Attenuated total reflectance Fourier-transform infrared spectroscopic imaging for breast histopathology

Providing potential solutions by using FT-IR spectroscopy for biofluid analysis: Clinical impact of optical screening and diagnostic tests

BACKGROUND

Currently, the potential of FT-IR spectroscopy for rapid diagnosis of many pathologies has been demonstrated by numerous research studies including those targeting COVID-19 detection. However, the number of clinicians aware of this potential and who are willing to use spectroscopy in their clinics and hospitals is still negligible. In addition, lack of awareness creates a huge gap between clinicians and researchers involved in clinical translation of current FT-IR technology hence hindering initiatives to bring basic and applied research together for the direct benefit of patients.

METHODS

Knowledge and medical training on FT-IR on the side of clinicians should be one of the first steps to be able to integrate it into the list of complementary exams which may be requested by health professionals. Countless FT-IR applications could have a life-changing impact on patients' lives, especially screening and diagnostic tests involving biofluids such as blood, saliva and urine which are routinely non-invasively or minimally-invasively.

RESULTS

Blood may be the most difficult to obtain by the invasive method of collection, but much can be evaluated in its components, and areas such as hematology, infectiology, oncology and endocrinology can be directly benefited. Urine with a relatively simple collection method can provide pertinent information from the entire urinary system, including the actual condition of the kidneys. Saliva collection can be simpler for the patient and can provide information on diseases affecting the mouth and digestive system and can be used to diagnose diseases such as oral cancer in its early-stages. An unavoidable second step is the active involvement of industries to design robust and portable instruments for specific purposes, as the medical community requires user-friendly instruments of advanced computational algorithms. A third step resides in the legal situation involving the global use of the technique as a new diagnostic modality.

CONCLUSIONS

It is important to note that decentralized funds for variety of technologies hinders the training of clinical and medical professionals for the use of newly arising technologies and affect the engagement of these professionals with technology developers. As a result of decentralized funding, research efforts are spread out over a range of technologies which take a long time to get validated and translated to the clinic. Partnership over similar groups of technologies and efforts to test the same technologies while overcoming barriers posed to technology validation in different areas around the globe may benefit the clinical/medical, research and industry community globally.

Potential Role of Fourier Transform Infrared Spectroscopy as a Screening Approach for Breast Cancer

Breast cancer is a heterogeneous disease, and its spread involves a succession of clinical and pathological stages. Screening is predominantly based on mammography, which has critical limitations related to the effectiveness and production of false-positive or false-negative results, generating discomfort and low adherence. In this context, infrared with attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy emerges as a non-destructive sample tool, which is non-invasive, label-free, has a low operating-cost, and requires only a small amount of sample, including liquid plasma samples. We sought to evaluate the clinical applicability of ATR FT-IR in breast cancer screening. ATR FT-IR spectroscopy through its highest potential spectral biomarker could distinguish, by liquid plasma biopsy, breast cancer patients and healthy controls, obtaining a sensitivity of 97%, specificity of 93%, a receiver operating characteristic ROC curve of 97%, and a prediction accuracy of 94%. The main variance between the groups was mainly in the band 1511 cm^-1 of the control group, 1502 and 1515 cm^-1 of the cancer group, which are the peaks of the bands referring to proteins and amide II. ATR FT-IR spectroscopy has demonstrated to be a promising tool for breast cancer screening, given its time efficiency, cost of approach, and its high ability to distinguish between the liquid plasma samples of breast cancer patients and healthy controls.

Vibrational Biospectroscopy: An Alternative Approach to Endometrial Cancer Diagnosis and Screening

Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects.

Spectroscopic assessment of oxidative damage in biomolecules and tissues

Oxidative damage is one of the main causes of cryopreservation injury compromising the use of cryopreserved biospecimens. The aim of this study was to evaluate the use of Fourier transform infrared spectroscopy (FTIR) as a non-invasive method to assess changes in biomolecular composition and structure, associated with oxidative stress in isolated biomolecules, acellular heart valve tissues, and ovarian cortex tissues. FTIR spectra of these specimens subjected to various treatments (H₂O₂- and Fenton-treatment or elevated temperatures) were vector normalized and selected spectral regions were analyzed by principal component analysis (PCA). Control and damaged biomolecules can easily be separated using PCA score plots. Acellular heart valve tissues that were subjected to different levels of oxidative damage formed separate cluster in PCA score plots. In hydrated ovarian tissue, large variation of the principal components was observed. Drying the ovarian tissues samples resulted in improved cluster separation of treatment groups. However, early signs of oxidative damage under mild stress conditions could not be detected by PCA of FTIR spectra. For the ovarian tissue samples, the standardly used nitro blue tetrazolium chloride (NBT) assay was used to monitor the amount of formazan production, reflecting reactive oxygen species (ROS) production at various temperatures. At 37 °C, formazan staining rapidly increased during the first 30 min, and then slowly reached a saturation level, but also at lower temperatures (i.e. 4 °C) formazan production was observed. In summary, we conclude that ATR-FTIR combined with PCA can be used to study oxidative damage in biomolecules as well as in tissues. In tissues, however, sample heterogeneity makes it difficult to detect early signs of oxidative damage.

Interrogation of IDH1 Status in Gliomas by Fourier Transform Infrared Spectroscopy

Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.

Label-free imaging of lipid-rich biological tissues by mid-infrared photoacoustic microscopy

SIGNIFICANCE

Mid-infrared (IR) imaging based on the vibrational transition of biomolecules provides good chemical-specific contrast in label-free imaging of biology tissues, making it a popular tool in both biomedical studies and clinical applications. However, the current technology typically requires thin and dried or extremely flat samples, whose complicated processing limits this technology's broader translation.

AIM

To address this issue, we report mid-IR photoacoustic microscopy (PAM), which can readily work with fresh and thick tissue samples, even when they have rough surfaces.

APPROACH

We developed a transmission-mode mid-IR PAM system employing an optical parametric oscillation laser operating in the wavelength range from 2.5 to 12  μm. Due to its high sensitivity to optical absorption and the low ultrasonic attenuation of tissue, our PAM achieved greater probing depth than Fourier transform IR spectroscopy, thus enabling imaging fresh and thick tissue samples with rough surfaces.

RESULTS

In our spectroscopy study, the CH2 symmetric stretching at 2850  cm  -  1 (3508 nm) was found to be an excellent source of endogenous contrast for lipids. At this wavenumber, we demonstrated label-free imaging of the lipid composition in fresh, manually cut, and unprocessed tissue sections of up to 3-mm thickness.

CONCLUSIONS

Our technology requires no time-consuming sample preparation procedure and has great potential in both fast clinical histological analysis and fundamental biological studies.

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

Infrared Spectroscopic Imaging Visualizes a Prognostic Extracellular Matrix-Related Signature in Breast Cancer

Molecular analysis techniques such as gene expression analysis and proteomics have contributed greatly to our understanding of cancer heterogeneity. In prior studies, gene expression analysis was shown to stratify patient outcome on the basis of tumor-microenvironment associated genes. A specific gene expression profile, referred to as ECM3 (Extracellular Matrix Cluster 3), indicated poorer survival in patients with grade III tumors. In this work, we aimed to visualize the downstream effects of this gene expression profile onto the tissue, thus providing a spatial context to altered gene expression profiles. Using infrared spectroscopic imaging, we identified spectral patterns specific to the ECM3 gene expression profile, achieving a high spectral classification performance of 0.87 as measured by the area under the curve of the receiver operating characteristic curve. On a patient level, we correctly identified 20 out of 22 ECM3 group patients and 19 out of 20 non-ECM3 group patients by using this spectroscopic imaging-based classifier. By comparing pixels that were identified as ECM3 or non-ECM3 with H&E and IHC images, we were also able to observe an association between tissue morphology and the gene expression clusters, showing the ability of our method to capture broad outcome associated features from infrared images.

Quantitative Histopathology of Stained Tissues using Color Spatial Light Interference Microscopy (cSLIM)

Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light interference microscopy (cSLIM) as a new whole-slide imaging modality that performs interferometric imaging on stained tissue, with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide stain-independent prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.

A comparison of mid-infrared spectral regions on accuracy of tissue classification

Infrared (IR) spectroscopic imaging, utilizing both the molecular and structural disease signatures, enables extensive profiling of tumors and their microenvironments. Here, we examine the relative merits of using either the fingerprint or the high frequency regions of the IR spectrum for tissue histopathology. We selected a complex model as a test case, evaluating both stromal and epithelial segmentation for various breast pathologies. IR spectral classification in each of these spectral windows is quantitatively assessed by estimating area under the curve (AUC) of the receiver operating characteristic curve (ROC) for pixel level accuracy and images for diagnostic ability. We found only small differences, though some that may be sufficiently important in diagnostic tasks to be clinically significant, between the two regions with the fingerprint region-based classifiers consistently emerging as more accurate. The work provides added evidence and comparison with fingerprint region, complex models, and previously untested tissue type (breast) - that the use of restricted spectral regions can provide high accuracy. Our study indicates that the fingerprint region is ideal for epithelial and stromal models to obtain high pixel level accuracies. Glass slides provide a limited spectral feature set but provides accurate information at the patient level.

An Innovative Platform Merging Elemental Analysis and Ftir Imaging for Breast Tissue Analysis

Histopathology and immunohistology remain the gold standard for breast cancer diagnostic. Yet, these approaches do not usually provide a sufficiently detailed characterization of the pathology. The purpose of this work is to demonstrate for the first time that elemental analysis and Fourier transform infrared spectroscopy microscopic examination of breast tissue sections can be merged into one dataset to provide a single set of markers based on both organic molecules and inorganic trace elements. For illustrating the method, 6 mammary tissue sections were used. Fourier transform infrared (FTIR) spectroscopy images reported a fingerprint of the organic molecules present in the tissue section and laser ablation elemental analysis (LA-ICP-MS) images brought inorganic element profiles. The 6 tissue sections provided 31 10⁶ and 150,000 spectra for FTIR and LA-ICP-MS spectra respectively. The results bring the proof of concept that breast tissue can be analyzed simultaneously by FTIR spectroscopy and laser ablation elemental analysis (LA-ICP-MS) to provide in both case reasonably high resolution images. We show how to bring the images obtained by the two methods to a same spatial resolution and how to use image registration to analyze the data originating from both techniques as one block of data. We finally demonstrates the elemental analysis is orthogonal to all FTIR markers as no significant correlation is found between FTIR and LA-ICP-MS data. Combining FTIR and LA-ICP-MS imaging becomes possible, providing two orthogonal methods which can bring an unprecedented diversity of information on the tissue. This opens a new avenue of tissue section analyses providing unprecedented diagnostic potential.

Standardization of complex biologically derived spectrochemical datasets

Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO₂ levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.

Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks

Current methods for cancer detection rely on tissue biopsy, chemical labeling/staining, and examination of the tissue by a pathologist. Though these methods continue to remain the gold standard, they are non-quantitative and susceptible to human error. Fourier transform infrared (FTIR) spectroscopic imaging has shown potential as a quantitative alternative to traditional histology. However, identification of histological components requires reliable classification based on molecular spectra, which are susceptible to artifacts introduced by noise and scattering. Several tissue types, particularly in heterogeneous tissue regions, tend to confound traditional classification methods. Convolutional neural networks (CNNs) are the current state-of-the-art in image classification, providing the ability to learn spatial characteristics of images. In this paper, we demonstrate that CNNs with architectures designed to process both spectral and spatial information can significantly improve classifier performance over per-pixel spectral classification. We report classification results after applying CNNs to data from tissue microarrays (TMAs) to identify six major cellular and acellular constituents of tissue, namely adipocytes, blood, collagen, epithelium, necrosis, and myofibroblasts. Experimental results show that the use of spatial information in addition to the spectral information brings significant improvements in the classifier performance and allows classification of cellular subtypes, such as adipocytes, that exhibit minimal chemical information but have distinct spatial characteristics. This work demonstrates the application and efficiency of deep learning algorithms in improving the diagnostic techniques in clinical and research activities related to cancer.

High definition infrared chemical imaging of colorectal tissue using a Spero QCL microscope

Mid-infrared microscopy has become a key technique in the field of biomedical science and spectroscopy. This label-free, non-destructive technique permits the visualisation of a wide range of intrinsic biochemical markers in tissues, cells and biofluids by detection of the vibrational modes of the constituent molecules. Together, infrared microscopy and chemometrics is a widely accepted method that can distinguish healthy and diseased states with high accuracy. However, despite the exponential growth of the field and its research world-wide, several barriers currently exist for its full translation into the clinical sphere, namely sample throughput and data management. The advent and incorporation of quantum cascade lasers (QCLs) into infrared microscopes could help propel the field over these remaining hurdles. Such systems offer several advantages over their FT-IR counterparts, a simpler instrument architecture, improved photon flux, use of room temperature camera systems, and the flexibility of a tunable illumination source. In this current study we explore the use of a QCL infrared microscope to produce high definition, high throughput chemical images useful for the screening of biopsied colorectal tissue.

Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)

Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects −34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.

Biofluid spectroscopic disease diagnostics: A review on the processes and spectral impact of drying

The complex patterns observed from evaporated liquid drops have been examined extensively over the last 20 years. Complete understanding of drop deposition is vital in many medical processes, and one which is essential to the translation of biofluid spectroscopic disease diagnostics. The promising use of spectroscopy in disease diagnosis has been hindered by the complicated patterns left by dried biological fluids which may inhibit the clinical translation of this technology. Coffee-ring formation, cracking and gelation patterns have all been observed in biofluid drops, and with surface homogeneity being a key element to many spectroscopic techniques, experimental issues have been found to arise. A better understanding of the fundamental processes involved in a drying droplet could allow efficient progression in this research field, and ultimately benefit the population with the development of a reliable cancer diagnostic.

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

Infrared spectroscopic tissue imaging is a potentially powerful adjunct tool to current histopathology techniques. By coupling the biochemical signature obtained through infrared spectroscopy to the spatial information offered by microscopy, this technique can selectively analyze the chemical composition of different features of unlabeled, unstained tissue sections. In the past, the tissue features that have received the most interest were parenchymal and epithelial cells, chiefly due to their involvement in dysplasia and progression to carcinoma; however, the field has recently turned its focus toward stroma and areas of fibrotic change. These components of tissue present an untapped source of biochemical information that can shed light on many diverse disease processes, and potentially hold useful predictive markers for these same pathologies. Here we review the recent applications of infrared spectroscopic imaging to stromal and fibrotic regions of diseased tissue, and explore the potential of this technique to advance current capabilities for tissue analysis.

Quantitative phase imaging for medical diagnosis

Optical microscopy is an indispensable diagnostic tool in modern healthcare. As a prime example, pathologists rely exclusively on light microscopy to investigate tissue morphology in order to make a diagnosis. While advances in light microscopy and contrast markers allow pathologists to visualize cells and tissues in unprecedented detail, the interpretation of these images remains largely subjective, leading to inter- and intra-observer discrepancy. Furthermore, conventional microscopy images capture qualitative information which makes it difficult to automate the process, reducing the throughput achievable in the diagnostic workflow. Quantitative Phase Imaging (QPI) techniques have been advanced in recent years to address these two challenges. By quantifying physical parameters of cells and tissues, these systems remove subjectivity from the disease diagnosis process and allow for easier automation to increase throughput. In addition to providing quantitative information, QPI systems are also label-free and can be easily assimilated into the current diagnostic workflow in the clinic. In this paper we review the advances made in disease diagnosis by QPI techniques. We focus on the areas of hematological diagnosis and cancer pathology, which are the areas where most significant advances have been made to date. [Image adapted from Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, Proc. Natl. Acad. Sci. 105, 13730-13735 (2008).].

Characterization of human breast cancer tissues by infrared imaging

Fourier Transform InfraRed (FTIR) spectroscopy coupled to microscopy (IR imaging) has shown unique advantages in detecting morphological and molecular pathologic alterations in biological tissues. The aim of this study was to evaluate the potential of IR imaging as a diagnostic tool to identify characteristics of breast epithelial cells and the stroma. In this study a total of 19 breast tissue samples were obtained from 13 patients. For 6 of the patients, we also obtained Non-Adjacent Non-Tumor tissue samples. Infrared images were recorded on the main cell/tissue types identified in all breast tissue samples. Unsupervised Principal Component Analyses and supervised Partial Least Square Discriminant Analyses (PLS-DA) were used to discriminate spectra. Leave-one-out cross-validation was used to evaluate the performance of PLS-DA models. Our results show that IR imaging coupled with PLS-DA can efficiently identify the main cell types present in FFPE breast tissue sections, i.e. epithelial cells, lymphocytes, connective tissue, vascular tissue and erythrocytes. A second PLS-DA model could distinguish normal and tumor breast epithelial cells in the breast tissue sections. A patient-specific model reached particularly high sensitivity, specificity and MCC rates. Finally, we showed that the stroma located close or at distance from the tumor exhibits distinct spectral characteristics. In conclusion FTIR imaging combined with computational algorithms could be an accurate, rapid and objective tool to identify/quantify breast epithelial cells and differentiate tumor from normal breast tissue as well as normal from tumor-associated stroma, paving the way to the establishment of a potential complementary tool to ensure safe tumor margins.

Attenuated total reflection Fourier-transform infrared (ATR-FTIR) imaging of tissues and live cells

FTIR spectroscopic imaging is a label-free, non-destructive and chemically specific technique that can be utilised to study a wide range of biomedical applications such as imaging of biopsy tissues, fixed cells and live cells, including cancer cells. In particular, the use of FTIR imaging in attenuated total reflection (ATR) mode has attracted much attention because of the small, but well controlled, depth of penetration and corresponding path length of infrared light into the sample. This has enabled the study of samples containing large amounts of water, as well as achieving an increased spatial resolution provided by the high refractive index of the micro-ATR element. This review is focused on discussing the recent developments in FTIR spectroscopic imaging, particularly in ATR sampling mode, and its applications in the biomedical science field as well as discussing the future opportunities possible as the imaging technology continues to advance.

Vibrational Micro-Spectroscopy of Human Tissues Analysis: Review

Vibrational spectroscopy (Infrared (IR) and Raman) and, in particular, micro-spectroscopy and micro-spectroscopic imaging have been used to characterize developmental changes in tissues, to monitor these changes in cell cultures and to detect disease and drug-induced modifications. The conventional methods for biochemical and histophatological tissue characterization necessitate complex and "time-consuming" sample manipulations and the results are rarely quantifiable. The spectroscopy of molecular vibrations using mid-IR or Raman techniques has been applied to samples of human tissue. This article reviews the application of these vibrational spectroscopic techniques for analysis of biological tissue published between 2005 and 2015.

Multi-functionality Redefined with Colloidal Carotene Carbon Nanoparticles for Synchronized Chemical Imaging, Enriched Cellular Uptake and Therapy

Typically, multiplexing high nanoparticle uptake, imaging, and therapy requires careful integration of three different functions of a multiscale molecular-particle assembly. Here, we present a simpler approach to multiplexing by utilizing one component of the system for multiple functions. Specifically, we successfully synthesized and characterized colloidal carotene carbon nanoparticle (C(3)-NP), in which a single functional molecule served a threefold purpose. First, the presence of carotene moieties promoted the passage of the particle through the cell membrane and into the cells. Second, the ligand acted as a potent detrimental moiety for cancer cells and, finally, the ligands produced optical contrast for robust microscopic detection in complex cellular environments. In comparative tests, C(3)-NP were found to provide effective intracellular delivery that enables both robust detection at cellular and tissue level and presents significant therapeutic potential without altering the mechanism of intracellular action of β-carotene. Surface coating of C(3) with phospholipid was used to generate C(3)-Lipocoat nanoparticles with further improved function and biocompatibility, paving the path to eventual in vivo studies.

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.

Perfluoroalkylated Substance Effects in Xenopus laevis A6 Kidney Epithelial Cells Determined by ATR-FTIR Spectroscopy and Chemometric Analysis

The effects of four perfluoroalkylated substances (PFASs), namely, perfluorobutanesulfonate (PFBS), perfluorooctanoic acid (PFOA), perfluorooctanesulfonate (PFOS), and perfluorononanoic acid (PFNA) were assessed in Xenopus laevis A6 kidney epithelial cells by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometric analysis. Principal component analysis–linear discriminant analysis (PCA-LDA) was used to visualize wavenumber-related alterations and ANOVA-simultaneous component analysis (ASCA) allowed data processing considering the underlying experimental design. Both analyses evidenced a higher impact of low-dose PFAS-treatments (10^–9 M) on A6 cells forming monolayers, while there was a larger influence of high-dose PFAS-treatments (10^–5 M) on A6 cells differentiated into dome structures. The observed dose–response PFAS-induced effects were to some extent related to their cytotoxicity: the EC₅₀-values of most influential PFAS-treatments increased (PFOS < PFNA < PFOA ≪ PFBS), and higher-doses of these chemicals induced a larger impact. Major spectral alterations were mainly attributed to DNA/RNA, secondary protein structure, lipids, and fatty acids. Finally, PFOS and PFOA caused a decrease in A6 cell numbers compared to controls, whereas PFBS and PFNA did not significantly change cell population levels. Overall, this work highlights the ability of PFASs to alter A6 cells, whether forming monolayers or differentiated into dome structures, and the potential of PFOS and PFOA to induce cell death.

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.

Introducing Discrete Frequency Infrared Technology for High-Throughput Biofluid Screening

Accurate early diagnosis is critical to patient survival, management and quality of life. Biofluids are key to early diagnosis due to their ease of collection and intimate involvement in human function. Large-scale mid-IR imaging of dried fluid deposits offers a high-throughput molecular analysis paradigm for the biomedical laboratory. The exciting advent of tuneable quantum cascade lasers allows for the collection of discrete frequency infrared data enabling clinically relevant timescales. By scanning targeted frequencies spectral quality, reproducibility and diagnostic potential can be maintained while significantly reducing acquisition time and processing requirements, sampling 16 serum spots with 0.6, 5.1 and 15% relative standard deviation (RSD) for 199, 14 and 9 discrete frequencies respectively. We use this reproducible methodology to show proof of concept rapid diagnostics; 40 unique dried liquid biopsies from brain, breast, lung and skin cancer patients were classified in 2.4 cumulative seconds against 10 non-cancer controls with accuracies of up to 90%.

High-throughput quantum cascade laser (QCL) spectral histopathology: a practical approach towards clinical translation

Infrared microscopy has become one of the key techniques in the biomedical research field for interrogating tissue. In partnership with multivariate analysis and machine learning techniques, it has become widely accepted as a method that can distinguish between normal and cancerous tissue with both high sensitivity and high specificity. While spectral histopathology (SHP) is highly promising for improved clinical diagnosis, several practical barriers currently exist, which need to be addressed before successful implementation in the clinic. Sample throughput and speed of acquisition are key barriers and have been driven by the high volume of samples awaiting histopathological examination. FTIR chemical imaging utilising FPA technology is currently state-of-the-art for infrared chemical imaging, and recent advances in its technology have dramatically reduced acquisition times. Despite this, infrared microscopy measurements on a tissue microarray (TMA), often encompassing several million spectra, takes several hours to acquire. The problem lies with the vast quantities of data that FTIR collects; each pixel in a chemical image is derived from a full infrared spectrum, itself composed of thousands of individual data points. Furthermore, data management is quickly becoming a barrier to clinical translation and poses the question of how to store these incessantly growing data sets. Recently, doubts have been raised as to whether the full spectral range is actually required for accurate disease diagnosis using SHP. These studies suggest that once spectral biomarkers have been predetermined it may be possible to diagnose disease based on a limited number of discrete spectral features. In this current study, we explore the possibility of utilising discrete frequency chemical imaging for acquiring high-throughput, high-resolution chemical images. Utilising a quantum cascade laser imaging microscope with discrete frequency collection at key diagnostic wavelengths, we demonstrate that we can diagnose prostate cancer with high sensitivity and specificity. Finally we extend the study to a large patient dataset utilising tissue microarrays, and show that high sensitivity and specificity can be achieved using high-throughput, rapid data collection, thereby paving the way for practical implementation in the clinic.

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.

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging of human tissue sections towards improving pathology

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging is an emerging approach to obtain detailed images that have associated biochemical information. FT-IR imaging of tissue is based on the principle that different regions of the mid-infrared are absorbed by different chemical bonds (e.g., C=O, C-H, N-H) within cells or tissue that can then be related to the presence and composition of biomolecules (e.g., lipids, DNA, glycogen, protein, collagen). In an FT-IR image, every pixel within the image comprises an entire Infrared (IR) spectrum that can give information on the biochemical status of the cells that can then be exploited for cell-type or disease-type classification. In this paper, we show: how to obtain IR images from human tissues using an FT-IR system, how to modify existing instrumentation to allow for high-definition imaging capabilities, and how to visualize FT-IR images. We then present some applications of FT-IR for pathology using the liver and kidney as examples. FT-IR imaging holds exciting applications in providing a novel route to obtain biochemical information from cells and tissue in an entirely label-free non-perturbing route towards giving new insight into biomolecular changes as part of disease processes. Additionally, this biochemical information can potentially allow for objective and automated analysis of certain aspects of disease diagnosis.

Breast cancer diagnosis using spatial light interference microscopy

The standard practice in histopathology of breast cancers is to examine a hematoxylin and eosin (H&E) stained tissue biopsy under a microscope to diagnose whether a lesion is benign or malignant. This determination is made based on a manual, qualitative inspection, making it subject to investigator bias and resulting in low throughput. Hence, a quantitative, label-free, and high-throughput diagnosis method is highly desirable. We present here preliminary results showing the potential of quantitative phase imaging for breast cancer screening and help with differential diagnosis. We generated phase maps of unstained breast tissue biopsies using spatial light interference microscopy (SLIM). As a first step toward quantitative diagnosis based on SLIM, we carried out a qualitative evaluation of our label-free images. These images were shown to two pathologists who classified each case as either benign or malignant. This diagnosis was then compared against the diagnosis of the two pathologists on corresponding H&E stained tissue images and the number of agreements were counted. The agreement between SLIM and H&E based diagnosis was 88% for the first pathologist and 87% for the second. Our results demonstrate the potential and promise of SLIM for quantitative, label-free, and high-throughput diagnosis.

Infrared imaging of MDA-MB-231 breast cancer cell line phenotypes in 2D and 3D cultures

Breast cancer cell lines in 2D (top) and 3D (bottom) culture: H&H, unstained bright field, and IR images.

Using Fourier transform IR spectroscopy to analyze biological materials

IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.

Integration of molecular profiling and chemical imaging to elucidate fibroblast-microenvironment impact on cancer cell phenotype and endocrine resistance in breast cancer

The tumor microenvironment is known to play a key role in altering the properties and behavior of nearby cancer cells. Its influence on resistance to endocrine therapy and cancer relapse, however, is poorly understood. Here we investigate the interaction of mammary fibroblasts and estrogen receptor-positive breast cancer cells in three-dimensional culture models in order to characterize gene expression, cellular changes, and the secreted protein factors involved in the cellular cross-talk. We show that fibroblasts, which are the predominant cell type found in the stroma adjacent to the cancer cells in a tumor, induce an epithelial-to-mesenchymal transition in the cancer cells, leading to hormone-independent growth, a more invasive phenotype, and resistance to endocrine therapy. Here, we applied a label-free chemical imaging modality, Fourier transform infrared (FT-IR) spectroscopic imaging, to identify cells that had transitioned to hormone-independent growth. Both the molecular and chemical profiles identified here were translated from cell culture to patient samples: a secreted protein signature was used to stratify patient populations based on gene expression and FT-IR was used to characterize breast tumor patient biopsies. Our findings underscore the role of mammary fibroblasts in promoting aggressiveness and endocrine therapy resistance in ER-positive breast cancers and highlight the utility of FT-IR for the further characterization of breast cancer samples.

Evaluation of FTIR spectroscopy as diagnostic tool for colorectal cancer using spectral analysis

The aim of this study is to confirm FTIR spectroscopy as a diagnostic tool for colorectal cancer. 180 freshly removed colorectal samples were collected from 90 patients for spectrum analysis. The ratios of spectral intensity and relative intensity (/I1460) were calculated. Principal component analysis (PCA) and Fisher's discriminant analysis (FDA) were applied to distinguish the malignant from normal. The FTIR parameters of colorectal cancer and normal tissues were distinguished due to the contents or configurations of nucleic acids, proteins, lipids and carbohydrates. Related to nitrogen containing, water, protein and nucleic acid were increased significantly in the malignant group. Six parameters were selected as independent factors to perform discriminant functions. The sensitivity for FTIR in diagnosing colorectal cancer was 96.6% by discriminant analysis. Our study demonstrates that FTIR can be a useful technique for detection of colorectal cancer and may be applied in clinical colorectal cancer diagnosis.

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.