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

Major advancements have recently been made in mass spectrometry-based proteomics, yielding an increasing number of datasets from various proteomics projects worldwide. In order to facilitate the sharing and reuse of promising datasets, it is important to construct appropriate, high-quality public data repositories. jPOSTrepo (https://repository.jpostdb.org/) has successfully implemented several unique features, including high-speed file uploading, flexible file management and easy-to-use interfaces. This repository has been launched as a public repository containing various proteomic datasets and is available for researchers worldwide. In addition, our repository has joined the ProteomeXchange consortium, which includes the most popular public repositories such as PRIDE in Europe for MS/MS datasets and PASSEL for SRM datasets in the USA. Later MassIVE was introduced in the USA and accepted into the ProteomeXchange, as was our repository in July 2016, providing important datasets from Asia/Oceania. Accordingly, this repository thus contributes to a global alliance to share and store all datasets from a wide variety of proteomics experiments. Thus, the repository is expected to become a major repository, particularly for data collected in the Asia/Oceania region.

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.

Rapid progress is being made in mass spectrometry (MS)-based proteomics, yielding an increasing number of larger datasets with higher quality and higher throughput. To integrate proteomics datasets generated from various projects and institutions, we launched a project named jPOST (Japan ProteOme STandard Repository/Database, https://jpostdb.org/) in 2015. Its proteomics data repository, jPOSTrepo, began operations in 2016 and has accepted more than 10 TB of MS-based proteomics datasets in the past two years. In addition, we have developed a new proteomics database named jPOSTdb in which the published raw datasets in jPOSTrepo are reanalyzed using standardized protocol. jPOSTdb provides viewers showing the frequency of detected post-translational modifications, the co-occurrence of phosphorylation sites on a peptide and peptide sharing among proteoforms. jPOSTdb also provides basic statistical analysis tools to compare proteomics datasets.