Jochen M. Schwenk

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.

Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics.

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.

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.

The Human Protein Atlas as a proteomic resource for biomarker discovery

Pontén F, Schwenk JM, Asplund A, Edqvist P‐HD (Uppsala University, Uppsala; and KTH – Royal Institute of Technology, Stockholm; Sweden). The Human Protein Atlas as a proteomic resource for biomarker discovery (Review). J Intern Med 2011; 270: 428–446.

Determination of Binding Specificities in Highly Multiplexed Bead-based Assays for Antibody Proteomics

One of the major challenges of antibody-based proteomics is the quality assurance of the generated antibodies to ensure specificity to the target protein. Here we describe a single tube multiplex approach to simultaneously analyze the binding of antibodies to a large number of different antigens. This bead-based assay utilizes the full multiplexing capacity theoretically offered by the Luminex suspension array technology. A protocol for an increased coupling throughput for the immobilization of antigens was developed and used to set up complex and stabile 100-plex bead mixtures. The possibility of using a two-dimensional multiplexing, in terms of high numbers of both analytes and samples or as in this case antigens and antibodies, enables the specificity of 96 antibodies versus 100 different antigens to be determined in 2 h. This high throughput analysis will potentially have great impact on the possibility for the utilization of different antibody proteomics approaches where the quality assessment of antibodies is of the utmost importance.

Antibody suspension bead arrays within serum proteomics.

Antibody microarrays offer a powerful tool to screen for target proteins in complex samples. Here, we describe an approach for systematic analysis of serum, based on antibodies and using color-coded beads for the creation of antibody arrays in suspension. This method, adapted from planar antibody arrays, offers a fast, flexible, and multiplexed procedure to screen larger numbers of serum samples, and no purification steps are required to remove excess labeling substance. The assay system detected proteins down to lower picomolar levels with dynamic ranges over 3 orders of magnitude. The feasibility of this workflow was shown in a study with more than 200 clinical serum samples tested for 20 serum proteins.

Protein microarrays: catching the proteome

After the completion of the human genome sequencing project, DNA microarrays and sophisticated bioinformatics platforms give scientists a global view of biological systems. In todays proteome era, efforts are undertaken to adapt microarray technology in order to analyse the expression of a large number of proteins simultaneously and screen entire genomes for proteins that interact with particular factors, catalyse particular reactions, act as substrates for protein-modifying enzymes and/or as targets of autoimmune responses. In this review, we will summarise the current stage of protein microarray technology. We will focus on the latest fields of application for the simultaneous determination of a variety of parameters from a minute amount of sample. Future challenges of this cutting-edge technology will be discussed.

Affinity proteomics within rare diseases: a BIO-NMD study for blood biomarkers of muscular dystrophies

Despite the recent progress in the broad‐scaled analysis of proteins in body fluids, there is still a lack in protein profiling approaches for biomarkers of rare diseases. Scarcity of samples is the main obstacle hindering attempts to apply discovery driven protein profiling in rare diseases. We addressed this challenge by combining samples collected within the BIO‐NMD consortium from four geographically dispersed clinical sites to identify protein markers associated with muscular dystrophy using an antibody bead array platform with 384 antibodies. Based on concordance in statistical significance and confirmatory results obtained from analysis of both serum and plasma, we identified eleven proteins associated with muscular dystrophy, among which four proteins were elevated in blood from muscular dystrophy patients: carbonic anhydrase III (CA3) and myosin light chain 3 (MYL3), both specifically expressed in slow‐twitch muscle fibers and mitochondrial malate dehydrogenase 2 (MDH2) and electron transfer flavoprotein A (ETFA). Using age‐matched sub‐cohorts, 9 protein profiles correlating with disease progression and severity were identified, which hold promise for the development of new clinical tools for management of dystrophinopathies.

Autoantibody Profiling in Multiple Sclerosis Using Arrays of Human Protein Fragments

Profiling the autoantibody repertoire with large antigen collections is emerging as a powerful tool for the identification of biomarkers for autoimmune diseases. Here, a systematic and undirected approach was taken to screen for profiles of IgG in human plasma from 90 individuals with multiple sclerosis related diagnoses. Reactivity pattern of 11,520 protein fragments (representing ∼38% of all human protein encoding genes) were generated on planar protein microarrays built within the Human Protein Atlas. For more than 2,000 antigens IgG reactivity was observed, among which 64% were found only in single individuals. We used reactivity distributions among multiple sclerosis subgroups to select 384 antigens, which were then re-evaluated on planar microarrays, corroborated with suspension bead arrays in a larger cohort (n = 376) and confirmed for specificity in inhibition assays. Among the heterogeneous pattern within and across multiple sclerosis subtypes, differences in recognition frequencies were found for 51 antigens, which were enriched for proteins of transcriptional regulation. In conclusion, using protein fragments and complementary high-throughput protein array platforms facilitated an alternative route to discovery and verification of potentially disease-associated autoimmunity signatures, that are now proposed as additional antigens for large-scale validation studies across multiple sclerosis biobanks.

Contribution of Antibody-based Protein Profiling to the Human Chromosome-centric Proteome Project (C-HPP)

A gene-centric Human Proteome Project has been proposed to characterize the human protein-coding genes in a chromosome-centered manner to understand human biology and disease. Here, we report on the protein evidence for all genes predicted from the genome sequence based on manual annotation from literature (UniProt), antibody-based profiling in cells, tissues and organs and analysis of the transcript profiles using next generation sequencing in human cell lines of different origins. We estimate that there is good evidence for protein existence for 69% (n = 13985) of the human protein-coding genes, while 23% have only evidence on the RNA level and 7% still lack experimental evidence. Analysis of the expression patterns shows few tissue-specific proteins and approximately half of the genes expressed in all the analyzed cells. The status for each gene with regards to protein evidence is visualized in a chromosome-centric manner as part of a new version of the Human Protein Atlas ( www.proteinatlas.org ).