Dataset Information

Genome-wide identification and analysis of microRNA expression in MEFs, ESCs, and iPSCs

ABSTRACT: Microarray analysis of miRNA—Two micrograms of total RNA was extended at the 3′ terminus with a poly(A) tail using poly(A) polymerase. An oligonucleotide tag was then ligated to the poly(A) tail for later fluorescent dye staining. Hybridization was performed overnight on a µParaflo microfluidic chip using a microcirculation pump (Atactic Technologies). On the microfluidic chip, each detection probe consisted of a chemically modified nucleotide coding segment complementary to the target microRNA (miRBase, http://mirbase.org) and a spacer segment of polyethylene glycol to extend the coding segment away from the substrate. The detection probes were made by in situ synthesis using photogenerated reagent chemistry. The hybridization melting temperatures were balanced by chemical modifications of the detection probes. Hybridization used 100 µl 6× SSPE buffer (0.90 M NaCl, 60 mM Na2HPO4, 6 mM EDTA, pH 6.8) containing 25% formamide at 34°C. After RNA hybridization, tag-conjugating Cy5 dye was circulated through the microfluidic chip for dye staining. Fluorescence images were collected by using a laser scanner (GenePix 4000B, Molecular Device) and digitized using Array-Pro image analysis software (Media Cybernetics). Data were analyzed by first subtracting the background and then normalizing the signals using a LOWESS filter (Locally-weighted Regression).

ORGANISM(S): Mus musculus

PROVIDER: GSE65596 | GEO | 2015/10/01

SECONDARY ACCESSION(S): PRJNA274448

REPOSITORIES: GEO

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Project description:Porphyromonas gingivalis is a pathogen in severe periodontal disease. Able to exploit an intracellular lifestyle within primary gingival epithelial cells (GECs), a reservoir of P. gingivalis can persist within the gingival epithelia. This process is facilitated by manipulation of the host cell signal transduction cascades which can impact cell cycle, cell death and cytokine responses. Using microarrays, we investigated the ability of P. gingivalis 33277 to regulate microRNA (miRNA) expression in GECs. One of several miRNAs differentially regulated by GECs in the presence of P. gingivalis was miR-203, which was upregulated 4-fold compared with uninfected controls. Differential regulation of miR-203 was confirmed by qRT-PCR. Putative targets of miR-203, suppressors of cytokine signaling (SOCS) 3 and 6, were evaluated by qRT-PCR. SOCS3 and SOCS6 mRNA levels were reduced >5-fold and >2-fold, respectively, in P. gingivalis-infected GECs compared with controls. Silencing miR-203 using a si-RNA construct reversed the inhibition of SOCS3 expression. A dual luciferase assay confirmed binding of miR-203 to the putative target binding site of SOCS3 3’ UTR. Western blot analysis demonstrated that activation of Stat3, a downstream target of SOCS, was diminished following miR-203 silencing. This study shows that induction of miRNAs by P. gingivalis can modulate important host signaling responses. MicroRNAs differentially expressed in human gingival epithelial cells in response to P. gingivalis infection were identified. P. gingivalis were reacted with primary gingival epithelial cells at an MOI of 100 for 6 hours at 37C in 5% CO2. Co-cultures were carried out in triplicate. The GECs were lysed with Trizol (Invitrogen) prior to RNA extraction and total RNA isolation. The microarray assay was performed using a service provider (LC Sciences). The assay started from 4 to 8 µg total RNA sample which was size fractionated using a YM-100 Microcon centrifugal filter (Millipore) and the small RNAs (< 300 nt) isolated were 3’-extended with a poly(A) tail using poly(A) polymerase. An oligonucleotide tag was then ligated to the poly(A) tail for later fluorescent dye staining. Hybridization was performed overnight on a µParaflo microfluidic chip using a micro-circulation pump (Atactic Technologies). The hybridization melting temperatures were balanced by chemical modifications of the detection probes. Hybridization used 100 uL 6xSSPE buffer (0.90 M NaCl, 60 mM Na2HPO4, 6 mM EDTA, pH 6.8) containing 25% formamide at 34 °C. After RNA hybridization, tag-conjugating Cy3 dyes were circulated through the microfluidic chip for dye staining. Fluorescence images were collected using a laser scanner (GenePix 4000B, Molecular Device) and digitized using Array-Pro image analysis software (Media Cybernetics). Microarray data were analyzed by first subtracting the background and then normalizing the signals using a LOWESS filter (Locally-weighted Regression). For a transcript to be listed as detectable it had a signal intensity higher than 3×(background standard deviation) and spot coefficient of variance (CV) < 0.5. CV was calculated by (standard deviation)/(signal intensity). When repeating probes are present on an array, a transcript is listed as detectable only if the signals from at least 50% of the repeating probes are above detection level. t-Test is performed between “control” and “test” sample groups. T-values are calculated for each miRNA, and p-values are computed from the theoretical t-distribution. miRNAs with p-values below a critical p-value (typically 0.01) are selected for cluster analysis. The clustering is done using hierarchical method and is performed with average linkage and Euclidean distance metric. This experiment includes a total of 6 samples which are divided in to 2 groups, each with 3 biological repeats: 3 untreated normal samples as controls and 3 infected samples as the treated condition.

Project description:Single-cell whole-transcriptome analysis is a powerful tool for quantifying gene expression heterogeneity in populations of cells. Many techniques have, thus, been recently developed to perform transcriptome sequencing (RNA-Seq) on individual cells. To probe subtle biological variation between samples with limiting amounts of RNA, more precise and sensitive methods are still required. We adapted a previously developed strategy for single-cell RNA-Seq that has shown promise for superior sensitivity and implemented the chemistry in a microfluidic platform for single-cell whole transcriptome analysis. In this approach, single cells are captured and lysed in a microfluidic device, where mRNAs with poly(A) tails are reverse-transcribed into cDNA. Double-stranded cDNA is then collected and sequenced using a next-generation sequencing platform. We prepared 94 libraries consisting of single mouse embryonic cells and technical replicates of extracted RNA and thoroughly characterized the performance of this technology. Microfluidic implementation increased mRNA detection sensitivity as well as improved measurement precision compared with tube-based protocols. With 0.2M reads per cell, we were able to reconstruct a majority of the bulk transcriptome with 10 single cells. We also quantified variation between and within different types of mouse embryonic cells and found that enhanced measurement precision, detection sensitivity, and experimental throughput aided the distinction between biological variability and technical noise. With this work, we validated the advantages of an early approach to single-cell RNA-Seq and showed that the benefits of combining microfluidic technology with high-throughput sequencing will be valuable for large-scale efforts in single-cell transcriptome analysis. We investigated gene expression in mouse embryonic cells using microfluidic-facilitated RNA-Seq to analyze 56 single mouse ES cell (mESC) transcriptomes and 6 single mouse embryonic fibroblast (MEF) transcriptomes. To quantitatively evaluate the sensitivity and precision of our technique, we also sequenced 23 libraries from extracted mESC RNA, representing three sets of technical replicates with varying starting amounts of material.

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Genome-wide identification and analysis of microRNA expression in MEFs, ESCs, and iPSCs

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