Infrared microspectroscopic imaging of benign breast tumor tissue sections

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging

Advances in infrared (IR) spectroscopic imaging instrumentation and data science now present unique opportunities for large validation studies of the concept of histopathology using spectral data. In this study, we examine the discrimination potential of IR metrics for different histologic classes to estimate the sample size needed for designing validation studies to achieve a given statistical power and statistical significance. Next, we present an automated annotation transfer tool that can allow large-scale training/validation, overcoming the limitations of sparse ground truth data with current manual approaches by providing a tool to transfer pathologist annotations from stained images to IR images across diagnostic categories. Finally, the results of a combination of supervised and unsupervised analysis provide a scheme to identify diagnostic groups/patterns and isolating pure chemical pixels for each class to better train complex histopathological models. Together, these methods provide essential tools to take advantage of the emerging capabilities to record and utilize large spectroscopic imaging datasets.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology

Cancer alters both the morphological and the biochemical properties of multiple cell types in a tissue. Generally, the morphology of epithelial cells is practical for routine disease diagnoses. Here, infrared spectroscopic imaging biochemically characterizes breast cancer, both epithelial cells and the tumor-associated microenvironment. Unfortunately, conventional spectral analyses are slow. Hence, we designed and built a laser confocal microscope that demonstrates a high signal-to-noise ratio for confident diagnoses. The instrument cuts down imaging time from days to minutes, making the technology feasible for research and clinical translation. Finally, automated human breast cancer biopsy imaging is reported in ∼1 hour, paving the way for routine research into the total tumor (epithelial plus microenvironment) properties and rapid, label-free diagnoses.

Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients’ caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.

Development of a practical spatial-spectral analysis protocol for breast histopathology using Fourier transform infrared spectroscopic imaging

Breast cancer screening provides sensitive tumor identification, but low specificity implies that a vast majority of biopsies are not ultimately diagnosed as cancer. Automated techniques to evaluate biopsies can prevent errors, reduce pathologist workload and provide objective analysis. Fourier transform infrared (FT-IR) spectroscopic imaging provides both molecular signatures and spatial information that may be applicable for pathology. Here, we utilize both the spectral and spatial information to develop a combined classifier that provides rapid tissue assessment. First, we evaluated the potential of IR imaging to provide a diagnosis using spectral data alone. While highly accurate histologic [epithelium, stroma] recognition could be achieved, the same was not possible for disease [cancer, no-cancer] due to the diversity of spectral signals. Hence, we employed spatial data, developing and evaluating increasingly complex models, to detect cancers. Sub-mm tumors could be very confidently predicted as indicated by the quantitative measurement of accuracy via receiver operating characteristic (ROC) curve analyses. The developed protocol was validated with a small set and statistical performance used to develop a model that predicts study design for a large scale, definitive validation. The results of evaluation on different instruments, at higher noise levels, under a coarser spectral resolution and two sampling modes [transmission and transflection], indicate that the protocol is highly accurate under a variety of conditions. The study paves the way to validating IR imaging for rapid breast tumor detection, its statistical validation and potential directions for optimization of the speed and sampling for clinical deployment.

Fundamental developments in infrared spectroscopic imaging for biomedical applications

Infrared chemical imaging is a rapidly emerging field with new advances in instrumentation, data acquisition and data analysis. These developments have had significant impact in biomedical applications and numerous studies have now shown that this technology offers great promise for the improved diagnosis of the diseased state. Relying on purely biochemical signatures rather than contrast from exogenous dyes and stains, infrared chemical imaging has the potential to revolutionise histopathology for improved disease diagnosis. In this review we discuss the recent advances in infrared spectroscopic imaging specifically related to spectral histopathology (SHP) and consider the current state of the field. Finally we consider the practical application of SHP for disease diagnosis and consider potential barriers to clinical translation highlighting current directions and the future outlook.

Biomolecular characterization of adrenal gland tumors by means of SR-FTIR

The adrenal glands are small endocrine organs located on the bottom pole of each kidney. Anatomically they are composed of cortical and medullar parts. Due to dysfunctional processes they can transform into the pathological lesions (in both cortex and medulla). The incidentally detected adrenal lesions have become an arising clinical problem nowadays. The crucial issue for an accurate treatment strategy is relevant diagnosis. Distinguishing between benign and malignant lesions is often difficult during the standard histological examination. Hence the alternative methods of differentiation are investigated. One of them is Fourier transform infrared spectroscopy which allows the analysis of the biomolecular composition of the studied tissue. In this paper we present the very preliminary FTIR studies for defining the biomolecular pattern of three types of adrenal lesions: adenoma (AA) and adrenal cortical hyperplasia (ACH) - both derived from adrenal cortex as well as pheochromocytoma (PCC) - from the medullar part of the gland. All studied cases were classified as benign lesions. The general observations show that cortically derived tissues are rich in lipids and they are rather protein depleted while for medullar pheochromocytoma there is the opposite relationship. Furthermore, the unequivocal differences were noticed within the "fingerprinting" range. In addition subtle shifts in absorption band positions were observed between studied cases.

Infrared imaging in breast cancer: automated tissue component recognition and spectral characterization of breast cancer cells as well as the tumor microenvironment

Current evaluation of histological sections of breast cancer samples remains unsatisfactory. The search for new predictive and prognostic factors is ongoing. Infrared spectroscopy and its potential to probe tissues and cells at the molecular level without requirement for contrast agents could be an attractive tool for clinical and diagnostic analysis of breast cancer. In this study, we report the successful application of FTIR (Fourier transform infrared) imaging for breast tissue component characterization. We show that specific FTIR spectral signatures can be assigned to the major tissue components of breast tumor samples. We demonstrate that a tissue component classifier can be built based on a spectral database of well-annotated tissues and successfully validated on independent breast samples. We also demonstrate that spectral features can reveal subtle differences within a tissue component, capturing for instance lymphocytic and stromal activation. By investigating in parallel lymph nodes, tonsils and wound healing tissues, we prove the uniqueness of the signature of both lymphocytic infiltrate and tumor microenvironment in the breast disease context. Finally, we demonstrate that the biochemical information reflected in the epithelial spectra might be clinically relevant for the grading purpose, suggesting potential to improve breast cancer management in the future.

Progress in Fourier Transform Infrared Spectroscopic Imaging Applied to Venereal Cancer Diagnosis

Fourier transform infrared imaging spectroscopy is a powerful technique that provides molecular and spatial information at the single-cell level. We report on the progress of this technology in the field of cancer research, focusing on human cervical cancer because of the inherent difficulty in grading this type of cancer and as a model for venereal cancers in dogs. Using a suite of multivariate imaging processing techniques, we demonstrate the potential of this technique to identify histologic features in the normal epithelium and cervical intraepithelial neoplasia stages I and III. We highlight the advantages and detail the barriers that need to be overcome before implementation of this technology in the clinical environment.

Distinction of broken cellular wall Ganoderma lucidum spores and G. lucidum spores using FTIR microspectroscopy

In this paper, FTIR microspectroscopy was used to identify broken cellular wall Ganoderma lucidum spores and G. lucidum spores. For IR spectra, broken cellular wall G. lucidum spores and G. lucidum spores were mainly different in the regions of 3000-2800, 1660-1600, 1400-1200 and 1100-1000 cm(-1). For curve fitting, the results showed the differences in the protein secondary structures and the polysaccharide structures/content between broken cellular wall G. lucidum spores and G. lucidum spores. Moreover, the value of A1078/A1741 might be a potentially useful factor to distinguish broken cellular wall G. lucidum spores from G. lucidum spores. Additionally, FTIR microspectroscopy could identify broken cellular wall G. lucidum spores and G. lucidum spores accurately when it was combined with hierarchical cluster analysis. The result suggests FTIR microspectroscopy is very simple and efficient for distinction of broken cellular wall G. lucidum spores and G. lucidum spores. The result also indicates FTIR microspectroscopy may be useful for TCM identification.

The study of a single BGC823 cell using Fourier transform infrared microspectroscopic imaging

In order to investigate gastric cancer at cellular and sub-cellular level, a single human gastric adenocarcinoma BGC823 cell was studied by an infrared microscope equipped with a focal plane array (FPA) detector. The spectra showed difference between the nucleus and the endoplasmic reticulum (ER) of the BGC823 cell. The peak of vasPO2- was shifted to a higher wavenumber at the nucleus compared with that at the ER. The height ratios of 2954 cm(-1)/2922 cm(-1) (CH3/CH2) and 1088 cm(-1)/1539 cm(-1) (DNA/amide II) of the nucleus were significantly higher than those of the ER. Furthermore, chemical images reveal the intensity distributions of lipids, proteins and DNA of the single BGC823 cell, and the intense absorptions of proteins and DNA were observed in the nuclear region of the cell while the intense absorption of lipids was found in the ER region of the cell. The Fourier transform infrared (FTIR) microspectroscopic imaging result indicates the study of the single gastric cancer cell at sub-cellular level can be beneficial for knowing gastric cancer more which will be of great importance for the study and diagnosis of gastric cancer. The result also suggests that FPA is a useful tool in the study of a single cell and may be a powerful tool for study and diagnosis of gastric cancer.

A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections

Background

Three-dimensional (3D) multivariate Fourier Transform Infrared (FTIR) image maps of tissue sections are presented. A villoglandular adenocarcinoma from a cervical biopsy with a number of interesting anatomical features was used as a model system to demonstrate the efficacy of the technique.

Methods

Four FTIR images recorded using a focal plane array detector of adjacent tissue sections were stitched together using a MATLAB^® routine and placed in a single data matrix for multivariate analysis using Cytospec™. Unsupervised Hierarchical Cluster Analysis (UHCA) was performed simultaneously on all 4 sections and 4 clusters plotted. The four UHCA maps were then stacked together and interpolated with a box function using SCIRun software.

Results

The resultant 3D-images can be rotated in three-dimensions, sliced and made semi-transparent to view the internal structure of the tissue block. A number of anatomical and histopathological features including connective tissue, red blood cells, inflammatory exudate and glandular cells could be identified in the cluster maps and correlated with Hematoxylin & Eosin stained sections. The mean extracted spectra from individual clusters provide macromolecular information on tissue components.

Conclusion

3D-multivariate imaging provides a new avenue to study the shape and penetration of important anatomical and histopathological features based on the underlying macromolecular chemistry and therefore has clear potential in biology and medicine.

Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 microm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets ("unknown spectra") were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.

A Fourier transform infrared microspectroscopic imaging investigation into an animal model exhibiting glioblastoma multiforme

Glioblastoma multiforme (GBM) is a highly malignant human brain tumour for which no cure is available at present. Numerous clinical studies as well as animal experiments are under way with the goal being to understand tumour biology and develop potential therapeutic approaches. C6 cell glioma in the adult rat is a frequently used and well accepted animal model for the malignant human glial tumour. By combining standard analytical methods such as histology and immunohistochemistry with Fourier Transform Infrared (FTIR) microspectroscopic imaging and multivariate statistical approaches, we are developing a novel approach to tumour diagnosis which allows us to obtain information about the structure and composition of tumour tissues that could not be obtained easily with either method alone. We have used a "Stingray" FTIR imaging spectrometer to analyse and compare the compositions of coronal brain tissue sections of a tumour-bearing animal and those from a healthy animal. We have found that the tumour tissue has a characteristic chemical signature, which distinguishes it from tumour-free brain tissue. The physical-chemical differences, determined by image and spectral comparison are consistent with changes in total protein absorbance, phosphodiester absorbance and physical dispersive artefacts. The results indicate that FTIR imaging analysis could become a valuable analytic method in brain tumour research and possibly in the diagnosis of human brain tumours.

Fourier Transform Infrared Imaging and Unsupervised Hierarchical Clustering Applied to Cervical Biopsies

FTIR images of cervical tissue from patient biopsies were processed with an unsupervised hierarchical clustering algorithm and compared with hematoxylin- and eosin-stained adjacent sections. Anatomical and potential histopathological features were clearly resolved in the resultant cluster maps. The mean extracted spectra assigned to each cluster indicate that the major spectral differences between the different cells in tissue predictably occur in the amide I region (1700–1570 cm−1) and the phosphodiester/glycogen region (1200–1000 cm−1). FTIR imaging in which a focal plane array mercury–cadmium–telluride detector and unsupervised hierarchical clustering is used shows potential as a rapid, non-subjective diagnostic tool in cervical pathology.