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WO2016201572A1 - Méthodes de détection de la stéatose - Google Patents

Méthodes de détection de la stéatose Download PDF

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Publication number
WO2016201572A1
WO2016201572A1 PCT/CA2016/050699 CA2016050699W WO2016201572A1 WO 2016201572 A1 WO2016201572 A1 WO 2016201572A1 CA 2016050699 W CA2016050699 W CA 2016050699W WO 2016201572 A1 WO2016201572 A1 WO 2016201572A1
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Prior art keywords
tissue
raman spectrum
fat
raman
spectrum
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Inventor
Ian ALWAYN
Kevin Cecil HEWITT
Haishan Zeng
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Dalhousie University
British Columbia Cancer Agency BCCA
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Dalhousie University
British Columbia Cancer Agency BCCA
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4222—Evaluating particular parts, e.g. particular organs
    • A61B5/4244—Evaluating particular parts, e.g. particular organs liver
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4869—Determining body composition
    • A61B5/4872—Body fat
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/20—Measuring for diagnostic purposes; Identification of persons for measuring urological functions restricted to the evaluation of the urinary system
    • A61B5/201—Assessing renal or kidney functions

Definitions

  • the present disclosure is directed to methods of detecting and quantifying steatosis using Raman Spectroscopy.
  • Steatosis is the term used to describe lipid retention in a mammalian cell due to the build-up or insufficient clearance of triglycerides.
  • the risk factors associated with steatosis in a person are varied and include obesity, sleep apnea, insulin resistance, tetracycline exposure, Reye's syndrome, hepatitis C, and acute fatty liver of pregnancy. Organs affected by steatosis are demonstrably less fit for their biological function.
  • steatotic organs that are used in transplantation are a significant contributing factor in allograft dysfunction, in ischemia reperfusion injury, and in lower survival rates for transplant patients.
  • Detection of steatosis has been previously attempted using a variety of methods, but all of these have certain drawbacks. The most accurate of these involves biopsy and pathological evaluation using a battery of stains (H&E, Sudan stain, OsO4). While sample collection for the biopsy is straightforward, the methodology for analysis requires an expert, and as such it is a low-throughput method unsuitable for screening organs designated for transplantation.
  • non-invasive techniques for detection of steatosis include computed tomography (CT) and ultrasonic (US) imaging. Other imaging techniques include magnetic resonance imaging and positron emission tomography, which is more quantitative. All of the imaging techniques are unfortunately rather expensive to perform, require specialized equipment that is not portable, and require experts to administer the test and analyze the results.
  • the method may include exciting the tissue with a laser at a near-infrared wavelength; recording a Raman spectrum associated with triglyceride C-H bonds; calibrating the Raman spectrum; applying principal component analysis to the calibrated Raman spectrum to generate a principal component score; and characterizing the amount of fat present in the tissue based on the principal component score.
  • calibrating the Raman spectrum may include, recording a background fluorescence spectrum overlapping with the recorded Raman spectrum; arranging the Raman spectrum data in a single matrix; subtracting the background from the Raman spectrum data; and removing the mean from the difference.
  • the method may further include converting the calibrated Raman spectrum into a 2 nd order derivative spectrum, and a Savitzky-Golay six-point quadratic polynomial may be used to convert the calibrated Raman spectrum into the 2 nd order derivative spectrum.
  • Characterizing the amount of fat present in the tissue may include comparing the estimated fat proportion from the principal component analysis to known measured fat content.
  • steatosis may be detected in the tissue if the amount of fat characterized in the tissue is above a threshold, for example, a threshold of about 5%.
  • the near-infrared wavelength of the laser may range from about 700 nm to about 1500 nm.
  • the Raman spectra is observed at a Raman shift in the range of about 2600 cm “1 to about 3100 cm “1 relative to the wavelength of the near-infrared laser.
  • the Raman spectrum data below about 2600 cm "1 is truncated and removed from further analysis.
  • characterizing the amount of fat present in the tissue may include observing an intensity of the Raman spectrum at a Raman shift of 2846 ⁇ 10 cm “ 1 , 2891 ⁇ 10 cm “1 , and 3014 ⁇ 10 cm “1 relative to the wavelength of the near-infrared laser.
  • the tissue may be selected from liver, kidney, heart, and muscle. In one aspect, the tissue may be liver.
  • the Raman spectrum may be acquired over a duration of about 1 second, and the characterization of the amount of fat may be performed in less than about 20 seconds. [0008] Further disclosed herein is a method of screening a tissue for transplantation potential.
  • the method may include exciting the tissue in situ with a laser at a near- infrared wavelength; characterizing the amount of fat present in the tissue based on a recorded, calibrated, and analyzed Raman spectrum; and rejecting the tissue for transplantation if the amount of fat characterized in the tissue is above a threshold of a predetermined acceptable level of fat.
  • the threshold may be about 5%.
  • the method may further include returning a value of the characterized amount of fat in a manner that does not require expert interpretation. The characterization of the amount of fat is performed in less than about 20 seconds.
  • the method may include acquiring at least one Raman spectrum of the tissue;
  • the method may further include calibrating the Raman spectrum prior to analyzing the magnitude of intensity.
  • Calibrating the Raman spectrum may include recording a background fluorescence spectrum overlapping with the recorded Raman spectrum; arranging the Raman spectrum data in a single matrix; subtracting the background from the Raman spectrum data; and removing the mean from the
  • the method may further include converting the calibrated Raman spectrum into a 2 nd order derivative spectrum, for example, a Savitzky-Golay six-point quadratic polynomial may be used to convert the calibrated Raman spectrum into the 2 nd order derivative spectrum.
  • Steatosis may be detected in the tissue if the amount of fat content in the tissue is above a threshold, such as 5%.
  • the tissue may be selected from liver, kidney, heart, and muscle. In one aspect, the tissue may be liver.
  • the Raman spectrum may be acquired over a duration of about 1 second, and generating an indication of fat content may be performed in less than about 20 seconds.
  • FIG. 3A shows Raman Spectra of a normal (dotted line) and fatty mice liver (solid line), using 785 nm excitation and an acquisition time of 1 second. Average spectra from three fatty liver and two normal liver spectra were preprocessed by removing the fluorescent background using a polynomial fit.
  • FIG. 4 shows the concentration of triglycerides (g/L) in the left liver lobe of MCD rats in Example 1 , as determined by thin-layer chromatography.
  • FIG. 5A shows pathological scores and FIG. 5B shows corresponding principal component scores over time for the Examples given below.
  • FIG. 6A shows Triglyceride content vs Pathologist rating
  • FIG. 6B shows PC score vs Pathologist rating
  • FIG. 6C shows PC score versus Triglyceride content, for the left lobe for the Examples given below.
  • FIG. 7 shows the PC score of Example 2 converted back to a spectrum for each week on the MCD or control diet for the animals of Example 1 . Spectra falling below arbitrary units of zero have lower lipid content than average, while spectra falling above zero have higher average lipid content.
  • FIG. 8 is a flowchart of one embodiment of the steps in the method of analyzing Raman spectra data.
  • FIG. 9A is a raw Raman spectrum of a rat liver with the excitation laser on, calibrated for wavelength (abscissa) and intensity (ordinate).
  • FIG. 9B is the dark current spectrum of a rat liver collected with the excitation laser off, calibrated for wavelength (abscissa) and intensity (ordinate).
  • FIG. 9C shows the spectrum data of FIG. 9A corrected for the dark current spectrum of FIG. 9B.
  • FIG. 10 shows the spectrum of FIG. 9C corrected to eliminate background fluorescence.
  • FIG. 11 shows a plot of singular values and the amount of information they reveal (ordinate) from the PCA of Raman spectra of 10 samples with different proportions of fatty liver in normal liver. From this figure, 6 PCs were assumed sufficient to model the relevant information regarding fat proportions in the livers.
  • FIG. 12 shows a regression analysis using PCR.
  • FIG. 13 shows regression coefficients versus wavelength (wavenumber) for liver-in-liver data.
  • the largest (positive) regression coefficients correspond to the wavenumbers that discriminate most for the presence of fat, especially in the region where the 2846 cm “1 mode due to symmetric stretching of C-H bonds in CH 2 and 2891 cm “1 due to symmetric stretching of C-H bonds in CH 3 are found as in FIG. 3A, corresponding to the presence of lipids.
  • FIG. 14 is an illustration of a Raman spectroscopy device that may be used with the methods disclosed herein.
  • FIG. 15 a block diagram of steps that may be used for determining fat content in a tissue in one aspect.
  • FIG. 16 is a diagram of the calibration methods and output that may be used by data acquisition software to analyze the Raman spectrum in one aspect.
  • Raman spectroscopy is an inelastic light scattering technique that is sensitive to molecular vibrations, the symmetry and frequencies of which are unique to the type of atoms and their spatial arrangement. Sensitivity to these properties is the basis of its ability to provide a spectral fingerprint of molecules in the illuminated region. It has been used to detect amino acids, nucleotide bases, fatty acids, saccharides, primary metabolites, and other constituents that form the protein, carbohydrates, fats and DNA/RNA of biological tissues. It is an attractive technology for bioanalysis because no sample preparation is required, and unlike IR absorption the signal from water can be isolated to a very narrow spectral range.
  • a conventional fibre-optic Raman system has not yet been shown to provide the sensitivity and specificity required for liver steatosis assessment.
  • a complete conventional fibre optic Raman system including continuous laser is cheaper, smaller, portable, robust than CARS/SRS and enables in-situ scans to be undertaken with very little experience required of the operator.
  • a fibre-optic based Raman probe with improved methods of analyzing the Raman spectra may take full advantage of the quantification strengths of Raman spectroscopy without the limitations of CARS/SRS microscopy.
  • the equipment required for these methods is inexpensive and portable compared to other methods, while retaining sensitivity and specificity.
  • the methods can quickly report the degree of steatosis without the need for expert interpretation.
  • the methods can be employed in organ retrieval or
  • a method of determining steatosis in a mammalian tissue may include exciting the tissue with a near-infrared laser and recording a Raman spectrum in the range associated with triglyceride C-H bonds.
  • the method also includes converting calibrated and smoothed Raman spectra into 2nd order derivative spectra with a Savitzky-Golay six-point quadratic polynomial.
  • the method may also include further applying a principal component analysis (PCA) to the spectra.
  • PCA principal component analysis
  • the method may include assaying fat, or the degree of steatosis, in a mammalian tissue by measuring triglyceride levels in the tissue using Raman spectroscopy.
  • the method may include detecting of the degree of steatosis in a cell, or the degree of steatosis on average in a mammalian tissue having cells.
  • the tissue may be liver, kidney, heart, muscle, or any organ to tissue to be transplanted.
  • the tissue may be liver.
  • the method may include assaying hepatic fat in a liver by measuring triglyceride levels in the tissue using Raman spectroscopy.
  • Measurement of hepatic fat may be correlated to determining the degree of steatosis in a human liver.
  • the degree of steatosis may be determined using Raman spectroscopic methods and apparatus as described in Example 1 below.
  • the method may further include screening an organ for transplantation potential.
  • the organ may be rejected for transplantation if the detection method returns a value of fat that is above a predetermined acceptable level.
  • the method may further include returning the value in a manner that does not require expert interpretation.
  • the method of determining steatosis in a mammalian tissue may include exciting the tissue with a laser at a near-infrared wavelength; recording a
  • Raman spectrum associated with triglyceride C-H bonds calibrating the Raman spectrum; applying principal component analysis to the calibrated Raman spectrum to generate a principal component score; and characterizing the amount of fat present in the tissue based on the principal component score.
  • the Raman spectrum signal is complicated because tissues have many chemicals that are mixed together in very different amounts and give a signal that reflects this complexity. A small part of the signal corresponds to fat. Therefore, the signal may need further processing to allow for quantification of the fat content in the tissue. In various aspects, the signal may need to be calibrated,
  • the method of determining steatosis in an organ may include acquiring a Raman spectrum, calibrating the spectrum, determining the principal components responsible for differences in spectrum, generating a principal component score, and calibrating the principal component score to a known concentration of fat.
  • the method may further include displaying the fat content to a user.
  • FIG. 15 illustrates steps in determining steatosis in an organ in one aspect.
  • any suitable apparatus may be used to acquire Raman and background spectra in a desired wavelength range.
  • a Raman spectroscopy system may be used to acquire/record the Raman spectrum.
  • FIG. 14 illustrates one embodiment of a Raman spectroscopy probe that may be used with the disclosed methods.
  • the methods disclosed herein may use a fibre optic Raman spectroscopy system which may include a probe with a central fibre for excitation surrounded by about 27 outer fibres for collection.
  • the Raman spectroscopy system may include, a light source, such as a laser that emits light in the near-infrared (NIR).
  • the light source may be a laser diode.
  • the probe may range from about 0.5 m to about 1 m long, or may be about 0.75 m long in one aspect.
  • the probe may have a diameter ranging from about 1 mm to about 2.5 mm, from about 1 mm to about 1 .5 mm, from about 1 .5 mm to about 2 mm, and from about 2 mm to about 2.5 mm.
  • the probe may have a diameter of about 1 .8 mm.
  • the central fibre may have a diameter ranging from about 100 m to about 300 m, from about 100 m to about 150 Mm, from about 150 Mm to about 200 Mm, from about 200 Mm to about 250 Mm, and from about 250 Mm to about 300 Mm.
  • the central fibre may be about 200 Mm in diameter.
  • the central fibre may have a numerical aperture of 0.22.
  • the outer fibres may have a diameter ranging from about 50 Mm to about 150 Mm, from about 50 Mm to about 100 Mm, and from about 100 Mm to about 150 Mm. In one aspect, the outer fibres may be about 100 Mm in diameter.
  • the Raman spectroscopy system may further include a collimator, a bandpass filter to create monochromatic light from the laser, a lens for focusing the light onto the tissue, a shutter to limit exposure of the tissue to the laser light, lenses to focus the backscattered light into the outer fibres, and a notch filter to limit the light to a wavelength range of interest.
  • Acquiring the Raman spectrum may include illuminating the tissue and receiving the dispersed light from the tissue.
  • the tissue may be illuminated using a stabilized diode laser.
  • the stabilized diode laser may have a near infrared excitation wavelength.
  • the near infrared excitation wavelength may minimize the fluorescent background, eliminate the risk of bleeding and infection, and improve the penetration depth of the laser while reducing the possibility of tissue damage.
  • the excitation wavelength may range from about 700 nm to about 1500 nm, from about 700 nm to about 800 nm, from about 750 nm to about 850 nm, from about 800 nm to about 900 nm, from about 850 nm to about 950 nm, and from about 1000 nm to about 1500 nm.
  • the stabilized diode laser may have a wavelength of about 785 nm and a power of about 300 mW.
  • the dispersed light from the sample may be analyzed by a NIR spectrometer.
  • the spectrometer may include a back-illuminated, deep depletion CCD detector.
  • the CCD detector may have 400 X 1340 pixels and may have a 20 X 20 Mm pixel size in one aspect.
  • the spectral range of the system may be set at about 1400 cm “ 1 to about 3500 cm “1 or about 1900 cm “1 to about 3100 cm "1 .
  • the Raman frequencies may be calibrated using tissues with known fat quantities to determine Raman peaks in the spectral region of interest.
  • the Raman spectroscopy system may be calibrated for the measurement of fat content in the tissue.
  • the details of the system calibration procedure are illustrated in FIG. 16.
  • System calibration may include wavelength calibration, intensity calibration, and PCA based fat content calibration.
  • the spectrometer may provide a raw spectrum which is a dependence of the intensity in arbitrary units versus CCD pixel number. The spectrometer may then have to be calibrated in advance in order to provide usable results.
  • Raman frequencies may be calibrated using materials having known Raman peaks in the spectral region of interest.
  • the wavelength calibration may be performed with Ne, Xe and Hg standard lamps. To take into account the CCD intensity versus wavelength sensitivity a calibrated tungsten halogen light source may be used after wavelength calibration.
  • the calibration curves may then be utilized by the spectrometer control and data acquisition software. Then in order to obtain fat content in the tissue, another calibration may be required. That may be done by means of performing measurements of several liver tissues with different known fat content, PCA based analysis of the obtained spectra and building a calibration model.
  • the calibration model may be introduced into the spectrometer control and data acquisition software to provide a fat content in the tissue under study.
  • a Raman spectrum may be acquired in about a one second duration.
  • the Raman spectrum may be acquired in less than one second.
  • the Raman spectra may be acquired and recorded about ten times and averaged. The averaged spectra may then be calibrated as described below. PCR analysis of the resulting spectra may provide an estimate of the fat content. Therefore, the steps of screening a tissue or organ for transplantation may be completed in less than about 20 seconds per sample point. In various aspects, the screening may be completed in less than about 50 s, less than about 40 s, less than about 30 s, less than about 20 s, or less than about 10 s.
  • the quick acquisition, calibration, and calculation for screening may ensure that the clinician has access to real-time assessment of the level of steatosis. This may allow for in situ analysis of the tissue prior to removing the organ from the donor.
  • raw Raman spectra may be standardized/normalized before they are analyzed. Normalization of the spectra may be performed to account for any fluctuations in laser power or differences in probe to sample distance. The usual procedure for standardization is to subtract the dark signal from the raw data of each spectrum, calibrate for the wavelength-dependent sensitivity of the system, smooth the spectra and remove the underlying fluorescence with a modified 5th order polynomial.
  • FIG. 8 illustrates standardizing the spectra by arranging spectra data in a single matrix and pre-processing the data by subtracting the background and removing the mean.
  • every spectrum acquired may be accompanied by a companion measurement of dark current. This dark current measurement may be subtracted from the sample measurement before further processing.
  • FIG. 9 along with the final spectrum obtained after removing the dark current.
  • the spectrum may be dominated by a fluorescence signal which may be corrected using an approach for baseline correction that minimizes a non-quadratic cost function.
  • a spectrum corrected for this background is displayed in FIG. 10 and shows that most of the important information is found in wavenumbers greater than 2600 cm "1 . Subsequently the data at wavenumbers below this arbitrary threshold may be truncated and removed from further analyses.
  • the underlying fluorescence may be minimized by converting the calibrated and smoothed raw spectra into a 2nd order derivative spectra with a Savitzky-Golay six-point quadratic polynomial. This may be accomplished by summing the squared derivative values of a spectrum and then dividing each variable in the spectrum by this sum. See A. Savitzky and A. Golay, Anal. Chem., 1964, 36, 1627- 1639; T. L. Weis, Y. N. Jiang and E. R. Grant, J.
  • the 2nd order derivative spectra may then be analyzed, preferably using multivariate statistics as illustrated in Example 2.
  • PCA principal component analysis
  • linear discrimination analysis with leave-one-out cross validation on single, or groups of, PCs that may individually account for 0.1 % or more of the variance.
  • Principal component regression is a method for building a calibration for measurements exhibiting multiple variables. Determining the optimum number of principal components (PCs) may further include applying singular value decomposition (SVD). The PCs may then be used to develop a transferable calibration model for determining fat content in an organ.
  • PCs principal components
  • SVD singular value decomposition
  • Spectroscopic analyses of intact samples alleviate the drawbacks of classical least squares regression analysis methods but yield spectra that are characterized by broad and overlapped signals which reflect the complexity in the mixture of individual chemical species that constitute the sample. In such instances it is still possible to develop calibration models using the inverse least squares approach. These methods may allow quantitative analysis of rapid spectroscopic measurements of intact samples even in the face of inherent non-selectivity by regressing the concentration, rather than the response, of analytes (known to be a part of the intact sample) on the corresponding measured spectra. This may be expressed as:
  • the matrix (X T X) is unstable to inversion. Collinearities in the data arise due to many factors including that the number of analytes and interferents may be smaller than the number of wavelength channels or that their levels correlate with each other. This means that the matrix X will have a small number of dominating factors that carry most of the information while the majority of it exhibits redundancies and noise that can be eliminated.
  • One of the most efficient ways to achieve this data reduction is by applying principal component analysis via singular value decomposition.
  • o ⁇ ⁇ 2 ... ⁇ 0 are the real valued square roots of the eigenvalues of X, referred to as the singular values. These singular values are usually arranged in order of decreasing significance and, in theory, o t » ⁇ ⁇ +1 when the most important information in the data has been accounted for. The point at o t becomes significantly more than ⁇ ⁇ +1 must be chosen carefully in order to keep only the relevant information in the data.
  • the columns of U are the orthonormal eigenvectors of X T X while the columns of V are the orthonormal eigenvectors of XX T .
  • concentration of the analyte can be regressed such that:
  • T is the small set of principal components chosen carefully to maximize the information in the data while ⁇ can be determined as in Eqn. (2) by substituting X for T. This becomes:
  • livers that have different amounts of fat may be measured and calibrated.
  • Between 6 and 7 parts of the Raman signal may then be used to generate a formula to predict how much fat is in the livers.
  • the amount of fat may be predicted using the generated formula. This is shown in FIG. 12 along with, at FIG. 13, the regression coefficients transformed for the original un-centered variables. These show the wavenumbers at which the response exhibits a linear relationship with the concentration.
  • the formula generated from the estimated concentrations and known proportions of fat in a tissue may be used to generate a principal component score for each tissue measured.
  • the PC score may follow a pathologist standard criteria for scoring tissue, such as 0: absent; 1 : mild; 2: moderate, and 3: severe and mixed (mix of two or more classifications).
  • the PC score and formula may be used for future measurements of the same tissue type to predict fat content of that tissue.
  • the predicted fat content may then be used to screen an intact organ prior to transplantation as further described herein below.
  • the method may be carried out on an endoscopic Raman system as described in Example 1 , further equipped with a computer and statistical software for carrying out the analysis as described in Example 2 and displaying the result in a manner that will portray the degree of steatosis in the organ being tested.
  • a method of screening or determining the fitness or potential of an organ for transplantation is disclosed.
  • the screening may be performed on an intact organ or tissue that has not yet been removed from the donor. Therefore, the surgeon may screen the organ in situ to determine if the organ is fit for later
  • one of the detection methods disclosed above may be performed on the organ for transplantation, and the organ may be rejected for transplantation if the detection method reports a value (e.g., a degree of steatosis or lipid content) that is above a predetermined acceptable level or threshold of fat concentration.
  • the acceptable level may be predetermined based upon such factors as the type of organ to be transplanted and the correlation of organs with such values with adverse outcomes, e.g., graft dysfunction.
  • the acceptable level of fat is less than about 2%, less than about 5%, less than about 7%, or less than about 10% of the organ.
  • Hepatic steatosis also known as non-alcoholic fatty liver disease (NAFLD)
  • NAFLD non-alcoholic fatty liver disease
  • a liver may be screened before transplantation and the liver may be rejected if the fat content of the liver is determined to be greater than about 5%.
  • the result may be presented in a manner corresponding to a data series in any of FIGS. 3 to 5B.
  • the fat content may be displayed to the user, such as the surgeon, to clearly indicate the fat content and/or the acceptability of the tissue for transplantation.
  • the fat content may be presented as a percentage or other graphical or numerical representation of the amount of fat in the tissue, a warning indicator such as a warning light, a graphical or textual indicator of a classification of the tissue, for example "Steatosis", other suitable indications, or combinations thereof.
  • a predetermined degree of steatosis may be programmed into the software such that the display will indicate merely whether the degree of steatosis is "HIGH” or “LOW", or whether the organ is “ACCEPTABLE” or “NOT ACCEPTABLE” for, e.g., transplantation purposes.
  • FIG. 16 illustrates one example of a representation of fat content that may be displayed to the user.
  • Raman spectra may be acquired over about a one second duration.
  • the Raman spectra may be acquired and recorded about ten times and averaged.
  • the screening may be completed in less than about 50 s, less than about 40 s, less than about 30 s, less than about 20 s, or less than about 10 s.
  • the quick acquisition, calibration, and calculation for screening may ensure that the clinician has access to real-time assessment of the level of steatosis. This may allow for in situ analysis of the tissue prior to removing the organ from the donor.
  • mice and rat livers were induced in mice and rat livers by using a diet deficient in methionine and choline (MCD), and quantified using biochemical analysis techniques, pathologist rating, and conventional Raman spectroscopy. Since steatosis in human liver tissue exhibit changes similar to the left lobe of mice and rats, it was used as a comparator to steatosis in humans. It has been shown to appear in the left lobe in a homogeneous manner. Comparing control and MCD fed mice at 4 weeks, H&E-stained liver sections from the latter show large fat droplets displacing the nucleus. In FIG.
  • mice livers showed a significant increase in hepatic steatosis.
  • mice n 10
  • isofluorane inhalation setting 2-3 at 1 L 02 per minute.
  • isofluorane flow rate 1 .5-2 %
  • oxygen flow rate 1 L/min.
  • liver harvesting the abdomen was shaved and prepped with 70 % alcohol.
  • a vertical thoraco-abdominal incision was made exposing the heart and liver. The whole liver was removed and placed in a petri dish for ex-vivo Raman analysis.
  • Rat livers were removed and sectioned. The animals were finally euthanized by cardiac puncture and exsanguination while still under anaesthesia. Rat and mice liver sections were sent for macro Raman analysis by an endoscopic Raman spectroscopy system and tissue samples were also sent for histological analysis.
  • the endoscopic Raman spectroscopy system was equipped with excitation of 785 nm and 300 mW stabilized diode laser (BRM - 785 B&W TEK).
  • the near infrared excitation wavelength was used to minimize the fluorescent background, eliminate the risk of bleeding and infection, and improve the penetration depth of the laser while reducing the possibility of tissue damage.
  • the spectral range of the system was set at 1900 - 3100 cm "1 and the dispersed light from the sample was analyzed with a back- illuminated, deep depletion CCD detector with 400 X 1340 pixels.
  • the 200 m-diameter excitation fibre has a numerical aperture of .22, and its end was held approximately 5 mm above the sample. Consequently, the diameter of the 150 mW excitation spot on the tissue surface was 2.25 mm.
  • the laser penetrates approximately to 1 mm in depth in tissue and laterally, by about 0.5 mm more than the diameter of the excitation spot at its widest point.
  • the shape of the illuminated volume is approximated as an oblate spheroid, resulting in a total sampling volume of 4.0 mm 3 . As seen in FIG.
  • the latter is present when using NIR and not visible (458 or 514.5 nm) light excitation.
  • NIR and not visible (458 or 514.5 nm) light excitation there is a reduction in vibrations due to CH 3 symmetric and asymmetric stretch.
  • Liver tissues were excised from each lobe of liver and homogenized. Lipids were extracted according to Hildebrandt et al (A. Hildebrandt, I. Bickmeyer and R. P. KCihnlein, PLoS One, 201 1 , 6, e23796; hereby incorporated by reference). Briefly, 1 part homogenate was mixed with 4 parts -methanol (CHCI3:MeOH) 1 :2 (v/v) with continuous agitation at room temperature for 3 hours. Mixtures were centrifuged at 3000 rpm for 10 minutes after which the supernatant were transferred.
  • Hildebrandt et al A. Hildebrandt, I. Bickmeyer and R. P. KCihnlein, PLoS One, 201 1 , 6, e23796; hereby incorporated by reference. Briefly, 1 part homogenate was mixed with 4 parts -methanol (CHCI3:MeOH) 1 :2 (v/v)
  • TLC Thin-layer chromatography
  • Paraffin-embedded liver tissues were then cut into thin sections using a Microtome and stained with haematoxylin and eosin (H&E stain) and Oil Red O stains. Prepared slides were examined under the light microscope (10X objective). A hepato-pathologist viewed the slides and scored the tissue samples for steatosis using the following, standard criteria: 0: absent; 1 : mild; 2: moderate, and 3: severe and mixed (mix of two or more classifications). The pathologist was blind to the experimental groups.
  • PCs Principal components
  • LDA linear discrimination analysis
  • FIG. 5 compares estimates over time of left lobe triglyceride content from the first principal component of the above analysis with the same-sample estimates as determined by blinded pathological examination.
  • a blinded histological rating ( Figure 7) of the fat content in sections from the left lobe shows a consistent increase followed by saturation over the course of the experiment.
  • the correlation between these estimates has an R2 of approximately 0.96, which is very high and shows that the methods of this invention have a quality that rivals that of a pathological analysis.
  • FIG. 7 shows that spectra from later weeks have more lipids than the average, while earlier weeks have less lipid than the average.
  • Spectra lying on the x- axis represents the average lipid signal for all six weeks.
  • the spectra of earlier weeks fell below the x-axis therefore they have fewer lipids than the average and spectra from later weeks have more lipids than the average.
  • the control is significantly lower than the rest of the spectra.
  • PC average Raman spectroscopic evidence
  • Every spectrum acquired was accompanied by a companion measurement of dark current. This dark current measurement was subtracted from the sample measurement before further processing. An example of the two measurements is shown in FIG. 9 along with the final spectrum obtained after removing the dark current. This spectrum is dominated by a fluorescence signal which was corrected using an approach for baseline correction that minimizes a non-quadratic cost function. The spectrum corrected for this background is displayed in FIG. 10 and shows that most of the important information is found in wavenumbers greater than 2600 cm "1 .

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Abstract

L'invention concerne des méthodes de détection de la stéatose qui utilisent un spectromètre Raman avec un laser d'excitation dans le proche infrarouge et une plage de détection associée aux liaisons C-H de triglycérides. L'invention concerne également des méthodes d'analyse de tels spectres, ainsi que des méthodes de criblage ou d'évaluation de la qualité d'organes à des fins de transplantation.
PCT/CA2016/050699 2015-06-16 2016-06-16 Méthodes de détection de la stéatose Ceased WO2016201572A1 (fr)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108896527A (zh) * 2018-06-08 2018-11-27 华中科技大学 一种拉曼光谱-主成分分析快速鉴别白酒真伪的方法
CN109507167A (zh) * 2018-11-16 2019-03-22 深圳达闼科技控股有限公司 一种物质检测方法、装置、计算设备及计算机存储介质
CN112505015A (zh) * 2019-09-16 2021-03-16 山东农业大学 一种拉曼光谱快速预判牛肉pH值的方法
CN114184600A (zh) * 2021-12-30 2022-03-15 江苏海洋大学 一种基于拉曼光谱背景扣除的水中溶质定量方法
WO2023137549A1 (fr) * 2022-01-20 2023-07-27 Dalhousie University Système d'analyse et procédé pour la détection synchrone en temps réel de longueurs d'onde caractéristiques dans le proche infrarouge de substances optiquement actives

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US20040186383A1 (en) * 1991-02-26 2004-09-23 Massachusetts Institute Of Technology Systems and methods of molecular spectroscopy to provide for the diagnosis of tissue
US20130231573A1 (en) * 2010-01-22 2013-09-05 British Columbia Cancer Agency Branch Apparatus and methods for characterization of lung tissue by raman spectroscopy

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US20040186383A1 (en) * 1991-02-26 2004-09-23 Massachusetts Institute Of Technology Systems and methods of molecular spectroscopy to provide for the diagnosis of tissue
US20130231573A1 (en) * 2010-01-22 2013-09-05 British Columbia Cancer Agency Branch Apparatus and methods for characterization of lung tissue by raman spectroscopy

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108896527A (zh) * 2018-06-08 2018-11-27 华中科技大学 一种拉曼光谱-主成分分析快速鉴别白酒真伪的方法
CN109507167A (zh) * 2018-11-16 2019-03-22 深圳达闼科技控股有限公司 一种物质检测方法、装置、计算设备及计算机存储介质
CN112505015A (zh) * 2019-09-16 2021-03-16 山东农业大学 一种拉曼光谱快速预判牛肉pH值的方法
CN114184600A (zh) * 2021-12-30 2022-03-15 江苏海洋大学 一种基于拉曼光谱背景扣除的水中溶质定量方法
WO2023137549A1 (fr) * 2022-01-20 2023-07-27 Dalhousie University Système d'analyse et procédé pour la détection synchrone en temps réel de longueurs d'onde caractéristiques dans le proche infrarouge de substances optiquement actives

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