We would like to acknowledge the Department of Pathology at the University of Illinois at Chicago for financial support. Histology and visible imaging services were provided by the Research Resources Center - Research Histology and Tissue Imaging Core at the University of Illinois at Chicago established with the support of the Vice Chancellor of Research, in particular we would like to thank Ryan Deaton and Andy Hall for their expertise. We would also like to thank Agilent Technologies, in particular Frank Weston for support and loaning of additional IR lens.

1. Setting up an FT-IR Microscope and Acquiring Tissue Images
<ol>
	<li>Section a formalin-fixed paraffin-embedded tissue block at 4 &#956;m thickness on to an IR compatible slide using a microtome. Alternately, section a liquid nitrogen frozen tissue at 4 &#956;m thickness onto an IR compatible slide using a cryostat. 
	NOTE: Typically, a serial section will also be acquired on a glass slide and stained with a special (such as Haematoxylin and Eosin (H&#38;E)) or immunohistochemical stain. In addition, cultured cell lines can be grown on IR compatible slides. IR compatible slides can be IR reflective slides for reflection mode imaging (e.g., MirrIR slide, gold) or IR transparent slides for transmission mode imaging (e.g., BaF2, CaF2).</li>
	<li>Purge the FT-IR microscope and spectrometer using either dry air or dry nitrogen gas to remove atmospheric water from the system. Wait at least 45 min before imaging to ensure a thorough purge.</li>
	<li>Cool both the FPA detector (to 79 K) and the internal MCT detector in the FT-IR microscope using liquid nitrogen. Cool the detectors approximately every 6 hr. 
	CAUTION! Liquid nitrogen is a cryogenic fluid and can cause cold burns, frostbite and in enclosed spaces asphyxiation.</li>
	<li>Mount the sample slide on the microscope stage for FT-IR imaging.</li>
	<li>Ensure the visible light is &#8220;ON&#8221; and then either using the binoculars or the sample capture program (which provides a real-time visible image of the tissue), focus on the sample.</li>
	<li>Open the bundle software package such as resolutions pro and click &#8216;Collect&#8217;, then click &#8216;Diagnostics&#8217; and then select &#8216;Align Spectrometer&#8217;. Ensure that the beam path to the spectrometer internal detector is not obstructed.</li>
	<li>Click on &#8216;Imaging Setup&#8217;.</li>
	<li>In the &#8216;Electronics&#8217; tab, select the appropriate &#8216;Speed&#8217;, &#8216;Under Sampling Ratio (UDR)&#8217; and &#8216;Filters&#8217; settings depending on the system. 
	NOTE: In this example, the setting is Speed = 2.5 KHz, UDR = 4 and Filters = None.</li>
	<li>In the &#8216;Optics&#8217; tab ensure that &#8216;Mid-IR source&#8217;, &#8216;Open aperture&#8217; and &#8216;100% beam attenuator&#8217; are selected.</li>
	<li>To calibrate the system, in the &#8216;Optics&#8217; tab, select &#8216;Detector&#8217; as &#8216;Ground&#8217;, &#8216;Microscope Detector&#8217; as &#8216;Left&#8217;, and then select either &#8216;Transmittance&#8217; or &#8216;Reflectance&#8217; under &#8216;Optics mode&#8217; depending on the type of slide being used.</li>
	<li>Click Setup, which will open up the &#8216;Lancer Control&#8217; window. 
	NOTE: For reflection mode follow instructions in 1.12 and 1.13 and for transmission mode follow instructions in 1.14 to 1.16. Continue from 1.17 for both reflection and transmission.</li>
	<li>For reflection mode, in &#8216;Lancer Control&#8217; click on &#8216;Raw&#8217;. Using the stage control joystick and watching the live view of the FT-IR interferogram image (top right image), move to a clean area on the slide.</li>
	<li>Adjust the integration time to approximately 8,000 counts. Next, move the stage to find a piece of tissue with structure, preferably the edge of a tissue, and perfect the focus of the image. Change &#8216;Warmth&#8217; and &#8216;Contrast&#8217; options to help form an image to improve focusing, this will have no effect on the IR data. Continue from 1.18.</li>
	<li>For transmission mode, in &#8216;Lancer Control&#8217; click on &#8216;Raw&#8217;. Using the stage control joystick and watching the live view of the FT-IR interferogram image (top right image), move to a clean area of the slide.</li>
	<li>Adjust the integration time to approximately 8,000 counts.</li>
	<li>Adjust the bottom focusing/condenser objective to increase the number of counts to its maximum. Watch the shape of the bottom right image in Lancer Control to ensure that it is Gaussian in appearance and relatively uniform. Adjust the integration time again to approximately 8,000 counts. 
	NOTE: If any of the pixels in the FT-IR interferogram image turn white, reduce the integration time.</li>
	<li>Move the stage to find a piece of tissue with structure, preferably the edge of a tissue and perfect the focus of the image. Optionally, change the warmth and contrast options to help form an image to improve focusing, but will have no effect on the IR data.</li>
	<li>Using the stage control joystick, move to a clean area of the slide. Press the &#8216;Calibrate&#8217; button. Ensure the &#8216;Out Of Range (OOR)&#8217; value is less than 50 and the difference between the &#8216;High Flux&#8217; and &#8216;Low Flux&#8217; counts is at least 4,000 counts.</li>
	<li>Select &#8216;OK&#8217; twice. In the optics tabs select; &#8216;Detector&#8217; = &#8216;MCT&#8217;, &#8216;Microscope Detector&#8217; = &#8216;Right&#8217; and then click &#8216;Setup&#8217;. At this point there will be a FT-IR interferogram on the screen. Click on &#8216;Find Centerburst&#8217;. Click &#8216;Okay&#8217;.</li>
	<li>In the &#8216;Optics&#8217; tab, reselect &#8216;Detector&#8217; = &#8216;Ground&#8217;, &#8216;Microscope Detector&#8217; = &#8216;Left&#8217; and select &#8216;Setup&#8217;.</li>
	<li>In Lancer Control &#8212; ensure the image is still on a clean area and click &#8216;Calibrate&#8217; again. Click &#8216;Okay&#8217;.</li>
	<li>Next, collect a background FT-IR image to correct for absorbance from the atmosphere, system and slide. Go to the &#8216;Electronics&#8217; tab and select an appropriate spectral resolution, typically of 4 cm-1 or 8 cm-1 for tissue.</li>
	<li>Go to the &#8216;Background&#8217; tab and type 128 in &#8216;Scans to co-add&#8217;. Select the &#8216;New File&#8230;&#8217; button and place the background file in appropriate folder. Check the &#8216;Imaging&#8217; tab to ensure mosaicing is 1 by 1. Click &#8216;Background&#8217;, wait for the scan to finish and confirm where to save the file. Click a region on the background FT-IR image and check the spectrum.</li>
	<li>To take a large mosaic image of the sample, select the &#8216;Imaging&#8217; tab and insert the number of X and Y frames for the mosaic size you wish to acquire.</li>
	<li>Click &#8216;Setup&#8217; and in Lancer Control use the live IR view to find the area of interest. If taking a mosaic, center the live feed in the middle of the area of interest. Click &#8216;Okay&#8217;. Go to the &#8216;Electronics&#8217; tab and type the number of scans to co-add (typically a value between 2 and 16 scans for tissue). Click &#8216;Scan&#8217;.</li>
	<li>Check the collected FT-IR image on the screen to ensure it looks focused. Click on the image to bring up an IR spectrum from that location and check that it looks to have an acceptable signal to noise. Acquire a visible image of the sample.</li>
	<li>Save the image and export it to the required format, such as ENVI-IDL.</li>
</ol>
2. Adapting an FT-IR Microscope for High-definition Capabilities
NOTE: Most FT-IR systems are equipped with an objective approximately 15X magnification and 0.5 numerical aperture (NA). To image in high-definition mode, an IR compatible 36X or 74X objective can be used to give diffraction limited imaging capabilities.
<ol>
	<li>Remove the system&#8217;s 15X objective by unscrewing counter clockwise.</li>
	<li>Screw in either the 36X or 74X objective in its place. Align the objective prior to use. Use lens extension tubes to bring the objective close enough to the sample.</li>
	<li>Place tissue under the new objective and focus using visible light using either the binoculars or Sample Capture program. 
	NOTE: High-definition imaging must be done in transmission mode. The very close working distance of these objectives (1-2 mm) and very shallow depth of focus requires focusing slowly and carefully, taking time to lower the objective without touching the sample.</li>
	<li>Follow instructions from 1.6 to 1.11. 
	NOTE: Due to being significantly more difficult to focus and much less light reaching the detector, the following protocol is adjusted based on 1.14.</li>
	<li>In transmission mode, click on &#8216;Raw&#8217;. Using the stage control joystick and watching the live view of the FT-IR interferogram image (top right image), move to the tissue and focus (ideally on an edge). 
	NOTE: This may be difficult, therefore adjust the &#8216;Warmth&#8217; and &#8216;Contrast&#8217; options to help form an image to improve focusing (these options have no affect on the IR data).</li>
	<li>Once focused, using the stage control joystick and the live IR view move to a clean area of the slide. Adjust the integration time to approximately 8,000 counts. Adjust the bottom focusing objective to increase the number of counts to its maximum. Adjust the integration time again to approximately 8,000 counts.</li>
	<li>Move the stage to find a piece of tissue with structure, preferably the edge of a tissue, and perfect the focus of the image.</li>
	<li>If mosaicking an image at high resolution, adjust the stage to allow correct movement required across distances for accurate mosaicing. To change the stage distance settings, go to &#8216;Setup stage&#8217; in Lancer Control and adjust the horizontal and vertical alignment settings to allow for correct mosaicking.</li>
	<li>Using the stage control joystick move to a clean area of the slide. Press the &#8216;Calibrate&#8217; button. Ensure the Out Of Range (OOR) value is less than 50 for both 36X and 74X objectives. Ensure the difference between the High Flux and Low Flux values is at least 1,000 counts for the 74X objective and 2,000 counts for the 36X objective.</li>
	<li>Continue from 1.19 to 1.27. The number of scans in 1.23 and 1.25 will need to be adjusted due to reduced signal to approximately 256 scans for the background image and 16 to 128 scans for the tissue image.</li>
</ol>
3. Visualizing and Classifying IR Spectral Datasets
NOTE: In this section, we will discuss how to visualize and extract data from spectral images using geospatial image processing and analyzing software such as ENVI+IDL, however the process is very similar for any alternate software such as MATLAB, free software such as CytoSpec, or the instrument developer&#8217;s own software. There are a few different spectral processing techniques that may be performed on the IR data.
<ol>
	<li>Open geospatial image processing and analyzing software and load the IR data file.</li>
	<li>Apply a baseline correction algorithm to the IR data by selecting &#8220;Spectral tools&#8221; and scroll down and click &#8220;Absorbance spectra&#8221;. Observe a pop-up menu and select &#8220;Baseline correction&#8221; 
	NOTE: Baseline correction will remove a baseline offset slope from the data due to scattering</li>
	<li>Perform Spectral normalization, by ratioing the data to a certain spectral peak (typically Amide I (1,650 cm-1) or Amide II (1,550 cm-1)) or area under a defined region of the spectra. Do this by selecting &#8220;Normal spectra&#8221; under the &#8220;Absorbance Spectra&#8221; menu options. 
	NOTE: Spectral normalization is performed so that any differences in spectra are real biochemical differences and not due to thickness or density differences between samples.</li>
	<li>Use a noise reduction algorithm to remove noise from the spectra if necessary28. 
	NOTE: There are multiple approaches available by which baseline correction, spectral normalization and noise reduction can be achieved, with most software having automated algorithms built in. In addition, there are an emerging number of approaches that will correct for spectral aberrations, which have been discussed in detail29-42; however, the community does not yet agree on which of these are needed.</li>
	<li>Observe a list of all the IR frequencies collected within the image (typically of a spectral range from 900 to 4,000 cm-1). Click on the frequencies that correspond to specific biomolecules to observe an image of the tissue at the selected frequency.
	<ol>
		<li>For example, click on 1,650 cm-1 band to observe the distribution of proteins in the sample. Alternatively click on 1,080 cm-1 band to observe the distribution of nucleic acids in the sample. Use wavenumber 1,650 cm-1.</li>
	</ol>
	</li>
	<li>Create additional images by computing spectral peak height ratios, peak area ratios etc. that will allow for visualization of different biomolecular components. Click on &#8216;Spectral Tools&#8217; and then select either &#8216;Peak Height Ratios&#8217; or &#8216;Calculate Image Area&#8217; to create images.</li>
	<li>Scan the corresponding adjacent tissue section which is stained using a separate whole slide imager system that captures brightfield images. Alongside the IR image, bring up the digital image of the stained tissue with a visible image program.</li>
	<li>Right click on the image at a region of interest and select &#8216;Z profile&#8217;. This will give the spectral information at the selected pixel.</li>
	<li>Look at the IR spectra of multiple pixels from multiple classes, such as comparing cell types, disease states. Mark specific pixels on the image using the &#8216;Regions Of Interest&#8217; (ROI) tool. To perform this, right click on the image and select &#8216;ROI tool&#8217;. Create the classes that are to be labeled, for example normal, dysplasia, and cancer classes. Then select, &#8216;ROI type&#8217; &#8212; &#8216;Point&#8217;. Then select the class to select pixels for and draw on the appropriate pixels on the IR image. Derive the average spectra for each of the classes using the &#8216;Average ROI&#8217; tool.</li>
	<li>Compare derived spectra by plotting in graphing software.</li>
</ol>

The authors have nothing to disclose.

FT-IR is an emerging modality for label-free biochemical imaging of tissue sections, with the potential to have an important role in improving the current standard of diagnosis in pathology. The current gold standard for pathology requires tissues to be biopsied, fixed in formalin, embedded in paraffin, sectioned multiple times, and stained with multiple stains. A highly trained pathologist has to subjectively visually assess the tissue structure and cellular morphology to determine a diagnosis. Here we show how to collect high-resolution IR images from the same type of sections and discuss some of the computational approaches to examine chemical differences between cell types and disease states.
The critical steps within this protocol are to ensure that the tissues are very carefully focused and that the system is well calibrated to ensure very high quality spectroscopic data. The care when setting up the system is particularly critical when working with high magnification objectives. To aid in troubleshooting, the following list covers some of the potential difficulties encountered;
Problem: Low IR intensity when imaging in reflection. Solution: Check IR slide orientation as the reflective coating may be on the wrong side of the slide.
Problem: Low signal/Red warning sign in Lancer Control. Solution: Cool detectors with LN2. Liquid nitrogen is required for the FPA detectors to function and requires periodically being topped up.
Problem: Velocity error/movement errors. Solution: Reset spectrometer and reduce vibrations. Vibrations will cause the moving mirror in the interferometer to be disturbed.
Problem: Water vapor spikes in data. Solution: Increase purge on system and protect sample from air.
Problem: Invalid centerburst. Solution: Find centerburst again.
Problem: Low flux difference in transmission, even though focused. Solution: Adjust bottom condenser. This will occur as the IR light is not being focused to a point on the sample.
In this paper, we have focused on how to acquire high definition IR images of tissues in either transmission or transflectance mode. The nature of FT-IR imaging, is that there are multiple modifications that can be made to the data acquisition, such as, type of substrate, fixation technique, sample thickness, spectral resolution, interferometer mirror speed etc. The effect of these parameters has been discussed in extensive detail recently 4,5,17,51.
There are a number of modifications that can be made to the imaging system including imaging in ATR mode10,24,26 and using nanoscale thermal approaches52,53 to allow for high resolution IR imaging. The main limitation with high resolution IR imaging is that the tissues must be carefully prepared and thin enough for IR to pass through (typically 4 &#956;m thickness). In addition, transmission and reflectance FT-IR imaging requires the samples to be dry due to the absorbance of IR by water. However, FT-IR imaging has significant advantages over other techniques, in that it can very rapidly image large areas of tissue while deriving rich and detailed biochemical information. Other similar techniques that derive biochemical information in a label-free fashion include Raman spectroscopy, however the time of data acquisition is much slower to acquire images. New Raman imaging approaches are emerging including Stimulated Raman scattering (SRS) and Coherent Antistokes Raman scattering (CARS); however, they have access limited spectral range or single frequency imaging.
The advances in speed of data acquisition, spatial resolution, and availability of computational approaches have been of tremendous value in making FT-IR imaging a more feasible approach for translation as a new imaging tool in pathology. The recent advances in spatial resolution have been particularly important for tissue pathology due to key cell types not being resolvable using conventional FT-IR imaging systems. The recent paper by Reddy et al. showed how to model an ideal system to obtain the optimal spatial resolution of an FT-IR imaging system5. The kidney tissue example presented in this paper demonstrates the importance of higher spatial resolutions in order to extract biochemical information from glomerular structures (Figure&#160;3 and Figure&#160;5). In the future, new advances in Quantum Cascade Lasers as very bright IR light sources54-57, 3D spectral imaging58, and breakthroughs in the field of nanoscale IR technologies52,53,59,60 hold exciting new avenues of research that may have huge implications in the future of tissue imaging.
We have presented examples of applications in liver and kidney disease where there is a need for additional biochemical information that can be of diagnostic value. The Spectral Pathology Lab in the Department of Pathology at the University of Illinois at Chicago is focused on the translation of IR imaging technologies towards improving disease diagnosis and improved prediction of patient outcome. FT-IR imaging may overcome some of the current limitations in pathology practice where quantitative and objective information is required. In particular, future work is focused on identifying areas in current pathology practice where current techniques fail to provide adequate diagnostic sensitivity or provide limited information. A clear need exists in improving the current practice of pathology and towards giving more information to the pathologist about a patient&#8217;s disease status, which may be achievable using high-definition FT-IR imaging.

IR spectroscopy has been an analytical tool available in some form since the 1930s; however, it is only been within the last decade that the area of tissue imaging with FT-IR has exploded. The advances in FT-IR for tissue imaging have been driven in a large part by three key developments: 1) increased speed of data acquisition due to the availability of large Focal Plane Array (FPA) detectors which typically have thousands of IR sensitive detectors1,2, 2) development of advanced processing algorithms and computational power to handle large hyperspectral data sets3, and 3) modeling of FT-IR imaging systems to maximize spatial resolution4,5. There have been numerous high quality and very extensive articles reviewing the field of FT-IR spectroscopy recently6-16, in addition to a Nature Protocols paper that details the steps to obtain point spectra or maps from tissues17. In this paper, we will focus on the protocol to obtain images of tissues using a 128 x 128 FPA detector in a modified FT-IR system with high-definition capabilities.
FT-IR imaging has long been suggested to be a potentially desirable tool for cell and tissue imaging due to the ability to obtain images in which every pixel has a wealth of biochemical information. FT-IR imaging is based on the principle that different biomolecules in a sample will quantitatively absorb different regions of the mid-infrared; this allows for the derivation of a &#8216;biochemical fingerprint&#8217;. This fingerprint had been shown in many studies to alter between different cell types and disease states. Unlike in conventional pathology practice where stains and immunohistochemical markers need to be used to visualize and identify cell types and tissue structures that are used to guide diagnosis and treatment options, the images from FT-IR are formed based on the inherent biochemistry of the tissue. The current technique of staining tissue for diagnosis is time-consuming, destructive, laborious, and requires subjective expertise of the pathologist, whereas FT-IR offers the potential to make this process rapid, non-destructive, highly automated, and more objective. In addition, FT-IR provides a novel route to obtaining additional biochemical information that may not be readily accessible using conventional staining techniques.
One of the most exciting advances in recent years has been the availability of high resolution imaging approaches that can now allow for the visualization and characterization of cell types and tissue structures that are critical for comprehensive disease diagnosis. One of these techniques is Attenuated Total Reflectance (ATR) FT-IR which incorporates a solid immersion lens (SIL) of a high refractive index which allows for high resolution imaging18, with many very exciting studies showing its applications19-25. In addition, it was recently demonstrated that the increased spatial resolution associated with ATR imaging can allow for the visualization and classification of endothelial and myoepithelial cells in breast tissue which form a key component of breast cancer diagnosis26. While ATR imaging is very useful, this technique requires the SIL to make contact with the tissue to form FT-IR images; therefore, its use is somewhat limited for tissue pathology where large regions of tissues must be quickly imaged.
A second approach was demonstrated by coupling a high magnification objective to an existing FT-IR system that uses a synchrotron as a bright source of IR, it is possible to fully illuminate an FPA and image with an effective pixel size of 0.54 x 0.54 &#956;m. This allowed for us to visualize key structures in breast and prostate tissues that were not resolvable using conventional FT-IR systems4. While these dramatic increases in IR image spatial resolution were exciting, its use remained limited due to requiring a synchrotron. Subsequently, an optimal system was designed that could also allow for high-definition imaging capabilities with a 1.1 x 1.1 &#956;m pixel size without the requirement of a synchrotron source but rather using a traditional globar IR source5. In this article, we show how to modify an existing commercial FT-IR imaging system to allow for diffraction limited IR imaging of tissues with an acceptable signal to noise ratio using multiple IR objectives (15X, 36X, and 74X). The effective pixel size with the three objectives is 5.5 x 5.5 &#956;m (15X), 2.2 x 2.2 &#956;m (36X) and 1.1 x 1.1 &#956;m (74X). We then give some examples of the importance of the gains in spatial resolution for disease detection in liver and kidney biopsies27.

<table><tbody><tr><td>Cary 600 Series FT-IR system</td><td>Agilent</td><td>Multiple configurations&amp;nbsp;</td><td>Alternate FT-IR imaging systems exist</td></tr><tr><td>Adjustable ReflX Objective 74X/0.65 NA IR</td><td>Edmund Optics</td><td>66-592</td><td></td></tr><tr><td>Adjustable ReflX Objective 36X/0.5 NA IR</td><td>Edmund Optics</td><td>66-586</td><td></td></tr><tr><td>MirrIR slide</td><td>Kevley Technologies</td><td>CFR</td><td>For FT-IR reflection-mode measurements</td></tr><tr><td>Barium Fluoride slides</td><td>International Crystal Laboratories</td><td>Multiple sizes</td><td>For FT-IR transmission-mode measurements</td></tr><tr><td>Calcium Fluoride slides</td><td>International Crystal Laboratories</td><td>Multiple sizes</td><td>For FT-IR transmission-mode measurements</td></tr><tr><td>Dry Nitrogen/Dry Air gas</td><td>Multiple gas suppliers</td><td>Multiple sizes</td><td></td></tr><tr><td>Hexane</td><td>Sigma Aldrich</td><td>Multiple sizes</td><td>For deparafinizing tissue</td></tr><tr><td>Liquid Nitrogen</td><td>Multiple cryogenic liquid suppliers</td><td>Multiple sizes</td><td></td></tr><tr><td>ENVI-IDL software</td><td>Exelis-Vis</td><td></td><td>Other software packages available</td></tr><tr><td>Whole slide Imager</td><td>Scanscope (Aperio) or Nanozoomer (Hamamatsu)</td><td></td><td>To image stained slides</td></tr></tbody></table>

high-definition fourier transform infrared (ft-ir) spectroscopic imaging of human tissue sections towards improving pathology

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging is an emerging approach to obtain detailed images that have associated biochemical information. FT-IR imaging of tissue is based on the principle that different regions of the mid-infrared are absorbed by different chemical bonds (e.g., C=O, C-H, N-H) within cells or tissue that can then be related to the presence and composition of biomolecules (e.g., lipids, DNA, glycogen, protein, collagen). In an FT-IR image, every pixel within the image comprises an entire Infrared (IR) spectrum that can give information on the biochemical status of the cells that can then be exploited for cell-type or disease-type classification. In this paper, we show: how to obtain IR images from human tissues using an FT-IR system, how to modify existing instrumentation to allow for high-definition imaging capabilities, and how to visualize FT-IR images. We then present some applications of FT-IR for pathology using the liver and kidney as examples. FT-IR imaging holds exciting applications in providing a novel route to obtain biochemical information from cells and tissue in an entirely label-free non-perturbing route towards giving new insight into biomolecular changes as part of disease processes. Additionally, this biochemical information can potentially allow for objective and automated analysis of certain aspects of disease diagnosis.

FT-IR imaging allows for the derivation of IR images of tissue that can give different contrasts depending on the IR frequency of interest. In addition, in an IR image, every pixel comprises of the entire IR spectrum, with different peaks corresponding to different biomolecules which can give information about the biochemical properties of cell types or disease states (Figure 1). Here, we have shown how to compare spectral signatures between classes, however more advanced automated classification is possible using additional algorithms3,43-50, such as Bayesian classification, Random Forests, Artificial Neural Networks, and Hierarchal Cluster Analysis can be performed on the data. Supervised classification approaches will allow for the construction of a classifier that can be trained to allow for automated recognition of cell types or disease states. Unsupervised classification approaches can be used to look for naturally occurring differences in tissue or cells due to biochemical variance.
FT-IR instrumentation has evolved over the past few decades, from measuring in a single point/mapping mode using IR opaque apertures to imaging mode using Cassegrain objectives, using either an illuminating objective coupled with a collecting objective in transmission-mode or a single objective that both illuminates and collects in reflection-mode (Figure 2). It was recently demonstrated that the collecting objective in transmission mode could be switched out for a higher magnification and numerical aperture objective to allow for diffraction limited IR imaging, that leads to substantial increases in the spatial resolution of collected IR images4,5. The advances in spatial resolution for tissue imaging have been of critical importance as we can now identify cell types and tissue structures, for example the functional units of the kidney, the glomeruli, using adapted in-house FT-IR systems (Figure 3).
High-definition FT-IR imaging allows for detailed images of tissues to be examined to identify abnormal regions and to identify biochemical differences between different cell types. In a liver tissue core, it is possible to visualize hepatocytes and regions of infiltrating fibrosis that divides two distinct areas of dysplasia and non-dysplastic cirrhosis (Figure 4). We are working to exploit this towards making automated diagnostic tools for use in difficult cases of liver disease.
Importantly, the increased spatial resolution can now allow us to isolate specific structural features that may be chemically modified by disease before histological changes are apparent. For example, we are focused on identifying biochemical changes in kidney glomerular structures such as the Bowman&#8217;s capsule, mesangium, glomerular basement membrane and tubular basement membrane, before changes identified by the pathologist can be observed (Figure 5). In particular, we are interested in identifying changes associated with the progression of diabetic nephropathy and chronic rejection in transplant patients, where current techniques fail to identify changes in an early enough fashion for successful intervention.
<img alt="Figure 1" src="/files/ftp_upload/52332/52332fig1.jpg" /> 
Figure 1. FT-IR images and spectrum from a liver core. Image of (A) H&#38;E stained core from liver biopsy and images of IR absorbance of serial section of the same core at (B) 3,286 cm-1 and (C) 2,603 cm-1, which highlighted different structural features. (D) Typical IR spectrum of tissue, with important peaks labeled. Scale bar = 100 &#956;m. <a href="https://www.jove.com/files/ftp_upload/52332/52332fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52332/52332fig2.jpg" /> 
Figure 2. Optics schematic detailing FT-IR microscope operation modes.&#160;(A) In transmission mode, the sample is illuminated through the bottom objective, and the light passing through the sample is collected by the top objective. (B) In reflection mode, the top objective serves both to illuminate the sample and to collect the reflected light. The bottom objective is not used. <a href="https://www.jove.com/files/ftp_upload/52332/52332fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52332/52332fig3.jpg" /> 
Figure 3. Comparison of different microscope objectives on FT-IR images of a kidney glomerulus at 2,925 cm-1. (A) 15X collecting objective with NA = 0.5 (5.5 x 5.5 &#181;m pixel size). (B) 36X collecting objective with NA = 0.5 (2.2 x 2.2 &#181;m pixel size). (C) 74X collecting objective with NA = 0.65 (1.1 x 1.1 &#181;m pixel size). Scale bar = 50 &#956;m. <a href="https://www.jove.com/files/ftp_upload/52332/52332fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/52332/52332fig4.jpg" /> 
Figure 4. Spectral differences between fibrosis and hepatocytes in a liver core. (A) H&#38;E stained core from liver biopsy. (B) Image of a serial section core scanned in FT-IR (36X objective setup). (C) Representative spectra of hepatocytes and fibrosis, taken from regions of tissue indicated by arrows in (A) and (B). Scale bar = 100 &#956;m. <a href="https://www.jove.com/files/ftp_upload/52332/52332fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/52332/52332fig5.jpg" /> 
Figure 5. Differentiation of kidney tissue biopsy features through the use of high-definition FT-IR imaging. (A) Periodic acid-Schiff stained section with the features to be extracted labeled. (B) High-definition FT-IR image of the CH2 asymmetric stretching region (36X objective setup) of a serial section of the same tissue. (C) Features labeled in (A) extracted using the FT-IR image in (B) to be able to chemically differentiate the four features of the tissue. Scale bar = 50 &#956;m. <a href="https://www.jove.com/files/ftp_upload/52332/52332fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology at JoVE.com

High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Fourier Transform Infrared (FT-IR) spectroscopic imaging is a fast and label-free approach to obtain biochemical data sets of cells and tissues. Here, we demonstrate how to obtain high-definition FT-IR images of tissue sections towards improving disease diagnosis.

High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging is an emerging approach to obtain detailed images that have associated ...

high-definition-fourier-transform-infrared-ft-ir-spectroscopic

Research

Education

JoVE Core

JoVE Journal

Electrical Engineering

Medicine

Unit 1

z-Transform

SCIENTISTS IN ACTION

33.1K Views.  University of Illinois at Chicago. Fourier Transform Infrared (FT-IR) spectroscopic imaging is a fast and label-free approach to obtain biochemical data sets of cells and tissues. Here, we demonstrate how to obtain high-definition FT-IR images of tissue sections towards improving disease diagnosis. 

Video: High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting airways, whereas adenocarcinomas are typically more peripheral in location. Lung malignancy detection early in the disease process may be difficult due to several limitations: radiological resolution, bronchoscopic limitations in evaluating tissue underlying the airway mucosa and identifying early pathologic changes, and small sample size and/or incomplete sampling in histology biopsies. High resolution imaging modalities, such as optical frequency domain imaging (OFDI), provide non-destructive, large area 3-dimensional views of tissue microstructure to depths approaching 2 mm in real time (Figure 1)2-6. OFDI has been utilized in a variety of applications, including evaluation of coronary artery atherosclerosis6,7 and esophageal intestinal metaplasia and dysplasia6,8-10.
Bronchoscopic OCT/OFDI has been demonstrated as a safe in vivo imaging tool for evaluating the pulmonary airways11-23 (Animation). OCT has been assessed in pulmonary airways16,23 and parenchyma17,22 of animal models and in vivo human airway14,15. OCT imaging of normal airway has demonstrated visualization of airway layering and alveolar attachments, and evaluation of dysplastic lesions has been found useful in distinguishing grades of dysplasia in the bronchial mucosa11,12,20,21. OFDI imaging of bronchial mucosa has been demonstrated in a short bronchial segment (0.8 cm)18. Additionally, volumetric OFDI spanning multiple airway generations in swine and human pulmonary airways in vivo has been described19. Endobronchial OCT/OFDI is typically performed using thin, flexible catheters, which are compatible with standard bronchoscopic access ports. Additionally, OCT and OFDI needle-based probes have recently been developed, which may be used to image regions of the lung beyond the airway wall or pleural surface17.
While OCT/OFDI has been utilized and demonstrated as feasible for in vivo pulmonary imaging, no studies with precisely matched one-to-one OFDI:histology have been performed. Therefore, specific imaging criteria for various pulmonary pathologies have yet to be developed. Histopathological counterparts obtained in vivo consist of only small biopsy fragments, which are difficult to correlate with large OFDI datasets. Additionally, they do not provide the comprehensive histology needed for registration with large volume OFDI. As a result, specific imaging features of pulmonary pathology cannot be developed in the in vivo setting. Precisely matched, one-to-one OFDI and histology correlation is vital to accurately evaluate features seen in OFDI against histology as a gold standard in order to derive specific image interpretation criteria for pulmonary neoplasms and other pulmonary pathologies. Once specific imaging criteria have been developed and validated ex vivo with matched one-to-one histology, the criteria may then be applied to in vivo imaging studies. Here, we present a method for precise, one to one correlation between high resolution optical imaging and histology in ex vivo lung resection specimens. Throughout this manuscript, we describe the techniques used to match OFDI images to histology. However, this method is not specific to OFDI and can be used to obtain histology-registered images for any optical imaging technique. We performed airway centered OFDI with a specialized custom built bronchoscopic 2.4 French (0.8 mm diameter) catheter. Tissue samples were marked with tissue dye, visible in both OFDI and histology. Careful orientation procedures were used to precisely correlate imaging and histological sampling locations. The techniques outlined in this manuscript were used to conduct the first demonstration of volumetric OFDI with precise correlation to tissue-based diagnosis for evaluating pulmonary pathology24. This straightforward, effective technique may be extended to other tissue types to provide precise imaging to histology correlation needed to determine fine imaging features of both normal and diseased tissues.

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

A method to image ex vivo pulmonary resection specimens with optical frequency domain imaging (OFDI) and obtain precise correlation to histology is described, which is essential to developing specific OFDI interpretation criteria for pulmonary pathology. This method is applicable to other tissue types and imaging techniques to obtain precise imaging to histology correlation for accurate image interpretation and assessment. Imaging criteria established with this technique would then be applicable to image assessment in future in vivo studies.

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting ...

Bioengineering

Three-dimensional Imaging of Nociceptive Intraepidermal Nerve Fibers in Human Skin Biopsies

A punch biopsy of the skin is commonly used to quantify intraepidermal nerve fiber densities (IENFD) for the diagnosis of peripheral polyneuropathy 1,2. At present, it is common practice to collect 3 mm skin biopsies from the distal leg (DL) and the proximal thigh (PT) for the evaluation of length-dependent polyneuropathies 3. However, due to the multidirectional nature of IENFs, it is challenging to examine overlapping nerve structures through the analysis of two-dimensional (2D) imaging. Alternatively, three-dimensional (3D) imaging could provide a better solution for this dilemma.
In the current report, we present methods for applying 3D imaging to study painful neuropathy (PN). In order to identify IENFs, skin samples are processed for immunofluorescent analysis of protein gene product 9.5 (PGP), a pan neuronal marker. At present, it is standard practice to diagnose small fiber neuropathies using IENFD determined by PGP immunohistochemistry using brightfield microscopy 4. In the current study, we applied double immunofluorescent analysis to identify total IENFD, using PGP, and nociceptive IENF, through the use of antibodies that recognize tropomyosin-receptor-kinase A (Trk A), the high affinity receptor for nerve growth factor 5. The advantages of co-staining IENF with PGP and Trk A antibodies benefits the study of PN by clearly staining PGP-positive, nociceptive fibers. These fluorescent signals can be quantified to determine nociceptive IENFD and morphological changes of IENF associated with PN. The fluorescent images are acquired by confocal microscopy and processed for 3D analysis. 3D-imaging provides rotational abilities to further analyze morphological changes associated with PN. Taken together, fluorescent co-staining, confocal imaging, and 3D analysis clearly benefit the study of PN.

In order to study the changes of nociceptive intraepidermal nerve fibers (IENFs) in painful neuropathies (PN), we developed protocols that could directly examine three-dimensional morphological changes observed in nociceptive IENFs. Three-dimensional analysis of IENFs has the potential to evaluate the morphological changes of IENF in PN.

A punch biopsy of the skin is commonly used to quantify intraepidermal nerve fiber densities (IENFD) for the diagnosis of peripheral polyneuropathy 1,2. ...

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

The exclusive combination of high optical contrast and excellent spatial resolution makes optoacoustics (photoacoustics) ideal for simultaneously attaining anatomical, functional and molecular contrast in deep optically opaque tissues. While enormous potential has been recently demonstrated in the application of optoacoustics for small animal research, vast efforts have also been undertaken in translating this imaging technology into clinical practice. We present here a newly developed optoacoustic tomography approach capable of delivering high resolution and spectrally enriched volumetric images of tissue morphology and function in real time. A detailed description of the experimental protocol for operating with the imaging system in both hand-held and stationary modes is provided and showcased for different potential scenarios involving functional and molecular studies in murine models and humans. The possibility for real time visualization in three dimensions along with the versatile handheld design of the imaging probe make the newly developed approach unique among the pantheon of imaging modalities used in today&#8217;s preclinical research and clinical practice.

We provide herein a detailed description of the experimental protocol for imaging with a newly developed hand-held optoacoustic (photoacoustic) system for three-dimensional functional and molecular imaging in real time. The demonstrated powerful performance and versatility may define new application areas of the optoacoustic technology in preclinical research and clinical practice.

The exclusive combination of high optical contrast and excellent spatial resolution makes optoacoustics (photoacoustics) ideal for simultaneously ...

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the tumor immune landscape. Although standard immunohistochemistry is suitable for studying tissue architecture, it is limited to the analysis of a small number of markers. Conversely, techniques such as flow cytometry can evaluate multiple markers simultaneously, although information about tissue morphology is lost. In recent years, multiplexed strategies that integrate phenotypic and spatial analysis have emerged as comprehensive approaches to the characterization of the tumor immune landscape. Herein, we discuss an innovative technology combining metal-labeled antibodies and secondary ion mass spectrometry focusing on the technical steps in assay development and optimization, tissue preparation, and image acquisition and processing. Before staining, a metal-labeled antibody panel must be developed and optimized. This hi-plex image system supports up to 40 metal-tagged antibodies in a single tissue section. Of note, the risk of signal interference increases with the number of markers included in the panel. After panel design, particular attention should be given to the metal isotope assignment to the antibody to minimize this interference. Preliminary panel testing is performed using a small subset of antibodies and subsequent testing of the entire panel in control tissues. Formalin-fixed, paraffin-embedded tissue sections are obtained and mounted on gold-coated slides and further stained. The staining takes 2 days and closely resembles standard immunohistochemical staining. Once samples are stained, they are placed in the image acquisition instrument. Fields of view are selected, and images are acquired, uploaded, and stored. The final stage is image preparation for the filtering and removal of interference using the system&#39;s image processing software. A disadvantage of this platform is the lack of analytical software. However, the images generated are supported by different computational pathology software.

In the era of cancer immunotherapy, interest in elucidating tumor microenvironment dynamics has increased strikingly. This protocol details a mass spectrometry imaging technique with respect to its staining and imaging steps, which allow for highly multiplexed spatial analysis.

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the ...

Cancer Research

Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

This manuscript presents a protocol to handle, characterize, and image freshly excised human breast tumors using pulsed terahertz imaging and spectroscopy techniques. The protocol involves terahertz transmission mode at normal incidence and terahertz reflection mode at an oblique angle of 30&#176;. The collected experimental data represent time domain pulses of the electric field. The terahertz electric field signal transmitted through a fixed point on the excised tissue is processed, through an analytical model, to extract the refractive index and absorption coefficient of the tissue. Utilizing a stepper motor scanner, the terahertz emitted pulse is reflected from each pixel on the tumor providing a planar image of different tissue regions. The image can be presented in time or frequency domain. Furthermore, the extracted data of the refractive index and absorption coefficient at each pixel are utilized to provide a tomographic terahertz image of the tumor. The protocol demonstrates clear differentiation between cancerous and healthy tissues. On the other hand, not adhering to the protocol can result in noisy or inaccurate images due to the presence of air bubbles and fluid remains on the tumor surface. The protocol provides a method for surgical margins assessment of breast tumors.

Freshly excised human breast cancer tumors are characterized with terahertz spectroscopy and imaging following fresh tissue handling protocols. Tissue positioning is taken into consideration to enable effective characterization while providing analysis in a timely manner for future intraoperative applications.

This manuscript presents a protocol to handle, characterize, and image freshly excised human breast tumors using pulsed terahertz imaging and spectroscopy ...

Engineering

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

Recent fiber-bundle microendoscopy techniques enable non-invasive analysis of in vivo tissue using either imaging techniques or a combination of spectroscopy techniques. Combining imaging and spectroscopy techniques into a single optical probe may provide a more complete analysis of tissue health. In this article, two dissimilar modalities are combined, high-resolution fluorescence microendoscopy imaging and diffuse reflectance spectroscopy, into a single optical probe. High-resolution fluorescence microendoscopy imaging is a technique used to visualize apical tissue micro-architecture and, although mostly a qualitative technique, has demonstrated effective real-time differentiation between neoplastic and non-neoplastic tissue. Diffuse reflectance spectroscopy is a technique which can extract tissue physiological parameters including local hemoglobin concentration, melanin concentration, and oxygen saturation. This article describes the specifications required to construct the fiber-optic probe, how to build the instrumentation, and then demonstrates the technique on in vivo human skin. This work revealed that tissue micro-architecture, specifically apical skin keratinocytes, can be co-registered with its associated physiological parameters. The instrumentation and fiber-bundle probe presented here can be optimized as either a handheld or endoscopically-compatible device for use in a variety of organ systems. Additional clinical research is needed to test the viability of this technique for different epithelial disease states.

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

The assembly and use of a multimodal microendoscope is described which can co-register superficial tissue image data with tissue physiological parameters including hemoglobin concentration, melanin concentration, and oxygen saturation. This technique can be useful for evaluating tissue structure and perfusion, and can be optimized for individual needs of the investigator.

Recent fiber-bundle microendoscopy techniques enable non-invasive analysis of in vivo tissue using either imaging techniques or a combination of ...

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

The spatial heterogeneity of the tumor microenvironment (TME) is a critical determinant of therapeutic response, particularly for immune-oncology agents, where success depends on the distribution of specific immune cell subpopulations. Over the past decade, multiple sophisticated technologies have been introduced to achieve detailed resolution of the TME using two-dimensional sections from either formalin-fixed, paraffin-embedded (FFPE) or fixed-frozen tissues. While these thin sections are easier to procure and analyze, they lack the three-dimensional architecture needed to reliably and comprehensively characterize a tumor. To address this limitation, a tissue mounting and imaging technique was developed to enable the three-dimensional analysis of tumor lesions in their native in vivo state. This protocol outlines the procurement of human tumor tissue, the mounting of samples on custom-printed platforms, and staining procedures for post-fixation samples. The multiplexed immunofluorescence technique, IBEX (Iterative Bleaching Extends Multiplexity), was adapted to characterize the three-dimensional TME with up to 15 markers for tumor, immune, and stromal cells using commercially available antibodies. Imaging depths of up to 100 &#181;m were achieved using an inverted white-light laser confocal microscope with a custom-printed imaging adaptor and commercial glass-bottom dishes to ensure optimal tissue orientation. This protocol highlights the potential of the IBEX method to expand multiplexed immunofluorescence studies, providing a more comprehensive understanding of TME composition.

This article describes a protocol for multiplex immunofluorescence optimized to characterize the three-dimensional architecture of peritoneal metastases.

The spatial heterogeneity of the tumor microenvironment (TME) is a critical determinant of therapeutic response, particularly for immune-oncology agents, ...

Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis

Stimulated Raman scattering (SRS) microscopy has emerged as a powerful optical imaging technique for tissue diagnosis. In recent years, two-color SRS has been shown to be able to provide hematoxylin and eosin (H&amp;E)-equivalent images that allow fast and reliable diagnosis of brain cancer. Such capability has enabled exciting intraoperative cancer diagnosis applications. Two-color SRS imaging of tissue can be done with either a picosecond or femtosecond laser source. Femtosecond lasers have the advantage of enabling flexible imaging modes, including fast hyperspectral imaging and real-time, two-color SRS imaging. A spectral-focusing approach with chirped laser pulses is typically used with femtosecond lasers to achieve high spectral resolution. 
Two-color SRS acquisition can be realized with orthogonal modulation and lock-in detection. The complexity of pulse chirping, modulation, and characterization is a bottleneck for the widespread adoption of this method. This article provides a detailed protocol to demonstrate the implementation and optimization of spectral-focusing SRS and real-time, two-color imaging of mouse brain tissue in the epi-mode. This protocol can be used for a broad range of SRS imaging applications that leverage the high speed and spectroscopic imaging capability of SRS.

Stimulated Raman scattering (SRS) microscopy is a powerful, nondestructive, and label-free imaging technique. One emerging application is stimulated Raman histology, where two-color SRS imaging at the protein and lipid Raman transitions are used to generate pseudo-hematoxylin and eosin images. Here, we demonstrate a protocol for real-time, two-color SRS imaging for tissue diagnosis.

Stimulated Raman scattering (SRS) microscopy has emerged as a powerful optical imaging technique for tissue diagnosis. In recent years, two-color SRS has ...

Comprehensive Protocol for Cell Identification and Metabolic Analysis Using OPTIR-FISH

Optical Photothermal Infrared-Fluorescence In Situ Hybridization (OPTIR-FISH)

Understanding the metabolic activities of individual cells within complex communities is critical for unraveling their role in human disease. Here, we present a comprehensive protocol for simultaneous cell identification and metabolic analysis with the OPTIR-FISH platform by combining rRNA-tagged FISH probes and isotope-labeled substrates. Fluorescence imaging provides cell identification by the specific binding of rRNA-tagged FISH probes, while OPTIR imaging provides metabolic activities within single cells by isotope-induced red shift on OPTIR spectra. Using bacteria cultured with 13C-glucose as a test bed, the protocol outlines microbial culture with isotopic labeling, fluorescence in situ hybridization (FISH), sample preparation, optimization of the OPTIR-FISH imaging setup, and data acquisition. We also demonstrate how to perform image analysis and interpret spectral data at the single-cell level with high throughput. This protocol&#39;s standardized and detailed nature will greatly facilitate its adoption by researchers from diverse backgrounds and disciplines within the broad single-cell metabolism research community.

Author Spotlight: Integrated OPTIR-FISH for Single-Cell Metabolic and Identity Analysis in Complex Environments

Here, we present a protocol using optical photothermal infrared-fluorescence in situ hybridization (OPTIR-FISH), also known as mid-infrared photothermal-FISH (MIP-FISH), to identify individual cells and understand their metabolism. This methodology can be applied broadly for diverse applications, including mapping cellular metabolism with single-cell resolution.

Understanding the metabolic activities of individual cells within complex communities is critical for unraveling their role in human disease. Here, we ...

Nanoscopic Imaging of Human Tissue Sections via Physical and Isotropic Expansion

In modern pathology, optical microscopy plays an important role in disease diagnosis by revealing microscopic structures of clinical specimens. However, the fundamental physical diffraction limit prevents interrogation of nanoscale anatomy and subtle pathological changes when using conventional optical imaging approaches. Here, we describe a simple and inexpensive protocol, called expansion pathology (ExPath), for nanoscale optical imaging of common types of clinical primary tissue specimens, including both fixed-frozen or formalin-fixed paraffin embedded (FFPE) tissue sections. This method circumvents the optical diffraction limit by chemically transforming the tissue samples into tissue-hydrogel hybrid and physically expanding them isotropically across multiple scales in pure water. Due to expansion, previously unresolvable molecules are separated and thus can be observed using a conventional optical microscope.

Nanoscale imaging of clinical tissue samples can improve understanding of disease pathogenesis. Expansion pathology (ExPath) is a version of expansion microscopy (ExM), modified for compatibility with standard clinical tissue samples, to explore the nanoscale configuration of biomolecules using conventional diffraction limited microscopes.

In modern pathology, optical microscopy plays an important role in disease diagnosis by revealing microscopic structures of clinical specimens. However, ...

High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Podsumowanie

Przeglądaj więcej filmów

PLAYLIST