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Toxicogenomics in Toxicologic Pathology
Published in Pritam S. Sahota, James A. Popp, Jerry F. Hardisty, Chirukandath Gopinath, Page R. Bouchard, Toxicologic Pathology, 2018
Arun R. Pandiri, David E. Malarkey, Mark J. Hoenerhoff
Several array platforms exist to evaluate large-scale changes in gene expression, and a more extensive technical description of various platforms is described elsewhere (Pandiri et al. 2011). The basic technology involves the concept that for every gene of interest, mRNA that is expressed represents a complementary copy of the DNA coding region of that gene, which when transcribed into cDNA, binds to complementary sequences of DNA from the coding region of its respective gene (Boorman et al. 2002a). Microarray platforms are created by attaching DNA sequences (probes) of hundreds to thousands of genes to solid strata, such as plastic, nylon, or glass. Once tissue samples are obtained, high quality RNA purification must be performed. The output of any microarray study is influenced by the quality of the RNA sample being analyzed, and the reliability of the data is directly proportional to the quality of the RNA (Pandiri et al. 2011). The quality of RNA for transcriptomic experiments in measured in terms of the RNA Integrity Number (RIN obtained from the Agilent bioanalyzer) and a RIN value of 7 and above is needed to obtain a good-quality transcriptomic data. Therefore, special care should be taken to properly and optimally collect the tissue samples, ensure proper handling and storage, and optimal RNA purification (Foley et al. 2006). Following isolation, RNA samples from each group being investigated (treatment vs control, for example) are reverse transcribed into cDNA, labeled with fluorescent markers, and allowed to competitively hybridize (bind) to the DNA sequences on the solid strata. Following hybridization, the samples are scanned and the fluorescence signal is quantified with image analysis software. The resulting strength of the fluorescent signal correlates with the relative expression of the transcript. While there are a number of platforms to evaluate gene expression changes, by far, the most common involves the use of high density synthetic oligonucleotide arrays (Figure 9.1a). Regardless of the technique used, one must realize that the output of microarray platforms is not free from technical problems and may be influenced by sample selection, RNA purification and processing, data analysis, and issues with gene annotation or gene location on the array.
Methods in molecular exercise physiology
Published in Adam P. Sharples, James P. Morton, Henning Wackerhage, Molecular Exercise Physiology, 2022
Adam P. Sharples, Daniel C. Turner, Stephen Roth, Robert A. Seaborne, Brendan Egan, Mark Viggars, Jonathan C. Jarvis, Daniel J. Owens, Jatin G. Burniston, Piotr P. Gorski, Claire E. Stewart
In the sections above, we focused on the isolation of RNA directly from skeletal muscle tissue to enable downstream analysis of mRNA expression. Specifically, the application of reverse transcription real-time quantitative polymerase chain reaction (rt-RT-qPCR) for assessing gene expression levels of a ‘target’ gene of interest. Targeted gene expression analysis is, and has been, very important in the field of molecular exercise physiology when determining changes in mRNA levels after exercise. While rt-RT-qPCR may be considered complex to the novice user, this method is well-recognised and a commonly used technique in the field of molecular biology. Indeed, it is now commonplace for laboratories to have their own thermal cycler machines. While RT-qPCR is relatively expensive (for PCR reagents and equipment) compared to traditional non-invasive physiology techniques, the cost is still affordable within moderately funded studies. A limitation to this technique, however, is that scientists are restricted to the number of individual genes that can be analysed at once. Given there are approximately 27,000 protein coding genes in the human genome, scientists may want to discover new genes or pathways associated with exercise, rather than focusing on well-known important regulators at the targeted gene level. This might be particularly important when analysing precious muscle biopsies from unique populations (e.g. elite/injured athletes, diseased/aged individuals or after complex exercise or nutritional interventions). With the advent of genome-wide analysis of transcription/gene expression (also known as ‘transcriptomics’) using microarrays or RNA-seq, scientists are now able to simultaneously assess the expression levels of larger numbers of genes (or even all genes) using comparable amounts of RNA used to investigate several genes via rt-RT-qPCR. However, these techniques are much more expensive making it less affordable for researchers to analyse the desired number of samples that are typically required for exercise studies, more so, if numerous time points after an acute bout of exercise or across a chronic exercise training intervention are to be explored. In Chapter 1, the emergence and historical narrative for the use of microarrays and RNA-seq technologies within molecular exercise physiology were discussed. Moreover, the use of microarray technology for genotyping/SNP and DNA methylation arrays has been discussed above. In terms of high-throughput sequencing techniques, similar methods described above for sequencing DNA are also used for the sequencing of RNA. As with rt-RT-qPCR, RNA must first undergo reverse transcription to produce cDNA before proceeding with downstream analysis. It is also fundamental that RNA has high integrity (how well conserved the RNA is) for microarray or RNA-seq. Therefore, as well as the standard ‘quality’ checks of RNA using UV spectrophotometry as described above, additional analyses of the integrity of the RNA are required using gel electrophoresis and analysing the ratios of 28S to 18S ribosomal bands (typically using an Agilent Bioanalyzer). This provides an RNA Integrity Number (RIN) between 1 and 10, with 10 being the highest quality samples with the least degradation.
Development of circulating microRNA-based biomarkers for medical decision-making: a friendly reminder of what should NOT be done
Published in Critical Reviews in Clinical Laboratory Sciences, 2023
Päivi Lakkisto, Louise Torp Dalgaard, Thalia Belmonte, Sara-Joan Pinto-Sietsma, Yvan Devaux, David de Gonzalo-Calvo
Fragment analyzers, such as Experion (Bio-Rad Laboratories, Hercules, CA, USA) or Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), constitute a useful tool to estimate size distribution of nucleic acid preparations using only minute amounts of sample in a controlled electrophoresis environment. The RNA integrity number (RIN) value developed by Agilent Technologies (Bioanalyzer) is based on the electrophoretic profile of RNA isolated from cells or tissue, which contain specific classes of RNA species of different lengths, such as 18 s and 28 s rRNA peaks, whose areas are used for the RIN algorithm [45]. Biofluids from plasma, serum, urine, or cerebrospinal fluid contain mostly small RNA species [46,47], such as miRNA, and only low amounts of 18 s rRNA and 28 s rRNA. Therefore, RNA samples from biofluids will have inherently low RIN values, and manual inspection of electropherograms is necessary.
Tumor-associated macrophages promote intratumoral conversion of conventional CD4+ T cells into regulatory T cells via PD-1 signalling
Published in OncoImmunology, 2022
Kevin Kos, Camilla Salvagno, Max D. Wellenstein, Muhammad A. Aslam, Denize A. Meijer, Cheei-Sing Hau, Kim Vrijland, Daphne Kaldenbach, Elisabeth A.M. Raeven, Martina Schmittnaegel, Carola H. Ries, Karin E. de Visser
For transcriptome analysis of Tregs from end-stage (225 mm2) KEP tumors, WT mammary gland and spleen, single-cell suspensions were prepared as described.8 A minimum of 70.000 Tregs (Live, CD45+, CD3+, CD4+, CD25high) or CD4+ Tconvs (Live, CD45+, CD3+, CD4+, CD25−) were sorted in RLT buffer with 1% β-mercapto ethanol. Due to low abundance of Tregs in WT mammary glands, tissue of 3 mice was pooled for each WT Treg sample prior to sorting. Library preparation was performed as previously described.64 Total RNA was extracted using RNAeasy mini kit (Qiagen). RNA quality and quantity control was performed using Agilent RNA 6000 Pico Kit and 2100 Bioanalyzer System. RNA samples with an RNA Integrity Number > 8 were subjected to library preparation. The strand-specific reads (65bp single-end) were sequenced with the HiSeq 2500 machine. Demultiplexing of the reads was performed with Illumina’s bcl2fastq. regDemultiplexed reads were aligned against the mouse reference genome (build 38) using TopHat (version 2.1.0, bowtie 1.1). TopHat was supplied with a known set of gene models (Ensembl version 77) and was guided to use the first-strand as the library-type. As additional parameters – prefilter-multihits and – no coverage were used. Normalized counts from DESeqDataSet from the DESeq2 package were subjected to calculate correlation among the samples by using ‘cor’ function using spearman method in R language (version 4.0.2).
LncRNA HOXC-AS3 increases non-small cell lung cancer cell migration and invasion by sponging premature miR-96
Published in Expert Review of Respiratory Medicine, 2022
Li Wan, Zaixing Cheng, Quanchao Sun, Ke Jiang
Direct-zol RNA Kit (ZYMO RESEARCH) was used for RNA isolation, followed by digestion with DNase I to remove genomic DNA. RNA integrity and density were analyzed by Bioanalyzer. A value of RNA integrity number (RIN) higher than 9 was confirmed in all samples. Next, reverse transcriptions were performed with 1 μg total RNA samples to prepare cDNA samples, which were used as the templates in qPCRs (1 μl in 20 μl system) to determine the expression levels of HOXC-AS3 with 18S rRNA as the internal control. The expression levels of miR-96 were determined using the All-in-One™ miRNA qRT-PCR Detection Kit (Genecopoeia) with U6 as the internal control. 2−ΔΔCt method was used to normalize Ct values of all reactions since an amplification rate close to 100% was reached in all cases.