Department of Chemical Engineering and Analytical Chemistry, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain.
ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss 3, 08860 Castelldefels, Barcelona, Spain.
Talanta. 2019 Mar 1;194:390-398. doi: 10.1016/j.talanta.2018.10.029. Epub 2018 Oct 12.
The use of hyperspectral imaging techniques in biological studies has increased in the recent years. Hyperspectral images (HSI) provide chemical information and preserve the morphology and original structure of heterogeneous biological samples, which can be potentially useful in environmental -omics studies when effects due to several factors, e.g., contaminant exposure, phenotype,…, at a specific tissue level need to be investigated. Yet, no available strategies exist to exploit adequately this kind of information. This work offers a novel chemometric strategy to pass from the raw image information to useful knowledge in terms of statistical assessment of the multifactor effects of interest in -omic studies. To do so, unmixing of the hyperspectral image measurement is carried out to provide tissue-specific information. Afterwards, several specific ANOVA-Simultaneous Component Analysis (ASCA) models are generated to properly assess and interpret the diverse effect of the factors of interest on the spectral fingerprints of the different tissues characterized. The unmixing step is performed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on multisets of biological images related to each studied condition and provides reliable HSI spectral signatures and related image maps for each specific tissue in the regions imaged. The variability associated with these signatures within a population is obtained through an MCR-based resampling step on representative pixel subsets of the images analyzed. All spectral fingerprints obtained for a particular tissue in the different conditions studied are used to obtain the related ASCA model that will help to assess the significance of the factors studied on the tissue and, if relevant, to describe the associated fingerprint modifications. The potential of the approach is assessed in a real case of study linked to the investigation of the effect of exposure time to chlorpyrifos-oxon (CPO) on ocular tissues of different phenotypes of zebrafish larvae from Raman HSI of eye cryosections. The study allowed the characterization of melanin, crystalline and internal eye tissue and the phenotype, exposure time and the interaction of the two factors were found to be significant in the changes found in all kind of tissues. Factor-related changes in the spectral fingerprint were described and interpreted per each kind of tissue characterized.
近年来，高光谱成像技术在生物研究中的应用日益增多。高光谱图像（HSI）提供了化学信息，并保留了异质生物样本的形态和原始结构，这在环境组学研究中可能非常有用，例如，当需要研究特定组织水平上由于多种因素（例如污染物暴露、表型等）而产生的影响时。然而，目前还没有充分利用这种信息的策略。本工作提出了一种新的化学计量学策略，用于从原始图像信息中提取有用的知识，以统计评估组学研究中感兴趣的多因素效应。为此，对高光谱图像测量进行解混处理，以提供组织特异性信息。然后，生成多个特定的方差分析-同时成分分析（ASCA）模型，以正确评估和解释感兴趣因素对不同组织特征的光谱指纹的多种影响。解混步骤通过多变量曲线分辨-交替最小二乘法（MCR-ALS）在与每个研究条件相关的多组生物图像上进行，为所成像区域中的每个特定组织提供可靠的 HSI 光谱特征和相关图像图。通过对分析图像的代表性像素子集进行基于 MCR 的重采样步骤，可以获得与这些特征相关的在人群中的变异性。在所研究的不同条件下，为特定组织获得的所有光谱指纹都用于获得相关的 ASCA 模型，该模型将有助于评估研究因素对组织的显著性，并且如果相关，还将描述相关的指纹变化。该方法的潜力在与研究暴露于毒死蜱氧（CPO）对不同表型斑马鱼幼虫眼部组织的影响相关的实际研究案例中进行了评估，该研究案例使用眼部组织的共聚焦拉曼高光谱成像（Raman HSI）获得。该研究实现了对黑色素、晶状体和内部眼部组织以及表型的表征，并且发现暴露时间和两个因素的相互作用在所有组织类型的变化中都具有显著性。描述并解释了每个特征组织的特征相关的指纹变化。
