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High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
Published on: January 21, 2015
Label-free investigation of infected acute pyelonephritis tissue by FTIR microspectroscopy with unsupervised and
Jingzhu Shao1, Xiangyu Zhao1, Ping Tang1
1Center for Biophotonics, Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Abstract:
Acute pyelonephritis (AP) is a severe urinary tract infection (UTI) syndrome with a large population of patients worldwide. Current approaches to confirming AP are limited to urinalysis, radiological imaging methods and histological assessment. Fourier transform infrared (FTIR) microspectroscopy is a promising label-free modality that can offer information about both morphological and molecular pathologic alterations from biological tissues. Here, FTIR microspectroscopy serves to investigate renal biological histology of a rat model with AP and classify normal cortex, normal medulla and infected acute pyelonephritis tissues. The spectra were experimentally collected by FTIR with an infrared Globar source through raster scanning procedure. Unsupervised analysis methods, including integrating, clustering and principal component analysis (PCA) were performed on such spectra data to form infrared histological maps of entire kidney section. In comparison to Hematoxylin & Eosin-stained results of the adjacent tissue sections, these infrared maps were proved to enable the differentiation of the renal tissue types. The results of both integration and clustering indicated that the concentration of amide II decreases in the infected acute pyelonephritis tissues, with an increased presence of nucleic acids and lipids. By means of PCA, the infected tissue was linearly separated from normal ones by plotting confident ellipses with the score values of the first and second principal components. Moreover, supervised analysis was performed based on the supported vector machines (SVM). Normal cortex, normal medulla and infected acute pyelonephritis tissues were classified by SVM models with the best accuracy of 96.11% in testing dataset. In addition, these analytical methods were further employed on synchrotron-based FTIR spectra data and successfully form high-resolution infrared histological maps of glomerulus and necrotic cell mass. This work demonstrates that FTIR microspectroscopy will be a powerful manner to investigate AP tissue and differentiate infected tissue from normal tissue in a renal infected model system.
Insights
Fourier transform infrared (FTIR) microspectroscopy effectively differentiates acute pyelonephritis (AP) kidney tissue from healthy tissue. This label-free method analyzes molecular and morphological changes, achieving high accuracy in classifying infected tissues.
Area of Science:
- Biomedical Optics
- Spectroscopy
- Pathology
Background:
- Acute pyelonephritis (AP) is a severe urinary tract infection (UTI) impacting many patients globally.
- Current diagnostic methods for AP include urinalysis, imaging, and histology.
- Fourier transform infrared (FTIR) microspectroscopy offers a label-free approach for analyzing tissue morphology and molecular composition.
Purpose of the Study:
- To investigate the utility of FTIR microspectroscopy for analyzing renal tissue in a rat model of AP.
- To differentiate normal kidney tissues (cortex and medulla) from infected AP tissues using FTIR spectral data.
- To assess the accuracy of unsupervised and supervised machine learning methods for classifying renal tissues based on FTIR spectra.
Main Methods:
- FTIR microspectroscopy was used to collect spectral data from rat kidney tissues.
- Unsupervised methods (integration, clustering, PCA) were applied to create infrared histological maps.
- Supervised analysis using Support Vector Machines (SVM) was employed for tissue classification.
- Synchrotron-based FTIR was utilized for high-resolution imaging of specific kidney structures.
Main Results:
- Infrared histological maps successfully differentiated renal tissue types compared to H&E staining.
- Analysis revealed decreased amide II and increased nucleic acids and lipids in infected AP tissues.
- PCA effectively separated infected from normal tissues.
- SVM models achieved a classification accuracy of 96.11% for differentiating tissue types.
- High-resolution maps of glomerulus and necrotic cell mass were generated using synchrotron FTIR.
Conclusions:
- FTIR microspectroscopy is a powerful tool for investigating AP tissue.
- This technique enables differentiation of infected renal tissue from normal tissue in a model system.
- FTIR offers a label-free, molecularly informative approach to AP diagnostics.
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