Characterization of tumor progression in engineered tissue using infrared spectroscopic imaging

FTIR microspectroscopic study of gastric cancer AGS cells apoptosis induced by As2O3

As2O3 has shown significant anti-gastric cancer effects, but the mechanism is still unclear. Thus, biomacromolecular changes induced by As2O3 were investigated by using human gastric cancer AGS cells as the model. Flow cytometry results confirmed that As2O3 induced AGS cells apoptosis. Fourier transform infrared (FTIR) microspectroscopy detected biomacromolecular changes during As2O3-induced AGS cells apoptosis sensitively: IR spectra showed significant changes in the lipids content and the proteins and DNA structure. Peak-area ratios indicated obvious changes in the lipids and DNA content and the proteins structure, while also showing a relatively good linear relationship between A1733/A969 and the apoptosis rate. PCA exhibited significant alteration in nucleic acids while curve fitting further revealed the changes in nucleic acids and proteins. On the whole, our study explored As2O3-induced gastric cancer cells apoptosis in depth on the basis of analyzing biomacromolecular changes, in addition, it also suggested FTIR microspectroscopy to be possibly useful in the research of apoptosis.

Engineering Heterogeneous Tumor Models for Biomedical Applications

Tumor tissue engineering holds great promise for replicating the physiological and behavioral characteristics of tumors in vitro. Advances in this field have led to new opportunities for studying the tumor microenvironment and exploring potential anti-cancer therapeutics. However, the main obstacle to the widespread adoption of tumor models is the poor understanding and insufficient reconstruction of tumor heterogeneity. In this review, the current progress of engineering heterogeneous tumor models is discussed. First, the major components of tumor heterogeneity are summarized, which encompasses various signaling pathways, cell proliferations, and spatial configurations. Then, contemporary approaches are elucidated in tumor engineering that are guided by fundamental principles of tumor biology, and the potential of a bottom-up approach in tumor engineering is highlighted. Additionally, the characterization approaches and biomedical applications of tumor models are discussed, emphasizing the significant role of engineered tumor models in scientific research and clinical trials. Lastly, the challenges of heterogeneous tumor models in promoting oncology research and tumor therapy are described and key directions for future research are provided.

Cell Phase Identification in a Three-Dimensional Engineered Tumor Model by Infrared Spectroscopic Imaging

Cell cycle progression plays a vital role in regulating proliferation, metabolism, and apoptosis. Three-dimensional (3D) cell cultures have emerged as an important class of in vitro disease models, and incorporating the variation occurring from cell cycle progression in these systems is critical. Here, we report the use of Fourier transform infrared (FT-IR) spectroscopic imaging to identify subtle biochemical changes within cells, indicative of the G1/S and G2/M phases of the cell cycle. Following previous studies, we first synchronized samples from two-dimensional (2D) cell cultures, confirmed their states by flow cytometry and DNA quantification, and recorded spectra. We determined two critical wavenumbers (1059 and 1219 cm-1) as spectral indicators of the cell cycle for a set of isogenic breast cancer cell lines (MCF10AT series). These two simple spectral markers were then applied to distinguish cell cycle stages in a 3D cell culture model using four cell lines that represent the main stages of cancer progression from normal cells to metastatic disease. Temporal dependence of spectral biomarkers during acini maturation validated the hypothesis that the cells are more proliferative in the early stages of acini development; later stages of the culture showed stability in the overall composition but unique spatial differences in cells in the two phases. Altogether, this study presents a computational and quantitative approach for cell phase analysis in tissue-like 3D structures without any biomarker staining and provides a means to characterize the impact of the cell cycle on 3D biological systems and disease diagnostic studies using IR imaging.

Design Considerations for Discrete Frequency Infrared Microscopy Systems

Discrete frequency infrared chemical imaging is transforming the practice of microspectroscopy by enabling a diversity of instrumentation and new measurement capabilities. While a variety of hardware implementations have been realized, design considerations that are unique to infrared (IR) microscopes have not yet been compiled in literature. Here, we describe the evolution of IR microscopes, provide rationales for design choices, and catalog some major considerations for each of the optical components in an imaging system. We analyze design choices that use these components to optimize performance, under their particular constraints, while providing illustrative examples. We then summarize a framework to assess the factors that determine an instrument's performance mathematically. Finally, we provide a validation approach by enumerating performance metrics that can be used to evaluate the capabilities of imaging systems or suitability for specific intended applications. Together, the presented concepts and examples should aid in understanding available instrument configurations, while guiding innovations in design of the next generation of IR chemical imaging spectrometers.

Infrared spectral histopathology using haematoxylin and eosin (H&E) stained glass slides: a major step forward towards clinical translation

Infrared spectral histopathology has shown great promise as an important diagnostic tool, with the potential to complement current pathological methods. While promising, clinical translation has been hindered by the impracticalities of using infrared transmissive substrates which are both fragile and prohibitively very expensive. Recently, glass has been proposed as a potential replacement which, although largely opaque in the infrared, allows unrestricted access to the high wavenumber region (2500-3800 cm^-1). Recent studies using unstained tissue on glass have shown that despite utilising only the amide A band, good discrimination between histological classes could be achieved, and suggest the potential of discriminating between normal and malignant tissue. However unstained tissue on glass has the potential to disrupt the pathologist workflow, since it needs to be stained following infrared chemical imaging. In light of this, we report on the very first infrared Spectral Histopathology SHP study utilising coverslipped H&E stained tissue on glass using samples as received from the pathologist. In this paper we present a rigorous study using results obtained from an extended patient sample set consisting of 182 prostate tissue cores obtained from 100 different patients, on 18 separate H&E slides. Utilising a Random Forest classification model we demonstrate that we can rapidly classify four classes of histology of an independent test set with a high degree of accuracy (>90%). We investigate different degrees of staining using nine separate prostate serial sections, and demonstrate that we discriminate on biomarkers rather than the presence of the stain. Finally, using a four-class model we show that we can discriminate normal epithelium, malignant epithelium, normal stroma and cancer associated stroma with classification accuracies over 95%.

Mid-infrared spectral microimaging of inflammatory skin lesions

Skin is one of the most important organs of the human body because of its characteristics and functions. There are many alterations, either pathological or physiological, that can disturb its functioning. However, at present all methods used to investigate skin diseases, non-invasive or invasive, are based on clinical examinations by physicians. Thus, diagnosis, prognosis and therapeutic management rely on the expertise of the practitioner, the quality of the method and the accessibility of distinctive morphological characteristics of each lesion. To overcome the high sensitivity of these parameters, techniques based on more objective criteria must be explored. Vibrational spectroscopy has become as a key technique for tissue analysis in the biomedical research field. Based on a non-destructive light/matter interaction, this tool provides information about specific molecular structure and composition of the analyzed sample, thus relating to its precise physiopathological state and permitting to distinguish lesional from normal tissues. This label-free optical method can be performed directly on the paraffin-embedded tissue sections without chemical dewaxing. In this study, the potential of the infrared microspectroscopy, combined with data classification methods was demonstrated, to characterize at the tissular level different types of inflammatory skin lesions, and this independently from conventional histopathology.

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.

Quantum Cascade Laser Spectral Histopathology: Breast Cancer Diagnostics Using High Throughput Chemical Imaging

Fourier transform infrared (FT-IR) microscopy coupled with machine learning approaches has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the prediseased state and diagnose cancer with high levels of accuracy has the potential to revolutionize current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors utilizing discrete frequency imaging have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes. However, this ground breaking technology is still in its infancy, and its applicability for routine disease diagnosis is, as yet, unproven. In light of this, we report on a large study utilizing a breast cancer TMA comprised of 207 different patients. We show that by using QCL imaging with continuous spectra acquired between 912 and 1800 cm^-1, we can accurately differentiate between 4 different histological classes. We demonstrate that we can discriminate between malignant and nonmalignant stroma spectra with high sensitivity (93.56%) and specificity (85.64%) for an independent test set. Finally, we classify each core in the TMA and achieve high diagnostic accuracy on a patient basis with 100% sensitivity and 86.67% specificity. The absence of false negatives reported here opens up the possibility of utilizing high throughput chemical imaging for cancer screening, thereby reducing pathologist workload and improving patient care.

FTIR imaging of the molecular burden around Aβ deposits in an early-stage 3-Tg-APP-PSP1-TAU mouse model of Alzheimer's disease

High spatial resolution FTIR imaging of early-stage 3-Tg-APP-PSP1-TAU mouse brain identifies molecular burden around Aβ deposits.

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.

Application of mid-infrared (MIR) microscopy imaging for discrimination between follicular hyperplasia and follicular lymphoma in transgenic mice

Mid-infrared (MIR) microscopy imaging is a vibrational spectroscopic technique that uses infrared radiation to image molecules of interest in thin tissue sections. A major advantage of this technology is the acquisition of local molecular expression profiles, while maintaining the topographic integrity of the tissue. Therefore, this technology has become an essential tool for the detection and characterization of the molecular components of many biological processes. Using this method, it is possible to investigate the spatial distribution of proteins and small molecules within biological systems by in situ analysis. In this study, we have evaluated the potential of mid-infrared microscopy imaging to study biochemical changes which distinguish between reactive lymphadenopathy and cancer in genetically modified mice with different phenotypes. We were able to demonstrate that MIR microscopy imaging and multivariate image analyses of different mouse genotypes correlated well with the morphological tissue features derived from HE staining. Using principal component analyses, we were also able to distinguish spectral clusters from different phenotype samples, particularly from reactive lymphadenopathy (follicular hyperplasia) and cancer (follicular lymphoma).

Improving prediction of prostate cancer recurrence using chemical imaging

Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. Clinically-useful tools to predict risk of adverse events (metastases, recurrence), however, remain deficient. Here, we report an approach to predict the risk of prostate cancer recurrence, at the time of initial diagnosis, using a combination of emerging chemical imaging, a diagnostic protocol that focuses simultaneously on the tumor and its microenvironment, and data analysis of frequent patterns in molecular expression. Fourier transform infrared (FT-IR) spectroscopic imaging was employed to record the structure and molecular content from tumors prostatectomy. We analyzed data from a patient cohort that is mid-grade dominant – which is the largest cohort of patients in the modern era and in whom prognostic methods are largely ineffective. Our approach outperforms the two widely used tools, Kattan nomogram and CAPRA-S score in a head-to-head comparison for predicting risk of recurrence. Importantly, the approach provides a histologic basis to the prediction that identifies chemical and morphologic features in the tumor microenvironment that is independent of conventional clinical information, opening the door to similar advances in other solid tumors.

Label-free route to rapid, nanoscale characterization of cellular structure and dynamics through opaque media

We report a novel technique for label-free, rapid visualization of structure and dynamics of live cells with nanoscale sensitivity through traditionally opaque media. Specifically, by combining principles of near-infrared (NIR) spectroscopy and quantitative phase imaging, functional characterization of cellular structure and dynamics through silicon substrates is realized in our study. We demonstrate the efficacy of the new approach by full-field imaging of erythrocyte morphology in their native states with a nm path length sensitivity. Additionally, we observe dynamic variations of human embryonic kidney cells, through a silicon substrate, in response to hypotonic stimulation with ms temporal resolution that also provides unique insight into the underlying biophysical changes. The proposed technology is fundamentally suited for high-performance investigations of biological specimens and significantly expands the options for visualization in complex microfluidic devices fabricated on silicon.

Real-time interactive data mining for chemical imaging information: application to automated histopathology

BACKGROUND

Vibrational spectroscopic imaging is now used in several fields to acquire molecular information from microscopically heterogeneous systems. Recent advances have led to promising applications in tissue analysis for cancer research, where chemical information can be used to identify cell types and disease. However, recorded spectra are affected by the morphology of the tissue sample, making identification of chemical structures difficult.

RESULTS

Extracting features that can be used to classify tissue is a cumbersome manual process which limits this technology from wide applicability. In this paper, we describe a method for interactive data mining of spectral features using GPU-based manipulation of the spectral distribution.

CONCLUSIONS

This allows researchers to quickly identify chemical features corresponding to cell type. These features are then applied to tissue samples in order to visualize the chemical composition of the tissue without the use of chemical stains.

Fourier transform infrared microspectroscopy identifies protein propionylation in histone deacetylase inhibitor treated glioma cells

Histone deacetylase inhibitors (HDIs) have attracted considerable attention as potential drug molecules in tumour biology. In order to optimise chemotherapy, it is important to understand the mechanisms of regulation of histone deacetylase (HDAC) enzymes and modifications brought by various HDIs. In the present study, we have employed Fourier transform infrared microspectroscopy (FT-IRMS) to evaluate modifications in cellular macromolecules subsequent to treatment with various HDIs. In addition to CH(3) (methyl) stretching bands at 2872 and 2960 cm(-1) , which arises due to acetylation, we also found major changes in bands at 2851 and 2922 cm(-1) , which originates from stretching vibrations of CH(2) (methylene) groups, in valproic acid treated cells. We further demonstrate that the changes in CH(2) stretching are concentration-dependent and also induced by several other HDIs. Recently, HDIs have been shown to induce propionylation besides acetylation [1]. Since propionylation involves CH(2) groups, we hypothesized that CH(2) vibrational frequency changes seen in HDI treated cells could arise due to propionylation. As verification, pre-treatment of cells with propionyl CoA synthetase inhibitor resulted in loss of CH(2) vibrational changes in histones, purified from valproic acid treated cells. This was further proved by western blot using propionyl-lysine specific antibody. Thus we demonstrate for the first time that propionylation could be monitored by studying CH(2) stretching using IR spectroscopy and further provide a platform for monitoring HDI induced multiple changes in cells.

Label-free characterization of cancer-activated fibroblasts using infrared spectroscopic imaging

Glandular tumors arising in epithelial cells comprise the majority of solid human cancers. Glands are supported by stroma, which is activated in the proximity of a tumor. Activated stroma is often characterized by the molecular expression of α-smooth muscle actin (α-SMA) within fibroblasts. However, the precise spatial and temporal evolution of chemical changes in fibroblasts upon epithelial tumor signaling is poorly understood. Here we report a label-free method to characterize fibroblast changes by using Fourier transform infrared spectroscopic imaging and comparing spectra with α-SMA expression in primary normal human fibroblasts. We recorded the fibroblast activation process by spectroscopic imaging using increasingly tissue-like conditions: 1), stimulation with the growth factor TGFβ1; 2), coculture with MCF-7 human breast cancerous epithelial cells in Transwell coculture; and 3), coculture with MCF-7 in three-dimensional cell culture. Finally, we compared the spectral signatures of stromal transformation with normal and malignant human breast tissue biopsies. The results indicate that this approach reveals temporally complex spectral changes and thus provides a richer assessment than simple molecular imaging based on α-SMA expression. Some changes are conserved across culture conditions and in human tissue, providing a label-free method to monitor stromal transformations.

Infrared spectroscopic imaging of renal tumor tissue

Fourier transform infrared (FTIR) spectroscopic imaging has been used to probe the biochemical composition of human renal tumor tissue and adjacent normal tissue. Freshly resected renal tumor tissue from surgery was prepared as a thin cryosection and examined by FTIR spectroscopic imaging. Tissue types could be discriminated by utilizing a combination of fuzzy k-means cluster analysis and a supervised classification algorithm based on a linear discriminant analysis. The spectral classification is compared and contrasted with the histological stained image. It is further shown that renal tumor cells have spread in adjacent normal tissue. This study demonstrates that FTIR spectroscopic imaging can potentially serve as a fast and objective approach for discrimination of renal tumor tissue from normal tissue and even in the detection of tumor infiltration in adjacent tissue.

Subcellular localization of early biochemical transformations in cancer-activated fibroblasts using infrared spectroscopic imaging

The tumor microenvironment, or stroma, is chemically and morphologically modified during carcinoma progression. The predominant cell type in the stroma, the fibroblast, maintains collagen properties in normal tissue and often transformed during tumor progression. Biochemical changes within fibroblasts upon initial cancer activation, however, are relatively poorly defined. Here, we hypothesized that Fourier transform infrared (FT-IR) spectroscopic imaging could potentially be employed to examine these early transformations. Further, we employ attenuated total reflectance (ATR) microscopy to characterize subcellular spectra and their changes upon transformation. We characterized fibroblast transitions upon stimulation with both a molecular agent and a carcinoma-mimicking cellular co-culture system. Changes were predominantly observed in the 1080 cm(-1) and 1224 cm(-1) peak absorbance, commonly associated with nucleic acids, as well as in the band at 2930 cm(-1) associated with the C-H stretching of proteins in the cytoplasmic compartment. In conclusion, biochemical changes in cancer-associated fibroblasts that express α-SMA are dominated by the cytoplasm, rather than the nucleus. This ensures that spectral changes are not associated with proliferation or cell cycle processes of the cells and the cells are undergoing a true phenotypic change denoted by protein modifications in the cell body.

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.