Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
UiT - The Arctic University of Norway, 9019, Tromsø, Norway.
Sci Rep. 2024 May 8;14(1):10524. doi: 10.1038/s41598-024-61178-0.
Extracellular matrix diseases like fibrosis are elusive to diagnose early on, to avoid complete loss of organ function or even cancer progression, making early diagnosis crucial. Imaging the matrix densities of proteins like collagen in fixed tissue sections with suitable stains and labels is a standard for diagnosis and staging. However, fine changes in matrix density are difficult to realize by conventional histological staining and microscopy as the matrix fibrils are finer than the resolving capacity of these microscopes. The dyes further blur the outline of the matrix and add a background that bottlenecks high-precision early diagnosis of matrix diseases. Here we demonstrate the multiple signal classification method-MUSICAL-otherwise a computational super-resolution microscopy technique to precisely estimate matrix density in fixed tissue sections using fibril autofluorescence with image stacks acquired on a conventional epifluorescence microscope. We validated the diagnostic and staging performance of the method in extracted collagen fibrils, mouse skin during repair, and pre-cancers in human oral mucosa. The method enables early high-precision label-free diagnosis of matrix-associated fibrotic diseases without needing additional infrastructure or rigorous clinical training.
细胞外基质疾病（如纤维化）很难早期诊断，否则会导致器官功能完全丧失，甚至癌症进展，因此早期诊断至关重要。用合适的染色剂和标记物对固定组织切片中的胶原蛋白等蛋白质的基质密度进行成像，是诊断和分期的标准方法。然而，由于基质纤维比这些显微镜的分辨率更细，常规的组织学染色和显微镜检查很难实现对基质密度的细微变化的检测。染料进一步模糊了基质的轮廓，并增加了背景，这限制了对基质疾病进行高精度早期诊断。在这里，我们展示了多重信号分类方法-MUSICAL-这是一种计算超分辨率显微镜技术，可使用常规荧光显微镜获取的图像堆栈，通过纤维自发荧光来精确估计固定组织切片中的基质密度。我们在提取的胶原蛋白纤维、修复过程中的小鼠皮肤以及人口腔黏膜的癌前病变中验证了该方法的诊断和分期性能。该方法无需额外的基础设施或严格的临床培训，即可实现早期高精度的、无标记的基质相关纤维化疾病的诊断。
