Choosing reference genes for qPCR normalisation

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=== Reference genes ===
=== Reference genes ===
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Most common method. Best practise is a panel not just a single reference gene including a data on it suitability as reference gene. Often ''housekeeping gene''  is used here instead of reference gene but the term is poorly defined and can be misleading.
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Most common method. Best practise is a panel, e.g. [http://www.bioline.com/h_prod_detail.asp?user_prodname=Human%20Endogenous%20Control%20Gene%20Panel] not just a single reference gene and including data on suitability as reference genes. Often ''housekeeping gene''  is used here instead of reference gene but the term is poorly defined and can be misleading.
=== RNA ===
=== RNA ===
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Total rRNA [http://scholar.google.com/scholar?hl=en&lr=&safe=off&cluster=12435126891737656303] [http://scholar.google.com/scholar?hl=en&lr=&safe=off&cluster=9547016096229453970], or total RNA. Drawback: rapidly dividing cells will have more rRNA and rRNA/mRNA ratio which will complicate comparison. Difference in cDNA synthesis not taken into account.
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Total rRNA [http://scholar.google.com/scholar?hl=en&lr=&safe=off&cluster=12435126891737656303] [http://scholar.google.com/scholar?hl=en&lr=&safe=off&cluster=9547016096229453970], or total RNA. Drawback: rapidly dividing cells will have more rRNA and different rRNA/mRNA ratio which will complicate comparison; difference in cDNA synthesis not taken into account.
=== Genomic DNA ===
=== Genomic DNA ===
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Genomic DNA or cell number. Drawbacks: RNA degrades faster than RNA which can distort the data; sample cannot be DNase treated; efficiency of cDNA synthesis not taken into account.  
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Genomic DNA or cell number. Drawbacks: RNA degrades faster than RNA which can distort the data; sample cannot be DNase treated; efficiency of cDNA synthesis not taken into account.
== The ideal reference gene ==
== The ideal reference gene ==

Revision as of 15:29, 7 February 2008

Quantifying mRNA via cDNA levels as in a QRT-PCR hinges on the references you choose. If you pick only one reference gene and your pick is not constant across different conditions or samples, your results will be skewed. Pick several and check whether they satisfy the criteria for a good reference gene.

Contents

Normalisation methods

There is an ongoing debate what is the best way to normalise qPCR data. Reference genes are the most common method, although single unverified reference genes invalidate the qPCR data generated. Total RNA, ribosomal RNA, and genomic DNA have been suggested as alternative methods.

Reference genes

Most common method. Best practise is a panel, e.g. [1] not just a single reference gene and including data on suitability as reference genes. Often housekeeping gene  is used here instead of reference gene but the term is poorly defined and can be misleading.

RNA

Total rRNA [2] [3], or total RNA. Drawback: rapidly dividing cells will have more rRNA and different rRNA/mRNA ratio which will complicate comparison; difference in cDNA synthesis not taken into account.

Genomic DNA

Genomic DNA or cell number. Drawbacks: RNA degrades faster than RNA which can distort the data; sample cannot be DNase treated; efficiency of cDNA synthesis not taken into account.

The ideal reference gene

A mRNA used as reference or standard of a QRT-PCR (and other experiments) should have the following properties:

  • expressed in all cells
  • constant copy number in all cells
  • medium copy number for more accuracy (or similar copy number to gene of interest)

Common reference genes

Common reference mRNAs linked to known mouse primer pairs:

  • β-actin (common cytoskeletal enzyme) [4], [5]
  • ribosomal proteins (e.g. RPLP0)
  • cyclophilin mRNA
  • MHC I (major histocompatibility complex I)

Common but not recommended references

  • glyceraldehyde-3-phosphate dehydrogenase GAPDH (common metabolic enzyme) [6], [7] - see QRT-PCR#Reference_mRNAs
  • ribosomal RNAs (28S or 18S) - see below

Reference genes across tissues

If you are comparing mRNA/cDNA levels from different tissue it is especially important that reference gene levels are close to constant across different tissues. Radonić et al compared 13 putative reference gene levels in 13 different human tissues [PMID 14706621]. The results are summarised below:

  • genes with the smallest range (most constant levels): TBP, RP2, Act, Tub, PLA
  • genes with the largest range (unsuitable for cross-tissue comparison): HPRT, Alb, PBGD, GAPDH, β2M
  • genes undetectable in tissue: Alb - colon; PPIA - ovaries; HPRT - prostate, testis, ovary, small intestine, colon; PBL, skeletal muscle; Tub - ovaries, PBGD - skeletal muscle; TBP - lung, prostate, colon; G6PDH - colon
  • genes detected in all tissues: GAPDH, Act, β2M, L13, PLA, RP2

(note the source Fig 2 is sometimes impossible to read and the describing text is incomplete; that might have lead to some errors above)

Primer collections

Search primer repositories like RTPrimerDB (see also below) and check the literature before doing it from scratch.
Check out the Eccles Lab collection of human and mouse qPCR reference genes on OWW.

Stability

  • Ajeffs 06:55, 21 April 2007 (EDT): In addition to the given requirements of good (well, acceptable) specificity and efficiency of the reference gene primers, the next most important aspect of reference gene selection is stability. I don't care if the CT value of my reference genes (yes, genes, not gene) is close to the target genes/s or not - as long as the efficiency of all the primers is similar, and they are all working within their respective limits of detection i.e. linear range, then the stability of the reference genes between samples, treatments, etc. is the most crucial aspect of generating believable qPCR results.

Selection

  • Ajeffs 06:55, 21 April 2007 (EDT): Screen a handful of ref genes, select the most stable using genorm, bestkeeper etc, use at least 2 reference genes for subsequent reactions and normalisation. Inlcude your genorm M values when publishing qPCR data.

Use of 18S

  • Ajeffs 06:55, 21 April 2007 (EDT): 18S is generally a terrible choice for a reference gene thanks to the combination of (i) high abundance (creating a 1:100 dilution of template to run in parallel with neat template just for 18S is a complete drag); and (ii) having different degradation characteristics to mRNAs (it appears to be more resistant to degradation). However, if you can show that you have screened 5-10 reference genes, and 18S is still the best for your specific situation then so be it (but do try 28S if you or you PI is hung-up on 18S).
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