Orthogonal projections to latent structures discriminant analysis modeling on in situ FT-IR spectral imaging of liver tissue for identifying sources of variability
(2008) In Analytical Chemistry 80. p.6898-6906- Abstract
- 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 mu m x 5 mu m, 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... (More)
- 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 mu m x 5 mu m, 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4332396
- author
- Stenlund, Hans ; Gorzsas, Andras ; Persson, Per LU ; Sundberg, Bjorn and Trygg, Johan
- publishing date
- 2008
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Analytical Chemistry
- volume
- 80
- pages
- 6898 - 6906
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- scopus:51949107088
- pmid:18714965
- ISSN
- 1520-6882
- DOI
- 10.1021/ac8005318
- language
- English
- LU publication?
- no
- additional info
- 18
- id
- ea1d8c3e-0623-4c2a-8535-4c3f69caade1 (old id 4332396)
- date added to LUP
- 2016-04-01 12:32:04
- date last changed
- 2022-04-21 08:43:55
@article{ea1d8c3e-0623-4c2a-8535-4c3f69caade1,
abstract = {{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 mu m x 5 mu m, 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.}},
author = {{Stenlund, Hans and Gorzsas, Andras and Persson, Per and Sundberg, Bjorn and Trygg, Johan}},
issn = {{1520-6882}},
language = {{eng}},
pages = {{6898--6906}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Analytical Chemistry}},
title = {{Orthogonal projections to latent structures discriminant analysis modeling on in situ FT-IR spectral imaging of liver tissue for identifying sources of variability}},
url = {{http://dx.doi.org/10.1021/ac8005318}},
doi = {{10.1021/ac8005318}},
volume = {{80}},
year = {{2008}},
}