Spatial resolution in infrared microspectroscopic imaging of tissues

Spatial resolution is one of the most critical measurement parameters in infrared microspectroscopy. Due to the distinct levels of morphologic heterogeneity in cells and tissues the spatial resolution in a given IR imaging setup strongly affects the character of the infrared spectral patterns obtained from the biomedical samples. This is particularly important when spectral data bases of reference microspectra from defined tissue structures are collected. In this paper we have also pointed out that the concept of spatial resolution in IR imaging is inseparable from the contrast. Based on infrared microspectroscopic transmittance data acquired from an USAF 1951 resolution target we have demonstrated how the spatial resolution can be determined experimentally and some numbers for the spatial resolution of popular IR imaging systems are provided. Finally, we have presented a new computational procedure which is suitable to improve the spatial resolution in IR imaging. A theoretical model of 3D-Fourier self-deconvolution (FSD) is given and advantages or pitfalls of this method are discussed. Based on synchrotron IR microspectroscopic data we have furthermore demonstrated that the technique of 3D-FSD can be successfully applied to increase the spatial resolution in a real IR imaging setup.     

Spatial resolution in infrared microspectroscopic imaging of tissues

Spatial resolution is one of the most critical measurement parameters in infrared microspectroscopy. Due to the distinct levels of morphologic heterogeneity in cells and tissues the spatial resolution in a given IR imaging setup strongly affects the character of the infrared spectral patterns obtained from the biomedical samples. This is particularly important when spectral data bases of reference microspectra from defined tissue structures are collected. In this paper we have also pointed out that the concept of spatial resolution in IR imaging is inseparable from the contrast. Based on infrared microspectroscopic transmittance data acquired from an USAF 1951 resolution target we have demonstrated how the spatial resolution can be determined experimentally and some numbers for the spatial resolution of popular IR imaging systems are provided. Finally, we have presented a new computational procedure which is suitable to improve the spatial resolution in IR imaging. A theoretical model of 3D-Fourier self-deconvolution (FSD) is given and advantages or pitfalls of this method are discussed. Based on synchrotron IR microspectroscopic data we have furthermore demonstrated that the technique of 3D-FSD can be successfully applied to increase the spatial resolution in a real IR imaging setup.     

High-resolution Fourier-transform infrared chemical imaging with multiple synchrotron beams

Conventional Fourier-transform infrared (FTIR) microspectroscopic systems are limited by an inevitable trade-off between spatial resolution, acquisition time, signal-to-noise ratio (SNR) and sample coverage. We present an FTIR imaging approach that substantially extends current capabilities by combining multiple synchrotron beams with wide-field detection. This advance allows truly diffraction-limited high-resolution imaging over the entire mid-infrared spectrum with high chemical sensitivity and fast acquisition speed while maintaining high-quality SNR.     

Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 microm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets ("unknown spectra") were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.     

Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 μm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets (“unknown spectra”) were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.     

Identification of B and T cells in human spleen sections by infrared microspectroscopic imaging.

BACKGROUND: Infrared spectroscopy probes the chemical composition and molecular structure of complex systems such as tissue and cells. Infrared spectroscopic imaging combines this spectral information with lateral resolution near the single-cell level. We analyzed whether this method is competitive with classic immunohistochemical methods for immunologic tissue and cells. METHODS: We recorded infrared microspectroscopic mapping datasets with a 90- x 90-microm2 aperture from a 3- x 3-mm2 unstained tissue area of human spleen. A secondary follicle containing a germinal center and a T zone were studied in more detail by infrared microspectroscopic imaging with lateral resolution near 5 mum. The results were compared with consecutive sections stained by immunoglobulin D antibodies. T and B lymphocytes were extracted from human blood and served as independent test samples. RESULTS: Cluster analysis of infrared datasets produced images that distinguished anatomical features such as primary and secondary follicles, T zones, arteries, and spleen red pulp. The assignments could be confirmed in consecutive sections by immunohistochemical staining. Main spectral variances between T and B lymphocytes in high-resolution measurements were attributed to specific spectral contributions of DNA and cytosol. CONCLUSIONS: Sensitivity and specificity of the infrared based methods are comparable to those of standard staining procedures for identification of B and T cells. However, infrared spectroscopic imaging can offer advantages in velocity, data throughput, and standardization because of minimal sample preparation. The results emphasize the potential of infrared spectroscopy as an innovative tool for the distinction of cell types, in particular in immunologic tissue.     

Molecular imaging of thin mammalian tissue sections by mass spectrometry

Imaging of tissue sections by mass spectrometry provides a detailed molecular picture containing information on both the abundance and distribution of many constituent compounds. Mass spectra are acquired directly from fresh frozen tissue sections using matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS); sample preparation and data collection mode determine the spatial resolution or surface area of the section represented in each mass spectrum. Statistical analyses of the individual ion signatures yield biomarkers whose abundances correlate to cell development processes, tumorigenesis and/or drug treatment. In an alternate mode, the generation of intensity maps for individual ions provides a visual representation of the distribution of each species throughout the section at spatial resolutions as small as 50 microm. The availability of this molecular information is likely to be of great value to clinicians and should lead to improved therapeutic efficacy in the future.     

Improvement of low-level light imaging performance using optical clearing method

Low-level light-emitting imaging technique often detects the light emerged at the tissue surface that is generated internally from a specific target. However, in most cases, the high scattering nature of biological tissue limits the sensitivity and spatial resolution of this imaging modality. In this paper, we report that a significant improvement of chemiluminescence (CL) imaging performance in terms of both sensitivity and spatial resolution can be achieved by use of the topical application of glycerol solution onto tissue sample, i.e. optical clearing approach. Monte Carlo (MC) simulation of internally-launched point source shows that the decrease of scattering coefficient of turbid medium, which can be achieved by optical tissue clearing approach, causes stronger peak intensity with a narrower full-width at half-maximum (FWHM). The improvement becomes more significant with the source depth increasing from 1 to 5 mm. The experimental results shows that tissue clearing with 50% glycerol solution could largely improve the brightness and the spatial resolution of CL imaging when the target is covered by biological tissue with a thickness of either 1 or 3mm. This method could have potential applications for the in vivo low-level light imaging techniques.     

Portable optical fiber probe-based spectroscopic scanner for rapid cancer diagnosis: a new tool for intraoperative margin assessment

There continues to be a significant clinical need for rapid and reliable intraoperative margin assessment during cancer surgery. Here we describe a portable, quantitative, optical fiber probe-based, spectroscopic tissue scanner designed for intraoperative diagnostic imaging of surgical margins, which we tested in a proof of concept study in human tissue for breast cancer diagnosis. The tissue scanner combines both diffuse reflectance spectroscopy (DRS) and intrinsic fluorescence spectroscopy (IFS), and has hyperspectral imaging capability, acquiring full DRS and IFS spectra for each scanned image pixel. Modeling of the DRS and IFS spectra yields quantitative parameters that reflect the metabolic, biochemical and morphological state of tissue, which are translated into disease diagnosis. The tissue scanner has high spatial resolution (0.25 mm) over a wide field of view (10 cm × 10 cm), and both high spectral resolution (2 nm) and high spectral contrast, readily distinguishing tissues with widely varying optical properties (bone, skeletal muscle, fat and connective tissue). Tissue-simulating phantom experiments confirm that the tissue scanner can quantitatively measure spectral parameters, such as hemoglobin concentration, in a physiologically relevant range with a high degree of accuracy (<5% error). Finally, studies using human breast tissues showed that the tissue scanner can detect small foci of breast cancer in a background of normal breast tissue. This tissue scanner is simpler in design, images a larger field of view at higher resolution and provides a more physically meaningful tissue diagnosis than other spectroscopic imaging systems currently reported in literatures. We believe this spectroscopic tissue scanner can provide real-time, comprehensive diagnostic imaging of surgical margins in excised tissues, overcoming the sampling limitation in current histopathology margin assessment. As such it is a significant step in the development of a platform technology for intraoperative management of cancer, a clinical problem that has been inadequately addressed to date.     

Magnetoacoustic imaging of electrical conductivity of biological tissues at a spatial resolution better than 2 mm

Magnetoacoustic tomography with magnetic induction (MAT-MI) is an emerging approach for noninvasively imaging electrical impedance properties of biological tissues. The MAT-MI imaging system measures ultrasound waves generated by the Lorentz force, having been induced by magnetic stimulation, which is related to the electrical conductivity distribution in tissue samples. MAT-MI promises to provide fine spatial resolution for biological tissue imaging as compared to ultrasound resolution. In the present study, we first estimated the imaging spatial resolution by calculating the full width at half maximum (FWHM) of the system point spread function (PSF). The actual spatial resolution of our MAT-MI system was experimentally determined to be 1.51 mm by a parallel-line-source phantom with Rayleigh criterion. Reconstructed images made from tissue-mimicking gel phantoms, as well as animal tissue samples, were consistent with the morphological structures of the samples. The electrical conductivity value of the samples was determined directly by a calibrated four-electrode system. It has been demonstrated that MAT-MI is able to image the electrical impedance properties of biological tissues with better than 2 mm spatial resolution. These results suggest the potential of MAT-MI for application to early detection of small-size diseased tissues (e.g. small breast cancer).     

Imaging of Biological Tissues by Desorption Electrospray Ionization Mass Spectrometry

Mass spectrometry imaging (MSI) provides untargeted molecular information with the highest specificity and spatial resolution for investigating biological tissues at the hundreds to tens of microns scale. When performed under ambient conditions, sample pre-treatment becomes unnecessary, thus simplifying the protocol while maintaining the high quality of information obtained. Desorption electrospray ionization (DESI) is a spray-based ambient MSI technique that allows for the direct sampling of surfaces in the open air, even in vivo. When used with a software-controlled sample stage, the sample is rastered underneath the DESI ionization probe, and through the time domain, m/z information is correlated with the chemical species'' spatial distribution. The fidelity of the DESI-MSI output depends on the source orientation and positioning with respect to the sample surface and mass spectrometer inlet. Herein, we review how to prepare tissue sections for DESI imaging and additional experimental conditions that directly affect image quality. Specifically, we describe the protocol for the imaging of rat brain tissue sections by DESI-MSI.     

Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis

In this paper, three different clustering algorithms were applied to assemble infrared (IR) spectral maps from IR microspectra of tissues. Using spectra from a colorectal adenocarcinoma section, we show how IR images can be assembled by agglomerative hierarchical (AH) clustering (Ward's technique), fuzzy C-means (FCM) clustering, and k-means (KM) clustering. We discuss practical problems of IR imaging on tissues such as the influence of spectral quality and data pretreatment on image quality. Furthermore, the applicability of cluster algorithms to the spatially resolved microspectroscopic data and the degree of correlation between distinct cluster images and histopathology are compared. The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to univariate methods of IR imaging (functional group mapping). Among the cluster imaging methods, AH clustering (Ward's algorithm) proved to be the best method in terms of tissue structure differentiation.     

Proteome profile of human breast cancer tissue generated by LC-ESI-MS/MS combined with sequential protein precipitation and solubilization

Comprehensive proteome profiling of breast cancer tissue samples is challenging, as the tissue samples contain many proteins with varying concentrations and modifications. We report an effective sample preparation strategy combined with liquid chromatography (LC) electrospray ionization (ESI) quadrupole time-of-flight (QTOF) MS/MS for proteome analysis of human breast cancer tissue. The complexity of the breast cancer tissue proteome was reduced by using protein precipitation from a tissue extract, followed by sequential protein solubilization in solvents of different solubilizing strength. The individual fractions of protein mixtures or subproteomes were subjected to trypsin digestion and the resultant peptides were separated by strong cation exchange (SCX) chromatography, followed by reversed-phase capillary LC combined with high resolution and high accuracy ESI-QTOF MS/MS. This approach identified 14407 unique peptides from 3749 different proteins based on peptide matches with scores above the threshold scores at the 95% confidence level in MASCOT database search of the acquired MS/MS spectra. The false positive rate of peptide matches was determined to be 0.95% by using the target-decoy sequence search strategy. On the basis of gene ontology categorization, the identified proteins represented a wide variety of biological functions, cellular processes, and cellular locations.     

Super-resolution segmentation of imaging mass spectrometry data: Solving the issue of low lateral resolution

In the last decade, imaging mass spectrometry has seen incredible technological advances in its applications to biological samples. One computational method of data mining in this field is the spatial segmentation of a sample, which produces a segmentation map highlighting chemically similar regions. An important issue for any imaging mass spectrometry technology is its relatively low spatial or lateral resolution (i.e. a large size of pixel) as compared with microscopy. Thus, the spatial resolution of a segmentation map is also relatively low, that complicates its visual examination and interpretation when compared with microscopy data, as well as reduces the accuracy of any automated comparison. We address this issue by proposing an approach to improve the spatial resolution of a segmentation map. Given a segmentation map, our method magnifies it up to some factor, producing a super-resolution segmentation map. The super-resolution map can be overlaid and compared with a high-res microscopy image. The proposed method is based on recent advances in image processing and smoothes the "pixilated" region boundaries while preserving fine details. Moreover, it neither eliminates nor splits any region. We evaluated the proposed super-resolution segmentation approach on three MALDI-imaging datasets of human tissue sections and demonstrated the superiority of the super-segmentation maps over standard segmentation maps.     

Forensic visualization of foreign matter in human tissue by near-infrared spectral imaging: methodology and data mining strategies.

BACKGROUND: Rapidity of data acquisition, high image fidelity and large field of view are of tremendous value when looking for chemical contaminants or for the proverbial "needle in the haystack" - in this case foreign inclusions in histologic sections of biopsy or autopsy tissues. Near infrared chemical imaging is one of three chemical imaging techniques (NIR, MIR and Raman) based on vibrational spectroscopy, and provides distinct technical advantages for this application. METHODS: We have chosen to utilize and evaluate near infrared (NIR) imaging for studies of foreign materials in tissue because the experimental configuration is relatively simple, data collection is rapid, and large sample areas can be screened with high image fidelity and spatial resolution. RESULTS: We have shown that NIR imaging can readily find and identify silicone gel inclusions in biological tissue samples. Additionally, preliminary results indicate that spectral signatures in the data set are also potentially sensitive to structural changes in the surrounding tissue that may be induced by the foreign body. CONCLUSIONS: NIR chemical imaging is a powerful, non-destructive tool for localization and identifying foreign contaminants in biological tissue. Preliminary results indicate that NIR imaging is also sensitive enough to differentiate tissue types (perhaps based on collagen structural differences), and provide data on the spatial localization of these components.     

A methodology for registration of a histological slide and in vivo MRI volume based on optimizing mutual information

We present a method for registering histology and in vivo imaging that requires minimal microtoming and is automatic following the user's initialization. In this demonstration, we register a single hematoxylin-and-eosin-stained histological slide of a coronal section of a rat brain harboring a 9L gliosarcoma with an in vivo 7T MR image volume of the same brain. Because the spatial resolution of the in vivo MRI is limited, we add the step of obtaining a high spatial resolution, ex vivo MRI in situ for intermediate registration. The approach taken was to maximize mutual information in order to optimize the registration between all pairings of image data whether the sources are MRI, tissue block photograph, or stained sample photograph. The warping interpolant used was thin plate splines with the appropriate basis function for either 2-D or 3-D applications. All registrations were implemented by user initialization of the approximate pose between the two data sets, followed by automatic optimization based on maximizing mutual information. Only the higher quality anatomical images were used in the registration process; however, the spatial transformation was directly applied to a quantitative diffusion image. Quantitative diffusion maps from the registered location appeared highly correlated with the H&E slide. Overall, this approach provides a robust method for coregistration of in vivo images with histological sections and will have broad applications in the field of functional and molecular imaging.     

Biological applications of synchrotron radiation infrared spectromicroscopy

Extremely brilliant infrared (IR) beams provided by synchrotron radiation sources are now routinely used in many facilities with available commercial spectrometers coupled to IR microscopes. Using these intense non-thermal sources, a brilliance two or three order of magnitude higher than a conventional source is achievable through small pinholes (< 10 μm) with a high signal to-noise ratio. IR spectroscopy is a powerful technique to investigate biological systems and offers many new imaging opportunities. The field of infrared biological imaging covers a wide range of fundamental issues and applied researches such as cell imaging or tissue imaging. Molecular maps with a spatial resolution down to the diffraction limit may be now obtained with a synchrotron radiation IR source also on thick samples. Moreover, changes of the protein structure are detectable in an IR spectrum and cellular molecular markers can be identified and used to recognize a pathological status of a tissue. Molecular structure and functions are strongly correlated and this aspect is particularly relevant for imaging. We will show that the brilliance of synchrotron radiation IR sources may enhance the sensitivity of a molecular signal obtained from small biosamples, e.g., a single cell, containing extremely small amounts of organic matter. We will also show that SR IR sources allow to study chemical composition and to identify the distribution of organic molecules in cells at submicron resolution is possible with a high signal-to-noise ratio. Moreover, the recent availability of two-dimensional IR detectors promises to push forward imaging capabilities in the time domain. Indeed, with a high current synchrotron radiation facility and a Focal Plane Array the chemical imaging of individual cells can be obtained in a few minutes. Within this framework important results are expected in the next years using synchrotron radiation and Free Electron Laser (FEL) sources for spectro-microscopy and spectral-imaging, alone or in combination with Scanning Near-field Optical Microscopy methods to study the molecular composition and dynamic changes in samples of biomedical interest at micrometric and submicrometric scales, respectively.     

Mass spectrometry imaging with high resolution in mass and space

Mass spectrometry (MS) imaging links molecular information and the spatial distribution of analytes within a sample. In contrast to most histochemical techniques, mass spectrometry imaging can differentiate molecular modifications and does not require labeling of targeted compounds. We have recently introduced the first mass spectrometry imaging method that provides highly specific molecular information (high resolution and accuracy in mass) at cellular dimensions (high resolution in space). This method is based on a matrix-assisted laser desorption/ionization (MALDI) imaging source working at atmospheric pressure which is coupled to an orbital trapping mass spectrometer. Here, we present a number of application examples and demonstrate the benefit of ‘mass spectrometry imaging with high resolution in mass and space.’ Phospholipids, peptides and drug compounds were imaged in a number of tissue samples at a spatial resolution of 5–10 μm. Proteins were analyzed after on-tissue tryptic digestion at 50-μm resolution. Additional applications include the analysis of single cells and of human lung carcinoma tissue as well as the first MALDI imaging measurement of tissue at 3 μm pixel size. MS image analysis for all these experiments showed excellent correlation with histological staining evaluation. The high mass resolution (R = 30,000) and mass accuracy (typically 1 ppm) proved to be essential for specific image generation and reliable identification of analytes in tissue samples. The ability to combine the required high-quality mass analysis with spatial resolution in the range of single cells is a unique feature of our method. With that, it has the potential to supplement classical histochemical protocols and to provide new insights about molecular processes on the cellular level.     

Cell-specific chemotyping and multivariate imaging by combined FT-IR microspectroscopy and orthogonal projections to latent structures (OPLS) analysis reveals the chemical landscape of secondary xylem

Fourier‐transform infrared (FT‐IR) spectroscopy combined with microscopy enables chemical information to be acquired from native plant cell walls with high spatial resolution. Combined with a 64 × 64 focal plane array (FPA) detector, 4096 spectra can be simultaneously obtained from a 0.3 × 0.3 mm image; each spectrum represents a compositional and structural ‘fingerprint’ of all cell wall components. For optimal use and analysis of such a large amount of information, multivariate approaches are preferred. Here, FT‐IR microspectroscopy with FPA detection is combined with orthogonal projections to latent structures discriminant analysis (OPLS‐DA). This allows for: (i) the extraction of spectra from single cell types, (ii) identification and characterization of different chemotypes using the full spectral information, and (iii) further visualization of the pattern of identified chemotypes by multivariate imaging. As proof of concept, the chemotypes of Populus tremula xylem cell types are described. The approach revealed unknown features about chemical plasticity and patterns of lignin composition in wood fibers that would have remained hidden in the dataset with traditional data analysis. The applicability of the method to Arabidopsis xylem and its usefulness in mutant chemotyping is also demonstrated. The methodological approach is not limited to xylem tissues but can be applied to any plant organ/tissue also using other techniques such as Raman and UV microspectroscopy.     

Combination of immunohistochemistry and laser ablation ICP mass spectrometry for imaging of cancer biomarkers

Laser ablation (LA) ICP-MS has been developed as a new tool for imaging of cancer biomarkers in tissue sections. The distribution of two breast cancer-associated proteins, MUC-1 and HER2 was studied based on multiple line rastering of tissue sections and measurement of relevant Au/Ag tagged antibodies bound to the tissue. Comparisons with optical microscopy indicated extremely high sensitivity for the LA technique and sufficiently good resolution to permit fine scale feature mapping at the cellular level. Application to the quantitative assessment of HER2 expression in tissue microarrays was demonstrated.