Dear all, just a quick comment regarding the Bioanalyzer to check the RNA quality. From my experience after extracting nearly 3000 RNA samples from human brain the results showed considerable variation in RIN values among RNA samples.

The algorithm for the RIN calculation is largely based on the ratio of ribosomal RNA 28S/18S (not mRNA directly) signal heights, areas under the signal peaks, position and separationtimes. In addition, the algorithm counts for the level of the signal in the fast, inter regions and post regions to look for RNA degradation products. It assigns relative weight to each of these factors and calculates the final RIN from the combination of these factors (Schroeder, Mueller et al. 2006).

In this study 2,318 RNA samples were assessed. The calculated RIN ranged from 1 to 8.5 with a mean of 3.85. 43% of the samples had RIN values < 3. Moreover, no difference was observed in the expected nucleotide lengths in cDNA (~200 - 400 nt) and cRNA (ranges from 200 to 2000 nt) production in the Agilent profiles when different RNA samples with different RIN values (from 1- 8) were used. Thus RIN was found to be a poor predictor of array quality.

Furthermore, it is important to note that the values for the RIN that were reported in this study are much lower than those reported for comparable samples in other laboratories. This is most likely due to differences in the RNA isolation kits that were used. Most RNA kits, including the RNAeasy kit, encourage the loss of small or degraded RNA species. For this project the miRNAeasy kit was used, which specifically allows for the conservation of short microRNA species. When short RNA is present, the RIN algorithm generates lower RIN values. As a result RIN values generated here are not directly comparable with those reported in other studies where a different RNA isolation kit was used.

The results of the work described in this chapter were published in the Journal of

Neurochemistry:

Trabzuni, D., Ryten, M., Walker, R., Smith, C., Imran, S., Ramasamy, A., Weale, M.E. and Hardy, J. (2011) Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. Journal of Neurochemistry, 119, 275-282.

Hope you find this useful, I am really concern to make researchers aware of this as people really get obsessed with RIN number.

Thanks

You can check the integrity of the RNA on Bioanalyzer for best results.

Dear Satyabrata,

How do you know your RNA is poor quality? Is it because it appears as a smear on the gel? If so, you should make sure that you clean the gel tank with RNase Away, use only RNase-free reagents to make the gel and in particular, use DEPC-treated or RNase-free water to make the gel. This is to make sure that your RNA is not degraded while running on the gel.

Dinesh is correct though - RNA quality is best measured with the Agilent Bioanalyzer or TapeStation or a similar instrument (BioRad or Qiagen QIAXcel), if you have access to one of the machines.

Here is a bit more info: http://www.gene-quantification.de/rna-integrity.html

Best wishes,

Petra Disterer

@ Petra Disterer: Thank you for your suggestion and help. I do follow all the necessary steps to make a RNAse free environment and also use DEPC. And in addition to the smeary appearance of RNA on the gel, I also get very low ratio value (

@Dinesh Sahu: Thank you for your suggestion.

The low 260/280 ratio on the Nanodrop suggests that you have protein contamination (likely RNases) in your sample. The low 260/230 ratio is a problem if you use Phenol (Trizol = Phenol) extraction as it strongly suggests that you have Phenol contamination in your sample. This will inhibit your reverse transcription. You can use a chloroform/isoamyl extraction after the phenol/trizol and then do ethanol precipitation, with at least one wash with ethanol.

If you use a RNA extraction kit such as RNeasy from Qiagen, then make sure that you do not overload your column with too much tissue (~20 mg tissue for the RNeasy Mini-Kit as a guide) and use 200 proof molecular biology grade ethanol to make up the buffer and 70% ethanol required by the kit. Protein contamination can also come if you haven't sheared genomic DNA enough in your sample - it clumps and gets stuck on the column filter thus clogging up the column and preventing protein from being washed through.

Yes, I am isolating RNA with Genepure Raflex Kit and I do follow the manufacturer's instructions. However, I treat the isolated RNA with DNAse I to make it free from any possible DNA contamination. Post-incubation with DNAse I at 37C, I again incubate the samples at 60 C for 10 mins to denature the DNAse. So I think the protein contamination may be coming from the leftover DNAse present in the samples.

Yes, that sounds likely - in the RNeasy kit you can do the DNase digest on the column, so you then get rid of it when washing the column, no need to heat-denature...

One point with DNase, don't buy it from Sigma - although it is supposed to be RNase-free, in my experience some of their lots aren't.

It may be worthwhile changing the kit if it is old or has been used by students (who may have contaminated the buffers).

The only other explanation I can come up with is the time your tissue is left before being flash-frozen or stored in RNAlater... even 30 min makes a difference in RNA quality. How do you dissociate the tissue? Again, if you go for flash-frozen, this needs to happen on dry ice with pre-cooled reagents and equipment.

I personally prefer to use RNAlater and then a Qiagen TissueLyser (a beadmill) for quick and easy dissociation, but then I usually extract from liver, so a very easy tissue to dissociate.

Dear all, just a quick comment regarding the Bioanalyzer to check the RNA quality. From my experience after extracting nearly 3000 RNA samples from human brain the results showed considerable variation in RIN values among RNA samples.

The algorithm for the RIN calculation is largely based on the ratio of ribosomal RNA 28S/18S (not mRNA directly) signal heights, areas under the signal peaks, position and separationtimes. In addition, the algorithm counts for the level of the signal in the fast, inter regions and post regions to look for RNA degradation products. It assigns relative weight to each of these factors and calculates the final RIN from the combination of these factors (Schroeder, Mueller et al. 2006).

In this study 2,318 RNA samples were assessed. The calculated RIN ranged from 1 to 8.5 with a mean of 3.85. 43% of the samples had RIN values < 3. Moreover, no difference was observed in the expected nucleotide lengths in cDNA (~200 - 400 nt) and cRNA (ranges from 200 to 2000 nt) production in the Agilent profiles when different RNA samples with different RIN values (from 1- 8) were used. Thus RIN was found to be a poor predictor of array quality.

Furthermore, it is important to note that the values for the RIN that were reported in this study are much lower than those reported for comparable samples in other laboratories. This is most likely due to differences in the RNA isolation kits that were used. Most RNA kits, including the RNAeasy kit, encourage the loss of small or degraded RNA species. For this project the miRNAeasy kit was used, which specifically allows for the conservation of short microRNA species. When short RNA is present, the RIN algorithm generates lower RIN values. As a result RIN values generated here are not directly comparable with those reported in other studies where a different RNA isolation kit was used.

The results of the work described in this chapter were published in the Journal of

Neurochemistry:

Trabzuni, D., Ryten, M., Walker, R., Smith, C., Imran, S., Ramasamy, A., Weale, M.E. and Hardy, J. (2011) Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. Journal of Neurochemistry, 119, 275-282.

Hope you find this useful, I am really concern to make researchers aware of this as people really get obsessed with RIN number.

Thanks