Transcriptomics analysis of LPS-induced changes in PMA-differentiated human THP-1 cells
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina HiSeq 4000
ORGANISM(S): Homo sapiens
SUBMITTER: Richard Kumaran Kandasamy
PROVIDER: E-MTAB-10513 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:(1) qPCR Gene Expression Data The THP-1 cell line was sub-cloned and one clone (#5) was selected for its ability to differentiate relatively homogeneously in response to phorbol 12-myristate-13-acetate (PMA) (Sigma). THP-1.5 was used for all subsequent experiments. THP-1.5 cells were cultured in RPMI, 10% FBS, Penicillin/Streptomycin, 10mM HEPES, 1mM Sodium Pyruvate, 50uM 2-Mercaptoethanol. THP-1.5 were treated with 30ng/ml PMA over a time-course of 96h. Total cell lysates were harvested in TRIzol reagent at 1, 2, 4, 6, 12, 24, 48, 72, 96 hours, including an undifferentiated control. Undifferentiated cells were harvested in TRIzol reagent at the beginning of the LPS time-course. One biological replicate was prepared for each time point. Total RNA was purified from TRIzol lysates according to manufacturerâs instructions. Genespecific primer pairs were designed using Primer3 software, with an optimal primer size of 20 bases, amplification size of 140bp, and annealing temperature of 60°C. Primer sequences were designed for 2,396 candidate genes including four potential controls: GAPDH, beta actin (ACTB), beta-2-microglobulin (B2M), phosphoglycerate kinase 1 (PGK1). The RNA samples were reverse transcribed to produce cDNA and then subjected to quantitative PCR using SYBR Green (Molecular Probes) using the ABI Prism 7900HT system (Applied Biosystems, Foster City, CA, USA) with a 384-well amplification plate; genes for each sample were assayed in triplicate. Reactions were carried out in 20μL volumes in 384-well plates; each reaction contained: 0.5 U of HotStar Taq DNA polymerase (Qiagen) and the manufacturerâs 1à amplification buffer adjusted to a final concentration of 1mM MgCl2, 160μM dNTPs, 1/38000 SYBR Green I (Molecular Probes), 7% DMSO, 0.4% ROX Reference Dye (Invitrogen), 300 nM of each primer (forward and reverse), and 2μL of 40-fold diluted first-strand cDNA synthesis reaction mixture (12.5ng total RNA equivalent). Polymerase activation at 95ºC for 15 min was followed by 40 cycles of 15 s at 94ºC, 30 s at 60ºC, and 30 s at 72ºC. The dissociation curve analysis, which evaluates each PCR product to be amplified from single cDNA, was carried out in accordance with the manufacturerâs protocol. Expression levels were reported as Ct values. The large number of genes assayed and the replicates measures required that samples be distributed across multiple amplification plates, with an average of twelve plates per sample. Because it was envisioned that GAPDH would serve as a single-gene normalization control, this gene was included on each plate. All primer pairs were replicated in triplicates. Raw qPCR expression measures were quantified using Applied Biosystems SDS software and reported as Ct values. The Ct value represents the number of cycles or rounds of amplification required for the fluorescence of a gene or primer pair to surpass an arbitrary threshold. The magnitude of the Ct value is inversely proportional to the expression level so that a gene expressed at a high level will have a low Ct value and vice versa. Replicate Ct values were combined by averaging, with additional quality control constraints imposed by a standard filtering method developed by the RIKEN group for the preprocessing of their qPCR data. Briefly this method entails: 1. Sort the triplicate Ct values in ascending order: Ct1, Ct2, Ct3. Calculate differences between consecutive Ct values: difference1 = Ct2 â Ct1 and difference2 = Ct3 â Ct2. 2. Four regions are defined (where Region4 overrides the other regions): Region1: difference ⦠0.2, Region2: 0.2 < difference ⦠1.0, Region3: 1.0 < difference, Region4: one of the Ct values in the difference calculation is 40 If difference1 and difference2 fall in the same region, then the three replicate Ct values are averaged to give a final representative measure. If difference1 and difference2 are in different regions, then the two replicate Ct values that are in the small number region are averaged instead. This particular filtering method is specific to the data set we used here and does not represent a part of the normalization procedure itself; Alternate methods of filtering can be applied if appropriate prior to normalization. Moreover while the presentation in this manuscript has used Ct values as an example, any measure of transcript abundance, including those corrected for primer efficiency can be used as input to our data-driven methods. (2) Quantile Normalization Algorithm Quantile normalization proceeds in two stages. First, if samples are distributed across multiple plates, normalization is applied to all of the genes assayed for each sample to remove plate-to-plate effects by enforcing the same quantile distribution on each plate. Then, an overall quantile normalization is applied between samples, assuring that each sample has the same distribution of expression values as all of the other samples to be compared. A similar approach using quantile ormalization has been previously described in the context of microarray normalization. Briefly, our method entails the following steps: i) qPCR data from a single RNA sample are stored in a matrix M of dimension k (maximum number of genes or primer pairs on a plate) rows by p (number of plates) columns. Plates with differing numbers of genes are made equivalent by padded plates with missing values to constrain M to a rectangular structure. ii) Each column is sorted into ascending order and stored in matrix Mâ. The sorted columns correspond to the quantile distribution of each plate. The missing values are placed at the end of each ordered column. All calculations in quantile normalization are performed on non-missing values. iii) The average quantile distribution is calculated by taking the average of each row in Mâ. Each column in Mâ is replaced by this average quantile distribution and rearranged to have the same ordering as the original row order in M. This gives the within-sample normalized data from one RNA sample. iv) Steps analogous to 1 â 3 are repeated for each sample. Between-sample normalization is performed by storing the within-normalized data as a new matrix N of dimension k (total number of genes, in our example k = 2,396) rows by n (number of samples) columns. Steps 2 and 3 are then applied to this matrix. (3) Rank-Invariant Set Normalization Algorithm We describe an extension of this method for use on qPCR data with any number of experimental conditions or samples in which we identify a set of stably-expressed genes from within the measured expression data and then use these to adjust expression between samples. Briefly, i) qPCR data from all samples are stored in matrix R of dimension g (total number of genes or primer pairs used for all plates) rows by s (total number of samples). ii) We first select gene sets that are rank-invariant across a single sample compared to a common reference. The reference may be chosen in a variety of ways, depending on the experimental design and aims of the experiment. As described in Tseng et al., the reference may be designated as a particular sample from the experiment (e.g. time zero in a time course experiment), the average or median of all samples, or selecting the sample which is closest to the average or median of all samples. Genes are considered to be rank-invariant if they retain their ordering or rank with respect to expression across the experimental sample versus the common reference sample. We collect sets of rank-invariant genes for all of the s pairwise comparisons, relative to a common reference. We take the intersection of all s sets to obtain the final set of rank-invariant genes that is used for normalization. iii) Let αj represent the average expression value of the rank-invariant genes in sample j. (α1, â¦, αs) then represents the vector of rank-invariant average expression values for all conditions 1 to s iv) We calculate the scale f The THP-1 cell line was sub-cloned and one clone (#5) was selected for its ability to differentiate relatively homogeneously in response to phorbol 12-myristate-13-acetate (PMA) (Sigma). THP-1.5 was used for all subsequent experiments. THP-1.5 cells were cultured in RPMI, 10% FBS, Penicillin/Streptomycin, 10mM HEPES, 1mM Sodium Pyruvate, 50uM 2-Mercaptoethanol. THP-1.5 were treated with 30ng/ml PMA over a time-course of 96h. Total cell lysates were harvested in TRIzol reagent at 1, 2, 4, 6, 12, 24, 48, 72, 96 hours, including an undifferentiated control. Total RNA was purifed from TRIzol lysates according to manufacturerâs instructions. The RNA samples were reverse transcribed to produce cDNA and then subjected to quantitative PCR using SYBR Green (Molecular Probes) using the ABI Prism 7900HT system (Applied Biosystems, Foster City, CA,USA) with a 384-well amplification plate; genes for each sample were assayed in triplicate.
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