Programmed fluctuations in sense/antisense transcript ratios drive sexual differentiation in S. pombe.

Strand-specific RNA sequencing of S. pombe revealed a highly structured programme of ncRNA expression at over 600 loci. Waves of antisense transcription accompanied sexual differentiation. A substantial proportion of ncRNA arose from mechanisms previously considered to be largely artefactual, including improper 3' termination and bidirectional transcription. Constitutive induction of the entire spk1+, spo4+, dis1+ and spo6+ antisense transcripts from an integrated, ectopic, locus disrupted their respective meiotic functions. This ability of antisense transcripts to disrupt gene function when expressed in trans suggests that cis production at native loci during sexual differentiation may also control gene function. Consistently, insertion of a marker gene adjacent to the dis1+ antisense start site mimicked ectopic antisense expression in reducing the levels of this microtubule regulator and abolishing the microtubule-dependent 'horsetail' stage of meiosis. Antisense production had no impact at any of these loci when the RNA interference (RNAi) machinery was removed. Thus, far from being simply 'genome chatter', this extensive ncRNA landscape constitutes a fundamental component in the controls that drive the complex programme of sexual differentiation in S. pombe.

A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling.

BACKGROUND: RNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci. RESULTS: We found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to t<1 suggesting that the background error rate is extremely low in RNA-Seq. We also found RNA-Seq more sensitive to detecting differentially expressed exons than the Exon array, reflecting the wider dynamic range achievable on the SOLiD platform. In addition, we find significant evidence of novel protein coding regions outside known exons, 93% of which map to Exon array probesets, and are able to infer the presence of thousands of novel transcripts through the detection of previously unreported exon-exon junctions. CONCLUSIONS: By focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.

Interesting times for microarray expression profiling.

The last 10 years have seen microarrays go from being a nascent technology available only in a limited range of research facilities to becoming a ubiquitous approach to expression profiling. Developments in microarray technology have allowed the content of arrays to increase to the point that complete transcriptomes can be assayed on a single array, whilst developments in RNA labelling technology have reduced the amount of RNA needed down to the point where single cell profiling is technically possible. Recently it has also become possible to generate expression data from formalin-fixed paraffin-embedded archival samples. With the range of samples that can now be successfully profiled by microarray analysis this should be a good time to be running a microarray core facility. However, the arrival of Next Generation Sequencers means that for the first time there is an alternate platform that can potentially give a more complete picture of cellular expression than a microarray. Next Generation Sequencers are still in their infancy as a platform for expression profiling. Currently there are simply not enough Next Generation Sequencers in operation to meet the level of demand for expression profiling that microarray facilities service, but more systems are constantly becoming available. Looking ahead it seems certain that some proportion of expression profiling work will move from microarrays to Next Generation Sequencers, so now is a good time to consider some of the factors that might affect how significant that switch will be.

The utility of MAS5 expression summary and detection call algorithms.

BACKGROUND: Used alone, the MAS5.0 algorithm for generating expression summaries has been criticized for high False Positive rates resulting from exaggerated variance at low intensities. RESULTS: Here we show, with replicated cell line data, that, when used alongside detection calls, MAS5 can be both selective and sensitive. A set of differentially expressed transcripts were identified that were found to be changing by MAS5, but unchanging by RMA and GCRMA. Subsequent analysis by real time PCR confirmed these changes. In addition, with the Latin square datasets often used to assess expression summary algorithms, filtered MAS5.0 was found to have performance approaching that of its peers. CONCLUSION: When used alongside detection calls, MAS5 is a sensitive and selective algorithm for identifying differentially expressed genes.

Successful downstream application of the Paxgene Blood RNA system from small blood samples in paediatric patients for quantitative PCR analysis.

BACKGROUND: The challenge of gene expression studies is to reliably quantify levels of transcripts, but this is hindered by a number of factors including sample availability, handling and storage. The PAXgene Blood RNA System includes a stabilizing additive in a plastic evacuated tube, but requires 2.5 mL blood, which makes routine implementation impractical for paediatric use. The aim of this study was to modify the PAXgene Blood RNA System kit protocol for application to small, sick children, without compromising RNA integrity, and subsequently to perform quantitative analysis of ICAM and interleukin-6 gene expression.Aliquots of 0.86 mL PAXgene reagent were put into microtubes and 0.3 mL whole blood added to maintain the same recommended proportions as in the PAXgene evacuated tube system. RNA quality was assessed using the Agilent BioAnalyser 2100 and an in-house TaqMan assay which measures GAPDH transcript integrity by determining 3' to 5' ratios. qPCR analysis was performed on an additional panel of 7 housekeeping genes. Three reference genes (HPRT1, YWHAZ and GAPDH) were identified using the GeNORM algorithm, which were subsequently used to normalising target gene expression levels. ICAM-1 and IL-6 gene expression were measured in 87 Malawian children with invasive pneumococcal disease. RESULTS: Total RNA yield was between 1,114 and 2,950 ng and the BioAnalyser 2100 demonstrated discernible 18s and 28s bands. The cycle threshold values obtained for the seven housekeeping genes were between 15 and 30 and showed good consistency. Median relative ICAM and IL-6 gene expression were significantly reduced in non-survivors compared to survivors (ICAM: 3.56 vs 4.41, p = 0.04, and IL-6: 2.16 vs 6.73, p = 0.02). CONCLUSION: We have successfully modified the PAXgene blood collection system for use in small children and demonstrated preservation of RNA integrity and successful quantitative real-time PCR analysis.

High correspondence between Affymetrix exon and standard expression arrays.

Exon arrays aim to provide comprehensive gene expression data at the level of individual exons, similar to that provided on a per-gene basis by existing expression arrays. This report describes the performance of Affymetrix GeneChip Human Exon 1.0 ST array by using replicated RNA samples from two human cell lines, MCF7 and MCF10A, hybridized both to Exon 1.0 ST and to HG-U133 Plus2 arrays. Cross-comparison between array types requires an appropriate mapping to be found between individual probe sets. Three possible mappings were considered, reflecting different strategies for dealing with probe sets that target different parts of the same transcript. Irrespective of the mapping used, Exon 1.0 ST and HG-U133 Plus2 arrays show a high degree of correspondence. More than 80% of HG-U133 Plus2 probe sets may be mapped to the Exon chip, and fold changes are found well preserved for over 96% of those probe sets detected present. Since HG-U133 Plus2 arrays have already been extensively validated, these results lend a significant degree of confidence to exon arrays.

Amplification protocols introduce systematic but reproducible errors into gene expression studies.

The desire to perform microarray experiments with small starting amounts of RNA has led to the development of a variety of protocols for preparing and amplifying mRNA. This has consequences not only for the standardization of experimental design, but also for reproducibility and comparability between experiments. Here we investigate the differences between the Affymetrix standard and small sample protocols and address the data analysis issues that arise when comparing samples and experiments that have been processed in different ways. We show that data generated on the same platform using different protocols are not directly comparable. Further, protocols introduce systematic biases that can be largely accounted for by using the correct data analysis techniques.

Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer.

Small-cell lung cancer (SCLC), an aggressive neuroendocrine tumor with early dissemination and dismal prognosis, accounts for 15-20% of lung cancer cases and ∼200,000 deaths each year. Most cases are inoperable, and biopsies to investigate SCLC biology are rarely obtainable. Circulating tumor cells (CTCs), which are prevalent in SCLC, present a readily accessible 'liquid biopsy'. Here we show that CTCs from patients with either chemosensitive or chemorefractory SCLC are tumorigenic in immune-compromised mice, and the resultant CTC-derived explants (CDXs) mirror the donor patient's response to platinum and etoposide chemotherapy. Genomic analysis of isolated CTCs revealed considerable similarity to the corresponding CDX. Most marked differences were observed between CDXs from patients with different clinical outcomes. These data demonstrate that CTC molecular analysis via serial blood sampling could facilitate delivery of personalized medicine for SCLC. CDXs are readily passaged, and these unique mouse models provide tractable systems for therapy testing and understanding drug resistance mechanisms.

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis.

BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power.

Amplification and translocation of 3q26 with overexpression of EVI1 in Fanconi anemia-derived childhood acute myeloid leukemia with biallelic FANCD1/BRCA2 disruption.

Fanconi anemia (FA) is an inherited disease with congenital abnormalities and an extreme risk of acute myeloid leukemia (AML). Genetic events occurring during malignant transformation in FA and the biology of FA-associated AML are poorly understood, but are often preceded by the development of chromosomal aberrations involving 3q26-29 in bone marrow of FA patients. We report here the molecular cytogenetic characterization of FA-derived AML cell lines SB1685CB and SB1690CB by conventional and array comparative genomic hybridization, fluorescence in situ hybridization, and SKY. We identified gains of a 3.7 MB chromosomal region on 3q26.2-26.31, which preceded transformation to overt leukemia. This region harbors the oncogenic transcription factor EVI1. A third FA-derived cell line, FA-AML1, carried a translocation with ectopic localization of 3q26 including EVI1. Rearrangements of 3q, which are rare in childhood AML, commonly result in overexpression of EVI1, which determines specific gene expression patterns and confers poor prognosis. We detected overexpression of EVI1 in all three FA-derived AML. Our results suggest a link between the FA defect, chromosomal aberrations involving 3q and overexpression of EVI1. We hypothesize that constitutional or acquired FA defects might be a common factor for the development of 3q abnormalities in AML. In addition, cryptic imbalances as detected here might account for overexpression of EVI1 in AML without overt 3q26 rearrangements.