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Global expression analysis of human cells and tissues using antibodies
KTH, School of Biotechnology (BIO), Proteomics.
2008 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

To construct a complete map of the human proteome landscape is a vital part of the total understanding of the human body. Such a map could enrich the mankind to the extent that many severe diseases could be fully understood and hence could be treated with appropriate methods.

In this study, immunohistochemical (IHC) data from ~6000 proteins, 65 cell types in 48 tissues and 47 cell lines has been used to investigate the human proteome regarding protein expression and localization. In order to analyze such a large data set, different statistical methods and algorithms has been applied and by using these tools, interesting features regarding the proteome was found. By using all available IHC data from 65 cell types in 48 tissues, it was found that the amount of tissue specific protein expression was surprisingly small, and the general impression from the analysis is that almost all proteins are present at all times in the cellular environment. Rather than tissue specific protein expression, the localization and minor concentration fluctuations of the proteins in the cell is responsible for molecular interaction and tissue specific cellular behavior. However, if a quarter of all proteins are used to distinguish different tissues types, there are a proportion of proteins that have certain expression profiles, which defines clusters of tissues of the same kind and embryonic origin.

The estimation of expression levels using IHC is a labor-intensive method, which suffers from large variation between manual annotators. An automated image software tool was developed to circumvent this problem. The automated image software was shown to be more robust then manual annotators, and the quantification of expressed protein levels of the stained imaged was in the same range as the manual annotations.

A more thorough investigation of the stained image estimations made by the automated software revield a significant correlation between the estimated protein expression and the cell size parameters provided by the automated software. To make it feasible to compare protein expression levels across different cell lines, without the cell line size bias, a normalization procedure was implemented and evaluated. It was found that when the normalization procedure was applied to the protein expression data, the correlation between protein expression values and cell size was minimized, and hence comparisons between cell lines regarding protein expression is possible.

In addition, using the normalized protein expression data, an analysis to investigate the degree of correlation between mRNA levels and proteins for 1065 gene products was performed. By using two individual microarray data sets for estimation of RNA levels, and normalized protein data measured by the automated software as estimation of the protein levels, a mean correlation of ~0.3 for was found. This result indicates that a significant proportion of the manufactured antibodies, when used in IHC setup, are indeed an accurate measurement of protein expression levels.

By using antibodies directed towards human proteins, plasma samples were investigated regarding metabolic dysfunctions. Since plasma is a complex sample, an optimization regarding protocol for quantification of expressed proteins was made. By using certain characteristics within the dataset, and by using a suspension bead microarray, the protocol could be evaluated. Expected characteristics within the dataset were found in the subsequent analysis, which showed that the protocol was functional. Using the same experimental outline will facilitate future applications, e.g. biomarker discovery. 

Place, publisher, year, edition, pages
Stockholm: KTH , 2008. , xi, 53 p.
Series
Trita-BIO-Report, ISSN 1654-2312 ; 2008:17
Keyword [en]
Immunohistochemistry, protein expression, Antibody, Tissue microarray, protein quanitification, RNA and protein correlation
National Category
Industrial Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-9116ISBN: 978-91-7415-113-8 (print)OAI: oai:DiVA.org:kth-9116DiVA: diva2:24267
Public defence
2008-10-10, Oscar Klein auditorium, Roslagstullsbacken 21, floor 4, Stockholm, 09:00 (English)
Opponent
Supervisors
Projects
Human Proteome Resource
Note
QC 20100728Available from: 2008-09-26 Created: 2008-09-19 Last updated: 2010-07-28Bibliographically approved
List of papers
1. Ubiquitous protein expression in human cells, tissues and organs
Open this publication in new window or tab >>Ubiquitous protein expression in human cells, tissues and organs
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(English)Manuscript (Other academic)
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-9205 (URN)
Note
QC 20100728Available from: 2008-10-03 Created: 2008-10-03 Last updated: 2010-07-28Bibliographically approved
2. A high-throughput strategy for protein profiling in cell microarrays using automated image analysis
Open this publication in new window or tab >>A high-throughput strategy for protein profiling in cell microarrays using automated image analysis
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2007 (English)In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 7, no 13, 2142-2150 p.Article in journal (Refereed) Published
Abstract [en]

Advances in antibody production render a growing supply of affinity reagents for immunohistochemistry (IHC), and tissue microarray (TMA) technologies facilitate simultaneous analysis of protein expression in a multitude of tissues. However, collecting validated IHC data remains a bottleneck problem, as the standard method is manual microscopical analysis. Here we present a high-throughput strategy combining IHC on a recently developed cell microarray with a novel, automated image-analysis application (TMAx). The software was evaluated on 200 digital images of IHC-stained cell spots, by comparing TMAx annotation with manual annotation performed by seven human experts. A high concordance between automated and manual annotation of staining intensity and fraction of IHC-positive cells was found. in a limited study, we also investigated the possibility to assess the correlation between mRNA and protein levels, by using TMAx output results for relative protein quantification and quantitative real-time PCR for the quantification of corresponding transcript levels. In conclusion, automated analysis of immunohistochemically stained in vitro-cultured cells in a microarray format can be used for high-throughput protein profiling, and extraction of RNA from the same cell lines provides a basis for comparing transcription and protein expression on a global scale.

Keyword
antibody proteornics, automated image analysis, cell line, immunohistochemistry, tissue microarray
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-14253 (URN)10.1002/pmic.200700199 (DOI)000248013700002 ()2-s2.0-34447566899 (Scopus ID)
Note
QC 20100728Available from: 2010-07-28 Created: 2010-07-28 Last updated: 2017-12-12Bibliographically approved
3. The correlation between cellular size and protein expression levels: Normalization for global protein profiling
Open this publication in new window or tab >>The correlation between cellular size and protein expression levels: Normalization for global protein profiling
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2008 (English)In: Journal of Proteomics, ISSN 1874-3919, Vol. 71, no 4, 448-460 p.Article in journal (Refereed) Published
Abstract [en]

An automated image analysis system was used for protein quantification of 1862 human proteins in 47 cancer cell lines and 12 clinical cell samples using cell microarrays and immunohistochemistry. The analysis suggests that most proteins are expressed in a cell size dependent manner, and that normalization is required for comparative protein quantification in order to correct for the inherent bias of cell size and systematic ambiguities associated with immunohistochemistry. Two reference standards were evaluated, and normalized protein expression values were found to allow for protein profiling across a panel of morphologically diverse cells, revealing putative patterns of over- and underexpression. Using this approach, proteins with stable expression as well as cell-line specific expression were identified. The results demonstrate the value of large-scale, automated proteome analysis using immunohistochemistry in revealing functional correlations and establishing methods to interpret and mine proteomic data.

Keyword
Immunohistochemistry; Protein quantification; Normalization; Expression profiling
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-4883 (URN)10.1016/j.jprot.2008.06.014 (DOI)000260816700005 ()2-s2.0-51749118509 (Scopus ID)
Note
QC 20100728. Uppdaterad från in press till published (20100728).Available from: 2008-09-16 Created: 2008-09-16 Last updated: 2010-07-28Bibliographically approved
4. Correlations between RNA and protein expression profiles in 23 human cell lines
Open this publication in new window or tab >>Correlations between RNA and protein expression profiles in 23 human cell lines
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2009 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 10Article in journal (Refereed) Published
Abstract [en]

Background: The Central Dogma of biology holds, in famously simplified terms, that DNA makes RNA makes proteins, but there is considerable uncertainty regarding the general, genome-wide correlation between levels of RNA and corresponding proteins. Therefore, to assess degrees of this correlation we compared the RNA profiles (determined using both cDNA- and oligo-based microarrays) and protein profiles (determined immunohistochemically in tissue microarrays) of 1066 gene products in 23 human cell lines. Results: A high mean correlation coefficient (0.52) was obtained from the pairwise comparison of RNA levels determined by the two platforms. Significant correlations, with correlation coefficients exceeding 0.445, between protein and RNA levels were also obtained for a third of the specific gene products. However, the correlation coefficients between levels of RNA and protein products of specific genes varied widely, and the mean correlations between the protein and corresponding RNA levels determined using the cDNA- and oligo-based microarrays were 0.25 and 0.20, respectively. Conclusion: Significant correlations were found in one third of the examined RNA species and corresponding proteins. These results suggest that RNA profiling might provide indirect support to antibodies’ specificity, since whenever a evident correlation between the RNA and protein profiles exists, this can sustain that the antibodies used in the immunoassay recognized their cognate antigens.

Keyword
RNA; protein, article; cell line; correlation coefficient; DNA microarray; gene expression profiling; human; human cell; immunohistochemistry; microarray analysis; oligo microarray; protein expression; reverse transcription polymerase chain reaction; tissue microarray; genetics; metabolism; protein microarray, Cell Line; Gene Expression Profiling; Humans; Oligonucleotide Array Sequence Analysis; Protein Array Analysis; Proteins; Reverse Transcriptase Polymerase Chain Reaction; RNA
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-14254 (URN)10.1186/1471-2164-10-365 (DOI)000269511200001 ()2-s2.0-68949205722 (Scopus ID)
Note
QC 20100728. Uppdaterad från manuskript till artikel (20100728). Tidigare titel: Correlation between RNA and protein expression profiles in 23 human cell linesAvailable from: 2010-07-28 Created: 2010-07-28 Last updated: 2017-12-12Bibliographically approved
5. Antibody suspension bead arrays within serum proteomics
Open this publication in new window or tab >>Antibody suspension bead arrays within serum proteomics
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2008 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 7, no 8, 3168-3179 p.Article in journal (Refereed) Published
Abstract [en]

Antibody microarrays offer a powerful tool to screen for target proteins in complex samples. Here, we describe an approach for systematic analysis of serum, based on antibodies and using color-coded beads for the creation of antibody arrays in suspension. This method, adapted from planar antibody arrays, offers a fast, flexible, and multiplexed procedure to screen larger numbers of serum samples, and no purification steps are required to remove excess labeling substance. The assay system detected proteins down to lower picomolar levels with dynamic ranges over 3 orders of magnitude. The feasibility of this workflow was shown in a study with more than 200 clinical serum samples tested for 20 serum proteins.

Keyword
antibody microarray, labeling, serum analysis, suspension bead arrays, antibody proteomics
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-14255 (URN)10.1021/pr700890b (DOI)000258200400011 ()2-s2.0-53049097707 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note
QC 20100728Available from: 2010-07-28 Created: 2010-07-28 Last updated: 2017-12-12Bibliographically approved

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