Infrared spectroscopic imaging: Label-free biochemical analysis of stroma and tissue fibrosis

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Digital Histopathology by Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

Autofluorescence spectroscopy and multivariate analysis for predicting the induced damages to other organs due to liver fibrosis

When our liver does not work well, it can induce damage to other organs causing their dysfunction. With this background, we aim to study the effect of liver fibrosis on other organs such as heart, lungs, kidney and spleen by assessing the variations in the inherent emission property of the tissue, using fluorescence spectroscopy. Fluorescence emission spectra from excised organs of liver fibrosis induced rats were collected at excitation wavelengths 320 and 410 nm. Optical redox ratio derived from the spectral data supported by multivariate statistical analysis, principal component analysis followed by linear discriminant analysis (PCA-LDA) distinguished between control and fibrosis induced groups. The two different excitation wavelength provided variations in the endogenous flurophores collagen, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), lipopigments and porphyrins. Additionally, evaluation of redox ratio provided variations in tissue metabolic activity of different organs. The PCA-LDA modelling yielded a sensitivity of 85 to 97% and specificity of 80 to 96% on 320 nm excitation and a sensitivity of 72 to 100% and specificity of 59 to 100% on 410 nm excitation. Fluorescence emission spectral study along with multivariate analysis paved way to identify the biochemical alterations caused to other organs due to the development of liver fibrosis, which could lead to their damage and dysfunction.

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

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.

Applications of hyperspectral imaging in the detection and diagnosis of solid tumors

Hyperspectral imaging (HSI) is an emerging new technology in solid tumor diagnosis and detection. It incorporates traditional imaging and spectroscopy together to obtain both spatial and spectral information from tissues simultaneously in a non-invasive manner. This imaging modality is based on the principle that different tissues inherit different spectral reflectance responses that present as unique spectral fingerprints. HSI captures those composition-specific fingerprints to identify cancerous and normal tissues. It becomes a promising tool for performing tumor diagnosis and detection from the label-free histopathological examination to real-time intraoperative assistance. This review introduces the basic principles of HSI and summarizes its methodology and recent advances in solid tumor detection. In particular, the advantages of HSI applied to solid tumors are highlighted to show its potential for clinical use.

Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning

Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FT-IR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes and can bypass the need for many chemical labels. Fourier transform infrared classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map IR spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FT-IR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FT-IR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.

Fluorescence spectroscopy as an efficient tool for staging the degree of liver fibrosis: an in vivo comparison with MRI

The study utilizes autofluorescence spectroscopy (AFS) along with multivariate spectral analysis for differentiating various stages of hepatic fibrosis. AFS has recently emerged as an efficient tool for evaluating the variations in different endogenous flurophores. In this study, the potential of AFS for differentiating the stages of liver fibrosis is assessed and compared with the results of enzyme evaluation, histopathology and the most advanced diagnostic tool, MRI. Using a fiber optic probe, the emission profile of the flurophores such as flavin adenine dinucleotide (FAD), lipofuscin-like lipopigments (lipopigments), porphyrins and the variation in the total hemoglobin concentration are evaluated in vivo on liver fibrosis induced animal models adopting a minimally invasive technique. Significant difference (p < 0.05) in the level of these biomarkers was observed between different stages of liver fibrosis. Normal hepatic tissue could be distinguished from mild and moderate hepatic fibrosis with a sensitivity of 95 to 100% and specificity of 90 to 100% using multivariate spectral analysis. The results are favourable to consider this technique as a potential tool for diagnosing liver fibrosis at an early stage, which is monumental as it otherwise can lead to cirrhosis and liver failure.

Predicting Fibrosis Progression in Renal Transplant Recipients Using Laser-Based Infrared Spectroscopic Imaging

Renal transplants have not seen a significant improvement in their 10-year graft life. Chronic damage accumulation often leads to interstitial fibrosis and tubular atrophy (IF/TA) and thus graft function loss over time. For this reason, IF/TA has been the chief suspect for a potential prognostic marker for long term outcomes. In this study, we have used infrared spectroscopic (IR) imaging to interrogate the biochemistry of regions of fibrosis from renal transplant biopsies to identify a biochemical signature that can predict rapid progression of fibrosis. IR imaging represents an approach that permits label-free biochemical imaging of human tissues towards identifying novel biomarkers for disease diagnosis or prognosis. Two cohorts were identified as progressors (n = 5, > 50% fibrosis increase between time points) and non-progressors (n = 5, < 5% increase between time points). Each patient had an early time point and late time point biopsy. Collagen associated carbohydrate moieties (ν(C–O), 1035 cm⁻¹ and ν(C–O–C),1079 cm⁻¹) spectral ratios demonstrated good separation between the two cohorts (p = 0.001). This was true for late and early time point biopsies suggesting the regions of fibrosis are biochemically altered in cases undergoing progressive fibrosis. Thus, IR imaging can potentially predict rapid progression of fibrosis using histologically normal early time point biopsies.