There are a number of leukemogenic protein-tyrosine kinases (PTKs) associated with leukemic transformation. Although each is linked with a specific disease their functional activity poses the question whether they have a degree of commonality in their effects upon target cells. Exon array analysis of the effects of six leukemogenic PTKs (BCR/ABL, TEL/PDGFR␤, FIP1/PDGFR␣, D816V KIT, NPM/ALK, and FLT3ITD) revealed few common effects on the transcriptome. It is apparent, however, that proteome changes are not directly governed by transcriptome changes. Therefore, we assessed and used a new generation of iTRAQ tagging, enabling eight-channel relative quantification discovery proteomics, to analyze the effects of these six leukemogenic PTKs. Again these were found to have disparate effects on the proteome with few common targets. BCR/ABL had the greatest effect on the proteome and had more effects in common with FIP1/PDGFR␣. The proteomic effects of the four type III receptor kinases were relatively remotely related. The only protein commonly affected was eosinophil-associated ribonuclease 7. Five of six PTKs affected the motility-related proteins CAPG and vimentin, although this did not correspond to changes in motility. However, correlation of the proteomics data with that from the exon microarray not only showed poor levels of correlation between transcript and protein levels but also revealed alternative patterns of regulation of the CAPG protein by different oncogenes, illustrating the utility of such a combined approach. Molecular & Cellular Proteomics 7: 853-863, 2008.

The proteome is determined by rates of transcription, translation, and protein turnover. Definition of stem cell populations therefore requires a stem cell proteome signature. However, the limit to the number of primary cells available has restricted extensive proteomic analysis. We present a mass spectrometric method using an isobaric covalent modification of peptides for relative quantification (iTRAQ) , IntroductionDefinition of cell form and function is derived from the transcription of specific sets of genes, followed by their translation and post-translational regulation. Cellular development alters phenotype via such changes in transcription and translation, and in this respect post-translational control of protein levels is key in many cell regulatory pathways, such as cell cycling. 1 Stem cell commitment to differentiation is a critical step in development, where proteomic changes will be observed as a cell undergoes successive commitment and developmental steps. The precise nature of the elements of gene expression that contribute to stem cell characteristics have been assessed by transcriptional profiling and the elements of a stem cell signature refined via comparative expression analyses. [2][3][4] Alternatively, the critical stem cell characteristics can be defined by analysis of the function of specific genes (in the hematopoietic system, SCL, HOX-B4, and BMI-1 5-7 ). Many key regulators are transcriptional activators or suppressors that will inevitably affect the transcriptomic profile of a cell. However, this process is known to be subject to further regulation via post-translational mechanisms. 8,9 Lin Ϫ Sca ϩ Kit ϩ (LSK ϩ ) cells and Lin Ϫ Sca ϩ Kit Ϫ (LSK Ϫ ) cells display differential abilities to reconstitute hematopoiesis, and the latter is a more mature cell type than the former, only able to support hematopoiesis in the short term. 10,11 Comparison of these 2 cell populations can help us derive knowledge of the intracellular systems that regulate the ability to maintain pluripotency and long-term self-renewal. Transcriptomic analysis, previously used to define a stem cell "signature," will not detail the true differences between stem cells and their progeny at the proteome level because of the importance of post-translational regulation of protein levels. 9 We therefore sought to establish a method for defining the proteome of stem cell populations where only limited sample is available. Furthermore, the method should not rely on transcriptome data to infer changes at the protein level. Given the limited material (approximately 1 million LSK ϩ and LSK Ϫ cells, respectively, per experiment), we developed a procedure employing isobaric tags for relative quantification (iTRAQ). This allows 4 samples to be analyzed simultaneously, giving relative quantification 12,13 on hundreds of proteins at any one time. Samples are separately proteolytically digested, and 4 isotope-coded, isobaric reagents (using differential stable isotope distribution between reporter group and balance group moieties) ...