Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue’s biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.
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The source data underlying Figs. 7–9 and Supplementary Fig. 1 are provided as Source Data files. Raw imaging files (Fig. 9 and Supplementary Fig. 1) can be opened with MATLAB via the MIA toolbox add-on. Source data for Fig. 6 can be obtained from the corresponding author upon reasonable request. Source data are provided with this paper.
Blanco, M. & Villarroya, I. NIR spectroscopy: a rapid-response analytical tool. Trends Anal. Chem. 21, 240–250 (2002).
Shenk, J. S., Workman Jr, J. J. & Westerhaus, O. Application of NIR spectroscopy to agricultural products. in Handbook of Near-Infrared Analysis (eds. Burns, D. A. & Ciurczak, E. W.) 347–386 (Marcel Dekker, 2008).
Anderson, C. A., Drennen, J. K. & Ciurczak, E. W. Pharmaceutical applications of near-infrared spectroscopy. in Handbook of Near-Infrared Analysis (eds. Burns, D. A. & Ciurczak, E. W.) 585–612 (Marcel Dekker, 2008).
Urbas, A. A. & Lodder, R. A. Near-infrared spectrometry in cardiovascular disease. in Handbook of Near-Infrared Analysis (eds. Burns, D. A. & Ciurczak, E. W.) 657–672 (Marcel Dekker, 2008).
Du, Y. P., Kasemsumran, S., Jiang, J.-H. & Ozaki, Y. In vivo and in vitro near-infrared spectroscopic determination of blood glucose and other biomedical components with chemometrics. in Handbook of Near-Infrared Analysis (eds. Burns, D. A. & Ciurczak, E. W.) 673–698 (Marcel Dekker, 2008).
Spahn, G. et al. Near-infrared spectroscopy for arthroscopic evaluation of cartilage lesions: results of a blinded, prospective, interobserver study. Am. J. Sports Med. 38, 2516–2521 (2010).
Afara, I. O. et al. Near infrared spectroscopy for rapid determination of Mankin score components: a potential tool for quantitative characterization of articular cartilage at surgery. Arthroscopy 30, 1146–1155 (2014).
Sarin, J. K. et al. Arthroscopic determination of cartilage proteoglycan content and collagen network structure with near-infrared spectroscopy. Ann. Biomed. Eng. 47, 1815–1826 (2019).
Huck, C., Ozaki, Y. & Huck-Pezzei, V. Critical review upon the role and potential of fluorescence and near-infrared imaging and absorption spectroscopy in cancer related cells, serum, saliva, urine and tissue analysis. Curr. Med. Chem. 23, 3052–3077 (2016).
Laimer, J. et al. Amalgam tattoo versus melanocytic neoplasm—differential diagnosis of dark pigmented oral mucosa lesions using infrared spectroscopy. PLoS One 13, 1–14 (2018).
Siesler, H. W. Basic principles of near-infrared spectroscopy. in Handbook of Near-Infrared Analysis (eds. Burns, D. A. & Ciurczak, E. W.) 7–20 (Marcel Dekker, 2008).
Baker, M. J. et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 9, 1771–1791 (2014).
Santos, M. C. D., Nascimento, Y. M., Araújo, J. M. G. & Lima, K. M. G. ATR-FTIR spectroscopy coupled with multivariate analysis techniques for the identification of DENV-3 in different concentrations in blood and serum: a new approach. RSC Adv 7, 25640–25649 (2017).
Afara, I., Singh, S. & Oloyede, A. Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage. Med. Eng. Phys. 35, 88–95 (2013).
Afara, I., Prasadam, I., Crawford, R., Xiao, Y. & Oloyede, A. Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score. Osteoarthritis Cartilage 20, 1367–1373 (2012).
Sakudo, A. et al. A novel diagnostic method for human immunodeficiency virus type-1 in plasma by near-infrared spectroscopy. Microbiol. Immunol. 49, 695–701 (2005).
Afara, I. O., Hauta-Kasari, M., Jurvelin, J. S., Oloyede, A. & Töyräs, J. Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition. Physiol. Meas. 36, 1913–1928 (2015).
Palukuru, U. P., McGoverin, C. M. & Pleshko, N. Assessment of hyaline cartilage matrix composition using near infrared spectroscopy. Matrix Biol 38, 3–11 (2014).
Palukuru, U. P. et al. Near infrared spectroscopic imaging assessment of cartilage composition: validation with mid infrared imaging spectroscopy. Anal. Chim. Acta 926, 79–87 (2016).
Sarin, J. K. et al. Near infrared spectroscopic mapping of functional properties of equine articular cartilage. Ann. Biomed. Eng. 44, 3335–3345 (2016).
Afara, I. O., Moody, H., Singh, S., Prasadam, I. & Oloyede, A. Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy. Biomed. Opt. Express 6, 144–154 (2015).
Ciurczak, E. W. Biomedical applications of near-infrared spectroscopy. in Handbook of Near-Infrared Analysis (eds. Burns, D. A. & Ciurczak, E. W.) 647–655 (Marcel Dekker, 2008).
Bale, G., Mitra, S., Meek, J., Robertson, N. & Tachtsidis, I. A new broadband near-infrared spectroscopy system for in-vivo measurements of cerebral cytochrome-c-oxidase changes in neonatal brain injury. Biomed. Opt. Express 5, 3450 (2014).
Gunadi, S., Leung, T. S., Elwell, C. E. & Tachtsidis, I. Spatial sensitivity and penetration depth of three cerebral oxygenation monitors. Biomed. Opt. Express 5, 2896 (2014).
Torricelli, A. et al. Time domain functional NIRS imaging for human brain mapping. Neuroimage 85, 28–50 (2014).
Wabnitz, H. et al. Time-resolved near-infrared spectroscopy and imaging of the adult human brain. in Advances in Experimental Medicine and Biology 143–148 (Springer, 2010).
Dehaes, M. et al. Cerebral oxygen metabolism in neonatal hypoxic ischemic encephalopathy during and after therapeutic hypothermia. J. Cereb. Blood Flow Metab. 34, 87–94 (2014).
Mitra, S., Bale, G., Meek, J., Tachtsidis, I. & Robertson, N. J. Cerebral near infrared spectroscopy monitoring in term infants with hypoxic ischemic encephalopathy—a systematic review. Front. Neurol. 11, 393 (2020).
Kleiser, S. et al. Comparison of tissue oximeters on a liquid phantom with adjustable optical properties: an extension. Biomed. Opt. Express 9, 86 (2018).
Sangani, S., Lamontagne, A. & Fung, J. Cortical mechanisms underlying sensorimotor enhancement promoted by walking with haptic inputs in a virtual environment. in Progress in Brain Research 313–330 (Elsevier, 2015).
Shah, N. J. et al. Quantitative cerebral water content mapping in hepatic encephalopathy. Neuroimage 41, 706–717 (2008).
Padalkar, M. V., Spencer, R. G. & Pleshko, N. Near infrared spectroscopic evaluation of water in hyaline cartilage. Ann. Biomed. Eng. 41, 2426–2436 (2013).
Kraats, E. B., van de, Munćan, J. & Tsenkova, R. N. Aquaphotomics – origin, concept, applications and future perspectives. Substantia 3, 13–28 (2019).
Tsenkova, R., Kovacs, Z. & Kubota, Y. Aquaphotomics: near infrared spectroscopy and water states in biological systems. Subcell. Biochem 71, 189–211 (2015).
Tsenkova, R., Muncan, J., Pollner, B. & Kovacs, Z. Essentials of aquaphotomics and its chemometrics approaches. Front. Chem. 6, 363 (2018).
Tuchin, V. V. Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis (SPIE, 2007).
Jacques, S. L. Optical properties of biological tissues: a review. Phys. Med. Biol. 58, 5007–5008 (2013).
Faris, F. et al. Noninvasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head. Clin. Phys. Physiol. Meas. 12, 353–358 (1991).
Sordillo, L. A., Pu, Y., Pratavieira, S., Budansky, Y. & Alfano, R. R. Deep optical imaging of tissue using the second and third near-infrared spectral windows. J. Biomed. Opt. 19, 056004 (2014).
Afara, I., Prasadam, I., Crawford, R., Xiao, Y. & Oloyede, A. Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models. Bone 53, 350–357 (2013).
Afara, I. O. et al. Characterizing human subchondral bone properties using near-infrared (NIR) spectroscopy. Sci. Rep. 8, 9733 (2018).
Sarin, J. K. et al. Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo. Sci. Rep. 8, 13409 (2018).
Afara, I. O., Singh, S., Moody, H., Zhang, L. & Oloyede, A. Characterization of articular cartilage recovery and its correlation with optical response in the near-infrared spectral range. Cartilage 8, 307–316 (2016).
Afara, I., Singh, S. & Oloyede, A. Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra. J. Mech. Behav. Biomed. Mater. 20, 249–258 (2013).
Prakash, M. et al. Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy. Osteoarthritis Cartilage 27, 1235–1243 (2019).
Spahn, G. et al. Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study. Med. Eng. Phys. 30, 285–292 (2008).
Sarin, J. K. et al. Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure. Sci. Rep. 7, 10586 (2017).
Afara, I. O., Prasadam, I., Arabshahi, Z., Xiao, Y. & Oloyede, A. Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy. Sci. Rep. 7, 11463 (2017).
Ala-Myllymäki, J., Honkanen, J. T. J., Töyräs, J. & Afara, I. O. Optical spectroscopic determination of human meniscus composition. J. Orthop. Res. 34, 270–278 (2016).
Ala-Myllymäki, J. et al. Optical spectroscopic characterization of human meniscus biomechanical properties. J. Biomed. Opt. 22, 1–10 (2017).
Torniainen, J. et al. Near infrared spectroscopic evaluation of ligament and tendon biomechanical properties. Ann. Biomed. Eng. 47, 213–222 (2019).
Prakash, M., Sarin, J. K., Rieppo, L., Afara, I. O. & Töyräs, J. Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage. Appl. Spectrosc. 71, 2253–2262 (2017).
Prakash, M., Sarin, J. K., Rieppo, L., Afara, I. O. & Töyräs, J. Accounting for spatial dependency in multivariate spectroscopic data. Chemom. Intell. Lab. Syst. 182, 166–171 (2018).
Sarin, J. K. et al. Multimodality scoring of chondral injuries in the equine fetlock joint ex vivo. Osteoarthritis Cartilage 25, 790–798 (2017).
Spahn, G. et al. Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study. BMC Musculoskelet. Disord. 8, 47 (2007).
Burns, D. A. & Ciurczak, E. W. Handbook of Near-Infrared Spectroscopy (Marcel Dekker, 2008).
Cozzolino, D. et al. Effect of temperature variation on the visible and near infrared spectra of wine and the consequences on the partial least square calibrations developed to measure chemical composition. Anal. Chim. Acta 588, 224–230 (2007).
Wachs, I. E. & Keturakis, C. J. Monolayer systems. in Comprehensive Inorganic Chemistry II (Second Edition): From Elements to Applications 131–151 (Elsevier, 2013).
Bjørsvik, H.-R. & Martens, H. Data analysis: calibration of NIR instruments by PLS regression. in Handbook of Near-Infrared Analysis (eds. Burns, A. D. & Ciurczak, E. W.) 189–207 (Marcel Dekker, 2008).
Wu, S. & Butt, H. J. Near-infrared-sensitive materials based on upconverting nanoparticles. Adv. Mater. 28, 1208–1226 (2016).
Nippolainen, E. et al. Near infrared spectroscopy enables differentiation of mechanically and enzymatically induced cartilage injuries. Ann. Biomed. Eng. 48, 2343–2353 (2020).
Kilpatrick-Liverman, L., Kazmi, P., Wolff, E. & Polefka, T. G. The use of near-infrared spectroscopy in skin care applications. Skin Res. Technol. 12, 162–169 (2006).
Gu, Y., Chen, W. R., Xia, M., Jeong, S. W. & Liu, H. Effect of photothermal therapy on breast tumor vascular contents: noninvasive monitoring by near-infrared spectroscopy. Photochem. Photobiol. 81, 1002–1009 (2005).
Ali, J. H., Wang, W. B., Zevallos, M. & Alfano, R. R. Near infrared spectroscopy and imaging to probe differences in water content in normal and cancer human prostate tissues. Technol. Cancer Res. Treat. 3, 491–497 (2004).
Kasemsumran, S., Du, Y. P., Murayama, K., Huehne, M. & Ozaki, Y. Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares. Anal. Chim. Acta 512, 223 (2004).
Samann, A. et al. Non-invasive blood glucose monitoring by means of near infrared spectroscopy: Investigation of long-term accuracy and stability. Exp. Clin. Endocrinol. Diabetes 108, 406–413 (2000).
Spahn, G., Felmet, G. & Hofmann, G. O. Traumatic and degenerative cartilage lesions: arthroscopic differentiation using near-infrared spectroscopy (NIRS). Arch. Orthop. Trauma Surg. 133, 997–1002 (2013).
García-Sánchez, F., Galvez-Sola, L., Martínez-Nicolás, J. J., Muelas-Domingo, R. & Nieves, M. Using near-infrared spectroscopy in agricultural systems. in Developments in Near-Infrared Spectroscopy, https://www.intechopen.com/books/developments-in-near-infrared-spectroscopy/using-near-infrared-spectroscopy-in-agricultural-systems (IntechOpen, 2017).
Jamrógiewicz, M. Application of the near-infrared spectroscopy in the pharmaceutical technology. J. Pharm. Biomed. Anal. 66, 1–10 (2012).
Linderholm, J., Geladi, P., Gorretta, N., Bendoula, R. & Gobrecht, A. Near infrared and hyperspectral studies of archaeological stratigraphy and statistical considerations. Geoarchaeology 34, 311–321 (2019).
Thomas, D. B., McGoverin, C. M., Chinsamy, A. & Manley, M. Near infrared analysis of fossil bone from the Western Cape of South Africa. J. Near Infrared Spectrosc 19, 151–159 (2011).
Türker-Kaya, S. & Huck, C. W. A review of mid-infrared and near-infrared imaging: principles, concepts and applications in plant tissue analysis. Molecules 22, 168 (2017).
Anand, S. et al. Effects of formalin fixation on tissue optical properties of in-vitro brain samples. in Optical Interactions with Tissue and Cells XXVI (SPIE, 2015).
Zhao, W. et al. Absorption spectroscopy of formaldehyde at 1.573 μm. J. Quant. Spectrosc. Radiat. Transf. 107, 331–339 (2007).
Padalkar, M. V. & Pleshko, N. Wavelength-dependent penetration depth of near infrared radiation into cartilage. Analyst 140, 2093–2100 (2015).
McGoverin, C. M., Lewis, K., Yang, X., Bostrom, M. P. G. & Pleshko, N. The contribution of bone and cartilage to the near-infrared spectrum of osteochondral tissue. Appl. Spectrosc. 68, 1168–1175 (2014).
Rajapakse, C. S., Padalkar, M. V., Yang, H. J., Ispiryan, M. & Pleshko, N. Non-destructive NIR spectral imaging assessment of bone water: comparison to MRI measurements. Bone 103, 116–124 (2017).
Ailavajhala, R., Oswald, J., Rajapakse, C. S. & Pleshko, N. Environmentally-controlled near infrared spectroscopic imaging of bone water. Sci. Rep. 9, 10199 (2019).
Loewen, E. G. & Popov, E. Diffraction Gratings and Applications (CRC Press, 2018).
Arthrospec ONE. http://www.arthrospec.de/.
Karchner, J. P., Querido, W., Kandel, S. & Pleshko, N. Spatial correlation of native and engineered cartilage components at micron resolution. Ann. NY Acad. Sci. 1442, 104–117 (2019).
Bhargava, R. Infrared spectroscopic imaging: the next generation. Appl. Spectrosc. 66, 1091–1120 (2012).
Lasch, P. & Naumann, D. Spatial resolution in infrared microspectroscopic imaging of tissues. Biochim. Biophys. Acta 1758, 814–829 (2006).
Savitzky, A. & Golay, M. J. E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964).
Kohler, A., Zimonja, M., Segtnan, V. & Martens, H. Standard normal variate, multiplicative signal correction and extended multiplicative signal correction preprocessing in biospectroscopy. in Comprehensive Chemometrics 139–162 (Elsevier, 2010).
Geladi, P., MacDougall, D. & Martens, H. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl. Spectrosc. 39, 491–500 (1985).
Guo, Q., Wu, W. & Massart, D. L. The robust normal variate transform for pattern recognition with near-infrared data. Anal. Chim. Acta 382, 87–103 (1999).
Li, Q., Gao, Q. & Zhang, G. Improved extended multiplicative scatter correction algorithm applied in blood glucose noninvasive measurement with FT-IR spectroscopy. J. Spectrosc. 2013, 916351 (2013).
Baykal, D. et al. Nondestructive assessment of engineered cartilage constructs using near-infrared spectroscopy. Appl. Spectrosc. 64, 1160–1166 (2010).
Hanifi, A., McCarthy, H., Roberts, S. & Pleshko, N. Fourier transform infrared imaging and infrared fiber optic probe spectroscopy identify collagen type in connective tissues. PLoS ONE 8, e64822 (2013).
Hanifi, A. et al. Infrared fiber optic probe evaluation of degenerative cartilage correlates with histological grading. Am. J. Sports Med. 40, 2853–2861 (2014).
Liu, F. T., Ting, K. M. & Zhou, Z. H. Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6, 3 (2012).
Workman, J. J. Jr. NIR spectroscopy calibration basics. in Handbook of Near-Infrared Analysis (eds. Burns, D. A. & Ciurczak, E. W.) 123–149 (Marcel Dekker, 2008).
Xiaobo, Z., Jiewen, Z., Povey, M. J. W., Holmes, M. & Hanpin, M. Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta 667, 14–32 (2010).
Afara, I. O. et al. Deep learning classification of cartilage integrity using near infrared spectroscopy. in Optics InfoBase Conference Papers, https://www.osapublishing.org/abstract.cfm?URI=Translational-2018-JTu3A.27 (2018).
Afara, I. O. et al. Machine learning classification of articular cartilage integrity using near infrared spectroscopy. Cell. Mol. Bioeng. 13, 219–228 (2020).
Diem, M., Romeo, M., Boydston-White, S., Miljković, M. & Matthäus, C. A decade of vibrational micro-spectroscopy of human cells and tissue (1994–2004). Analyst 129, 880–885 (2004).
Lasch, P., Haensch, W., Naumann, D. & Diem, M. Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis. Biochim. Biophys. Acta 1688, 176–186 (2004).
Kolmas, J., Marek, D. & Kolodziejski, W. Near-infrared (NIR) spectroscopy of synthetic hydroxyapatites and human dental tissues. Appl. Spectrosc. 69, 902–912 (2015).
Szarko, M., Muldrew, K. & Bertram, J. E. Freeze-thaw treatment effects on the dynamic mechanical properties of articular cartilage. BMC Musculoskelet. Disord. 11, 231 (2010).
Loessner, D. et al. Functionalization, preparation and use of cell-laden gelatin methacryloyl-based hydrogels as modular tissue culture platforms. Nat. Protoc. 11, 727–746 (2016).
Torniainen, J. et al. Open-source Python module for automated preprocessing of near infrared spectroscopic data. Anal. Chim. Acta 1108, 1–9 (2020).
Kandel, S., Querido, W., Falcon, J. M., Reiners, D. J. & Pleshko, N. Approaches for in situ monitoring of matrix development in hydrogel-based engineered cartilage. Tissue Eng. Part C Methods 26, 225–238 (2020).
Viscarra Rossel, R. A. ParLeS: software for chemometric analysis of spectroscopic data. Chemom. Intell. Lab. Syst. 90, 72–83 (2008).
Stevens, A., Ramirez-Lopez, L., Stevens, M. A. & Rcpp, L. Package ‘prospectr’. R Package (2020).
Li, H.-D., Xu, Q.-S. & Liang, Y.-Z. libPLS: an integrated library for partial least squares regression and linear discriminant analysis. Chemom. Intell. Lab. Syst. 176, 34–43 (2018).
Hanson, B. A. ChemoSpec: an R package for the chemometric analysis of spectroscopic data. https://cran.r-project.org/web/packages/ChemoSpec/index.html (2014).
Beleites, C. & Sergo, V. hyperSpec: a package to handle hyperspectral data sets in R. https://cran.r-project.org/web/packages/hyperSpec/hyperSpec.pdf (2020).
Liland, K. H., Mevik, B. H. & Canteri, R. Baseline: baseline correction of spectra. https://cran.r-project.org/web/packages/baseline/baseline.pdf (2020).
Le Losq, C. Rampy: a Python library for processing spectroscopic (IR, Raman, XAS) data. https://zenodo.org/record/1168730#.X8uFwc6SlPY (2018).
Ramirez-Lopez, L. & Stevens, A. resemble: regression and similarity evaluation for memory-based learning in spectral chemometrics. https://www.r-project.org/nosvn/pandoc/resemble.html (2016).
Baumann P. Simplerspec: Beta Release 0.1.0 for Zenodo (2019).
Roudier, P. et al. spectacles: storing and manipulating spectroscopy data in R. https://cran.r-project.org/web/packages/spectacles/spectacles.pdf (2020).
Le Losq, C. Spectra. jl: a Julia package for processing spectroscopic data. https://zenodo.org/record/53940#.X8uG5M6SlPY (2016).
This work was financially supported by the Academy of Finland (315820), the European Union (Horizon 2020 Research and Innovation Programme, 780598), the SCITECO Doctoral Programme of the University of Eastern Finland, State Research Funding of Kuopio University Hospital (5203111) and the U.S. National Institutes of Health (R01 AR056145).
The authors declare no competing interests as defined by Nature Research or other interests that might be perceived to influence the interpretation of the article.
Peer review information Nature Protocols thanks Daniel Cozzolino, Ilias Tachtsidis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Key references using this protocol:
Afara, I. O. et al. Sci. Rep. 7, 11463 (2017): https://doi.org/10.1038/s41598-017-11844-3
Ala-Myllymäki, J. et al. Ann. Biomed. Eng. 2020, https://doi.org/10.1007/s10439-020-02578-x
Rajapakse, C. S. et al. Bone 103, 116–124 (2017): https://doi.org/10.1016/j.bone.2017.06.015
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Afara, I.O., Shaikh, R., Nippolainen, E. et al. Characterization of connective tissues using near-infrared spectroscopy and imaging. Nat Protoc 16, 1297–1329 (2021). https://doi.org/10.1038/s41596-020-00468-z