Change search
ReferencesLink to record
Permanent link

Direct link
A global view of protein expression in human cells, tissues, and organs
KTH, School of Biotechnology (BIO), Proteomics.
KTH, School of Biotechnology (BIO), Proteomics.ORCID iD: 0000-0003-0198-7137
KTH, School of Biotechnology (BIO), Proteomics.ORCID iD: 0000-0001-7034-0850
Show others and affiliations
2009 (English)In: Molecular Systems Biology, ISSN 1744-4292, Vol. 5Article in journal (Refereed) Published
Abstract [en]

Defining the protein profiles of tissues and organs is critical to understanding the unique characteristics of the various cell types in the human body. In this study, we report on an anatomically comprehensive analysis of 4842 protein profiles in 48 human tissues and 45 human cell lines. A detailed analysis of over 2 million manually annotated, high-resolution, immunohistochemistry- based images showed a high fraction (>65%) of expressed proteins in most cells and tissues, with very few proteins (<2%) detected in any single cell type. Similarly, confocal microscopy in three human cell lines detected expression of more than 70% of the analyzed proteins. Despite this ubiquitous expression, hierarchical clustering analysis, based on global protein expression patterns, shows that the analyzed cells can be still subdivided into groups according to the current concepts of histology and cellular differentiation. This study suggests that tissue specificity is achieved by precise regulation of protein levels in space and time, and that different tissues in the body acquire their unique characteristics by controlling not which proteins are expressed but how much of each is produced. Molecular Systems Biology 5: 337; published online 22 December 2009; doi:10.1038/msb.2009.93

Place, publisher, year, edition, pages
2009. Vol. 5
Keyword [en]
antibody-based analysis, bioimaging, global protein expression, immunofluorescence, immunohistochemistry, human genome, antisense transcription, gene-expression, immunohistochemistry, quantification, identification, association, microarrays, prediction, discovery
URN: urn:nbn:se:kth:diva-19103DOI: 10.1038/msb.2009.93ISI: 000273359200006OAI: diva2:337150
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2011-03-17Bibliographically approved
In thesis
1. Mapping the human proteome using bioinformatic methods
Open this publication in new window or tab >>Mapping the human proteome using bioinformatic methods
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The fundamental goal of proteomics is to gain an understanding of the expression and function of the proteome on the level of individual proteins, on the level of defined cell types and on the level of the entire organism. In this thesis, the human proteome is explored using membrane protein topology prediction methods to define the human membrane proteome and by global protein expression profiling, which relies on a complex study of the location and expression levels of proteins in tissues and cells.

A whole-proteome analysis was performed based on the predicted protein-coding genes of humans using a selection of membrane protein topology prediction methods. The study used a majority decision-based method, which estimated that approximately 26% of the human genes encode for a membrane protein. The prediction results are displayed in a visualization tool to facilitate the selection of antigens to be used for antibody generation.

Global protein expression profiles in a large number of cells and tissues in the human body were analyzed for more than 4000 protein targets, based on data from the antibody-based immunohistochemistry and immunofluorescence methods within the framework of the Human Protein Atlas project. The results revealed few cell-type specific proteins and a high fraction of human proteins expressed in most cells, suggesting that cell and tissue specificity is attained by a fine-tuned regulation of protein levels. The expression profiles were also used to analyze the relationship between 45 cell lines by hierarchical clustering and principal component analysis. The global protein expression patterns overall reflected the tumor origin of the cells, and also allowed for identification of proteins of importance for distinguishing different categories of cell lines, as defined by phenotype of progenitor cell. In addition, the protein distribution in 16 subcellular compartments in three of the human cell lines was mapped. A large fraction of proteins were localized in two or more compartments and, in line with previous results, a majority of proteins were detected in all three cell lines.

Finally, mass spectrometry-based protein expression levels were compared to RNA-seq-based transcript expression levels in three cell lines. Highly ubiquitous mRNA expression was found and the changes of expression levels between the cell lines showed high correlations between proteins and transcripts. Large general differences in abundance of proteins from various functional classes were observed. A comparison between categories based on expression levels revealed that, in general, genes with varying expression levels between the cell lines or only expressed in one cell line were highly enriched for cell-surface proteins.

These studies show a path for a systematic analysis to characterize the proteome in human cells, tissues and organs.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology., 2011. 66 p.
Trita-BIO-Report, ISSN 1654-2312 ; 2011:4
proteome, transcriptome, bioinformatics, membrane protein prediction, subcellular localization, protein expression level, cell line, immunohistochemistry, immunofluorescence
National Category
Bioinformatics and Systems Biology
Research subject
SRA - Molecular Bioscience
urn:nbn:se:kth:diva-31477 (URN)978-91-7415-886-1 (ISBN)
Public defence
2011-04-08, F3, Lindstedtsvägen 26, KTH, Stockholm, 14:41 (English)
The Human Protein Atlas project
Knut and Alice Wallenberg Foundation
QC 20110317Available from: 2011-03-17 Created: 2011-03-16 Last updated: 2011-03-17Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Gry, MarcusFagerberg, LinnLundberg, EmmaBerglund, LisaOksvold, PerBjörling, ErikHober, SophiaNilsson, PeterOttosson, JennyPersson, AnjaWernérus, HenrikUhlén, Mathias
By organisation
In the same journal
Molecular Systems Biology

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 63 hits
ReferencesLink to record
Permanent link

Direct link