Performing Department
Biomedical Sciences
Non Technical Summary
Chronic wasting disease (CWD) is an invariably fatal transmissaible spongiform encephalopathy of cervids (e.g. deer and elk). CWD is extremely contatious and can be transmitted from one animal to the next by contact with various excreta. The incubation period for CWD is 2-4 years. Infected animals have a relatively long 'preclinical' period of disease where they show no clinical signs, but are shedding infectious material. There is a greater prevalence of CWD within captive cervid populations. Control of CWD within captive populations is considered essential in order to protect the wild population. CWD is an economically important disease. Deer farming alone contributes an estimated 893.5 million dollars of direct economic activity annually.Control of CWD depends upon identification and removal of infected animals as early as possible during their preclinical period. Currently, identification of these animals depends upon detection of misfolded prion protein (PrP Sc) in a biopsy of recoanal mucosal tissue (e. g. RAMALT). The reported sensitivity of this technique varies from 68% to 90%, however collection of the sample is relatively invasive and requires significant restraint of the animals.We propose to evaluate the effectiveness of a test that uses Raman spectroscopy on skin biopsies to detect white-tailed deer infected wiht CWD. Raman spectroscopy is a rapid, repeatable and non-destructive analysis technology that has been widely used in many applications including diagnostic medicine. Application of Raman spectroscopy to an easily obtainable skin biopsy could transform our ability to identify CWD infected animals and control the spread of the disease.The proposed work is part of a much larger project to evaluate several different tests to detect animals infected with CWD. Thus, the effectiveness of this technique can be directly compared to all current diagnostic standards and developing approaches, as all assays will be done with samples from the same animals collected at the same time.
Animal Health Component
100%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
Aim 1: Determine the most significant differences in Raman spectrum in skin from animals with CWD compared with healthy animals. A Raman spectrum provides abundance information for 100s of biochemical components in a given sample. The objective of Aim 1 is to determine the most significant biochemical differences in skin from animals that are clinically ill with CWD compared to healthy control animals. This work will use archived samples from the National Animal Disease Center. Skin samples that have been formalin fixed and paraffin embedded will be sectioned onto gold-coated microscope slides. Raman measurements will be collected with a commercially available dispersive Raman microscope (DXR Raman microscope, Thermo Scientific, Madison) equipped with three excitation lasers (532 nm, 633 nm and 780 nm). A prinicple components analysis will be used to identify which spectral elements account for most of the differences between the samples. The known positive and negative samples will be used to train a classifier that will be used to identify samples from infected animals.Aim 2: Determine the earliest time point in the incubation of CWD that Raman spectra can be used to identify infected animals. This aim will utilize samples collected every three months at NADC from white-tailed deer infected with CWD. Skin samples will be fixed in formalin, embedded in paraffin and sectioned onto gold-coated slides. There are eight experimental animals currently in the study. Samples are collected every three months. Twenty spectra will be collected from each sample and averaged. A classifier (trained using the data collected in Aim 1) will be used to make a positive/negative call on the samples. The accuracy of the classifier will be quantified for samples from each collection time point.Aim 3: Compare the sensitivity of a Raman skin test with other methods. The work proposed here provides a somewhat unique opportunity to directly compare the sensitivity of our assay with other assays using samples from the same animals collected at the same time. The work described in Aim 2 will be used to calculate the sensitivity of our assay at each time point collected. Specifically, for each animal, at each time point we will have classified that sample as either postive or negative. We will collate data from collaborators to determine how our assay compares to all other assays for each animal at each time point. We will directly compare our results to immunohistochemistry of RAMALT (done by J. Greenlee, NADC), RT-QuIC of blood (done by A. Kanthasamy, ISU) and RT-QuIC of skin (done by Qingzhong Kong, Case Western Reserve University).
Project Methods
Plan of WorkExperimental samples: The proposed work would be an adjunct project to an ongoing study at the National Disease Center. Eight white-tailed deer were inoculated oronasally with CWD. Prior to inoculation, baseline samples of blood, saliva, and skin were collected and archived. To identify the earliest markers of CWD infection, animals are sampled every three months. In addition to blood, saliva and skin, nasal brushings and rectal biopsy samples are also collected. The work described in this proposal will utilize the skin samples (Aim 1 and Aim 2). Other samples will be analyzed in house by Dr. J. Greenlee's group at NADC, or by other collaborators (Dr. Anumantha Kanthasamy, BMS; Dr. Qingzhong Kong, Case Western Reserve University). By participating as a part of this consortium, we will be able to directly compare the sensitivity and specificity of our assay to currently used diagnostics ((Aim 3) i.e. RAMALT biopsy)) and assays under development (e.g. RT-QuIC). Skin samples will be collected by knotching the animal's ear. Tissue will be fixed in formalin, embedded in paraffin and a 5uM histologic section will be collected onto a gold-plated histologic slide for the collection of Raman spectra.Aim 1 analysis: All spectra will be baseline corrected, smoothed and normalized to reduce the baseline variability and background noises at the region between 550 and 2000 cm-1 using Omnic professional Software Suite (Thermo Scientific, Inc., Madision, Wisconsin), following the same procedure established by our earlier work. Through our earlier work we have developed multivariate statistical discriminant models to differentiate diseased skin samples from healthy ones. For discriminant anlaysis, the dimensions of the data set (i.e., each wave number in the spectral data represents an independent dimension) become large and limitatation on the capability of detecting distinguishable classes becomes severe. Therefore, principle component analysis (PCA) is used first for dimensionality reduction. The spectral data sets are compressed into PC scores, and 10 to 50 PC scores (which is the number that accounted for up to 99% of the total variance in canine glaucoma analysis), will be selected from approximately 1500 dimensional hyperspectral data. These principle componenets will be the inputs for the multivariate discriminant classification model generated using a Support Vector Machine implemented with MATLAB SVM toolbox (The Mathworks, Inc., Natick, Massachusetts) which uses a polynomial kernel function. Training sets and testing sets will be randomly chosen from the measured spectra (from diseased vs. healthy groups). Average classification accuracy will be calculated from 10 random replications of the discriminant process. To make the discriminant analysis robust, we will measure 10-15 skin samples from each group (diseased vs. healthy) and 20-30 spectra will be acquired from each sample.Aim 2 analysis: We will use two analysis approaches with the spectra collected from time course samples. First, spectra from baseline (pre-inoculation) samples will be compared to samples from given time points. Details of spectra collection and analysis are the same as in Aim 1. With this approach we will determine if it is possible to train a classifier that can effectively separate samples from a given time point (e.g. 9, 12, 15, 18, etc. months incubation time) from baseline samples. Second, we will use the classifier developed in Aim 1 to determine if any individual time point sample would be classified as CWD positive or CWD negative.Aim 3 analysis: Samples from each deer (eg. skin, blood, etc), at each timepoint (et. pre-inocultion, 3 months, 6 months, etc) will be tested by our collaborators to determine the preformance of each method (eg. QuIC, IHC of RAMALT, Raman skin test, etc). After all samples from all timepoints have been tested, we will compare the sensitivity and specificity of the Raman skin test to all other avaialble methods.