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.
"It should be noted that IHC as well as MS are limited by a lower level of sensitivity and resolution. This is exemplified by a previous study, in which mRNA levels were compared with protein levels, as detected using SILAC-based MS and antibody-based confocal microscopy, showing that lowabundant transcripts, as exemplified by the functional group of G protein-coupled receptors (GPCRs), were often not detected on a protein level . Within the Human Protein Atlas project, the three cell lines routinely analyzed using IHC as well as IF have also been analyzed using next-generation RNA sequencing using the Illumina system (Illumina Inc. "
[Show abstract][Hide abstract] ABSTRACT: In this review, we present an update on the progress of the Human Protein Atlas, with an emphasis on strategies for validating immunohistochemistry-based protein expression patterns and on the possibilities to extend the map of protein expression patterns for cancer research projects. The objectives underlying the Human Protein Atlas include (i) the generation of validated antibodies toward a major isoform of all proteins encoded by the human genome, (ii) creating an information database of protein expression patterns in normal human tissues, in cells, and in cancer, and (iii) utilizing generated antibodies and protein expression data as tools to identify clinically useful biomarkers. The success of such an effort is dependent on the validity of antibodies as specific binders of intended targets in applications used to map protein expression patterns. The development of strategies to support specific target binding is crucial and remains a challenge as a large fraction of proteins encoded by the human genome is poorly characterized, including the approximately one-third of all proteins lacking evidence of existence. Conceivable methods for validation include the use of paired antibodies, i.e. two independent antibodies targeting different and nonoverlapping epitopes on the same protein as well as comparative analysis of mRNA expression patterns with corresponding proteins.
"Global protein expression in 45 human cell lines As all the immunohistochemical images from the TMAs were manually annotated by pathologists involving subjective scoring, we decided to carry out the same analysis on 45 human cell lines in which an automated image analysis algorithm have been used (Stromberg et al, 2007; Lundberg et al, 2008). The data from 5349 antibodies corresponding to 4349 genes were analyzed, involving more than 450 000 additional images, and the results are shown in Figure 3. "
[Show abstract][Hide abstract] ABSTRACT: 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 12/2009; 5(1):337. DOI:10.1038/msb.2009.93 · 10.87 Impact Factor
"The summed values were then divided by the number of cells present in the respective spots, generating average values of protein expression level per cell. In order to correct for bias introduced by the correlation between cell size and the level of protein expression, as described by Lundberg et al. , the protein expression levels obtained per cell were adjusted with respect to cell size. Using image analysis data, the average cross-sectional area for each cell line was calculated from 100 CMAs, and by setting the cell size of the largest cell to 1, a relative average size for each cell type was computed. "
[Show abstract][Hide abstract] ABSTRACT: 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.
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.
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.
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