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A proteomics and transcriptomics approach to identify leukemic stem cell (LSC) markers.

ABSTRACT: Interactions between hematopoietic stem cells and their niche are mediated by proteins within the plasma membrane (PM) and changes in these interactions might alter hematopoietic stem cell fate and ultimately result in acute myeloid leukemia (AML). Here, using nano-LC/MS/MS, we set out to analyze the PM profile of two leukemia patient samples. We identified 867 and 610 unique CD34(+) PM (-associated) proteins in these AML samples respectively, including previously described proteins such as CD47, CD44, CD135, CD96, and ITGA5, but also novel ones like CD82, CD97, CD99, PTH2R, ESAM, MET, and ITGA6. Further validation by flow cytometry and functional studies indicated that long-term self-renewing leukemic stem cells reside within the CD34(+)/ITGA6(+) fraction, at least in a subset of AML cases. Furthermore, we combined proteomics with transcriptomics approaches using a large panel of AML CD34(+) (n = 60) and normal bone marrow CD34(+) (n = 40) samples. Thus, we identified eight subgroups of AML patients based on their specific PM expression profile. GSEA analysis revealed that these eight subgroups are enriched for specific cellular processes.

SUBMITTER: Bonardi F

PROVIDER: S-EPMC3591656 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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A proteomics and transcriptomics approach to identify leukemic stem cell (LSC) markers.

Bonardi Francesco F Fusetti Fabrizia F Deelen Patrick P van Gosliga Djoke D Vellenga Edo E Schuringa Jan Jacob JJ

Molecular & cellular proteomics : MCP 20121211 3

Interactions between hematopoietic stem cells and their niche are mediated by proteins within the plasma membrane (PM) and changes in these interactions might alter hematopoietic stem cell fate and ultimately result in acute myeloid leukemia (AML). Here, using nano-LC/MS/MS, we set out to analyze the PM profile of two leukemia patient samples. We identified 867 and 610 unique CD34(+) PM (-associated) proteins in these AML samples respectively, including previously described proteins such as CD47 ...[more]

PMID: 23233446

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Project description:BackgroundTo unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome.ResultsIn the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over-expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides.ConclusionOur proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets.

Dataset Information

A proteomics and transcriptomics approach to identify leukemic stem cell (LSC) markers.

Publications

A proteomics and transcriptomics approach to identify leukemic stem cell (LSC) markers.

Similar Datasets

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