Can you freeze rnalater

Post your comment. Anti Spam Code: Can't read the image? International Conferences Gene expression analysis provides information at which a particular gene pattern may be expressed by cellular responses. RNA quality is influenced by warm and cold ischemic durations, cellular stress responses, tissue processing protocols and storage conditions [ 2 , 3 ].

Isolation of highly pure intact RNA is vital for successful quantification of gene expression [ 1 , 4 ] so that RNA preservation is an essential subject during handling processes [ 5 ]. RNA degradation correlates with the length of delay in sample fixation or preservation. Most pathology departments still rely on tissue preservation with formalin fixation followed by paraffin embedding step [ 3 ].

Although it is suitable for morphologic assessment, it is unable to keep intact RNA [ 5 ]. In fact, formalin fixative keeps tissue structure via cross-linking of cellular proteins. In addition, there also happen cross-links between proteins and nucleic acids. Although it makes formalin an ideal fixative hardening agent for histopathological analysis, cross-linking of proteins and nucleic acids causes intact mRNA extraction from formalin-fixed tissues to be difficult [ 3 , 4 , 6 , 7 ].

On the other hand, labile and low mRNA transcripts of specific genes may also remain unamplifiable [ 4 ]. Therefore, biopsy tissues have to be preserved with alternative methods providing higher-quality RNA for gene expression analysis. It should be noted that small RNA molecules such as mature mi RNA are less affected by formalin and can be recovered more easily and efficiently in the extraction process [ 8 ]. These fixatives would avoid hydroxymethylene cross-linking between proteins and nucleic acids and, keep tissue morphology and RNA integrity [ 5 , 12 , 13 ].

Flash freezing also called snap freezing is a better method for providing high quality RNA for high throughput expression analysis as compared to formalin fixation [ 4 ]. RNA stability in intact fresh frozen tissue is due to both preserved cellular structure [ 14 ] and RNase inactivation [ 4 ].

In this method, small fragments of fresh tissue approximately 0. Although optimal RNA quality is preserved using flash freezing, this method also has a few limitations.

It may not be practical if there is not access for freezing facilities and sample collection is decentralized i. To reach the place where freezing facilities are available, transportation of fresh tissues on ice would be a practical solution, as it does not compromise RNA integrity and gene expression profiles in tissue [ 4 , 18 ].

Even brief thawing of flash frozen sample before homogenization in a guanidinium-based lysis solution containing 4 M guanidine thiocyanate, 25 mM sodium citrate, 0.

In general, flash freezing disadvantages including availability, expense of cold shipping and storage as well as risk of RNA degradation prior to homogenization [ 3 , 5 , 6 , 12 , 18 ] impose great needsfor applying other preservative methods. Other chemical reagent called TRIzol, consisting of phenol and guanidine isothiocyanate can preserve high RNA quality and quantity [ 21 ].

However, it destroys tissue structure, making it unamenable for histomorphological studies [ 22 ]. This solution preserves intact RNA by precipitating out RNases into an aqueous sulfate salt solution [ 4 , 18 , 20 ]. Based on the passive diffusion of reagent, thin tissue pieces must be prepared to penetrate rapidly into the tissue before RNA destruction by RNases.

We studied the effects of differences between storage conditions on gene expression as measured by expression array. Duplicate uterine myometrial tissue samples from three women were processed under each of 4 fixed storage conditions — fresh, frozen, 24 hours RNA-later and 72 hours RNA-later. Then, for each microarray a data matrix was generated of 22, probe sets genes by quantitative expression levels in each RNA sample, and the effect of subject source, tissue processing, and replicates Table 1 determined by ANOVA.

Subset analysis by gene functional class was then performed to determine if storage condition has a specific effect on particular groups of genes. Experimental design. Tissue aliquots from 3 women were aliquoted, in duplicate, into four storage groups before RNA isolation and microarray hybridization. We found no systematic bias in measured quantitative level of gene expression by processing method, indicating that short term storage in RNALater is a valid alternative to traditional frozen storage.

Of the 22, genes, 14, did not have absolutely null expression across all 24 samples. The permutation distribution was used to assess the significance of F statistics calculated for each gene in the dataset. In this approach all 13, or 4!

For each of these, the F statistics were computed for each gene. To control the overall error rate, the distributions of the maximum F statistics over the genes were used. That is, for each gene, the p-value is the proportion of permutations with the maximum F statistics over all genes greater or equal to the observed value for a particular gene.

Similar analyses were performed replacing the distribution of the maximum F statistic with the distribution of the F statistics at the 95 th percentile and then at the 90 th percentile. In fact, within a storage condition, 2 out of 3 patients exhibited null expression while the third patient showed expression values other than null but less than for at least one of their replicates.

Therefore, as an additional analysis, any expression values less than were recoded as Genes that showed expression levels of across all 24 samples, and therefore lacked variability, were then removed from the analysis. This resulted in 7, genes for which there was at least one sample with expression level greater than across all 24 samples. Patient subjects provided the greatest sources of variation in the mixed model ANOVA, with replicates and processing method the least Figure 2.

This is strong evidence that those individual expression profiles characteristic of the source tissue woman are unlikely to be obscured by the small amount of variation introduced by the processing method chosen. Distribution of Mean Squares Errors by variation source are plotted for those genes with at least one tissue showing expression at a level above LN genes. Note that individual women emerge as the dominant source of variation. Variation contributed by tissue storage is of approximately the same magnitude as that seen between duplicate samples within the same storage group.

Boxes encompass inner quartiles, horizontal line represents the median or the second quartile, and whiskers delimit 1. Because of the very large number of data points, outliers were suppressed in this summary plot. The distribution of ANOVA test statistics on those 7, genes where at least one sample of 24 had expression at a level exceeding is compared in Figure 3 to those seen in the randomly permuted dataset.

In the actual dataset, the maximum observed F statistic was Corresponding p-values were 0. This indicates that the model variation contributed by processing method is of the same magnitude as that seen randomly. Distribution of actual test statistics vs. The maximum, 95th percentile, and 90th percentile F-Statistics in the permuted dataset provide an index of the distribution of test results expected for a random sample.

Genes were included if at least one tissue showed expression above LN genes. Subset analysis of the test statistic according to gene functional class Table 2 showed that the frequency of "outlier" ANOVA results within each functional class is overall no greater than expected by chance.

Test statistic distribution was compiled by functional annotation for those genes which had at least one sample with detectable expression above a level of Nine separate classification schemas containing a total of functional classes were studied.

Nine publically available gene functional annotation schemas listed in Table 2 , each composed of multiple functional gene classes, were used to determine if specific functional subsets of expressed genes were more likely to show significant change in RNA detection between tissue treatments. Results for functional classes of genes with a minimum of 50 available expressed genes are shown as individual data points circles the distribution of which is summarized for each schema by the superimposed notch plot.

There are only two high outliers arrows amongst the gene classes shown. Storage of fresh whole human tissues for up to 72 hours at room temperature in RNALater does not introduce quantitative bias into RNA expression determinations with the Affymetrix UA array. Several differing standards justify this conclusion. First, by construction of a test model Figure 1 incorporating both random reproducibility estimates replicate determinations and between-sample differences it has been possible to demonstrate that the magnitude of variation introduced by RNALater processing is equivalent to that seen within routine repeat specimens in a common processing group Figure 2.

Second, the extent of result variation conferred by RNALater processing is not statistically significant when measured against the randomly permuted dataset Figure 3. This is an important element in evaluation of large datasets in which small numbers of individual variables may randomly demonstrate extreme values of the test statistic. Lastly, there is no evidence that specific functional subgroups of genes have aberrant behavior in this regard Figure 4.

Uterine myometrium was selected for these experiments because its components myocytes, fibroblasts, vascular elements are evenly intermingled throughout the myometrial compartment, lending itself to physical subdivision into equivalent aliquots. This would not be possible with more complex tissues in which differing cell types are distributed asymmetrically within the specimen.

Despite the equivalency of subdivided fractions that underwent varying storage treatments, it must be noted that this is a hormonally responsive tissue whose expression patterns would be expected to differ between individual women as a function of monthly changes in circulating sex hormones. We did not control for hormonal factors or indication for hysterectomy prolapse or fibroids but selected patients randomly.

It comes as no surprise that expression differences between women, irrespective of processing method, emerged as the dominant source of inter-sample variation. This was anticipated in constructing the model, by assigning the subject source of specimens as a random variable which could be measured against the fixed processing effects. It is likely that if a larger number of women had been included in the study, the observed biologic variation attributable to subject would have been even greater.

Since our goal was to compare magnitude of variation contributed by subjects to that conferred by processing method, we achieved a balanced design by having comparable degrees of freedom for those two variables. Proteins were extracted according to Basu et al. The collected protein extract was centrifuged at 10, g for 60 min to precipitate the microsomal fraction.

The resultant supernatant was used to precipitate soluble proteins by addition of 0. The pellet washed twice with 0. The microsomal pellet was washed with sterile mM sodium carbonate Sigma solution to remove any soluble protein contamination [ 13 ]. All samples from the soluble protein extraction were washed additionally with 1 x 0.

All were then dried to prepare for iTRAQ-labeling. The membrane protein samples were centrifuged, and the supernatant assayed for protein using the CBX assay. To increase protein yield from the pellets, RapiGest 0. The proteins were then digested by adding 2. Digested samples were centrifuged, the supernatant was decanted, and the samples were dried for iTRAQ8-labeling. Replicates for each sample were then pooled and dried. The pooled samples were reconstituted in 0.

Peptides were separated using the following gradient mobile phase A, 0. The mass spectrometer was operated in positive ionization mode. Fragmentation was accomplished with normalized collision energy of 40 and activation time of 0. Mass spectrometry data analyses were automated using Mascot Daemon 2. The raw data was processed to. Mascot searches used parent ion tolerance of Fixed modifications were set to assume iTRAQ 8-plex modifications at the N-terminus and lysine residues and carbamidomethyl modifications at cysteine residues.

Deamidation of asparagine and glutamine residues, oxidation of methionine residues and phosphorylation of serine, threonine and tyrosine residues were specified as variable modifications. The iTRAQ labeling efficiency was also evaluated and calculated to be The relative quantification of each protein identified in the RNAlater and liquid N 2 control were expressed as a ratio between the spectral counts of the isobaric tags.

Proteins with a p-value less than 0. The analysis returned a list of biological processes with greater than expected frequency among the genes submitted. The authors thank Dr. Sophie Alvarez, Dr. Their work was supported by Grant No. Software: CPSK. Visualization: CPSK. Writing — original draft: CPSK. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Tissue preservation is a minimal requirement for the success of plant RNA and protein expression studies.

Introduction For the success of transcriptomic and proteomic experiments sufficient quality and quantity of RNA or protein is essential. Results RNAlater treatment alters transcript levels in Arabidopsis seedlings One caution with using RNAlater is the rate of its diffusion through tissues that could lead to uneven preservation of cells across tissues.

Download: PPT. Fig 1. Number of differentially expressed genes identified by RNAseq analysis. RNAlater fixation allows for proteomics analysis To determine if RNAlater fixation is sufficient to prevent proteolysis in Arabidopsis seedlings, etiolated plants were grown for 72 hours then either flash frozen in liquid N 2 or submerged in RNAlater for 12 hours prior to protein extraction.

Differentially detected proteins RNAlater successfully preserved protein integrity to allow for reproducible detection of proteins, however RNAlater-fixed samples have altered protein expression levels.

Fig 2. Proteomic analysis of RNAlater vs. Liquid N 2 treated samples. Post-translational modifications altered by RNAlater fixation A growing focus of proteomics research is the detection of alterations in post-translational modifications. Table 1. Number of peptides with altered post-translational modifications due to RNAlater. Discussion RNAlater is a valuable fixative used for preservation of biological samples.

Methods Plant material Arabidopsis thaliana L. Protein extraction For extraction of soluble and membrane proteins, five plates were pooled for each replicate. Protein sample preparation All samples from the soluble protein extraction were washed additionally with 1 x 0. Protein data analysis Mass spectrometry data analyses were automated using Mascot Daemon 2. Supporting information. S1 Table. Unique peptide modifications found in the membrane protein fraction.

S2 Table. Unique peptide modifications found in the soluble protein fraction.