Initial Quantitative Proteomic Map of 28 Mouse Tissues Using the SILAC Mouse

Abstract

Identifying the building blocks of mammalian tissues is a precondition for understanding their function. In particular, global and quantitative analysis of the proteome of mammalian tissues would point to tissue-specific mechanisms and place the function of each protein in a whole-organism perspective. We performed proteomic analyses of 28 mouse tissues using high-resolution mass spectrometry and used a mix of mouse tissues labeled via stable isotope labeling with amino acids in cell culture as a “spike-in” internal standard for accurate protein quantification across these tissues. We identified a total of 7,349 proteins and quantified 6,974 of them. Bioinformatic data analysis showed that physiologically related tissues clustered together and that highly expressed proteins represented the characteristic tissue functions. Tissue specialization was reflected prominently in the proteomic profiles and is apparent already in their hundred most abundant proteins. The proportion of strictly tissue-specific proteins appeared to be small. However, even proteins with household functions, such as those in ribosomes and spliceosomes, can have dramatic expression differences among tissues. We describe a computational framework with which to correlate proteome profiles with physiological functions of the tissue. Our data will be useful to the broad scientific community as an initial atlas of protein expression of a mammalian species. Identifying the building blocks of mammalian tissues is a precondition for understanding their function. In particular, global and quantitative analysis of the proteome of mammalian tissues would point to tissue-specific mechanisms and place the function of each protein in a whole-organism perspective. We performed proteomic analyses of 28 mouse tissues using high-resolution mass spectrometry and used a mix of mouse tissues labeled via stable isotope labeling with amino acids in cell culture as a “spike-in” internal standard for accurate protein quantification across these tissues. We identified a total of 7,349 proteins and quantified 6,974 of them. Bioinformatic data analysis showed that physiologically related tissues clustered together and that highly expressed proteins represented the characteristic tissue functions. Tissue specialization was reflected prominently in the proteomic profiles and is apparent already in their hundred most abundant proteins. The proportion of strictly tissue-specific proteins appeared to be small. However, even proteins with household functions, such as those in ribosomes and spliceosomes, can have dramatic expression differences among tissues. We describe a computational framework with which to correlate proteome profiles with physiological functions of the tissue. Our data will be useful to the broad scientific community as an initial atlas of protein expression of a mammalian species. The proteins expressed by a tissue are the principal active units that determine its function. Mapping tissue proteomes can therefore identify the molecular regulators and effectors of their physiological activity. A broad and quantitative proteome of a mammalian species has, however, not been determined yet. Instead, global analyses of mouse tissues have previously been performed mainly on the mRNA level using microarray or deep sequencing technologies (1Su A.I. Wiltshire T. Batalov S. Lapp H. Ching K.A. Block D. Zhang J. Soden R. Hayakawa M. Kreiman G. Cooke M.P. Walker J.R. Hogenesch J.B. A gene atlas of the mouse and human protein-encoding transcriptomes.Proc. Natl. Acad. Sci. U.S.A. 2004; 101: 6062-6067Crossref PubMed Scopus (2851) Google Scholar, 2Ramskold D. Wang E.T. Burge C.B. Sandberg R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data.PLoS Comput. Biol. 2009; 5: e1000598Crossref PubMed Scopus (632) Google Scholar). Although these methods can provide a near-comprehensive view of the biological system at the transcriptional level, mRNA levels do not necessarily predict protein expression levels and therefore miss an important determinant of biological function. Recent studies comparing mRNA to protein levels showed a correlation of 0.4 to 0.6 (see, for example, Refs. 3Lundberg E. Fagerberg L. Klevebring D. Matic I. Geiger T. Cox J. Algenas C. Lundeberg J. Mann M. Uhlen M. Defining the transcriptome and proteome in three functionally different human cell lines.Mol. Syst. Biol. 2010; 6: 450Crossref PubMed Scopus (271) Google Scholar and 4Nagaraj N. Wisniewski J.R. Geiger T. Cox J. Kircher M. Kelso J. Paabo S. Mann M. Deep proteome and transcriptome mapping of a human cancer cell line.Mol. Syst. Biol. 2011; 7: 548Crossref PubMed Scopus (757) Google Scholar), and it appears that the main process additionally determining protein levels is the regulation of translation rates (5Schwanhausser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4091) Google Scholar). These observations highlight the importance of determining the proteome in addition to the transcriptome. Mass spectrometry (MS)-based 1The abbreviations used are:IHCimmunohistochemical analysisLC-MS/MSliquid chromatography–tandem mass spectrometryMSmass spectrometrySILACstable isotope labeling with amino acids in cell culture. 1The abbreviations used are:IHCimmunohistochemical analysisLC-MS/MSliquid chromatography–tandem mass spectrometryMSmass spectrometrySILACstable isotope labeling with amino acids in cell culture. proteomics is the primary technology that enables a system-wide view of proteomes and their changes (6Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Crossref PubMed Scopus (5596) Google Scholar, 7Cox J. Mann M. Quantitative, high-resolution proteomics for data-driven systems biology.Annu. Rev. Biochem. 2011; 80: 273-299Crossref PubMed Scopus (532) Google Scholar, 8Yates 3rd, J.R. Gilchrist A. Howell K.E. Bergeron J.J. Proteomics of organelles and large cellular structures.Nat. Rev. Mol. Cell Biol. 2005; 6: 702-714Crossref PubMed Scopus (345) Google Scholar). In “shotgun” proteomics, proteins are digested to peptides, and the peptides are analyzed via liquid chromatography coupled to mass spectrometry (LC-MS/MS). The development of mass spectrometers with high resolution, high mass accuracy, and high sequencing speed now allows routine identification of large proportions of the mammalian proteome with high confidence (9Mann M. Kelleher N.L. Precision proteomics: the case for high resolution and high mass accuracy.Proc. Natl. Acad. Sci. U.S.A. 2008; 105: 18132-18138Crossref PubMed Scopus (353) Google Scholar). Previously, Kislinger et al. performed a proteomic analysis of six mouse tissues and identified ∼2,000 proteins per tissue using low-resolution MS (10Kislinger T. Cox B. Kannan A. Chung C. Hu P. Ignatchenko A. Scott M.S. Gramolini A.O. Morris Q. Hallett M.T. Rossant J. Hughes T.R. Frey B. Emili A. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling.Cell. 2006; 125: 173-186Abstract Full Text Full Text PDF PubMed Scopus (399) Google Scholar). A recent deep phospho-proteomic study in mouse also analyzed the proteome of nine mouse tissues (11Huttlin E.L. Jedrychowski M.P. Elias J.E. Goswami T. Rad R. Beausoleil S.A. Villen J. Haas W. Sowa M.E. Gygi S.P. A tissue-specific atlas of mouse protein phosphorylation and expression.Cell. 2010; 143: 1174-1189Abstract Full Text Full Text PDF PubMed Scopus (1217) Google Scholar). These studies as on and to a of tissues. to correlate protein expression and tissue with quantification on a Although not proteome the and of the study a of tissue-specific functions. analysis liquid chromatography–tandem mass spectrometry mass spectrometry stable isotope labeling with amino acids in cell culture. analysis liquid chromatography–tandem mass spectrometry mass spectrometry stable isotope labeling with amino acids in cell culture. MS quantitative with the development of methods in which the of and of the peptides are determined Mann M. Mass spectrometry-based proteomics Biol. 2005; PubMed Scopus Google Scholar). isotope labeling with amino acids in cell culture is as the most accurate technology for protein on in which amino and are proteins their B. I. H. A. Mann M. isotope labeling by amino acids in cell as a and accurate to expression Full Text Full Text PDF PubMed Scopus Google Scholar). In the was to in it now been to and human tissues T. Cox J. P. Wisniewski J.R. Mann M. mix for quantitative proteomics of human 2010; 7: PubMed Scopus Google Scholar, J. proteomics by labeling of 2010; Full Text Full Text PDF PubMed Scopus Google Scholar, Chen Selbach M. The allows for accurate protein quantification in 2010; Full Text Full Text PDF PubMed Scopus Google Scholar, M. E. G. S. A. A.I. labeling with amino acids in 2011; PubMed Scopus Google Scholar). We have that for on a as their of amino to labeling of their proteome M. M. S. I. S. S. R. Mann M. mouse for quantitative proteomics as an for cell 2008; Full Text Full Text PDF PubMed Scopus Google Scholar, S. M. Mann M. In quantitative proteomics: the Mol. Biol. PubMed Scopus Google Scholar). provide a of tissues that can as a for quantification and which the of the tissue in In study of the mouse technology to 28 mouse tissues to a mix of these tissues. We provide a deep proteomic of the proteins and functions across these tissues and highlight regulators of tissue via and 28 tissues and and in liquid protein tissues at in and in at and the was in of proteins in and was performed to determine the protein three to each and to of protein tissues combined to the protein standard for The standard was with each of the tissues in and digested in with via on an in a as S. Mann M. with is an to protein for proteome 2008; PubMed Scopus Google Scholar). each of the using J. Mann M. and with for and in 2006; 5: PubMed Scopus Google Scholar). performed on an coupled to an mass on a and using a of MS performed in the in the at resolution and a of The most each MS a of and in the analysis was performed in on an N. Cox J. N. Mann M. analysis with of the proteome by on a Full Text Full Text PDF PubMed Scopus Google coupled to a mass A. E. A. A. N. Cox J. Mann M. S. Mass spectrometry-based proteomics using a mass 2011; Full Text Full Text PDF PubMed Scopus Google Scholar). on a and using a of MS performed in the in the at resolution and a of The most each MS and in the with a resolution of mass analyzed in the J. Mann M. enables high identification mass and protein 2008; PubMed Scopus Google Scholar), using the J. N. A. Mann M. a the 2011; PubMed Scopus Google the with such as human and and and mass and mass with initial mass of and in the and and in the The of and and a of The of was at and the was at six amino The was at for and protein identified peptides combined and as protein In to determine a of was We used the of the proteins as a for protein and by the of each to its the analysis of protein in the the The used for across the different tissues. that the most is the that changes in protein abundance and analysis was performed with was by biological molecular and cellular the the and the for was the the data as to have at of 28 with per the data to data by a of with a standard of to the standard of the and standard of the to the of of proteins was performed in on of the using analysis was on The in in to the principal The protein by the among for protein the for of the expression The is a with a that was by using a of that the for at of the to of the The for each are the differences of the was performed with a of Tissue in for at by standard tissues and using a The tissues in and and on L. A. A. S. C. The of tissue on and protein in tissues and 2010; PubMed Scopus Google Scholar). Tissue in in of and for in for was performed in for at using a analysis was performed using an and the system for each at in as was performed in an using and the in an using at using a a quantitative of the mouse protein tissues and tissue of accurate of the mouse M. M. S. I. S. S. R. Mann M. mouse for quantitative proteomics as an for cell 2008; Full Text Full Text PDF PubMed Scopus Google and used it as a standard and We combined protein of each of these 28 tissues a mouse and standard with an of each of the tissues. We digested each tissue proteome with and the peptides to their point of the was analyzed via on a mass for a total of of the data with J. Mann M. enables high identification mass and protein 2008; PubMed Scopus Google and the J. N. A. Mann M. a the 2011; PubMed Scopus Google identified 7,349 proteins at a of and quantified 6,974 of These data a of expressed mouse proteins and their profiles across the mouse tissues. these proteomes the proteins expressed in the and not necessarily in cell in the tissue. highlight tissue-specific proteins that have high the standard in or a related tissues and can determine proteins with expression levels in the tissues. example, is highly expressed in the tissues in not in tissue protein is expressed mainly in the levels are also in the and protein was the to the of the expressed an of the is expressed in the In are and expressed at levels in tissues Our analysis of of proteomes three the performed analyses of and which quantified to the We identified a total of and proteins in each tissue We high correlation the of the tissue These that in with quantification highly quantitative data of proteins. mass with an performed of mouse and is not a quantitative proteins that showed the tissue with expression in the or to the tissues. 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Biol. 2009; 5: PubMed Scopus Google Scholar), which proteins. a of tissue-specific proteins determined via proteomics, to proteins and nine for which the high sequence mouse and We to to the mass are expressed in the tissues. of the proteins with the mouse proteins and with the MS In with the MS data the and highly expressed in the tissues the of and of and the of to the MS is highly expressed in the and the in the it mainly in the and was in the the high in the MS the of expression in the as with expression in the the expression in the tissues and a expression in the to the MS The identified high expression levels in the and in the was in the and in the The related to the expression in the is related to the of which not the differences in protein for the of the proteomic data to the MS using highlight the internal in the for the cell in the tissue have different expression The MS analysis identified to proteins in tissues was high of the identified proteins the with proteins identified in of and proteins that in and The large differences in the of identified proteins are of the of the MS A highly abundant proteins in tissues can with the identification of of the abundance the of the of the most abundant proteins to the total protein mass of each to the protein of the the three and proteins and of the protein In the most abundant proteins to of the and the the of protein mass that in the most abundant already of the total protein and proteins for of it In of proteins in tissues. 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Gygi S.P. A. of 2010; 143: Full Text Full Text PDF PubMed Scopus Google Scholar, T. M. A. of on cellular and cell in PubMed Scopus Google Scholar). In with these data that the of large molecular are in a across the mouse tissues. the the internal standard in tissues The also with a of the of the of the large and of as in The levels of proteins the with the tissue the high levels in the and and the levels in the tissues. levels the with ribosomes in the in the been for that the high levels of ribosomes G. in Natl. Acad. Sci. U.S.A. PubMed Google Scholar, E. I. and Biol. Full Text PDF PubMed Google Scholar), and is by the high translation rates that are in to the of data provide an of the of each tissue. the showed of of their The tissue the levels of these by and These are by the high of these for its and the for activity. 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W. B. B. D. M. M. D. G. S. J. S. S.P. A. J. E. I. R. J. A. G. J. 2011; PubMed Scopus Google Scholar). We these to genes proteins in using each analysis as the to the protein The revealed biological and tissue The of the performed in cell mouse data identified of of in the tissue. and clustered with the protein of protein in proteins in that highly expressed in the tissue to the These the and A with high expression levels in the tissue as as in the and the was for and These with Recent the importance of in in with to its in J. A. as a of and protein Rev. 2010; PubMed Scopus Google Scholar). Our analysis to tissue and a for in tissue and in tissues that high protein translation a global view of the tissues to a of and tissues in to identify their and differences a proteomic tissues in and their is by and P. and the of Rev. Biochem. 2008; PubMed Scopus Google Scholar), the tissue high that enables its P. and the of Rev. Biochem. 2008; PubMed Scopus Google Scholar). the of tissues each among that are high in in or to tissue or the The of the regulators of and and high in tissues In the of the that not with of the of development and a Our data not a of that for was a large of These related to the the main the tissues. proteomic analysis can the main differences tissues and can therefore be used as a of on tissue the correlation of the proteomic data to mRNA expression proteome data to a used microarray expression (1Su A.I. Wiltshire T. Batalov S. Lapp H. Ching K.A. Block D. Zhang J. Soden R. Hayakawa M. Kreiman G. Cooke M.P. Walker J.R. Hogenesch J.B. A gene atlas of the mouse and human protein-encoding transcriptomes.Proc. Natl. Acad. Sci. U.S.A. 2004; 101: 6062-6067Crossref PubMed Scopus (2851) Google Scholar), correlation of to However, correlation to recent expression profiles A. L. B. Mapping and mammalian by 2008; 5: PubMed Scopus Google was at to 0.6 which is to for cell using high-resolution MS N. Wisniewski J.R. Geiger T. Cox J. Kircher M. Kelso J. Paabo S. Mann M. Deep proteome and transcriptome mapping of a human cancer cell line.Mol. Syst. Biol. 2011; 7: 548Crossref PubMed Scopus (757) Google Scholar, B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4091) Google Scholar). the correlation of the data to the microarray data was also the correlation of microarray to proteomics data of microarray and the proteome of different and tissues The not the and the proteomics data studies of the of the mRNA and protein the of proteome We performed a deep and quantitative proteome analysis of mouse as as using the mouse as a We identified a total of 7,349 proteins and to proteins in each tissue. These of per are to those in studies that tissues (10Kislinger T. Cox B. Kannan A. Chung C. Hu P. Ignatchenko A. Scott M.S. Gramolini A.O. Morris Q. Hallett M.T. Rossant J. Hughes T.R. Frey B. Emili A. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling.Cell. 2006; 125: 173-186Abstract Full Text Full Text PDF PubMed Scopus (399) Google Scholar, E.L. Jedrychowski M.P. Elias J.E. Goswami T. Rad R. Beausoleil S.A. Villen J. Haas W. Sowa M.E. Gygi S.P. A tissue-specific atlas of mouse protein phosphorylation and expression.Cell. 2010; 143: 1174-1189Abstract Full Text Full Text PDF PubMed Scopus (1217) Google Scholar). However, data by the quantified of the main in tissue analysis is the high abundance of proteins that the of the that tissues have large of as in the that with a tissue analyses in a of protein initial mouse proteome expression study also the for even in the broad of the analysis combined with quantification protein of tissue analysis and of the MS data to and mRNA data it with to the and the the correlation of to 0.6 that the mRNA and the protein levels the to study proteins and not the of have expression profiles across the 28 tissues that can be used by the scientific community to proteins of to tissues. The been in the which a for the levels of proteins of tissues and the expression each tissue C. Geiger T. G. Cox J. Mann M. of high accuracy, quantitative proteomics data in the Full Text Full Text PDF PubMed Scopus Google Scholar). can in the and of tissue-specific of or which tissues be by at proteins. that tissue is already in the most abundant proteins. that the characteristic tissue functions are the most at the proteome level, the of specialization of tissues. These large differences in the proteins do not a large of strictly tissue-specific proteins. is in with a study on cell E. Fagerberg L. Klevebring D. Matic I. Geiger T. Cox J. Algenas C. Lundeberg J. Mann M. Uhlen M. Defining the transcriptome and proteome in three functionally different human cell lines.Mol. Syst. Biol. 2010; 6: 450Crossref PubMed Scopus (271) Google and a transcriptome study D. Wang E.T. Burge C.B. Sandberg R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data.PLoS Comput. Biol. 2009; 5: e1000598Crossref PubMed Scopus (632) Google Scholar), which also a proportion of genes expressed by cell or tissue. the tissue proteome the cell data high at and quantitative levels of gene can be to the of cancer cell which are highly and to cell culture the differences the studies the large of proteome tissue also by comparing the proteome of a cancer cell to a primary cell C. C. S. Mann M. proteomic of cell and primary to of cell 2009; Full Text Full Text PDF PubMed Scopus Google Scholar). In have a framework for protein expression to protein which to the functions of the tissue. We combined the proteomic with tissue functions and regulators of tissue in In data provide a of tissue-specific proteins proteins with functions with protein example, the proteomic of and transcriptional regulators of tissue of these in and tissues. In the the of the function of its and activity. tissue was with of and which are in cell and protein of and a of these related to cell and these mechanisms of transcriptional proteomic data the of of regulation of protein expression in the different tissues. levels of ribosomes in the and the high of protein translation in these tissues. the expression of in the tissue to their importance in the regulation of In the that is not by the of the by regulation via such as protein and in with analyses (5Schwanhausser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4091) Google Scholar), these levels point to the of protein mechanisms in the regulation of protein levels to protein levels that these are and that of these levels would be to and tissues. In initial quantitative proteomic of a a useful for the community and protein protein and tissue in a We the of the of Proteomics and for to for the in the MS and data with