Transcription factors (TFs) and their specific interactions with targets are crucial for specifying gene-expression programs. To gain insights into the transcriptional regulatory networks in embryonic stem (ES) cells, we use chromatin immunoprecipitation coupled with ultra-high-throughput DNA sequencing (ChIP-seq) to map the locations of 13 sequence-specific TFs (Nanog, Oct4, STAT3, Smad1, Sox2, Zfx, c-Myc, n-Myc, Klf4, Esrrb, Tcfcp2l1, E2f1, and CTCF) and 2 transcription regulators (p300 and Suz12). These factors are known to play different roles in ES-cell biology as components of the LIF and BMP signaling pathways, self-renewal regulators, and key reprogramming factors. Our study provides insights into the integration of the signaling pathways into the ES-cell-specific transcription circuitries. Intriguingly, we find specific genomic regions extensively targeted by different TFs. Collectively, the comprehensive mapping of TF-binding sites identifies important features of the transcriptional regulatory networks that define ES-cell identity.

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

Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.