Molecular pathology via IR and Raman spectral imaging

Potential use of MCR-ALS for the identification of coeliac-related biochemical changes in hyperspectral Raman maps from pediatric intestinal biopsies

Raman hyperspectral imaging is an emerging practice in biological and biomedical research for label free analysis of tissues and cells. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. The current study reports the first Raman microspectroscopic characterisation of colon tissues from patients with Coeliac Disease (CD). The aim was to assess if Raman imaging coupled with hyperspectral multivariate image analysis is capable of detecting the alterations in the biochemical composition of intestinal tissues associated with CD. The analytical approach was based on a multi-step methodology: duodenal biopsies from healthy and coeliac patients were measured and processed with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS). Based on the distribution maps and the pure spectra of the image constituents obtained from MCR-ALS, interesting biochemical differences between healthy and coeliac patients has been derived. Noticeably, a reduced distribution of complex lipids in the pericryptic space, and a different distribution and abundance of proteins rich in beta-sheet structures was found in CD patients. The output of the MCR-ALS analysis was then used as a starting point for two clustering algorithms (k-means clustering and hierarchical clustering methods). Both methods converged with similar results providing precise segmentation over multiple Raman images of studied tissues.

Integrated Fourier Transform Infrared Imaging and Proteomics for Identification of a Candidate Histochemical Biomarker in Bladder Cancer

Histopathological differentiation between severe urocystitis with reactive urothelial atypia and carcinoma in situ (CIS) can be difficult, particularly after a treatment that deliberately induces an inflammatory reaction, such as intravesical instillation of Bacillus Calmette-Guèrin. However, precise grading in bladder cancer is critical for therapeutic decision making and thus requires reliable immunohistochemical biomarkers. Herein, an exemplary potential biomarker in bladder cancer was identified by the novel approach of Fourier transform infrared imaging for label-free tissue annotation of tissue thin sections. Identified regions of interest are collected by laser microdissection to provide homogeneous samples for liquid chromatography-tandem mass spectrometry-based proteomic analysis. This approach afforded label-free spatial classification with a high accuracy and without interobserver variability, along with the molecular resolution of the proteomic analysis. Cystitis and invasive high-grade urothelial carcinoma samples were analyzed. Three candidate biomarkers were identified and verified by immunohistochemistry in a small cohort, including low-grade urothelial carcinoma samples. The best-performing candidate AHNAK2 was further evaluated in a much larger independent verification cohort that also included CIS samples. Reactive urothelial atypia and CIS were distinguishable on the basis of the expression of this newly identified and verified immunohistochemical biomarker, with a sensitivity of 97% and a specificity of 69%. AHNAK2 can differentiate between reactive urothelial atypia in the setting of an acute or chronic cystitis and nonmuscle invasive-type CIS.

Resolving Interobserver Discrepancies in Lung Cancer Diagnoses by Spectral Histopathology

This paper reports the results of a collaborative lung cancer study between City of Hope Cancer Center (Duarte, California) and CIRECA, LLC (Cambridge, Massachusetts), comprising 328 samples from 249 patients, that used an optical technique known as spectral histopathology (SHP) for tissue classification. Because SHP is based on a physical measurement, it renders diagnoses on a more objective and reproducible basis than methods based on assessing cell morphology and tissue architecture. This report demonstrates that SHP provides distinction of adenocarcinomas from squamous cell carcinomas of the lung with an accuracy comparable to that of immunohistochemistry and highly reliable classification of adenosquamous carcinoma. Furthermore, this report shows that SHP can be used to resolve interobserver differences in lung pathology. Spectral histopathology is based on the detection of changes in biochemical composition, rather than morphologic features, and is therefore more akin to methods such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry imaging. Both matrix-assisted laser desorption ionization time-of-flight mass spectrometry and SHP imaging modalities demonstrate that changes in tissue morphologic features observed in classical pathology are accompanied by, and may be correlated to, changes in the biochemical composition at the cellular level. Thus, these imaging methods provide novel insight into biochemical changes due to disease.

Determination of meningioma brain tumour grades using Raman microspectroscopy imaging

Raman microspectroscopy imaging was used to distinguish 90 brain tissue samples into meningiomas Grade I and Grade II.

Virtual staining of colon cancer tissue by label-free Raman micro-spectroscopy

The great capability of the label-free classification of tissue via vibrational spectroscopy, like Raman or infrared imaging, is shown in numerous publications (review: Diem et al., J. Biophotonics, 2013, 6, 855-886). Herein, we present a new approach, virtual staining, that improves the Raman spectral histopathology (SHP) images of colorectal cancer tissue by combining the integrated Raman intensity image in the C-H stretching region (2800-3050 cm^-1) with the pseudo-colour Raman image. This allows the display of fine structures such as the filamentous composition of muscle tissue. The morphology of the virtually stained images is in agreement with the gold standard in medical diagnosis, the haematoxylin-eosin staining. The virtual staining image also represents the whole biochemical fingerprint, and several tissue components including carcinoma were identified automatically with high sensitivity and specificity. For fast tissue classifications, a similar approach was applied on coherent anti-Stokes Raman scattering (CARS) spectral data that is faster and therefore potentially more suitable for clinical applications.

Spectroscopic analysis of normal and neoplastic (WI-FTC) thyroid tissue

Thyroid cancer holds the first place of the malignant tumors of the endocrine system. One of the less common thyroid cancers is follicular thyroid carcinoma (FTC), which is very difficult to diagnose because it gives the same image as adenoma, which is benign. Certainty of the diagnosis is gained only when FTC gives metastases. Therefore, it was decided to compare normal and neoplastic (FTC) thyroid tissues with Fourier Transform Infrared (FTIR) spectroscopy. The obtained FTIR spectra and Principal Component Analysis (PCA) allowed us to conclude that there are differences in the FTIR spectrum between normal tissues and those affected by cancer. In addition, the results indicate that there is a decrease in the number of functional groups that build cellular and tissue structures in tumoral tissues. The shifts of wave numbers corresponding to the protein and lipid function group vibrations, as well as the calculated second derivative of the FTIR spectra showed the structural changes in neoplastic tissues. Moreover, the deconvolution of the amide I massif indicates that in cancerous tissues the prevailing secondary structure is β-sheet structure, while in normal tissues it is α-helix. The obtained results allow us to conclude that infrared spectroscopy, in addition to providing information on the composition of tested samples, can be an excellent diagnostic tool contributing to understanding the FTC substrate.

Development of a memetic clustering algorithm for optimal spectral histology: application to FTIR images of normal human colon

The coupling between Fourier-transform infrared (FTIR) imaging and unsupervised classification is effective in revealing the different structures of human tissues based on their specific biomolecular IR signatures; thus the spectral histology of the studied samples is achieved. However, the most widely applied clustering methods in spectral histology are local search algorithms, which converge to a local optimum, depending on initialization. Multiple runs of the techniques estimate multiple different solutions. Here, we propose a memetic algorithm, based on a genetic algorithm and a k-means clustering refinement, to perform optimal clustering. In addition, this approach was applied to the acquired FTIR images of normal human colon tissues originating from five patients. The results show the efficiency of the proposed memetic algorithm to achieve the optimal spectral histology of these samples, contrary to k-means.

Biochemical fingerprint of colorectal cancer cell lines using label-free live single-cell Raman spectroscopy

Label-free live single-cell Raman spectroscopy was used to obtain a chemical fingerprint of colorectal cancer cells including the classification of the SW480 and SW620 cell line model system, derived from primary and secondary tumour cells from the same patient. High-quality Raman spectra were acquired from hundreds of live cells, showing high reproducibility between experiments. Principal component analysis with linear discriminant analysis yielded the best cell classification, with an accuracy of 98.7 ± 0.3% (standard error) when compared with discrimination trees or support vector machines. SW480 showed higher content of the disordered secondary protein structure Amide III band, whereas SW620 showed larger α-helix and β-sheet band content. The SW620 cell line also displayed higher nucleic acid, phosphates, saccharide, and CH2 content. HL60, HT29, HCT116, SW620, and SW480 live single-cell spectra were classified using principal component analysis or linear discriminant analysis with an accuracy of 92.4 ± 0.4% (standard error), showing differences mainly in the β-sheet content, the cytochrome C bands, the CH-stretching regions, the lactate contributions, and the DNA content. The lipids contributions above 2,900 cm-1 and the lactate contributions at 1,785 cm-1 appeared to be dependent on the colorectal adenocarcinoma stage, the advanced stage cell lines showing lower lipid, and higher lactate content. The results demonstrate that these cell lines can be distinguished with high confidence, suggesting that Raman spectroscopy on live cells can distinguish between different disease stages, and could play an important role clinically as a diagnostic tool for cell phenotyping.

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

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.

A study of hormonal effects in cervical smear samples using Raman spectroscopy

Raman spectroscopy is a powerful tool that has the potential to be used as a screening method for cervical cancer. It is a label-free, low-cost method providing a biochemical fingerprint of a given sample. The objective of this study was to address patient-to-patient variability contributed by hormonal effects due to the menstrual cycle, the use of hormone-based contraceptives (HC) and the onset of menopause, and to determine if these changes would affect the ability to successfully identify dyskaryotic cells. Raman spectra were recorded from unstained ThinPrep cervical samples (45 cytology negative and 15 high-grade dyskaryosis (high-grade squamous intraepithelial lesion, HSIL) samples using a HORIBA Jobin Yvon XploRA system. HPV DNA testing was also performed. Clinical data collected included date of the last menstrual period, the use of HC and/or menopausal status. Spectral changes were observed depending on the day of the menstrual cycle and on the use of HC. Despite this, HSIL could be discriminated from normal cells regardless of the day on which the sample was taken or the use of HC.

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Raman spectroscopy for cytopathology of exfoliated cervical cells

Cervical cancer is the fourth most common cancer affecting women worldwide but mortality can be decreased by early detection of pre-malignant lesions. The Pap smear test is the most commonly used method in cervical cancer screening programmes. Although specificity is high for this test, it is widely acknowledged that sensitivity can be poor mainly due to the subjective nature of the test. There is a need for new objective tests for the early detection of pre-malignant cervical lesions. Over the past two decades, Raman spectroscopy has emerged as a promising new technology for cancer screening and diagnosis. The aim of this study was to evaluate the potential of Raman spectroscopy for cervical cancer screening using both Cervical Intraepithelial Neoplasia (CIN) and Squamous Intraepithelial Lesion (SIL) classification terminology. ThinPrep® Pap samples were recruited from a cervical screening population. Raman spectra were recorded from single cell nuclei and subjected to multivariate statistical analysis. Normal and abnormal ThinPrep® samples were discriminated based on the biochemical fingerprint of the cells using Principal Component Analysis (PCA). Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) was employed to build classification models based on either CIN or SIL terminology. This study has shown that Raman spectroscopy can be successfully applied to the study of routine cervical cytology samples from a cervical screening programme and that the use of CIN terminology resulted in improved sensitivity for high grade cases.

Infrared micro-spectroscopy of human tissue: principles and future promises

This article summarizes the methods employed, and the progress achieved over the past two decades in applying vibrational (Raman and IR) micro-spectroscopy to problems of medical diagnostics and cellular biology. During this time, several research groups have verified the enormous information contained in vibrational spectra; in fact, information on protein, lipid and metabolic composition of cells and tissues can be deduced by decoding the observed vibrational spectra. This decoding process is aided by the availability of computer workstations and advanced algorithms for data analysis. Furthermore, commercial instrumentation for the fast collection of both Raman and infrared micro-spectral data has enabled the collection of images of cells and tissues based solely on vibrational spectroscopic data. The progress in the field has been manifested by a steady increase in the number and quality of publications submitted by established and new research groups in vibrational spectroscopy in the biological and biomedical arenas.

Clinical applications of infrared and Raman spectroscopy: state of play and future challenges

This review examines the state-of-the-art of clinical applications of infrared absorption and Raman spectroscopy, outstanding challenges, and progress towards translation.

Prospective on using fibre mid-infrared supercontinuum laser sources for in vivo spectral discrimination of disease

Mid-infrared (MIR) fibre-optics may play a future role in in vivo diagnosis of disease, including cancer.

Optical Analysis of Glioma: Fourier-Transform Infrared Spectroscopy Reveals the IDH1 Mutation Status

Purpose: Somatic mutations in the human cytosolic isocitrate dehydrogenase 1 (IDH1) gene cause profound changes in cell metabolism and are a common feature of gliomas with unprecedented predictive and prognostic impact. Fourier-transform infrared (FT-IR) spectroscopy addresses the molecular composition of cells and tissue and was investigated to deduct the IDH1 mutation status.Experimental Design: We tested the technique on human cell lines that were transduced with wild-type IDH1 or mutated IDH1 and on 34 human glioma samples. IR spectra were acquired at 256 positions from cell pellets or tissue cryosections. Moreover, IR spectra were obtained from fresh, unprocessed biopsies of 64 patients with glioma.Results:IDH1 mutation was linked to changes in spectral bands assigned to molecular groups of lipids and proteins in cell lines and human glioma. The spectra of cryosections of brain tumor samples showed high interpatient variability, for example, bands related to calcifications at 1113 cm^-1 However, supervised classification recognized relevant spectral regions at 1103, 1362, 1441, 1485, and 1553 cm^-1 and assigned 88% of the tumor samples to the correct group. Similar spectral positions allowed the classification of spectra of fresh biopsies with an accuracy of 86%.Conclusions: Here, we show that vibrational spectroscopy reveals the IDH1 genotype of glioma. Because it can provide information in seconds, an implementation into the intraoperative workflow might allow simple and rapid online diagnosis of the IDH1 genotype. The intraoperative confirmation of IDH1 mutation status might guide the decision to pursue definitive neurosurgical resection and guide future in situ therapies of infiltrative gliomas. Clin Cancer Res; 24(11); 2530-8. ©2017 AACRSee related commentary by Hollon and Orringer, p. 2467.

Position-resolved Raman spectra from a laser-trapped single airborne chemical droplet

It could be very useful to detect and monitor the molecules and molecular reactions located at different positions within a microsized particle as they respond to various micro-local environments. In this Letter, a particular optical trap using two focusing counterpropagating hollow beams was able to stably trap both absorbing and nonabsorbing particles in air for lengthy observation. A technique that can measure the Raman spectra from different submicrometer positions of a laser-trapped single airborne particle was developed. Spontaneous and stimulated Raman scattering spectra originating from different positions of a diethyl phthalate droplet were recorded, and the strong Raman scattering signals are the result of cavity-enhanced effects and the localized strong light illumination.

The Dual Role of Cellular Senescence in Developing Tumors and Their Response to Cancer Therapy

Cellular senescence describes an irreversible growth arrest characterized by distinct morphology, gene expression pattern, and secretory phenotype. The final or intermediate stages of senescence can be reached by different genetic mechanisms and in answer to different external and internal stresses. It has been maintained in the literature but never proven by clearcut experiments that the induction of senescence serves the evolutionary purpose of protecting the individual from development and growth of cancers. This hypothesis was recently scrutinized by new experiments and found to be partly true, but part of the gene activities now known to happen in senescence are also needed for cancer growth, leading to the view that senescence is a double-edged sword in cancer development. In current cancer therapy, cellular senescence is, on the one hand, intended to occur in tumor cells, as thereby the therapeutic outcome is improved, but might, on the other hand, also be induced unintentionally in non-tumor cells, causing inflammation, secondary tumors, and cancer relapse. Importantly, organismic aging leads to accumulation of senescent cells in tissues and organs of aged individuals. Senescent cells can occur transiently, e.g., during embryogenesis or during wound healing, with beneficial effects on tissue homeostasis and regeneration or accumulate chronically in tissues, which detrimentally affects the microenvironment by de- or transdifferentiation of senescent cells and their neighboring stromal cells, loss of tissue specific functionality, and induction of the senescence-associated secretory phenotype, an increased secretory profile consisting of pro-inflammatory and tissue remodeling factors. These factors shape their surroundings toward a pro-carcinogenic microenvironment, which fuels the development of aging-associated cancers together with the accumulation of mutations over time. We are presenting an overview of well-documented stress situations and signals, which induce senescence. Among them, oncogene-induced senescence and stress-induced premature senescence are prominent. New findings about the role of senescence in tumor biology are critically reviewed with respect to new suggestions for cancer therapy leveraging genetic and pharmacological methods to prevent senescence or to selectively kill senescent cells in tumors.

Macromolecular Profiling of Organelles in Normal Diploid and Cancer Cells

To advance an understanding of cellular regulation and function it is crucial to identify molecular contents in cellular organelles, which accommodate specific biochemical processes. Toward achievement of this goal, we applied micro-Raman-Biomolecular Component Analysis assay for molecular profiling of major organelles in live cells. We used this assay for comparative analysis of proteins 3D conformation and quantification of proteins, RNA, and lipids concentrations in nucleoli, endoplasmic reticulum, and mitochondria of WI 38 diploid lung fibroblasts and HeLa cancer cells. Obtained data show substantial differences in the concentrations and conformations of proteins in the studied organelles. Moreover, differences in the intraorganellar concentrations of RNA and lipids between these cell lines were found. We report the biological significance of obtained macromolecular profiles and advocate for micro-Raman BCA assay as a valuable proteomics tool.

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

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

Label-free molecular imaging of the kidney

In this review, we will highlight technologies that enable scientists to study the molecular characteristics of tissues and/or cells without the need for antibodies or other labeling techniques. Specifically, we will focus on matrix-assisted laser desorption/ionization imaging mass spectrometry, infrared spectroscopy, and Raman spectroscopy.

An Automated Algorithm of Peak Recognition Based on Continuous Wavelet Transformation and Local Signal-to-Noise Ratio

Raman peaks carry valuable information about constituent chemical bonds. Therefore, peak recognition is a very essential part of spectral analysis. The fully automated peak recognition is convenient in practical application. A fully automated Raman peaks recognition algorithm based on continuous wavelet transformation and local signal-to-noise ratio (LSNR) is proposed. This algorithm extracts feature points through continuous wavelet transformation and recognizes peaks through LSNR. This algorithm also can be used to eliminate spike, noise, and baseline. Both simulated and experimental data are used to evaluate the performance of the CWT-LSNR algorithm compared with the other two algorithms. The results show that CWT-LSNR gives better accuracy and has the advantage of easy use.

Raman fiber-optical method for colon cancer detection: Cross-validation and outlier identification approach

Endoscopy plays a major role in early recognition of cancer which is not externally accessible and therewith in increasing the survival rate. Raman spectroscopic fiber-optical approaches can help to decrease the impact on the patient, increase objectivity in tissue characterization, reduce expenses and provide a significant time advantage in endoscopy. In gastroenterology an early recognition of malign and precursor lesions is relevant. Instantaneous and precise differentiation between adenomas as precursor lesions for cancer and hyperplastic polyps on the one hand and between high and low-risk alterations on the other hand is important. Raman fiber-optical measurements of colon biopsy samples taken during colonoscopy were carried out during a clinical study, and samples of adenocarcinoma (22), tubular adenomas (141), hyperplastic polyps (79) and normal tissue (101) from 151 patients were analyzed. This allows us to focus on the bioinformatic analysis and to set stage for Raman endoscopic measurements. Since spectral differences between normal and cancerous biopsy samples are small, special care has to be taken in data analysis. Using a leave-one-patient-out cross-validation scheme, three different outlier identification methods were investigated to decrease the influence of systematic errors, like a residual risk in misplacement of the sample and spectral dilution of marker bands (esp. cancerous tissue) and therewith optimize the experimental design. Furthermore other validations methods like leave-one-sample-out and leave-one-spectrum-out cross-validation schemes were compared with leave-one-patient-out cross-validation. High-risk lesions were differentiated from low-risk lesions with a sensitivity of 79%, specificity of 74% and an accuracy of 77%, cancer and normal tissue with a sensitivity of 79%, specificity of 83% and an accuracy of 81%. Additionally applied outlier identification enabled us to improve the recognition of neoplastic biopsy samples.

A machine learning framework to analyze hyperspectral stimulated Raman scattering microscopy images of expressed human meibum

We develop and discuss a methodology for batch-level analysis of hyperspectral stimulated Raman scattering (hsSRS) data sets of human meibum in the CH-stretching vibrational range. The analysis consists of two steps. The first step uses a training set (n=19) to determine chemically meaningful reference spectra that jointly constitute a basis set for the sample. This procedure makes use of batch-level vertex component analysis (VCA), followed by unsupervised k-means clustering to express the data set in terms of spectra that represent lipid and protein mixtures in changing proportions. The second step uses a random forest classifier to rapidly classify hsSRS stacks in terms of the pre-determined basis set. The overall procedure allows a rapid quantitative analysis of large hsSRS data sets, enabling a direct comparison among samples using a single set of reference spectra. We apply this procedure to assess 50 specimens of expressed human meibum, rich in both protein and lipid, and show that the batch-level analysis reveals marked variation among samples that potentially correlate with meibum health quality.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics

Diffuse malignant mesothelioma (DMM) is a heterogeneous malignant neoplasia manifesting with three subtypes: epithelioid, sarcomatoid and biphasic. DMM exhibit a high degree of spatial heterogeneity that complicates a thorough understanding of the underlying different molecular processes in each subtype. We present a novel approach to spatially resolve the heterogeneity of a tumour in a label-free manner by integrating FTIR imaging and laser capture microdissection (LCM). Subsequent proteome analysis of the dissected homogenous samples provides in addition molecular resolution. FTIR imaging resolves tumour subtypes within tissue thin-sections in an automated and label-free manner with accuracy of about 85% for DMM subtypes. Even in highly heterogeneous tissue structures, our label-free approach can identify small regions of interest, which can be dissected as homogeneous samples using LCM. Subsequent proteome analysis provides a location specific molecular characterization. Applied to DMM subtypes, we identify 142 differentially expressed proteins, including five protein biomarkers commonly used in DMM immunohistochemistry panels. Thus, FTIR imaging resolves not only morphological alteration within tissue but it resolves even alterations at the level of single proteins in tumour subtypes. Our fully automated workflow FTIR-guided LCM opens new avenues collecting homogeneous samples for precise and predictive biomarkers from omics studies.

Determining Gender by Raman Spectroscopy of a Bloodstain

The development of novel methods for forensic science is a constantly growing area of modern analytical chemistry. Raman spectroscopy is one of a few analytical techniques capable of nondestructive and nearly instantaneous analysis of a wide variety of forensic evidence, including body fluid stains, at the scene of a crime. In this proof-of-concept study, Raman microspectroscopy was utilized for gender identification based on dry bloodstains. Raman spectra were acquired in mapping mode from multiple spots on a bloodstain to account for intrinsic sample heterogeneity. The obtained Raman spectroscopic data showed highly similar spectroscopic features for female and male blood samples. Nevertheless, support vector machines (SVM) and artificial neuron network (ANN) statistical methods applied to the spectroscopic data allowed for differentiating between male and female bloodstains with high confidence. More specifically, the statistical approach based on a genetic algorithm (GA) coupled with an ANN classification showed approximately 98% gender differentiation accuracy for individual bloodstains. These results demonstrate the great potential of the developed method for forensic applications, although more work is needed for method validation. When this method is fully developed, a portable Raman instrument could be used for the infield identification of traces of body fluids and to obtain phenotypic information about the donor, including gender and race, as well as for the analysis of a variety of other types of forensic evidence.

Developments in spontaneous and coherent Raman scattering microscopic imaging for biomedical applications

First, the potential role of Raman-based techniques in biomedicine is introduced. Second, an overview about the instrumentation for spontaneous and coherent Raman scattering microscopic imaging is given with a focus of recent developments. Third, imaging strategies are summarized including sequential registration with laser scanning microscopes, line imaging and global or wide-field imaging. Finally, examples of biomedical applications are presented in the context of single cells, laser tweezers, tissue sections, biopsies and whole animals.

Micro-Raman spectroscopic study of thyroid tissues

Thyroid carcinomas are the most common endocrine malignancy. Inconclusive results for the analysis of malignancies are an issue in the diagnosis of thyroid carcinomas; 20% of thyroid cancer diagnoses are indeterminate or suspicious, resulting in a surgical procedure without immediate need. The use of Raman spectroscopy may help improve the diagnosis of thyroid carcinoma. In this study, 30 thyroid samples, including normal thyroid, goiter and thyroid cancer, were analyzed by confocal Raman spectroscopy. Principal component analysis (PCA), linear discriminant analysis (LDA) with cross validation and binary logistic regression (BLR) analysis were applied to discriminate among tissues. Significant discrimination was observed, with a consistent rate of concordant pairs of 89.2% for normal thyroid versus cancer, 85.7% for goiter versus cancer and 80.6% for normal thyroid versus goiter using just the amide III region. Raman spectroscopy was thus proven to be an important and fast tool for the diagnosis of thyroid tissues. The spectral region of 1200-1400cm^-1 discriminated normal versus goiter tissues despite the great similarity of these tissues.

Screening the low molecular weight fraction of human serum using ATR-IR spectroscopy

Vibrational spectroscopic techniques can detect small variations in molecular content, linked with disease, showing promise for screening and early diagnosis. Biological fluids, particularly blood serum, are potentially valuable for diagnosis purposes. The so-called Low Molecular Weight Fraction (LMWF) contains the associated peptidome and metabolome and has been identified as potentially the most relevant molecular population for disease-associated biomarker research. Although vibrational spectroscopy can deliver a specific chemical fingerprint of the samples, the High Molecular Weight Fraction (HMWF), composed of the most abundant serum proteins, strongly dominates the response and ultimately makes the detection of minor spectral variations a challenging task. Spectroscopic detection of potential serum biomarkers present at relatively low concentrations can be improved using pre-analytical depletion of the HMWF. In the present study, human serum fractionation by centrifugal filtration was used prior to analysis by Attenuated Total Reflection infrared spectroscopy. Using a model sample based on glycine spiked serum, it is demonstrated that the screening of the LMWF can be applied to quantify blinded concentrations up to 50 times lower. Moreover, the approach is easily transferable to different bodily fluids which would support the development of more efficient and suitable clinical protocols exploring vibrational spectroscopy based ex-vivo diagnostic tools. Revealing serum LMWF for spectral serological diagnostic applications.

Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool

Background

Due to the steadily increasing number of cancer patients worldwide the early diagnosis and treatment of cancer is a major field of research. The diagnosis of cancer is mostly performed by an experienced pathologist via the visual inspection of histo-pathological stained tissue sections. To save valuable time, low quality cryosections are frequently analyzed with diagnostic accuracies that are below those of high quality embedded tissue sections. Thus, alternative means have to be found that enable for fast and accurate diagnosis as the basis of following clinical decision making.

Methods

In this contribution we will show that the combination of the three label-free non-linear imaging modalities CARS (coherent anti-Stokes Raman-scattering), TPEF (two-photon excited autofluorescence) and SHG (second harmonic generation) yields information that can be translated into computational hematoxylin and eosin (HE) images by multivariate statistics. Thereby, a computational HE stain is generated resulting in pseudo-HE overview images that allow for identification of suspicious regions. The latter are analyzed further by Raman-spectroscopy retrieving the tissue’s molecular fingerprint.

Results

The results suggest that the combination of non-linear multimodal imaging and Raman-spectroscopy possesses the potential as a precise and fast tool in routine histopathology.

Conclusions

As the key advantage, both optical methods are non-invasive enabling for further pathological investigations of the same tissue section, e.g. a direct comparison with the current pathological gold-standard.

Biochemical fingerprints of human papillomavirus infection and cervical dysplasia using cervical fluids: Spectral pattern investigation

The Pap smear is the primary screening tool for invasive cervical cancer resulting from a persistent infection with oncogenic human papillomavirus (HPV); however, there are the problems such as the inability to distinguish between HPV infection and cervical dysplasia and a low sensitivity remain. We present preliminary findings of a label-free method to detect and classify HPV infection and cervical dysplasia using human cervical fluids. Three experimental groups, defined as normal, HPV-positive, and cervical dysplasia, were evaluated through their Raman spectral patterns for noise-independence, high reproducibility, and uniformity. Clinical diagnosis was performed through liquid-based cervical cytology, HPV test, and cervical histologic examination. Healthy cervical fluids showed a strong Raman intensity at 877 cm^-1 (symmetric C-C stretching), and at 963 cm^-1 (phosphate), compared to a reference Raman peak at 1003 cm^-1 (phenylalanine symmetric ring breath). The HPV-positive cervical fluids showed a strong intensity of a Raman peak at 1448 cm^-1 corresponding to C-H deformation vibration mode and the highest similarity between the central and ring zones among the three groups. The cervical dysplasia fluids showed the presence of strong peaks compared to the control and HPV-positive groups. In addition, different Raman spectra were acquired according to HPV type. Therefore, all ranges of cervical fluid-induced Raman spectra could be used to detect the presence of cervical pre-cancer. Raman peak-gated assessment provides a label-free and nondestructive tool for the clinical diagnosis of HPV infection and cervical precancerous changes.

Chemico-mechanical imaging of Barrett's oesophagus

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

Label-free and non-invasive discrimination of HaCaT and melanoma cells in a co-culture model by hyperspectral confocal reflectance microscopy

A novel hyperspectral confocal microscopy method to separate different cell populations in a co-culture model is presented here. The described methodological and instrumental approach allows discrimination of different cell types using a non-invasive, label free method with good accuracy with a single cell resolution. In particular, melanoma cells are discriminated from HaCaT cells by hyperspectral confocal imaging, principal component analysis and optical frequencies signing, as confirmed by fluorescence labelling cross check. The identification seems to be quite robust to be insensitive to the cellular shape within the studied samples, enabling to separate cells according to their cytotype down to a single cell sensitivity. Set of hyperspectral images of melanoma-keratinocytes co-culture model (left), score plot of principal component analysis and spectral analysis of principal components coefficients (center), label-free spectral identification of cell populations (right).

Systematic evaluation of the biological variance within the Raman based colorectal tissue diagnostics

Being among the most common cancers worldwide screening and early diagnosis of colorectal cancer is of high interest for the health system, the patients and for research. Raman microspectroscopy as a label-free, non-invasive and non-destructive technique is a promising tool for an early diagnosis. However, to ensure a reliable diagnosis specially designed statistical analysis workflows are required. Several statistical approaches have been introduced leading to varying results in the overall accuracy, sensitivity and specificity. In this study a systematic evaluation of different statistical analysis approaches has been performed using a colon cancer mouse model with genotypic identical individuals. Based on the inter-individual Raman spectral variances a measure for the biological variance can be estimated. By applying a leave-one-individual-out cross-validation a clinically relevant discrimination of healthy tissue versus adenoma and carcinoma with an accuracy of 95% is shown. Furthermore, the transfer of a model from tissue to biopsy specimen is demonstrated.

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.

Raman-based microarray readout: a review

For a quarter of a century, microarrays have been part of the routine analytical toolbox. Label-based fluorescence detection is still the commonest optical readout strategy. Since the 1990s, a continuously increasing number of label-based as well as label-free experiments on Raman-based microarray readout concepts have been reported. This review summarizes the possible concepts and methods and their advantages and challenges. A common label-based strategy is based on the binding of selective receptors as well as Raman reporter molecules to plasmonic nanoparticles in a sandwich immunoassay, which results in surface-enhanced Raman scattering signals of the reporter molecule. Alternatively, capture of the analytes can be performed by receptors on a microarray surface. Addition of plasmonic nanoparticles again leads to a surface-enhanced Raman scattering signal, not of a label but directly of the analyte. This approach is mostly proposed for bacteria and cell detection. However, although many promising readout strategies have been discussed in numerous publications, rarely have any of them made the step from proof of concept to a practical application, let alone routine use. Graphical Abstract Possible realization of a SERS (Surface-Enhanced Raman Scattering) system for microarray readout.

Infrared imaging of small molecules in living cells: from in vitro metabolic analysis to cytopathology

A major topic in InfraRed (IR) spectroscopic studies of living cells is the complexity of the vibrational spectra, involving hundreds of overlapping absorption bands from all the cellular components present at detectable concentrations. We focus on the relative contribution of both small-molecule metabolites and macromolecules, while defining the spectroscopic properties of cells and tissue in the middle IR (midIR) region. As a consequence, we show the limitations of current interpretative schemes that rely on a small number of macromolecules for IR band assignment. The discussion is framed specifically around the glycolytic metabolism of cancer cells because of the potential pharmacological applications. Several metabolites involved in glycolysis by A549 lung cancer cells can be identified by this approach, which we refer to as Correlated Cellular Spectro-Microscopy (CSM). It is noteworthy that the rate of formation or consumption of specific molecules could be quantitatively assessed by this approach. We now extend this analysis to the two-dimensional case by performing IR imaging on single cells and cell clusters, detecting variations of metabolite concentration in time and space across the sample. The molecular detail obtained from this analysis allows its use in evaluating the pharmacological effect of inhibitors of glycolytic enzymes with potential consequences for in vitro drug testing. Finally we highlight the implications of the spectral contribution from cellular metabolites on applications in IR spectral cytopathology (SCP).

Label-free classification of colon cancer grading using infrared spectral histopathology

In recent years spectral histopathology (SHP) has been established as a label-free method to identify cancer within tissue. Herein, this approach is extended. It is not only used to identify tumour tissue with a sensitivity of 94% and a specificity of 100%, but in addition the tumour grading is determined. Grading is a measure of how much the tumour cells differ from the healthy cells. The grading ranges from G1 (well-differentiated), to G2 (moderately differentiated), G3 (poorly differentiated) and in rare cases to G4 (anaplastic). The grading is prognostic and is needed for the therapeutic decision of the clinician. The presented results show good agreement between the annotation by SHP and by pathologists. A correlation matrix is presented, and the results show that SHP provides prognostic values in colon cancer, which are obtained in a label-free and automated manner. It might become an important automated diagnostic tool at the bedside in precision medicine.

Recent advances in optical diagnosis of oral cancers: Review and future perspectives

Optical diagnosis techniques offer several advantages over traditional approaches, including objectivity, speed, and cost, and these label-free, noninvasive methods have the potential to change the future workflow of cancer management. The oral cavity is particularly accessible and, thus, such methods may serve as alternate/adjunct tools to traditional methods. Recently, in vivo human clinical studies have been initiated with a view to clinical translation of such technologies. A comprehensive review of optical methods in oral cancer diagnosis is presented. After an introduction to the epidemiology and etiological factors associated with oral cancers currently used, diagnostic methods and their limitations are presented. A thorough review of fluorescence, infrared absorption, and Raman spectroscopic methods in oral cancer diagnosis is presented. The applicability of minimally invasive methods based on serum/saliva is also discussed. The review concludes with a discussion on future demands and scope of developments from a clinical point of view. © 2015 Wiley Periodicals, Inc. Head Neck 38: E2403-E2411, 2016.