Fagerberg Linn, Hallström Björn M, Oksvold Per, Kampf Caroline, Djureinovic Dijana, Odeberg Jacob, Habuka Masato, Tahmasebpoor Simin, Danielsson Angelika, Edlund Karolina, Asplund Anna, Sjöstedt Evelina, Lundberg Emma, Szigyarto Cristina Al-Khalili, Skogs Marie, Takanen Jenny Ottosson, Berling Holger, Tegel Hanna, Mulder Jan, Nilsson Peter, Schwenk Jochen M, Lindskog Cecilia, Danielsson Frida, Mardinoglu Adil, Sivertsson Asa, von Feilitzen Kalle, Forsberg Mattias, Zwahlen Martin, Olsson IngMarie, Navani Sanjay, Huss Mikael, Nielsen Jens, Ponten Fredrik, Uhlén Mathias
Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden;
Mol Cell Proteomics. 2014 Feb;13(2):397-406. doi: 10.1074/mcp.M113.035600. Epub 2013 Dec 5.
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 ∼80% 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.
根据人体各器官和组织的空间表达模式对人类蛋白质进行全局分类,对于人类生物学和疾病研究至关重要。在这里,我们使用定量转录组学分析(RNA测序)对所有主要人类器官和组织的代表性样本中的基因组织特异性表达进行分类,并将此分析与相同组织的基于抗体的分析相结合。为了展示数据,我们推出了新版人类蛋白质图谱,该图谱整合了约80%人类蛋白质编码基因的RNA和蛋白质表达数据,并可在单个基因水平上获取RNA和蛋白质分析的原始数据。我们展示了所有人类蛋白质编码基因在组织特异性和空间表达模式方面的分类。这种整合的人类表达图谱可作为探索人体分子组成的起点。