Dataassist 2

Manufactured by Thermo Fisher Scientific

Sourced in Spain, United States

DataAssist 2.0 is a software application designed for data analysis and visualization of real-time PCR data. It provides tools for data processing, statistical analysis, and report generation.

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Lab products found in correlation

7900ht fast real time pcr system, by Thermo Fisher Scientific (3 mentions) 7500 fast system software 1, by Thermo Fisher Scientific (2 mentions) Taqman gene expression assay, by Thermo Fisher Scientific (1 mentions) Rq manager 1, by Thermo Fisher Scientific (1 mentions) Taqman rt qpcr assay, by Thermo Fisher Scientific (1 mentions)

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DataAssist (16 citations)

6 protocols using dataassist 2

Microarray Time Course Analysis

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Microarray data were evaluated using Extraction of Differential Gene Expression software version 1.0 (University of Washington, Seattle, WA, USA) as a time course study using a bootstrap method,^{12 (link)} with a false-discovery rate (FDR) cutoff of α < 0.05.¹³ The resulting data table was annotated using the annotation by Tsai et al.^{14 (link)} Graphs of single cell line time courses and principal component analysis (PCA) of comparisons between cell lines were run in JMP Genomics 4.1 (SAS Americas, Cary, NC, USA). Time course cluster graphs of each cell line were generated in Expander 5.1 (Tel Aviv University, Tel Aviv, Israel) using the Cluster identification via connectivity kernels (CLICK) analysis method, as this analysis does not make any prior assumptions about the underlying structure of the data,^{15 (link)} and clusters are numbered based on the size of the cluster. Individual gene expression changes were considered significant when fold change > |±2-fold| and α < 0.05.
PCR analyses were performed by the ΔΔC_t method (7500 Fast System software 1.4 and DataAssist 2.0; Applied Biosystems), and graphs were generated using JMP software 4.1 (SAS Americas). All data points are expressed as mean ± standard error (SE). Statistical difference was determined using a Student’s t test. Data were considered significantly different when P < 0.05.

Yang J., Menges S., Gu P., Tongbai R., Samuel M., Prather R.S, & Klassen H. (2017). Porcine Neural Progenitor Cells Derived from Tissue at Different Gestational Ages Can Be Distinguished by Global Transcriptome. Cell Transplantation, 26(9), 1582-1595.

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Gene Expression Analysis of Inflammation and Angiogenesis

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A custom gene expression low density array designed by Applied Biosystems (Life Technologies, Madrid, Spain) to characterize inflammation and angiogenesis was performed by quantitative PCR using TaqMan^® Gene Expression assays (Supplemental Table S1) using 200 ng of every sample following manufacturer’s instructions. TaqMan™ fluorescent real-time PCR primer used for tissue factor (TF) (Hs00175225_m1), VEGFA (Hs00900055_m1), miR126-3p (477887_miR), miR145-5p (477916_miR) and GAPDH (Hs99999905_m1), GUSB (Hs99999908_m1) and miR186-5p (477940_miR) (Applied Biosystems, Madrid, Spain) which were used as endogenous control. PCR data were analyzed with RQ Manager 1.2.1 and DataAssist 2.0 softwares (Applied Biosystems, Life Technologies, Madrid, Spain) against the endogens controls to obtain expression values for every gene (2-ΔCt).

Arderiu G., Lambert C., Ballesta C., Moscatiello F., Vilahur G, & Badimon L. (2020). Cardiovascular Risk Factors and Differential Transcriptomic Profile of the Subcutaneous and Visceral Adipose Tissue and Their Resident Stem Cells. Cells, 9(10), 2235.

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Quantifying SAA3 and VEGF Expression

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Expression of SAA3 (Oc03397898_m1) and VEGF (Oc03395999_m1) was evaluated by TaqMan quantitative real-time PCR assays (Applied Biosystems, Life Technologies, Grand Island, NY, USA) using RNA extracted from freshly frozen tissues. β-Actin (Oc03824857_g1) was used as the endogenous control to normalize target gene expression. Quantitative real-time PCR was performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems) and data were collected and analyzed using SDS 2.3 software. All assays were performed in triplicate. Gene expression was quantified by calculating δCt values, where Ct = threshold cycle and δCt = Ct of target gene − Ct of β-Actin. DataAssist 2.0 (Applied Biosystems) was used to analyze changes in expression.

Ito Y., Yoshida M., Maeda D., Takahashi M., Nanjo H., Masuda H, & Goto A. (2018). Neovasculature can be induced by patching an arterial graft into a vein: A novel in vivo model of spontaneous arteriovenous fistula formation. Scientific Reports, 8, 3156.

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Quantitative Analysis of ICD-Related Genes

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The expression of ICD-related genes, which were previously reported, were evaluated by real-time PCR^{17 (link)}. Expression of CDH2 (Hs00983053_m1), CTNNB1 (Hs00355045_m1), DSC2 (Hs00951428_m1), DSG2 (Hs00170071_m1), GJA1 (Hs00748445_s1), TRPV2 (Hs00901648_m1), and VCL (Hs00419715_m1) was evaluated by TaqMan quantitative real-time PCR assays (Thermo Fisher Scientific, Waltham, MA, USA) using RNA extracted from freshly frozen tissues. GAPDH (Hs02786624_g1) was used as an endogenous control to normalize target gene expression. Quantitative real-time PCR was performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Life Technologies, Grand Island, NY, USA), and data were collected and analyzed using the SDS 2.3 software. All assays were performed in triplicate. Gene expression was quantified by calculating ∆Ct values, where Ct = threshold cycle and ∆Ct = Ct of target gene – Ct of GAPDH. Changes in expression were analyzed using DataAssist 2.0 (Applied Biosystems).

Ito Y., Yoshida M., Masuda H., Maeda D., Kudo-Asabe Y., Umakoshi M., Nanjo H, & Goto A. (2021). Disorganization of intercalated discs in dilated cardiomyopathy. Scientific Reports, 11, 11852.

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Differential Gene Expression Analysis

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Microarray data was evaluated using Extraction of Differential Gene Expression (EDGE) software (University of Washington, Seattle, WA) as a time course study using a bootstrap method^{12 (link)} with an FDR cutoff of α<0.05¹³. The resulting data table was annotated using the annotation by Tsai et al.^{14 (link)}. Graphs of single cell-line time courses and Principal Component Analysis (PCA) of comparisons between cell-lines were run in JMP Genomics 4.1 (SAS Americas). Time course cluster graphs of each cell line were generated in Expander 5.1 using the CLICK-analysis method as this analysis does not make any prior assumptions about the underlying structure of the data^{15 (link)}, and clusters are numbered based on the size of the cluster. Individual gene expression changes were considered significant when fold change >|±2-fold| and α<0.05.
PCR analyses were performed by the ΔΔC_t method (7500 Fast system software 1.4 and DataAssist 2.0, Applied Biosystems, Irvine, USA), and graphs were generated using JMP software 4.1 (SAS Americas). All data points are expressed as mean ± Standard Error (SE). Statistical difference was determined using a Student’s t-test. Data were considered significantly different when p<0.05.

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Single-Cell Transcriptional Profiling of Cardiomyocytes and Progenitors

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cDNA synthesis from individual cell of additional single cardiomyocytes and mCPCs was performed using Cell-to-Ct protocol with pre-amplification option (Ambion). Cardiomyocyte-specific gene (Myh6) and putative progenitor and stem cell genes (c-kit and Sox2) together with two endogenous controls (Actb and Gapdh) were validated by TaqMan RT-qPCR assays (Applied Biosystems) at single-cell level. Moreover, a panel of genes for stem cell pluripotency and differentiation covered by TaqMan Low Density Array (TLDA, 96 genes including house-keeping genes such as Gapdh, Applied Biosystems) were profiled in a separated subset of single cells using our optimized streamline protocols including single-cell lysis, cDNA synthesis, and pre-amplification. Real-time qPCR was performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems) and data collected and analyzed using SDS 2.3 software suite. Ct values were normalized to endogenous controls, and comparative 2^−ΔΔCt method was used to evaluate the relative gene expression in mCPCs vs. cardiomyocytes9 (link). DataAssist 2.0 (Applied Biosystems) was used to analyze the expression changes. As for comparison, normalized intensity of probe(s) of specific genes was used to calculate gene expression fold changes detected by microarray.

Zhang Y., Zhong J.F., Qiu H., Robb MacLellan W., Marbán E, & Wang C. (2015). Epigenomic Reprogramming of Adult Cardiomyocyte-Derived Cardiac Progenitor Cells. Scientific Reports, 5, 17686.

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