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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

Structured Abstract

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.

Abstract

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.
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Supplementary Material

Summary

Materials and Methods
Figs. S1 to S8
Tables S1 to S18
References (4161)

Resources

File (1260419__excel_tabless1-s18.xlsx)
File (1260419_uhlen.sm.pdf)

REFERENCES AND NOTES

1
Flicek P., Ahmed I., Amode M. R., Barrell D., Beal K., Brent S., Carvalho-Silva D., Clapham P., Coates G., Fairley S., Fitzgerald S., Gil L., García-Girón C., Gordon L., Hourlier T., Hunt S., Juettemann T., Kähäri A. K., Keenan S., Komorowska M., Kulesha E., Longden I., Maurel T., McLaren W. M., Muffato M., Nag R., Overduin B., Pignatelli M., Pritchard B., Pritchard E., Riat H. S., Ritchie G. R., Ruffier M., Schuster M., Sheppard D., Sobral D., Taylor K., Thormann A., Trevanion S., White S., Wilder S. P., Aken B. L., Birney E., Cunningham F., Dunham I., Harrow J., Herrero J., Hubbard T. J., Johnson N., Kinsella R., Parker A., Spudich G., Yates A., Zadissa A., Searle S. M., Ensembl 2013. Nucleic Acids Res. 41 (Database), D48–D55 (2013).
2
Pruitt K. D., Tatusova T., Brown G. R., Maglott D. R., NCBI Reference Sequences (RefSeq): Current status, new features and genome annotation policy. Nucleic Acids Res. 40 (Database), D130–D135 (2012).
3
Kawaji H., Severin J., Lizio M., Forrest A. R., van Nimwegen E., Rehli M., Schroder K., Irvine K., Suzuki H., Carninci P., Hayashizaki Y., Daub C. O., Update of the FANTOM web resource: From mammalian transcriptional landscape to its dynamic regulation. Nucleic Acids Res. 39 (Database), D856–D860 (2011).
4
Brazma A., Parkinson H., Sarkans U., Shojatalab M., Vilo J., Abeygunawardena N., Holloway E., Kapushesky M., Kemmeren P., Lara G. G., Oezcimen A., Rocca-Serra P., Sansone S. A., ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71 (2003).
5
Magrane M., Consortium U., UniProt Knowledgebase: A hub of integrated protein data. Database (Oxford) 2011, bar009 (2011).
6
Gaudet P., Argoud-Puy G., Cusin I., Duek P., Evalet O., Gateau A., Gleizes A., Pereira M., Zahn-Zabal M., Zwahlen C., Bairoch A., Lane L., neXtProt: Organizing protein knowledge in the context of human proteome projects. J. Proteome Res. 12, 293–298 (2013).
7
Dunham I., Kundaje A., Aldred S. F., Collins P. J., Davis C. A., Doyle F., Epstein C. B., Frietze S., Harrow J., Kaul R., Khatun J., Lajoie B. R., Landt S. G., Lee B.-K., Pauli F., Rosenbloom K. R., Sabo P., Safi A., Sanyal A., Shoresh N., Simon J. M., Song L., Trinklein N. D., Altshuler R. C., Birney E., Brown J. B., Cheng C., Djebali S., Dong X., Dunham I., Ernst J., Furey T. S., Gerstein M., Giardine B., Greven M., Hardison R. C., Harris R. S., Herrero J., Hoffman M. M., Iyer S., Kellis M., Khatun J., Kheradpour P., Kundaje A., Lassmann T., Li Q., Lin X., Marinov G. K., Merkel A., Mortazavi A., Parker S. C. J., Reddy T. E., Rozowsky J., Schlesinger F., Thurman R. E., Wang J., Ward L. D., Whitfield T. W., Wilder S. P., Wu W., Xi H. S., Yip K. Y., Zhuang J., Bernstein B. E., Birney E., Dunham I., Green E. D., Gunter C., Snyder M., Pazin M. J., Lowdon R. F., Dillon L. A. L., Adams L. B., Kelly C. J., Zhang J., Wexler J. R., Green E. D., Good P. J., Feingold E. A., Bernstein B. E., Birney E., Crawford G. E., Dekker J., Elnitski L., Farnham P. J., Gerstein M., Giddings M. C., Gingeras T. R., Green E. D., Guigó R., Hardison R. C., Hubbard T. J., Kellis M., Kent W. J., Lieb J. D., Margulies E. H., Myers R. M., Snyder M., Stamatoyannopoulos J. A., Tenenbaum S. A., Weng Z., White K. P., Wold B., Khatun J., Yu Y., Wrobel J., Risk B. A., Gunawardena H. P., Kuiper H. C., Maier C. W., Xie L., Chen X., Giddings M. C., Bernstein B. E., Epstein C. B., Shoresh N., Ernst J., Kheradpour P., Mikkelsen T. S., Gillespie S., Goren A., Ram O., Zhang X., Wang L., Issner R., Coyne M. J., Durham T., Ku M., Truong T., Ward L. D., Altshuler R. C., Eaton M. L., Kellis M., Djebali S., Davis C. A., Merkel A., Dobin A., Lassmann T., Mortazavi A., Tanzer A., Lagarde J., Lin W., Schlesinger F., Xue C., Marinov G. K., Khatun J., Williams B. A., Zaleski C., Rozowsky J., Röder M., Kokocinski F., Abdelhamid R. F., Alioto T., Antoshechkin I., Baer M. T., Batut P., Bell I., Bell K., Chakrabortty S., Chen X., Chrast J., Curado J., Derrien T., Drenkow J., Dumais E., Dumais J., Duttagupta R., Fastuca M., Fejes-Toth K., Ferreira P., Foissac S., Fullwood M. J., Gao H., Gonzalez D., Gordon A., Gunawardena H. P., Howald C., Jha S., Johnson R., Kapranov P., King B., Kingswood C., Li G., Luo O. J., Park E., Preall J. B., Presaud K., Ribeca P., Risk B. A., Robyr D., Ruan X., Sammeth M., Sandhu K. S., Schaeffer L., See L.-H., Shahab A., Skancke J., Suzuki A. M., Takahashi H., Tilgner H., Trout D., Walters N., Wang H., Wrobel J., Yu Y., Hayashizaki Y., Harrow J., Gerstein M., Hubbard T. J., Reymond A., Antonarakis S. E., Hannon G. J., Giddings M. C., Ruan Y., Wold B., Carninci P., Guigó R., Gingeras T. R., Rosenbloom K. R., Sloan C. A., Learned K., Malladi V. S., Wong M. C., Barber G. P., Cline M. S., Dreszer T. R., Heitner S. G., Karolchik D., Kent W. J., Kirkup V. M., Meyer L. R., Long J. C., Maddren M., Raney B. J., Furey T. S., Song L., Grasfeder L. L., Giresi P. G., Lee B.-K., Battenhouse A., Sheffield N. C., Simon J. M., Showers K. A., Safi A., London D., Bhinge A. A., Shestak C., Schaner M. R., Ki Kim S., Zhang Z. Z., Mieczkowski P. A., Mieczkowska J. O., Liu Z., McDaniell R. M., Ni Y., Rashid N. U., Kim M. J., Adar S., Zhang Z., Wang T., Winter D., Keefe D., Birney E., Iyer V. R., Lieb J. D., Crawford G. E., Li G., Sandhu K. S., Zheng M., Wang P., Luo O. J., Shahab A., Fullwood M. J., Ruan X., Ruan Y., Myers R. M., Pauli F., Williams B. A., Gertz J., Marinov G. K., Reddy T. E., Vielmetter J., Partridge E., Trout D., Varley K. E., Gasper C., Bansal A., Pepke S., Jain P., Amrhein H., Bowling K. M., Anaya M., Cross M. K., King B., Muratet M. A., Antoshechkin I., Newberry K. M., McCue K., Nesmith A. S., Fisher-Aylor K. I., Pusey B., DeSalvo G., Parker S. L., Balasubramanian S., Davis N. S., Meadows S. K., Eggleston T., Gunter C., Newberry J. S., Levy S. E., Absher D. M., Mortazavi A., Wong W. H., Wold B., Blow M. J., Visel A., Pennachio L. A., Elnitski L., Margulies E. H., Parker S. C. J., Petrykowska H. M., Abyzov A., Aken B., Barrell D., Barson G., Berry A., Bignell A., Boychenko V., Bussotti G., Chrast J., Davidson C., Derrien T., Despacio-Reyes G., Diekhans M., Ezkurdia I., Frankish A., Gilbert J., Gonzalez J. M., Griffiths E., Harte R., Hendrix D. A., Howald C., Hunt T., Jungreis I., Kay M., Khurana E., Kokocinski F., Leng J., Lin M. F., Loveland J., Lu Z., Manthravadi D., Mariotti M., Mudge J., Mukherjee G., Notredame C., Pei B., Rodriguez J. M., Saunders G., Sboner A., Searle S., Sisu C., Snow C., Steward C., Tanzer A., Tapanari E., Tress M. L., van Baren M. J., Walters N., Washietl S., Wilming L., Zadissa A., Zhang Z., Brent M., Haussler D., Kellis M., Valencia A., Gerstein M., Reymond A., Guigó R., Harrow J., Hubbard T. J., Landt S. G., Frietze S., Abyzov A., Addleman N., Alexander R. P., Auerbach R. K., Balasubramanian S., Bettinger K., Bhardwaj N., Boyle A. P., Cao A. R., Cayting P., Charos A., Cheng Y., Cheng C., Eastman C., Euskirchen G., Fleming J. D., Grubert F., Habegger L., Hariharan M., Harmanci A., Iyengar S., Jin V. X., Karczewski K. J., Kasowski M., Lacroute P., Lam H., Lamarre-Vincent N., Leng J., Lian J., Lindahl-Allen M., Min R., Miotto B., Monahan H., Moqtaderi Z., Mu X. J., O’Geen H., Ouyang Z., Patacsil D., Pei B., Raha D., Ramirez L., Reed B., Rozowsky J., Sboner A., Shi M., Sisu C., Slifer T., Witt H., Wu L., Xu X., Yan K.-K., Yang X., Yip K. Y., Zhang Z., Struhl K., Weissman S. M., Gerstein M., Farnham P. J., Snyder M., Tenenbaum S. A., Penalva L. O., Doyle F., Karmakar S., Landt S. G., Bhanvadia R. R., Choudhury A., Domanus M., Ma L., Moran J., Patacsil D., Slifer T., Victorsen A., Yang X., Snyder M., White K. P., Auer T., Centanin L., Eichenlaub M., Gruhl F., Heermann S., Hoeckendorf B., Inoue D., Kellner T., Kirchmaier S., Mueller C., Reinhardt R., Schertel L., Schneider S., Sinn R., Wittbrodt B., Wittbrodt J., Weng Z., Whitfield T. W., Wang J., Collins P. J., Aldred S. F., Trinklein N. D., Partridge E. C., Myers R. M., Dekker J., Jain G., Lajoie B. R., Sanyal A., Balasundaram G., Bates D. L., Byron R., Canfield T. K., Diegel M. J., Dunn D., Ebersol A. K., Frum T., Garg K., Gist E., Hansen R. S., Boatman L., Haugen E., Humbert R., Jain G., Johnson A. K., Johnson E. M., Kutyavin T. V., Lajoie B. R., Lee K., Lotakis D., Maurano M. T., Neph S. J., Neri F. V., Nguyen E. D., Qu H., Reynolds A. P., Roach V., Rynes E., Sabo P., Sanchez M. E., Sandstrom R. S., Sanyal A., Shafer A. O., Stergachis A. B., Thomas S., Thurman R. E., Vernot B., Vierstra J., Vong S., Wang H., Weaver M. A., Yan Y., Zhang M., Akey J. M., Bender M., Dorschner M. O., Groudine M., MacCoss M. J., Navas P., Stamatoyannopoulos G., Kaul R., Dekker J., Stamatoyannopoulos J. A., Dunham I., Beal K., Brazma A., Flicek P., Herrero J., Johnson N., Keefe D., Lukk M., Luscombe N. M., Sobral D., Vaquerizas J. M., Wilder S. P., Batzoglou S., Sidow A., Hussami N., Kyriazopoulou-Panagiotopoulou S., Libbrecht M. W., Schaub M. A., Kundaje A., Hardison R. C., Miller W., Giardine B., Harris R. S., Wu W., Bickel P. J., Banfai B., Boley N. P., Brown J. B., Huang H., Li Q., Li J. J., Noble W. S., Bilmes J. A., Buske O. J., Hoffman M. M., Sahu A. D., Kharchenko P. V., Park P. J., Baker D., Taylor J., Weng Z., Iyer S., Dong X., Greven M., Lin X., Wang J., Xi H. S., Zhuang J., Gerstein M., Alexander R. P., Balasubramanian S., Cheng C., Harmanci A., Lochovsky L., Min R., Mu X. J., Rozowsky J., Yan K.-K., Yip K. Y., Birney E.ENCODE Project Consortium, An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
8
Paik Y. K., Jeong S. K., Omenn G. S., Uhlen M., Hanash S., Cho S. Y., Lee H. J., Na K., Choi E. Y., Yan F., Zhang F., Zhang Y., Snyder M., Cheng Y., Chen R., Marko-Varga G., Deutsch E. W., Kim H., Kwon J. Y., Aebersold R., Bairoch A., Taylor A. D., Kim K. Y., Lee E. Y., Hochstrasser D., Legrain P., Hancock W. S., The Chromosome-Centric Human Proteome Project for cataloging proteins encoded in the genome. Nat. Biotechnol. 30, 221–223 (2012).
9
Wilhelm M., Schlegl J., Hahne H., Moghaddas Gholami A., Lieberenz M., Savitski M. M., Ziegler E., Butzmann L., Gessulat S., Marx H., Mathieson T., Lemeer S., Schnatbaum K., Reimer U., Wenschuh H., Mollenhauer M., Slotta-Huspenina J., Boese J. H., Bantscheff M., Gerstmair A., Faerber F., Kuster B., Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014).
10
Kim M. S., Pinto S. M., Getnet D., Nirujogi R. S., Manda S. S., Chaerkady R., Madugundu A. K., Kelkar D. S., Isserlin R., Jain S., Thomas J. K., Muthusamy B., Leal-Rojas P., Kumar P., Sahasrabuddhe N. A., Balakrishnan L., Advani J., George B., Renuse S., Selvan L. D., Patil A. H., Nanjappa V., Radhakrishnan A., Prasad S., Subbannayya T., Raju R., Kumar M., Sreenivasamurthy S. K., Marimuthu A., Sathe G. J., Chavan S., Datta K. K., Subbannayya Y., Sahu A., Yelamanchi S. D., Jayaram S., Rajagopalan P., Sharma J., Murthy K. R., Syed N., Goel R., Khan A. A., Ahmad S., Dey G., Mudgal K., Chatterjee A., Huang T. C., Zhong J., Wu X., Shaw P. G., Freed D., Zahari M. S., Mukherjee K. K., Shankar S., Mahadevan A., Lam H., Mitchell C. J., Shankar S. K., Satishchandra P., Schroeder J. T., Sirdeshmukh R., Maitra A., Leach S. D., Drake C. G., Halushka M. K., Prasad T. S., Hruban R. H., Kerr C. L., Bader G. D., Iacobuzio-Donahue C. A., Gowda H., Pandey A., A draft map of the human proteome. Nature 509, 575–581 (2014).
11
Mann M., Functional and quantitative proteomics using SILAC. Nat. Rev. Molec. Cell Biol. 7, 952–958 (2006).
12
Ezkurdia I., Juan D., Rodriguez J. M., Frankish A., Diekhans M., Harrow J., Vazquez J., Valencia A., Tress M. L., Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum. Mol. Genet. 23, 5866–5878 (2014).
13
Lange V., Picotti P., Domon B., Aebersold R., Selected reaction monitoring for quantitative proteomics: A tutorial. Mol. Syst. Biol. 4, 222 (2008).
14
Uhlen M., Oksvold P., Fagerberg L., Lundberg E., Jonasson K., Forsberg M., Zwahlen M., Kampf C., Wester K., Hober S., Wernerus H., Björling L., Ponten F., Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol. 28, 1248–1250 (2010).
15
Fagerberg L., Hallström B. M., Oksvold P., Kampf C., Djureinovic D., Odeberg J., Habuka M., Tahmasebpoor S., Danielsson A., Edlund K., Asplund A., Sjöstedt E., Lundberg E., Szigyarto C. A., Skogs M., Takanen J. O., Berling H., Tegel H., Mulder J., Nilsson P., Schwenk J. M., Lindskog C., Danielsson F., Mardinoglu A., Sivertsson A., von Feilitzen K., Forsberg M., Zwahlen M., Olsson I., Navani S., Huss M., Nielsen J., Pontén F., Uhlén M., Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteomics 13, 397–406 (2014).
16
Kampf C., Mardinoglu A., Fagerberg L., Hallström B. M., Edlund K., Lundberg E., Pontén F., Nielsen J., Uhlen M., The human liver-specific proteome defined by transcriptomics and antibody-based profiling. FASEB J. 28, 2901–2914 (2014).
17
Djureinovic D., Fagerberg L., Hallström B., Danielsson A., Lindskog C., Uhlén M., Pontén F., The human testis-specific proteome defined by transcriptomics and antibody-based profiling. Mol. Hum. Reprod. 20, 476–488 (2014).
18
Gremel G., Wanders A., Cedernaes J., Fagerberg L., Hallström B., Edlund K., Sjöstedt E., Uhlén M., Pontén F., The human gastrointestinal tract-specific transcriptome and proteome as defined by RNA sequencing and antibody-based profiling. J. Gastroenterol. 50, 46–57 (2014).
19
Law V., Knox C., Djoumbou Y., Jewison T., Guo A. C., Liu Y., Maciejewski A., Arndt D., Wilson M., Neveu V., Tang A., Gabriel G., Ly C., Adamjee S., Dame Z. T., Han B., Zhou Y., Wishart D. S., DrugBank 4.0: Shedding new light on drug metabolism. Nucleic Acids Res. 42 (D1), D1091–D1097 (2014).
20
COSMIC catalogue of somatic mutations in cancer (2014); http://cancer.sanger.ac.uk/cancergenome/projects/census.
21
Lundberg E., Fagerberg L., Klevebring D., Matic I., Geiger T., Cox J., Algenäs C., Lundeberg J., Mann M., Uhlen M., Defining the transcriptome and proteome in three functionally different human cell lines. Mol. Syst. Biol. 6, 450 (2010).
22
Taniguchi Y., Choi P. J., Li G. W., Chen H., Babu M., Hearn J., Emili A., Xie X. S., Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–538 (2010).
23
Petersen T. N., Brunak S., von Heijne G., Nielsen H., SignalP 4.0: Discriminating signal peptides from transmembrane regions. Nat. Methods 8, 785–786 (2011).
24
Käll L., Krogh A., Sonnhammer E. L., Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server. Nucleic Acids Res. 35 (Web server), W429–W432 (2007).
25
Viklund H., Bernsel A., Skwark M., Elofsson A., SPOCTOPUS: A combined predictor of signal peptides and membrane protein topology. Bioinformatics 24, 2928–2929 (2008).
26
Ramsköld D., Wang E. T., Burge C. B., Sandberg R., An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLOS Comput. Biol. 5, e1000598 (2009).
27
Wingender E., Schoeps T., Dönitz J., TFClass: An expandable hierarchical classification of human transcription factors. Nucleic Acids Res. 41 (D1), D165–D170 (2013).
28
Tamborero D., Gonzalez-Perez A., Perez-Llamas C., Deu-Pons J., Kandoth C., Reimand J., Lawrence M. S., Getz G., Bader G. D., Ding L., Lopez-Bigas N., Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 3, 2650 (2013).
29
Muzny D. M., Bainbridge M. N., Chang K., Dinh H. H., Drummond J. A., Fowler G., Kovar C. L., Lewis L. R., Morgan M. B., Newsham I. F., Reid J. G., Santibanez J., Shinbrot E., Trevino L. R., Wu Y.-Q., Wang M., Gunaratne P., Donehower L. A., Creighton C. J., Wheeler D. A., Gibbs R. A., Lawrence M. S., Voet D., Jing R., Cibulskis K., Sivachenko A., Stojanov P., McKenna A., Lander E. S., Gabriel S., Getz G., Ding L., Fulton R. S., Koboldt D. C., Wylie T., Walker J., Dooling D. J., Fulton L., Delehaunty K. D., Fronick C. C., Demeter R., Mardis E. R., Wilson R. K., Chu A., Chun H.-J. E., Mungall A. J., Pleasance E., Gordon Robertson A., Stoll D., Balasundaram M., Birol I., Butterfield Y. S. N., Chuah E., Coope R. J. N., Dhalla N., Guin R., Hirst C., Hirst M., Holt R. A., Lee D., Li H. I., Mayo M., Moore R. A., Schein J. E., Slobodan J. R., Tam A., Thiessen N., Varhol R., Zeng T., Zhao Y., Jones S. J. M., Marra M. A., Bass A. J., Ramos A. H., Saksena G., Cherniack A. D., Schumacher S. E., Tabak B., Carter S. L., Pho N. H., Nguyen H., Onofrio R. C., Crenshaw A., Ardlie K., Beroukhim R., Winckler W., Getz G., Meyerson M., Protopopov A., Zhang J., Hadjipanayis A., Lee E., Xi R., Yang L., Ren X., Zhang H., Sathiamoorthy N., Shukla S., Chen P.-C., Haseley P., Xiao Y., Lee S., Seidman J., Chin L., Park P. J., Kucherlapati R., Todd Auman J., Hoadley K. A., Du Y., Wilkerson M. D., Shi Y., Liquori C., Meng S., Li L., Turman Y. J., Topal M. D., Tan D., Waring S., Buda E., Walsh J., Jones C. D., Mieczkowski P. A., Singh D., Wu J., Gulabani A., Dolina P., Bodenheimer T., Hoyle A. P., Simons J. V., Soloway M., Mose L. E., Jefferys S. R., Balu S., O’Connor B. D., Prins J. F., Chiang D. Y., Neil Hayes D., Perou C. M., Hinoue T., Weisenberger D. J., Maglinte D. T., Pan F., Berman B. P., Van Den Berg D. J., Shen H., Triche T., Baylin S. B., Laird P. W., Getz G., Noble M., Voet D., Saksena G., Gehlenborg N., DiCara D., Zhang J., Zhang H., Wu C.-J., Yingchun Liu S., Shukla S., Lawrence M. S., Zhou L., Sivachenko A., Lin P., Stojanov P., Jing R., Park R. W., Nazaire M.-D., Robinson J., Thorvaldsdottir H., Mesirov J., Park P. J., Chin L., Thorsson V., Reynolds S. M., Bernard B., Kreisberg R., Lin J., Iype L., Bressler R., Erkkilä T., Gundapuneni M., Liu Y., Norberg A., Robinson T., Yang D., Zhang W., Shmulevich I., de Ronde J. J., Schultz N., Cerami E., Ciriello G., Goldberg A. P., Gross B., Jacobsen A., Gao J., Kaczkowski B., Sinha R., Arman Aksoy B., Antipin Y., Reva B., Shen R., Taylor B. S., Chan T. A., Ladanyi M., Sander C., Akbani R., Zhang N., Broom B. M., Casasent T., Unruh A., Wakefield C., Hamilton S. R., Craig Cason R., Baggerly K. A., Weinstein J. N., Haussler D., Benz C. C., Stuart J. M., Benz S. C., Zachary Sanborn J., Vaske C. J., Zhu J., Szeto C., Scott G. K., Yau C., Ng S., Goldstein T., Ellrott K., Collisson E., Cozen A. E., Zerbino D., Wilks C., Craft B., Spellman P., Penny R., Shelton T., Hatfield M., Morris S., Yena P., Shelton C., Sherman M., Paulauskis J., Gastier-Foster J. M., Bowen J., Ramirez N. C., Black A., Pyatt R., Wise L., White P., Bertagnolli M., Brown J., Chan T. A., Chu G. C., Czerwinski C., Denstman F., Dhir R., Dörner A., Fuchs C. S., Guillem J. G., Iacocca M., Juhl H., Kaufman A., Kohl B., Van Le X., Mariano M. C., Medina E. N., Meyers M., Nash G. M., Paty P. B., Petrelli N., Rabeno B., Richards W. G., Solit D., Swanson P., Temple L., Tepper J. E., Thorp R., Vakiani E., Weiser M. R., Willis J. E., Witkin G., Zeng Z., Zinner M. J., Zornig C., Jensen M. A., Sfeir R., Kahn A. B., Chu A. L., Kothiyal P., Wang Z., Snyder E. E., Pontius J., Pihl T. D., Ayala B., Backus M., Walton J., Whitmore J., Baboud J., Berton D. L., Nicholls M. C., Srinivasan D., Raman R., Girshik S., Kigonya P. A., Alonso S., Sanbhadti R. N., Barletta S. P., Greene J. M., Pot D. A., Mills Shaw K. R., Dillon L. A. L., Buetow K., Davidsen T., Demchok J. A., Eley G., Ferguson M., Fielding P., Schaefer C., Sheth M., Yang L., Guyer M. S., Ozenberger B. A., Palchik J. D., Peterson J., Sofia H. J., Thomson E.Cancer Genome Atlas Network, Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).
30
Grasso C. S., Wu Y. M., Robinson D. R., Cao X., Dhanasekaran S. M., Khan A. P., Quist M. J., Jing X., Lonigro R. J., Brenner J. C., Asangani I. A., Ateeq B., Chun S. Y., Siddiqui J., Sam L., Anstett M., Mehra R., Prensner J. R., Palanisamy N., Ryslik G. A., Vandin F., Raphael B. J., Kunju L. P., Rhodes D. R., Pienta K. J., Chinnaiyan A. M., Tomlins S. A., The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).
31
Danielsson F., Wiking M., Mahdessian D., Skogs M., Ait Blal H., Hjelmare M., Stadler C., Uhlén M., Lundberg E., RNA deep sequencing as a tool for selection of cell lines for systematic subcellular localization of all human proteins. J. Proteome Res. 12, 299–307 (2013).
32
Schnabel M., Marlovits S., Eckhoff G., Fichtel I., Gotzen L., Vécsei V., Schlegel J., Dedifferentiation-associated changes in morphology and gene expression in primary human articular chondrocytes in cell culture. Osteoarthritis Cartilage 10, 62–70 (2002).
33
Birney E., Stamatoyannopoulos J. A., Dutta A., Guigó R., Gingeras T. R., Margulies E. H., Weng Z., Snyder M., Dermitzakis E. T., Thurman R. E., Kuehn M. S., Taylor C. M., Neph S., Koch C. M., Asthana S., Malhotra A., Adzhubei I., Greenbaum J. A., Andrews R. M., Flicek P., Boyle P. J., Cao H., Carter N. P., Clelland G. K., Davis S., Day N., Dhami P., Dillon S. C., Dorschner M. O., Fiegler H., Giresi P. G., Goldy J., Hawrylycz M., Haydock A., Humbert R., James K. D., Johnson B. E., Johnson E. M., Frum T. T., Rosenzweig E. R., Karnani N., Lee K., Lefebvre G. C., Navas P. A., Neri F., Parker S. C., Sabo P. J., Sandstrom R., Shafer A., Vetrie D., Weaver M., Wilcox S., Yu M., Collins F. S., Dekker J., Lieb J. D., Tullius T. D., Crawford G. E., Sunyaev S., Noble W. S., Dunham I., Denoeud F., Reymond A., Kapranov P., Rozowsky J., Zheng D., Castelo R., Frankish A., Harrow J., Ghosh S., Sandelin A., Hofacker I. L., Baertsch R., Keefe D., Dike S., Cheng J., Hirsch H. A., Sekinger E. A., Lagarde J., Abril J. F., Shahab A., Flamm C., Fried C., Hackermüller J., Hertel J., Lindemeyer M., Missal K., Tanzer A., Washietl S., Korbel J., Emanuelsson O., Pedersen J. S., Holroyd N., Taylor R., Swarbreck D., Matthews N., Dickson M. C., Thomas D. J., Weirauch M. T., Gilbert J., Drenkow J., Bell I., Zhao X., Srinivasan K. G., Sung W. K., Ooi H. S., Chiu K. P., Foissac S., Alioto T., Brent M., Pachter L., Tress M. L., Valencia A., Choo S. W., Choo C. Y., Ucla C., Manzano C., Wyss C., Cheung E., Clark T. G., Brown J. B., Ganesh M., Patel S., Tammana H., Chrast J., Henrichsen C. N., Kai C., Kawai J., Nagalakshmi U., Wu J., Lian Z., Lian J., Newburger P., Zhang X., Bickel P., Mattick J. S., Carninci P., Hayashizaki Y., Weissman S., Hubbard T., Myers R. M., Rogers J., Stadler P. F., Lowe T. M., Wei C. L., Ruan Y., Struhl K., Gerstein M., Antonarakis S. E., Fu Y., Green E. D., Karaöz U., Siepel A., Taylor J., Liefer L. A., Wetterstrand K. A., Good P. J., Feingold E. A., Guyer M. S., Cooper G. M., Asimenos G., Dewey C. N., Hou M., Nikolaev S., Montoya-Burgos J. I., Löytynoja A., Whelan S., Pardi F., Massingham T., Huang H., Zhang N. R., Holmes I., Mullikin J. C., Ureta-Vidal A., Paten B., Seringhaus M., Church D., Rosenbloom K., Kent W. J., Stone E. A., Batzoglou S., Goldman N., Hardison R. C., Haussler D., Miller W., Sidow A., Trinklein N. D., Zhang Z. D., Barrera L., Stuart R., King D. C., Ameur A., Enroth S., Bieda M. C., Kim J., Bhinge A. A., Jiang N., Liu J., Yao F., Vega V. B., Lee C. W., Ng P., Shahab A., Yang A., Moqtaderi Z., Zhu Z., Xu X., Squazzo S., Oberley M. J., Inman D., Singer M. A., Richmond T. A., Munn K. J., Rada-Iglesias A., Wallerman O., Komorowski J., Fowler J. C., Couttet P., Bruce A. W., Dovey O. M., Ellis P. D., Langford C. F., Nix D. A., Euskirchen G., Hartman S., Urban A. E., Kraus P., Van Calcar S., Heintzman N., Kim T. H., Wang K., Qu C., Hon G., Luna R., Glass C. K., Rosenfeld M. G., Aldred S. F., Cooper S. J., Halees A., Lin J. M., Shulha H. P., Zhang X., Xu M., Haidar J. N., Yu Y., Ruan Y., Iyer V. R., Green R. D., Wadelius C., Farnham P. J., Ren B., Harte R. A., Hinrichs A. S., Trumbower H., Clawson H., Hillman-Jackson J., Zweig A. S., Smith K., Thakkapallayil A., Barber G., Kuhn R. M., Karolchik D., Armengol L., Bird C. P., de Bakker P. I., Kern A. D., Lopez-Bigas N., Martin J. D., Stranger B. E., Woodroffe A., Davydov E., Dimas A., Eyras E., Hallgrímsdóttir I. B., Huppert J., Zody M. C., Abecasis G. R., Estivill X., Bouffard G. G., Guan X., Hansen N. F., Idol J. R., Maduro V. V., Maskeri B., McDowell J. C., Park M., Thomas P. J., Young A. C., Blakesley R. W., Muzny D. M., Sodergren E., Wheeler D. A., Worley K. C., Jiang H., Weinstock G. M., Gibbs R. A., Graves T., Fulton R., Mardis E. R., Wilson R. K., Clamp M., Cuff J., Gnerre S., Jaffe D. B., Chang J. L., Lindblad-Toh K., Lander E. S., Koriabine M., Nefedov M., Osoegawa K., Yoshinaga Y., Zhu B., de Jong P. J.ENCODE Project Consortium; NISC Comparative Sequencing Program; Baylor College of Medicine Human Genome Sequencing Center; Washington University Genome Sequencing Center; Broad Institute; Children’s Hospital Oakland Research Institute, Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).
34
Gonzàlez-Porta M., Frankish A., Rung J., Harrow J., Brazma A., Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene. Genome Biol. 14, R70 (2013).
35
Mardinoglu A., Nielsen J., Systems medicine and metabolic modelling. J. Intern. Med. 271, 142–154 (2012).
36
Mardinoglu A., Agren R., Kampf C., Asplund A., Uhlen M., Nielsen J., Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5, 3083 (2014).
37
Agren R., Mardinoglu A., Asplund A., Kampf C., Uhlen M., Nielsen J., Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol. Syst. Biol. 10, 721 (2014).
38
Human Metabolic Atlas, (2014); www.metabolicatlas.org/.
39
Crosswell L. C., Thornton J. M., ELIXIR: A distributed infrastructure for European biological data. Trends Biotechnol. 30, 241–242 (2012).
40
Rual J. F., Venkatesan K., Hao T., Hirozane-Kishikawa T., Dricot A., Li N., Berriz G. F., Gibbons F. D., Dreze M., Ayivi-Guedehoussou N., Klitgord N., Simon C., Boxem M., Milstein S., Rosenberg J., Goldberg D. S., Zhang L. V., Wong S. L., Franklin G., Li S., Albala J. S., Lim J., Fraughton C., Llamosas E., Cevik S., Bex C., Lamesch P., Sikorski R. S., Vandenhaute J., Zoghbi H. Y., Smolyar A., Bosak S., Sequerra R., Doucette-Stamm L., Cusick M. E., Hill D. E., Roth F. P., Vidal M., Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).
41
Kampf C., Olsson I., Ryberg U., Sjöstedt E., Pontén F., Production of tissue microarrays, immunohistochemistry staining and digitalization within the human protein atlas. J. Vis. Exp. 2012, 3620 (2012).
42
Trapnell C., Pachter L., Salzberg S. L., TopHat: Discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).
43
Trapnell C., Williams B. A., Pertea G., Mortazavi A., Kwan G., van Baren M. J., Salzberg S. L., Wold B. J., Pachter L., Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
44
Flicek P., Amode M. R., Barrell D., Beal K., Billis K., Brent S., Carvalho-Silva D., Clapham P., Coates G., Fitzgerald S., Gil L., Girón C. G., Gordon L., Hourlier T., Hunt S., Johnson N., Juettemann T., Kähäri A. K., Keenan S., Kulesha E., Martin F. J., Maurel T., McLaren W. M., Murphy D. N., Nag R., Overduin B., Pignatelli M., Pritchard B., Pritchard E., Riat H. S., Ruffier M., Sheppard D., Taylor K., Thormann A., Trevanion S. J., Vullo A., Wilder S. P., Wilson M., Zadissa A., Aken B. L., Birney E., Cunningham F., Harrow J., Herrero J., Hubbard T. J., Kinsella R., Muffato M., Parker A., Spudich G., Yates A., Zerbino D. R., Searle S. M., Ensembl 2014. Nucleic Acids Res. 42 (Database), D749–D755 (2014).
45
Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources v6.7 (2014); http://david.abcc.ncifcrf.gov/.
46
Fagerberg L., Jonasson K., von Heijne G., Uhlén M., Berglund L., Prediction of the human membrane proteome. Proteomics 10, 1141–1149 (2010).
47
Jones D. T., Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23, 538–544 (2007).
48
Nugent T., Jones D. T., Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics 10, 159 (2009).
49
Käll L., Krogh A., Sonnhammer E. L., A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol. 338, 1027–1036 (2004).
50
Bernsel A., Viklund H., Falk J., Lindahl E., von Heijne G., Elofsson A., Prediction of membrane-protein topology from first principles. Proc. Natl. Acad. Sci. U.S.A. 105, 7177–7181 (2008).
51
E. L. Sonnhammer, G. von Heijne, A. Krogh, A hidden Markov model for predicting transmembrane helices in protein sequences, in Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, J. Glasgow, Ed., Montréal, Québec, 28 June to 1 July 1998 (AAAI Press, Menlo Park, CA, 1998).
52
Zhou H., Zhou Y., Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method. Protein Sci. 12, 1547–1555 (2003).
57
Bendtsen J. D., Nielsen H., von Heijne G., Brunak S., Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–795 (2004).
58
Nickel W., Pathways of unconventional protein secretion. Curr. Opin. Biotechnol. 21, 621–626 (2010).
59
Cotter D., Guda P., Fahy E., Subramaniam S., MitoProteome: Mitochondrial protein sequence database and annotation system. Nucleic Acids Res. 32, D463–D467 (2004).
60
Zola H., Swart B., Nicholson I., Aasted B., Bensussan A., Boumsell L., Buckley C., Clark G., Drbal K., Engel P., Hart D., Horejsí V., Isacke C., Macardle P., Malavasi F., Mason D., Olive D., Saalmueller A., Schlossman S. F., Schwartz-Albiez R., Simmons P., Tedder T. F., Uguccioni M., Warren H., CD molecules 2005: Human cell differentiation molecules. Blood 106, 3123–3126 (2005).
61
UniProt Consortium, Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res. 42 (D1), D191–D198 (2014).

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Science
Volume 347 | Issue 6220
23 January 2015

Submission history

Received: 25 August 2014
Accepted: 5 December 2014
Published in print: 23 January 2015

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Acknowledgments

We acknowledge the entire staff of the Human Protein Atlas program; the Science for Life Laboratory; and the pathology team in Mumbai, India, for valuable contributions. We thank the Department of Pathology at the Uppsala Akademiska Hospital, Uppsala, Sweden, and Uppsala Biobank for kindly providing clinical diagnostics and specimens used in this study. We also acknowledge support from Science for Life Laboratory, the National Genomics Infrastructure (NGI), and Uppmax for providing assistance in massive parallel sequencing and computational infrastructure. Funding was provided by the Knut and Alice Wallenberg Foundation. The authors declare that they have no conflict of interest. Correspondence and requests for materials should be addressed to M.U. The mRNA levels of all genes in each tissue sample (n = 122) are available in table S18. The supplementary Excel tables are available in the supplementary material and at www.proteinatlas.org/about/publicationdata. The raw sequencing data are available at ArrayExpress (www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-2836/) and BioProject (NIH) (www.ncbi.nlm.nih.gov/bioproject/PRJNA183192). All Protein Atlas (protein) data are available in structured XML format and can be downloaded from www.proteinatlas.org/about/download.

Authors

Affiliations

Mathias Uhlén* [email protected]
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark.
Linn Fagerberg
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Björn M. Hallström
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Cecilia Lindskog
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
Per Oksvold
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Adil Mardinoglu
Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
Åsa Sivertsson
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Caroline Kampf
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
Evelina Sjöstedt
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
Anna Asplund
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
IngMarie Olsson
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
Karolina Edlund
Leibniz Research Centre for Working Environment and Human Factors (IfADo) at Dortmund TU, D-44139 Dortmund, Germany.
Emma Lundberg
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Sanjay Navani
Lab Surgpath, Mumbai, India.
Cristina Al-Khalili Szigyarto
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Jacob Odeberg
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Dijana Djureinovic
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
Jenny Ottosson Takanen
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Sophia Hober
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Tove Alm
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Per-Henrik Edqvist
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
Holger Berling
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Hanna Tegel
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Jan Mulder
Science for Life Laboratory, Department of Neuroscience, Karolinska Institute, SE-171 77 Stockholm, Sweden.
Johan Rockberg
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Peter Nilsson
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Jochen M. Schwenk
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Marica Hamsten
Department of Proteomics, KTH—Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Kalle von Feilitzen
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Mattias Forsberg
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Lukas Persson
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Fredric Johansson
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Martin Zwahlen
Science for Life Laboratory, KTH—Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
Gunnar von Heijne
Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
Jens Nielsen
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark.
Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
Fredrik Pontén
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·

Notes

*Corresponding author. E-mail: [email protected]

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