IR spectral imaging of secreted mucus: a promising new tool for the histopathological recognition of human colonic adenocarcinomas

A comprehensive survey of intestine histopathological image analysis using machine vision approaches

Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.

Deep learning for the prediction of the chemotherapy response of metastatic colorectal cancer: comparing and combining H&E staining histopathology and infrared spectral histopathology

Colorectal cancer is a global public health problem with one of the highest death rates. It is the second most deadly type of cancer and the third most frequently diagnosed in the world. The present study focused on metastatic colorectal cancer (mCRC) patients who had been treated with chemotherapy-based regimen for which it remains uncertainty about the efficacy for all eligible patients. This is a major problem, as it is not yet possible to test different therapies in view of the consequences on the health of the patients and the risk of progression. Here, we propose a method to predict the efficacy of an anticancer treatment in an individualized way, using a deep learning model constructed on the retrospective analysis of the primary tumor of several patients. Histological sections from tumors were imaged by standard hematoxylin and eosin (HE) staining and infrared spectroscopy (IR). Images obtained were then processed by a convolutional neural network (CNN) to extract features and correlate them with the subsequent progression-free survival (PFS) of each patient. Separately, HE and IR imaging resulted in a PFS prediction with an error of 6.6 and 6.3 months respectively (28% and 26% of the average PFS). Combining both modalities allowed to decrease the error to 5.0 months (21%). The inflammatory state of the stroma seemed to be one of the main features detected by the CNN. Our pilot study suggests that multimodal imaging analyzed with deep learning methods allow to give an indication of the effectiveness of a treatment when choosing.

Mucins as contrast agent targets for fluorescence-guided surgery of pancreatic cancer

Pancreatic cancer is difficult to resect due to its unique challenges, often leading to incomplete tumor resections. Fluorescence-guided surgery (FGS), also known as intraoperative molecular imaging and optical surgical navigation, is an intraoperative tool that can aid surgeons in complete tumor resection through an increased ability to detect the tumor. To target the tumor, FGS contrast agents rely on biomarkers aberrantly expressed in malignant tissue compared to normal tissue. These biomarkers allow clinicians to identify the tumor and its stage before surgical resection and provide a contrast agent target for intraoperative imaging. Mucins, a family of glycoproteins, are upregulated in malignant tissue compared to normal tissue. Therefore, these proteins may serve as biomarkers for surgical resection. Intraoperative imaging of mucin expression in pancreatic cancer can potentially increase the number of complete resections. While some mucins have been studied for FGS, the potential ability to function as a biomarker target extends to the entire mucin family. Therefore, mucins are attractive proteins to investigate more broadly as FGS biomarkers. This review summarizes the biomarker traits of mucins and their potential use in FGS for pancreatic cancer.

Research on NCFCP compact broadband NIR detector imaging and energy transfer function

Nonlinear crystal frequency conversion imaging with direct detection by silicon-based detectors is an effective way to break through the limitations for existing near-infrared (NIR) detectors with expensive cost and high noise. In this paper, a broadband NIR detector imaging scheme based on the principle of nonlinear crystal frequency conversion (NCFCP) was proposed. A thin film of nonlinear crystal frequency conversion material (NCFCM) combined with a silicon-based detector was used to form a broadband NIR detector. The theoretically investigated energy transfer function was used as a guidance for experiment. Meanwhile, the relationship between the imaging effect and the energy transfer of the NCFCP-based compact broadband NIR detector in the NIR band was measured experimentally. The accuracy of the theoretical study had been verified by the measured transfer results.

Enhancing scanning electrochemical microscopy's potential to probe dynamic co-culture systems via hyperspectral assisted-imaging

Precise determination of boundaries in co-culture systems is difficult to achieve with scanning electrochemical microscopy alone. Thus, biological scanning electrochemical microscope platforms generally consist of a scanning electrochemical microscope positioner mounted on the stage of an inverted microscope for correlated electrochemical and optical imaging. Use of a fluorescence microscope allows for site-specific fluorescence labeling to obtain more clearly resolved spatial and electrochemical data. Here, we construct a unique hyperspectral assisted-biological scanning electrochemical microscope platform to widen the scope of biological imaging. Specifically, we incorporate a variable fluorescence bandpass source into a biological scanning electrochemical microscope platform for simultaneous optical, spectral, and electrochemical imaging. Not only does this platform serve as a cost-effective alternative to white light laser imaging, but additionally it provides multi-functional analysis of biological samples. Here, we demonstrate the efficacy of our platform to discern the electrochemical contribution of site-specific cells by optically and spectroscopically resolving boundaries as well as cell types within a complex biological system.

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.

Identification of remodeled collagen fibers in tumor stroma by FTIR Micro-spectroscopy: A new approach to recognize the colon carcinoma

The tumor stroma plays a pivotal role in colon cancer genesis and progression. It was observed that collagen fibers in the extracellular matrix (ECM) of cancer stroma, undergo a strong remodeling. These fibrous proteins result more aligned and compact than in physiological conditions, creating a microenvironment that favors cancer development. In this work, micro-FTIR spectroscopy was applied to investigate the chemical modifications in the tumor stroma. Using Fuzzy C-means clustering, mean spectra from diseased and normal stroma were compared and collagen was found to be responsible for the main differences between them. Specifically, the modified absorptions at 1203, 1238, 1284 cm^-1 and 1338 cm^-1 wavenumbers, were related to the amide III band and CH₂ bending of side chains. These signals are sensitive to the interactions between the α-chains in the triple helices of collagen structure. This provided robust chemical evidence that in cancer ECM, collagen fibers are more parallelized, stiff and ordered than in normal tissue. Principal Component Analysis (PCA) applied to the spectra from malignant and normal stroma confirmed these findings. Using LDA (Linear Discriminant Analysis) classification, the absorptions 1203, 1238, 1284 and 1338 cm^-1 were examined as spectral biomarkers, obtaining quite promising results. The use of a PCA-LDA prediction model on samples with moderate tumor degree further showed that the stroma chemical modifications are more indicative of malignancy compared to the epithelium. These preliminary findings have shown that micro-FTIR spectroscopy, focused on collagen signals, could become a promising tool for colon cancer diagnosis.

Automatic Identification of Paraffin Pixels on FTIR Images Acquired on FFPE Human Samples

The transfer of mid-infrared spectral histopathology to the clinic will be possible provided that its application in clinical practice is simple. Rapid analysis of formalin-fixed paraffin-embedded (FFPE) tissue section is thus a prerequisite. The chemical dewaxing of these samples before image acquisition used by the majority of studies is in contradiction with this principle. Fortunately, the in silico analysis of the images acquired on FFPE samples is possible using extended multiplicative signal correction (EMSC). However, the removal of pure paraffin pixels is essential to perform a relevant classification of tissue spectra. So far, this task was possible only if using manual and subjective histogram analysis. In this article, we thus propose a new automatic and multivariate methodology based on the analysis of optimized combinations of EMSC regression coefficients by validity indices and KMeans clustering to separate paraffin and tissue pixels. The validation of our method is performed using simulated infrared spectral images by measuring the Jaccard index between our partitions and the image model, with values always over 0.90 for diverse baseline complexity and signal-to-noise ratio. These encouraging results were also validated on real images by comparing our method with classical ones and by computing the Jaccard index between our partitions and the KMeans partitions obtained on the infrared image acquired on the same samples but after chemical dewaxing, with values always over 0.84.

Concurrent Vibrational Circular Dichroism Measurements with Infrared Spectroscopic Imaging

Vibrational circular dichroism (VCD) spectroscopy has emerged as a powerful platform to quantify chirality, a vital biological property that performs a pivotal role in the metabolism of life organisms. With a photoelastic modulator (PEM) integrated into an infrared spectrometer, the differential response of a sample to the direction of circularly polarized light can be used to infer conformation handedness. However, these optical components inherently exhibit chromatic behavior and are typically optimized at discrete spectral frequencies. Advancements of discrete frequency infrared (DFIR) spectroscopic microscopes in spectral image quality and data throughput are promising for use toward analytical VCD measurements. Utilizing the PEM advantages incorporated into a custom-built QCL microscope, we demonstrate a point scanning VCD instrument capable of acquiring spectra rapidly across all fingerprint region wavelengths in transmission configuration. Moreover, for the first time, we also demonstrate the VCD imaging performance of our instrument for site-specific chirality mapping of biological tissue samples. This study offers some insight into future possibilities of examining small, localized changes in tissue that have major implications for systemic diseases and their progression, while also laying the groundwork for additional modeling and validation in advancing the capability of VCD spectroscopy and imaging.

Evaluating biochemical differences in thyroglobulin from normal and goiter tissues by infrared spectral imaging

Thyroglobulin is a glycoiodoprotein that is produced by thyroid follicular cells; it is stored in follicles in structures known as colloids. The main function of this protein is to stock the hormones triiodothyronine (T3) and thyroxine (T4) until the body requires them. This study aims to demonstrate that infrared spectral imaging with appropriate multivariate analysis can reveal biochemical changes in this glycoprotein. The results achieved herein point out biochemical differences in the colloid samples obtained from normal and goiter patients including glycosylation and changes in the secondary conformational structure. We have presented the first spectral histopathology-based method to detect biochemical differences in thyroid colloids, such as TG iodination, glycosylation, and changes in the secondary structure in normal and goiter patients. The observed changes in the colloids were mainly due to the alterations in amide I and amide II (secondary conformation of proteins) and there is a correlation with different glycosylation between normal and goiter tissues.

Morphogenetic (Mucin Expression) as Well as Potential Anti-Corona Viral Activity of the Marine Secondary Metabolite Polyphosphate on A549 Cells

The mucus layer of the nasopharynx and bronchial epithelium has a barrier function against inhaled pathogens such as the coronavirus SARS-CoV-2. We recently found that inorganic polyphosphate (polyP), a physiological, metabolic energy (ATP)-providing polymer released from blood platelets, blocks the binding of the receptor binding domain (RBD) to the cellular ACE2 receptor in vitro. PolyP is a marine natural product and is abundantly present in marine bacteria. Now, we have approached the in vivo situation by studying the effect of polyP on the human alveolar basal epithelial A549 cells in a mucus-like mucin environment. These cells express mucins as well as the ectoenzymes alkaline phosphatase (ALP) and adenylate kinase (ADK), which are involved in the extracellular production of ATP from polyP. Mucin, integrated into a collagen-based hydrogel, stimulated cell growth and attachment. The addition of polyP to the hydrogel significantly increased cell attachment and also the expression of the membrane-tethered mucin MUC1 and the secreted mucin MUC5AC. The increased synthesis of MUC1 was also confirmed by immunostaining. This morphogenetic effect of polyP was associated with a rise in extracellular ATP level. We conclude that the nontoxic and non-immunogenic polymer polyP could possibly also exert a protective effect against SARS-CoV-2-cell attachment; first, by stimulating the innate antiviral response by strengthening the mucin barrier with its antimicrobial proteins, and second, by inhibiting virus attachment to the cells, as deduced from the reduction in the strength of binding between the viral RBD and the cellular ACE2 receptor.

Chemical and Microstructural Characterization of pH and [Ca2+] Dependent Sol-Gel Transitions in Mucin Biopolymer

Mucus is responsible for controlling transport and barrier function in biological systems, and its properties can be significantly affected by compositional and environmental changes. In this study, the impacts of pH and CaCl₂ were examined on the solution-to-gel transition of mucin, the primary structural component of mucus. Microscale structural changes were correlated with macroscale viscoelastic behavior as a function of pH and calcium addition using rheology, dynamic light scattering, zeta potential, surface tension, and FTIR spectroscopic characterization. Mucin solutions transitioned from solution to gel behavior between pH 4–5 and correspondingly displayed a more than ten-fold increase in viscoelastic moduli. Addition of CaCl₂ increased the sol-gel transition pH value to ca. 6, with a twofold increase in loss moduli at low frequencies and ten-fold increase in storage modulus. Changing the ionic conditions—specifically [H⁺] and [Ca²⁺] —modulated the sol-gel transition pH, isoelectric point, and viscoelastic properties due to reversible conformational changes with mucin forming a network structure via  non-covalent cross-links between mucin chains.

Programmable hyperspectral microscopy for high-contrast biomedical imaging in a snapshot

SIGNIFICANCE

Hyperspectral microscopy has been intensively explored in biomedical applications. However, due to its huge three-dimensional hyperspectral data cube, it typically suffers from slow data acquisition, mass data transmission and storage, and computationally expensive postprocessing.

AIM

To overcome the above limitations, a programmable hyperspectral microscopy technique was developed, which can perform hardware-based hyperspectral data postprocessing by the physical process of optical imaging in a snapshot.

APPROACH

A programmable hyperspectral microscopy system was developed to collect coded microscopic images from samples under multiplexed illumination. Principal component analysis followed by linear discriminant analysis scheme was coded into multiplexed illumination and realized by the physical process of optical imaging. The contrast enhancement was evaluated on two representative types of microscopic samples, i.e., tissue section and cell samples.

RESULTS

Compared to the microscopic images collected under white light illumination, the contrasts of coded microscopic images were significantly improved by 41% and 59% for tissue section and cell samples, respectively.

CONCLUSIONS

The proposed method can perform hyperspectral data acquisition and postprocessing simultaneously by its physical process, while preserving the most important spectral information to maximize the difference between the target and background, thus opening a new avenue for high-contrast microscopic imaging in a snapshot.

Characterization of colorectal mucus using infrared spectroscopy: a potential target for bowel cancer screening and diagnosis

Biological materials presenting early signs of cancer would be beneficial for cancer screening/diagnosis. In this respect, the suitability of potentially exploiting mucus in colorectal cancer was tested using infrared spectroscopy in combination with statistical modeling. Twenty-six paraffinized colon tissue biopsy sections containing mucus regions from 20 individuals (10 normal and 16 cancerous) were measured using mid-infrared spectroscopic imaging. A digital de-paraffinization, followed by cluster analysis driven digital color-coded multi-staining segmented the infrared images into various histopathological features such as epithelium, connective tissue, stroma, and mucus regions within the tissue sections. Principal component analysis followed by supervised linear discriminant analysis was carried out on pure mucus and epithelial spectra from normal and cancerous regions of the tissue. For the mucus-based classification, a sensitivity of 96%, a specificity of 83%, and an area under the curve performance of 95% was obtained. For the epithelial tissue-based classification, a sensitivity of 72%, a specificity of 88%, and an area under the curve performance of 89% was obtained. The mucus spectral profiles further showed contributions indicative of glycans including that of sialic acid changes between these pathology groups. The study demonstrates that infrared spectroscopic analysis of mucus discriminates colorectal cancers with high sensitivity. This concept could be exploited to develop screening/diagnostic approaches complementary to histopathology.

Digital de-waxing on FTIR images

This paper presents a procedure that digitally neutralizes the contribution of paraffin to FTIR hyperspectral images. A brief mathematical derivation of the procedure is demonstrated and applied on one normal human colon sample to exemplify the de-waxing procedure. The proposed method includes construction of a paraffin model based on PCA, EMSC normalization and application of two techniques for spectral quality control. We discuss every step in which the researcher needs to take a subjective decision during the de-waxing procedure, and we explain how to make an adequate choice of parameters involved. Application of this procedure to 71 hyperspectral images collected from 55 human colon biopsies (20 normal, 17 ulcerative colitis, and 18 adenocarcinoma) showed that paraffin was appropriately neutralized, which made the de-waxed images adequate for analysis by pattern-recognition techniques such as k-means clustering or PCA-LDA.

Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging

Tissue histology utilizing chemical and immunohistochemical labels plays an important role in biomedicine and disease diagnosis. Recent research suggests that mid-infrared (IR) spectroscopic imaging may augment histology by providing quantitative molecular information. One of the major barriers to this approach is long acquisition time using Fourier-transform infrared (FTIR) spectroscopy. Recent advances in discrete frequency sources, particularly quantum cascade lasers (QCLs), may mitigate this problem by allowing selective sampling of the absorption spectrum. However, DFIR imaging only provides a significant advantage when the number of spectral samples is minimized, requiring a priori knowledge of important spectral features. In this paper, we demonstrate the use of a GPU-based genetic algorithm (GA) using linear discriminant analysis (LDA) for DFIR feature selection. Our proposed method relies on pre-acquired broadband FTIR images for feature selection. Based on user-selected criteria for classification accuracy, our algorithm provides a minimal set of features that can be used with DFIR in a time-frame more practical for clinical diagnosis.

Mid-infrared upconversion based hyperspectral imaging

Mid-infrared hyperspectral imaging has in the past decade emerged as a promising tool for medical diagnostics. In this work, nonlinear frequency upconversion based hyperspectral imaging in the 6 to 8 µm spectral range is presented for the first time, using both broadband globar and narrowband quantum cascade laser illumination. AgGaS₂ is used as the nonlinear medium for sum frequency generation using a 1064 nm mixing laser. Angular scanning of the nonlinear crystal provides broad spectral coverage at every spatial position in the image. This study demonstrates the retrieval of series of monochromatic images acquired by a silicon based CCD camera, using both broadband and narrowband illumination and a comparison is made between the two illumination sources for hyperspectral imaging.

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

Pathology is essential for research in disease and development, as well as for clinical decision making. For more than 100 years, pathology practice has involved analyzing images of stained, thin tissue sections by a trained human using an optical microscope. Technological advances are now driving major changes in this paradigm toward digital pathology (DP). The digital transformation of pathology goes beyond recording, archiving, and retrieving images, providing new computational tools to inform better decision making for precision medicine. First, we discuss some emerging innovations in both computational image analytics and imaging instrumentation in DP. Second, we discuss molecular contrast in pathology. Molecular DP has traditionally been an extension of pathology with molecularly specific dyes. Label-free, spectroscopic images are rapidly emerging as another important information source, and we describe the benefits and potential of this evolution. Third, we describe multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology. Finally, we provide examples of application areas in telepathology, education, and precision medicine. We conclude by discussing challenges and emerging opportunities in this area.

Label Free Detection of Sensitive Mid-Infrared Biomarkers of Glomerulonephritis in Urine Using Fourier Transform Infrared Spectroscopy

More reliable biomarkers using near-patient technologies are needed to improve early diagnosis and intervention for patients with renal disease. Infrared (IR) vibrational spectroscopy/microspectroscopy is an established analytical method that was first used in biomedical research over 20 years ago. With the advances in instrumentation, computational and mathematical techniques, this technology has now been applied to a variety of diseases; however, applications in nephrology are just beginning to emerge. In the present study, we used attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to analyze urine samples collected from rodent models of inflammatory glomerulonephritis (GN) as well as from patients with crescentic GN, with the aim of identifying potential renal biomarkers; several characteristic mid-IR spectral markers were identified in urine samples. Specifically, a 1545 cm⁻¹ band increased in intensity with the progression and severity of GN in rats, mice and humans. Furthermore, its intensity declined significantly in response to corticosteroid treatment in nephritic rats. In conclusion, our results suggest that specific urinary FTIR biomarkers may provide a rapid, sensitive and novel non-invasive means of diagnosing inflammatory forms of GN, and for real-time monitoring of progress, and response to treatment.

Process Analytical Quality Control of Tailored Drug Release Formulation Prepared via Hot-Melt Extrusion Technology

The objective of the present study was to compare the influence of Eudragit^® RS PO and RL PO blends on the release of water-soluble and insoluble drugs from hot-melt extruded formulations. In addition, we aimed to evaluate drug content uniformity and distribution by Fourier transform-infrared (FT-IR) chemical imaging. Theophylline (TP) and carbamazepine (CBZ) were selected as the water-soluble and insoluble model drugs, respectively. Eudragit^® RS PO and RL PO were selected as the polymeric matrices. FT-IR chemical imaging clearly demonstrated the content uniformity and distribution for both drugs in the extrudates, which was confirmed by HPLC. Increasing the ratio of Eudragit^® RL PO led to an increase in the in vitro drug release, whereas an increase in the ratio of Eudragit^® RS PO sustained the drug release for up to 12 h. The hot-melt extrusion of TP and CBZ with varying ratios of Eudragit^® RS PO and RL PO can be employed to tailor the drug release profiles. In this study, we demonstrated, for the first time, the use of FT-IR chemical imaging as a process analytical technique to determine the drug content uniformity and distribution. Our data correlated well with the results of HPLC analysis in the study of tailored drug release from the prepared hot-melt extruded formulation.

Quantum cascade lasers (QCLs) in biomedical spectroscopy

This review focuses on the recent applications of QCLs in mid-IR spectroscopy of clinically relevant samples.

Retrospective analysis of canine gallbladder contents in biliary sludge and gallbladder mucoceles

The pathophysiology of canine gallbladder diseases, including biliary sludge, gallbladder mucoceles and gallstones, is poorly understood. This study aimed to evaluate the component of gallbladder contents and bacterial infection of the gallbladder in order to elucidate the pathophysiology of biliary sludge and gallbladder mucoceles. A total of 43 samples of canine gallbladder contents (biliary sludge, 21 and gallbladder mucoceles, 22) were subjected to component analysis by infrared spectroscopy, and the resultant infrared spectra were compared with that of swine mucin. Of the 43 samples, 41 were also evaluated by aerobic and anaerobic bacterial culture. The contents of 20 (95.2%) biliary sludge and 22 (100%) gallbladder mucocele samples exhibited similar infrared spectra as swine mucin. Although biliary sludge and gallbladder mucocele contents exhibited similar infrared spectra, one sample of biliary sludge (4.8%) was determined to be composed of proteins. The rate of bacterial infection of the gallbladder was 10.0% for biliary sludge and 14.3% for gallbladder mucoceles. Almost all of the identified bacterial species were intestinal flora. These results indicate that the principal components of gallbladder contents in both gallbladder mucoceles and biliary sludge are mucins and that both pathophysiologies exhibit low rates of bacterial infection of the gallbladder. Therefore, it is possible that gallbladder mucoceles and biliary sludge have the same pathophysiology, and, rather than being independent diseases, they could possibly represent a continuous disease. Thus, biliary sludge could be considered as the stage preceding the appearance of gallbladder mucoceles.

Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples

Fourier transform infrared (FT-IR) spectroscopic imaging has been widely tested as a tool for stainless digital histology of biomedical specimens, including for the identification of infiltration and fibrosis in endomyocardial biopsy samples to assess transplant rejection. A major barrier in clinical translation has been the slow speed of imaging. To address this need, we tested and report here the viability of using high speed discrete frequency infrared (DFIR) imaging to obtain stain-free biochemical imaging in cardiovascular samples collected from patients. Images obtained by this method were classified with high accuracy by a Bayesian classification algorithm trained on FT-IR imaging data as well as on DFIR data. A single spectral feature correlated with instances of fibrosis, as identified by the pathologist, highlights the advantage of the DFIR imaging approach for rapid detection. The speed of digital pathologic recognition was at least 16 times faster than the fastest FT-IR imaging instrument. These results indicate that a fast, on-site identification of fibrosis using IR imaging has potential for real time assistance during surgeries. Further, the work describes development and applications of supervised classifiers on DFIR imaging data, comparing classifiers developed on FT-IR and DFIR imaging modalities and identifying specific spectral features for accurate identification of fibrosis. This addresses a topic of much debate on the use of training data and cross-modality validity of IR measurements. Together, the work is a step toward addressing a clinical diagnostic need at acquisition time scales that make IR imaging technology practical for medical use.

Chemico-mechanical imaging of Barrett's oesophagus

Barrett's oesophagus is a condition characterized by a change in the lining of the oesophagus that markedly increases the risk of adenocarcinoma. We demonstrate the first site-matched application of Brillouin microscopy, Raman microscopy and FTIR micro-spectroscopic imaging to ex-vivo epithelial tissue - Barrett's oesophagus. The mechanical and chemical characters of the epithelium were assessed in histological sections from a patient subjected to endoscopic oesophageal biopsy. Previous studies have shown that both these properties change within the oesophageal wall, owing to the presence of distinct cellular and extracellular constituents which are putatively affected by oesophageal cancer. Brillouin microscopy enables maps of elasticity of the epithelium to be obtained, whilst Raman and FTIR imaging provide 'chemical images' without the need for labelling or staining. This site-matched approach provides a valuable platform for investigating the structure, biomechanics and composition of complex heterogeneous systems. A combined Brillouin-Raman device has potential for in-vivo diagnosis of pathology. First application of site-matched micro Brillouin, Raman and FTIR spectroscopic imaging to epithelial tissue in Barrett's oesophagus.

Fully unsupervised inter-individual IR spectral histology of paraffinized tissue sections of normal colon

In label-free Fourier-transform infrared histology, spectral images are individually recorded from tissue sections, pre-processed and clustered. Each single resulting color-coded image is annotated by a pathologist to obtain the best possible match with tissue structures revealed after Hematoxylin-Eosin staining. However, the main limitations of this approach are the empirical choice of the number of clusters in unsupervised classification, and the marked color heterogeneity between the clustered spectral images. Here, using normal murine and human colon tissues, we developed an automatic multi-image spectral histology to simultaneously analyze a set of spectral images (8 images mice samples and 72 images human ones). This procedure consisted of a joint Extended Multiplicative Signal Correction (EMSC) to numerically deparaffinize the tissue sections, followed by an automated joint K-Means (KM) clustering using the hierarchical double application of Pakhira-Bandyopadhyay-Maulik (PBM) validity index. Using this procedure, the main murine and human colon histological structures were correctly identified at both the intra- and the inter-individual levels, especially the crypts, secreted mucus, lamina propria and submucosa. Here, we show that batched multi-image spectral histology procedure is insensitive to the reference spectrum but highly sensitive to the paraffin model of joint EMSC. In conclusion, combining joint EMSC and joint KM clustering by double PBM application allows to achieve objective and automated batched multi-image spectral histology.

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.

High-resolution FTIR imaging of colon tissues for elucidation of individual cellular and histopathological features

Comparison of spectral-histopathological features of a colon tissue measured using a conventional (5.5 μm × 5.5 μm, left) and a high-magnification (1.1 μm × 1.1 μm, right) FTIR imaging system with respect to HE stained tissue (middle).

Chemically tunable mucin chimeras assembled on living cells

Mucins are a family of secreted and transmembrane glycoproteins characterized by a massive domain of dense O-glycosylation on serine and threonine residues. Mucins are intimately involved in immunity and cancer, yet elucidation of the biological roles of their glycodomains has been complicated by their massive size, domain polymorphisms, and variable glycosylation patterns. Here we developed a synthetic route to a library of compositionally defined, high-molecular weight, dual end-functionalized mucin glycodomain constructs via N-carboxyanhydride polymerization. These glycopolypeptides are the first synthetic analogs to our knowledge to feature the native α-GalNAc linkage to serine with molecular weights similar to native mucins, solving a nearly 50-year synthetic challenge. Physical characterization of the mimics revealed insights into the structure and properties of mucins. The synthetic glycodomains were end-functionalized with an optical probe and a tetrazine moiety, which allowed site-specific bioorthogonal conjugation to an engineered membrane protein on live mammalian cells. This strategy in protein engineering will open avenues to explore the biological roles of cell surface mucins.

Rapid identification of goblet cells in unstained colon thin sections by means of quantum cascade laser-based infrared microspectroscopy

Changes in the volume covered by mucin-secreting goblet cell regions within colon thin sections may serve as a means to differentiate between ulcerative colitis and infectious colitis. Here we show that rapid, quantum cascade laser-based mid-infrared microspectroscopy might be able to contribute to the differential diagnosis of colitis ulcerosa, an inflammatory bowel disease. Infrared hyperspectral images of mouse colon thin sections were obtained within 7.5 minutes per section with a pixel size of 3.65 × 3.65 μm(2) and a field of view of 2.8 × 3.1 mm(2). The spectra were processed by training a random decision forest classifier on the basis of k-means clustering on one thin section. The trained algorithm was then applied to 5 further thin sections for a blinded validation and it was able to identify goblet cells in all sections. The rapid identification of goblet cells within these unstained, paraffinized thin sections of colon tissue was enabled by the high content of glycopeptides within the goblet cells as revealed by the pronounced spectral signatures in the 7.6 μm-8.6 μm and the 9.2 μm-9.7 μm wavelength ranges of the electromagnetic spectrum. More so, the simple calculation of the ratio between the absorbance values at 9.29 μm and 8.47 μm provides the potential to further shorten the time for measurement and analysis of a thin section down to well below 1 minute.

Development of a hierarchical double application of crisp cluster validity indices: a proof-of-concept study for automated FTIR spectral histology

Fourier-transform infrared (FTIR) spectral imaging is currently used as a non-destructive and label-free method for analyzing biological specimens. However, to highlight the different tissue regions, unsupervised clustering methods are commonly used leading to a subjective choice of the number of clusters. Here, we develop a hierarchical double application of 9 selected crisp cluster validity indices (CCVIs) using K-Means clustering. This approach when tested first on an artificial dataset showed that the indices Pakhira-Bandyopadhyay-Maulik (PBM) and Sym-Index (SI) perfectly estimated the expected 9 sub-clusters. Then, the concept was applied to a real dataset consisting of FTIR spectral images of normal human colon tissue samples originating from 5 patients. PBM and SI were revealed to be the most efficient indices that correctly identified the different colon histological components including crypts, lamina propria, muscularis mucosae, submucosa, and lymphoid aggregates. In conclusion, these results strongly suggest that the hierarchical double CCVI application is a promising method for automated and informative spectral histology.

Label-free phenotyping of peripheral blood lymphocytes by infrared imaging

It is now widely accepted that the immune microenvironment of tumors and more precisely Tumor Infiltrating Lymphocytes (TIL) play an important role in cancer development and outcome. TILs are considered to be important prognostic and predictive factors based on a growing body of clinical evidence; however, their presence at the tumor site is not currently assessed routinely. FTIR (Fourier transform infrared) imaging has proven it has value in studying a range of tumors, particularly for characterizing tumor cells. Currently, very little is known about the potential for FTIR imaging to characterize TIL. The present proof of concept study investigates the ability of FTIR imaging to identify the principal lymphocyte subpopulations present in human peripheral blood (PB). A negative cell isolation method was employed to select pure, label-free, helper T cells (CD4(+)), cytotoxic T cells (CD8(+)) and B cells (CD19(+)) from six healthy donors PB by Fluorescence Activated Cell Sorting (FACS). Cells were centrifuged onto Barium Fluoride windows and ten infrared images were recorded for each lymphocyte subpopulation from all six donors. After spectral pre-treatment, statistical analyses were performed. Unsupervised Principal Component Analyses (PCA) revealed that in the absence of donor variability, CD4(+) T cells, CD8(+) T cells and B cells each display distinct IR spectral features. Supervised Partial Least Square Discriminant Analyses (PLS-DA) demonstrated that the differences between the three lymphocyte subpopulations are reflected in their IR spectra, permitting their individual identification even when significant donor variability is present. Our results also show that a distinct spectral signature is associated with antibody binding. To our knowledge this is the first study reporting that FTIR imaging can effectively identify T and B lymphocytes and differentiate helper T cells from cytotoxic T cells. This proof of concept study demonstrates that FTIR imaging is a reliable tool for the identification of lymphocyte subpopulations and has the potential for use in characterizing TIL.

Automated Quantification of Tumor Viability in a Rabbit Liver Tumor Model after Chemoembolization Using Infrared Imaging

The rabbit VX2 tumor is a fast-growing carcinoma model commonly used to study new therapeutic devices, such as catheter-based therapies for patients with inoperable hepatocellular carcinoma. The evaluation of tumor viability after such locoregional therapies is essential to directing hepatocellular carcinoma management. We used infrared microspectroscopy for the automatic characterization and quantification of the VX2 liver tumor viability after drug-eluting beads transarterial chemoembolization (DEB-TACE). The protocol consisted of K-means clustering followed by principal component analysis (PCA) and linear discriminant analysis (LDA). The K-means clustering was used to classify the spectra from the infrared images of control or treated tumors and to build a database of many tissue spectra. On the basis of this reference library, the PCA-LDA analysis was used to build a predictive model to identify and quantify automatically tumor viability on unknown tissue sections. For the DEB group, the LDA model determined that the surface of tumor necrosis represented 91.6% ± 8.9% (control group: 33.1% ± 19.6%; Mann-Whitney P = 0.0004) and the viable tumor 2.6% ± 4% (control group: 62.2% ± 15.2%; Mann-Whitney P = 0.0004). Tissue quantification measurements correlated well with tumor necrosis (r = 0.827, P < 0.0001) and viable tumor (r = 0.840, P < 0.0001). Infrared imaging and PCA-LDA analysis could be helpful for easily assessing tumor viability.

Infrared and Raman imaging for characterizing complex biological materials: a comparative morpho-spectroscopic study of colon tissue

Complementary diagnostic methods to conventional histopathology are currently being investigated for developing rapid and objective molecular-level understanding of various disorders, especially cancers. Spectral histopathology using vibrational spectroscopic imaging has been put in the frontline as potentially promising in this regard as it provides a "spectral fingerprint" of the biochemical composition of cells and tissues. In order to ascertain the feasible conditions of vibrational spectroscopic methods for tissue-imaging analysis, vibrational multimodal imaging (infrared transmission, infrared-attenuated total reflection, and Raman imaging) of the same colon tissue has been implemented. The spectral images acquired were subjected to multivariate clustering analysis in order to identify on a molecular level the constituent histological organization of the colon tissue such as the epithelium, connective tissue, etc., by comparing the cluster images with the histological reference images. Based on this study, a comparative analysis of important factors involved in the vibrational multimodal imaging approaches such as image resolution, time constraints, their advantages and limitations, and their applicability to biological tissues has been carried out. Out of the three different vibrational imaging modalities tested, infrared-attenuated total reflection mode of imaging appears to provide a good compromise between the tissue histology and the time constraints in achieving similar image contrast to that of Raman imaging at an approximately 33-fold faster measurement time. The present study demonstrates the advantages, the limitations of the important parameters involved in vibrational multimodal imaging approaches, and their potential application toward imaging of biological tissues.

Infrared spectral histopathology for cancer diagnosis: a novel approach for automated pattern recognition of colon adenocarcinoma

Automated and label-free colon cancer diagnosis and identification of tumor-associated features using FTIR spectral histopathology directly on paraffinized tissue arrays.

Breast cancer and melanoma cell line identification by FTIR imaging after formalin-fixation and paraffin-embedding

Over the past few decades, Fourier transform infrared (FTIR) spectroscopy coupled to microscopy has been recognized as an emerging and potentially powerful tool in cancer research and diagnosis. For this purpose, histological analyses performed by pathologists are mostly carried out on biopsied tissue that undergoes the formalin-fixation and paraffin-embedding (FFPE) procedure. This processing method ensures an optimal and permanent preservation of the samples, making FFPE-archived tissue an extremely valuable source for retrospective studies. Nevertheless, as highlighted by previous studies, this fixation procedure significantly changes the principal constituents of cells, resulting in important effects on their infrared (IR) spectrum. Despite the chemical and spectral influence of FFPE processing, some studies demonstrate that FTIR imaging allows precise identification of the different cell types present in biopsied tissue, indicating that the FFPE process preserves spectral differences between distinct cell types. In this study, we investigated whether this is also the case for closely related cell lines. We analyzed spectra from 8 cancerous epithelial cell lines: 4 breast cancer cell lines and 4 melanoma cell lines. For each cell line, we harvested cells at subconfluence and divided them into two sets. We first tested the "original" capability of FTIR imaging to identify these closely related cell lines on cells just dried on BaF2 slides. We then repeated the test after submitting the cells to the FFPE procedure. Our results show that the IR spectra of FFPE processed cancerous cell lines undergo small but significant changes due to the treatment. The spectral modifications were interpreted as a potential decrease in the phospholipid content and protein denaturation, in line with the scientific literature on the topic. Nevertheless, unsupervised analyses showed that spectral proximities and distances between closely related cell lines were mostly, but not entirely, conserved after FFPE processing. Finally, PLS-DA statistical analyses highlighted that closely related cell lines are still successfully identified and efficiently distinguished by FTIR spectroscopy after FFPE treatment. This last result paves the way towards identification and characterization of cellular subtypes on FFPE tissue sections by FTIR imaging, indicating that this analysis technique could become a potential useful tool in cancer research.

Infrared imaging as a cancer diagnostic tool: introducing a new concept of spectral barcodes for identifying molecular changes in colon tumors

Complementary diagnostic methods to conventional histopathology are under scrutiny for various types of cancers for rapid and molecular level diagnostics. In this perspective, a biophotonic approach based on infrared spectral micro-imaging combined with multivariate statistical analysis has been implemented on colon tissues. The ability of infrared imaging to investigate the intrinsic biochemical features of cells and tissues has been exploited to develop a new concept of spectral bar coding. To implement this concept, 10 frozen colon tissue samples (five nontumoral and tumoral pairs from five patients) were imaged using infrared spectral micro-imaging in a nondestructive manner. The spectral images were processed by a multivariate clustering method to identify the histological organization in a label-free manner. Spectral information from the epithelial components was then automatically recovered on the basis of their intrinsic biochemical composition, and compared using a statistical method (Mann-Whitney U-test) to construct spectral barcodes specific to each patient. The spectral barcodes representing the discriminant infrared spectral wavenumbers (900-1,800 cm(-1) ) enabled characterization of some of the malignancy-associated biochemical alterations associated with mucin, nucleotides, carbohydrates, and protein regions. This approach not only allowed the identification of common biochemical alterations among all the colon cancer patients, but also revealed a difference of gradient within individual patients. This new concept of spectral bar coding gives insight into the potential of infrared spectral micro-imaging as a complementary diagnostic tool to conventional histopathology, for biochemical level understanding of malignancy in colon cancers in an objective and label-free manner.

Design of a specific colonic mucus marker using a human commensal bacterium cell surface domain

Imaging living cells and organs requires innovative, specific, efficient, and well tolerated fluorescent markers targeting cellular components. Such tools will allow proceeding to the dynamic analysis of cells and the adaptation of tissues to environmental cues. In this study, we have identified and synthesized a novel non-toxic fluorescent marker allowing a specific fluorescent staining of the human colonic mucus. Our strategy to identify a molecule able to specifically bind to the human colonic mucus was on the basis of the mucus adhesion properties of commensal bacteria. We identified and characterized the mucus-binding property of a 70-amino acid domain (MUB(70)) expressed on the surface of Lactobacillus strains. The chemical synthesis of MUB(70) was achieved using the human commensal bacterium Lactobacillus reuteri AF120104 protein as a template. The synthesized Cy5-conjugated MUB(70) marker specifically stained the colonic mucus on fixed human, rabbit, and guinea pig tissues. Interestingly, murine tissue was not stained, suggesting significant differences in the composition of the murine colonic mucus. In addition, this marker stained the mucus of living cultured human colonic cells (HT29-MTX) and human colonic tissue explants. Using a biotinylated derivative of MUB(70), we demonstrated that this peptide binds specifically to Muc2, the most abundant secreted mucin, through its glycosylated moieties. Hence, Cy5-MUB(70) is a novel and specific fluorescent marker for mammalian colonic mucus. It may be used for live imaging analysis but also, as demonstrated in this study, as a marker for the diagnosis and the prognosis of colonic mucinous carcinomas.

Automation of an algorithm based on fuzzy clustering for analyzing tumoral heterogeneity in human skin carcinoma tissue sections

This study aims to develop a new FT-IR spectral imaging of tumoral tissue permitting a better characterization of tumor heterogeneity and tumor/surrounding tissue interface. Infrared (IR) data were acquired on 13 biopsies of paraffin-embedded human skin carcinomas. Our approach relies on an innovative fuzzy C-means (FCM)-based clustering algorithm, allowing the automatic and simultaneous estimation of the optimal FCM parameters (number of clusters K and fuzziness index m). FCM seems more suitable than classical 'hard' clusterings, as it permits the assignment of each IR spectrum to every cluster with a specific membership value. This characteristic allows differentiating the nuances in the assignment of pixels, particularly those corresponding to tumoral tissue and those located at the tumor/peritumoral tissue interface. FCM images permit to highlight a marked heterogeneity within the tumor and characterize the interconnection between tissular structures. For the infiltrative tumors, a progressive gradient in the membership values of the pixels of the invasive front was also revealed.