Orthogonal Projections to Latent Structures Discriminant Analysis Modeling on in Situ FT-IR Spectral Imaging of Liver Tissue for Identifying Sources of Variability

• Published in: Analytical chemistry (Washington) 2008-09, Vol.80 (18), p.6898-6906
• Main Authors: Stenlund, Hans, Gorzsás, András, Persson, Per, Sundberg, Björn, Trygg, Johan
• Format: Article
• Language: English
• Subjects: Analysis of Variance; Analytical chemistry; Animals; Chemistry; Discriminant Analysis; Erythrocytes - chemistry; Exact sciences and technology; Female; Hepatocytes - chemistry; Least-Squares Analysis; Liver; Liver - cytology; Mice; Models, Biological; Principal Component Analysis; Rodents; Spectrometric and optical methods; Spectroscopy, Fourier Transform Infrared; Tissues
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/ac8005318

In this study, the orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to assess the in situ chemical composition of two different cell types in mouse liver samples, hepatocytes and erythrocytes. High spatial resolution FT-IR microspectroscopy equipped with a focal plan array (FPA) detector is capable of simultaneously recording over 4000 spectra from 64 × 64 pixels with a maximum spatial resolution of about 5 μm × 5 μm, which allows for the differentiation of individual cells. The main benefit with OPLS-DA lies in the ability to separate predictive variation (between cell type) from variation that is uncorrelated to cell type in order to facilitate understanding of different sources of variation. OPLS-DA was able to differentiate between chemical properties and physical properties (e.g., edge effects). OPLS-DA model interpretation of the chemical features that separated the two cell types clearly highlighted proteins and lipids/bile acids. The modeled variation that was uncorrelated to cell type made up a larger portion of the total variation and displayed strong variability in the amide I region. This could be traced back to a gradient in the high intensity (high-density) areas vs the low intensity areas (close to empty areas) that as a result of normalization had an adverse effect on FT-IR spectral profiles. This highlights that OPLS-DA provides an effective solution to identify different sources of variability, both predictive and uncorrelated, and also facilitates understanding of any sampling, experimental, or preprocessing issues.