Assessment of tissue-specific multifactor effects in environmental -omics studies of heterogeneous biological samples: Combining hyperspectral image information and chemometrics.

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.

[1]  K. Leung,et al.  Spectral Interpretation and Qualitative Analysis of Organophosphorus Pesticides Using FT-Raman and FT-Infrared Spectroscopy , 1996 .

[2]  Daniel L Villeneuve,et al.  Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment , 2010, Environmental toxicology and chemistry.

[3]  Romà Tauler,et al.  Chemometrics applied to unravel multicomponent processes and mixtures: Revisiting latest trends in multivariate resolution , 2003 .

[4]  Romà Tauler,et al.  Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies , 2015, Analytical and Bioanalytical Chemistry.

[5]  Henk A. L. Kiers,et al.  Simultaneous Components Analysis , 1992 .

[6]  Combining Hyperspectral Imaging and Chemometrics to Assess and Interpret the Effects of Environmental Stressors on the Organism at Tissue Level , 2018 .

[7]  P. Fratzl,et al.  Simultaneous Raman Microspectroscopy and Fluorescence Imaging of Bone Mineralization in Living Zebrafish Larvae , 2014, Biophysical journal.

[8]  Delong Zhang,et al.  Coherent Raman Scattering Microscopy in Biology and Medicine. , 2015, Annual review of biomedical engineering.

[9]  Carol R Flach,et al.  Infrared and Raman imaging spectroscopy of ex vivo skin , 2013, International journal of cosmetic science.

[10]  Gene H. Golub,et al.  Singular value decomposition and least squares solutions , 1970, Milestones in Matrix Computation.

[11]  J. J. Jansen,et al.  ASCA: analysis of multivariate data obtained from an experimental design , 2005 .

[12]  S. Skoulika,et al.  FT-Raman spectroscopy - analytical tool for routine analysis of diazinon pesticide formulations. , 2000, Talanta.

[13]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[14]  Jürgen Popp,et al.  The many facets of Raman spectroscopy for biomedical analysis , 2014, Analytical and Bioanalytical Chemistry.

[15]  R. Bro PARAFAC. Tutorial and applications , 1997 .

[16]  Romà Tauler,et al.  Multiset Data Analysis: Extended Multivariate Curve Resolution , 2020, Comprehensive Chemometrics.

[17]  Tao Liu,et al.  Determination of Pesticide Residues on the Surface of Fruits Using Micro-Raman Spectroscopy , 2010, CCTA.

[18]  P. Eilers A perfect smoother. , 2003, Analytical chemistry.

[19]  Romà Tauler,et al.  1H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis , 2016, Scientific Reports.

[20]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

[21]  Romà Tauler,et al.  Metabolic profiling of Daphnia magna exposed to environmental stressors by GC–MS and chemometric tools , 2016, Metabolomics.

[22]  Jérémie Teyssier,et al.  Precise colocalization of interacting structural and pigmentary elements generates extensive color pattern variation in Phelsuma lizards , 2013, BMC Biology.

[23]  Brian Everitt,et al.  Cluster analysis , 1974 .

[24]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[25]  Romà Tauler,et al.  Combining hyperspectral imaging and chemometrics to assess and interpret the effects of environmental stressors on zebrafish eye images at tissue level , 2018, Journal of biophotonics.

[26]  R. Sanz-Pamplona,et al.  Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy. , 2014, Biochimica et biophysica acta.

[27]  Luisa Orsini,et al.  Daphnia magna transcriptome by RNA-Seq across 12 environmental stressors , 2016, Scientific Data.

[28]  R. Ferrari,et al.  Metabolomic and proteomic investigations of impacts of titanium dioxide nanoparticles on Escherichia coli , 2017, PloS one.

[29]  P. Sopp Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[30]  Pablo Villoslada,et al.  Dynamic molecular monitoring of retina inflammation by in vivo Raman spectroscopy coupled with multivariate analysis , 2014, Journal of biophotonics.

[31]  Haishan Zeng,et al.  Raman spectroscopy of in vivo cutaneous melanin. , 2004, Journal of biomedical optics.

[32]  A. Talari,et al.  Raman Spectroscopy of Biological Tissues , 2007 .

[33]  J. Mansfield,et al.  Infrared and Raman imaging of biological and biomimetic samples , 2000, Fresenius' journal of analytical chemistry.

[34]  Carlos Barata,et al.  Ecological relevance of biomarkers in monitoring studies of macro-invertebrates and fish in Mediterranean rivers. , 2016, The Science of the total environment.

[35]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[36]  S. Piqueras,et al.  Chemometric Tools for Image Analysis , 2014 .

[37]  Romà Tauler,et al.  Vibrational spectroscopic image analysis of biological material using multivariate curve resolution–alternating least squares (MCR-ALS) , 2015, Nature Protocols.

[38]  Romà Tauler,et al.  Relevant aspects of unmixing/resolution analysis for the interpretation of biological vibrational hyperspectral images , 2017 .

[39]  Romà Tauler,et al.  Perfluoroalkylated Substance Effects in Xenopus laevis A6 Kidney Epithelial Cells Determined by ATR-FTIR Spectroscopy and Chemometric Analysis , 2016, Chemical research in toxicology.

[40]  Age K. Smilde,et al.  Multilevel component analysis of time-resolved metabolic fingerprinting data , 2005 .

[41]  Demetrio Raldúa,et al.  Zebrafish Models for Human Acute Organophosphorus Poisoning , 2015, Scientific Reports.

[42]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[43]  Romà Tauler,et al.  Analysis of multiple mass spectrometry images from different Phaseolus vulgaris samples by multivariate curve resolution. , 2017, Talanta.

[44]  C. Martyniuk,et al.  Exposure to Deepwater Horizon oil and Corexit 9500 at low concentrations induces transcriptional changes and alters immune transcriptional pathways in sheepshead minnows. , 2017, Comparative biochemistry and physiology. Part D, Genomics & proteomics.

[45]  W. Windig,et al.  Interactive self-modeling mixture analysis , 1991 .

[46]  R. Spang,et al.  State-of-the art data normalization methods improve NMR-based metabolomic analysis , 2011, Metabolomics.

[47]  C. Tyler,et al.  Information to : Ecotoxicological assessment of nanoparticle-containing acrylic copolymer dispersions in fairy shrimp and zebrafish embryos , 2017 .

[48]  R Tauler,et al.  Resolution and segmentation of hyperspectral biomedical images by multivariate curve resolution-alternating least squares. , 2011, Analytica chimica acta.

[49]  J Popp,et al.  Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues. , 2015, Analytica chimica acta.

[50]  Age K. Smilde,et al.  Statistical validation of megavariate effects in ASCA , 2007, BMC Bioinformatics.

[51]  Age K. Smilde,et al.  ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data , 2005, Bioinform..

[52]  Romà Tauler,et al.  Phenotypic malignant changes and untargeted lipidomic analysis of long-term exposed prostate cancer cells to endocrine disruptors. , 2015, Environmental research.

[53]  A. Singer Spectral independent component analysis , 2006 .

[54]  Romà Tauler,et al.  MCR-ALS GUI 2.0: New features and applications , 2015 .