Issue 9, 2019

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

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

Supplementary files

Article information

Article type
Paper
Submitted
08 10月 2018
Accepted
28 1月 2019
First published
29 1月 2019

Analyst, 2019,144, 2954-2964

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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