Limitations of mRNA amplification from small-size cell samples

LINCATRA: Two-cycle method to amplify RNA for transcriptome analysis from formalin-fixed paraffin-embedded tissue

Whole transcriptome analysis (WTA) using RNA extracted from Formalin Fixed Paraffin Embedded (FFPE) tissue is an invaluable tool to understand the molecular pathology of disease. RNA extracted from FFPE tissue is either degraded and/or in very low quantities hampering gene expression analysis. Earlier studies described protocols applied for cellular RNA using poly-A primer-based linear amplification. The current study describes a method, LINCATRA (LINear amplifiCAtion of RNA for whole TRAnscriptome analysis). It employs random nonamer primer based method which can amplify short, fragmented RNA with high fidelity from as low as 5 ng to obtain enough material for WTA. The two-cycle method significantly amplified RNA at ∼1000 folds (p < 0.0001) improving the mean read lengths (p < 0.05) in WTA. Overall, increased mean read length positively correlated with on-target reads (Pearson's r = 0.71, p < 0.0001) in both amplified and unamplified RNA-seq analysis. Gene expression analysis compared between unamplified and amplified group displayed substantial overlap of the differentially expressed genes (DEGs) (log2 fold change cut-off < -2 and >2, p < 0.05) identified between lung cancer and asthma cohorts validating the method developed. This method is applicable in clinical molecular pathology field for both diagnostics and elucidation of disease mechanisms.

Selective gene expression analysis of the neuroepithelial body microenvironment in postnatal lungs with special interest for potential stem cell characteristics

BACKGROUND

The pulmonary neuroepithelial body (NEB) microenvironment (ME) consists of innervated cell clusters that occur sparsely distributed in the airway epithelium, an organization that has so far hampered reliable selective gene expression analysis. Although the NEB ME has been suggested to be important for airway epithelial repair after ablation, little is known about their potential stem cell characteristics in healthy postnatal lungs. Here we report on a large-scale selective gene expression analysis of the NEB ME.

METHODS

A GAD67-GFP mouse model was used that harbors GFP-fluorescent NEBs, allowing quick selection and pooling by laser microdissection (LMD) without further treatment. A panel of stem cell-related PCR arrays was used to selectively compare mRNA expression in the NEB ME to control airway epithelium (CAE). For genes that showed a higher expression in the NEB ME, a ranking was made based on the relative expression level. Single qPCR and immunohistochemistry were used to validate and quantify the PCR array data.

RESULTS

Careful optimization of all protocols appeared to be essential to finally obtain high-quality RNA from pooled LMD samples of NEB ME. About 30% of the more than 600 analyzed genes showed an at least two-fold higher expression compared to CAE. The gene that showed the highest relative expression in the NEB ME, Delta-like ligand 3 (Dll3), was investigated in more detail. Selective Dll3 gene expression in the NEB ME could be quantified via single qPCR experiments, and Dll3 protein expression could be localized specifically to NEB cell surface membranes.

CONCLUSIONS

This study emphasized the importance of good protocols and RNA quality controls because of the, often neglected, fast RNA degradation in postnatal lung samples. It was shown that sufficient amounts of high-quality RNA for reliable complex gene expression analysis can be obtained from pooled LMD-collected NEB ME samples of postnatal lungs. Dll3 expression, which has also been reported to be important in high-grade pulmonary tumor-initiating cells, was used as a proof-of-concept to confirm that the described methodology represents a promising tool for further unraveling the molecular basis of NEB ME physiology in general, and its postnatal stem cell capacities in particular.

A microaliquoting technique for precise histological annotation and optimization of cell content in frozen tissue specimens

Knowledge of the exact cell content of frozen tissue samples is of growing importance in genomic research. We developed a microaliquoting technique to measure and optimize the cell composition of frozen tumor specimens for molecular studies. Frozen samples of 31 mesothelioma cases were cut in alternating thin and thick sections. Thin sections were stained and evaluated visually. Thick sections, i.e., microaliquots, were annotated using bordering stained sections. A range of cellular heterogeneity was observed among and within samples. Precise annotation of samples was obtained by integration and compared to conventional single face and "front and back"’ section estimates of cell content. Front and back estimates were more highly correlated with block annotation by microaliquoting than were single face estimates. Both methods yielded discrepant estimates, however, and for some studies may not adequately account for the heterogeneity of mesothelioma or other malignancies with variable cellular composition. High yield and quality RNA was extracted from precision annotated, tumor-enriched subsamples prepared by combining individual microaliquots with the highest tumor cellularity estimates. Microaliquoting provides accurate cell content annotation and permits genomic analysis of enriched subpopulations of cells without fixation or amplification.

Melanoma brain colonization involves the emergence of a brain-adaptive phenotype

The brain offers a unique microenvironment that plays an important role in the establishment and progression of metastasis. However, the molecular determinants that promote development of melanoma brain metastases are largely unknown. Utilizing two species of immune-compromised animals, with in vivo cultivated metastatic tissues along with their corresponding host tissues in a metastasis model, we here identify molecular events associated with melanoma brain metastases. We find that the transcriptional changes in the melanoma cells, as induced by the brain-microenvironment in both host species, reveal the opportunistic nature of melanoma in this biological context in rewiring the molecular framework of key molecular players with their associated biological processes. Specifically, we identify the existence of a neuron-like melanoma phenotype, which includes synaptic characteristics and a neurotransmission-like circuit involving glutamate. Regulation of gene transcription and neuron-like plasticity by Ca(2+)-dependent signaling appear to occur through glutamate receptor activation. The brain-adaptive phenotype was found as more prominent in the early metastatic growth phases compared to a later phase, emphasizing a temporal requirement of critical events in the successful colonization of the brain. Analysis of the host tissue uncovered a cooperative inflammatory microenvironment formed by activated host cells that permitted melanoma growth at the expense of the host organism. Combined experimental and computational approaches clearly highlighted genes and signaling pathways being shared with neurodegenerative diseases. Importantly, the identification of essential molecular networks that operate to promote the brain-adaptive phenotype is of clinical relevance, as they represent leads to urgently needed therapeutic targets.

Procurement and cytological features of human fallopian tube fimbrial cells by ex vivo imprinting and washing

INTRODUCTION

Fallopian tube intraepithelial cancer is a postulated precursor of epithelial ovarian carcinomas. As research continues on epithelial ovarian carcinomas' developmental pathways, representative tubal tissue must be procured for diagnostic, biological, and molecular studies without compromising pathological diagnosis.

MATERIALS AND METHODS

Fallopian tube fimbrial epithelia were harvested from postmenopausal women undergoing surgery for non-neoplastic gynecologic lesions (n = 16) and epithelial ovarian carcinomas (n = 6). Cytological imprints and washings were obtained from each fimbria and stained by Diff-Quik and rapid Papanicolaou for general cytomorphology; by Trypan blue for cell viability; and by rapid immunohistochemistry for evaluation of low molecular weight cytokeratin, MIB-1, p53, and high-mobility group A (HMGA2) expression.

RESULTS

Benign and malignant tubal imprints harvests yielded means of 3.5 × 10⁵ and 1.2 × 10⁶ cells/fimbria, respectively, with viabilities higher than 85%. A mean of 2.5 × 10⁵ cells/fimbria was obtained from fimbrial washings. The mean DNA, RNA, and protein contents of benign imprints were 2.4, 1.5, and 67 μg/fimbria, respectively. Benign cell populations contained nearly 97% cytokeratin-positive and p53/HMGA2-negative cells, which were dispersed within a watery to proteinaceous material and rare microcalcifications. Fimbrial imprints from serous carcinomas involving the fimbriae exhibited abnormal p53 and HMGA2 expression, high proliferation, and diagnostic criteria of malignancy, including prominent nucleoli and cell crowding.

CONCLUSIONS

Ex vivo harvest from operative specimens allows for collection of cell populations representative of native fimbrial epithelium and free of significant contaminants. Tubal harvest facilitates triaging of cellular material for basic, clinical, and translational studies on cancer pathobiology and also represents a potential diagnostic adjunct to emerging in vivo high-resolution optical technologies.

Gene expression changes in the colon epithelium are similar to those of intact colon during late inflammation in interleukin-10 gene deficient mice

In addition to their role in absorption and secretion, epithelial cells play an important role in the protection of the colon mucosa from the resident microbiota and are important for the maintenance of homeostasis. Microarray analysis of intact colon samples is widely used to gain an overview of the cellular pathways and processes that are active in the colon during inflammation. Laser microdissection of colon epithelial cells allows a more targeted analysis of molecular pathways in the mucosa, preceding and during inflammation, with potentially increased sensitivity to changes in specific cell populations. The aim of this study was to investigate the molecular changes that occur in early and late inflammation stages in colon epithelium of a mouse model of inflammatory bowel diseases. Microarray analysis of intact colon samples and microdissected colon epithelial cell samples from interleukin-10 gene deficient and control mice at 6 and 12 weeks of age was undertaken. Results of gene set enrichment analysis showed that more immune-related pathways were identified between interleukin-10 gene deficient and control mice at 6 weeks of age in epithelial cells than intact colon. This suggests that targeting epithelial cells could increase sensitivity for detecting immune changes that occur early in the inflammatory process. However, in the later stages of inflammation, microarray analyses of intact colon and epithelium both provide a similar overview of gene expression changes in the colon mucosa at the pathway level.

Comparison of progestin transcriptional profiles in rat mammary gland using Laser Capture Microdissection and whole tissue-sampling

INTRODUCTION

Investigation of molecular mechanisms by gene expression profiling gains increasingly importance in preclinical safety evaluation. However, assigning expressed genes to specific cell populations is nearly impossible if the investigated RNA originates from whole tissue extracts. In this regard, Laser Capture Microdissection (LCM) can be used to detect changes specific to individual cell types. The objective of this study was to investigate the use of LCM for characterisation of progestin-related gene expression changes in the mammary gland. Thus, transcriptional profiles of the mammary gland of rats treated with a non-steroidal progesterone-receptor ligand, promegestone, medroxyprogesterone acetate, progesterone or vehicle were compared using whole tissue homogenates or LCM-captured epithelial cells.

METHODS

Total RNA from 30 mammary glands was isolated from snap-frozen specimen of the whole tissue and from approximately 25.000-30.000 cells of cresyl violet stained frozen sections employing LCM. After amplification of averaged 0.2μg total RNA of LCM-captured samples, RNA was labelled, hybridised to Affymetrix GeneChips and analysed.

RESULTS

LCM-captured samples showed up to 3-fold more differentially expressed probe sets (progesterone) and up to 10-fold more downregulated (promegestone) probe sets than whole tissue samples implying high cell specificity. Moreover, mammary gland specific differentiation markers like whey acidic protein, alpha lactalbumin, casein alpha s1 and casein kappa showed up to 3.4-fold (alpha lactalbumin, vehicle) higher expression values. Multivariate data analyses revealed a clear separation of gene expression profiles according to the method used, suggesting an amplification dependent bias.

DISCUSSION

LCM transcriptional profiling provides highly cell-specific information. An amplification dependent bias was observed. The technical variability was shown to be smaller than the biological variability. For progestin-related transcriptional profiling of the mammary gland, whole tissue-sampling proved to yield more informative results. Therefore LCM should only be considered when cell-type specific gene expression profiles are necessary for an in depth evaluation.

Characterization of in vitro transcription amplification linearity and variability in the low copy number regime using External RNA Control Consortium (ERCC) spike-ins

Using spike-in controls designed to mimic mammalian mRNA species, we used the quantitative reverse transcription polymerase chain reaction (RT-qPCR) to assess the performance of in vitro transcription (IVT) amplification process of small samples. We focused especially on the confidence of the transcript level measurement, which is essential for differential gene expression analyses. IVT reproduced gene expression profiles down to approximately 100 absolute input copies. However, a RT-qPCR analysis of the antisense RNA showed a systematic bias against low copy number transcripts, regardless of sequence. Experiments also showed that noise increases with decreasing copy number. First-round IVT preserved the gene expression information within a sample down to the 100 copy level, regardless of total input sample amount. However, the amplification was nonlinear under low total RNA input/long IVT conditions. Variability of the amplification increased predictably with decreasing input copy number. For the small enrichments of interest in typical differential gene expression studies (e.g., twofold changes), the bias from IVT reactions is unlikely to affect the results. In limited cases, some transcript-specific differential gene expression values will need adjustment to reflect this bias. Proper experimental design with reasonable detection limits will yield differential gene expression capability even between low copy number transcripts.

SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data

MOTIVATION

The application of next-generation sequencing (NGS) technologies to RNAs directly extracted from a community of organisms yields a mixture of fragments characterizing both coding and non-coding types of RNAs. The task to distinguish among these and to further categorize the families of messenger RNAs and ribosomal RNAs (rRNAs) is an important step for examining gene expression patterns of an interactive environment and the phylogenetic classification of the constituting species.

RESULTS

We present SortMeRNA, a new software designed to rapidly filter rRNA fragments from metatranscriptomic data. It is capable of handling large sets of reads and sorting out all fragments matching to the rRNA database with high sensitivity and low running time.

A simple, cost-effective and flexible method for processing of snap-frozen tissue to prepare large amounts of intact RNA using laser microdissection

Understanding the molecular basis of disease requires gene expression profiling of normal and pathological tissue. Although the advent of laser microdissection (LMD) has greatly facilitated the procurement of specific cell populations, often only small amounts of low quality RNA is recovered. This precludes the use of global approaches of gene expression profiling which require sizable amounts of high quality RNA. Here we report a method for processing of snap-frozen tissue to prepare large amounts of intact RNA using LMD. Portions of small intestine from piglets (n = 6) were snap-frozen in Optimum Cutting Temperature compound (experimental) and in RNAlater (control). A randomly selected sample was laser microdissected using the developed protocol in multiple sessions totalling 4 h each day on four consecutive days. RNAs were extracted from these samples and its control and their quality (RIN) determined. RINs of the experimental samples were independent of time (p = 0.12) and day (p = 0.56) of the microdissection thereby suggesting that their RNA quality remained unaltered. These samples exhibited high quality (RIN ≥ 8) with good recovery (81.2%) and excellent yield (1539 ng/1.2 × 10(7) μm(2)). Their overall RIN, 8.029 ± 0.116, was not significantly different from 8.2 (p = 0.123), the value obtained from the control, non-laser microdissected, sample. This indicated that the RNA quality from the laser microdissected and non-microdissected samples was comparable. The method allowed LMD for up to 4 h each day for a total of four days. The microdissected samples can be pooled thereby increasing amount of RNA at least by ten-fold. The procedure did not require any expensive limited-shelf life RNase inhibitors, RNA protectors, staining kits or toxic chemicals. Furthermore, it was flexible and enabled the processing without affecting routine laboratory workflow. The method developed was simple, inexpensive and provided substantial amounts of high quality RNA suitable for gene expression profiling and other cellular and molecular analyses for biology and molecular medicine.

Identifying individual DNA species in a complex mixture by precisely measuring the spacing between nicking restriction enzymes with atomic force microscope

We discuss a novel atomic force microscope-based method for identifying individual short DNA molecules (<5000 bp) within a complex mixture by measuring the intra-molecular spacing of a few sequence-specific topographical labels in each molecule. Using this method, we accurately determined the relative abundance of individual DNA species in a 15-species mixture, with fewer than 100 copies per species sampled. To assess the scalability of our approach, we conducted a computer simulation, with realistic parameters, of the hypothetical problem of detecting abundance changes in individual gene transcripts between two single-cell human messenger RNA samples, each containing roughly 9000 species. We found that this approach can distinguish transcript species abundance changes accurately in most cases, including transcript isoforms which would be challenging to quantitate with traditional methods. Given its sensitivity and procedural simplicity, our approach could be used to identify transcript-derived complementary DNAs, where it would have substantial technical and practical advantages versus established techniques in situations where sample material is scarce.

Potential role of atomic force microscopy in systems biology

Systems biology is a quantitative approach for understanding a biological system at its global level through systematic perturbation and integrated analysis of all its components. Simultaneous acquisition of information data sets pertaining to the system components (e.g., genome, proteome) is essential to implement this approach. There are limitations to such an approach in measuring gene expression levels and accounting for all proteins in the system. The success of genomic studies is critically dependent on polymerase chain reaction (PCR) for its amplification, but PCR is very uneven in amplifying the samples, ineffective in scarce samples and unreliable in low copy number transcripts. On the other hand, lack of amplifying techniques for proteins critically limits their identification to only a small fraction of high concentration proteins. Atomic force microscopy (AFM), AFM cantilever sensors, and AFM force spectroscopy in particular, could address these issues directly. In this article, we reviewed and assessed their potential role in systems biology.

Global array-based transcriptomics from minimal input RNA utilising an optimal RNA isolation process combined with SPIA cDNA probes

BACKGROUND

Technical advances in the collection of clinical material, such as laser capture microdissection and cell sorting, provide the advantage of yielding more refined and homogenous populations of cells. However, these attractive advantages are counter balanced by the significant difficulty in obtaining adequate nucleic acid yields to allow transcriptomic analyses. Established technologies are available to carry out global transcriptomics using nanograms of input RNA, however, many clinical samples of low cell content would be expected to yield RNA within the picogram range. To fully exploit these clinical samples the challenge of isolating adequate RNA yield directly and generating sufficient microarray probes for global transcriptional profiling from this low level RNA input has been addressed in the current report. We have established an optimised RNA isolation workflow specifically designed to yield maximal RNA from minimal cell numbers. This procedure obtained RNA yield sufficient for carrying out global transcriptional profiling from vascular endothelial cell biopsies, clinical material not previously amenable to global transcriptomic approaches. In addition, by assessing the performance of two linear isothermal probe generation methods at decreasing input levels of good quality RNA we demonstrated robust detection of a class of low abundance transcripts (GPCRs) at input levels within the picogram range, a lower level of RNA input (50 pg) than previously reported for global transcriptional profiling and report the ability to interrogate the transcriptome from only 10 pg of input RNA. By exploiting an optimal RNA isolation workflow specifically for samples of low cell content, and linear isothermal RNA amplification methods for low level RNA input we were able to perform global transcriptomics on valuable and potentially informative clinically derived vascular endothelial biopsies here for the first time. These workflows provide the ability to robustly exploit ever more common clinical samples yielding extremely low cell numbers and RNA yields for global transcriptomics.

Microgenomic analysis in skeletal muscle: expression signatures of individual fast and slow myofibers

Background

Skeletal muscle is a complex, versatile tissue composed of a variety of functionally diverse fiber types. Although the biochemical, structural and functional properties of myofibers have been the subject of intense investigation for the last decades, understanding molecular processes regulating fiber type diversity is still complicated by the heterogeneity of cell types present in the whole muscle organ.

Methodology/Principal Findings

We have produced a first catalogue of genes expressed in mouse slow-oxidative (type 1) and fast-glycolytic (type 2B) fibers through transcriptome analysis at the single fiber level (microgenomics). Individual fibers were obtained from murine soleus and EDL muscles and initially classified by myosin heavy chain isoform content. Gene expression profiling on high density DNA oligonucleotide microarrays showed that both qualitative and quantitative improvements were achieved, compared to results with standard muscle homogenate. First, myofiber profiles were virtually free from non-muscle transcriptional activity. Second, thousands of muscle-specific genes were identified, leading to a better definition of gene signatures in the two fiber types as well as the detection of metabolic and signaling pathways that are differentially activated in specific fiber types. Several regulatory proteins showed preferential expression in slow myofibers. Discriminant analysis revealed novel genes that could be useful for fiber type functional classification.

Conclusions/Significance

As gene expression analyses at the single fiber level significantly increased the resolution power, this innovative approach would allow a better understanding of the adaptive transcriptomic transitions occurring in myofibers under physiological and pathological conditions.

Identification of genes and pathways associated with cytotoxic T lymphocyte infiltration of serous ovarian cancer

Background:

Tumour-infiltrating lymphocytes (TILs) are predictors of disease-specific survival (DSS) in ovarian cancer. It is largely unknown what factors contribute to lymphocyte recruitment. Our aim was to evaluate genes and pathways contributing to infiltration of cytotoxic T lymphocytes (CTLs) in advanced-stage serous ovarian cancer.

Methods:

For this study global gene expression was compared between low TIL (n=25) and high TIL tumours (n=24). The differences in gene expression were evaluated using parametric T-testing. Selectively enriched biological pathways were identified with gene set enrichment analysis. Prognostic influence was validated in 157 late-stage serous ovarian cancer patients. Using immunohistochemistry, association of selected genes from identified pathways with CTL was validated.

Results:

The presence of CTL was associated with 320 genes and 23 pathways (P<0.05). In addition, 54 genes and 8 pathways were also associated with DSS in our validation cohort. Immunohistochemical evaluation showed strong correlations between MHC class I and II membrane expression, parts of the antigen processing and presentation pathway, and CTL recruitment.

Conclusion:

Gene expression profiling and pathway analyses are valuable tools to obtain more understanding of tumour characteristics influencing lymphocyte recruitment in advanced-stage serous ovarian cancer. Identified genes and pathways need to be further investigated for suitability as therapeutic targets.

Microarray analysis at single-molecule resolution

Bioanalytical chip-based assays have been enormously improved in sensitivity in the recent years; detection of trace amounts of substances down to the level of individual fluorescent molecules has become state-of-the-art technology. The impact of such detection methods, however, has yet not fully been exploited, mainly due to a lack of appropriate mathematical tools for robust data analysis. One particular example relates to the analysis of microarray data. While classical microarray analysis works at resolutions of 2-20 microm and quantifies the abundance of target molecules by determining average pixel intensities, a novel high-resolution approach directly visualizes individual bound molecules as diffraction-limited peaks. The now possible quantification via counting is less susceptible to labeling artifacts and background noise. We have developed an approach for the analysis of high-resolution microarray images. First, it consists of a single-molecule detection step, based on undecimated wavelet transforms, and second, a spot identification step via spatial statistics approach (corresponding to the segmentation step in the classical microarray analysis). The detection method was tested on simulated images with a concentration range of 0.001 to 0.5 molecules per square micrometer and signal-to-noise ratio (SNR) between 0.9 and 31.6. For SNR above 15, the false negatives relative error was below 15%. Separation of foreground/background is proved reliable, in case foreground density exceeds background by a factor of 2. The method has also been applied to real data from high-resolution microarray measurements.

Challenges in defining predictive markers for response to endocrine therapy in breast cancer

Endocrine therapy is a major treatment modality for hormone-dependent breast cancer. It has a relatively low morbidity, and there is evidence that antihormonal treatments have had a significant effect in reducing mortality for breast cancer. Despite this, resistance to endocrine therapy, either primary or acquired during treatment, occurs in the majority of patients, and is a major obstacle to optimal clinical management. There is therefore an urgent need to identify, on an individual basis, those tumors that are most likely to respond to endocrine therapy (so sparing patients with resistant tumors the needless side effects of ineffective therapy), and the mechanisms of resistance in tumors that are nonresponsive to treatment (so these can be bypassed). These needs are the focus of this review, which discusses the particular issues encountered when investigating the potential of multigene expression signatures as predictive factors for response to aromatase inhibitors, which have recently become front-line endocrine therapies for postmenopausal patients with breast cancer.

An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data

The parts of the genome transcribed by a cell or tissue reflect the biological processes and functions it carries out. We characterized the features of mammalian tissue transcriptomes at the gene level through analysis of RNA deep sequencing (RNA-Seq) data across human and mouse tissues and cell lines. We observed that roughly 8,000 protein-coding genes were ubiquitously expressed, contributing to around 75% of all mRNAs by message copy number in most tissues. These mRNAs encoded proteins that were often intracellular, and tended to be involved in metabolism, transcription, RNA processing or translation. In contrast, genes for secreted or plasma membrane proteins were generally expressed in only a subset of tissues. The distribution of expression levels was broad but fairly continuous: no support was found for the concept of distinct expression classes of genes. Expression estimates that included reads mapping to coding exons only correlated better with qRT-PCR data than estimates which also included 3′ untranslated regions (UTRs). Muscle and liver had the least complex transcriptomes, in that they expressed predominantly ubiquitous genes and a large fraction of the transcripts came from a few highly expressed genes, whereas brain, kidney and testis expressed more complex transcriptomes with the vast majority of genes expressed and relatively small contributions from the most expressed genes. mRNAs expressed in brain had unusually long 3′UTRs, and mean 3′UTR length was higher for genes involved in development, morphogenesis and signal transduction, suggesting added complexity of UTR-based regulation for these genes. Our results support a model in which variable exterior components feed into a large, densely connected core composed of ubiquitously expressed intracellular proteins.

A variety of genes are active within the nuclei of our cells. Some are needed for the day-to-day maintenance of cell functions, while others have roles that are more specific to certain tissues or particular cell types; for example, only the pancreas produces insulin. As a result, every tissue has its own profile of gene activity. Since active genes produce RNA, tissue differences in gene activity can be probed by characterizing the RNA they contain. Essentially the entire set of RNAs or ‘transcriptome’ has been sequenced from various tissues, and we used these data to compare the degree of specialization of different tissues and to investigate the set of ‘core’ genes active in every tissue. A central observation was that there are an abundance of such core genes, and that these genes account for the majority of the transcriptome in each tissue. These findings will aid in the understanding of what makes tissues, and cell types, different from each other and what each requires to function.

Murine spinal cord transcriptome analysis following reduction of prevalent myelin cDNA sequences

From 1,000 randomly selected colonies from cDNA libraries derived from murine spinal cord subtracted against white matter by means of suppression subtractive hybridization, 220 clones were identified as differentially expressed by dot blot analysis. Sequence analysis by the BLAST programming identified 140 unique genes. (1) The percentage of known sequences from myelin and other glial sources was reduced by approximately 75% over previous, similar subtractions employing visual cortex as the driver. (2) Differentially expressed genes tended to reflect existing expectations concerning structure and function of the spinal cord. (3) About 35% of all genes differentially expressed in the spinal cord in this study are also known to be differentially expressed for this structure as tabulated in the UniGene database. (4) About 33% of all genes differentially expressed in the present study are recorded as not present when measured in the spinal cord according to the UniGene database indicating that present techniques are not recording about a third of differentially expressed genes in this structure. (5) About 15% of all differentially expressed genes are for unknown, putative or hypothetical protein products. (6) About 4% of all differentially expressed genes are novel expressed sequence tags for the mouse. The current study demonstrates the importance of reducing the presence of glial associated sequences when comparing brain regions. It is concluded that the persistence of some myelin sequences in the spinal cord when white matter is used as the driver indicates that myelination is more active in this structure than for those areas represented by white matter and corpus callosum.

Modified PAXgene method allows for isolation of high-integrity total RNA from microlitre volumes of mouse whole blood

Analysis of gene expression is often used to evaluate the effects of experimental manipulations in laboratory animals. Blood is a rich source of potential biomarkers, including gene expression information, which may be obtained from whole blood. When compared with the end of a study, when whole blood samples can be easily obtained for gene expression measurements, the limiting volumes of whole blood obtainable from animals during the course of an experiment requires a method for RNA isolation from a minimal volume of whole blood. The PAXgene Blood RNA Extraction System originally designed for isolation of total RNA from 2.5 mL of human whole blood, was modified and successfully used to isolate high-integrity total RNA from as little as 50 microL of mouse whole blood. Fifty microlitres of mouse whole blood yielded an average of 2.3 microg highly intact total RNA, of sufficient quality and quantity allowing for multiple gene expression determinations. The utility of this method was demonstrated by confirming the time- and dose-dependent upregulation of haem oxygenase-1 (Hmox1) mRNA in response to a single injection of cobalt protoporphyrin. The successful isolation of total RNA from small volumes of mouse whole blood can allow for serial sampling on the same animals, thereby reducing the number of animals required for experimentation.

Evaluation of methods for amplification of picogram amounts of total RNA for whole genome expression profiling

Background

For more than a decade, microarrays have been a powerful and widely used tool to explore the transcriptome of biological systems. However, the amount of biological material from cell sorting or laser capture microdissection is much too small to perform microarray studies. To address this issue, RNA amplification methods have been developed to generate sufficient targets from picogram amounts of total RNA to perform microarray hybridisation.

Results

In this study, four commercial protocols for amplification of picograms amounts of input RNA for microarray expression profiling were evaluated and compared. The quantitative and qualitative performances of the methods were assessed. Microarrays were hybridised with the amplified targets and the amplification protocols were compared with respect to the quality of expression profiles, reproducibility within a concentration range of input RNA, and sensitivity. The results demonstrate significant differences between these four methods.

Conclusion

In our hands, the WT-Ovation pico system proposed by Nugen appears to be the most suitable for RNA amplification. This comparative study will be useful to scientists needing to choose an amplification method to carry out microarray experiments involving samples comprising only a few cells and generating picogram amounts of RNA.

Atomic force microscope observation of branching in single transcript molecules derived from human cardiac muscle

We have used an atomic force microscope to examine a clinically derived sample of single-molecule gene transcripts, in the form of double-stranded cDNA, (c: complementary) obtained from human cardiac muscle without the use of polymerase chain reaction (PCR) amplification. We observed a log-normal distribution of transcript sizes, with most molecules being in the range of 0.4-7.0 kilobase pairs (kb) or 130-2300 nm in contour length, in accordance with the expected distribution of mRNA (m: messenger) sizes in mammalian cells. We observed novel branching structures not previously known to exist in cDNA, and which could have profound negative effects on traditional analysis of cDNA samples through cloning, PCR and DNA sequencing.

Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells

Background

Phenotypically identical cells demonstrate predictable, robust behaviours. However, there is uncertainty as to whether phenotypically identical cells are equally similar at the underlying transcriptional level or if cellular systems are inherently noisy. To answer this question, it is essential to distinguish between technical noise and true variation in transcript levels. A critical issue is the contribution of sampling effects, introduced by the requirement to globally amplify the single cell mRNA population, to observed measurements of relative transcript abundance.

Results

We used single cell microarray data to develop simple mathematical models, ran Monte Carlo simulations of the impact of technical and sampling effects on single cell expression data, and compared these with experimental microarray data generated from single embryonic neural stem cells in vivo. We show that the actual distribution of measured gene expression ratios for pairs of neural stem cells is much broader than that predicted from our sampling effect model.

Conclusion

Our results confirm that significant differences in gene expression levels exist between phenotypically identical cells in vivo, and that these differences exceed any noise contribution from global mRNA amplification.

Validation of oligoarrays for quantitative exploration of the transcriptome

BACKGROUND

Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5.105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE.

RESULTS

A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8.105 (oligoarrays), 1.1.105 (MPSS) and 7.6.104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1.106 (oligoarrays), 2.8.105 (MPSS) and 3.8.105 (SAGE).

CONCLUSION

The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5.105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.

Single molecule transcription profiling with AFM

Established techniques for global gene expression profiling, such as microarrays, face fundamental sensitivity constraints. Due to greatly increasing interest in examining minute samples from micro-dissected tissues, including single cells, unorthodox approaches, including molecular nanotechnologies, are being explored in this application. Here, we examine the use of single molecule, ordered restriction mapping, combined with AFM, to measure gene transcription levels from very low abundance samples. We frame the problem mathematically, using coding theory, and present an analysis of the critical error sources that may serve as a guide to designing future studies. We follow with experiments detailing the construction of high density, single molecule, ordered restriction maps from plasmids and from cDNA molecules, using two different enzymes, a result not previously reported. We discuss these results in the context of our calculations.

Gene expression in epithelial ovarian cancer: a study of intratumor heterogeneity

The aim of this study was to investigate the intratumor heterogeneity of gene expression profiles in epithelial ovarian cancer (EOC). This was done to evaluate whether sampling of a single macrodissected tissue sample from each EOC case would bias the data and result in, eg, prognostic studies based on gene expression microarray experiments. From nine EOCs removed at Odense University Hospital, Denmark, three tumor samples of 200-300 mg each were taken with greatest possible mutual distance. The samples were immediately flash frozen. A parallel section was taken for histopathologic comparison. RNA was extracted from the tissue samples. Five micrograms of each RNA sample was used for labeling. The fragmented biotin-labeled complementary RNA was hybridized to Affymetrix GeneChip Human Genome U133 plus 2.0 arrays, and scanning was performed on the GeneArray scanner 3000 (Affymetrix, Santa Clara, CA). Data were evaluated using hierarchical clustering and intraclass correlation coefficient (ICC) from reliability analysis. All evaluation methods revealed low intratumor heterogeneity. Intratumor ICCs ranged from 0.888 to 0.978. In contrast, "between-tumor" ICC was 0.549 indicating much lower intra- than intertumor heterogeneity. Due to a low degree of intratumor variation, we conclude that it is sufficiently accurate in a clinical setup to use single, macrodissected tumor samples in the evaluation of gene expression in EOCs.

Electrophysiological and gene expression profiling of neuronal cell types in mammalian neocortex

It is a challenging question to understand how different neuronal types are organized into a complex architecture in the cortex, an architecture which is also adapted in different regions to subserve very different functions. Recent developments in genetic and molecular techniques have opened up the possibility of using gene expression profiling for neuronal cell typing, with the aim of uncovering novel cell types and the underlying mechanisms which generate and maintain neuronal heterogeneity in the cortex. This review introduces some current ideas about neuronal cell types in the cortex and describes recent approaches to expression profiling for defining cortical neuronal cell types.

Sensitivity enhancement of DNA microarray on nano-scale controlled surface by using a streptavidin-fluorophore conjugate

High throughput analysis of DNA in low concentration and small volume is an important issue and a continuing challenge in the field of DNA microarray and sensor. Recently, we have demonstrated that the DNA microarray on nano-scale controlled surface provides ample space for hybridization resulting in the best discrimination efficiency for SNP analysis. Here, we report the utility of the nano-scale controlled surface in conjunction with a multiply tagged protein. Application of streptavidin-fluorophore conjugates in combination with the highly controlled surface that suppresses non-specific binding of DNA allows highly sensitive detection of DNA while maintaining superior SNP discrimination efficiency comparable to our earlier results. The sensitivity of DNA microarray on the mesospaced surface is two orders of magnitude higher than that of the generic surface when a streptavidin-fluorophore conjugate was employed, and the detection limit on the former surface was found to be 50 fM of 15-mer target DNA. Various streptavidin-fluorophore conjugates including streptavidin-Cy3, streptavidin-Cy5, streptavidin-Alexa Flour 555 and streptavidin-phycoerythrin were examined.

Options available for profiling small samples: a review of sample amplification technology when combined with microarray profiling

The possibility of performing microarray analysis on limited material has been demonstrated in a number of publications. In this review we approach the technical aspects of mRNA amplification and several important implicit consequences, for both linear and exponential procedures. Amplification efficiencies clearly allow profiling of extremely small samples. The conservation of transcript abundance is the most important issue regarding the use of sample amplification in combination with microarray analysis, and this aspect has generally been found to be acceptable, although demonstrated to decrease in highly diluted samples. The fact that variability and discrepancies in microarray profiles increase with minute sample sizes has been clearly documented, but for many studies this does appear to have affected the biological conclusions. We suggest that this is due to the data analysis approach applied, and the consequence is the chance of presenting misleading results. We discuss the issue of amplification sensitivity limits in the light of reports on fidelity, published data from reviewed articles and data analysis approaches. These are important considerations to be reflected in the design of future studies and when evaluating biological conclusions from published microarray studies based on extremely low input RNA quantities.