Project description:Detect lncRNA/mRNA expression profiling for 10 human lung samples from NSCLC patients to elucidate the dysregulation of lncRNAs and mRNA in tumorigenesis
Project description:We have completed the Human lncRNA microarray analysis of the 4 samples that you submitted. Total RNA from each sample was quantified using the Nanodrop 1000 and the RNA integrity was assessed by Agilent 2100 Bioanalyzer. About 5 μg total RNA of each sample was used for labeling and array hybridization as the following steps: 1) Reverse transcription with invitrogen Superscript ds-cDNA synthesis kit; 2) ds-cDNA labeling with Nimblegen one-color DNA labeling kit; 3) Array hybridization using the NimbleGen Hybridization System and followed by washing with the Nimblegen wash buffer kit. 4) Array scanning using the Axon GenePix 4000B microarray scanner (Molecular Devices Corporation). Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and mRNA level (*_RMA.calls) files were generated after normalization. The 4 mRNA level files were imported into Agilent GeneSpring Software (version 11.0) for further analysis. lncRNAs and mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 (“All Targets Value”) were chosen for data analysis. Differentially expressed lncRNAs and mRNAs were identified through Fold Change filtering. Pathway Analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show distinguishable lncRNA and mRNA expression profiling among samples. Array Information: Human 4x44K lncRNA expression array information. Data Analysis for lncRNAs. Data Analysis for lncRNAs. Data Analysis for mRNAs. 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. 1. Raw lncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity lncRNAs were filtered. (lncRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis). 2. Quality assessment of lncRNA data after filtering: Contains Box Plot and Scatter Plot for lncRNAs after filtering. 3. Differentially expressed lncRNAs screening: Contains significant differentially expressed lncRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the lncRNAs after filtering. Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. 1. Raw lncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity lncRNAs were filtered. (lncRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis). 2. Quality assessment of lncRNA data after filtering: Contains Box Plot and Scatter Plot for lncRNAs after filtering. 3. Differentially expressed lncRNAs screening: Contains significant differentially expressed lncRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the lncRNAs after filtering. Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs.
Project description:We employed LncRNA/mRNA expression profiling as a discovery platform identify aberrantly expressed LncRNAs that fail to reverse back after hyperglycemia is terminated. Using rat model of diabetic retinopathy- metabolic memory, LncRNA array was performed in the retina of rats diabetic for eight months, or in poor glycemic control for four months followed by good glycemic control for four additional months. Expression of selected LncRNAs and their associated genes from these profiles were confirmed by quantitative real-time PCR.
Project description:By using the method of “small samples for high throughput screening + large samples for validation”, to find the risk-related long non-coding RNAs and encoding-genes in gastric cancer. To improve the level of clinical diagnosis and treatment of gastric cancer by studying the biological function and its molecular mechanism of these molecules
Project description:Background: Although chamber specialization is critical for proper cardiac function, a comprehensive, genome-wide analysis of the cardiac transcriptome, including identification of regional differences in mRNA and lncRNA expression patterns for the four chambers and interventricular septum of the non-failing human heart, has not been performed. Methods and Results: mRNA and long noncoding RNA (lncRNA) transcriptional profiling of the left (LA) and right (RA) atria, left (LV) and right (RV) ventricles, and the interventricular septum (IVS) of non-failing human hearts (N=8) was performed by deep sequencing. Analysis of the mRNA and lncRNA expression profiles revealed that the different regions of the heart are distinct. Differential expression analysis of paired tissue samples identified 5,747 mRNAs and 2,794 lncRNAs with chamber-enriched expression patterns. The largest differences in mRNA and lncRNA expression were evident between atria and ventricular samples, including regional differences in ~20% of all cardiac expressed mRNA and lncRNA transcripts. Regional differences in mRNA and lncRNA expression were also evident, although to a lesser extent, between the LA and RA, and between the LV, RV and IVS. Gene ontology classification of differentially expressed gene sets revealed regional differences in chamber specialization, including differences in signaling, metabolism, and muscle contraction. Sex differences in mRNA and lncRNA gene expression profiles were also identified between male and female LA and RA samples. Conclusions: There are marked regional differences in the mRNA and lncRNA expression profiles in non-failing adult human heart, and are associated with chamber specialization.
Project description:In microarray analysis, we have characterized the lncRNA and mRNA profiles in three advanced atherosclerosis samples and in three normal intima tissues. A total of 30,586 lncRNAs and 26,109 coding transcripts were detected by microarray analysis. The microarray profiling analysis not only provides new insights into the pathogenesis of atherosclerosis, but may also reveal new biomarkers for its diagnosis and treatment.