Effects of mRNA amplification on gene expression ratios in cDNA experiments estimated by analysis of variance

Abstract

Background: A limiting factor of cDNA microarray technology is the need for a substantial amount of RNA per labeling reaction. Thus, 20-200 micro-grams total RNA or 0.5-2 micro-grams poly (A) RNA is typically required for monitoring gene expression. In addition, gene expression profiles from large, heterogeneous cell populations provide complex patterns from which biological data for the target cells may be difficult to extract. In this study, we chose to investigate a widely used mRNA amplification protocol that allows gene expression studies to be performed on samples with limited starting material. We present a quantitative study of the variation and noise present in our data set obtained from experiments with either amplified or non-amplified material.

Evaluation of Global RNA Amplification and Its Use for High-Throughput Transcript Analysis of Laser-Microdissected Endosperm

“…The between-replicate reproducibility of the unamplified samples was high with a Pearson correlation of r = .95. The single-round IVT amplification gave an even higher between-replicate correlation ( r = .98), a phenomenon that has been reported by others [35, 36]. The extreme amplifications also showed high between-replicate correlations of r = .8 and r = .9 for PCR and 2IVT, respectively.…”

The Stage-specific Testicular Germ Cell Apoptotic Response to Low-dose X-irradiation and 2,5-hexanedione Combined Exposure. I

“…LCM isolation of cell populations is a highly manipulated process that generates additional questions, including whether amplification bias occurs and where bias originates (Caretti et al 2008; Duftner et al 2008; Lang et al 2009; Mazurek et al 2013; Nygaard et al 2003). Researchers have compared the use of pooled LCM RNA with different array platforms (Affymetrix U133 plus 2.0 vs. X3P arrays) and different amplification methods (NuGen vs. Arcturus) and have found minimal differences between the methods (Caretti et al 2008).…”

Statistical analysis of MPSS measurements: Application to the study of LPS-activated macrophage gene expression

“…Because a signature may resist sequencing by one or the other stepper protocol, the stepper with the largest count is most likely to be better suited for measuring that signature. Once the stepper is chosen for each signature, the values of the k independent sequencing replicates are combined to give an aggregate tpm 6 , where the i s and the Ns are the bead counts for the given signature i and the total number of sequenced beads in each MPSS run, respectively. If, for a given signature, ij ϭ 0, then the MPSS replicate j is excluded from both the numerator and the denominator.…”

“…), this transcription profiling method exhibits significant technology-dependent noise. Models that characterize this noise through the study of replicate measurements have been developed (3)(4)(5)(6)(7)(8)(9) and provide a measure of security against false discoveries. An alternative gene expression profiling method uses the sequencing of short sequence tags derived from the ends of messenger RNA.…”