Imaging Mass Spectrometry and Proteome Analysis of Marek's Disease Virus-Induced Tumors

Abstract

The highly oncogenic alphaherpesvirus Marek's disease virus (MDV) causes immense economic losses in the poultry industry. MDV induces a variety of symptoms in infected chickens, including neurological disorders and immunosuppression. Most notably, MDV induces transformation of lymphocytes, leading to T cell lymphomas in visceral organs with a mortality of up to 100%. While several factors involved in MDV tumorigenesis have been identified, the transformation process and tumor composition remain poorly understood. Here we developed an imaging mass spectrometry (IMS) approach that allows sensitive visualization of MDV-induced lymphoma with a specific mass profile and precise differentiation from the surrounding tissue. To identify potential tumor markers in tumors derived from a very virulent wild-type virus and a telomerase RNA-deficient mutant, we performed laser capture microdissection (LCM) and thereby obtained tumor samples with no or minimal contamination from surrounding nontumor tissue. The proteomes of the LCM samples were subsequently analyzed by quantitative mass spectrometry based on stable isotope labeling. Several proteins, like interferon gamma-inducible protein 30 and a 70-kDa heat shock protein, were identified that are differentially expressed in tumor tissue compared to surrounding tissue and naive T cells. Taken together, our results demonstrate for the first time that MDV-induced tumors can be visualized using IMS, and we identified potential MDV tumor markers by analyzing the proteomes of virus-induced tumors.IMPORTANCE Marek's disease virus (MDV) is an oncogenic alphaherpesvirus that infects chickens and causes the most frequent clinically diagnosed cancer in the animal kingdom. Not only is MDV an important pathogen that threatens the poultry industry but it is also used as a natural virus-host model for herpesvirus-induced tumor formation. In order to visualize MDV-induced lymphoma and to identify potential biomarkers in an unbiased approach, we performed imaging mass spectrometry (IMS) and noncontact laser capture microdissection. This study provides a first description of the visualization of MDV-induced tumors by IMS that could be applied also for diagnostic purposes. In addition, we identified and validated potential biomarkers for MDV-induced tumors that could provide the basis for future research on pathogenesis and tumorigenesis of this malignancy.

Keywords: Marek’s disease virus; imaging mass spectrometry; lymphoma; noncontact laser capture microdissection; proteome; tumor; tumor markers.

FIG 1

Protein and peptide IMS analysis of MDV-induced lymphomas. (A to D) Cryosections were prepared from the liver of a MDV-infected chicken, and an IMS protein scan was performed in the mass range between 2 and 20 kDa. The selected masses represent a marker for liver tissue (4,964 Da in panel B) and two for MDV-induced T cell tumors (4,663 Da and 9,318 Da in panels C and D, respectively). The remaining panels, panels E to J, show FFPE chicken liver (E to H) and chicken skeletal muscle (I and J) sections scanned in the typical peptide range (700 to 3,500 Da) after trypsin digestion of the sections. Panels A and E show HE-stained sections with or without (control) tumor; the regions that were measured by IMS are outlined in blue, and the tumor regions are outlined in black. IMS scans based on negative (B) and positive (C to D and F to H) tumor markers are shown. Panels I and J show the distributions of two tumor-specific masses (1,745 and 2,917 Da, respectively) that were identified in liver and skeletal muscle tumors from different animals. Intensities are rainbow color coded from 0% (black) to 100% (white) relative intensity according to the color code bar on the right. The represented masses are given for each panel. Bars, 1 mm.

FIG 2

Statistical evaluation of MDV lymphoma peptide spectra. (A to D) Cluster analysis of spectra from a FFPE chicken liver section. (A) HE stain, the contour highlights the tumor region. (B) Joint cluster analysis of spectra from the section shown in panel A and a tumor-free control section with a given number of five clusters. Red and blue regions indicate tumor-free regions, which are clearly distinguished from the tumor appearing in magenta. Allowance of higher numbers of clusters as given in the parentheses in panels C and D resulted in a more fine-grained pattern. The margins of the tumor form a distinct red cluster in panel C, and the central region of the tumor shows microheterogeneity in, e.g., the green, dark blue, and red clusters in panel D. The color coding applies to each panel separately. The analysis was performed with in-house scripts using the statistical programming language R (63). (E1 to E3) Statistical models based on tumor-specific mass patterns of the chicken breast muscle tissue sample E1 were calculated using ClinProTools (Bruker) software. Spectra from the region outlined by the blue contour of the micrograph (E1) were used as training data for tumor (outlined in black) and tumor-free (outside the black outline) tissue. Models were then used to classify spectra from tissue sections of different animals that contained a tumor (E2) or were tumor-free controls (E3). Cross-validation of the models using the training data (E1) was correct for >99% of the data points. The regions predicted as tumor or tumor-free in the test sections E2 and E3 are shown in green and blue, respectively, and corresponded very well to the histological assessment of the sections (see Fig. S2 in the supplemental material). (F) The confusion matrix gives the prediction results of spectra from E2 and E3 in raster spots. In the tumor section, >95% of the area was correctly identified (n = 416), and prediction of the tumor-free region was correct for >99% (n = 3,036) showing that detection of MDV-induced tumors by IMS is feasible and exhibited high sensitivity and specificity.