Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis.
Reverse transcription and real-time PCR (RT-qPCR) has been widely used for rapid quantification of relative gene expression. To offset technical confounding variations, stably-expressed internal reference genes are measured simultaneously along with target genes for data normalization. Statistic met...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2011-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3058000?pdf=render |
| _version_ | 1811195609889112064 |
|---|---|
| author | Daijun Ling Paul M Salvaterra |
| author_facet | Daijun Ling Paul M Salvaterra |
| author_sort | Daijun Ling |
| collection | DOAJ |
| description | Reverse transcription and real-time PCR (RT-qPCR) has been widely used for rapid quantification of relative gene expression. To offset technical confounding variations, stably-expressed internal reference genes are measured simultaneously along with target genes for data normalization. Statistic methods have been developed for reference validation; however normalization of RT-qPCR data still remains arbitrary due to pre-experimental determination of particular reference genes. To establish a method for determination of the most stable normalizing factor (NF) across samples for robust data normalization, we measured the expression of 20 candidate reference genes and 7 target genes in 15 Drosophila head cDNA samples using RT-qPCR. The 20 reference genes exhibit sample-specific variation in their expression stability. Unexpectedly the NF variation across samples does not exhibit a continuous decrease with pairwise inclusion of more reference genes, suggesting that either too few or too many reference genes may detriment the robustness of data normalization. The optimal number of reference genes predicted by the minimal and most stable NF variation differs greatly from 1 to more than 10 based on particular sample sets. We also found that GstD1, InR and Hsp70 expression exhibits an age-dependent increase in fly heads; however their relative expression levels are significantly affected by NF using different numbers of reference genes. Due to highly dependent on actual data, RT-qPCR reference genes thus have to be validated and selected at post-experimental data analysis stage rather than by pre-experimental determination. |
| first_indexed | 2024-04-12T00:46:02Z |
| format | Article |
| id | doaj.art-5cdf17db040a47f89a0a7105812db31d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T00:46:02Z |
| publishDate | 2011-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5cdf17db040a47f89a0a7105812db31d2022-12-22T03:54:53ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0163e1776210.1371/journal.pone.0017762Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis.Daijun LingPaul M SalvaterraReverse transcription and real-time PCR (RT-qPCR) has been widely used for rapid quantification of relative gene expression. To offset technical confounding variations, stably-expressed internal reference genes are measured simultaneously along with target genes for data normalization. Statistic methods have been developed for reference validation; however normalization of RT-qPCR data still remains arbitrary due to pre-experimental determination of particular reference genes. To establish a method for determination of the most stable normalizing factor (NF) across samples for robust data normalization, we measured the expression of 20 candidate reference genes and 7 target genes in 15 Drosophila head cDNA samples using RT-qPCR. The 20 reference genes exhibit sample-specific variation in their expression stability. Unexpectedly the NF variation across samples does not exhibit a continuous decrease with pairwise inclusion of more reference genes, suggesting that either too few or too many reference genes may detriment the robustness of data normalization. The optimal number of reference genes predicted by the minimal and most stable NF variation differs greatly from 1 to more than 10 based on particular sample sets. We also found that GstD1, InR and Hsp70 expression exhibits an age-dependent increase in fly heads; however their relative expression levels are significantly affected by NF using different numbers of reference genes. Due to highly dependent on actual data, RT-qPCR reference genes thus have to be validated and selected at post-experimental data analysis stage rather than by pre-experimental determination.http://europepmc.org/articles/PMC3058000?pdf=render |
| spellingShingle | Daijun Ling Paul M Salvaterra Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis. PLoS ONE |
| title | Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis. |
| title_full | Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis. |
| title_fullStr | Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis. |
| title_full_unstemmed | Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis. |
| title_short | Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis. |
| title_sort | robust rt qpcr data normalization validation and selection of internal reference genes during post experimental data analysis |
| url | http://europepmc.org/articles/PMC3058000?pdf=render |
| work_keys_str_mv | AT daijunling robustrtqpcrdatanormalizationvalidationandselectionofinternalreferencegenesduringpostexperimentaldataanalysis AT paulmsalvaterra robustrtqpcrdatanormalizationvalidationandselectionofinternalreferencegenesduringpostexperimentaldataanalysis |