Infrared spectral microscopy as a tool to monitor lung fibrosis development in a model system

Abstract

Tissue fibrosis is a progressive and destructive disease process that can occur in many different organs including the liver, kidney, skin, and lungs. Fibrosis is typically initiated by inflammation as a result of chronic insults such as infection, chemicals and autoimmune diseases. Current approaches to examine organ fibrosis are limited to radiological and histological analyses. Infrared spectroscopic imaging offers a potential alternative approach to gain insight into biochemical changes associated with fibrosis progression. In this study, we demonstrate that IR imaging of a mouse model of pulmonary fibrosis can identify biochemical changes observed with fibrosis progression and the beginning of resolution using K-means analysis, spectral ratios and multivariate data analysis. This study demonstrates that IR imaging may be a useful approach to understand the biochemical events associated with fibrosis initiation, progression and resolution for both the clinical setting and for assessing novel anti-fibrotic drugs in a model system.

High-Resolution Fourier Transform Infrared (FT-IR) Spectroscopic Imaging for Detection of Lung Structures and Cancer-Related Abnormalities in a Murine Model

“…Some studies reported investigations of the lungs by spectroscopic imaging but none of them showed structural details discussed here. 20,22,23,25 The reason of that is certainly the projected pixel size implemented in standard optics in FT-IR microscopes that does not exceed approximately 5 mm. In our previous works, we employed this setup of FT-IR microscope to study murine lungs with the same model as in this work and we did not obtain such detailed segmentation of the lung morphological structures as presented here.…”

“…22 Infrared spectral microscopy supported by multivariate data analysis was showed to be useful for temporal monitoring of lung fibrosis in a murine model based on spectral regions specific for collagen, glycosylation process, and protein conformations. 23…”

“…22 Infrared spectral microscopy supported by multivariate data analysis was showed to be useful for temporal monitoring of lung fibrosis in a murine model based on spectral regions specific for collagen, glycosylation process, and protein conformations. 23 In pathological conditions, the structure of the lungs undergoes morphological and biochemical alternations. Therefore, the question arises if FT-IR spectroscopic imaging is capable to visualize their composition and to identify specific markers which could be further used to discriminate between healthy and pathologically modified lung parenchyma.…”

Infrared Microspectroscopy With Multivariate Analysis to Differentiate Oral Hyperplasia From Squamous Cell Carcinoma: A Proof of Concept for Early Diagnosis

“…The algorithm for UHCA was run on a hyperspectral image dataset that underwent processing like second derivative, standard normal variate normalization, and mean centering. The number of clusters for UHCA was restricted to 6 to extract the major biochemical difference in the tissue between the four different histological groups [30,33]. The mean second derivative and corresponding raw spectrum for each cluster were extracted to compare the spectral profile of each cluster with others.…”

“…UHCA allows the identification of biospatial patterns within the data that contain additional information on diagnosis and prognosis [15,41]. Cluster analysis identifies biochemical spatial patterns that may not be identified using conventional histology [33].…”