Rapid acquisition of Raman spectral maps through minimal sampling: applications in tissue imaging

Intraoperative spectroscopic evaluation of sentinel lymph nodes in breast cancer surgery

BACKGROUND AND OBJECTIVES

Sentinel lymph node (SLN) biopsy is a standard procedure for patients with breast cancer and normal axilla on imaging. Positive SLNs on histological examination can lead to a subsequent surgery for axillary lymph node clearance (ALNC). Here we report a non-destructive technique based on autofluorescence (AF) imaging and Raman spectroscopy for intra-operative assessment of SLNs excised in breast cancer surgery.

METHODS

A microscope integrating AF imaging and Raman spectroscopy modules was built to allow scanning of lymph node biopsy samples. During AF-Raman measurements, AF imaging determined optimal sampling locations for Raman spectroscopy measurements. After optimisation of the AF image analysis and training of classification models based on data from 85 samples, the AF-Raman technique was tested on an independent set of 81 lymph nodes comprising 58 fixed and 23 fresh specimens. The sensitivity and specificity of AF-Raman were calculated using post-operative histology as a standard of reference.

RESULTS

The independent test set contained 66 negative lymph nodes and 15 positive lymph nodes according to the reference standard, collected from 78 patients. For this set of specimens, the area under the receiver operating characteristic (ROC) curve for the AF-Raman technique was 0.93 [0.83-0.98]. AF-Raman was then operated in a regime that maximised detection specificity, producing a 94% detection accuracy: 80% sensitivity and 97% specificity. The main confounders for SLN metastasis were areas rich in histiocytes clusters, for which only few Raman spectra had been included in the training dataset.

DISCUSSION

This preliminary study indicates that with further development and extension of the training dataset by inclusion of additional Raman spectra of histiocytes clusters and capsule, the AF-Raman may become a promising technique for intra-operative assessment of SLNs. Intra-operative detection of positive biopsies could avoid second surgery for axillary clearance.

On-the-fly Raman microscopy guaranteeing the accuracy of discrimination

Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back "optimal" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis.

Rapid Intracellular Detection and Analysis of Lipid Droplets' Morpho-Chemical Composition by Phase-Guided Raman Sampling

Intracellular lipid droplets (LDs) are dynamic, complex organelles involved in nearly all aspects of cellular metabolism. In situ characterization methods are primarily limited to fluorescence imaging, which yields limited chemical information, or Raman spectroscopy, which provides excellent chemical profiling but very low throughput. Here, we propose a new paradigm where locations of both large and small droplets are obtained automatically from high-resolution phase images and fed into a galvomirror-controlled Raman sampling arm to obtain the full spectrum of each LD efficiently. Using this phase-guided Raman sampling, we can characterize hundreds of LDs within a single cell in minutes and easily acquire more than 40,000 high-quality spectra. The data set revealed strong, cell line-dependent, cell-dependent, and individual droplet-dependent composition changes to various culture conditions. In particular, we revealed a strong competitive relationship between mono- and polyunsaturated fatty acids, where supplementation with one led to a relative decrease in the other.

Quantification of Drugs in Brain and Liver Mimetic Tissue Models Using Raman Spectroscopy

Quantitative analysis of drug delivery with in biological systems is an integral challenge in drug development. Analytical techniques are important for assessing both drug target delivery, target action, and drug toxicology. Using mimetic tissue models, we have investigated the efficacy of Raman spectroscopy in quantitative detection of alkyne group and deuterated drugs in rat brain and rat liver tissue models. Lasers with 671 nm and 785 nm wavelengths were assessed for their feasibility in this application due to opposing relative benefits and disadvantages. Thin tissue sections have been tested as a practical means of reducing autofluorescent background by minimizing out-of-focus tissue and therefore maximizing photobleaching rates. Alkyne-tagged drugs were quantitatively measured at 18 ± 5 μg/g drug/tissue mass ratio in rat brain and at 34 ± 6 μg/g in rat liver. Quantification calibration curves were generated for a range of concentrations from 0-500 μg/g. These results show the potential of Raman spectroscopy as a diffraction-limited spatially resolved imaging technique for assessing drug delivery in tissue applications.

Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging

This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information-nucleic acids, proteins, and lipids-from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.

Classification of Soft Tissue Sarcoma Specimens with Raman Spectroscopy as Smart Sensing Technology

Intraoperative confirmation of negative resection margins is an essential component of soft tissue sarcoma surgery. Frozen section examination of samples from the resection bed after excision of sarcomas is the gold standard for intraoperative assessment of margin status. However, it takes time to complete histologic examination of these samples, and the technique does not provide real-time diagnosis in the operating room (OR), which delays completion of the operation. This paper presents a study and development of sensing technology using Raman spectroscopy that could be used for detection and classification of the tumor after resection with negative sarcoma margins in real time. We acquired Raman spectra from samples of sarcoma and surrounding benign muscle, fat, and dermis during surgery and developed (i) a quantitative method (QM) and (ii) a machine learning method (MLM) to assess the spectral patterns and determine if they could accurately identify these tissue types when compared to findings in adjacent H&E-stained frozen sections. High classification accuracy (>85%) was achieved with both methods, indicating that these four types of tissue can be identified using the analytical methodology. A hand-held Raman probe could be employed to further develop the methodology to obtain spectra in the OR to provide real-time in vivo capability for the assessment of sarcoma resection margin status.

Translational biophotonics with Raman imaging: clinical applications and beyond

Clinical medicine continues to seek novel rapid non-invasive tools capable of providing greater insight into disease progression and management. Raman scattering based technologies constitute a set of tools under continuing development to address outstanding challenges spanning diagnostic medicine, surgical guidance, therapeutic monitoring, and histopathology. Here we review the mechanisms and clinical applications of Raman scattering, specifically focusing on high-speed imaging methods that can provide spatial context for translational biomedical applications.

Selective-sampling Raman imaging techniques for ex vivo assessment of surgical margins in cancer surgery

One of the main challenges in cancer surgery is to ensure the complete excision of the tumour while sparing as much healthy tissue as possible. Histopathology, the gold-standard technique used to assess the surgical margins on the excised tissue, is often impractical for intra-operative use because of the time-consuming tissue cryo-sectioning and staining, and availability of histopathologists to assess stained tissue sections. Raman micro-spectroscopy is a powerful technique that can detect microscopic residual tumours on ex vivo tissue samples with accuracy, based entirely on intrinsic chemical differences. However, raster-scanning Raman micro-spectroscopy is a slow imaging technique that typically requires long data acquisition times wich are impractical for intra-operative use. Selective-sampling Raman imaging overcomes these limitations by using information regarding the spatial properties of the tissue to reduce the number of Raman spectra. This paper reviews the latest advances in selective-sampling Raman techniques and applications, mainly based on multimodal optical imaging. We also highlight the latest results of clinical integration of a prototype device for non-melanoma skin cancer. These promising results indicate the potential impact of Raman spectroscopy for providing fast and objective assessment of surgical margins, helping surgeons ensure the complete removal of tumour cells while sparing as much healthy tissue as possible.

Fast confocal Raman imaging via context-aware compressive sensing

Raman hyperspectral imaging is a powerful method to obtain detailed chemical information about a wide variety of organic and inorganic samples noninvasively and without labels. However, due to the weak, nonresonant nature of spontaneous Raman scattering, acquiring a Raman imaging dataset is time-consuming and inefficient. In this paper we utilize a compressive imaging strategy coupled with a context-aware image prior to improve Raman imaging speed by 5- to 10-fold compared to classic point-scanning Raman imaging, while maintaining the traditional benefits of point scanning imaging, such as isotropic resolution and confocality. With faster data acquisition, large datasets can be acquired in reasonable timescales, leading to more reliable downstream analysis. On standard samples, context-aware Raman compressive imaging (CARCI) was able to reduce the number of measurements by ∼85% while maintaining high image quality (SSIM >0.85). Using CARCI, we obtained a large dataset of chemical images of fission yeast cells, showing that by collecting 5-fold more cells in a given experiment time, we were able to get more accurate chemical images, identification of rare cells, and improved biochemical modeling. For example, applying VCA to nearly 100 cells' data together, cellular organelles were resolved that were not faithfully reconstructed by a single cell's dataset.

Combined total internal reflection AF spectral-imaging and Raman spectroscopy for fast assessment of surgical margins during breast cancer surgery

The standard treatment for breast cancer is surgical removal mainly through breast-conserving surgery (BCS). We developed a new technique based on auto-fluorescence (AF) spectral imaging and Raman spectroscopy for fast intraoperative assessment of excision margins in BCS. A new wide-field AF imaging unit based on total internal reflection (TIR) was combined with a Raman spectroscopy microscope equipped with a 785 nm laser. The wavelength of the AF excitation was optimized to 365 nm in order to maximize the discrimination of adipose tissue. This approach allows for the non-adipose regions of tissue, which are at a higher risk of containing a tumor, to be targeted more efficiently by the Raman spectroscopy measurements. The integrated TIR-AF-Raman was tested on small tissue samples as well as fresh wide local excisions, delivering the analysis of the entire cruciate surface of BCS specimens (5.1 × 7.6 cm²) in less than 45 minutes and also providing information regarding the location of the tumor in the specimen. Full automation of the instrument and selection of a faster translation stage would allow for the measurement of BCS specimens within an intraoperative time scale (20 minutes). This study demonstrates that the TIR-AF Raman microscope represents a feasible step towards the development of a technique for intraoperative assessment of large WLE within intraoperative timescales.

Raman spectroscopy for rapid intra-operative margin analysis of surgically excised tumour specimens

Raman spectroscopy, a form of vibrational spectroscopy, has the ability to provide sensitive and specific biochemical analysis of tissue. This review article provides an in-depth analysis of the suitability of different Raman spectroscopy techniques in providing intra-operative margin analysis in a range of solid tumour pathologies. Surgical excision remains the primary treatment of a number of solid organ cancers. Incomplete excision of a tumour and positive margins on histopathological analysis is associated with a worse prognosis, the need for adjuvant therapies with significant side effects and a resulting financial burden. The provision of intra-operative margin analysis of surgically excised tumour specimens would be beneficial for a number of pathologies, as there are no widely adopted and accurate methods of margin analysis, beyond histopathology. The limitations of Raman spectroscopic studies to date are discussed and future work necessary to enable translation to clinical use is identified. We conclude that, although there remain a number of challenges in translating current techniques into a clinically effective tool, studies so far demonstrate that Raman Spectroscopy has the attributes to successfully perform highly accurate intra-operative margin analysis in a clinically relevant environment.

Applications of Spatial Light Modulators in Raman Spectroscopy

Advances in consumer display screen technologies have historically been adapted by researchers across the fields of optics as they can be used as electronically controlled spatial light modulators (SLMs) for a variety of uses. The performance characteristics of such SLM devices based on liquid crystal (LC) and digital micromirror device (DMD) technologies, in particular, has developed to the point where they are compatible with increasingly sensitive instrumental applications, for example, Raman spectroscopy. Spatial light modulators provide additional flexibility, from modulation of the laser excitation (including multiple laser foci patterns), manipulation of microscopic samples (optical trapping), or selection of sampling volume (adaptive optics or spatially offset Raman spectroscopy), to modulation in the spectral domain for high-resolution spectral filtering or multiplexed/compressive fast detection. Here, we introduce the benefits of different SLM devices as a part of Raman instrumentation and provide a variety of recent example applications which have benefited from their incorporation into a Raman system.

Investigation of Issues for the Accurate and Precise Measurement of an Analyte Using Surface-Enhanced Raman Scattering (SERS)

This paper builds on an earlier examination of the influence of sampling size and analyte surface density on the accuracy and precision of measurements using surface-enhanced Raman scattering (SERS) to read out heterogeneous immunoassays. Quantitation using SERS typically relies on interrogating a small area on the sample surface by using a micrometer-sized laser spot for signal generation. The information obtained using such a small portion of sample is then projected as being representative of the much larger sample, which can compromise the accuracy and precision of the measurement due to undersampling. For a heterogeneous immunoassay interrogated by SERS, quantitation is, therefore, sensitive to the size of the analyzed area and the surface density of the measured analyte. To identify conditions in which sampling error poses a threat to accuracy and precision, a simulation of a SERS immunoassay was developed and compared to experimental results. The simulation randomly distributes adsorbates across the capture surface and then measures the density of adsorbates inside areas of analysis of different sizes. This approach mimics the analysis of a heterogeneous immunoassay when using a Raman microscope with different laser spot sizes. The results of the simulations, which were confirmed experimentally by comparison to an immunoassay of human immunoglobulin G (IgG) show that the accuracy and precision of the measurement improved with larger analysis areas and higher analyte concentrations due to the increased apparent homogeneity of the analyte within the area of analysis. By imposing a threshold on precision (5%), we also begin to establish a framework for the parameters necessary to achieve reliable quantitative measurements (e.g., laser spot size, analyte concentration, and sample volume).

Intra-operative spectroscopic assessment of surgical margins during breast conserving surgery

Background

In over 20% of breast conserving operations, postoperative pathological assessment of the excised tissue reveals positive margins, requiring additional surgery. Current techniques for intra-operative assessment of tumor margins are insufficient in accuracy or resolution to reliably detect small tumors. There is a distinct need for a fast technique to accurately identify tumors smaller than 1 mm² in large tissue surfaces within 30 min.

Methods

Multi-modal spectral histopathology (MSH), a multimodal imaging technique combining tissue auto-fluorescence and Raman spectroscopy was used to detect microscopic residual tumor at the surface of the excised breast tissue. New algorithms were developed to optimally utilize auto-fluorescence images to guide Raman measurements and achieve the required detection accuracy over large tissue surfaces (up to 4 × 6.5 cm²). Algorithms were trained on 91 breast tissue samples from 65 patients.

Results

Independent tests on 121 samples from 107 patients - including 51 fresh, whole excision specimens - detected breast carcinoma on the tissue surface with 95% sensitivity and 82% specificity. One surface of each uncut excision specimen was measured in 12–24 min. The combination of high spatial-resolution auto-fluorescence with specific diagnosis by Raman spectroscopy allows reliable detection even for invasive carcinoma or ductal carcinoma in situ smaller than 1 mm².

Conclusions

This study provides evidence that this multimodal approach could provide an objective tool for intra-operative assessment of breast conserving surgery margins, reducing the risk for unnecessary second operations.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1002-2) contains supplementary material, which is available to authorized users.

Dynamic Sparse Sampling for Confocal Raman Microscopy

The total number of data points required for image generation in Raman microscopy was greatly reduced using sparse sampling strategies, in which the preceding set of measurements informed the next most information-rich sampling location. Using this approach, chemical images of pharmaceutical materials were obtained with >99% accuracy from 15.8% sampling, representing an ∼6-fold reduction in measurement time relative to full field of view rastering with comparable image quality. This supervised learning approach to dynamic sampling (SLADS) has the distinct advantage of being directly compatible with standard confocal Raman instrumentation. Furthermore, SLADS is not limited to Raman imaging, potentially providing time-savings in image reconstruction whenever the single-pixel measurement time is the limiting factor in image generation.

Automated multimodal spectral histopathology for quantitative diagnosis of residual tumour during basal cell carcinoma surgery

Multimodal spectral histopathology (MSH), an optical technique combining tissue auto-fluorescence (AF) imaging and Raman micro-spectroscopy (RMS), was previously proposed for detection of residual basal cell carcinoma (BCC) at the surface of surgically-resected skin tissue. Here we report the development of a fully-automated prototype instrument based on MSH designed to be used in the clinic and operated by a non-specialist spectroscopy user. The algorithms for the AF image processing and Raman spectroscopy classification had been first optimised on a manually-operated laboratory instrument and then validated on the automated prototype using skin samples from independent patients. We present results on a range of skin samples excised during Mohs micrographic surgery, and demonstrate consistent diagnosis obtained in repeat test measurement, in agreement with the reference histopathology diagnosis. We also show that the prototype instrument can be operated by clinical users (a skin surgeon and a core medical trainee, after only 1-8 hours of training) to obtain consistent results in agreement with histopathology. The development of the new automated prototype and demonstration of inter-instrument transferability of the diagnosis models are important steps on the clinical translation path: it allows the testing of the MSH technology in a relevant clinical environment in order to evaluate its performance on a sufficiently large number of patients.

Compressed sensing laser scanning microscopy

We present a measurement and reconstruction method for laser-scanning microscopy based on compressed sensing, which enables significantly higher frame rates and reduced photobleaching. The image reconstruction accuracy is ensured by including a model of the physical imaging process into the compressed sensing reconstruction procedure. We demonstrate its applicability to unmodified commercial confocal fluorescence microscopy systems and for Raman imaging, showing a potential data reduction of 10-15 times, which directly leads to improvements in acquisition speed, or reduction of photobleaching, without significant loss of spatial resolution. Furthermore, the reconstruction model is also robust to noise, and effective for low-light applications. This method has promising applications for all imaging modalities based on laser-scanning acquisition, including fluorescence, Raman, and nonlinear microscopy.

Tissue diagnosis using power-sharing multifocal Raman micro-spectroscopy and auto-fluorescence imaging

We describe a multifocal Raman micro-spectroscopy detection method based on a digital micromirror device, which allows for simultaneous "power-sharing" acquisition of Raman spectra from ad hoc sampling points. As the locations of the points can be rapidly updated in real-time via software control of a liquid-crystal spatial light modulator (LC-SLM), this technique is compatible with automated adaptive- and selective-sampling Raman spectroscopy techniques, the latter of which has previously been demonstrated for fast diagnosis of skin cancer tissue resections. We describe the performance of this instrument and show examples of multiplexed measurements on a range of test samples. Following this, we show the feasibility of reducing measurement time for power-shared multifocal Raman measurements combined with confocal auto-fluorescence imaging to provide guided diagnosis of tumours in human skin samples.

Raman Imaging from Microscopy to Nanoscopy, and to Macroscopy

Raman spectroscopy can not only provide intrinsic fingerprint information about a sample, but also utilize the merits of the narrow bandwidth and low background of Raman spectra, offering itself as a promising multiplex analytical technique. Raman microscopy has become particularly attractive recently because it has demonstrated itself as an important imaging technique for various samples, from biological samples and chemical systems to industrially important silicon-based wafers. In this Concept article, some of the most recent advances in Raman imaging techniques are critically reviewed, and the advantages and problems associated with the current techniques are discussed. Particular emphasis is placed on its future directions, from both the technical and application sides.

Optimization of multimodal spectral imaging for assessment of resection margins during Mohs micrographic surgery for basal cell carcinoma

Multimodal spectral imaging (MSI) based on auto-fluorescence imaging and Raman micro-spectroscopy was used to detect basal cell carcinoma (BCC) in tissue specimens excised during Mohs micrographic surgery. In this study, the MSI algorithm was optimized to maximize the diagnosis accuracy while minimizing the number of Raman spectra: the segmentation of the auto-fluorescence images was optimized according to the type of BCC, sampling points for Raman spectroscopy were generated based on auto-fluorescence intensity variance and segment area, additional Raman spectra were acquired when performance of the segmentation algorithm was sub-optimal. The results indicate that accurate diagnosis can be achieved with a sampling density of ~2,000 Raman spectra/cm(2), based on sampling points generated by the MSI algorithms. The key benefit of MSI is that diagnosis of BCC is obtained based on intrinsic chemical contrast of the tissue, within time scales similar to frozen-section histopathology, but without requiring laborious sample preparation and subjective interpretation of stained frozen-sections.

Towards intra-operative diagnosis of tumours during breast conserving surgery by selective-sampling Raman micro-spectroscopy

Breast-conserving surgery (BCS) is increasingly employed for the treatment of early stage breast cancer. One of the key challenges in BCS is to ensure complete removal of the tumour while conserving as much healthy tissue as possible. In this study we have investigated the potential of Raman micro-spectroscopy (RMS) for automated intra-operative evaluation of tumour excision. First, a multivariate classification model based on Raman spectra of normal and malignant breast tissue samples was built and achieved diagnosis of mammary ductal carcinoma (DC) with 95.6% sensitivity and 96.2% specificity (5-fold cross-validation). The tumour regions were discriminated from the healthy tissue structures based on increased concentration of nucleic acids and reduced concentration of collagen and fat. The multivariate classification model was then applied to sections from fresh tissue of new patients to produce diagnosis images for DC. The diagnosis images obtained by raster scanning RMS were in agreement with the conventional histopathology diagnosis but were limited to long data acquisition times (typically 10,000 spectra mm(-2), which is equivalent to ~5 h mm(-2)). Selective-sampling based on integrated auto-fluorescence imaging and Raman spectroscopy was used to reduce the number of Raman spectra to ~20 spectra mm(-2), which is equivalent to an acquisition time of ~15 min for 5 × 5 mm(2) tissue samples. This study suggests that selective-sampling Raman microscopy has the potential to provide a rapid and objective intra-operative method to detect mammary carcinoma in tissue and assess resection margins.

Molecular pathology via IR and Raman spectral imaging

During the last 15 years, vibrational spectroscopic methods have been developed that can be viewed as molecular pathology methods that depend on sampling the entire genome, proteome and metabolome of cells and tissues, rather than probing for the presence of selected markers. First, this review introduces the background and fundamentals of the spectroscopies underlying the new methodologies, namely infrared and Raman spectroscopy. Then, results are presented in the context of spectral histopathology of tissues for detection of metastases in lymph nodes, squamous cell carcinoma, adenocarcinomas, brain tumors and brain metastases. Results from spectral cytopathology of cells are discussed for screening of oral and cervical mucosa, and circulating tumor cells. It is concluded that infrared and Raman spectroscopy can complement histopathology and reveal information that is available in classical methods only by costly and time-consuming steps such as immunohistochemistry, polymerase chain reaction or gene arrays. Due to the inherent sensitivity toward changes in the bio-molecular composition of different cell and tissue types, vibrational spectroscopy can even provide information that is in some cases superior to that of any one of the conventional techniques.

Characterization of atherosclerotic plaque depositions by Raman and FTIR imaging

Spectroscopy-based imaging techniques can provide useful biochemical information about tissue samples. Here, we employ Raman and Fourier transform infrared (IR) imaging to characterize composition and constitution of atherosclerotic plaques of rabbits, fed with a high cholesterol diet. The results were compared with conventional light microscopy after staining with hematoxylin eosin, and elastica van Gieson. The spectral unmixing algorithm vertex component analysis was applied for data analysis and image reconstruction. IR microscopy allowed for differentiation between lipids and proteins in plaques of full aortic cross sections. Raman microscopy further discriminated cholesterol esters, cholesterol and triglycerides. FTIR and Raman images were recorded at a resolution near 20 micrometer per pixel for a large field of view. High resolution Raman images at 1 micrometer per pixel revealed structural details at selected regions of interest. The intima-media and the lipid-protein ratio were determined in five specimens for quantitation. These results correlate well with histopathology. The described method is a promising tool for easy and fast molecular imaging of atherosclerosis.

Advances in optical biopsy--correlation of malignancy and cell density of primary brain tumors using Raman microspectroscopic imaging

Raman spectroscopy is a promising tool towards biopsy under vision as it provides label-free image contrast based on intrinsic vibrational spectroscopic fingerprints of the specimen. The current study applied the spectral unmixing algorithm vertex component analysis (VCA) to probe cell density and cell nuclei in Raman images of primary brain tumor tissue sections. Six Raman images were collected at 785 nm excitation that consisted of 61 by 61 spectra at a step size of 2 micrometers. After data acquisition the samples were stained with hematoxylin and eosin for comparison. VCA abundance plots coincided well with histopathological findings. Raman spectra of high grade tumor cells were found to contain more intense spectral contributions of nucleic acids than those of low grade tumor cells. Similarly, VCA endmember signatures of Raman images from high grade gliomas showed increased nucleic acid bands. Further abundance plots and endmember spectra were assigned to tissue containing proteins and lipids, and cholesterol microcrystals. Since no sample preparation is required, an important advantage of the Raman imaging methodology is that all tissue components can be observed - even those that may be lost in sample staining steps. The results demonstrate how morphology and chemical composition obtained by Raman imaging correlate with histopathology and provide complementary, diagnostically relevant information at the cellular level.