Fourier transform infrared spectroscopic imaging of colon tissues: evaluating the significance of amide I and C-H stretching bands in diagnostic applications with machine learning

Full fingerprint hyperspectral imaging of prostate cancer tissue microarrays within clinical timeframes using quantum cascade laser microscopy

One of the major limitations for clinical applications of infrared spectroscopic imaging modalities is the acquisition time required to obtain reasonable images of tissues with high spatial resolution and good signal-to-noise ratio (SNR). The time to acquire a reasonable signal to noise spectroscopic scan of a standard microscope slide region of tissue can take many hours. As a trade-off, systems can allow for discrete wavenumber acquisitions, sacrificing potentially vital chemical bands in order to reach specific acquisition targets. Recent instrumentation developments now allow for the full fingerprint imaging of entire microscope slides in under 30 minutes, enabling rapid, high quality spectroscopic imaging of tissues within clinical timeframes without sacrificing frequency bands. Here we compare the data from a novel QCL microscope to an FTIR microscope covering multiple aspects of spectroscopic imaging of a large, clinically relevant, prostate cancer tissue cohort (N = 1281). Comparisons of hyperspectral data acquisition quality in both achieved signal to noise and image contrast alongside the capacity for unsupervised and supervised modelling of tissue constituents are reported. We conclude that it is now possible to collect full fingerprint spectra and derive clinically relevant data in a timeframe suitable for translation into the pathology laboratory without the need to resort to discrete frequency imaging with subsequent loss of information.

Safe-by-Design Strategies for Intranasal Drug Delivery Systems: Machine and Deep Learning Solutions to Differentiate Epithelial Tissues via Attenuated Total Reflection Fourier Transform Infrared Spectroscopy

The development of nasal drug delivery systems requires advanced analytical techniques and tools that allow for distinguishing between the nose-to-brain epithelial tissues with better precision, where traditional bioanalytical methods frequently fail. In this study, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is coupled to machine learning (ML) and deep learning (DL) techniques to discriminate effectively between epithelial tissues. The primary goal of this work was to develop Safe-by-Design models for intranasal drug delivery using ex vivo pig tissues experiment, which were analyzed by way of ML modeling. We compiled an ATR-FTIR spectral data set from olfactory epithelium (OE), respiratory epithelium (RE), and tracheal tissues. The data set was used to train and test different ML algorithms. Accuracy, sensitivity, specificity, and F1 score metrics were used to evaluate optimized model performance and their abilities to identify specific spectral signatures relevant to each tissue type. The used feedforward neural network (FNN) has shown 0.99 accuracy, indicating that it had performed a discrimination with a high level of trueness estimates, without overfitting, unlike the built support vector machine (SVM) model. Important spectral features detailing the assignment and site of two-dimensional (2D) protein structures per tissue type were determined by the SHapley Additive exPlanations (SHAP) value analysis of the FNN model. Furthermore, a denoising autoencoder was built to improve spectral quality by reducing noise, as confirmed by higher Pearson correlation coefficients for denoised spectra. The combination of spectroscopic analysis with ML modeling offers a promising strategy called, Safe-by-Design, as a monitoring strategy for intranasal drug delivery systems, also for designing the analysis of tissue for diagnosis purposes.

Comparative Blood Profiling Based on ATR-FTIR Spectroscopy and Chemometrics for Differential Diagnosis of Patients with Amyotrophic Lateral Sclerosis-Pilot Study

Amyotrophic lateral sclerosis (ALS) is a motor neurodegenerative disease characterized by poor prognosis. Currently, screening and diagnostic methods for ALS remain challenging, often leading to diagnosis at an advanced stage of the disease. This delay hinders the timely initiation of therapy, negatively impacting patient well-being. Additionally, misdiagnosis with other neurodegenerative disorders that present similar profiles often occurs. Therefore, there is an urgent need for a cost-effective, rapid, and user-friendly tool capable of predicting ALS onset. In this pilot study, we demonstrate that infrared spectroscopy, coupled with chemometric analysis, can effectively identify and predict disease profiles from blood samples drawn from ALS patients. The selected predictive spectral markers, which are used in various discriminant models, achieved an AUROC sensitivity of almost 80% for distinguishing ALS patients from controls. Furthermore, the differentiation of ALS at both the initial and advanced stages from other neurodegenerative disorders showed even higher AUROC values, with sensitivities of 87% (AUROC: 0.70-0.97). These findings highlight the elevated potential of ATR-FTIR spectroscopy for routine clinical screening and early diagnosis of ALS.

Biomolecular alterations detected in multiple sclerosis skin fibroblasts using Fourier transform infrared spectroscopy

Multiple sclerosis (MS) is the leading cause of non-traumatic disability in young adults. New avenues are needed to help predict individuals at risk for developing MS and aid in diagnosis, prognosis, and outcome of therapeutic treatments. Previously, we showed that skin fibroblasts derived from patients with MS have altered signatures of cell stress and bioenergetics, which likely reflects changes in their protein, lipid, and biochemical profiles. Here, we used Fourier transform infrared (FTIR) spectroscopy to determine if the biochemical landscape of MS skin fibroblasts were altered when compared to age- and sex-matched controls (CTRL). More so, we sought to determine if FTIR spectroscopic signatures detected in MS skin fibroblasts are disease specific by comparing them to amyotrophic lateral sclerosis (ALS) skin fibroblasts. Spectral profiling of skin fibroblasts from MS individuals suggests significant alterations in lipid and protein organization and homeostasis, which may be affecting metabolic processes, cellular organization, and oxidation status. Sparse partial least squares-discriminant analysis of spectral profiles show that CTRL skin fibroblasts segregate well from diseased cells and that changes in MS and ALS may be unique. Differential changes in the spectral profile of CTRL, MS, and ALS cells support the development of FTIR spectroscopy to detect biomolecular modifications in patient-derived skin fibroblasts, which may eventually help establish novel peripheral biomarkers.

Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples

The early and accurate detection of colorectal cancer (CRC) significantly affects its prognosis and clinical management. However, current standard diagnostic procedures for CRC often lack sensitivity and specificity since most rely on visual examination. Hence, there is a need to develop more accurate methods for its diagnosis. Support vector machine (SVM) and feedforward neural network (FNN) models were designed using the Fourier transform infrared (FT-IR) spectral data of several colorectal tissues that were unanimously identified as either benign or malignant by different unrelated pathologists. The set of samples in which the pathologists had discordant readings were then analyzed using the AI models described above. Between the SVM and NN models, the NN model was able to outperform the SVM model based on their prediction confidence scores. Using the spectral data of the concordant samples as training set, the FNN was able to predict the histologically diagnosed malignant tissues (n = 118) at 59.9-99.9% confidence (average = 93.5%). Of the 118 samples, 84 (71.18%) were classified with an above average confidence score, 34 (28.81%) classified below the average confidence score, and none was misclassified. Moreover, it was able to correctly identify the histologically confirmed benign samples (n = 83) at 51.5-99.7% confidence (average = 91.64%). Of the 83 samples, 60 (72.29%) were classified with an above average confidence score, 22 (26.51%) classified below the average confidence score, and only 1 sample (1.20%) was misclassified. The study provides additional proof of the ability of attenuated total reflection (ATR) FT-IR enhanced by AI tools to predict the likelihood of CRC without dependence on morphological changes in tissues.

Evaluation of Proteomic and Lipidomic Changes in Aeromonas-Infected Trout Kidney Tissue with the Use of FT-IR Spectroscopy and MALDI Mass Spectrometry Imaging

Aeromonas species are opportunistic bacteria causing a vast spectrum of human diseases, including skin and soft tissue infections, meningitis, endocarditis, peritonitis, gastroenteritis, and finally hemorrhagic septicemia. The aim of our research was to indicate the molecular alterations in proteins and lipids profiles resulting from Aeromonas sobria and A. salmonicida subsp. salmonicida infection in trout kidney tissue samples. We successfully applied FT-IR (Fourier transform infrared) spectroscopy and MALDI-MSI (matrix-assisted laser desorption/ionization mass spectrometry imaging) to monitor changes in the structure and compositions of lipids, secondary conformation of proteins, and provide useful information concerning disease progression. Our findings indicate that the following spectral bands’ absorbance ratios (spectral biomarkers) can be used to discriminate healthy tissue from pathologically altered tissue, for example, lipids (CH2/CH3), amide I/amide II, amide I/CH2 and amide I/CH3. Spectral data obtained from 10 single measurements of each specimen indicate numerous abnormalities concerning proteins, lipids, and phospholipids induced by Aeromonas infection, suggesting significant disruption of the cell membranes. Moreover, the increase in the content of lysolipids such as lysophosphosphatidylcholine was observed. The results of this study suggest the application of both methods MALDI-MSI and FT-IR as accurate methods for profiling biomolecules and identifying biochemical changes in kidney tissue during the progression of Aeromonas infection.

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning

Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissue using hematoxylin and eosin (H&E) stained tissue sections. Fourier transform infrared spectroscopic (FT-IR) imaging provides an alternate view of tissue in which spatially specific molecular information from unstained tissue can be utilized. Here, we examine the potential of IR imaging for grading colon cancer in biopsy samples. We used a 148-patient cohort to develop a deep learning classifier to estimate the tumor grade using IR absorption. We demonstrate that FT-IR imaging can be a viable tool to determine colorectal cancer grades, which we validated on an independent cohort of surgical resections. This work demonstrates that harnessing molecular information from FT-IR imaging and coupling it with morphometry is a potential path to develop clinically relevant grade prediction models.

Clinical Spectroscopy: Lost in Translation?

This Focal Point Review paper discusses the developments of biomedical Raman and infrared spectroscopy, and the recent strive towards these technologies being regarded as reliable clinical tools. The promise of vibrational spectroscopy in the field of biomedical science, alongside the development of computational methods for spectral analysis, has driven a plethora of proof-of-concept studies which convey the potential of various spectroscopic approaches. Here we report a brief review of the literature published over the past few decades, with a focus on the current technical, clinical, and economic barriers to translation, namely the limitations of many of the early studies, and the lack of understanding of clinical pathways, health technology assessments, regulatory approval, clinical feasibility, and funding applications. The field of biomedical vibrational spectroscopy must acknowledge and overcome these hurdles in order to achieve clinical efficacy. Current prospects have been overviewed with comment on the advised future direction of spectroscopic technologies, with the aspiration that many of these innovative approaches can ultimately reach the frontier of medical diagnostics and many clinical applications.

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.

Lipidomic landscape in cancer: Actionable insights for membrane-based therapy and diagnoses

Cancer cells display altered cellular lipid metabolism, including disruption in endogenous lipid synthesis, storage, and exogenous uptake for membrane biogenesis and functions. Altered lipid metabolism and, consequently, lipid composition impacts cellular function by affecting membrane structure and properties, such as fluidity, rigidity, membrane dynamics, and lateral organization. Herein, we provide an overview of lipid membranes and how their properties affect cellular functions. We also detail how the rewiring of lipid metabolism impacts the lipidomic landscape of cancer cell membranes and influences the characteristics of cancer cells. Furthermore, we discuss how the altered cancer lipidome provides cues for developing lipid-inspired innovative therapeutic and diagnostic strategies while improving our limited understanding of the role of lipids in cancer initiation and progression. We also present the arcade of membrane characterization techniques to cement their relevance in cancer diagnosis and monitoring of treatment response.

Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets

The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms - AdaBoost and Random Forests - on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

Perspectives on infrared spectroscopic imaging from cancer diagnostics to process analysis

This perspective paper discusses the recent and potential developments in the application of infrared spectroscopic imaging, with a focus on Fourier transform infrared (FTIR) spectroscopic imaging. The current state-of-the-art has been briefly reported, that includes recent trends and advances in applications of FTIR spectroscopic imaging to biomedical systems. Here, some new opportunities for research in the biomedical field, particularly for cancer diagnostics, and also in the engineering field of process analysis; as well as challenges in FTIR spectroscopic imaging are discussed. Current and future prospects that will bring spectroscopic imaging technologies to the frontier of advanced medical diagnostics and to process analytics in engineering applications will be outlined in this opinion paper.

Application of artificial intelligence to the diagnosis and therapy of colorectal cancer

Artificial intelligence (AI) is a relatively new branch of computer science involving many disciplines and technologies, including robotics, speech recognition, natural language and image recognition or processing, and machine learning. Recently, AI has been widely applied in the medical field. The effective combination of AI and big data can provide convenient and efficient medical services for patients. Colorectal cancer (CRC) is a common type of gastrointestinal cancer. The early diagnosis and treatment of CRC are key factors affecting its prognosis. This review summarizes the research progress and clinical application value of AI in the investigation, early diagnosis, treatment, and prognosis of CRC, to provide a comprehensive theoretical basis for AI as a promising diagnostic and treatment tool for CRC.

Collagen maturity and mineralization in mesenchymal stem cells cultured on the hydroxyapatite-based bone scaffold analyzed by ATR-FTIR spectroscopic imaging

Modern bone tissue engineering is based on the use of implants in the form of biomaterials, which are used as scaffolds for osteoprogenitor or stem cells. The task of the scaffolds is to temporarily sustain the function, proliferation and differentiation of bone tissue to enable its regeneration. The aim of this work is to use the macro ATR-FTIR spectroscopic imaging for analysis of the ceramic-based biomaterial (chitosan/β-1,3-glucan/hydroxyapatite). Specifically, during long-term culture of mesenchymal cells derived from adipose tissue (ADSCs) and bone marrow (BMDSCs) on the surface of scaffold. Infrared spectroscopy allows the acquisition of information on both the organic and inorganic parts of the tested composite. This innovative spectroscopic approach proved to be very suitable for studying the formation of new bone tissue and ECM components, sample staining and demineralization are not required and consequently the approach is rapid and cost-effective. The novelty of this study focuses on the innovatory use of ATR-FTIR imaging to evaluate the molecular structure and maturity of collagen as well as mineral matrix formation and crystallization in the context of bone regenerative medicine. Our research has shown that the biomaterial investigated on this work facilitates the formation of valid bone ECM of the stem cells types studied, as a result of the synthesis of type I collagen and mineral content deposition. Nevertheless, ADSC cells have been proven to produce a greater amount of collagen with a lower content of helical secondary structures, at the same time showing a higher mineralization intensity compared to BMDSC cells. Considering the above results, it could be stated that the developed scaffold is a promising material for biomedical applications, including modification of bone implants to increase their biocompatibility.

The emerging role of super enhancer-derived noncoding RNAs in human cancer

Super enhancers (SEs) are large clusters of adjacent enhancers that drive the expression of genes which regulate cellular identity; SE regions can be enriched with a high density of transcription factors, co-factors, and enhancer-associated epigenetic modifications. Through enhanced activation of their target genes, SEs play an important role in various diseases and conditions, including cancer. Recent studies have shown that SEs not only activate the transcriptional expression of coding genes to directly regulate biological functions, but also drive the transcriptional expression of non-coding RNAs (ncRNAs) to indirectly regulate biological functions. SE-derived ncRNAs play critical roles in tumorigenesis, including malignant proliferation, metastasis, drug resistance, and inflammatory response. Moreover, the abnormal expression of SE-derived ncRNAs is closely related to the clinical and pathological characterization of tumors. In this review, we summarize the functions and roles of SE-derived ncRNAs in tumorigenesis and discuss their prospective applications in tumor therapy. A deeper understanding of the potential mechanism underlying the action of SE-derived ncRNAs in tumorigenesis may provide new strategies for the early diagnosis of tumors and targeted therapy.

Effect of Gelation Temperature on the Molecular Structure and Physicochemical Properties of the Curdlan Matrix: Spectroscopic and Microscopic Analyses

In order to determine the effect of different gelation temperatures (80 °C and 90 °C) on the structural arrangements in 1,3-β-d-glucan (curdlan) matrices, spectroscopic and microscopic approaches were chosen. Attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) and Raman spectroscopy are well-established techniques that enable the identification of functional groups in organic molecules based on their vibration modes. X-ray photoelectron spectroscopy (XPS) is a quantitative analytical method utilized in the surface study, which provided information about the elemental and chemical composition with high surface sensitivity. Contact angle goniometer was applied to evaluate surface wettability and surface free energy of the matrices. In turn, the surface topography characterization was obtained with the use of atomic force microscopy (AFM) and scanning electron microscopy (SEM). Described techniques may facilitate the optimization, modification, and design of manufacturing processes (such as the temperature of gelation in the case of the studied 1,3-β-d-glucan) of the organic polysaccharide matrices so as to obtain biomaterials with desired characteristics and wide range of biomedical applications, e.g., entrapment of drugs or production of biomaterials for tissue regeneration. This study shows that the 1,3-β-d-glucan polymer sample gelled at 80 °C has a distinctly different structure than the matrix gelled at 90 °C.

The FT-IR and Raman Spectroscopies as Tools for Biofilm Characterization Created by Cariogenic Streptococci

Fourier transform infrared (FT-IR) and Raman spectroscopy and mapping were applied to the analysis of biofilms produced by bacteria of the genus Streptococcus. Bacterial biofilm, also called dental plaque, is the main cause of periodontal disease and tooth decay. It consists of a complex microbial community embedded in an extracellular matrix composed of highly hydrated extracellular polymeric substances and is a combination of salivary and bacterial proteins, lipids, polysaccharides, nucleic acids, and inorganic ions. This study confirms the value of Raman and FT-IR spectroscopies in biology, medicine, and pharmacy as effective tools for bacterial product characterization.

Biomolecular and bioanalytical applications of infrared spectroscopy - A review

Infrared (IR; or mid-infrared, MIR; 4000-400 cm^-1; 2500-25,000 nm) spectroscopy has become one of the most powerful and versatile tools at the disposal of modern bioscience. Because of its high molecular specificity, applicability to wide variety of samples, rapid measurement and non-invasivity, IR spectroscopy forms a potent approach to elucidate qualitative and quantitative information from various kinds of biological material. For these reasons, it became an established bioanalytical technique with diverse applications. This work aims to be a comprehensive and critical review of the recent accomplishments in the field of biomolecular and bioanalytical IR spectroscopy. That progress is presented on a wider background, with fundamental characteristics, the basic principles of the technique outlined, and its scientific capability directly compared with other methods being used in similar fields (e.g. near-infrared, Raman, fluorescence). The article aims to present a complete examination of the topic, as it touches the background phenomena, instrumentation, spectra processing and data analytical methods, spectra interpretation and related information. To suit this goal, the article includes a tutorial information essential to obtain a thorough perspective of bio-related applications of the reviewed methodologies. The importance of the fundamental factors to the final performance and applicability of IR spectroscopy in various areas of bioscience is explained. This information is interpreted in critical way, with aim to gain deep understanding why IR spectroscopy finds extraordinarily intensive use in this remarkably diverse and dynamic field of research and utility. The major focus is placed on the diversity of the applications in which IR biospectroscopy has been established so far and those onto which it is expanding nowadays. This includes qualitative and quantitative analytical spectroscopy, spectral imaging, medical diagnosis, monitoring of biophysical processes, and studies of physicochemical properties and dynamics of biomolecules. The application potential of IR spectroscopy in light of the current accomplishments and the future prospects is critically evaluated and its significance in the progress of bioscience is comprehensively presented.

Micro ATR-FTIR spectroscopic imaging of colon biopsies with a large area Ge crystal

A new large-area germanium ATR crystal is utilised with an FTIR microscope to improve the acquired images of de-paraffinized colon biopsy sections, without recourse to a synchrotron source. The large crystal (⌀ = 28 mm) offers significant improvements compared to slide-on small germanium crystal (⌀ = 3.5 mm); for example, it facilitates more uniform distribution of higher signal intensity within the field of view and more rapid acquisition time. Mapping of a larger sample area up to ca. 350 × 350 μm² with this new set-up, coupled with imaging using an FPA detector, is demonstrated for the first time on biological specimens. The performance of k-means clustering algorithm applied to classify the different anatomical structures of the colon biopsies is greatly improved with mapping. Comparison of H&E stained adjacent tissue sections with false-colour k-means images strongly support differentiation of five distinct regions within tissues. The efficiency of the methodology to categorise colon tissues at various stages of malignancy is analysed via multivariate chemometrics. The second derivative spectra extracted from the crypt region of the colon were subjected to Partial Least Squares classification. Good separation between data in clusters occurs when projecting spectra on a PLS score plot on a plane made by the first 3 principal components. Important spectral biomarkers for colon malignancy classification were identified to exist mostly in the fingerprint region of the FTIR spectrum based on the chemometrics analysis.