Dataset Information

LOPIT-DC subcellular map of U-2 OS cells

ABSTRACT: Generation of a high-resolution subcellular localisation map for the proteome of the human cancer cell line U-2 OS, using the newly developed LOPIT-DC method. Comparison of the results generated using LOPIT-DC with data generated using the well-established hyperLOPIT method and data mining regarding multi-localising proteins, signalling pathways, isoforms and large protein complexes. Integration of the two methods using transfer learning.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell, Cell Culture

DISEASE(S): Bone Osteosarcoma

SUBMITTER: Aikaterini Geladaki

LAB HEAD: Kathryn Lilley

PROVIDER: PXD011254 | Pride | 2019-01-23

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 364

Publications

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics.

Geladaki Aikaterini A Kočevar Britovšek Nina N Breckels Lisa M LM Smith Tom S TS Vennard Owen L OL Mulvey Claire M CM Crook Oliver M OM Gatto Laurent L Lilley Kathryn S KS

Nature communications 20190118 1

The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Dif ...[more]

PMID: 30659192

Similar Datasets

Project description:Gliomas are the most frequent type of primary tumor of the central nervous system in adults with significant morbidity and mortality. Despite the development of novel, complex, multidisciplinary, and targeted therapies, glioma therapy has not progressed much over the last decades. Therefore, there is an urgent need to develop novel patient-adjusted immunotherapies that actively stimulate antitumor T cells generating long-term memory and thus resulting in significant clinical benefits. This work aimed to investigate the efficacy and molecular mechanism of dendritic cell (DC) vaccines loaded with glioma cells undergoing immunogenic cell death (ICD) induced by photosens based photodynamic therapy (PS-PDT) and to identify reliable prognostic genes’ signatures for predicting patients’ overall survival (OS). The analysis of the transcriptional program of the ICD-based DC vaccine led to the identification of robust induction of Th17 signature when used as a vaccine, these DCs demonstrate retinoic acid receptor-related orphan receptor-γt dependent efficacy in orthotopic mouse model. Moreover, by comparative analysis of transcriptome program of the ICD-based DC vaccine with transcriptome data from the TCGA-low grade glioma (LGG) dataset the four-gene signature (CFH, GALNT3, SMC4, VAV3) associated with OS of glioma patients have been identified. This model has been validated on OS of CGGA-LGG, TCGA-glioblastoma multiforme (GBM) and CGGA-GBM datasets to determine whether it has a similar prognostic value. For this the sensitivity and specificity of the prognostic model for predicting the OS were evaluated by calculating the area under curve (AUC) of the time-dependent receiver operating characteristic (ROC) curve. The values of AUC for TCGA-LGG, CGGA-LGG, TCGA-GBM and CGGA-GBM were, respectively, 0.75, 0.73, 0.9 and 0.69 for predicting 5-year survival rates. These data open attractive prospects for improving glioma therapy by employing ICD and PS-PDT-based DC vaccines to induce Th17 immunity and to use this prognostic model to predict the OS of glioma patients.

			Action	DRS
	DC_REP1_F1.dat	Other
	DC_REP1_F1.raw	Raw
	DC_REP1_F10.dat	Other
	DC_REP1_F10.raw	Raw
	DC_REP1_F11.dat	Other