Gene‐specific correlation of RNA and protein levels in human cells and tissues
An 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 mea...
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| Published in: | Molecular systems biology Vol. 12; no. 10; pp. 883 - n/a |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
01.10.2016
EMBO Press John Wiley and Sons Inc Springer Nature |
| Subjects: | |
| ISSN: | 1744-4292, 1744-4292 |
| Online Access: | Get full text |
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| Abstract | An 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.
Synopsis
A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced.
A targeted proteomics approach based on spike‐in of stable isotope‐labeled protein fragments is developed to measure absolute protein copy numbers across human tissues and cell lines.
Transcript and protein levels within a sample do not correlate well, unless a gene‐specific RNA‐to‐protein (RTP) factor is introduced.
The RTP‐ratio varies significantly between genes, ranging from thousands to millions of protein copies per mRNA molecule, but does not vary across tissues.
Transcriptome analysis can be used as a tool to predict protein copy numbers per cell, thus forming an attractive link between genomics and proteomics.
Graphical Abstract
A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced. |
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| AbstractList | An 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. Abstract An 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. An 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. Synopsis A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced. A targeted proteomics approach based on spike‐in of stable isotope‐labeled protein fragments is developed to measure absolute protein copy numbers across human tissues and cell lines. Transcript and protein levels within a sample do not correlate well, unless a gene‐specific RNA‐to‐protein (RTP) factor is introduced. The RTP‐ratio varies significantly between genes, ranging from thousands to millions of protein copies per mRNA molecule, but does not vary across tissues. Transcriptome analysis can be used as a tool to predict protein copy numbers per cell, thus forming an attractive link between genomics and proteomics. Graphical Abstract A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced. An 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. Synopsis A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced. A targeted proteomics approach based on spike‐in of stable isotope‐labeled protein fragments is developed to measure absolute protein copy numbers across human tissues and cell lines. Transcript and protein levels within a sample do not correlate well, unless a gene‐specific RNA‐to‐protein (RTP) factor is introduced. The RTP‐ratio varies significantly between genes, ranging from thousands to millions of protein copies per mRNA molecule, but does not vary across tissues. Transcriptome analysis can be used as a tool to predict protein copy numbers per cell, thus forming an attractive link between genomics and proteomics. A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced. An important issue for molecular biology is to establish if 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 protein copies per mRNA molecule for others. In conclusion, our data suggests 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. |
| Author | Danielsson, Frida Lundberg, Emma Edfors, Fredrik Forsström, Björn Uhlén, Mathias Pontén, Fredrik Hallström, Björn M Käll, Lukas |
| AuthorAffiliation | 2 Department of Immunology, Genetics and Pathology Rudbeck Laboratory Uppsala University Uppsala Sweden 3 Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Hørsholm Denmark 1 Science for Life Laboratory KTH – Royal Institute of Technology Stockholm Sweden |
| AuthorAffiliation_xml | – name: 1 Science for Life Laboratory KTH – Royal Institute of Technology Stockholm Sweden – name: 2 Department of Immunology, Genetics and Pathology Rudbeck Laboratory Uppsala University Uppsala Sweden – name: 3 Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Hørsholm Denmark |
| Author_xml | – sequence: 1 givenname: Fredrik surname: Edfors fullname: Edfors, Fredrik organization: Science for Life Laboratory, KTH – Royal Institute of Technology – sequence: 2 givenname: Frida surname: Danielsson fullname: Danielsson, Frida organization: Science for Life Laboratory, KTH – Royal Institute of Technology – sequence: 3 givenname: Björn M surname: Hallström fullname: Hallström, Björn M organization: Science for Life Laboratory, KTH – Royal Institute of Technology – sequence: 4 givenname: Lukas surname: Käll fullname: Käll, Lukas organization: Science for Life Laboratory, KTH – Royal Institute of Technology – sequence: 5 givenname: Emma surname: Lundberg fullname: Lundberg, Emma organization: Science for Life Laboratory, KTH – Royal Institute of Technology – sequence: 6 givenname: Fredrik orcidid: 0000-0003-0703-3940 surname: Pontén fullname: Pontén, Fredrik organization: Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University – sequence: 7 givenname: Björn surname: Forsström fullname: Forsström, Björn organization: Science for Life Laboratory, KTH – Royal Institute of Technology – sequence: 8 givenname: Mathias orcidid: 0000-0002-4858-8056 surname: Uhlén fullname: Uhlén, Mathias email: mathias.uhlen@scilifelab.se organization: Science for Life Laboratory, KTH – Royal Institute of Technology, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27951527$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193966$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan) https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196993$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan) https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-308938$$DView record from Swedish Publication Index (Uppsala universitet) https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-318132$$DView record from Swedish Publication Index (Uppsala universitet) |
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| References | 2010; 10 2015; 347 2013; 45 2005; 41 2011; 10 2008; 5 2015; 348 2012; 489 2012; 13 2012; 11 2016; 15 2011; 7 2016; 34 2011; 473 2014; 507 2010; 26 2009; 10 2013; 13 2013; 35 2015; 40 2014; 509 2009; 583 1997; 18 2008; 26 1999; 11 2014; 13 2000; 80 2009; 6 2007; 2 2013; 495 2009; 4 2003; 100 2010; 6 1989 2016; 44 e_1_2_8_28_1 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_30_1 27951528 - Mol Syst Biol. 2016 Oct 20;12(10):885 |
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