Orthogonal projections to latent structures discriminant analysis modeling on in situ FT-IR spectral imaging of liver tissue for identifying sources of variability.
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 x 64 pixels with a maximum spatial resolution of about 5 microm x 5 microm, 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.
Stenlund
Hans
H
Computational Life Science Cluster (CLIC), KBC, Umeå University, SE-901 87 Umeå, Sweden.
Gorzsás
András
A
Persson
Per
P
Sundberg
Björn
B
Trygg
Johan
J
D000704:Analysis of Variance; D000818:Animals; D016002:Discriminant Analysis; D004912:Erythrocytes / Q000737:chemistry; D005260:Female; D022781:Hepatocytes / Q000737:chemistry; D016018:Least-Squares Analysis; D008099:Liver / Q000166:cytology*; D051379:Mice; D008954:Models, Biological; D025341:Principal Component Analysis; D017550:Spectroscopy, Fourier Transform Infrared