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Issue 9, 2019
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Three-dimensional depth profiling of prostate tissue by micro ATR-FTIR spectroscopic imaging with variable angles of incidence

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Abstract

The depth of penetration and effective thickness in ATR-FTIR spectroscopic imaging are dependent on the wavelength and angle of incidence of the incoming light beam. We have demonstrated, for the first time, that variable angle micro ATR-FTIR, which is created via the insertion of circular apertures, is intrinsic at examining embedded components within a prostate tissue specimen. This is done by constructing a 3D model from the stacks of 2D chemical images obtained, each of which represents the spatial distribution of a chosen spectral band assigned to the component of interest at a different probing depth. ATR-FTIR imaging is also shown to have the ability to resolve subcellular components of cells such as organelles. For differentiation of diseased and non-diseased tissues, statistical tests are employed to analyse the spectral datasets obtained. When the second derivative of the spectral datasets was subjected to t-test analysis, the spectral differences between both samples in the fingerprint region are shown to be more significant at a shallow depth of penetration, with the greatest variance at the spectral band of 1235 cm−1 (vasPO2), depicted by plotting the scores of PCA on its first two PCs. Overall, this paper demonstrates a non-destructive, label-free approach for examining heterogeneous biological samples in the z-direction to construct a 3D model using micro ATR-FTIR imaging, in a qualitative and semi-quantitative manner.

Graphical abstract: Three-dimensional depth profiling of prostate tissue by micro ATR-FTIR spectroscopic imaging with variable angles of incidence

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Supplementary files

Article information


Submitted
08 Oct 2018
Accepted
28 Jan 2019
First published
29 Jan 2019

Analyst, 2019,144, 2954-2964
Article type
Paper

Three-dimensional depth profiling of prostate tissue by micro ATR-FTIR spectroscopic imaging with variable angles of incidence

C. L. Song and S. G. Kazarian, Analyst, 2019, 144, 2954
DOI: 10.1039/C8AN01929K

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