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LC/MS data from proteomics melanoma study on YB-1 expression

ABSTRACT: Malignant melanoma is the most lethal form of skin cancer. Y-box binding protein 1 (YB-1) plays a prominent role in mediating metastatic behavior by promoting epithelial-to-mesenchymal transition (EMT). Migratory melanoma cells exhibit two major migration modes: elongated mesenchymal or rounded amoeboid. Using A375 melanoma cell line and the YB-1 knock-out model, we aimed to elucidate biochemical and biomechanical changes in migration signaling pathways in the context of melanoma metastases. We subjected A375 YB-1 knock-out and parental cells to atomic force microscopy (stiffness determination), immunolabelling, and proteome analysis. We found that YB-1 expressing cells were significantly stiffer compared to the corresponding YB-1 knock-out cell line. Our study demonstrated that the constitutive expression of YB-1 in A375 melanoma cell line appears to be closely related to known biomarkers of epithelial-to-mesenchymal transition, nestin, and vimentin, resulting in a stiffer phenotype, as well as a wide array of proteins involved in RNA, ribosomes, and spliceosomes. YB-1 knock-out resulted in nestin depletion and significantly lower vimentin expression, as well as global upregulation of proteins related to the cytoskeleton and migration. YB-1 knock-out cells demonstrated both morphological features and biochemical drivers of mesenchymal/ameboid migration. Melanoma is a highly plastic, adaptable, and aggressive tumor entity, capable of exhibiting characteristics of different migratory modes.

ORGANISM(S): Homo sapiens (human)

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PROVIDER: S-BSST1067 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundAlthough fold change is a commonly used criterion in quantitative proteomics for differentiating regulated proteins, it does not provide an estimation of false positive and false negative rates that is often desirable in a large-scale quantitative proteomic analysis. We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 98:5116-5121) to a differential proteomics problem of two samples with replicates. The quantitative proteomic analysis was carried out with nanoliquid chromatography/linear iron trap-Fourier transform mass spectrometry. The biological sample model included two Mycobacterium smegmatis unlabeled cell cultures grown at pH 5 and pH 7. The objective was to compare the protein relative abundance between the two unlabeled cell cultures, with an emphasis on significance analysis of protein differential expression using the SAM method. Results using the SAM method are compared with those obtained by fold change and the conventional t-test.ResultsWe have applied the SAM method to solve the two-sample significance analysis problem in liquid chromatography/mass spectrometry (LC/MS) based quantitative proteomics. We grew the pH5 and pH7 unlabelled cell cultures in triplicate resulting in 6 biological replicates. Each biological replicate was mixed with a common 15N-labeled reference culture cells for normalization prior to SDS/PAGE fractionation and LC/MS analysis. For each biological replicate, one center SDS/PAGE gel fraction was selected for triplicate LC/MS analysis. There were 121 proteins quantified in at least 5 of the 6 biological replicates. Of these 121 proteins, 106 were significant in differential expression by the t-test (p < 0.05) based on peptide-level replicates, 54 were significant in differential expression by SAM with Delta = 0.68 cutoff and false positive rate at 5%, and 29 were significant in differential expression by the t-test (p < 0.05) based on protein-level replicates. The results indicate that SAM appears to overcome the false positives one encounters using the peptide-based t-test while allowing for identification of a greater number of differentially expressed proteins than the protein-based t-test.ConclusionWe demonstrate that the SAM method can be adapted for effective significance analysis of proteomic data. It provides much richer information about the protein differential expression profiles and is particularly useful in the estimation of false discovery rates and miss rates.

Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.

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LC/MS data from proteomics melanoma study on YB-1 expression

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