Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments.

Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.

Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.

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The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

We describe an approach for the accurate quantification and concurrent sequence identification of the individual proteins within complex mixtures. The method is based on a class of new chemical reagents termed isotope-coded affinity tags (ICATs) and tandem mass spectrometry. Using this strategy, we com- pared protein expression in the yeast Saccharomyces cerevisiae, using either ethanol or galactose as a carbon source. The measured differences in protein expression correlated with known yeast metabolic function under glucose-repressed conditions. The method is redundant if multiple cysteinyl residues are present, and the relative quantification is highly accurate because it is based on stable isotope dilution techniques. The ICAT approach should provide a widely applicable means to compare quantitatively glob- al protein expression in cells and tissues.

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Quantitative analysis of complex protein mixtures using isotope-coded affinity tags

We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.

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Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using Amine-reactive Isobaric Tagging Reagents

The availability of complete genomic sequences and technologies that allow comprehensive analysis of global expression profiles of messenger RNA have greatly expanded our ability to monitor the internal state of a cell. Yet biological systems ultimately need to be explained in terms of the activity, regulation and modification of proteins--and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy for such information. To facilitate global protein analyses, we have created a Saccharomyces cerevisiae fusion library where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location. Through immunodetection of the common tag, we obtain a census of proteins expressed during log-phase growth and measurements of their absolute levels. We find that about 80% of the proteome is expressed during normal growth conditions, and, using additional sequence information, we systematically identify misannotated genes. The abundance of proteins ranges from fewer than 50 to more than 10(6) molecules per cell. Many of these molecules, including essential proteins and most transcription factors, are present at levels that are not readily detectable by other proteomic techniques nor predictable by mRNA levels or codon bias measurements.

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Global analysis of protein expression in yeast

The description of the state of a biological system by the quantitative measurement of the system constituents is an essential but largely unexplored area of biology. With recent technical advances including the development of differential display-PCR (21), of cDNA microarray and DNA chip technology (20, 27), and of serial analysis of gene expression (SAGE) (34, 35), it is now feasible to establish global and quantitative mRNA expression profiles of cells and tissues in species for which the sequence of all the genes is known. However, there is emerging evidence which suggests that mRNA expression patterns are necessary but are by themselves insufficient for the quantitative description of biological systems. This evidence includes discoveries of posttranscriptional mechanisms controlling the protein translation rate (15), the half-lives of specific proteins or mRNAs (33), and the intracellular location and molecular association of the protein products of expressed genes (32).

Proteome analysis, defined as the analysis of the protein complement expressed by a genome (26), has been suggested as an approach to the quantitative description of the state of a biological system by the quantitative analysis of protein expression profiles (36). Proteome analysis is conceptually attractive because of its potential to determine properties of biological systems that are not apparent by DNA or mRNA sequence analysis alone. Such properties include the quantity of protein expression, the subcellular location, the state of modification, and the association with ligands, as well as the rate of change with time of such properties. In contrast to the genomes of a number of microorganisms (for a review, see reference 11) and the transcriptome of Saccharomyces cerevisiae (35), which have been entirely determined, no proteome map has been completed to date.

The most common implementation of proteome analysis is the combination of two-dimensional gel electrophoresis (2DE) (isoelectric focusing-sodium dodecyl sulfate [SDS]-polyacrylamide gel electrophoresis) for the separation and quantitation of proteins with analytical methods for their identification. 2DE permits the separation, visualization, and quantitation of thousands of proteins reproducibly on a single gel (18, 24). By itself, 2DE is strictly a descriptive technique. The combination of 2DE with protein analytical techniques has added the possibility of establishing the identities of separated proteins (1, 2) and thus, in combination with quantitative mRNA analysis, of correlating quantitative protein and mRNA expression measurements of selected genes.

The recent introduction of mass spectrometric protein analysis techniques has dramatically enhanced the throughput and sensitivity of protein identification to a level which now permits the large-scale analysis of proteins separated by 2DE. The techniques have reached a level of sensitivity that permits the identification of essentially any protein that is detectable in the gels by conventional protein staining (9, 29). Current protein analytical technology is based on the mass spectrometric generation of peptide fragment patterns that are idiotypic for the sequence of a protein. Protein identity is established by correlating such fragment patterns with sequence databases (10, 22, 37). Sophisticated computer software (8) has automated the entire process such that proteins are routinely identified with no human interpretation of peptide fragment patterns.

In this study, we have analyzed the mRNA and protein levels of a group of genes expressed in exponentially growing cells of the yeast S. cerevisiae. Protein expression levels were quantified by metabolic labeling of the yeast proteins to a steady state, followed by 2DE and liquid scintillation counting of the selected, separated protein species. Separated proteins were identified by in-gel tryptic digestion of spots with subsequent analysis by microspray liquid chromatography-tandem mass spectrometry (LC-MS/MS) and sequence database searching. The corresponding mRNA transcript levels were calculated from SAGE frequency tables (35).

This study, for the first time, explores a quantitative comparison of mRNA transcript and protein expression levels for a relatively large number of genes expressed in the same metabolic state. The resultant correlation is insufficient for prediction of protein levels from mRNA transcript levels. We have also compared the relative amounts of protein and mRNA with the respective codon bias values for the corresponding genes. This comparison indicates that codon bias by itself is insufficient to accurately predict either the mRNA or the protein expression levels of a gene. In addition, the results demonstrate that only highly expressed proteins are detectable by 2DE separation of total cell lysates and that therefore the construction of complete proteome maps with current technology will be very challenging, irrespective of the type of organism.

Correlation between Protein and mRNA Abundance in Yeast

The p210 bcr-abl protein tyrosine kinase (PTK) appears to be directly responsible for the initial manifestations of chronic myelogenous leukemia (CML). In contrast to the extensive characterization of the PTK and its effects on cell function, relatively little is known about the nature of the protein tyrosine phosphatases (PTPs) that may modulate p210 bcr-abl-induced signalling. In this study, we have demonstrated that expression of PTP1B is enhanced specifically in various cells expressing p210 bcr-abl, including a cell line derived from a patient with CML. This effect on expression of PTP1B required the kinase activity of p210 bcr-abl and occurred rapidly, concomitant with maximal activation of a temperature-sensitive mutant of the PTK. The effect is apparently specific for PTP1B since, among several PTPs tested, we detected no change in the levels of TCPTP, the closest relative of PTP1B. We have developed a strategy for identification of physiological substrates of individual PTPs which utilizes substrate-trapping mutant forms of the enzymes that retain the ability to bind to substrate but fail to catalyze efficient dephosphorylation. We have observed association between a substrate-trapping mutant of PTP1B (PTP1B-D181A) and p210 bcr-abl, but not v-Abl, in a cellular context. Consistent with the trapping data, we observed dephosphorylation of p210 bcr-abl, but not v-Abl, by PTP1B in vivo. We have demonstrated that PTP1B inhibited binding of the adapter protein Grb2 to p210 bcr-abl and suppressed p210 bcr-abl-induced transcriptional activation that is dependent on Ras. These results illustrate selectivity in the effects of PTPs in a cellular context and suggest that PTP1B may function as a specific, negative regulator of p210 bcr-abl signalling in vivo.

Protein Tyrosine Phosphatase 1B Antagonizes Signalling by Oncoprotein Tyrosine Kinase p210 bcr-abl In Vivo

Several methods have been developed for the comprehensive analysis of gene expression in complex biological systems Generally these procedures assess either a portion of the cellular transcriptome or a portion of the cellular proteome Each approach has distinct conceptual and methodological advantages and disadvantages We have investigated the application of both methods to characterize the gene expression pathway mediated by androgens and the androgen receptor in prostate cancer cells This pathway is of critical importance for the development and progression of prostate cancer Of clinical importance, modulation of androgens remains the mainstay of treatment for patients with advanced disease To facilitate global gene expression studies we have first sought to define the prostate transcriptome by assembling and annotating prostate-derived expressed sequence tags (ESTs) A total of 55 000 prostate ESTs were assembled into a set of 15 953 clusters putatively representing 15 953 distinct transcripts These clusters were used to construct cDNA microarrays suitable for examining the androgen-response pathway at the level of transcription The expression of 20 genes was found to be induced by androgens This cohort included known androgen-regulated genes such as prostate-specific antigen (PSA) and several novel complementary DNAs (cDNAs) Protein expression profiles of androgen-stimulated prostate cancer cells were generated by two-dimensional electrophoresis (2-DE) Mass spectrometric analysis of androgen-regulated proteins in these cells identified the metastasis-suppressor gene NDKA/nm23, a finding that may explain a marked reduction in metastatic potential when these cells express a functional androgen receptor pathway

Comprehensive analyses of prostate gene expression: convergence of expressed sequence tag databases, transcript profiling and proteomics.

To be successful, ubicomp applications must be designed with their environment and users in mind and evaluated to confirm that they do not disrupt the users' natural workflow. Well-established techniques for understanding users and their environment exist, but are not specifically designed to assess how well the computing and physical task environments blend. We present strengths and weaknesses of several qualitative and quantitative user study techniques for ubicomp. We applied these techniques to the design and evaluation of a ubicomp application for cell biology laboratories (Labscape). We describe how these techniques helped identify design considerations that were crucial for Lab-scape's adoption and demonstrate their ability to measure how effectively applications blend into an environment.

User Study Techniques in the Design and Evaluation of a Ubicomp Environment

Methods for the determination of the rate of synthesis of biopolymer synthesis and degradation in cells, tissues, or cell-free systems using monomer which has been labeled with a stable isotope are provided. Further, the present invention provides methods for the determination or identification of an unknown biopolymer and for the identification of an unknown cell type, a physiological state of a cell or tissue. Also, the present invention provides a database of descriptors which can be used to define an organism, tissue type, cell type, and the like, and which database can be used in conjunction with other public and private databases to identify or characterize an organism, tissue type, cell type, state of differentiation, or physiologic state of an organism, or tissue or cell sample.

B. Robert Franza

Papers

Correlation between Protein and mRNA Abundance in Yeast

Protein Tyrosine Phosphatase 1B Antagonizes Signalling by Oncoprotein Tyrosine Kinase p210 bcr-abl In Vivo

Comprehensive analyses of prostate gene expression: convergence of expressed sequence tag databases, transcript profiling and proteomics.

User Study Techniques in the Design and Evaluation of a Ubicomp Environment

Stable isotope metabolic labeling for analysis of biopolymers