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Fan X, Sun AR, Young RSE, Afara IO, Hamilton BR, Ong LJY, Crawford R, Prasadam I. Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications. Bone Res 2024; 12:7. [PMID: 38311627 PMCID: PMC10838951 DOI: 10.1038/s41413-023-00304-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 02/06/2024] Open
Abstract
Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.
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Affiliation(s)
- Xiwei Fan
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Antonia Rujia Sun
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Reuben S E Young
- Central Analytical Research Facility, Queensland University of Technology, Brisbane, QLD, Australia
- Molecular Horizons, University of Wollongong, Wollongong, NSW, Australia
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland, Brisbane, QLD, Australia
| | - Brett R Hamilton
- Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, QLD, Australia
| | - Louis Jun Ye Ong
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ross Crawford
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Indira Prasadam
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia.
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Li CL, Fisher CJ, Komolibus K, Lu H, Burke R, Visentin A, Andersson-Engels S. Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications. Sci Data 2024; 11:136. [PMID: 38278822 PMCID: PMC10817894 DOI: 10.1038/s41597-024-02972-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/15/2024] [Indexed: 01/28/2024] Open
Abstract
Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355-1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.
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Affiliation(s)
- Celina L Li
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland.
| | - Carl J Fisher
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Katarzyna Komolibus
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Huihui Lu
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Ray Burke
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Andrea Visentin
- Insight Centre for Data Analytics, School of Computer Science and Information Technology, University College Cork, Cork, Ireland
| | - Stefan Andersson-Engels
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland.
- Department of Physics, University College Cork, Cork, Ireland.
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Jaabar IL, Cornette P, Miche A, Wanherdrick K, Dupres V, Ehkirch FP, Cambon Binder A, Berenbaum F, Houard X, Landoulsi J. Deciphering pathological remodelling of the human cartilage extracellular matrix in osteoarthritis at the supramolecular level. NANOSCALE 2022; 14:8691-8708. [PMID: 35673929 DOI: 10.1039/d2nr00474g] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The extracellular matrix (ECM) of articular cartilage is a three-dimensional network mainly constituted of entangled collagen fibrils and interfibrillar aggrecan aggregates. During the development of osteoarthritis (OA), the most common musculoskeletal disorder, the ECM is subjected to a combination of chemical and structural changes that play a pivotal role in the initiation and the progress of the disease. While the molecular mechanisms involved in the pathological remodelling of the ECM are considered as decisive, they remain, however, not completely elucidated. Herein, we report a relevant way for unravelling the role and nature of OA progress on human cartilage tissues, in terms of chemical composition and morphological and mechanical properties at the level of supramolecular assemblies constituting the cartilage ECM. For this purpose, we used X-ray photoelectron spectroscopy (XPS), and developed an innovative methodological approach that provides the molecular composition of the ECM. Moreover, we used atomic force microscopy (AFM) to probe the tissues at the level of individual collagen fibrils, both imaging and force spectroscopy modes being explored to this end. Taken together, these nanoscale characterization studies reveal the existence of two stages in the OA progress. At the early stage, a marked increase in the aggrecan and collagen content is observed, reflecting the homeostatic chondrocyte activity that tends to repair the cartilage ECM. At the late stage, we observe a failed attempt to stabilize and/or restore the tissue, yielding significant degradation of the supramolecular assemblies. This suggests an imbalance in the chondrocyte activity that turns in favor of catabolic events. Chemical changes are also accompanied by ECM structural changes and stiffening. Interestingly, we showed the possibility to mimic the imbalanced activities of chondrocytes by applying enzymatic digestions of healthy cartilage, through the combined action of hyaluronidase and collagenase. This yields damage strictly analogous to that observed at high OA severity. These findings bring mechanistic insights leading to a better understanding of the mechanism by which OA is initiated and progresses in the cartilage ECM. They offer guidelines for the development of curative treatments, such as targeting the homeostatic balance of chondrocyte metabolism through the control of enzymatic reactions involved in catabolic processes.
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Affiliation(s)
- Ilhem Lilia Jaabar
- Sorbonne Université, CNRS, Laboratoire de Réactivité de Surface, LRS, F-75005 Paris, France.
- Sorbonne Université, INSERM (UMR_S938), Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France.
| | - Pauline Cornette
- Sorbonne Université, CNRS, Laboratoire de Réactivité de Surface, LRS, F-75005 Paris, France.
- Sorbonne Université, INSERM (UMR_S938), Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France.
| | - Antoine Miche
- Sorbonne Université, CNRS, Laboratoire de Réactivité de Surface, LRS, F-75005 Paris, France.
| | - Kristell Wanherdrick
- Sorbonne Université, INSERM (UMR_S938), Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France.
| | - Vincent Dupres
- Université Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 9017 - CIIL - Center for Infection and Immunity of Lille, F-59000, Lille, France
| | - François-Paul Ehkirch
- Sorbonne Université, INSERM (UMR_S938), Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France.
- Clinique Maussins-Nollet, F-75019 Paris, France
| | - Adeline Cambon Binder
- Sorbonne Université, INSERM (UMR_S938), Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France.
- Orthopedics and Hand Surgery Department, Saint-Antoine Hospital, 184 Rue du Faubourg Saint-Antoine, Paris, 75012, France
| | - Francis Berenbaum
- Sorbonne Université, INSERM (UMR_S938), Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France.
- Rheumatology Department, AP-HP Saint-Antoine Hospital, 184, rue du Faubourg Saint-Antoine, 75012, Paris, France
| | - Xavier Houard
- Sorbonne Université, INSERM (UMR_S938), Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France.
| | - Jessem Landoulsi
- Sorbonne Université, CNRS, Laboratoire de Réactivité de Surface, LRS, F-75005 Paris, France.
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Vibrational Spectroscopy in Assessment of Early Osteoarthritis-A Narrative Review. Int J Mol Sci 2021; 22:ijms22105235. [PMID: 34063436 PMCID: PMC8155859 DOI: 10.3390/ijms22105235] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/07/2021] [Accepted: 05/13/2021] [Indexed: 12/21/2022] Open
Abstract
Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.
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Torniainen J, Afara IO, Prakash M, Sarin JK, Stenroth L, Töyräs J. Open-source python module for automated preprocessing of near infrared spectroscopic data. Anal Chim Acta 2020; 1108:1-9. [PMID: 32222230 DOI: 10.1016/j.aca.2020.02.030] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 12/20/2019] [Accepted: 02/12/2020] [Indexed: 12/26/2022]
Abstract
Near infrared spectroscopy (NIRS) is an analytical technique for determining the chemical composition or structure of a given sample. For several decades, NIRS has been a frequently used analysis tool in agriculture, pharmacology, medicine, and petrochemistry. The popularity of NIRS is constantly growing as new application areas are discovered. Contrary to mid infrared spectral region, the absorption bands in near infrared spectral region are often non-specific, broad, and overlapping. Analysis of NIR spectra requires multivariate methods which are highly subjective to noise arising from instrumentation, scattering effects, and measurement setup. NIRS measurements are also frequently performed outside of a laboratory which further contributes to the presence of noise. Therefore, preprocessing is a critical step in NIRS as it can vastly improve the performance of multivariate models. While extensive research regarding various preprocessing methods exists, selection of the best preprocessing method is often determined through trial-and-error. A more powerful approach for optimizing preprocessing in NIRS models would be to automatically compare a large number of preprocessing techniques (e.g., through grid-search or hyperparameter tuning). To enable this, we present, nippy, an open-source Python module for semi-automatic comparison of NIRS preprocessing methods (available at https://github.com/uef-bbc/nippy). We provide here a brief overview of the capabilities of nippy and demonstrate the typical usage through two examples with public datasets.
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Affiliation(s)
- Jari Torniainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - Isaac O Afara
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Mithilesh Prakash
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Jaakko K Sarin
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Lauri Stenroth
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Juha Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
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Dataset on equine cartilage near infrared spectra, composition, and functional properties. Sci Data 2019; 6:164. [PMID: 31471536 PMCID: PMC6717194 DOI: 10.1038/s41597-019-0170-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 06/19/2019] [Indexed: 12/14/2022] Open
Abstract
Near infrared (NIR) spectroscopy is a well-established technique that is widely employed in agriculture, chemometrics, and pharmaceutical engineering. Recently, the technique has shown potential in clinical orthopaedic applications, for example, assisting in the diagnosis of various knee-related diseases (e.g., osteoarthritis) and their pathologies. NIR spectroscopy (NIRS) could be especially useful for determining the integrity and condition of articular cartilage, as the current arthroscopic diagnostics is subjective and unreliable. In this work, we present an extensive dataset of NIRS measurements for evaluating the condition, mechanical properties, structure, and composition of equine articular cartilage. The dataset contains NIRS measurements from 869 different locations across the articular surfaces of five equine fetlock joints. A comprehensive library of reference values for each measurement location is also provided, including results from a mechanical indentation testing, digital densitometry imaging, polarized light microscopy, and Fourier transform infrared spectroscopy. The published data can either be used as a model of human cartilage or to advance equine veterinary research.
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