aRNA-longSAGE: a new approach to generate SAGE libraries from microdissected cells

Large-scale gene expression analyses of microdissected primary tissue are still difficult because generally only a limited amount of mRNA can be obtained from microdissected cells. The introduction of the T7-based RNA amplification technique was an important step to reduce the amount of RNA needed for such analyses. This amplification technique produces amplified antisense RNA (aRNA), which so far has precluded its direct use for serial analysis of gene expression (SAGE) library production. We describe a method, termed ‘aRNA-longSAGE’, which is the first to allow the direct use of aRNA for standard longSAGE library production. The aRNA-longSAGE protocol was validated by comparing two aRNA-longSAGE libraries with two Micro-longSAGE libraries that were generated from the same RNA preparations of two different cell lines. Using a conservative validation approach, we were able to verify 68% of the differentially expressed genes identified by aRNA-longSAGE. Furthermore, the identification rate of differentially expressed genes was roughly twice as high in our aRNA-longSAGE libraries as in the standard Micro-longSAGE libraries. Using our validated aRNA-longSAGE protocol, we were able to successfully generate longSAGE libraries from as little as 40 ng of total RNA isolated from 2000–3000 microdissected pancreatic ductal epithelial cells or cells from pancreatic intraepithelial neoplasias.     

Identifying set-wise differential co-expression in gene expression microarray data

Background  

Previous differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions.     

Representation of differential expression: a new approach to study differential gene expression in trypanosomatids

During the past five years, several methods have been described that allow the isolation and cloning of stage-specific or cell-specific genes. The characterization of genes expressed at different stages of parasite development is of the utmost importance for the understanding of the mechanisms involved in the regulation of gene expression. Here, Samuel Goldenberg and Marco Aurelio Krieger describe a method for the amplification and cloning of Trypanosoma cruzi genes expressed specifically at different times of the metacyclogenesis process. This method, representation of differential expression (RDE), should be useful for the isolation and cloning of any trypanosomatid gene transcribing differentially expressed messenger RNA.     

Improved differential screening approach to analyse transcriptional variations in organized cDNA libraries

A cDNA library was generated from rat brain tissues and organized into 1536-well plates, using a fluorescence activated cell sorter (FACS), acting as a single cell deposition system. The organized library containing 10 000 clones, with 60% full-length cDNA inserts, allowed the generation of multiple identical membrane replicas. Each replica was hybridized with a complex probe obtained from a particular brain tissue or a given cultured cell. The signal intensity for each of the clones present on the membrane, quantified with a standard image-analysis software, is proportional both to the abundance of the corresponding mRNA in the probe and to the amount of plasmid template on the membrane. The latter value was thus used to normalize the signals produced with complex probes, to optimize the comparison of mRNA expression levels for the different systems under study. The construction of high-quality cDNA libraries, the generation of identical membrane replicas and comparable probes, and the utilization of an image-analysis software package, coupled with the normalization of the spot intensity by assaying plasmid quantity, significantly improves the differential screening approach. Altogether, these technical improvements open the possibility to compare a great number of different probes and, in consequence, to accumulate biological information for each clone present in an organized cDNA library. The functional information obtained should complement data from DNA sequencing projects.     

Identification and prevention of a GC content bias in SAGE libraries

Serial Analysis of Gene Expression (SAGE) is becoming a widely used gene expression profiling method for the study of development, cancer and other human diseases. Investigators using SAGE rely heavily on the quantitative aspect of this method for cataloging gene expression and comparing multiple SAGE libraries. We have developed additional computational and statistical tools to assess the quality and reproducibility of a SAGE library. Using these methods, a critical variable in the SAGE protocol was identified that has the potential to bias the Tag distribution relative to the GC content of the 10 bp SAGE Tag DNA sequence. We also detected this bias in a number of publicly available SAGE libraries. It is important to note that the GC content bias went undetected by quality control procedures in the current SAGE protocol and was only identified with the use of these statistical analyses on as few as 750 SAGE Tags. In addition to keeping any solution of free DiTags on ice, an analysis of the GC content should be performed before sequencing large numbers of SAGE Tags to be confident that SAGE libraries are free from experimental bias.     

Differential expression in SAGE: accounting for normal between-library variation

MOTIVATION: In contrasting levels of gene expression between groups of SAGE libraries, the libraries within each group are often combined and the counts for the tag of interest summed, and inference is made on the basis of these larger 'pseudolibraries'. While this captures the sampling variability inherent in the procedure, it fails to allow for normal variation in levels of the gene between individuals within the same group, and can consequently overstate the significance of the results. The effect is not slight: between-library variation can be hundreds of times the within-library variation. RESULTS: We introduce a beta-binomial sampling model that correctly incorporates both sources of variation. We show how to fit the parameters of this model, and introduce a test statistic for differential expression similar to a two-sample t-test.     

A comparative analysis of the information content in long and short SAGE libraries

Background  

Serial Analysis of Gene Expression (SAGE) is a powerful tool to determine gene expression profiles. Two types of SAGE libraries, ShortSAGE and LongSAGE, are classified based on the length of the SAGE tag (10 vs. 17 basepairs). LongSAGE libraries are thought to be more useful than ShortSAGE libraries, but their information content has not been widely compared. To dissect the differences between these two types of libraries, we utilized four libraries (two LongSAGE and two ShortSAGE libraries) generated from the hippocampus of Alzheimer and control samples. In addition, we generated two additional short SAGE libraries, the truncated long SAGE libraries (tSAGE), from LongSAGE libraries by deleting seven 5' basepairs from each LongSAGE tag.     

Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates

Background  

Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed.     

On a log-linear approach to detecting ecological interactions in monitored populations

We discuss a log-linear model for series of regular bird counts taken at a number of survey sites. The model is parameterized in terms of annual growth rates rather than actual indices of abundance, as is more frequently done. This not only permits easy estimation of and inference about these rates, but also allows us to model the effects upon population growth of covariates, such as the local presence of a competitor or predator, which may themselves vary in space and over time. A recursive relationship permits the expected count at a site to be functionally dependent upon the expected count at the previous visit. We discuss the advantages of using this relationship, rather than replacing the latter with their observed counterparts, as has been used previously.     

Effects of long‐term differential fertilization on eukaryotic microbial communities in an arable soil: a multiple barcoding approach

To understand the fine‐scale effects of changes in nutrient availability on eukaryotic soil microorganisms communities, a multiple barcoding approach was used to analyse soil samples from four different treatments in a long‐term fertilization experiment. We performed PCR amplification on soil DNA with primer pairs specifically targeting the 18S rRNA genes of all eukaryotes and three protist groups (Cercozoa, Chrysophyceae‐Synurophyceae and Kinetoplastida) as well as the ITS gene of fungi and the 23S plastid rRNA gene of photoautotrophic microorganisms. Amplicons were pyrosequenced, and a total of 88 706 quality filtered reads were clustered into 1232 operational taxonomic units (OTU) across the six data sets. Comparisons of the taxonomic coverage achieved based on overlapping assignment of OTUs revealed that half of the eukaryotic taxa identified were missed by the universal eukaryotic barcoding marker. There were only little differences in OTU richness observed between organic‐ (farmyard manure), mineral‐ and nonfertilized soils. However, the community compositions appeared to be strongly structured by organic fertilization in all data sets other than that generated using the universal eukaryotic 18S rRNA gene primers, whereas mineral fertilization had only a minor effect. In addition, a co‐occurrence based network analysis revealed complex potential interaction patterns between OTUs from different trophic levels, for example between fungivorous flagellates and fungi. Our results demonstrate that changes in pH, moisture and organic nutrients availability caused shifts in the composition of eukaryotic microbial communities at multiple trophic levels.