ArticleBMC genomics2005
Limitations of mRNA amplification from small-size cell samples.
Article in BMC genomics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- LINCATRA: Two-cycle method to amplify RNA for transcriptome analysis from formalin-fixed paraffin-embedded tissue.Heliyon · 2024Article
- Selective gene expression analysis of the neuroepithelial body microenvironment in postnatal lungs with special interest for potential stem cell characteristics.Respiratory research · 2017Article
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- Identifying individual DNA species in a complex mixture by precisely measuring the spacing between nicking restriction enzymes with atomic force microscope.Journal of the Royal Society, Interface · 2012Article
- Global array-based transcriptomics from minimal input RNA utilising an optimal RNA isolation process combined with SPIA cDNA probes.PloS one · 2011Article
- Microgenomic analysis in skeletal muscle: expression signatures of individual fast and slow myofibers.PloS one · 2011Article
- Identification of genes and pathways associated with cytotoxic T lymphocyte infiltration of serous ovarian cancer.British journal of cancer · 2010Article
- Microarray analysis at single-molecule resolution.IEEE transactions on nanobioscience · 2010Article
- An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data.PLoS computational biology · 2009Article
- Murine spinal cord transcriptome analysis following reduction of prevalent myelin cDNA sequences.Cellular and molecular neurobiology · 2009Article
- Evaluation of methods for amplification of picogram amounts of total RNA for whole genome expression profiling.BMC genomics · 2009Article
- Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells.BMC genomics · 2008Article
- Validation of oligoarrays for quantitative exploration of the transcriptome.BMC genomics · 2008Article
- Single molecule transcription profiling with AFM.Nanotechnology · 2007Article
- Electrophysiological and gene expression profiling of neuronal cell types in mammalian neocortex.The Journal of physiology · 2006Review
- Options available for profiling small samples: a review of sample amplification technology when combined with microarray profiling.Nucleic acids research · 2006Review
- Potential role of atomic force microscopy in systems biology.Wiley interdisciplinary reviews. Systems biology and medicineReview
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlobal mRNA amplification has become a widely used approach to obtain gene expression profiles from limited material. An important concern is the reliable reflection of the starting material in the results obtained. This is especially important with extremely low quantities of input RNA where stochastic effects due to template dilution may be present. This aspect remains under-documented in the literature, as quantitative measures of data reliability are most often lacking. To address this issue, we examined the sensitivity levels of each transcript in 3 different cell sample sizes. ANOVA analysis was used to estimate the overall effects of reduced input RNA in our experimental design. In order to estimate the validity of decreasing sample sizes, we examined the sensitivity levels of each transcript by applying a novel model-based method, TransCount.
resultsFrom expression data, TransCount provided estimates of absolute transcript concentrations in each examined sample. The results from TransCount were used to calculate the Pearson correlation coefficient between transcript concentrations for different sample sizes. The correlations were clearly transcript copy number dependent. A critical level was observed where stochastic fluctuations became significant. The analysis allowed us to pinpoint the gene specific number of transcript templates that defined the limit of reliability with respect to number of cells from that particular source. In the sample amplifying from 1000 cells, transcripts expressed with at least 121 transcripts/cell were statistically reliable and for 250 cells, the limit was 1806 transcripts/cell. Above these thresholds, correlation between our data sets was at acceptable values for reliable interpretation.
conclusionThese results imply that the reliability of any amplification experiment must be validated empirically to justify that any gene exists in sufficient quantity in the input material. This finding has important implications for any experiment where only extremely small samples such as single cell analyses or laser captured microdissected cells are available.
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