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A combination of Let-7d, Let-7g and Let-7i serves as a stable reference for normalization of serum microRNAs.

Chen X, Liang H, Guan D, Wang C, Hu X, Cui L, Chen S, Zhang C, Zhang J, Zen K, Zhang CY - PLoS ONE (2013)

Bottom Line: Recent studies have indicated that circulating microRNAs (miRNAs) in serum and plasma are stable and can serve as biomarkers of many human diseases.A combination of let-7d, let-7g and let-7i is selected as a reference for the normalization of serum miRNAs and it is statistically superior to the commonly used reference genes U6, RNU44, RNU48 and miR-16.This has important implications for proper experimental design and accurate data interpretation.

View Article: PubMed Central - PubMed

Affiliation: Jiangsu Engineering Research Center for microRNA Biology and Biotechnology, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China.

ABSTRACT
Recent studies have indicated that circulating microRNAs (miRNAs) in serum and plasma are stable and can serve as biomarkers of many human diseases. Measurement of circulating miRNAs with sufficient sensitivity and precision, however, faces some special challenges, among which proper normalization is the most critical but often an underappreciated issue. The primary aim of this study was to identify endogenous reference genes that maintain consistent levels under various conditions to serve as an internal control for quantification of serum miRNAs. We developed a strategy combining Illumina's sequencing by synthesis (SBS) technology, reverse transcription quantitative polymerase chain reaction (RT-qPCR) assay, literature screening and statistical analysis to screen and validate the most suitable reference genes. A combination of let-7d, let-7g and let-7i is selected as a reference for the normalization of serum miRNAs and it is statistically superior to the commonly used reference genes U6, RNU44, RNU48 and miR-16. This has important implications for proper experimental design and accurate data interpretation.

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Related in: MedlinePlus

Selection of the most stable reference genes by SBS technology.(A) Sera from cancer patients and healthy participants were pooled separately as described above, and miRNA levels were determined using SBS technology. SBS reads were converted to the log2 scale. The average log2-transformed read of each miRNA was plotted against the standard deviation of the log2-transformed read. MiRNAs highlighted in red are those with higher abundance (log2-transformed reads > 10) and lower standard deviations (< 1) in the dataset. (B) The average expression values (SBS reads ± standard errors) of the selected miRNAs were plotted. (C) Selection of the most stable reference genes from a panel of 25 genes using geNorm. The geNorm program calculates the average expression stability value (M) for each gene. Genes with the lowest M values are considered the most stable. The least stable gene with the highest M value was automatically excluded for the next calculation round. The x-axis from left to right indicates the ranking of the reference genes according to their expression stability from the least to the most stable, and the y-axis represents the M values of the remaining reference genes. (D) Identification of the optimal number of reference genes for accurate normalization using geNorm. V is the pairwise variation (Vn/Vn+1) between two sequential normalization factors (NFn and NFn+1). The magnitude of the change in the normalization factor after the inclusion of an additional reference gene reflects the improvement that is obtained. The authors of geNorm suggest that V > 0.15 should be considered the threshold for including an extra reference gene in the assay, and the least number of genes for each V < 0.15 is selected as the optimal set of genes for normalization. (E) Selection of the most stable reference gene or gene combinations using geNorm. In this case, geNorm indicated that the combination of let-7d, let-7g and let-7i was statistically superior to other combinations or each individually. (F) Identification of the most stable reference genes using NormFinder. The NormFinder algorithm ranks the set of candidate normalization genes according to their expression stability in different groups (e.g., disease versus normal). According to this algorithm, lower stability values of the individual genes indicate greater gene stability. In this case, 23 samples were divided into two groups (12 normal controls and 11 cancer patients). Blue bars represent the stability values of the candidate genes.
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pone-0079652-g002: Selection of the most stable reference genes by SBS technology.(A) Sera from cancer patients and healthy participants were pooled separately as described above, and miRNA levels were determined using SBS technology. SBS reads were converted to the log2 scale. The average log2-transformed read of each miRNA was plotted against the standard deviation of the log2-transformed read. MiRNAs highlighted in red are those with higher abundance (log2-transformed reads > 10) and lower standard deviations (< 1) in the dataset. (B) The average expression values (SBS reads ± standard errors) of the selected miRNAs were plotted. (C) Selection of the most stable reference genes from a panel of 25 genes using geNorm. The geNorm program calculates the average expression stability value (M) for each gene. Genes with the lowest M values are considered the most stable. The least stable gene with the highest M value was automatically excluded for the next calculation round. The x-axis from left to right indicates the ranking of the reference genes according to their expression stability from the least to the most stable, and the y-axis represents the M values of the remaining reference genes. (D) Identification of the optimal number of reference genes for accurate normalization using geNorm. V is the pairwise variation (Vn/Vn+1) between two sequential normalization factors (NFn and NFn+1). The magnitude of the change in the normalization factor after the inclusion of an additional reference gene reflects the improvement that is obtained. The authors of geNorm suggest that V > 0.15 should be considered the threshold for including an extra reference gene in the assay, and the least number of genes for each V < 0.15 is selected as the optimal set of genes for normalization. (E) Selection of the most stable reference gene or gene combinations using geNorm. In this case, geNorm indicated that the combination of let-7d, let-7g and let-7i was statistically superior to other combinations or each individually. (F) Identification of the most stable reference genes using NormFinder. The NormFinder algorithm ranks the set of candidate normalization genes according to their expression stability in different groups (e.g., disease versus normal). According to this algorithm, lower stability values of the individual genes indicate greater gene stability. In this case, 23 samples were divided into two groups (12 normal controls and 11 cancer patients). Blue bars represent the stability values of the candidate genes.

Mentions: We first screened a SBS dataset to identify stable serum miRNAs across various physiological and pathological conditions. A total of 23 pooled serum samples were analyzed, including 8 healthy male or female samples of different ages (baby boy, baby girl, young boy, young girl, middle-aged man, middle-aged woman, old man and old woman; each pool was created by combining 10 individual serum samples), 2 mixed healthy samples (middle-aged and old; each pooled from 5 male and 5 female) and 13 cancer patients (3 non-small cell lung cancer, 2 breast cancer, 2 gastric cancer, 2 esophageal cancer, 1 colorectal cancer, 1 pancreatic cancer, 1 cervical cancer and 1 hepatocellular carcinoma; each pool was created by combining 10 individual serum samples). MiRNAs were considered stable if they fulfilled the following criteria: (1) expressed in all samples; (2) highly expressed, as measured by the mean; and (3) consistently expressed, as measured by the standard deviations. According to these criteria, 25 miRNAs were selected as candidate reference genes. As shown in Figure 2A, SBS reads were converted to the log2 scale, and genes were sorted by the mean expression levels and standard deviations. Among the miRNAs detected, 25 miRNAs had high abundance (log2-transformed reads > 10) and low standard deviations (


A combination of Let-7d, Let-7g and Let-7i serves as a stable reference for normalization of serum microRNAs.

Chen X, Liang H, Guan D, Wang C, Hu X, Cui L, Chen S, Zhang C, Zhang J, Zen K, Zhang CY - PLoS ONE (2013)

Selection of the most stable reference genes by SBS technology.(A) Sera from cancer patients and healthy participants were pooled separately as described above, and miRNA levels were determined using SBS technology. SBS reads were converted to the log2 scale. The average log2-transformed read of each miRNA was plotted against the standard deviation of the log2-transformed read. MiRNAs highlighted in red are those with higher abundance (log2-transformed reads > 10) and lower standard deviations (< 1) in the dataset. (B) The average expression values (SBS reads ± standard errors) of the selected miRNAs were plotted. (C) Selection of the most stable reference genes from a panel of 25 genes using geNorm. The geNorm program calculates the average expression stability value (M) for each gene. Genes with the lowest M values are considered the most stable. The least stable gene with the highest M value was automatically excluded for the next calculation round. The x-axis from left to right indicates the ranking of the reference genes according to their expression stability from the least to the most stable, and the y-axis represents the M values of the remaining reference genes. (D) Identification of the optimal number of reference genes for accurate normalization using geNorm. V is the pairwise variation (Vn/Vn+1) between two sequential normalization factors (NFn and NFn+1). The magnitude of the change in the normalization factor after the inclusion of an additional reference gene reflects the improvement that is obtained. The authors of geNorm suggest that V > 0.15 should be considered the threshold for including an extra reference gene in the assay, and the least number of genes for each V < 0.15 is selected as the optimal set of genes for normalization. (E) Selection of the most stable reference gene or gene combinations using geNorm. In this case, geNorm indicated that the combination of let-7d, let-7g and let-7i was statistically superior to other combinations or each individually. (F) Identification of the most stable reference genes using NormFinder. The NormFinder algorithm ranks the set of candidate normalization genes according to their expression stability in different groups (e.g., disease versus normal). According to this algorithm, lower stability values of the individual genes indicate greater gene stability. In this case, 23 samples were divided into two groups (12 normal controls and 11 cancer patients). Blue bars represent the stability values of the candidate genes.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3818225&req=5

pone-0079652-g002: Selection of the most stable reference genes by SBS technology.(A) Sera from cancer patients and healthy participants were pooled separately as described above, and miRNA levels were determined using SBS technology. SBS reads were converted to the log2 scale. The average log2-transformed read of each miRNA was plotted against the standard deviation of the log2-transformed read. MiRNAs highlighted in red are those with higher abundance (log2-transformed reads > 10) and lower standard deviations (< 1) in the dataset. (B) The average expression values (SBS reads ± standard errors) of the selected miRNAs were plotted. (C) Selection of the most stable reference genes from a panel of 25 genes using geNorm. The geNorm program calculates the average expression stability value (M) for each gene. Genes with the lowest M values are considered the most stable. The least stable gene with the highest M value was automatically excluded for the next calculation round. The x-axis from left to right indicates the ranking of the reference genes according to their expression stability from the least to the most stable, and the y-axis represents the M values of the remaining reference genes. (D) Identification of the optimal number of reference genes for accurate normalization using geNorm. V is the pairwise variation (Vn/Vn+1) between two sequential normalization factors (NFn and NFn+1). The magnitude of the change in the normalization factor after the inclusion of an additional reference gene reflects the improvement that is obtained. The authors of geNorm suggest that V > 0.15 should be considered the threshold for including an extra reference gene in the assay, and the least number of genes for each V < 0.15 is selected as the optimal set of genes for normalization. (E) Selection of the most stable reference gene or gene combinations using geNorm. In this case, geNorm indicated that the combination of let-7d, let-7g and let-7i was statistically superior to other combinations or each individually. (F) Identification of the most stable reference genes using NormFinder. The NormFinder algorithm ranks the set of candidate normalization genes according to their expression stability in different groups (e.g., disease versus normal). According to this algorithm, lower stability values of the individual genes indicate greater gene stability. In this case, 23 samples were divided into two groups (12 normal controls and 11 cancer patients). Blue bars represent the stability values of the candidate genes.
Mentions: We first screened a SBS dataset to identify stable serum miRNAs across various physiological and pathological conditions. A total of 23 pooled serum samples were analyzed, including 8 healthy male or female samples of different ages (baby boy, baby girl, young boy, young girl, middle-aged man, middle-aged woman, old man and old woman; each pool was created by combining 10 individual serum samples), 2 mixed healthy samples (middle-aged and old; each pooled from 5 male and 5 female) and 13 cancer patients (3 non-small cell lung cancer, 2 breast cancer, 2 gastric cancer, 2 esophageal cancer, 1 colorectal cancer, 1 pancreatic cancer, 1 cervical cancer and 1 hepatocellular carcinoma; each pool was created by combining 10 individual serum samples). MiRNAs were considered stable if they fulfilled the following criteria: (1) expressed in all samples; (2) highly expressed, as measured by the mean; and (3) consistently expressed, as measured by the standard deviations. According to these criteria, 25 miRNAs were selected as candidate reference genes. As shown in Figure 2A, SBS reads were converted to the log2 scale, and genes were sorted by the mean expression levels and standard deviations. Among the miRNAs detected, 25 miRNAs had high abundance (log2-transformed reads > 10) and low standard deviations (

Bottom Line: Recent studies have indicated that circulating microRNAs (miRNAs) in serum and plasma are stable and can serve as biomarkers of many human diseases.A combination of let-7d, let-7g and let-7i is selected as a reference for the normalization of serum miRNAs and it is statistically superior to the commonly used reference genes U6, RNU44, RNU48 and miR-16.This has important implications for proper experimental design and accurate data interpretation.

View Article: PubMed Central - PubMed

Affiliation: Jiangsu Engineering Research Center for microRNA Biology and Biotechnology, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China.

ABSTRACT
Recent studies have indicated that circulating microRNAs (miRNAs) in serum and plasma are stable and can serve as biomarkers of many human diseases. Measurement of circulating miRNAs with sufficient sensitivity and precision, however, faces some special challenges, among which proper normalization is the most critical but often an underappreciated issue. The primary aim of this study was to identify endogenous reference genes that maintain consistent levels under various conditions to serve as an internal control for quantification of serum miRNAs. We developed a strategy combining Illumina's sequencing by synthesis (SBS) technology, reverse transcription quantitative polymerase chain reaction (RT-qPCR) assay, literature screening and statistical analysis to screen and validate the most suitable reference genes. A combination of let-7d, let-7g and let-7i is selected as a reference for the normalization of serum miRNAs and it is statistically superior to the commonly used reference genes U6, RNU44, RNU48 and miR-16. This has important implications for proper experimental design and accurate data interpretation.

Show MeSH
Related in: MedlinePlus