Global classification of the human proteins with regards to spatial expression patterns across organs and tissues is important for studies of human biology and disease.Here, we used a quantitative transcriptomics analysis (RNA-Seq) to classify the tissue-specific expression of genes across a representative set of all major human organs and tissues and combined this analysis with antibody-based profiling of the same tissues. To present the data, we launch a new version of the Human Protein Atlas that integrates RNA and protein expression data corresponding to ϳ80% of the human protein-coding genes with access to the primary data for both the RNA and the protein analysis on an individual gene level. We present a classification of all human protein-coding genes with regards to tissue-specificity and spatial expression pattern. The integrative human expression map can be used as a starting point to explore the molecular constituents of the human body. Molecular & Cellular Proteomics 13: 10.1074/mcp.M113.035600, 397-406, 2014.Central questions in human biology relate to how cells, tissues, and organs differ in the expression of genes and proteins and what consequences the global expression pattern has for the phenotype of various cells with different functions in the body. Therefore, the annotation of the human protein-coding genes with regards to the spatial, temporal, and functional space represents one of the greatest challenges in human biology (1). Important questions related to this are how many of the genes actually code for functional proteins, how many are expressed in a tissue-specific manner, and how many proteins have "housekeeping" functions and are therefore expressed in all cells? These questions have a major impact not only on efforts to try to understand human biology, but also for applied medical research, such as pharmaceutical drug development and biomarker discovery in the field of translational medicine.Several efforts have been initiated in the aftermath of the genome project to systematically annotate the putative protein-coding part of the human genome. Genome annotation efforts, such as Ensembl (2) and RefSeq (3), have provided an increasingly accurate map with at present ϳ20,000 proteincoding genes. Similarly, the ENCODE consortium has been launched to provide an integrated encyclopedia of DNA eleFrom the ‡Science for Life Laboratory, KTH -Royal Institute of Technology, SE-171 21 Stockholm, Sweden; §Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden; ¶Department

Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.

An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non‐secreted proteins based on parallel reaction monitoring to measure, at steady‐state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene‐specific RNA‐to‐protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics.