Does Epitranscriptomic array use MeRIP?

When using an antibody to immunoprecipitate RNAs modified by a methyl group (e.g. m6A/m5C/m1A/m7G), Epitranscriptomic array is the same as MeRIP (Methylated RNA Immunoprecipitation). However, other covered RNA modifications (e.g. ac4C/Ψ) are not necessarily methylation, or m6A modified RNA can also be pulled down by m6A reader protein (YTH) instead of m6A antibody, so the broader term “Epitranscriptomic array” is used.

back to top ↑

Does Epitranscriptomics array profile only one modification at a time?

Correct. You can choose one of m6A/m5C/m1A/ac4C/m7G/Ψ RNA modifications (not all modifications at once) per Epitranscriptomics array experiment.

back to top ↑

For m6A, should I choose m6A-MeRIP method or YTH pull-down method?

M6A MeRIP method has more comprehensive coverage for m6A modifications in the RNAs, while having cross-reactivity to m6Am, a minor modification.
YTH reader protein pull-down has very high specificity only to native m6A modifications recognizable by YTH-DF2. The pull-down efficiency of m6A recognized by other m6A readers is not known.

back to top ↑

What controls are used in Epitranscriptomic array?

Each total RNA sample is spiked in with synthetic positive RNA control containing known modification. The immunoprecipitated RNA (IP), supernatant (Supe), and non-immune IgG (mock) aliquots are qPCR quantified to ensure the success of RNA modification enrichment.

back to top ↑

MazF enzyme used in m6A Single Nucleotide Array does not cut all m6A sites?

MazF has the recognition sequence of “ACA”, which cuts a subset of all m6A modification sites having the consensus motif sequence of “RRACH”. Thus, m6A Single Nucleotide Array is used to profile a cross-section of the entire epitranscriptome.

back to top ↑

How to choose between Epitranscriptomic Array and m6A Single Nucleotide Array?

The table below helps to make the choice.

back to top ↑

Why use Small RNA Array instead of small RNA-seq?

Arraystar Small RNA Array uses one single labeling reaction to covers major small RNA classes (i.e. miRNA, pre-miRNA, tRNA, tsRNA, snoRNA) at once, whereas multiple different sequencings would be required for biochemically different small RNA classes (i.e. different RNA pretreatments and sequencing library constructions). The direct end labeling for the array effectively does away complicated RNA pretreatments, de-modification, and size selection (particularly for tRNA and tsRNA) needed for reverse transcription by sequencing, achieving higher sensitivity and quantification accuracy. The array also requires less samples to run and is more tolerant for lower RNA qualities (e.g. biofluid, FFPE RNA).

back to top ↑

Any special considerations for small RNA array sample prep?

Standard TRIzol reagent method for total RNA extraction is preferred (see RNA Sample Submission). For other commercial RNA purification kits, please use the total RNA protocol that also recovers small RNA size range.

back to top ↑

Are there any validation recommendations for circular RNA expression analysis?

1. Design outward-facing primers
2. Perform reverse transcription with random hexamer primer.
3. Treat total RNA with RNase R to remove linear RNAs, thus reducing false positive signals.

You may refer to "Circular RNAs are abundant, conserved, and associated with ALU repeats" for detailed information.

back to top ↑

Does Arraystar provide the complete annotation of array probe sets (at least genomic coordinates and gene names) that are needed to compare the Arraystar results with our RNA seq data?

Complete annotations for all circRNAs are not disclosed. However, the probe IDs and the probe sequences, but not annotation for the genes, will be deposited as a GEO platform in the NCBI GEO repository.

back to top ↑

Does Arraystar provide circular RNA expression vector?

We do not supply individual circRNAs.  However, investigagtors may construct, transfect and produce recombinant circular RNA using circular RNA expression vector by themselves. A description of recombinant circular RNA production can be found in the publication:

Liang and Wilusz (2014) "Short intronic repeat sequences facilitate circular RNA production" Genes Dev. 2014 Oct 15; 28(20): 2233–2247. [PMCID: PMC4201285]

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201285/

back to top ↑

Does each lncRNA have a polyA tail?

Most of the lncRNAs (75%) have a poly-A tail, while some of them don't.

back to top ↑

Which should I use for Long non-coding RNA (LncRNA) profiling: Microarray or Next Generation Sequencing?

There are several scientifically-sound reasons for why you should choose microarrays over next-generation sequencing for LncRNA expression profiling. First, it has been shown that most LncRNAs are expressed at significantly lower levels than mRNAs (1, 2). Therefore, many more sequencing reads are required for LncRNA analysis than for mRNAs due to their low abundance. Recent publications used more than 120 million raw reads per sample in order to obtain acceptable coverage of LncRNAs (2,3). Second, only 1,000-4,000 LncRNAs are detected by greater than 120 million sequencing reads (3), while 7,000-12,000 LncRNAs are normally detectable using Arraystar LncRNA arrays. Therefore, LncRNA arrays can detect more LncRNAs than Next Generation Sequencing, at a lower cost.

References

1. Guttman, et al. (2010). Nature Biotechnology 28, 503.
2. Cabili, et al. (2011). Genes Dev. 25, 1950.
3. Markus et al. (2012). Genes Dev. 26, 338-343

back to top ↑

Is the DRIPc-seq RNA strand-specific?

Yes. We extract RNA from the immunoprecipitated R-loops for strand-specific RNA sequencing.

back to top ↑

Should RNase treatment be included to remove free RNA for the genomic DNA sample prep?

We do not recommend using RNase A in gDNA sample prep. Free RNAs do not interfere appreciably with DRIPc-seq results. Although RNA in the R-loops is protected from RNases and optimized RNase A treatment condition does not compromise DRIPc-seq results, bulk RNase A treatment can greatly reduce the R-loop signals. Also, RNase carry-over contamination risks degradation of the extracted R-loop RNA.

back to top ↑

What is the total RNA sample amount for MeRIP-seq?

For the best results, 50 ug total RNA is preferred. If your sample amounts are limiting, 10 ug total RNA has been done in the past.

back to top ↑

How specific is your antibody used in Epitranscriptomic-seq?

The outsourced antibody is ChIP grade certified by the vendor.

back to top ↑

How to ensure the reliability of MeDIP experiment?

Several stringent quality control measures are implemented:

• Sample DNA QC to ensure the submitted samples meet the amount and quality requirements of MeDIP.
• Sonication QC to ensure the genomic DNA is fragmented to the desired size distribution.
• H19 imprinting gene is used as the positive control of DNA methylation.
• GAPDH housekeeping gene is used as a negative control of DNA methylation.
• qPCR QC on the MeDIP and input DNAs of the positive and negative control gene loci to ensure the enrichment efficiency and specificity.
• QC steps for microarray (MeDIP-chip) or sequencing (MeDIP-seq) from the MeDIP materials.

back to top ↑

What are the quality control steps to determine whether the hMeDIP procedure was successful? Are there any particular genes Arraystar tests for with qPCR that are constitutively hydroxymethylated in normal tissue irrespective of origin?

Before immunoprecipitation, a synthetic oligonucleotide containing 5hmC at CpG positions is mixed with the fragmented genomic DNA as a spike-in positive control. The enrichment of hydroxymethylated DNA by immunoprecipitation is assessed by qPCR using the positive control sequence.

back to top ↑

Can tRNA and tRF/tiRNA be profiled in the same sequencing experiment?

tRNA (60~100 nt) and tRF/tiRNA (15~45 nt) have different size ranges, RNA pretreatment, cDNA synthesis, and sequencing library construction requirements. Currently, we use enhanced hydro-tRNA-seq for tRNA, where full length mature tRNAs are first isolated by gel electrophoresis and then partially hydrolyzed to smaller fragments to overcome strong tRNA structure and modification problems. Such a work flow is not compatible with tRF/tiRNA-seq.

If the species is human or mouse, we recommend using nrStar™ tRNA PCR Array (https://www.arraystar.com/nrstar-trna-pcr-array/) and nrStar™ tRF&tiRNA PCR Array (https://www.arraystar.com/nrstar-trfandtirna-pcr-array/). These qPCR panels have the coverage for all anticodon tRNAs and tRF/tiRNAs in the databases. Additional benefits compared with sequencing:

• Fast turn-around time. You can run the qPCR yourself.
• Gold standard qPCR quantification accuracy better than sequencing. No need for follow-up validation as sequencing results.
• Highly sensitive and low sample amount requirement. Good enough even for serum/plasma/biofluid sample sources.

back to top ↑

Do you provide tRNA/tRF/tiRNA sequencing for non-modeling organism such as Plasmodium falciparum? Sequencing-only service for raw data without data analysis?

If you can provide the transcriptome, tRNA and genome reference databases for non-model species other than human, mouse or rat, such as Plasmodium falciparum, we can provide the sequencing and data analysis service.

If no reference database is available, we may provide raw data-only sequencing services for non-modeling organisms. Please inquire to confirm.

back to top ↑

Is it possible to get RNA-seq for total RNA and not just poly(A) selected?

RNA-seq is primarily used for mRNAs. Sequencing total RNA without mRNA enrichment is not recommended, as rRNAs alone constitute ~90% of the total RNA and can severely depress sequencing read coverage for mRNA class.  For intact RNA samples, mRNA is enriched by poly(A) selection. For significantly degraded RNA samples, rRNA removal method is used instead for better mRNA fragment retention.

back to top ↑

Would it be possible to get RNA-Seq for all RNA types including noncoding and specifically miRNAs?

Different RNA classes/biotypes have very substantially different properties that need separate procedures for effective profiling. For example, miRNA sizes are very small and need special end extension by adapter ligation and library sizing in miRNA size range (15~35 nt). tRNAs are heavily modified and structured, which need special enzymatic pretreatments. Long noncoding RNAs are often expressed and function at low levels and can be short in half-life, which need high sensitivity lncRNA microarray oligo probe capture for accurate profiling. For human canonical miRNAs, our 384-well qPCR panel can entirely cover them at gold standard accuracy and very high sensitivity, particularly better than miRNA-seq for miRNAs in biofluids.

back to top ↑

Is rtStar™ tRNA-optimized First-Strand cDNA Synthesis Kit (AS-FS-004) required for nrStar™ tRNA PCR Array? Is rtStar™ tRF&tiRNA Pretreatment Kit (AS-FS-005) required for nrStar™ tRF&tiRNA PCR Array?

If RNA is not pretreated before cDNA synthesis, the cDNA synthesis quality is impaired.

For tRNA cDNA synthesis, rtStar™ tRNA-optimized First-Strand cDNA Synthesis Kit (AS-FS-004) includes an RNA demethylation step that enzymatically removes m1A, m1G and m3C.

For tRF&tiRNA cDNA synthesis, a separate rtStar™ tRF&tiRNA Pretreatment Kit (AS-FS-005) is used first on the RNA sample before cDNA synthesis, which prepares the tRNA fragment termini for adapter ligation and also removes m1A, m1G and m3C.

For more details, please visit Arraystar tRNA/tRF/tiRNA Research Area (tRNA/tRF/tiRNA Research ).

back to top ↑

For FFPE samples, should we send FFPE samples or the extracted RNA?

The main problem with FFPE samples is that the RNA is typically degraded. Therefore, obtaining the highest yield and quality of RNA from the de-paraffinization and extraction procedures is essential for obtaining high quality profiling data, for both microarrays and sequencing. Arraystar has many years of experience performing extraction of RNA from FFPE samples. Therefore, we strongly recommend that you send us your FFPE samples, and we will extract the RNA for you using our optimized protocols.

back to top ↑

Which method do Arraystar recommend to extracting total RNA from samples?

At Arraystar, we use TRIzol® Reagent from Life Technologies for extraction of total RNA from cells and tissues (http://www.lifetechnologies.com/order/catalog/product/15596026). However, if you have experience with total RNA isolation, then we recommend using whichever method you are most comfortable with. There are several other good RNA extraction methods as well, such as Qiagen’s RNeasy kit (http://www.qiagen.com/qdm/rna/rneasy-plus-kits?cmpid=Qven10GARneasy). The most important objective for total RNA isolation is to obtain as high a yield as possible with minimal degradation and contamination from proteins, DNA, and organic solvents, regardless of the extraction method used.

back to top ↑

Should I submit total RNA or isolated miRNA for Arraystar’s miRNA seq and miRNA PCR array services?

1. Our seq and miRStar^TM systems have been specifically optimized for use on total RNA extracted using Trizol method.
2. To avoid miRNA loss during purification, we recommend you submit total RNA not small RNAs.
3. If you have isolated miRNA, technically we can do it, but we shall not assure the success of sequencing library preparation that is affected by the extraction efficiency of small RNA. We highly recommend that you should monitor the extraction efficiency with Agilent 2100 Bioanalyzer, and provide the results to us.

back to top ↑

Why do I need biological replicates? Isn’t one sample per group enough?

Biological replicates increase the probability that your array results are statistically significant. As with any experiment in biology, genetic differences exist between individuals with the same apparent phenotype, even in inbred laboratory strains. If you were to test only one individual per experimental group, you would not be able to conclude with confidence that any observed difference is due to the difference in experimental conditions. It is always possible that an underlying, otherwise “invisible” genotypic variation is contributing to your results. Only by examining more than one individual per group can you determine whether or not a differential expression result is real. This is especially important in order to reduce the occurrence of false negatives, which you will never see.

back to top ↑

How many biological replicates per group do you recommend?

We strongly recommend a minimum of three biological replicates per group to achieve the most statistically significant data. There are two major reasons for why we do not recommend using fewer than three. First, if you had only 1 sample per group, there is a chance, that that one individual represents an “outlier”. However, since you only tested one sample in that group, you would never know whether it is an outlier or not. Further, if you had two biological replicates per group, one of those individuals could represent the outlier. However, if such was the case, you would not know which of the two individuals in that group represented the outlier. Only if you have three or more replicates per group can you be reasonably confident that any genetic differences observed between groups are real, because it is highly unlikely to have two outliers in a group of three. For mammalian tissues, we prefer six biological replicates per group, due to the heterogeneity of cell populations in tissues.

back to top ↑

Do you need to run technical replicates?

No. The arrays have been validated many times, and contain control probes. So, technical replicates are a waste of money.

back to top ↑

How are the fold changes in a microarray experiment calculated? In my Excel file neither the raw nor the normalized signal intensities seem to correlate with the Fold change values.

First, we do not use raw signal intensity values to determine fold changes. We only use the normalized values. Second, those normalized intensity values are log2 transformed. We calculate Fold Change by subtracting the normalized intensity value of one group from the normalized intensity value of another group. For example, if the normalized intensity of the control group is 3.5819511 and the normalized intensity of the experimental group is 5.984653. Subtracting the normalized intensity of the control group from the normalized intensity of the experimental group gives you 5.984653 - 3.5819511 = 2.4027019. Since the normalized intensities are actually log2 transformed, this result is actually the log2 of the fold change. Thus, the actual fold change is 2^2.4027019=5.2879257, which is the level of fold increase of expression in the control group vs. the experimental group. This is the value indicated in the fold change column labeled “FC (abs)”.

back to top ↑

Is there a way to identify individual transcripts from the volcano plot?

It is possible to identify transcripts from the volcano plot. However, this can only be done with the GeneSpring analysis software.

back to top ↑

How to pick differentially expressed transcripts to validate?

Researchers can take at least two different approaches to validating their microarray data, depending on their individual research needs. One way is to validate all of the differentially expressed transcripts on the array, in an unbiased, high-throughput manner. Another way is to just focus on those differentially expressed transcripts that appear to be associated with a biological function of interest, using the GO and Pathway analysis data provided in your report. However, this latter approach might not be very effective in the case of LncRNAs, because there are a great many LncRNAs whose function and roles in cellular processes are unknown. In any case, we strongly recommend eliminating those transcripts that have extremely low raw signal intensities, and then picking transcripts with larger fold change, as well as lower p values.

back to top ↑