Sophia Hober

Tissue-based map of the human proteome

Protein expression across human tissues Sequencing the human genome gave new insights into human biology and disease. However, the ultimate goal is to understand the dynamic expression of each of the approximately 20,000 protein-coding genes and the function of each protein. Uhlén et al. now present a map of protein expression across 32 human tissues. They not only measured expression at an RNA level, but also used antibody profiling to precisely localize the corresponding proteins. An interactive website allows exploration of expression patterns across the human body. Science, this issue 10.1126/science.1260419 Transcriptomics and immunohistochemistry map protein expression across 32 human tissues. INTRODUCTION Resolving the molecular details of proteome variation in the different tissues and organs of the human body would greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on quantitative transcriptomics on a tissue and organ level combined with protein profiling using microarray-based immunohistochemistry to achieve spatial localization of proteins down to the single-cell level. We provide a global analysis of the secreted and membrane proteins, as well as an analysis of the expression profiles for all proteins targeted by pharmaceutical drugs and proteins implicated in cancer. RATIONALE We have used an integrative omics approach to study the spatial human proteome. Samples representing all major tissues and organs (n = 44) in the human body have been analyzed based on 24,028 antibodies corresponding to 16,975 protein-encoding genes, complemented with RNA-sequencing data for 32 of the tissues. The antibodies have been used to produce more than 13 million tissue-based immunohistochemistry images, each annotated by pathologists for all sampled tissues. To facilitate integration with other biological resources, all data are available for download and cross-referencing. RESULTS We report a genome-wide analysis of the tissue specificity of RNA and protein expression covering more than 90% of the putative protein-coding genes, complemented with analyses of various subproteomes, such as predicted secreted proteins (n = 3171) and membrane-bound proteins (n = 5570). The analysis shows that almost half of the genes are expressed in all analyzed tissues, which suggests that the gene products are needed in all cells to maintain “housekeeping” functions such as cell growth, energy generation, and basic metabolism. Furthermore, there is enrichment in metabolism among these genes, as 60% of all metabolic enzymes are expressed in all analyzed tissues. The largest number of tissue-enriched genes is found in the testis, followed by the brain and the liver. Analysis of the 618 proteins targeted by clinically approved drugs unexpectedly showed that 30% are expressed in all analyzed tissues. An analysis of metabolic activity based on genome-scale metabolic models (GEMS) revealed liver as the most metabolically active tissue, followed by adipose tissue and skeletal muscle. CONCLUSIONS A freely available interactive resource is presented as part of the Human Protein Atlas portal (www.proteinatlas.org), offering the possibility to explore the tissue-elevated proteomes in tissues and organs and to analyze tissue profiles for specific protein classes. Comprehensive lists of proteins expressed at elevated levels in the different tissues have been compiled to provide a spatial context with localization of the proteins in the subcompartments of each tissue and organ down to the single-cell level. The human tissue–enriched proteins. All tissue-enriched proteins are shown for 13 representative tissues or groups of tissues, stratified according to their predicted subcellular localization. Enriched proteins are mainly intracellular in testis, mainly membrane bound in brain and kidney, and mainly secreted in pancreas and liver. Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray–based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

Towards a knowledge-based Human Protein Atlas

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A Human Protein Atlas for Normal and Cancer Tissues Based on Antibody Proteomics

Antibody-based proteomics provides a powerful approach for the functional study of the human proteome involving the systematic generation of protein-specific affinity reagents. We used this strategy to construct a comprehensive, antibody-based protein atlas for expression and localization profiles in 48 normal human tissues and 20 different cancers. Here we report a new publicly available database containing, in the first version, ∼400,000 high resolution images corresponding to more than 700 antibodies toward human proteins. Each image has been annotated by a certified pathologist to provide a knowledge base for functional studies and to allow queries about protein profiles in normal and disease tissues. Our results suggest it should be possible to extend this analysis to the majority of all human proteins thus providing a valuable tool for medical and biological research.

A Genecentric Human Protein Atlas for Expression Profiles Based on Antibodies

An attractive path forward in proteomics is to experimentally annotate the human protein complement of the genome in a genecentric manner. Using antibodies, it might be possible to design protein-specific probes for a representative protein from every protein-coding gene and to subsequently use the antibodies for systematical analysis of cellular distribution and subcellular localization of proteins in normal and disease tissues. A new version (4.0) of the Human Protein Atlas has been developed in a genecentric manner with the inclusion of all human genes and splice variants predicted from genome efforts together with a visualization of each protein with characteristics such as predicted membrane regions, signal peptide, and protein domains and new plots showing the uniqueness (sequence similarity) of every fraction of each protein toward all other human proteins. The new version is based on tissue profiles generated from 6120 antibodies with more than five million immunohistochemistry-based images covering 5067 human genes, corresponding to ∼25% of the human genome. Version 4.0 includes a putative list of members in various protein classes, both functional classes, such as kinases, transcription factors, G-protein-coupled receptors, etc., and project-related classes, such as candidate genes for cancer or cardiovascular diseases. The exact antigen sequence for the internally generated antibodies has also been released together with a visualization of the application-specific validation performed for each antibody, including a protein array assay, Western blot analysis, immunohistochemistry, and, for a large fraction, immunofluorescence-based confocal microscopy. New search functionalities have been added to allow complex queries regarding protein expression profiles, protein classes, and chromosome location. The new version of the protein atlas thus is a resource for many areas of biomedical research, including protein science and biomarker discovery.

A global view of protein expression in human cells, tissues, and organs

Defining the protein profiles of tissues and organs is critical to understanding the unique characteristics of the various cell types in the human body. In this study, we report on an anatomically comprehensive analysis of 4842 protein profiles in 48 human tissues and 45 human cell lines. A detailed analysis of over 2 million manually annotated, high‐resolution, immunohistochemistry‐based images showed a high fraction (>65%) of expressed proteins in most cells and tissues, with very few proteins (<2%) detected in any single cell type. Similarly, confocal microscopy in three human cell lines detected expression of more than 70% of the analyzed proteins. Despite this ubiquitous expression, hierarchical clustering analysis, based on global protein expression patterns, shows that the analyzed cells can be still subdivided into groups according to the current concepts of histology and cellular differentiation. This study suggests that tissue specificity is achieved by precise regulation of protein levels in space and time, and that different tissues in the body acquire their unique characteristics by controlling not which proteins are expressed but how much of each is produced.

A pathology atlas of the human cancer transcriptome.

Modeling the cancer transcriptome Recent initiatives such as The Cancer Genome Atlas have mapped the genome-wide effect of individual genes on tumor growth. By unraveling genomic alterations in tumors, molecular subtypes of cancers have been identified, which is improving patient diagnostics and treatment. Uhlen et al. developed a computer-based modeling approach to examine different cancer types in nearly 8000 patients. They provide an open-access resource for exploring how the expression of specific genes influences patient survival in 17 different types of cancer. More than 900,000 patient survival profiles are available, including for tumors of colon, prostate, lung, and breast origin. This interactive data set can also be used to generate personalized patient models to predict how metabolic changes can influence tumor growth. Science, this issue p. eaan2507 A systems biology approach should allow genome-wide exploration of the effect of individual proteins on cancer clinical outcomes. INTRODUCTION Cancer is a leading cause of death worldwide, and there is great need to define the molecular mechanisms driving the development and progression of individual tumors. The Hallmarks of Cancer has provided a framework for a deeper molecular understanding of cancer, and the focus so far has been on the genetic alterations in individual cancers, including genome rearrangements, gene amplifications, and specific cancer-driving mutations. Using systems-level approaches, it is now also possible to define downstream effects of individual genetic alterations in a genome-wide manner. RATIONALE In our study, we used a systems-level approach to analyze the transcriptome of 17 major cancer types with respect to clinical outcome, based on a genome-wide transcriptomics analysis of ~8000 individual patients with clinical metadata. The study was made possible through the availability of large open-access knowledge-based efforts such as the Cancer Genome Atlas and the Human Protein Atlas. Here, we used the data to perform a systems-level analysis of 17 major human cancer types, describing both interindividual and intertumor variation patterns. RESULTS The analysis identified candidate prognostic genes associated with clinical outcome for each tumor type; the results show that a large fraction of cancer protein-coding genes are differentially expressed and, in many cases, have an impact on overall patient survival. Systems biology analyses revealed that gene expression of individual tumors within a particular cancer varied considerably and could exceed the variation observed between distinct cancer types. No general prognostic gene necessary for clinical outcome was applicable to all cancers. Shorter patient survival was generally associated with up-regulation of genes involved in mitosis and cell growth and down-regulation of genes involved in cellular differentiation. The data allowed us to generate personalized genome-scale metabolic models for cancer patients to identify key genes involved in tumor growth. In addition, we explored tissue-specific genes associated with the dedifferentiation of tumor cells and the role of specific cancer testis antigens on a genome-wide scale. For lung and colorectal cancer, a selection of prognostic genes identified by the systems biology effort were analyzed in independent, prospective cancer cohorts using immunohistochemistry to validate the gene expression patterns at the protein level. CONCLUSION A Human Pathology Atlas has been created as part of the Human Protein Atlas program to explore the prognostic role of each protein-coding gene in 17 different cancers. Our atlas uses transcriptomics and antibody-based profiling to provide a standalone resource for cancer precision medicine. The results demonstrate the power of large systems biology efforts that make use of publicly available resources. Using genome-scale metabolic models, cancer patients are shown to have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. With more than 900,000 Kaplan-Meier plots, this resource allows exploration of the specific genes influencing clinical outcome for major cancers, paving the way for further in-depth studies incorporating systems-level analyses of cancer. All data presented are available in an interactive open-access database (www.proteinatlas.org/pathology) to allow for genome-wide exploration of the impact of individual proteins on clinical outcome in major human cancers. Schematic overview of the Human Pathology Atlas. A systems-level approach enables analysis of the protein-coding genes of 17 different cancer types from ~8000 patients. Results are available in an interactive open-access database. Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

Protein engineering of an IgG-binding domain allows milder elution conditions during affinity chromatography.

One of the problems in the recovery of antibodies by affinity chromatography is the low pH, which is normally essential to elute the bound material from the column. Here, we have addressed this problem by constructing destabilized mutants of a domain analogue (domain Z) from an IgG-binding bacterial receptor, protein A. In order to destabilize the IgG-binding domain, two protein engineered variants were constructed using site-directed mutagenesis of the second loop of this antiparallel three-helix bundle domain. In the first mutant (Z6G), the second loop was extended with six glycines in order to evaluate the significance of the loop length. In the second mutant (ZL4G), the original loop sequence was exchanged for glycines in order to evaluate the importance of the loop forming residues. Both mutated variants have a lower alpha-helical content, as well as a lower thermal and chemical stability compared to the parent Z-molecule. The affinity to IgG was slightly lowered in both cases, mainly due to higher dissociation rates. Interestingly, the elution studies showed that most of the bound IgG-molecules could be eluted at a pH as high as 4.5 from columns with the engineered ligands, while only 70% of the bound IgG could be eluted from the matrix with the parent Z as ligand.

A Web-based Tool for in Silico Biomarker Discovery Based on Tissue-specific Protein Profiles in Normal and Cancer Tissues

Here we report the dev elopment of a publicly available Web-based analysis tool for exploring proteins expressed in a tissue- or cancer-specific manner. The search queries are based on the human tissue profiles in normal and cancer cells in the Human Protein Atlas portal and rely on the individual annotation performed by pathologists of images representing immunohistochemically stained tissue sections. Approximately 1.8 million images representing more than 3000 antibodies directed toward human proteins were used in the study. The search tool allows for the systematic exploration of the protein atlas to discover potential protein biomarkers. Such biomarkers include tissue-specific markers, cell type-specific markers, tumor type-specific markers, markers of malignancy, and prognostic or predictive markers of cancers. Here we show examples of database queries to generate sets of candidate biomarker proteins for several of these different categories. Expression profiles of candidate proteins can then subsequently be validated by examination of the underlying high resolution images. The present study shows examples of search strategies revealing several potential protein biomarkers, including proteins specifically expressed in normal cells and in cancer cells from specified tumor types. The lists of candidate proteins can be used as a starting point for further validation in larger patient cohorts using both immunological approaches and technologies utilizing more classical proteomics tools.

Improving the tolerance of a protein a analogue to repeated alkaline exposures using a bypass mutagenesis approach

Staphylococcal protein A (SPA) is a cell surface protein expressed by Staphylococcus aureus. It consists of five repetitive domains. The five SPA‐domains show individual interaction to the Fc‐fragment as well as certain Fab‐fragments of immunoglobulin G (IgG) from most mammalian species. Due to the high affinity and selectivity of SPA, it has a widespread use as an affinity ligand for capture and purification of antibodies. One of the problems with proteinaceous affinity ligands in large‐scale purification is their sensitivity to alkaline conditions. SPA however, is considered relatively stable to alkaline treatment. Nevertheless, it is desirable to further improve the stability in order to enable an SPA‐based affinity medium to withstand even longer exposure to the harsh conditions associated with cleaning‐in‐place (CIP) procedures. For this purpose, a protein engineering strategy, which was used earlier for stabilization and consists of replacing the asparagine residues, is employed. Since Z in its “nonengineered” form already has a significant tolerance to alkaline treatment, small changes in stability due to the mutations are difficult to assess. Hence, in order to enable detection of improvements regarding the alkaline resistance of the Z domain, we chose to use a bypass mutagenesis strategy using a mutated variant Z(F30A) as a surrogate framework. Z(F30A) has earlier been shown to possess an affinity to IgG that is similar to the wild‐type but also demonstrates decreased structural stability. Since the contribution of the different asparagine residues to the deactivation rate of a ligand is dependent on the environment and also the structural flexibility of the particular region, it is important to consider all sensitive amino acids one by one. The parental Z‐domain contains eight asparagine residues, each with a different impact on the alkaline stability of the domain. By exchanging asparagine 23 for a threonine, we were able to increase the stability of the Z(F30A) domain in alkaline conditions. Also, when grafting the N23T mutation to the Z scaffold, we were able to detect an increased tolerance to alkaline treatment compared to the native Z molecule. Proteins 2004.

The Impact of Tissue Fixatives on Morphology and Antibody-based Protein Profiling in Tissues and Cells

Pathology archives harbor large amounts of formalin-fixed, paraffin-embedded tissue samples, used mainly in clinical diagnostics but also for research purposes. Introduction of heat-induced antigen retrieval has enabled the use of tissue samples for extensive immunohistochemical analysis, despite the fact that antigen retrieval may not recover all epitopes, owing to alterations of the native protein structure induced by formalin. The aim of this study was to investigate how different fixatives influence protein recognition by immunodetection methods in tissues, cell preparations, and protein lysates, as compared with formalin. Seventy-two affinity-purified polyclonal antibodies were used to evaluate seven different fixatives. The aldehyde-based fixative Glyo-fixx proved to be excellent for preservation of proteins in tissue detected by immunohistochemistry (IHC), similar to formalin. A non-aldehyde–based fixative, NEO-FIX was superior for fixation of cultured cells, in regard to morphology, and thereby also advantageous for IHC. Large variability in the amount of protein extracted from the differently fixed tissues was observed, and the HOPE fixative provided the overall highest yield of protein. In conclusion, morphological resolution and immunoreactivity were superior in tissues fixed with aldehyde-based fixatives, whereas the use of non-aldehyde–based fixatives can be advantageous in obtaining high protein yield for Western blot analysis. This manuscript contains online supplemental material at http://www.jhc.org. Please visit this article online to view these materials.