Departamento de Bioingeniería, Universidad Carlos III de Madrid, Madrid, Spain.
Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.
Sci Rep. 2024 May 27;14(1):12084. doi: 10.1038/s41598-024-61020-7.
Selective Plane Illumination Microscopy (SPIM) has become an emerging technology since its first application for 3D in-vivo imaging of the development of a living organism. An extensive number of works have been published, improving both the speed of acquisition and the resolution of the systems. Furthermore, multispectral imaging allows the effective separation of overlapping signals associated with different fluorophores from the spectrum over the whole field-of-view of the analyzed sample. To eliminate the need of using fluorescent dyes, this technique can also be applied to autofluorescence imaging. However, the effective separation of the overlapped spectra in autofluorescence imaging necessitates the use of mathematical tools. In this work, we explore the application of a method based on Principal Component Analysis (PCA) that enables tissue characterization upon spectral autofluorescence data without the use of fluorophores. Thus, enabling the separation of different tissue types in fixed and living samples with no need of staining techniques. Two procedures are described for acquiring spectral data, including a single excitation based method and a multi-excitation scanning approach. In both cases, we demonstrate the effective separation of various tissue types based on their unique autofluorescence spectra.
选择平面照明显微镜 (SPIM) 自首次应用于活体生物 3D 体内成像以来，已成为一种新兴技术。已经发表了大量的研究工作，提高了系统的采集速度和分辨率。此外，多光谱成像允许有效地分离与整个分析样本视场相关的不同荧光团的重叠信号。为了消除对荧光染料的需求，该技术也可应用于自发荧光成像。然而，在自发荧光成像中有效分离重叠光谱需要使用数学工具。在这项工作中，我们探索了一种基于主成分分析 (PCA) 的方法的应用，该方法可以在不使用荧光团的情况下，通过光谱自发荧光数据进行组织特征描述。因此，可以在无需染色技术的情况下，分离固定和活体样本中的不同组织类型。本文描述了两种获取光谱数据的方法，包括基于单激发的方法和多激发扫描方法。在这两种情况下，我们都证明了基于其独特的自发荧光光谱，可以有效地分离各种组织类型。
