Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of mouse pre-leukemic stem cells and leukemic stem cells

ABSTRACT: The molecular pathways mediating unlimited self-renewal and alleged drug resistant properties that are believed to be key for maintenance and re-initiation of leukemia during relapse are largely unexplored area. To gain further insights into the potential pathways responsible for the conversion and establishment of leukemic stem cells (LSC), we molecularly dissected and compared the cell populations enriched in pre-LSC and LSC. In this model, pre-LSC were identified as early transduced primary cells carrying the initiating event and endowed with the ability to induce leukemia in vivo with a long latency. LSC were defined as leukemic cells harvested from primary mice transplanted with pre-LSC that had then acquired additional events and were capable of inducing leukemia in secondary mice with a very short latency.

Pre-LSC were generated by transformation of cKit+ purified cells derived from mouse bone marrow with MLL-ENL retrovirus and subjected to several rounds of replating in methylcellulose. A cell line (pre-LSC bulk population) was subsequently generated and injected into mice. Cells derived from a leukemic mouse were used to generate the LSC cell line (LSC bulk population). Pre-LSC and LSC bulk populations and their clonal deriviatives were used in microarray experiments to help identify key events which convert pre-LSC to LSC.

ORGANISM(S): Mus musculus

SUBMITTER: JENNY YEUNG

PROVIDER: E-MEXP-2618 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Many diseases have been associated with gut microbiome abnormalities. The root cause of such diseases is not only due to bacterial dysbiosis, but also to change in bacterial functions, which are best studied by proteomic approaches. Although bacterial proteomics is well established, metaproteomics is hindered by challenges associated with the sample physical structure, contaminating proteins, the simultaneous analysis of hundreds of species and the subsequent data analysis. Here, we present a systematic assessment of sample preparation and data analysis methodologies applied to LC-MS/MS metaproteomics experiment. We could show that low speed centrifugation (LSC) has a significant impact on both peptide identifications and reproducibility. LSC led to increase in peptide and proteins identifications compare to no LSC. Notably, the dominant bacterial phyla, i.e. Firmicutes and Bacteroidetes, showed divergent representation between LSC and no-LSC. In terms of data processing, protein sequence databases derived from mouse faeces metagenome provided at least four times more MS/MS identification compared to databases of concatenated single organisms. We also demonstrated that two-steps database search strategy comes at the expense of a dramatic rise in number of false positives compared to single-step strategy. Overall, we found a positive correlation between matching metaproteome and metagenome abundance, which could be linked to core microbial functions, such as glycolysis-gluconeogenesis, citrate cycle and carbon metabolism. We observed significant overlap and correlation at the phylum, class, order and family taxonomic levels between taxonomy-derived from metagenome and metaproteome. Notably, nearly all functional categories (e.g., membrane transport, translation, transcription) were differentially abundant in the metaproteome (activity) compared to what would be expected from the metagenome (potential). In conclusion, these results highlight the need to perform metaproteomics when studying complex microbiome samples.

Project description:A Metaproteomic Workflow for Sample Preparation and Data Analysis Applied to Mouse Faeces: 1 MTD project_description Many diseases have been associated with gut microbiome abnormalities. The root cause of such diseases is not only due to bacterial dysbiosis, but also to change in bacterial functions, which are best studied by proteomic approaches. Although bacterial proteomics is well established, metaproteomics is hindered by challenges associated with the sample physical structure, contaminating proteins, the simultaneous analysis of hundreds of species and the subsequent data analysis. Here, we present a systematic assessment of sample preparation and data analysis methodologies applied to LC-MS/MS metaproteomics experiment. We could show that low speed centrifugation (LSC) has a significant impact on both peptide identifications and reproducibility. LSC led to increase in peptide and proteins identifications compare to no LSC. Notably, the dominant bacterial phyla, i.e. Firmicutes and Bacteroidetes, showed divergent representation between LSC and no-LSC. In terms of data processing, protein sequence databases derived from mouse faeces metagenome provided at least four times more MS/MS identification compared to databases of concatenated single organisms. We also demonstrated that two-steps database search strategy comes at the expense of a dramatic rise in number of false positives compared to single-step strategy. Overall, we found a positive correlation between matching metaproteome and metagenome abundance, which could be linked to core microbial functions, such as glycolysis-gluconeogenesis, citrate cycle and carbon metabolism. We observed significant overlap and correlation at the phylum, class, order and family taxonomic levels between taxonomy-derived from metagenome and metaproteome. Notably, nearly all functional categories (e.g., membrane transport, translation, transcription) were differentially abundant in the metaproteome (activity) compared to what would be expected from the metagenome (potential). In conclusion, these results highlight the need to perform metaproteomics when studying complex microbiome samples.

Dataset Information

Transcription profiling of mouse pre-leukemic stem cells and leukemic stem cells

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure