Chum-RNA allows preparation of a high-quality cDNA library from a single-cell quantity of mRNA without PCR amplification

Linear RNA amplification using T7 RNA polymerase is useful in genome-wide analysis of gene expression using DNA microarrays, but exponential amplification using polymerase chain reaction (PCR) is still required for cDNA library preparation from single-cell quantities of RNA. We have designed a small RNA molecule called chum-RNA that has enabled us to prepare a single-cell cDNA library after four rounds of T7-based linear amplification, without using PCR amplification. Chum-RNA drove cDNA synthesis from only 0.49 femtograms of mRNA (730 mRNA molecules) as a substrate, a quantity that corresponds to a minor population of mRNA molecules in a single mammalian cell. Analysis of the independent cDNA clone of this library (6.6 × 10⁵ cfu) suggests that 30-fold RNA amplification occurred in each round of the amplification process. The size distribution and representation of mRNAs in the resulting one-cell cDNA library retained its similarity to that of the million-cell cDNA library. The use of chum-RNA might also facilitate reactions involving other DNA/RNA modifying enzymes whose Michaelis constant (K_m) values are around 1 mM, allowing them to be activated in the presence of only small quantities of substrate.     

Scalable transcriptome preparation for massive parallel sequencing

Background

The tremendous output of massive parallel sequencing technologies requires automated robust and scalable sample preparation methods to fully exploit the new sequence capacity.

Methodology

In this study, a method for automated library preparation of RNA prior to massively parallel sequencing is presented. The automated protocol uses precipitation onto carboxylic acid paramagnetic beads for purification and size selection of both RNA and DNA. The automated sample preparation was compared to the standard manual sample preparation.

Conclusion/Significance

The automated procedure was used to generate libraries for gene expression profiling on the Illumina HiSeq 2000 platform with the capacity of 12 samples per preparation with a significantly improved throughput compared to the standard manual preparation. The data analysis shows consistent gene expression profiles in terms of sensitivity and quantification of gene expression between the two library preparation methods.     

RNA Amplification in Brain Tissues

Recent developments in gene array technologies, specifically cDNA microarray platforms, have made it easier to try to understand the constellation of gene alterations that occur within the CNS. Unlike an organ that is comprised of one principal cell type, the brain contains a multiplicity of both neuronal (e.g., pyramidal neurons, interneurons, and others) and noneuronal (e.g., astrocytes, microglia, oligodendrocytes, and others) populations of cells. An emerging goal of modern molecular neuroscience is to sample gene expression from similar cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subtypes and noneuronal cells. At present, an optimal methodology to assess gene expression is to evaluate single cells, either identified physiologically in living preparations, or by immunocytochemical or histochemical procedures in fixed cells in vitro or in vivo. Unfortunately, the quantity of RNA harvested from a single cell is not sufficient for standard RNA extraction methods. Therefore, exponential polymerase-chain reaction (PCR) based analyses and linear RNA amplifications, including a newly developed terminal continuation (TC) RNA amplification methodology, have been used in combination with single cell microdissection procedures to enable the use of cDNA microarray analysis within individual populations of cells obtained from postmortem brain samples as well as the brains of animal models of neurodegeneration.     

Analysis of blood processing conditions to obtain high-quality total RNA from human leukocyte concentrate

Blood samples are used as a biological source to discover biomarkers of hematological and non-hematological disorders. The present study shows the impact of different experimental conditions associated with cell lysis buffer, TRI-reagent protocol and blood cell storage buffer and their correlation with the quantity, quality and Adrenomedullin gene expression levels of total RNA when RT-PCR technique is used. A leukocyte cell bank protocol is also proposed for further mRNA expression analysis using RNAlater as storage buffer. There is evidence that total RNA isolated from leukocyte concentrate stored for 1 month at -70 degrees C did not show significant differences concerning quality, purity and Adrenomedullin gene expression compared with the freshly processed leukocyte sample.     

Use of RNA and genomic DNA references for inferred comparisons in DNA microarray analyses

In most microarray assays, labeled cDNA molecules derived from reference and query RNA samples are co-hybridized to probes arrayed on a glass surface. Gene expression profiles are then calculated for each gene based on the relative hybridization intensities measured between the two samples. The most commonly used reference samples are typically isolates from a single representative RNA source (RNA-0) or pooled mixtures of RNA derived from a plurality of sources (RNA-p). Genomic DNA offers an alternative reference nucleic acid with a number of potential advantages, including stability, reproducibility, and a potentially uniform representation of all genes, as each unique gene should have equal representation in a haploid genome. Using hydrogen peroxide-treated Arabidopsis thaliana plants as a model, we evaluated genomic DNA and RNA-p as reference samples and compared expression levels inferred through the reference relative to unexposed plants with expression levels measured directly using an RNA-0 reference. Our analysis demonstrates that while genomic DNA can serve as a reasonable reference source for microarray assays, a much greater correlation with direct measurements can be achieved using an RNA-based reference sample.