mrna与蛋白质水平

AbstractABSTRACT We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeastSaccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Over 150 protein spots were excised and identified by capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein spots were quantified by metabolic labeling and scintillation counting. Corresponding mRNA levels were calculated from serial analysis of gene expression (SAGE) frequency tables (V. E. Velculescu, L. Zhang, W. Zhou, J. Vogelstein, M. A. Basrai, D. E. Bassett, Jr., P. Hieter, B. Vogelstein, and K. W. Kinzler, Cell 88:243–251, 1997). We found that the correlation between mRNA and protein levels was insufficient to predict protein expression levels from quantitative mRNA data. Indeed, for some genes, while the mRNA levels were of the same value the protein levels varied by more than 20-fold. Conversely, invariant steady-state levels of certain proteins were observed with respective mRNA transcript levels that varied by as much as 30-fold. Another interesting observation is that codon bias is not a predictor of either protein or mRNA levels. Our results clearly delineate the technical boundaries of current approaches for quantitative analysis of protein expression and reveal that simple deduction from mRNA transcript analysis is insufficient.

AbstractTechniques for systematically monitoring protein translation have lagged far behind methods for measuring messenger RNA (mRNA) levels. Here, we present a ribosome-profiling strategy that is based on the deep sequencing of ribosome-protected mRNA fragments and enables genome-wide investigation of translation with subcodon resolution. We used this technique to monitor translation in budding yeast under both rich and starvation conditions. These studies defined the protein sequences being translated and found extensive translational control in both determining absolute protein abundance and responding to environmental stress. We also observed distinct phases during translation that involve a large decrease in ribosome density going from early to late peptide elongation as well as widespread regulated initiation at non–adenine-uracil-guanine (AUG) codons. Ribosome profiling is readily adaptable to other organisms, making high-precision investigation of protein translation experimentally accessible.

摘要mRNA所包含的核苷酸序列通过三联体密码子决定了蛋白质的氨基酸序列。但是, 由于对氨基酸同义密码使用频率上的差异, 密码子与反密码子相互作用效率上的不同, 以及密码子上下文关系和mRNA 不同区域二级结构上的差异, 造成了核糖体对mRNA 不同区域翻译速度上的差异, 加之共翻译折叠的作用, 使得mRNA 的序列和结构影响着蛋白质空间结构的形成。

AbstractAn 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.

AbstractAbstract Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data.

摘要近日,来自美国爱因斯坦医学院的研究人员在著名国际学术期刊cell发表了一项最新研究进展,他们利用双光子荧光涨落分析技术实现了对mRNA-蛋白质相互作用的定量检测,这一技术对于研究mRNA在细胞内不同时空条件下的表达具有重要推动作用。(源自:药品资讯网)

AbstractThe influence of mRNA abundance on protein expression continues to be a topic of significant interest and debate. In various systems, the correlations between these molecules has been reported to be anywhere from 20–70%. Many of these studies have been conducted in complex, in vivo systems, and assayed just a fraction of the genes present due to difficulties in quantitation of many proteins simultaneously. Our approach is to examine this relationship as the result of a single translational event, in a global manner, and without the additional factors introduced by in vivo systems. We created a protein library whose constituents can be decoded by next generation sequencing using cDNA display techniques, a synthetic, bacterial based, translation system, and an expression library derived from an mRNA population. This data, along with sequencing data from the precursor mRNA library, allow for a correlation analysis across all genes contained within the mRNA library. Preliminary results indicates that in this system, ~60% of the variance in protein expression can be explained by mRNA abundance. To determine the stability of the correlation, we modified the translation sequences in the gene library and reanalyzed the protein to mRNA relationship; variations in the correlation reflect the influence of the translational sequences on the protein expression. This system can be utilized to explore the mRNA and protein relationship for wild‐type translational systems, or to evaluate the effects of modifications or synthetic sequences.

AbstractABSTRACT We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeastSaccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Over 150 protein spots were excised and identified by capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein spots were quantified by metabolic labeling and scintillation counting. Corresponding mRNA levels were calculated from serial analysis of gene expression (SAGE) frequency tables (V. E. Velculescu, L. Zhang, W. Zhou, J. Vogelstein, M. A. Basrai, D. E. Bassett, Jr., P. Hieter, B. Vogelstein, and K. W. Kinzler, Cell 88:243–251, 1997). We found that the correlation between mRNA and protein levels was insufficient to predict protein expression levels from quantitative mRNA data. Indeed, for some genes, while the mRNA levels were of the same value the protein levels varied by more than 20-fold. Conversely, invariant steady-state levels of certain proteins were observed with respective mRNA transcript levels that varied by as much as 30-fold. Another interesting observation is that codon bias is not a predictor of either protein or mRNA levels. Our results clearly delineate the technical boundaries of current approaches for quantitative analysis of protein expression and reveal that simple deduction from mRNA transcript analysis is insufficient.

摘要mRNA所包含的核苷酸序列通过三联体密码子决定了蛋白质的氨基酸序列。但是, 由于对氨基酸同义密码使用频率上的差异, 密码子与反密码子相互作用效率上的不同, 以及密码子上下文关系和mRNA 不同区域二级结构上的差异, 造成了核糖体对mRNA 不同区域翻译速度上的差异, 加之共翻译折叠的作用, 使得mRNA 的序列和结构影响着蛋白质空间结构的形成。

AbstractThe deviation from the uniform usage of synonymous codons is termed codon usage bias. A lot has been explained from the translational viewpoint for the observed phenomenon. To understand codon usage bias from the transcriptional perspective, we present here a holistic picture of this phenomenon in Saccharomyces cerevisiae, using both wild type and computationally mutated mRNAs. Although in wild type, both codon usage bias and mRNA stability positively regulate the gene (mRNA) expression level and are positively correlated with each other, any deviation from natural situation breaks such equilibrium. Computational examination of mRNA sequences with different sets of synonymous codon composition reveals that in mutated condition, the mRNA expression becomes reduced. Furthermore, constraining codon usage pattern to wild type and carrying out randomization of codons resulted in less stable mRNA. Further, we realized a Boolean Expression explaining the gene expression under various conditions of bias and mRNA stability. These studies suggest that selection of codons is favored for regulation of gene expression through potential formation of messenger RNA structures which contribute to folding stability. The naturally occurring codon composition is responsible for optimization of gene expression, and under such composition, the mRNA structure having highest stability is selected by nature.

AbstractTechniques for systematically monitoring protein translation have lagged far behind methods for measuring messenger RNA (mRNA) levels. Here, we present a ribosome-profiling strategy that is based on the deep sequencing of ribosome-protected mRNA fragments and enables genome-wide investigation of translation with subcodon resolution. We used this technique to monitor translation in budding yeast under both rich and starvation conditions. These studies defined the protein sequences being translated and found extensive translational control in both determining absolute protein abundance and responding to environmental stress. We also observed distinct phases during translation that involve a large decrease in ribosome density going from early to late peptide elongation as well as widespread regulated initiation at non–adenine-uracil-guanine (AUG) codons. Ribosome profiling is readily adaptable to other organisms, making high-precision investigation of protein translation experimentally accessible.

摘要近日,来自美国爱因斯坦医学院的研究人员在著名国际学术期刊cell发表了一项最新研究进展,他们利用双光子荧光涨落分析技术实现了对mRNA-蛋白质相互作用的定量检测,这一技术对于研究mRNA在细胞内不同时空条件下的表达具有重要推动作用。(源自:药品资讯网)

摘要近日,来自美国爱因斯坦医学院的研究人员在著名国际学术期刊cell发表了一项最新研究进展,他们利用双光子荧光涨落分析技术实现了对mRNA-蛋白质相互作用的定量检测,这一技术对于研究mRNA在细胞内不同时空条件下的表达具有重要推动作用。(源自:药品资讯网)

AbstractSelective recognition and removal of faulty transcripts and misfolded polypeptides are crucial for cell viability. In eukaryotic cells, nonsense-mediated mRNA decay (NMD) constitutes an mRNA surveillance pathway for sensing and degrading aberrant transcripts harboring premature termination codons (PTCs). NMD functions also as a post-transcriptional gene regulatory mechanism by downregulating naturally occurring mRNAs. As NMD is activated only after a ribosome reaches a PTC, PTC-containing mRNAs inevitably produce truncated and potentially misfolded polypeptides as byproducts. To cope with the emergence of misfolded polypeptides, eukaryotic cells have evolved sophisticated mechanisms such as chaperone-mediated protein refolding, rapid degradation of misfolded polypeptides through the ubiquitin–proteasome system, and sequestration of misfolded polypeptides to the aggresome for autophagy-mediated degradation. In this review, we discuss how UPF1, a key NMD factor, contributes to the selective removal of faulty transcripts via NMD at the molecular level. We then highlight recent advances on UPF1-mediated communication between mRNA surveillance and protein quality control.

AbstractUp-frameshift protein 1 (UPF1) plays the role of a vital controller for transcripts, ready to react in the event of an incorrect translation mechanism. It is well known as one of the key elements involved in mRNA decay pathways and participates in transcript and protein quality control in several different aspects. Firstly, UPF1 specifically degrades premature termination codon (PTC)-containing products in a nonsense-mediated mRNA decay (NMD)-coupled manner. Additionally, UPF1 can potentially act as an E3 ligase and degrade target proteins independently from mRNA decay pathways. Thus, UPF1 protects cells against the accumulation of misfolded polypeptides. However, this multitasking protein may still hide many of its functions and abilities. In this article, we summarize important discoveries in the context of UPF1, its involvement in various cellular pathways, as well as its structural importance and mutational changes related to the emergence of various pathologies and disease states. Even though the state of knowledge about this protein has significantly increased over the years, there are still many intriguing aspects that remain unresolved.

AbstractAn 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.

AbstractIn this work targeted (selected reaction monitoring, SRM, PASSEL: PASS00697) and panoramic (shotgun LC-MS/MS, PRIDE: PXD00244) mass-spectrometric methods as well as transcriptomic analysis of the same samples using RNA-Seq and PCR methods (SRA experiment IDs: SRX341198, SRX267708, SRX395473, SRX390071) were applied for quantification of chromosome 18 encoded transcripts and proteins in human liver and HepG2 cells. The obtained data was used for the estimation of quantitative mRNA-protein ratios for the 275 genes of the selected chromosome in the selected tissues. The impact of methodological limitations of existing analytical proteomic methods on gene-specific mRNA-protein ratios and possible ways of overcoming these limitations for detection of missing proteins are also discussed.

AbstractAn 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.

AbstractTranscriptomic data are widely available, and the extent to which they are predictive of protein abundances remains debated. Using multiple public databases, we calculate mRNA and mRNA-to-protein ratio variability across human tissues to quantify and classify genes for protein abundance predictability confidence. We propose that such predictability is best understood as a spectrum. A gene-specific, tissue-independent mRNA-to-protein ratio plus mRNA levels explains ∼80% of protein abundance variance for more predictable genes, as compared to ∼55% for less predictable genes. Protein abundance predictability is consistent with independent mRNA and protein data from two disparate cell lines, and mRNA-to-protein ratios estimated from publicly-available databases have predictive power in these independent datasets. Genes with higher predictability are enriched for metabolic function, tissue development/cell differentiation roles, and transmembrane transporter activity. Genes with lower predictability are associated with cell adhesion, motility and organization, the immune system, and the cytoskeleton. Surprisingly, many genes that regulate mRNA-to-protein ratios are constitutively expressed but also exhibit ratio variability, suggesting a general autoregulation mechanism whereby protein expression profile changes can be implemented quickly, or homeostatic sensing stabilizes protein abundances under fluctuating conditions. Gene classifications and their mRNA-to-protein ratios are provided as a resource to facilitate protein abundance predictions by others.

AbstractTechniques for systematically monitoring protein translation have lagged far behind methods for measuring messenger RNA (mRNA) levels. Here, we present a ribosome-profiling strategy that is based on the deep sequencing of ribosome-protected mRNA fragments and enables genome-wide investigation of translation with subcodon resolution. We used this technique to monitor translation in budding yeast under both rich and starvation conditions. These studies defined the protein sequences being translated and found extensive translational control in both determining absolute protein abundance and responding to environmental stress. We also observed distinct phases during translation that involve a large decrease in ribosome density going from early to late peptide elongation as well as widespread regulated initiation at non–adenine-uracil-guanine (AUG) codons. Ribosome profiling is readily adaptable to other organisms, making high-precision investigation of protein translation experimentally accessible.

摘要缺乏必需氨基酸,会通过抑制mRNA翻译的起始阶段而引起细胞内所有蛋白质合成的下降,但是不同蛋白质合成受抑制的程度并不相同。某些蛋白质,尤其是由具有5′末端寡嘧啶（5-t′erm ina l o ligopyrim id ine,TOP）特征的mRNA编码的蛋白质,其受影响的程度要高于其余大多数蛋白质。TOP mRNA翻译的特异性下降,是核糖体蛋白S6激酶、S6K 1受抑制及S6磷酸化同时降低所致。由于许多TOP mRNA编码的蛋白质（如真核细胞延长因子eEF 1A和eEF 2以及核糖体蛋白质）参与mRNA的翻译,因此,必需氨基酸的缺乏不仅会快速、直接地抑制所有mRNA的翻译,而且会潜在地导致机体合成蛋白质的能力下降。所以持续供给完全平衡的必需氨基酸是使肝脏和骨骼肌中的蛋白质合成维持在最佳速度的先决条件。

AbstractAbstract Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data.

AbstractThe deviation from the uniform usage of synonymous codons is termed codon usage bias. A lot has been explained from the translational viewpoint for the observed phenomenon. To understand codon usage bias from the transcriptional perspective, we present here a holistic picture of this phenomenon in Saccharomyces cerevisiae, using both wild type and computationally mutated mRNAs. Although in wild type, both codon usage bias and mRNA stability positively regulate the gene (mRNA) expression level and are positively correlated with each other, any deviation from natural situation breaks such equilibrium. Computational examination of mRNA sequences with different sets of synonymous codon composition reveals that in mutated condition, the mRNA expression becomes reduced. Furthermore, constraining codon usage pattern to wild type and carrying out randomization of codons resulted in less stable mRNA. Further, we realized a Boolean Expression explaining the gene expression under various conditions of bias and mRNA stability. These studies suggest that selection of codons is favored for regulation of gene expression through potential formation of messenger RNA structures which contribute to folding stability. The naturally occurring codon composition is responsible for optimization of gene expression, and under such composition, the mRNA structure having highest stability is selected by nature.

AbstractProtein replacement therapy is an umbrella term used for medical treatments that aim to substitute or replenish specific protein deficiencies that result either from the protein being absent or non-functional due to mutations in affected patients. Traditionally, such an approach requires a well characterized but arduous and expensive protein production procedure that employs in vitro expression and translation of the pharmaceutical protein in host cells, followed by extensive purification steps. In the wake of the SARS-CoV-2 pandemic, mRNA-based pharmaceuticals were recruited to achieve rapid in vivo production of antigens, proving that the in vivo translation of exogenously administered mRNA is nowadays a viable therapeutic option. In addition, the urgency of the situation and worldwide demand for mRNA-based medicine has led to an evolution in relevant technologies, such as in vitro transcription and nanolipid carriers. In this review, we present preclinical and clinical applications of mRNA as a tool for protein replacement therapy, alongside with information pertaining to the manufacture of modified mRNA through in vitro transcription, carriers employed for its intracellular delivery and critical quality attributes pertaining to the finished product.

AbstractThe deviation from the uniform usage of synonymous codons is termed codon usage bias. A lot has been explained from the translational viewpoint for the observed phenomenon. To understand codon usage bias from the transcriptional perspective, we present here a holistic picture of this phenomenon in Saccharomyces cerevisiae, using both wild type and computationally mutated mRNAs. Although in wild type, both codon usage bias and mRNA stability positively regulate the gene (mRNA) expression level and are positively correlated with each other, any deviation from natural situation breaks such equilibrium. Computational examination of mRNA sequences with different sets of synonymous codon composition reveals that in mutated condition, the mRNA expression becomes reduced. Furthermore, constraining codon usage pattern to wild type and carrying out randomization of codons resulted in less stable mRNA. Further, we realized a Boolean Expression explaining the gene expression under various conditions of bias and mRNA stability. These studies suggest that selection of codons is favored for regulation of gene expression through potential formation of messenger RNA structures which contribute to folding stability. The naturally occurring codon composition is responsible for optimization of gene expression, and under such composition, the mRNA structure having highest stability is selected by nature.

摘要近日,来自美国爱因斯坦医学院的研究人员在著名国际学术期刊cell发表了一项最新研究进展,他们利用双光子荧光涨落分析技术实现了对mRNA-蛋白质相互作用的定量检测,这一技术对于研究mRNA在细胞内不同时空条件下的表达具有重要推动作用。(源自:药品资讯网)

AbstractINTRODUCTION. Protein families are distinguished by members that exhibit sequence and biomedical function similarity. For most gene changes in protein abundance are related to changes in mRNA abundance, which are immensely informative about cell state and the activity of genes. LifeExpress RNA (LE) database (Incyte Genomics, Inc) is a large-scale genome expression database. Based on LE we derived the functional relationship of Pfam[1], a database of protein domain families, by studying the global expression profile of the corresponding genes of Pfam family members. The expression profiles for 135 largest Pfam families were summarized and relationships were analyzed. The study present a simple model for conceptualizing the complex genetic regulatory network.

AbstractProtein 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.

AbstractProtein 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.