Dev Isha, Mehmood Sofia, Pleshko Nancy, Obeid Iyad, Querido William
Department of Bioengineering, Temple University, Philadelphia, PA, 19122, USA.
Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19122, USA.
J Struct Biol X. 2024 Oct 9;10:100111. doi: 10.1016/j.yjsbx.2024.100111. eCollection 2024 Dec.
Understanding the composition of bone tissue at the submicron level is crucial to elucidate factors contributing to bone disease and fragility. Here, we introduce a novel approach utilizing optical photothermal infrared (O-PTIR) spectroscopy and imaging coupled with machine learning analysis to assess bone tissue composition at 500 nm spatial resolution. This approach was used to evaluate thick bone samples embedded in typical poly(methyl methacrylate) (PMMA) blocks, eliminating the need for cumbersome thin sectioning. We demonstrate the utility of O-PTIR imaging to assess the distribution of bone tissue mineral and protein, as well as to explore the structure-composition relationship surrounding microporosity at a spatial resolution unattainable by conventional infrared imaging modalities. Using bone samples from wildtype (WT) mice and from a mouse model of osteogenesis imperfecta (OIM), we further showcase the application of O-PTIR spectroscopy to quantify mineral content, crystallinity, and carbonate content in spatially defined regions across the cortical bone. Notably, we show that machine learning analysis using support vector machine (SVM) was successful in identifying bone phenotypes (typical in WT, fragile in OIM) based on input of spectral data, with over 86 % of samples correctly identified when using the collagen spectral range. Our findings highlight the potential of O-PTIR spectroscopy and imaging as valuable tools for exploring bone submicron composition.
了解亚微米级骨组织的组成对于阐明导致骨疾病和骨脆性的因素至关重要。在此，我们介绍一种利用光学光热红外（O-PTIR）光谱和成像结合机器学习分析的新方法，以在500 nm空间分辨率下评估骨组织组成。该方法用于评估嵌入典型聚甲基丙烯酸甲酯（PMMA）块中的厚骨样品，无需繁琐的薄切片。我们展示了O-PTIR成像在评估骨组织矿物质和蛋白质分布方面的效用，以及在传统红外成像模式无法达到的空间分辨率下探索围绕微孔的结构-组成关系。使用来自野生型（WT）小鼠和成骨不全（OIM）小鼠模型的骨样品，我们进一步展示了O-PTIR光谱在定量皮质骨中空间定义区域的矿物质含量、结晶度和碳酸盐含量方面的应用。值得注意的是，我们表明，使用支持向量机（SVM）的机器学习分析基于光谱数据输入成功识别了骨表型（WT中典型，OIM中易碎），在使用胶原蛋白光谱范围时，超过86%的样品被正确识别。我们的研究结果突出了O-PTIR光谱和成像作为探索骨亚微米组成的有价值工具的潜力。
