Journal: Nature Communications

Article Title: The non-coding RNA landscape of human hematopoiesis and leukemia

doi: 10.1038/s41467-017-00212-4

Figure Lengend Snippet: Microarray-based profiling of the ncRNA landscape in normal hematopoiesis. a Blood cell populations purified by multicolor flow cytometry from different healthy individuals: hematopoietic stem cells ( HSCs ), common myeloid progenitor cells ( CMPs ), granulocyte-monocyte progenitor cells ( GMPs ), megakaryocytes ( MEGA ), erythroid precursors ( ERY ), granulocytes ( GRAN ), monocytes ( MONO ), CD4 and CD8 T-cells, NK cells ( NKC ), and B-cells. Cytospins were prepared after sorting. For HSCs, CMPs, and GMPs representative cytospins from in vitro expanded CB HSPCs are depicted. b Annotation, distribution, and functional classes of the Arraystar Human lncRNA Microarray V2.0 probes according to the indicated databases. c Overlapping features between the Arraystar Human lncRNA Microarray V2.0 and the NCode Human Long Non-coding RNA microarray. d Annotation, distribution, and functional classes of the NCode Human Long Non-coding RNA microarray probes. e Box plots of log 2 -probe intensities for mRNAs ( n = 978,840), ncRNAs ( n = 784,530), and lincRNAs ( n = 90,855) from all NCode Human Long Non-coding RNA microarrays. P -values were calculated using the two-tailed Welsh’s t -test

Article Snippet: All depicted data refer to the Arraystar Human lncRNA Microarray V2.0 platform. a – c t -SNE of all samples using the most variable a mRNAs (3926), b ncRNAs (3151), and c lincRNAs (767). d Self-organizing maps ( SOMs ) trained using the 17,655 most variable mRNAs and ncRNAs in 11 sample groups.

Techniques: Microarray, Purification, Flow Cytometry, In Vitro, Functional Assay, Two Tailed Test

Journal: Nature Communications

Article Title: The non-coding RNA landscape of human hematopoiesis and leukemia

doi: 10.1038/s41467-017-00212-4

Figure Lengend Snippet: Distinct ncRNA expression profiles characterize cells of the different human blood lineages. All depicted data refer to the Arraystar Human lncRNA Microarray V2.0 platform. a – c t -SNE of all samples using the most variable a mRNAs (3926), b ncRNAs (3151), and c lincRNAs (767). d Self-organizing maps ( SOMs ) trained using the 17,655 most variable mRNAs and ncRNAs in 11 sample groups. Black rectangles : group-specific overexpression spots. Center : neighbor-joining tree built using the 68 lineage-specific spot metagenes. e Heatmaps of ( left ) 2493 fingerprint ncRNAs and ( right ) 581 anti-fingerprint ncRNAs, defined by integrating SOM and limma analyses. f Guilt-by-association workflow for the fingerprint/anti-fingerprint ncRNAs and all protein-coding genes. g Enrichment map network analysis for HOTAIRM1 (FDR < 0.05, see the methods section for details). Circle size corresponds to the size of the gene set, and connecting line thickness represents the degree of similarity between two gene sets. Red and blue nodes indicate positive and negative correlation to HOTAIRM1 expression, respectively. Gene set labels printed in bold indicate a similar association (FDR < 0.05) observed in at least one AML validation cohort

Article Snippet: All depicted data refer to the Arraystar Human lncRNA Microarray V2.0 platform. a – c t -SNE of all samples using the most variable a mRNAs (3926), b ncRNAs (3151), and c lincRNAs (767). d Self-organizing maps ( SOMs ) trained using the 17,655 most variable mRNAs and ncRNAs in 11 sample groups.

Techniques: Expressing, Microarray, Over Expression, Biomarker Discovery

Journal: Nature Communications

Article Title: The non-coding RNA landscape of human hematopoiesis and leukemia

doi: 10.1038/s41467-017-00212-4

Figure Lengend Snippet: LINC00173 is a granulocyte-specific lincRNA. a Averaged expression ( top ) and heatmap of granulocyte fingerprint ncRNAs (top 30 without pseudogenes) which show increasing expression from HSCs to CMPs to GMPs. b RNA-seq of human myelopoiesis: PCA on the 1373 most variable ncRNAs in the data set. The arrow indicates the main trajectory of myeloid maturation. Bl/PM blasts/promyelocytes, MM metamyelocytes, PMN polymorphonuclear neutrophils. c SOM representation of RNA-seq data set revealing three spots of co-regulated metagenes (modules), whose expression properties are depicted in the bar charts below. d – f LINC00173 expression normalized to granulocytes as measured by d the Arraystar Human lncRNA Microarray V2.0 ( n = 3–5 per data point), e qRT-PCR ( n = 3), and f RNA-Seq ( n = 2–4). Error bars indicate ± s.e.m. g The LINC00173 gene locus depicting the array probe and alternative isoforms (according to ENSEMBL GRCh38.p5), together with UCSC genome browser tracks ( http://genome.ucsc.edu ; assembly : GRCh38/hg38) of RNA-Seq and ChIP-seq data (BLUEPRINT) , CAGE-Seq Signals (FANTOM5) , and sequence conservation (GERP-elements) in mature human neutrophils. h Guilt-by-association results for LINC00173 . Circle size corresponds to the size of the gene set, and connecting line thickness represents the degree of similarity between two gene sets. Red and blue nodes indicate positive and negative correlation to LINC00173 expression, respectively

Article Snippet: All depicted data refer to the Arraystar Human lncRNA Microarray V2.0 platform. a – c t -SNE of all samples using the most variable a mRNAs (3926), b ncRNAs (3151), and c lincRNAs (767). d Self-organizing maps ( SOMs ) trained using the 17,655 most variable mRNAs and ncRNAs in 11 sample groups.

Techniques: Expressing, RNA Sequencing, Microarray, Quantitative RT-PCR, ChIP-sequencing, Sequencing

Journal: Nature Communications

Article Title: The non-coding RNA landscape of human hematopoiesis and leukemia

doi: 10.1038/s41467-017-00212-4

Figure Lengend Snippet: ncRNAs of the DLK1-DIO3 locus control human megakaryopoiesis. a t -SNE of all samples using 240 variance-filtered miRNAs from the NCode Human miRNA Microarray V3 platform. b Heat map of 174 cell-type-specific fingerprint miRNAs. c , d Genome-wide view of log 2 -FC for c miRNAs (NCode Human miRNA Microarray V3) and d ncRNAs (NCode Human Long Non-coding RNA microarray) in megakaryocytes compared to all other cell types. Red dots : log 2 -FC ≥ 3. The heatmaps show qRT-PCR validation results of z -score transformed 2 ΔΔCt values. e Schematic of the imprinted DLK1-DIO3 locus on human chromosome 14. Below are UCSC genome browser tracks (GRCh38/hg38) of ChIP-seq and RNA-Seq data in CD41 + megakaryocytic cells . f Lentiviral expression of miR-770, miR-136, miR-379, and miR-410 ( n = 4) and g RNAi (shRNA)-mediated knockdown of DLK1, MEG3, MEG8 , and MEG9 ( n = 5) in CD34 + HSPCs in vitro. Both plots show the percentage of CD41 + /CD42b + cells. f , g Data are presented as mean ± s.e.m. * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001; ns not significant; P -values were calculated using one-way ANOVA with Dunnett’s post hoc test

Article Snippet: All depicted data refer to the Arraystar Human lncRNA Microarray V2.0 platform. a – c t -SNE of all samples using the most variable a mRNAs (3926), b ncRNAs (3151), and c lincRNAs (767). d Self-organizing maps ( SOMs ) trained using the 17,655 most variable mRNAs and ncRNAs in 11 sample groups.

Techniques: Control, Microarray, Genome Wide, Quantitative RT-PCR, Biomarker Discovery, Transformation Assay, ChIP-sequencing, RNA Sequencing, Expressing, shRNA, Knockdown, In Vitro

Journal: Journal of Experimental & Clinical Cancer Research : CR

Article Title: The expression profile of microRNAs in a model of 7,12-dimethyl-benz[ a ]anthrance-induced oral carcinogenesis in Syrian hamster

doi: 10.1186/1756-9966-28-64

Figure Lengend Snippet: Experimental variation and reproducibility assessment from twelve microarray hybridizations in six different samples . Scatter diagram showing high reproducibility between the replicate experiments of every sample. The R-value in each microarray analysis showing that most of the average correlations are well above 0.9, indicating high reproducibility. Panel A~C: self-hybridization results obtained after probing the microarray with the same RNA sample prepared from three normal tissues and labeled separately with Cy3 dye. Panel D~F: self-hybridization results obtained after probing the microarray with the same RNA sample prepared from three cancer tissues and labeled separately with Cy3 dye.

Article Snippet: Our miRNA microarray detection platform was constructed by CapitalBio, and several previous comparative studies between microarray platforms and analysis procedures had indicated the very high sensitivity, reproducibility, and specificity using their recommended methods [ ].

Techniques: Microarray, Hybridization, Labeling

Journal: Journal of Experimental & Clinical Cancer Research : CR

Article Title: The expression profile of microRNAs in a model of 7,12-dimethyl-benz[ a ]anthrance-induced oral carcinogenesis in Syrian hamster

doi: 10.1186/1756-9966-28-64

Figure Lengend Snippet: Supervised hierarchical clustering analysis of miRNA expression . 17 miRNAs expression profile (from SAM result) of 6 samples were clustered using Cluster 3.0. 6 samples were successfully separated into 2 discrete groups.

Article Snippet: Our miRNA microarray detection platform was constructed by CapitalBio, and several previous comparative studies between microarray platforms and analysis procedures had indicated the very high sensitivity, reproducibility, and specificity using their recommended methods [ ].

Techniques: Expressing

Journal: Journal of Experimental & Clinical Cancer Research : CR

Article Title: The expression profile of microRNAs in a model of 7,12-dimethyl-benz[ a ]anthrance-induced oral carcinogenesis in Syrian hamster

doi: 10.1186/1756-9966-28-64

Figure Lengend Snippet: MicroRNAs microarray SAM results and correlation with cancer

Article Snippet: Our miRNA microarray detection platform was constructed by CapitalBio, and several previous comparative studies between microarray platforms and analysis procedures had indicated the very high sensitivity, reproducibility, and specificity using their recommended methods [ ].

Techniques: Microarray

Journal: Toxicological Sciences

Article Title: Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

doi: 10.1093/toxsci/kfx241

Figure Lengend Snippet: Neurogenic Transcription Factors that Are Potentially Targeted by the miR-466/669 Cluster

Article Snippet: The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform {"type":"entrez-geo","attrs":{"text":"GPL21312","term_id":"21312"}} GPL21312 ).

Techniques:

Journal: Toxicological Sciences

Article Title: Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

doi: 10.1093/toxsci/kfx241

Figure Lengend Snippet: The miR-466-669 cluster share sequence similarities. Sequences of mature miRNA from the miR-466-669 cluster were aligned using Clustal Omega. All mature sequences were initially aligned together and then were subdivided into 4 major groups which have high similarity. The highlighted nucleotides were used to derive the 4 consensus sequences (as light blue). An asterisk indicates the sequence identity among all miRNAs within the group.

Article Snippet: The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform {"type":"entrez-geo","attrs":{"text":"GPL21312","term_id":"21312"}} GPL21312 ).

Techniques: Sequencing

Journal: Toxicological Sciences

Article Title: Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

doi: 10.1093/toxsci/kfx241

Figure Lengend Snippet: Expression profiles of microRNAs (miRNAs) between control and arsenic exposure in differentiating P19 cells. P19 cells were induced to differentiate with or without 0.5 µM of arsenic for 9 days. MicroRNA expression was detected via a miRNA microarray and plotted as a heat map using CIM Miner (NIH). Darker shading indicates increased expression. Only statistically different miRNAs are listed in the map (Student’s t test; P value < .05).

Article Snippet: The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform {"type":"entrez-geo","attrs":{"text":"GPL21312","term_id":"21312"}} GPL21312 ).

Techniques: Expressing, Control, Microarray

Journal: Toxicological Sciences

Article Title: Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

doi: 10.1093/toxsci/kfx241

Figure Lengend Snippet: Validation of miRNA transcript levels by qPCR. Day 9 differentiated P19 cells were used to confirm miRNA expression by qPCR (n = 3 per treatment). Significantly changed miRNAs with known roles in development were examined, including miR-92a (A), miR-291a (B), miR-709 (C), miR-199a (D), and miR-9 (E). Expression values were normalized with U6 snRNA and fold differences calculated from control cells using the delta Ct method. Values are expressed as mean ± SD and statistical differences were determined by Student’s t test (*P < .05).

Article Snippet: The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform {"type":"entrez-geo","attrs":{"text":"GPL21312","term_id":"21312"}} GPL21312 ).

Techniques: Biomarker Discovery, Expressing, Control

Journal: Toxicological Sciences

Article Title: Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

doi: 10.1093/toxsci/kfx241

Figure Lengend Snippet: Arsenic exposure induces members of the miR-466-669 cluster along with its host gene, Sfmbt2. P19 cells were differentiated and RNA was extracted from cells exposed to 0 or 0.5 μM arsenite on days 0, 2, 5, and 9 (n = 3 per treatment per day). MicroRNA or mRNA expression was determined by qPCR. Day 9 samples were used to determine the expression of miRNA-466-669 cluster genes (A) and the host gene Sfmbt2 (B). Samples from days 0, 2, 5, to 9 were used to determine the expression of miR-467d (C), miR-669p (D), and Sfmbt2 (E). Expression values were normalized with U6 snRNA for the miRNAs, and Gapdh for Sfmbt2. Fold changes were compared with unexposed cells, and time-dependent qPCR expression fold changes were compared with day 0 unexposed cells. Data are shown as mean ± SD. Statistical differences were determined by ANOVA followed by Tukey’s test or by Student’s t test (*P < .05).

Article Snippet: The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform {"type":"entrez-geo","attrs":{"text":"GPL21312","term_id":"21312"}} GPL21312 ).

Techniques: Expressing

Journal: Toxicological Sciences

Article Title: Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

doi: 10.1093/toxsci/kfx241

Figure Lengend Snippet: Inhibiting the miR-466-669 cluster during differentiation rescues the morphological loss of neurons following arsenic exposure. P19 cells were transfected with 100 nM anti-miRNA oligonucleotides which target 4 consensus sequences of the miRNA-466-467-669 cluster. Cells were coexposed to 0 or 0.5 μM arsenic for the 5 days of embryoid body formation. Only the arsenic exposure was maintained for the entire 9 days of differentiation, after which cell morphology was observed. Transfections include oligonucleotides sequences that do not target any miRNAs, designated as negative control, (N.C.), and a mixed transfection that combined all consensus anti-miRNAs. Arrows indicate neuronal cells (A). The distance of cells differentiating away from the embryoid body was quantitated using ImageJ and is expressed in mm (n = 6 replicate embryoid bodies per group) (B). mRNA levels of the neuronal cell marker NeuroD1 on day 9 was assessed by qPCR (C). mRNA expression levels were normalized with Gapdh using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#).

Article Snippet: The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform {"type":"entrez-geo","attrs":{"text":"GPL21312","term_id":"21312"}} GPL21312 ).

Techniques: Transfection, Negative Control, Marker, Expressing

Journal: Toxicological Sciences

Article Title: Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

doi: 10.1093/toxsci/kfx241

Figure Lengend Snippet: Consensus anti-miRNAs are active and functional and can rescue the expression of miR-466-467-669 target genes. P19 cells were transfected with all 4 miRNA inhibitors, with or without 0.5 μM arsenic (n = 3 replicates), allowed to form embryoid bodies for 5 days, and examined for mRNA expression of each of the 4 consensus sequences (A–D), 1 individual miRNA, miR-669a-3p (E), the host gene Sfmbt2 (F), and a known target gene for the consensus 1 cluster, Lats2 (G). mRNA expression levels were normalized with Gapdh, and miRNA expression levels were normalized with shRNA U6, using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#) in (A–F). A 1-way ANOVA followed by Tukey’s test (P < .05) was run to determine significance (#) in (G).

Article Snippet: The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform {"type":"entrez-geo","attrs":{"text":"GPL21312","term_id":"21312"}} GPL21312 ).

Techniques: Functional Assay, Expressing, Transfection, shRNA

Journal: Toxicological Sciences

Article Title: Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

doi: 10.1093/toxsci/kfx241

Figure Lengend Snippet: Mixed consensus miRNA inhibitors rescue arsenic’s inhibitory effects on NeuroD1 expression. P19 cells were transfected with a combined mixture of the 4 anti-miRNAs, with or without 0.5 μM arsenic (n = 3 replicates per anti-miRNA and per exposure group), and allowed to form embryoid bodies for 5 days. Immunohistochemistry was used to examine expression of NeuroD1 protein (red) in the embryoid bodies. Cells were counterstained with DAPI (blue) to indicate the nuclei (A). High magnification images of cells are shown in the (A) inserts. For 10 representative cells (examples are shown in the blue boxes), expression of NeuroD1 in the whole cell, cytoplasm, and nuclei were quantified using ImageJ (B). NeuroD1 transcript levels were quantified by qPCR, and normalized with Gapdh using the comparative delta Ct method with fold changes compared with N.C. (B). Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#).

Article Snippet: The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform {"type":"entrez-geo","attrs":{"text":"GPL21312","term_id":"21312"}} GPL21312 ).

Techniques: Expressing, Transfection, Immunohistochemistry

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Differentially expressed in blood and biopsy by microarrays and RNA-seq using statistical confidence levels of FDR <10% and p <0.005 and p< 0.05

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: Microarray

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Correlation between the blood findings and the biopsy findings comparing the two analytical methodologies (microarrays and RNA-seq). The similarity between the technologies was also reflected in the consistently higher number of differentially expressed genes in the biopsy compared to the blood. The M (log ratios) and A (average) scale (MA) plots for all comparisons are shown in Figures 1a for RNAseq (NGS) and b for the microarrays.

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: RNA Sequencing Assay

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Classifiers and predictive performance

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: Microarray

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Shared and differentially expressed genes blood and biopsy by microarrays and RNA-seq using statistical confidence levels of FDR <10%, p <0.005 and p <0.05

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: Microarray

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Comparison of the directionality of fold-changes among the shared genes between blood and biopsies by microarrays and RNA-seq.

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: Microarray

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Scatter plots of the fold changes for cAR vs TX (x-axes) and subAR vs. TX (y-axes) to demonstrate that that cAR fold changes are of greater magnitude than subAR changes. 3a, Microarrays - Blood 3b, RNA-seq - Blood 3c, Microarray - Biopsies 3d RNA-seq - Biopsies. Green dots denote greater cAR vs TX fold changes and blue dots denote greater subAR vs. TX fold changes. The table shows the number of genes in each comparison and the overlapping genes that were plotted to create figures 3a-d.

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: RNA Sequencing Assay, Microarray

Journal: Biology Open

Article Title: Effects of β4 integrin expression on microRNA patterns in breast cancer

doi: 10.1242/bio.20121628

Figure Lengend Snippet: ( A ) qNPA microarray was performed in triplicate on MCF10CA1a siCtrl cells and MCF10CA1a siβ4 cells at 72 hours post-transfection. The heat map depicts the 44 miRNAs undergoing a statistically significant change in expression following transient depletion of β4 subunit in this system. ( B ) qNPA microarray was performed in triplicate on two subclones of the MDA-MB-435/β4 transfectants (3A7 and 5B3), and two subclones of the MDA-MB-435/mock transfectants (6D2 and 6D7). The heat map depicts the 50 miRNAs undergoing a statistically significant change in expression following introduction of the β4 subunit into this system. ( C ) qNPA microarray was performed in triplicate on ten β4 positive and ten β4 negative invasive breast carcinomas. The heat map depicts the 74 miRNAs differentially expressed between tumor subsets. For all array analyses, a p-value < 0.05 and a ±1.2-fold change cut-off was applied. Color was assigned to each miRNA based on relative expression across samples.

Article Snippet: A novel qNPA based miRNA Microarray high throughput platform from High Throughput Genomics (HTG Molecular Diagnostics, Inc.; Tuscon, AZ, USA) was used to study 1050 mature miRNAs in human, rat, and mouse based upon the Sanger miRBase release 9.1.

Techniques: Microarray, Transfection, Expressing

Journal: Biology Open

Article Title: Effects of β4 integrin expression on microRNA patterns in breast cancer

doi: 10.1242/bio.20121628

Figure Lengend Snippet: ( A ) Venn diagram of overlapping miRNAs that undergo differential expression in response to β4 across all three arrays. ( B ) Venn diagram of overlapping miRNA families that undergo differential expression in response to β4 across all three arrays.

Article Snippet: A novel qNPA based miRNA Microarray high throughput platform from High Throughput Genomics (HTG Molecular Diagnostics, Inc.; Tuscon, AZ, USA) was used to study 1050 mature miRNAs in human, rat, and mouse based upon the Sanger miRBase release 9.1.

Techniques: Quantitative Proteomics

Journal: Biology Open

Article Title: Effects of β4 integrin expression on microRNA patterns in breast cancer

doi: 10.1242/bio.20121628

Figure Lengend Snippet: GeneChip derived mRNA levels were ranked from the most upregulated in β4 transfected cells to the most downregulated (x-axis, 1 to 12,300, respectively). Red shading indicates mRNA is upregulated in β4 transfectants, while blue shading indicates mRNA is downregulated. Each vertical black line represents a miRNA target. The left-to-right position of each black line indicates the relative position of the predicted target within the rank ordered mRNA list. ( A ) miR-92ab predicted target gene are enriched among mRNAs up-regulated in the β4 transfectants, as illustrated by the increasing number of black lines on the left side of each graphic and the positive running enrichment scores (ES) marked by the red lines (p = 0.028). No enrichment was detected for and miR-99ab/100. ( B ) miR-15abc/16/16abc/195/322/424/497/1907 (p = 0.039), miR-23abc/23b-3p (p = 0/034), miR-27abc/27a-3p (p = 0.003), and miR-30abcdef/30abe-5p/384-5p (p = 0.0) predicted target genes are enriched among mRNAs up-regulated in the β4 transfectants.

Article Snippet: A novel qNPA based miRNA Microarray high throughput platform from High Throughput Genomics (HTG Molecular Diagnostics, Inc.; Tuscon, AZ, USA) was used to study 1050 mature miRNAs in human, rat, and mouse based upon the Sanger miRBase release 9.1.

Techniques: Derivative Assay, Transfection