Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.
There is a growing interest in elucidating the mechanisms that govern atherosclerotic plaque instability, which has so far highlighted the role of dysregulations in lipid, carbohydrate, and amino acid metabolism. However, where these impairments occur in a plaque has yet to be revealed. This is critically important because lipid deposition, cell composition, and matrix stiffness are heterogeneous throughout human atheromas, particularly in regions of the fibrous cap and near the necrotic core. Furthermore, lesional cells are constantly bombarded with various intrinsic and extrinsic metabolic insults that influence cell phenotype. Mass spectrometry imaging (MSI) is a powerful technology for the visualization of the spatial localization and relative abundance of hundreds to thousands of analytes in thin tissue sections without the need for a priori knowledge of analytes present in the tissue. Imaging is performed using matrix-assisted laser desorption/ionization (MALDI) to interrogate small areas from a tissue surface after the addition of a chemical matrix to the section which serves to extract, co-crystallize, and ionize molecules from the sample. In the present study, we performed MSI of late-stage, stable and vulnerable human atherosclerotic plaques using MALDI-MSI to visualize the distribution of metabolites in the fibrous cap and necrotic core. This platform was able to identify 856 metabolites assigned with chemical formulas. We were then able to confidently annotate 189 metabolites using a data filtering system whereby we queried detected and quantified metabolites from the Human Metabolome Database (HMDB). Coupled with RNAseq examining human stable and unstable atheromas, we identified differences in pathways related to nitric oxide metabolism, collagen catabolism, arginine metabolism, tryptophan metabolism, lipid metabolism, and reactive oxygen species. This work represents the first study to begin defining an atlas of metabolic pathways involved in plaque destabilization in human atherosclerosis. We anticipate this work will be a valuable resource for the scientific community and will ultimately open new avenues of research in cardiovascular disease.
Tumors often display a high degree of intratumoral heterogeneity as manifested by dynamic changes in gene expression, protein expression, and on gross examination of histology, among many other features. Clinically, this underlying heterogeneity can drive tumor evolution and progression towards a more aggressive neoplastic state and a worse prognosis for patients; therefore, identifying the diverse composition of a tumor for early risk stratification is of critical importance. To elucidate intratumoral heterogeneity and intracellular hierarchy in a novel manner, we first conducted a low-cost quantitative proteomics analysis using MALDI-TOF mass spectrometry on over 1900 samples from different histological regions of individual tumors from 35 lung cancer patients, as well as from 3 mesenchymal stem cell samples. The histologies identified were acinar, basal cells, bronchial epithelium, lepidic, complex gland, micropapillary, near tumor normal, normal alveolar, papillary, papillary lepidic, papillary mucinous, and solid. Patient-specific information including survival status, sex, age, smoking status, SUV by FDG-PET scan, tumor size, EGFR, KRAS, and ERCC1 mutation status, among other variables was obtained. We then compared the proteomes derived from each tumor to the stem cell proteomes, and using computational strategies, mapped the distance of each histological sample from the mesenchymal stem cell state; using clustering techniques, we organized the major histological subtypes into a phylogenetic tree from stem cells to normal lung. We hypothesized that by applying and improving upon map of tumor evolution based on the distance of each individual histological sample from a stem cell state. Apart from liquid tumors, there have thus far been limited studies on the prognostic significance of different subclones in solid tumors, and therefore we treated each histological sample as a subclone within each patient. We also aimed to identify survival-associated subclones and prognostic molecular signatures across combinations of subclones. Identifying these subclones may provide insight into malignant micrometastases to other organs. Using co-expression network analysis, we further pinpointed distinctive significantly dysregulated co-regulatory protein networks within each histological subtype. Based on these networks, we sought to identify important hub proteins within each histology. Ultimately, using proteomic profiling in solid tumors can be a novel approach in functionally characterizing intratumoral heterogeneity, and may allow for a more robust analysis of the diverse molecular expression of single tumor samples. Our results may help inform the field of targeted broad-scale proteomics profiling for therapeutic use. Citation Format: Charlotte Lee, Hua-Jun Wu, Andre L. Moreira, Erin H. Seeley, Callee Walsh, Robert J. Downey, Franziska Michor. Proteomic profiling to elucidate intratumoral heterogeneity and cancer evolution in lung cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A21.
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