Gene expression analysis of in vivo fluorescent cells

Background

The analysis of gene expression for tissue homogenates is of limited value because of the considerable cell heterogeneity in tissues. However, several methods are available to isolate a cell type of interest from a complex tissue, the most reliable one being Laser Microdissection (LMD). Cells may be distinguished by their morphology or by specific antigens, but the obligatory staining often results in RNA degradation. Alternatively, particular cell types can be detected in vivo by expression of fluorescent proteins from cell type-specific promoters.

Methodology/Principal Findings

We developed a technique for fixing in vivo fluorescence in brain cells and isolating them by LMD followed by an optimized RNA isolation procedure. RNA isolated from these cells was of equal quality as from unfixed frozen tissue, with clear 28S and 18S rRNA bands of a mass ratio of ∼2∶1. We confirmed the specificity of the amplified RNA from the microdissected fluorescent cells as well as its usefulness and reproducibility for microarray hybridization and quantitative real-time PCR (qRT-PCR).

Conclusions/Significance

Our technique guarantees the isolation of sufficient high quality RNA obtained from specific cell populations of the brain expressing soluble fluorescent marker, which is a critical prerequisite for subsequent gene expression studies by microarray analysis or qRT-PCR.     

Gene Expression and Functional Annotation of the Human Ciliary Body Epithelia

Purpose

The ciliary body (CB) of the human eye consists of the non-pigmented (NPE) and pigmented (PE) neuro-epithelia. We investigated the gene expression of NPE and PE, to shed light on the molecular mechanisms underlying the most important functions of the CB. We also developed molecular signatures for the NPE and PE and studied possible new clues for glaucoma.

Methods

We isolated NPE and PE cells from seven healthy human donor eyes using laser dissection microscopy. Next, we performed RNA isolation, amplification, labeling and hybridization against 44×k Agilent microarrays. For microarray conformations, we used a literature study, RT-PCRs, and immunohistochemical stainings. We analyzed the gene expression data with R and with the knowledge database Ingenuity.

Results

The gene expression profiles and functional annotations of the NPE and PE were highly similar. We found that the most important functionalities of the NPE and PE were related to developmental processes, neural nature of the tissue, endocrine and metabolic signaling, and immunological functions. In total 1576 genes differed statistically significantly between NPE and PE. From these genes, at least 3 were cell-specific for the NPE and 143 for the PE. Finally, we observed high expression in the (N)PE of 35 genes previously implicated in molecular mechanisms related to glaucoma.

Conclusion

Our gene expression analysis suggested that the NPE and PE of the CB were quite similar. Nonetheless, cell-type specific differences were found. The molecular machineries of the human NPE and PE are involved in a range of neuro-endocrinological, developmental and immunological functions, and perhaps glaucoma.     

Sets of RNA repeated tags and hybridization-sensitive fluorescent probes for distinct images of RNA in a living cell

Background

Imaging the behavior of RNA in a living cell is a powerful means for understanding RNA functions and acquiring spatiotemporal information in a single cell. For more distinct RNA imaging in a living cell, a more effective chemical method to fluorescently label RNA is now required. In addition, development of the technology labeling with different colors for different RNA would make it easier to analyze plural RNA strands expressing in a cell.

Methodology/Principal Findings

Tag technology for RNA imaging in a living cell has been developed based on the unique chemical functions of exciton-controlled hybridization-sensitive oligonucleotide (ECHO) probes. Repetitions of selected 18-nucleotide RNA tags were incorporated into the mRNA 3′-UTR. Pairs with complementary ECHO probes exhibited hybridization-sensitive fluorescence emission for the mRNA expressed in a living cell. The mRNA in a nucleus was detected clearly as fluorescent puncta, and the images of the expression of two mRNAs were obtained independently and simultaneously with two orthogonal tag–probe pairs.

Conclusions/Significance

A compact and repeated label has been developed for RNA imaging in a living cell, based on the photochemistry of ECHO probes. The pairs of an 18-nt RNA tag and the complementary ECHO probes are highly thermostable, sequence-specifically emissive, and orthogonal to each other. The nucleotide length necessary for one tag sequence is much shorter compared with conventional tag technologies, resulting in easy preparation of the tag sequences with a larger number of repeats for more distinct RNA imaging.     

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

Background

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.

Results

Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this “gold-standard” comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.

Conclusions

Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-649) contains supplementary material, which is available to authorized users.     

Highly parallel genome-wide expression analysis of single mammalian cells

Background

We have developed a high-throughput amplification method for generating robust gene expression profiles using single cell or low RNA inputs.

Methodology/Principal Findings

The method uses tagged priming and template-switching, resulting in the incorporation of universal PCR priming sites at both ends of the synthesized cDNA for global PCR amplification. Coupled with a whole-genome gene expression microarray platform, we routinely obtain expression correlation values of R²∼0.76–0.80 between individual cells and R²∼0.69 between 50 pg total RNA replicates. Expression profiles generated from single cells or 50 pg total RNA correlate well with that generated with higher input (1 ng total RNA) (R²∼0.80). Also, the assay is sufficiently sensitive to detect, in a single cell, approximately 63% of the number of genes detected with 1 ng input, with approximately 97% of the genes detected in the single-cell input also detected in the higher input.

Conclusions/Significance

In summary, our method facilitates whole-genome gene expression profiling in contexts where starting material is extremely limiting, particularly in areas such as the study of progenitor cells in early development and tumor stem cell biology.     

Expression Variation: Its Relevance to Emergence of Chronic Disease and to Therapy

Background

Stochastic fluctuations in the protein turnover underlie the random emergence of neural precursor cells from initially homogenous cell population. If stochastic alteration of the levels in signal transduction networks is sufficient to spontaneously alter a phenotype, can it cause a sporadic chronic disease as well – including cancer?

Methods

Expression in >80 disease-free tissue environments was measured using Affymetrix microarray platform comprising 54675 probe-sets. Steps were taken to suppress the technical noise inherent to microarray experiment. Next, the integrated expression and expression variability data were aligned with the mechanistic data covering major human chronic diseases.

Results

Measured as class average, variability of expression of disease associated genes measured in health was higher than variability of random genes for all chronic pathologies. Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes. Same held for magnitude of gene expression. The genes known to participate in multiple chronic disorders demonstrated the highest variability. Disease-related gene categories displayed on average more intricate regulation of biological function vs random reference, were enriched in adaptive and transient functions as well as positive feedback relationships.

Conclusions

A possible causative link can be suggested between normal (healthy) state gene expression variation and inception of major human pathologies, including cancer. Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization. The results of the study suggest the need to advance personalized therapy development.     

Clustered gene expression changes flank targeted gene loci in knockout mice

Background

Gene expression profiling using microarrays is a powerful technology widely used to study regulatory networks. Profiling of mRNA levels in mutant organisms has the potential to identify genes regulated by the mutated protein.

Methodology/Principle Findings

Using tissues from multiple lines of knockout mice we have examined genome-wide changes in gene expression. We report that a significant proportion of changed genes were found near the targeted gene.

Conclusions/Significance

The apparent clustering of these genes was explained by the presence of flanking DNA from the parental ES cell. We provide recommendations for the analysis and reporting of microarray data from knockout mice     

Systematic biases in DNA copy number originate from isolation procedures

Background

The ability to accurately detect DNA copy number variation in both a sensitive and quantitative manner is important in many research areas. However, genome-wide DNA copy number analyses are complicated by variations in detection signal.

Results

While GC content has been used to correct for this, here we show that coverage biases are tissue-specific and independent of the detection method as demonstrated by next-generation sequencing and array CGH. Moreover, we show that DNA isolation stringency affects the degree of equimolar coverage and that the observed biases coincide with chromatin characteristics like gene expression, genomic isochores, and replication timing.

Conclusion

These results indicate that chromatin organization is a main determinant for differential DNA retrieval. These findings are highly relevant for germline and somatic DNA copy number variation analyses.     

Possible Roles of mmu-miR-141 in the Endometrium of Mice in Early Pregnancy Following Embryo Implantation

Objective

Embryo implantation is directly affected by genes related to uterine receptivity. Studies have demonstrated the important roles of miRNAs in the regulation of gene expression. Our early miRNA chip analyses revealed that the mmu-miR-141 expression in endometrial tissue is lower after embryo implantation than before it. However, the possible roles of miR-141 in embryo implantation have not yet been elucidated. Here, mmu-miR-141 was designed to detect the expression and role of miR-141 in the endometria of mice in early pregnancy following embryo implantation.

Methods

Real-time PCR and in-situ hybridization were used to study mmu-miR-141 expression in mouse uterus. Cell proliferation was detected by tetrazolium dye (MTT) assay and flow cytometry. Real-time PCR and Western blot analysis were used to confirm the mRNA and protein levels of phosphatase and tensin homolog (PTEN) to determine whether it was the target gene of mmu-miR-141. Enhanced green fluorescent protein (EGFP) fluorescence reporter vector analysis was also performed. A functional study was performed by injecting mice uteri with mmu-miR-141 inhibitor or mimic vectors.

Results

mmu-miR-141 expression was lower on day 6 (D6) than day 4 (D4) and could be increased by progesterone. Reduced mmu-miR-141 could decrease the proliferation activity of stromal cells and promote apoptosis. Upregulation of mmu-miR-141 inhibited PTEN protein expression but downregulation of mmu-miR-141 increased it, while the mRNA level remained unchanged. EGFP fluorescence reporter vector analysis showed that miR-141 targets the 3′-untranslated region of the PTEN mRNA. In addition, when the physiological mmu-miR-141 level was altered on D2 by injecting with inhibitor or mimic, the embryo implantation sites were significantly decreased on D7.

Conclusions

This study demonstrated that mmu-miR-141 might influence cell proliferation and apoptosis in the endometrium by negatively regulating PTEN expression, and could also influence the number of embryo implantation sites. mmu-miR-141 plays an essential role in embryo implantation.     

Using GFP video to track 3D movement and conditional gene expression in free-moving flies

Background

In vivo imaging and quantification of fluorescent reporter molecules is increasingly useful in biomedical research. For example, tracking animal movement in 3D with simultaneous quantification of fluorescent transgenic reporters allows for correlations between behavior, aging and gene expression. However implementation has been hindered in the past by the complexity of operating the systems.

Results

We report significant technical improvements and user-friendly software (called FluoreScore) that enables tracking of 3D movement and the dynamics of gene expression in adult Drosophila, using two cameras and recorded GFP videos. Expression of a transgenic construct encoding eGFP was induced in free-moving adult flies using the Gene-Switch system and RU486 drug feeding. The time course of induction of eGFP expression was readily quantified from internal tissues including central nervous tissue.

Conclusions

FluoreScore should facilitate a variety of future studies involving quantification of movement behaviors and fluorescent molecules in free-moving animals.     

A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

Background

Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results.

Methods

To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor.

Results

This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients'' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status.

Conclusions

Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.     

Transcriptional Instability during Evolving Sepsis May Limit Biomarker Based Risk Stratification

Background

Sepsis causes extensive morbidity and mortality in children worldwide. Prompt recognition and timely treatment of sepsis is critical in reducing morbidity and mortality. Genomic approaches are used to discover novel pathways, therapeutic targets and biomarkers. These may facilitate diagnosis and risk stratification to tailor treatment strategies.

Objective

To investigate the temporal gene expression during the evolution of sepsis induced multi-organ failure in response to a single organism, Neisseria meningitidis, in previously healthy children.

Method

RNA was extracted from serial blood samples (6 time points over 48 hours from presentation) from five critically ill children with meningococcal sepsis. Extracted RNA was hybridized to Affymetrix arrays. The RNA underwent strict quality control and standardized quantitation. Gene expression results were analyzed using GeneSpring software and Ingenuity Pathway Analysis.

Result

A marked variability in differential gene expression was observed between time points and between patients revealing dynamic expression changes during the evolution of sepsis. While there was evidence of time-dependent changes in expected gene networks including those involving immune responses and inflammatory pathways, temporal variation was also evident in specific “biomarkers” that have been proposed for diagnostic and risk stratification functions. The extent and nature of this variability was not readily explained by clinical phenotype.

Conclusion

This is the first study of its kind detailing extensive expression changes in children during the evolution of sepsis. This highlights a limitation of static or single time point biomarker estimation. Serial estimations or more comprehensive network approaches may be required to optimize risk stratification in complex, time-critical conditions such as evolving sepsis.