Proteomic LC-MS approaches combined with genome-annotated databases currently allow identification of thousands of proteins from complex mixtures (1). Approaches have also been developed for relative quantitation using stable isotope labeling (2-4). Recently not only comprehensive quantitation studies between two states (5, 6) but also protein-protein (7, 8), protein-peptide (9), and protein-drug (10) interaction analyses have been reported. So far, however, a comprehensive approach for determining protein concentrations in one sample has not been established. Protein concentrations are one of the most basic and important parameters in quantitative proteomics because the kinetics/dynamics of the cellular proteome is described in terms of changes in the concentrations of proteins in particular compartments. Biological experiments often require at least some information on protein abundance for correct interpretation. In the past, crude quantitative information could be drawn from the intensity of gel staining in comparison to a known amount of marker protein. However, in complex mixture analysis, individual proteins cannot be stained individually, and usually all information about protein abundance is lost. So far, isotope-labeled synthetic peptides have been used as internal standards for absolute quantitation of particular proteins of interest (11,12). This approach is in principle applicable to comprehensive analysis but is hampered by the high cost of isotope-labeled peptides as well as the difficulty of quantitative digestion of proteins in-gel (13).Even a single nano-LC-MS/MS analysis can easily generate a long list of identified proteins with the help of database searching, and additional information can be extracted, such as the hit rank in identification, the probability score, the number of identified peptides per protein, ion counts of identified peptides, LC retention times, and so on. Qualitatively some parameters, such as the hit rank, the score, and the number of peptides per protein (14), can be considered as indicators for protein abundance in the analyzed sample. Among them, the integrated ion counts of the peptides identifying each protein would be the most direct parameter to describe the abundance and has been used to compare protein expression in different states (15). However, a mass spectrometer is not as versatile as an absorbance detector because of limited linearity and possibly because of background and ionization suppression effects (16). Therefore, it is necessary to normalize these parameters to obtain at least approximate quantitative information. The first approach to achieve this, to our knowledge, was to use the number of peptides per protein normalized by the theoretical number of

Adult T cell leukemia/lymphoma (ATL) is a peripheral T cell neoplasm of largely unknown genetic basis, associated with human T cell leukemia virus type-1 (HTLV-1) infection. Here we describe an integrated molecular study in which we performed whole-genome, exome, transcriptome and targeted resequencing, as well as array-based copy number and methylation analyses, in a total of 426 ATL cases. The identified alterations overlap significantly with the HTLV-1 Tax interactome and are highly enriched for T cell receptor-NF-κB signaling, T cell trafficking and other T cell-related pathways as well as immunosurveillance. Other notable features include a predominance of activating mutations (in PLCG1, PRKCB, CARD11, VAV1, IRF4, FYN, CCR4 and CCR7) and gene fusions (CTLA4-CD28 and ICOS-CD28). We also discovered frequent intragenic deletions involving IKZF2, CARD11 and TP73 and mutations in GATA3, HNRNPA2B1, GPR183, CSNK2A1, CSNK2B and CSNK1A1. Our findings not only provide unique insights into key molecules in T cell signaling but will also guide the development of new diagnostics and therapeutics in this intractable tumor.

We have developed a simple, highly specific enrichment procedure for phosphopeptides, by increasing the specificity of an immobilized metal affinity column (IMAC) without using any chemical reaction. The method employs a biphasic IMAC-C18 tip, in which IMAC beads are packed on an Empore C18 disk in a 200-microL pipet tip. Phosphopeptides are separated from non-phosphopeptides on the IMAC in an optimized solvent without any chemical reaction, then desorbed from the IMAC using a phosphate buffer, reconcentrated, and desalted on the C18 disk. The increase in selectivity was achieved by (a) using a strong acid to discriminate phosphates from carboxyl groups of peptides and (b) using a high concentration of acetonitrile to remove hydrophobic non-phosphopeptides. The entire procedure was optimized by using known phosphoproteins such as Akt1 kinase and protein kinase A. Although it was difficult to detect phosphopeptides in MALDI-MS spectra of tryptic peptide mixtures before enrichment, after the IMAC procedure, we could successfully detect phosphopeptides with almost no non-phosphopeptides. Next, we constructed an array of IMAC-IMAC/C18 tips, such that number of arrayed tips on a 96-well plate could easily be changed depending on the loading amount of sample. Applying this approach to mouse forebrain resulted in the identification of 162 phosphopeptides (166 phosphorylation sites) from 135 proteins using nano-LC/MS.

An important challenge for proteomics is to be able to compare absolute protein levels across biological samples. Here we introduce an approach based on the use of culture-derived isotope tags (CDITs) for quantitative tissue proteome analysis. We cultured Neuro2A cells in a stable isotope-enriched medium and mixed them with mouse brain samples to serve as internal standards. Using CDITs, we identified and quantified a total of 1,000 proteins, 97-98% of which were expressed in both mouse whole brain and Neuro2A cells. CDITs also allow comprehensive and absolute protein quantification. Synthetic unlabeled peptides were used to quantify the corresponding proteins labeled with stable isotopes in Neuro2A cells, and the results were used to obtain the absolute amounts of 103 proteins in mouse whole brain. The expression levels correlated well with those in Neuro2A cells. Thus, the use of CDITs allows both relative and absolute quantitative proteome studies.