An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE4309 | GEO | 2006/03/20
SECONDARY ACCESSION(S): PRJNA94845
REPOSITORIES: GEO
Similar Datasets
Project description:A systems-level understanding of a small but essential population of cells in development or adulthood (e.g. somatic stem cells) requires accurate quantitative monitoring of genome-wide gene expression, ideally from single cells. We report here a strategy to globally amplify mRNAs from single cells for highly quantitative high-density oligonucleotide microarray analysis that combines a small number of directional PCR cycles with subsequent linear amplification. Using this strategy, both the representation of gene expression profiles and reproducibility between individual experiments are unambiguously improved from the original method, along with high coverage and accuracy. The immediate application of this method to single cells in the undifferentiated inner cell masses of mouse blastocysts at embryonic day (E) 3.5 revealed the presence of two populations of cells, one with primitive endoderm (PE) expression and the other with pluripotent epiblast-like gene expression. The genes expressed differentially between these two populations were well preserved in morphologically differentiated PE and epiblast in the embryos one day later (E4.5), demonstrating that the method successfully detects subtle but essential differences in gene expression at the single-cell level among seemingly homogeneous cell populations. This study provides a strategy to analyze biophysical events in medicine as well as in neural, stem cell and developmental biology, where small numbers of distinctive or diseased cells play critical roles.
| S-EPMC1409679 | biostudies-literature
Project description:BACKGROUND:Data from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable. RESULTS:This paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if required. CONCLUSIONS:The performance of DBNorm is compared with z-score, average difference, quantile normalization and ComBat on a set of datasets, including several that are publically available. The performance of these normalization methods are compared using statistics, visualization, and classification when class labels are known based on a number of self-generated and public microarray datasets. The experimental results show that DBNorm achieves better normalization results than conventional methods. Finally, the approach has the potential to be applicable outside bioinformatics analysis.
| S-EPMC5706403 | biostudies-literature
Project description:This SuperSeries is composed of the following subset Series:; GSE4307: Expression data from single cells from ICMs of mouse blastocysts at E3.5; GSE4308: Expression data for validation of single cell cDNA amplification method (V1V3 method) Experiment Overall Design: Refer to individual Series
2008-06-13 | E-GEOD-4309 | biostudies-arrayexpress
Project description:A systems-level understanding of a small but essential population of cells in development or adulthood (e.g., somatic stem cells) requires accurate quantitative monitoring of genome-wide gene expression, ideally from single cells. We report here a strategy to globally amplify mRNAs from single cells for highly quantitative high-density oligonucleotide microarray analysis that combines a small number of directional PCR cycles with subsequent linear amplification. Using this strategy, both the representation of gene expression profiles and reproducibility between individual experiments are unambiguously improved from the original method, along with high coverage and accuracy. Keywords: Method validation
2006-04-26 | GSE4308 | GEO
Project description:In large-scale genome-wide association studies based on high-density single nucleotide polymorphism (SNP) genotyping array, the quantity and quality of available genomic DNA (gDNA) is a practical problem. We examined the feasibility of using the Multiple Displacement Amplification (MDA) method of whole-genome amplification (WGA) for such a platform. The Affymetrix Early Access Mendel Nsp 250K GeneChip was used for genotyping 224,940 SNPs per sample for 28 DNA samples. We compared the call concordance using 14 gDNA samples and their corresponding 14 WGA samples. The overall mean genotype call rates in gDNA and the corresponding WGA samples were comparable at 97.07% [95% confidence interval (CI), 96.17-97.97] versus 97.77% (95% CI, 97.26-98.28; P = 0.154), respectively. Reproducibility of the platform, calculated as concordance in duplicate samples, was 99.45%. Overall genotypes for 97.74% (95% CI, 97.03-98.44) of SNPs were concordant between gDNA and WGA samples. When the analysis was restricted to well-performing SNPs (successful genotyping in gDNA and WGA in >90% of samples), 99.11% (95% CI, 98.80-99.42) of the SNPs, on average, were concordant, and overall a SNP showed a discordant call in 0.92% (95% CI, 0.90-0.94) of paired samples. In a pair of gDNA and WGA DNA, similar concordance was reproducible on Illumina's Infinium 610 Quad platform as well. Although copy number analysis revealed a total of seven small telomeric regions in six chromosomes with loss of copy number, the estimated genome representation was 99.29%. In conclusion, our study confirms that high-density oligonucleotide array-based genotyping can yield reproducible data and MDA-WGA DNA products can be effectively used for genome-wide SNP genotyping analysis.
| S-EPMC2871542 | biostudies-literature
Project description:Analysis of single-cell gene expression promises a more precise understanding of molecular mechanisms of a living system. Most techniques only allow studies of the expressions for limited numbers of gene species. When amplification of cDNA was carried out for analysing more genes, amplification biases were frequently reported. A non-biased and efficient global-amplification method, which uses a single-cell cDNA library immobilized on beads, was developed for analysing entire gene expressions for single cells. Every step in this analysis from reverse transcription to cDNA amplification was optimized. By removing degrading excess primers, the bias due to the digestion of cDNA was prevented. Since the residual reagents, which affect the efficiency of each subsequent reaction, could be removed by washing beads, the conditions for uniform and maximized amplification of cDNAs were achieved. The differences in the amplification rates for randomly selected eight genes were within 1.5-folds, which could be negligible for most of the applications of single-cell analysis. The global amplification gives a large amount of amplified cDNA (>100 μg) from a single cell (2-pg mRNA), and that amount is enough for downstream analysis. The proposed global-amplification method was used to analyse transcript ratios of multiple cDNA targets (from several copies to several thousand copies) quantitatively.
| S-EPMC3902946 | biostudies-literature
Project description:Although DNA microarrays are powerful tools for profiling gene expression, the dynamic range and the sheer number of signals produced require efficient procedures for distinguishing false positive results (noise) from changes in expression that are 'real' (independently reproducible). We have developed an approach to filter noise from datasets generated when high density oligonucleotide-based microarrays are used to compare two distinct RNA populations. First, we performed comparisons between chips hybridized with cRNAs prepared from an identical starting RNA population; an 'Increase' or 'Decrease' call in such a comparison was defined as a false positive. Plotting the average distribution of these false positive signal intensities across 18 such comparisons of nine independent RNA preparations allowed us to develop a series of noise-filtering look-up tables (LUTs). Using a database of 70 separate chip-to-chip comparisons between distinct RNA preparations prepared by different workers at different sites and at different times, we show that the LUTs can be used to predict the likelihood that a given transcript called Increased or Decreased in one comparison will again be called Increased or Decreased in a replicate comparison. Evidence is presented that this LUT-based scoring system provides greater predictive value for reproducible microarray results than imposition of arbitrary fold-change thresholds and accurately predicts which microarray-identified changes will be validated by independent assays such as quantitative real-time PCR.
| S-EPMC55837 | biostudies-literature
Project description:BackgroundNormalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples.ResultsWe propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data.ConclusionsThe new normalization method is effective for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed.
| S-EPMC552315 | biostudies-literature
Project description:MotivationHigh-density DNA microarrays provide useful tools to analyze gene expression comprehensively. However, it is still difficult to obtain accurate expression levels from the observed microarray data because the signal intensity is affected by complicated factors involving probe-target hybridization, such as non-linear behavior of hybridization, non-specific hybridization, and folding of probe and target oligonucleotides. Various methods for microarray data analysis have been proposed to address this problem. In our previous report, we presented a benchmark analysis of probe-target hybridization using artificially synthesized oligonucleotides as targets, in which the effect of non-specific hybridization was negligible. The results showed that the preceding models explained the behavior of probe-target hybridization only within a narrow range of target concentrations. More accurate models are required for quantitative expression analysis.ResultsThe experiments showed that finiteness of both probe and target molecules should be considered to explain the hybridization behavior. In this article, we present an extension of the Langmuir model that reproduces the experimental results consistently. In this model, we introduced the effects of secondary structure formation, and dissociation of the probe-target duplex during washing after hybridization. The results will provide useful methods for the understanding and analysis of microarray experiments.AvailabilityThe method was implemented for the R software and can be downloaded from our website (http://www-shimizu.ist.osaka-u.ac.jp/shimizu_lab/FHarray/).
| S-EPMC2373920 | biostudies-literature
Project description:BACKGROUND: An algorithm for the analysis of Affymetrix Genechips is presented. This algorithm, referred to as the Inverse Langmuir Method (ILM), estimates the binding of transcripts to complementary probes using DNA/RNA hybridization free energies, and the hybridization between partially complementary transcripts in solution using RNA/RNA free energies. The balance between these two competing reactions allows for the translation of background-subtracted intensities into transcript concentrations. RESULTS: To validate the ILM, it is applied to publicly available microarray data from a multi-lab comparison study. Here, microarray experiments are performed on samples which deviate only in few genes. The log2 fold change between these two samples, as obtained from RT-PCR experiments, agrees well with the log2 fold change as obtained with the ILM, indicating that the ILM determines changes in the expression level accurately. We also show that the ILM allows for the identification of outlying probes, as it yields independent concentration estimates per probe. CONCLUSION: The ILM is robust and offers an interesting alternative to purely statistical algorithms for microarray data analysis.
| S-EPMC2661052 | biostudies-literature