An Innovative Platform Merging Elemental Analysis and Ftir Imaging for Breast Tissue Analysis

Abstract

Histopathology and immunohistology remain the gold standard for breast cancer diagnostic. Yet, these approaches do not usually provide a sufficiently detailed characterization of the pathology. The purpose of this work is to demonstrate for the first time that elemental analysis and Fourier transform infrared spectroscopy microscopic examination of breast tissue sections can be merged into one dataset to provide a single set of markers based on both organic molecules and inorganic trace elements. For illustrating the method, 6 mammary tissue sections were used. Fourier transform infrared (FTIR) spectroscopy images reported a fingerprint of the organic molecules present in the tissue section and laser ablation elemental analysis (LA-ICP-MS) images brought inorganic element profiles. The 6 tissue sections provided 31 10⁶ and 150,000 spectra for FTIR and LA-ICP-MS spectra respectively. The results bring the proof of concept that breast tissue can be analyzed simultaneously by FTIR spectroscopy and laser ablation elemental analysis (LA-ICP-MS) to provide in both case reasonably high resolution images. We show how to bring the images obtained by the two methods to a same spatial resolution and how to use image registration to analyze the data originating from both techniques as one block of data. We finally demonstrates the elemental analysis is orthogonal to all FTIR markers as no significant correlation is found between FTIR and LA-ICP-MS data. Combining FTIR and LA-ICP-MS imaging becomes possible, providing two orthogonal methods which can bring an unprecedented diversity of information on the tissue. This opens a new avenue of tissue section analyses providing unprecedented diagnostic potential.

Figure 1

Image of an H&E stained fibroadenoma section described in the text. Bottom: enlargement of the area contained in the rectangle draw in the upper part of the figure (will be detailed in Fig. 2).

Figure 2

Example of a processed FTIR image. Here the ratio A¹²³⁰/A¹⁶⁵⁵ is reported, evidencing the epithelial cells surrounding the ducts. The pixels where the SNR is below 150 have been turned to black. This image corresponds to the framed region in the section shown in Fig. 1.

Figure 3

Illustration of the process followed for background subtraction. A. the rectangles represent the areas selected to be used as background in this ¹³C image. In this example, 1918 spectra were included in the rectangles and their mean was subtracted from all spectra. B. intensity distribution before subtraction of the background, C. intensity distribution after subtraction of the background, D. intensity distribution after subtraction of the mean and division by the standard deviation.

Figure 4

FTIR image reporting the absorbance at 1652 cm⁻¹ of 3 breast tissues (left column) and elemental analysis image reporting the abundance of ¹³C for the same 3 breast tissues (right column). Data have been processed as described below in the text. Regions with SNR < 150 have been turned to dark blue.

Figure 5

⁶⁴Zn distribution in the 6 tissue sections described in Table 1. The areas in grey have values below 0 for both ¹³C and ⁶⁴Zn.

Figure 6

: 2D correlation analysis of the abundance of elements (¹³C, ³¹P, ³⁴S, ⁵²Cr, ⁵⁵Mn, ⁵⁶Fe, ⁵⁸Ni, ⁶³Cu and ⁶⁴Zn) in the 6 breast tissue sections.

Figure 7

Top: shape of the first 2 principal components PC1 and PC2. Bottom score maps for PC1 and PC2 of 6 tissue sections. PCA was computed only on the spectra with ¹³C values above 0 as shown in Fig. 5.

Figure 8

Left: represents the ⁶⁴Zn/A¹⁶⁵⁴ FTIR ratio. The two rectangles include a total of 1705 spectra whose average was subtracted from all spectra of the image. For all values at each wavenumber/element, the mean was subtracted and it was divided by the standard deviation. Right: distribution of the SNR through the FTIR image. The red curve reports the integrated counts.

Figure 9

Correlation analysis of the FTIR/LA-ICP-MS concatenate spectra. The LA-ICP-MS data are represented by 9 points present below 900 cm⁻¹ as indicated by the purple circle. Elements are in the same sequence as previously: ¹³C, ³¹P, ³⁴S, ⁵²Cr, ⁵⁵Mn, ⁵⁶Fe, ⁵⁸Ni, ⁶³Cu and ⁶⁴Zn. The white line present on the figure corresponds to points of the spectra where there is no variance because a baseline has been drawn.

Figure 10

(A) PCs 1 to 10 (from bottom to top) obtained after PCA of the data presented in Fig. 8. The mean spectrum has not been subtracted prior to PCA. (B) fraction of the variance explained as a function of the number of PCs. The red line reports the cumulative fraction of the variance explained.

Figure 11

Representation of the intensities of the 10,780 FTIR/LA-ICP-MS spectra of section #3 presented on Fig. 8 passing a SNR threshold of 500 after double clustering analysis. Spectra were processed by subtraction of the mean and normalization by the standard deviation prior to clustering. The 10,780 spectra were sorted according to a hierarchical cluster analysis shown on top of the figure. The wavenumbers/elements were sorted in 4 clusters by K-means clustering. The dotted line on the left side of the figure indicates the limits of the clusters. The mean spectrum after sorting the wavenumbers/elements by the K-means (“sorted spectrum”) is also presented on the left side of the figure. For the sake of the clarity, the “sorted spectrum” is shown prior to mean subtraction and normalization by standard deviation.