Improving DNA and RNA quant with plate based fluorimetry

We quantify NGS libraries all the time and qPCR works brilliantly, but nucleic acids need to be handled differently. We don't actually run that much quantifiaction on DNA and RNA as most of our users have already done this; we asked them to do it so we could more efficiently run larger batches of library prep to keep costs down and turnaround times as short as possible. Over the last few years we've been running the Nextera exome preps and DNA quant has become more important than ever before, in fact we started running a secondary quant just to be certain about DNA concentration.

Most of the time DNA and RNA quant works well and we've favoured the fluorescent Qubit assay recommended by Illumina in their protocols. A nanodrop or plate reading spec at 260:280nM measures total nucleic acid and is confounded by ssDNA, RNA, and oligos so can give inaccurate results. We run the Qubit dsDNA BR Assay from Molecular Probes on the PHERAstar fluorescent plate reader (here's their handy protocol). We have only been using 1ul of DNA (Illumina suggest 2) for each sample but we run triplicate assays to get a high-quality quantitation.

Problems with the Qubit assay: Recently some users have reported problems with the accuracy of the QuBit assay on our plate reader and the manager of our Research Instrumentation Core helped us to get to the bottom of the issues and some excellent results. The main problem turned out to be addition of DNA into the working dye solution, it was the DNA coating the outside of the tips that appeared to be making the results so flaky. Changing the protocol to add DNA to the plate first fixed it and the results are looking great.

It ca also be very important to be certain which assay you should use; BR (Broad range) or HS (High Sensitivity). If you are working with low concentration nucleic acids then the HS assay is probably the one to use. For really accurate quant we'd suggest a quick QT check first, then normalisation of samples to about twice what you need; a second triplicate and robust quant will allow you to dilute the samples to the perfect working concentration.

Here are our top tips:

• Add DNA to the measurement plate/tubes before anything else
• Use a repeat pipette to make sure each well gets the same/right amount of dye solution
• Shake the tubes/plate in the dark for at least 10 minutes (quant will be inaccurate if the dye has not intercalated properly, you can check your standard curve replicates to verify if this is an issue)
• The triplicates really are worth the effort - especially if you're doing a Nextera prep

When will my sequencing be done?

Will my sequencing be done before the dying of the sun,
Will wildcats once more roam the land
Will the desert still have sand
Will Norfolk be swallowed by the sea
Do I have time for a cup of tea?
Will rhinos and the manatee
Be urban legend, just like me
Oh, it's done.

Nature reports on "careers in a core lab"

In this weeks issue of Nature a feature by Julie Gould covers what life as a core lab manager is like: Core facilities: Shared support. She interviews several core lab managers/directors from the US and Europe including me. If you've ever fancied a job in a core then I'd recommend the article.

• Stefan Green: DNA-services laboratory at the University of Illinois at Chicago
• Patrick Descombes: Functional Genomics Core at the Nestlé Institute of Health Sciences in Lausanne, Switzerland
• Mónica Morales: Core Facilities and Internal Scientific Services at the Centre for Genomic Regulation, Spain
• Yuriy Alekseyev: Microarray Resource at Boston Universit
• James Hadfield: Cancer Research UK Cambridge Institute Genomics core
• Gerd Prehna: Center for Structural Biology at the University of Illinois

If you have any questions about the realities of running a core and what sort of career move it might be feel free to get i touch. If you are in the CRUK-CI then you've got lots of other core managers who can give you there views as well.

Is your antibody any good

"Doesn't necessarily do what it says on the tin!" 

Probably not is the simple answer, and only if you've verified it is a more comprehensive one. The lack of reproducibility from antibody data in scientific publications is shocking, Nature published a commentary signed by over 100 researchers: Reproducibility: Standardise antibodies used in research, in which they describe the pretty poor state of antibody reproducibility. In this they cite a 2008 BioTechniques article, and a 2012 Nature commentary that discuss the state of affairs with antibodies in particular, and with reproducibility in general. In the BioTechniques paper the authors finish by saying that "for the meantime, however, the responsibility ultimately lies with the researcher or laboratory director to ensure that the antibodies used in their labs are validated for specificity and reproducibility."

Antibodies sold as being specific for a protein are oftentimes not, they can be very promiscuous in what else they bind and sometimes don't even bind the targeted protein. To make sure you are not affected by poor choices of antibodies make sure you run some validation studies before diving into your ChIP-seq experiments!

Not doing this risks wasting money (a lot according to the Nature article -see figure below). But more importantly you might waste your time, or even worse publish something that is erroneous. Hopefully you've already validated that your MCF7 cells are actually MCF7s with the BioRepository, so why not do the same with your antibody before starting your next experiment?

Figure from Bradbury and Plückthun Nature 2015.

Use your local support team

We have a half-day workshop on Thursday for NGS newbies, the focus of which is library prep for next-generation sequencing. We organise seminars from commercial providers of new technologies throughout the year; but this is a semi-annual event where local users get a chance to present their work, and new users get to hear about what's possible with NGS.

This year we have presentations about RNA-seq, ChIP-seq, Exome-seq, FFPE genomes, DNA methylation, targeted resequencing and a talk on the UoC 10,000 Genomes Project; and afterwards we'll wrap up with beer and pizza. These days require lots of organisation (thanks to Fatimah for organising this years event) but, for the new users especially, turn out to be well worth the effort.

Making use of your local support teams: We also make sure we keep a good relationship with our local technical support teams and run a series of commercial presentations throughout the year. This works out to be much easier to organise as they do the prep work! While we're here in the Genomics Core to help our local users, we get lots of queries from people outside the Cambridge Institute, and this is one way we've found to increase the support we can offer.

Every other month we have Illumina come in to present on a specific library prep, or talk about recent updates. Sandra (Field Application Specialist), and Carla (Marketing Technology Specialist) generally talk for 30 minutes followed by Q&A, and then spend some time with users on a one-to-one basis troubleshooting their problems.

We also try to arrange a training session once per quarter with Thermo. We've been using their ABI 7900 qPCR instruments for eight years and buy in quite a lot of their SYBR and TaqMan master-mixes. Ever since we started working with them we've run "An introduction to qPCR" course for new users. The last one was run by Emma and everyone said it was a great introductory session.

What's in it for them: Neither Illumina or Thermo would do this for free if there was nothing in it for them. They get to interact directly with potential new customers, and get feedback on how their technologies are working in the real world. Some of these conversations might end up as research collaborations. Some of the contacts might end up as new sales contracts too (I know why they are really here)!

What's in it for us: These talks have been reasonably well attended and increase the support we can offer (albeit indirectly), and the feedback from users has been almost universally positive. I'd encourage you to get in touch with your local sales or technical rep and ask if they can help you too. They might even supply doughnuts!

PS: Thanks very much to Carla and Sandra at Illumina for the seminars over the past 12 months. And to Emma for the most recent qPCR training.

PPS: If you missed the registration link to the event on Thursday, send us message via a comment below!

How many reads do I need to sequence?

A common question we're asked is "how many reads should I use to sequence a sample?" I'm going to focus on genomes, exomes and amplicomes in this post and introduce the Lander-Waterman equation [1]. Other apps are more complex because the number is very much 'how long is a piece of string' for RNA-seq, ChIP-seq and other counting applications - it depends on the complexity of your sample and the sensitivity you'd like to get, but is also affected by the number of replicates you have.

The Lander-Waterman equation

Lander-Waterman: Almost everyone doing NGS is using this equation, even if they are not aware of it. Anyone under 27 was born after it was published (1988), but it is an equation that is good to understand if you are sequencing. Basically it allows you to estimate how many reads of a specific length you need to sequence your genome.

The general equation is C = LN/G where: C = redundancy of coverage, G is the haploid genome size, L is the sequence read length, and N is the number of sequence reads. It can be rearranged to N = CG/L allowing you to compute the number of reads to sequence a genome, exome or amplicome (amplicon-panel) to a desired coverage (this is what we typically discuss when designing experiments).

In the examples below paired-end reads of 125bp from each end of a fragment are used, but these are converted to single 250bp reads for simplicity.

• Human genome (3Gb) 30x coverage = 360M reads.
• Human exome (150Mb) 50x coverage = 30M reads.
• Human amplicome (30x250bp amplicons 0.075Gb) 1000x coverage = 0.3M reads.
  

[1] Lander, E. S. & Waterman, S. Genomic Mapping by Fingerprinting Random Clones : A Mathematical Analysis. Genomics 239, 231–239 (1988).  

 

Eric Lander founded both the Whitehead and Broad Institutes. Michael S. Waterman is one of the founders of computational biology and gave his name to another important algorithm: Smith-Waterman alignment, he also wrote Computational Genome Analysis with our Director Simon Tavare while at the University of Southern California

Is my NGS library any good?

We've all been there. You bought the extortionately priced kit, you ran the gels, you lovingly removed every single SPRI bead, you sweated in a lab coat for days, and finally you elute your first ever NGS libraries. The question is, how can you tell if you were wasting your time? What if your tube turns out to contain nothing but buffer? Or worse, what if it can be sequenced, but it produces nothing more than a load of expensive gobbledegook?

Never fear, if your experimental design is up to scratch, then you need only three simple quality checks to tell you if your library is a Science paper in the making, or a bit of a dud:

1. Bioanalyzer for Library Length
2. qPCR for Concentration
3. Nanodrop for Chemical Contamination (optional)

1. Bioanalyzer for Size Distribution

The Agilent Bioanalyzer or Tapestation runs 1ul of your library in a microfluidics gel-like cartridge, and shows you the range of sizes in your library, as well as an estimate of library quantity.

A good Bioanalyzer trace will look different depending on the type of library you are assaying. Preferably, your library should appear as a single discrete peak approximating a bell curve. It should be larger than ~150bp, but smaller than ~700bp.

The Bioanalyzer trace is essential for detecting Illumina adapter contamination, which can be spotted is a sharp peak between 100 - 150bp. If you are a member of the CRUK Cambridge Institute, we can train you on how to run the Bioanalyzer and offer you advice on interpreting your Bioanalyzer trace.

A clean library on the Bioanalyzer: this will sequence like a dream

A problematic library on the Bioanalyzer: it will be difficult to sequence this library well.

Once you have run your library on the Bioanalyzer, use manual integration or the region table to select the entire trace and determine the average size of your library. You will need this to calculate your nanomolar concentration later. If you sequence with us, we will ask for this information at submission - it must be accurate in order for us to provide you with a high sequencing yield and quality.

Look out! Certain library prep types do not give an accurate length estimate on the Bioanalyzer due to the presence of secondary structures in the DNA (e.g. Truseq DNA PCR-free). If you're using a kit, the protocol should clearly state if this is the case - and should give you the length to use in quantification calculations.

I wouldn't recommend you use use the Bioanalyzer nmol/l concentration for multiplexing, unless you really know what you are doing - or it is explicitly recommended in your protocol or kit. After all, the Bioanalyzer nmol/l value is only accurate for quantifying certain library prep types, and it is biased by any DNA in your sample which does not contain Illumina adapters.

2. qPCR for Quantification

I like to recommend quantification of libraries by qPCR, using primers designed to target the Illumina adapters. Our NGS service currently uses the KAPA library quantification kit (LQK) for this, and we find it very reliable - but there are alternative kits out there which we haven't tested.

A high quality library should be high concentration, ideally >10nM, but also not too high concentration, ideally <100nM. 

If you find your libraries are consistently very high yield (>100nM), then it is likely that you are performing more cycles of PCR than you need; this is likely to give you unnecessarily high PCR duplicate rates in your data. Reduce your protocol 1 PCR cycle at a time until you are reliably getting 10nM - 100nM libraries. Make sure you remember to dilute your library pools to within our submission requirements, currently 10nM - 20nM.

My top tips for high quality qPCR quantification:

• Aliquot your qPCR mastermix and your standards into single-use batches prior to first use, to avoid template contamination and the effects of repeated freeze-thaw cycles

• Wipe down all working surfaces and pipettes with a DNA degrading cleaning agent e.g. DNA Away/DNAoff/DNAZap, before starting work

• Make a serial dilution and take triplicate measurements, use the median concentration result

• Check your serial dilution and your replicate measurements give highly reproducible concentration values

• Check that your results are all comfortably within the range of your standard curve

If you use our NGS service and you choose to use the KAPA LQK, we can provide you with aliquots of the recommended DNA dilution buffer (Tris-Hcl with 0.05% tween). Also, if you are within the CRUK-CI, we offer training on how to perform real-time PCR, and you can sign out a KAPA qPCR kit from the Genomics Core to take advantage of the Institute’s bulk discount.

If you must know about the Qubit...

Other quant methods like Qubit or Bioanalyzer can be great for some library types, as long as you know what you are doing - but both will over-estimate your library concentration if you have an inefficient adapter ligation reaction. So use them with care.

Our submission guidelines are in nmol/l (nM), so if you use the Qubit you need to convert ng/ul to nM using the following equation:

x: concentration in ng/ul 

L: average library length (bp)

y: concentration in nM.

3. Nanodrop for Chemical Contamination

The Nanodrop is a quick and dirty assay for protein and chemical contaminants which interfere with sequencing - including the real killers ethanol and phenol. Test 1ul of each NGS library, preferably before you pool them for submission. I recommend you check that the 260/280 ratio is greater than 1.8, and that the 260/230 ratio is greater than 2.0. The trace should like like this:

A good Nanodrop profile

A bad Nanodrop profile. Do you see the peak at 230nm?

A library with a 260/230 ratio less than 1.8, or a 260/280 measurement less than 2.0, may cluster poorly, and therefore generate low quality data. If you're new to the library preparation process and you can spare the sample I recommend you throw this one away and start again - while paying very careful attention to each cleanup step.

Always use the recommended cleanup method, don't be tempted to swap a bead cleanup for a column, or vice versa, even if it is more convenient! That will waste your time in the long run.

If you've got a contaminant and your library is irreplaceable, consider whether your yield is sufficiently high for you to repeat the final cleanup step. If not, have a chat with your NGS provider and ask if they will try sequencing it anyway. If you sequence with us here at CRUK-CI, we will always try our best to get you sequence data - as long as you know the you run the risk of paying for a lane of data which you can't use.

Whatever happens, do NOT use the Nanodrop quantity measurement for quantifying your DNA/RNA prior to library preparation, OR your final library concentration. DON'T DO IT. This is the most easily avoidable mistake in NGS. Don't be that scientist!

I hope that is enough to get you started. As ever, if you want advice on whether your library is going to sequence well on the Illumina platform, the best place to go is your local NGS facility (if you have one), or Illumina's technical support team: techsupport@illumina.com.

Happy Sequencing!