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The MTA 1.0 is a laboratory instrument designed for the measurement of thermal analysis. It provides quantitative data on the thermal properties of materials, including their melting points, glass transition temperatures, and decomposition temperatures. The MTA 1.0 utilizes advanced thermal analysis techniques to deliver accurate and reliable results.

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5 protocols using mta 1

1

Microarray data analysis pipeline

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Microarray raw data was pre-processed using Robust Multi-array Averaging
(RMA)[17 (link)] and normalized using
quantile normalization. Probes were annotated using manufacturer’s
annotation files (Affymetrix MTA 1.0). Differential expression between
experimental groups was assessed using two-tailed t-test and false discovery
rate correction was performed using Benjamini-Hochberg method. Enrichment of
Gene Ontology terms was done using hypergeometric test and enrichment of
transcription factors was done using oPOSSUM [18 (link)].
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2

Microarray Analysis of Tissue Samples

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Total RNA was isolated using Trizol (Life Technologies) and purified using the RNeasy Mini Kit (QIAGEN). It was then assessed for degradation using an Advanced Analytical Agilent Fragment Analyzer. For each condition, the 3 best quality samples were selected for liver and muscle and 4 for heart and taken further for microarray analysis. Microarray analysis was performed using the Affymetrix MoGene 1.0ST for liver and quadriceps and the similar but slightly enhanced Affymetrix MTA 1.0 (an array with probes that target splice junctions that is also known as Clariom D) for heart. Microarray data were normalized using the rma function from the R Oligo package (Carvalho and Irizarry, 2010 (link)). Differential expression was performed using the limma package (Smyth, 2005 ).
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3

FFPE Tissue RNA Extraction and Expression Profiling

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RNA was extracted from 6 – 10 FFPE tissue sections using
QUICK RNA FFPE kit (Zymo Research). Expression profiling was performed using
Affymetrix Clariom D Pico Assay, mouse and analyzed using Transcriptome
Analysis Console as previously described. (Andrzejewski et al., 2017 (link); Nassal et al., 2017 (link)). Heatmaps were plotted in R using the
Robust Multi-array Average (RMA) procedure (Bolstad et al., 2003 (link); Carvalho
and Irizarry, 2010
; Irizarry et
al., 2003a
; Irizarry et al.,
2003b
). Expression values were normalized using Affymetrix mta10
annotation data and gene names were translated to MGI symbols. Heatmaps of
signature gene sets were extracted from a study of Brant et al. (Brant et al., 2017 (link)).
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4

FFPE Tissue RNA Extraction and Expression Profiling

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RNA was extracted from 6 – 10 FFPE tissue sections using
QUICK RNA FFPE kit (Zymo Research). Expression profiling was performed using
Affymetrix Clariom D Pico Assay, mouse and analyzed using Transcriptome
Analysis Console as previously described. (Andrzejewski et al., 2017 (link); Nassal et al., 2017 (link)). Heatmaps were plotted in R using the
Robust Multi-array Average (RMA) procedure (Bolstad et al., 2003 (link); Carvalho
and Irizarry, 2010
; Irizarry et
al., 2003a
; Irizarry et al.,
2003b
). Expression values were normalized using Affymetrix mta10
annotation data and gene names were translated to MGI symbols. Heatmaps of
signature gene sets were extracted from a study of Brant et al. (Brant et al., 2017 (link)).
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5

Mouse Transcriptome Profiling for Long Noncoding RNAs

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We used a mouse transcriptome assay (MTA 1.0; Affymetrix, Santa Clara, CA, USA) for digital lncRNA profiling using 500 ng total RNA extracted from each of the aforementioned murine ocular tissues. This microarray provides a whole-transcriptome expression data set, which contains approximately 600 million probes targeted against more than 23,000 protein-coding genes and more than 55,000 noncoding genes. RNA amplification, labeling, and array hybridization were performed according to the manufacturer's instructions. The hybridization signaling was detected using a commercial scanner (Affymetrix Gene-Chip Scanner 3000; Affymetrix), and probe cell intensity data were summarized with the default settings of commercial software (Command Console Software; Affymetrix). After performing a quality assessment procedure, raw microarray data were then normalized by the expression console. Only probes perfectly matched to one gene were retained, while probes targeting several transcripts were discarded. To filter for expressed genes, a threshold cutoff-point of 5.0 was selected, and values for each gene in all biologic samples below this cutoff-point were considered to be indicative of relevant protein-coding or ncRNA expression. Accordingly, only those gene expressions satisfying this criterion were taken as candidates for the following bioinformatics analysis.
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