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A high-throughput strategy for protein profiling in cell microarrays using automated image analysis
Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University.
KTH, School of Biotechnology (BIO), Proteomics.
KTH, School of Biotechnology (BIO), Proteomics.
KTH, School of Biotechnology (BIO), Proteomics.
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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.

Place, publisher, year, edition, pages
2007. Vol. 7, no 13, 2142-2150 p.
Keyword [en]
antibody proteornics, automated image analysis, cell line, immunohistochemistry, tissue microarray
National Category
Industrial Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-14253DOI: 10.1002/pmic.200700199ISI: 000248013700002Scopus ID: 2-s2.0-34447566899OAI: oai:DiVA.org:kth-14253DiVA: diva2:331923
Note
QC 20100728Available from: 2010-07-28 Created: 2010-07-28 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Global expression analysis of human cells and tissues using antibodies
Open this publication in new window or tab >>Global expression analysis of human cells and tissues using antibodies
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
Immunohistochemistry, protein expression, Antibody, Tissue microarray, protein quanitification, RNA and protein correlation
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-9116 (URN)978-91-7415-113-8 (ISBN)
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

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