Journal: Mediators of Inflammation

Article Title: A Peptide Analogue of Selectin Ligands Attenuated Atherosclerosis by Inhibiting Monocyte Activation

doi: 10.1155/2019/8709583

Figure Lengend Snippet: IELLQAR decreases PLCA-induced atherosclerosis and impaired Ly-6C high monocyte recruitment to the arterial walls of ApoE −/− mice. Eight-week-old male ApoE-deficient mice randomized into four treatment groups, sham group, normal saline (NS), and two IELLQAR treatment groups (1 and 3 mg/kg), and fed a normal chow diet for 4 weeks. In PLCA surgery, three (left external carotid, internal carotid, and occipital artery) of the four branches of ApoE −/− mice were ligated, and only the superior thyroid artery was left to provide blood circulation. (a) Representative histological analysis results of the carotid artery following H&E staining ( n ≥ 5). Scale bar: 50 μ m. (b) Flow cytometric dot plots showing the percentage of CD45 + CD11b + Ly-6C high monocytes in circulation. (c) Representative histological analysis results of the carotid artery following immunofluorescence staining with Ly-6C ( n ≥ 5). Scale bar: 50 μ m and 25 μ m, respectively. (d) The mRNA expression levels of MCP-1, CCR2, and ICAM-1 in carotid artery tissue were measured by qRT-PCR ( n ≥ 5). Data are presented as the mean ± SEM; ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001.

Article Snippet: Primer sequences for quantitative real-time polymerase chain reaction (qRT-PCR) analysis were designed and purchased from GenePharma Co. Ltd. (Shanghai, China).

Techniques: Staining, Immunofluorescence, Expressing, Quantitative RT-PCR

Journal: Mediators of Inflammation

Article Title: A Peptide Analogue of Selectin Ligands Attenuated Atherosclerosis by Inhibiting Monocyte Activation

doi: 10.1155/2019/8709583

Figure Lengend Snippet: IELLQAR inhibited monocyte activation by P-selectin-dependent activation of the NF- κ B pathways. (a) PBMCs were treated with P-selectin (10 μ g/mL) for different times. Representative Western blots showing the expression levels of total and phosphorylated I κ B α and P65. (b) PBMCs were treated with IELLQAR (1, 3, or 10 μ M) and coincubated with or without 10 μ g/mL of P-selectin for 30 min. Representative Western blots showing the expression levels of total and phosphorylated I κ B α and P65. (c) PBMCs were treated with PBS, LPS (1 μ g/ml), P-selectin, IELLQAR (10 μ M) plus P-selectin, or IELLQAR plus LPS for 2 h, and the mRNA expression levels of IL-6, TNF- α , and MCP-1 were measured by qRT-PCR. Data are presented as the mean ± SEM ( n = 3). ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001.

Article Snippet: Primer sequences for quantitative real-time polymerase chain reaction (qRT-PCR) analysis were designed and purchased from GenePharma Co. Ltd. (Shanghai, China).

Techniques: Activation Assay, Western Blot, Expressing, Quantitative RT-PCR

Journal: BMC Genomics

Article Title: RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice

doi: 10.1186/s12864-015-1527-z

Figure Lengend Snippet: Comparing RNA-Seq and Agilent microarrays for detecting genes expressed in the liver and differentially expressed by TCDD. (A) RNA-Seq reads were aligned to mouse genome GRCm38 (release 74) and subsampled to represent 3–5 independent biological replicates. The number of differentially expressed genes (DEGs) was determined under varying |fold change| and P1( t ) criteria. (B) Microarray features were examined for DEGs under varying |fold change| and P1( t ) criteria. (C) RNA-Seq and microarray detected genes (yellow boxes) were examined for common and unique detected genes and DEGs. (D) Distribution of log 2 (fold change) and (E) P1( t ) values (P1 ( t ) ≥ 0) in RNA-Seq (blue) and Agilent (pink) datasets.

Article Snippet: Additional file 7: WaferGen SmartChip QRTPCR analysis of false-positive RNA-Seq genes.

Techniques: RNA Sequencing Assay, Microarray

Journal: BMC Genomics

Article Title: RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice

doi: 10.1186/s12864-015-1527-z

Figure Lengend Snippet: Effect of the number of independent biological replicates on RNA-Seq analysis. (A) DEGs were identified using a |fold change| ≥ 2.0 and P1( t ) ≥ 0.8 when examining 5 independent biological replicates or a subset of 3 or 4 replicates and compared for identified genes. Correlation of gene expression fold changes and P1( t ) values are illustrated comparing (B) 3 and 4, (C) 3 and 5, and (D) 4 and 5 biological replicates.

Article Snippet: Additional file 7: WaferGen SmartChip QRTPCR analysis of false-positive RNA-Seq genes.

Techniques: RNA Sequencing Assay, Expressing

Journal: BMC Genomics

Article Title: RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice

doi: 10.1186/s12864-015-1527-z

Figure Lengend Snippet: Comparison of RNA-Seq and Agilent DEGs. (A) Overlapping, RNA-Seq-specific and Agilent-specific DEGs were identified. Correlation of fold changes and P1( t ) values were examined for (B) all 12,165 expressed genes detected by both platforms, (C) the union of 1,722 differentially expressed genes (DEGs), and (D) the 449 DEGs detected by both platforms.

Article Snippet: Additional file 7: WaferGen SmartChip QRTPCR analysis of false-positive RNA-Seq genes.

Techniques: RNA Sequencing Assay

Journal: BMC Genomics

Article Title: RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice

doi: 10.1186/s12864-015-1527-z

Figure Lengend Snippet: Verification of RNA-Seq and Agilent DEG identification by WaferGen SmartChip Real-Time PCR analysis. (A) QRTPCR data (n = 5) was used as the “gold standard” to determine true and false positives and negatives for RNA-Seq (n = 3) and Agilent (n = 3) datasets. (B) Performance metrics of RNA-Seq and Agilent validated by QRTPCR. (C) Representative example of a false-negative and (D) false-positive response in the RNA-Seq dataset. Official gene symbols are indicated in upper left corner with the number of RNA-Seq aligned reads in parentheses () and number of samples with C t values lower than background in brackets [] for vehicle control samples. Bars represent mean fold-change determined by WaferGen technology (±SEM), the red line represents RNASeq fold-change, and the green line represents Agilent fold change. Significant differences within WaferGen data were determined by one-way ANOVA followed by Dunnett’s post-hoc test and indicated by an asterisk (*) with the exception of Fam83a whose undetectable levels prevented statistical testing. Red (RNA-Seq) and green (Agilent) dots represent P1 ( t ) values with size indicating level of significance (small ~0.8, large ~1). Labels on the X-axis indicate the dose of TCDD (μg/kg), number of aligned RNA-Seq reads, and number of samples with C t values lower than background. Dashed lines indicate 1.5 and 2.0 |fold-change| thresholds to identify DEGs. (E) UCSC genome browser track illustrating PCR primer and Agilent probe alignments for Zfp846. The Zfp846 loci is presented in blue with exons (closed boxes) and introns (solid line). The arrowheads indicate the direction of transcription.

Article Snippet: Additional file 7: WaferGen SmartChip QRTPCR analysis of false-positive RNA-Seq genes.

Techniques: RNA Sequencing Assay, Real-time Polymerase Chain Reaction

Journal: BMC Genomics

Article Title: RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice

doi: 10.1186/s12864-015-1527-z

Figure Lengend Snippet: Comparison of RNA-Seq and Agilent dose–response modeling. (A) The ToxResponse modeler was used to identify genes fitting a sigmoidal response for the estimation of ED 50 s while (B) BMDExpress was used to find the best fit curves for point of departure (BMD and BMDL) estimates. Correlation of (C) ED 50 s and (D) BMD (blue circles) and BMDL (red circles) estimates were examined for all comparable genes.

Article Snippet: Additional file 7: WaferGen SmartChip QRTPCR analysis of false-positive RNA-Seq genes.

Techniques: RNA Sequencing Assay

Journal: BMC Research Notes

Article Title: A novel mean-centering method for normalizing microRNA expression from high-throughput RT-qPCR data

doi: 10.1186/1756-0500-4-555

Figure Lengend Snippet: Effect of normalization method on variation of miRNA expression . Each point represents the mean standard deviation from all miRNAs (All miRNAs; n = 377) or the restricted set of miRNAs (Restricted miRNAs; n = 19) on the TaqMan array, but calculated separately across all samples within a given group (Sample Origin). The restricted set of miRNAs is the core set of miRNAs detected across all samples in all titrations. Note that all data were normalized together, and this is most important for methods that share information across samples. NormFinder was parameterized to use the sample origin for grouping. GeNorm, NormFinder, and CCR results are based on the selection of two miRNAs as normalizers.

Article Snippet: Normalization of singleplex miRNA RT-qPCR data from solid tissue samples has been thoroughly evaluated at Asuragen [ ] and elsewhere [ - ].

Techniques: Expressing, Standard Deviation, Selection

Journal: BMC Research Notes

Article Title: A novel mean-centering method for normalizing microRNA expression from high-throughput RT-qPCR data

doi: 10.1186/1756-0500-4-555

Figure Lengend Snippet: Variance explained by sample origin . Bars show the percent variance explained by sample origin (tissue type) based on weighting results from a univariate random effect model using the eigenvalues from principal component analysis (PCA). We used the first three principal components and their corresponding eigenvalues for weighting (See reference for more information). In general, MCR normalization tends to reveal more of the biological differences between samples, and shows nominal improvement over other miRNA (gene)-specific normalization methods.

Article Snippet: Normalization of singleplex miRNA RT-qPCR data from solid tissue samples has been thoroughly evaluated at Asuragen [ ] and elsewhere [ - ].

Techniques:

Journal: Stem Cell Reports

Article Title: Retinal Ganglion Cell Diversity and Subtype Specification from Human Pluripotent Stem Cells

doi: 10.1016/j.stemcr.2018.02.010

Figure Lengend Snippet: hPSCs Give Rise to Multiple DS-RGC Subtypes (A and B) ON-OFF DS-RGCs were identified by the co-expression of the RGC marker BRN3 with either (A) CART or (B) CDH6. (C) ON DS-RGCs were identified by the expression of FSTL4 co-localized with BRN3. (D) Quantification of immunocytochemistry results identified the expression of CART, CDH6, and FSTL4 within the BRN3-RGC population at 25.95% ± 0.46%, 17.16% ± 0.51%, and 30.49% ± 0.58%, respectively. (E) Single-cell qRT-PCR analysis demonstrated the combinatorial expression of DS-RGC markers, in conjunction with RGC-associated markers. Error bars represent the SEM (n = 36 technical replicates from 3 biological replicates for each bar using miPS2, H9, and H7 cell lines). Scale bars, 20 μm.

Article Snippet: The Ct values from the Biomark qRT-PCR analysis were sorted based on the expression of BRN3B and their expression levels were further constructed into heatmaps of specific RGC subtype classes using GraphPad Prism software.

Techniques: Expressing, Marker, Immunocytochemistry, Quantitative RT-PCR

Journal: Stem Cell Reports

Article Title: Retinal Ganglion Cell Diversity and Subtype Specification from Human Pluripotent Stem Cells

doi: 10.1016/j.stemcr.2018.02.010

Figure Lengend Snippet: Identification of α-RGCs in hPSC-Derived RGCs (A and B) α-RGCs were identified by the co-localization of BRN3 with either (A) SPP1 or (B) CB2. (C) Quantification of immunocytochemistry results indicated SPP1 and CB2 were co-expressed with BRN3 in 21.10% ± 0.42% and 15.72% ± 0.45% of all cells, respectively. (D) Single-cell qRT-PCR analyses demonstrated the combinatorial expression of α-RGC markers, along with the expression of pan-RGC markers. Error bars represent the SEM (n = 36 technical replicates from 3 biological replicates for each bar using miPS2, H9, and H7 cell lines). Scale bars, 30 μm.

Article Snippet: The Ct values from the Biomark qRT-PCR analysis were sorted based on the expression of BRN3B and their expression levels were further constructed into heatmaps of specific RGC subtype classes using GraphPad Prism software.

Techniques: Derivative Assay, Immunocytochemistry, Quantitative RT-PCR, Expressing

Journal: Stem Cell Reports

Article Title: Retinal Ganglion Cell Diversity and Subtype Specification from Human Pluripotent Stem Cells

doi: 10.1016/j.stemcr.2018.02.010

Figure Lengend Snippet: Characterization of Intrinsically Photosensitive RGCs Derived from hPSCs (A and B) A subset of hPSC-derived cells exhibited the expression of melanopsin co-expressing either (A) with or (B) without BRN3. (C) Quantification of immunocytochemistry results was performed as a percentage of the total DAPI-positive population and melanopsin-positive/BRN3-negative cells comprised 2.75% ± 0.13% of all cells, while melanopsin-positive/BRN3-positive comprised 1.17% ± 0.06% of all cells. (D) Single-cell qRT-PCR revealed the combinatorial expression of melanopsin with a variety of other RGC-related markers. Error bars represent the SEM (n = 36 technical replicates from 3 biological replicates for each bar using miPS2, H9, and H7 cell lines). Scale bar, 50 μm.

Article Snippet: The Ct values from the Biomark qRT-PCR analysis were sorted based on the expression of BRN3B and their expression levels were further constructed into heatmaps of specific RGC subtype classes using GraphPad Prism software.

Techniques: Derivative Assay, Expressing, Immunocytochemistry, Quantitative RT-PCR