: 99%

Label-free 3-D molecular imaging of living tissues using Raman Spectral Projection Tomography

Stepula,

Walther,

Mehrotra

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Kroupa et al [ 42 ] utilized a needle arthroscopic Raman probe for acquiring spectra, which were decomposed into composition-specific scores for GAGs, collagen, and water. In addition, polarized Raman spectra were collected to quantify collagen anisotropy in abraded cartilage samples.…”

Section: Discussionmentioning

confidence: 99%

Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties

Shehata¹,

Nippolainen²,

Shaikh³

et al. 2023

Ann Biomed Eng

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Objective To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). Design Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared (n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties. Results The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%). Conclusion RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS.

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“…32 To date, Raman needle arthroscopy has been developed and reported. 33 Kroupa et al 34 demonstrated Raman needle arthroscopy in both ex vivo and in vivo measurements for probing the cartilage surface of the knee joint. Our group has been developing a fiber-optic Raman spectroscopic system for endoscopic applications in animal and human optical biopsies.…”

Section: Discussionmentioning

confidence: 99%

“…To date, Raman needle arthroscopy has been developed and reported 33 . Kroupa et al 34 . demonstrated Raman needle arthroscopy in both ex vivo and in vivo measurements for probing the cartilage surface of the knee joint.…”

Section: Discussionmentioning

confidence: 99%

Prognostic potential and pathological validation of a diagnostic application using Raman spectroscopy in the characterization of degenerative changes in the cartilage of the humeral head

et al. 2022

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Significance: Raman spectroscopy is a well-established analytical method in the fields of chemistry, industry, biology, pharmaceutics, and medicine. Previous studies have investigated optical imaging and Raman spectroscopy for osteoarthritis (OA) diagnosis in weight-bearing joints such as hip and knee joints. However, to realize early diagnosis or a curable treatment, it is still challenging to understand the correlations with intrinsic factors or patients' background.Aim: To elucidate the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage.Approach: Osteoarthritic cartilage specimens excised from the humeral heads of 14 patients who underwent shoulder arthroplasty were assessed by a confocal Raman microscope and histological staining. The Raman spectroscopic dataset of degenerative cartilage was further analyzed by principal component analysis and hierarchical cluster analysis.Results: Multivariate association of the Raman spectral data generated three major clusters. The first cluster of patients shows a relatively high Raman intensity of collagen. The second cluster displays relatively low Raman intensities of proteoglycans (PGs) and glycosaminoglycans (GAGs), whereas the third cluster shows relatively high Raman intensities of PGs and GAGs. The reduced PGs and GAGs are typical changes in OA cartilage, which have been confirmed by safranin-O staining. In contrast, the increased Raman intensities of collagen, PGs, and GAGs may reflect the instability of the cartilage matrix structure in OA patients. Conclusions:The results obtained confirm the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage. Unsupervised machine learning methods successfully yielded a clinically meaningful classification between the shoulder OA patients. This approach not only has potential to confirm severity of cartilage defects but also to determine the origin of an individual's OA by evaluating the cartilage quality.

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Raman needle arthroscopy for in vivo molecular assessment of cartilage

Cited by 10 publications

References 42 publications

Label-free 3-D molecular imaging of living tissues using Raman Spectral Projection Tomography

Label-free 3-D molecular imaging of living tissues using Raman Spectral Projection Tomography

Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties

Prognostic potential and pathological validation of a diagnostic application using Raman spectroscopy in the characterization of degenerative changes in the cartilage of the humeral head

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