New Ways to Track Transcripts - Genome Technology 11-2008

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N O V E M B E R 2 0 0 8 GENOME TECHNOLOGY 35

TRANSCRIPTOMICS

New WaysTo TrackTranscripts

Fo r a w h i l e , t opeek into a cellst r a n s c r i p t s ,researchers mainlyrelied on micro-

arrays. But with the rise of tran-scriptomics, the failings of arrays the biases of using probes, potentialfor cross-hybridization, and back-ground noise are thrown intosharp relief as researchers begin tolook globally at transcripts andwant to have even more informationabout them. The big challenge is tomap out all the isoforms and go alot deeper than anyones ever goneb efor e, s ay s Ya le Uni ve rs it ys

Michael Snyder.Instead of relying on arrays,researchers are turning to sequenc-ing-based methods to study geneexpression. In particular, next-generation sequencing technolo-gies are opening up transcrip-tomics to move beyond just geneexpression to include alternativesplicing, different isoforms, lowabundance transcripts, and more.

Ever s in ce h igh- th roughp utsequencing became a viable tech-

nology, I think one of the obvious

analysis of gene expression and

RNA-seq to microarrays. So far, itseems the next-gen-based tech-niques are holding their own oreven slightly surpassing arrays fordelving into the transcriptome.

SAGE

One method that has benefitedfrom the advent of next-gensequencing technologies is serialanalysis of gene expression. InSAGE, the mRNA is converted tocDNA and a tag is added to thepoly-A tail of the transcript at a par-ticular restriction site of choice. Thetags can be short or long, ranging inlength from 10 to 17 base pairs. Allthe transcripts then making up aSAGE library can be sequenced.Thanks to new platforms, suchlibraries can now be sequencedquite rapidly.

These next-generation technolo-gies make long SAGE a much more

cost-effective method, where previ-ously we were doing everything bySanger sequencing. It was moreexpensive to do long SAGE effec-tively, says Isobel Parkin at Saska-toon Research Centre. She adds, Ithink its a fantastic technology.

Unlike arrays, SAGE is independ-ent of what is already known aboutthe transcript being studied though having genomic informationcan make the analysis much richer

and SAGE can be quantitative.At the Universit de Lyon, OlivierGandrillon uses SAGE to studyg en e e xp re ss io n d if fe re nc esbetween chicken cells undergoingself-renewal and ones that areundergoing differentiation to deter-mine what signals the process of dif-ferentiation. After discussing itwith a couple of colleagues, wethought SAGE was much moreadapted towards what we werelooking for because we had no idea

what genes might be involved in

Next-gen sequencing is changing the face of

transcriptomics studies, with improvements

to SAGE and the new RNA-seq.

BYCIARA CURTIN

applications has been to study gene

expression levels and particu-larly it offers a lot of potential to useover arrays, says the University ofChicagos John Marioni. For exam-ple, particularly for genes that areonly moderately expressed, thatsdifficult to discriminate the signalfrom the background in an array.

You might be able to identify thoseaccurately using sequencing tech-nology.

In a spate of papers, researchersare comparing and developing

next-gen sequencing-based serial

MICHAELSNYDER

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[differentiation] because it is a very

transient state, Gandrillon says.We wanted to have the most exten-sive covering of our transcriptome.

We thought SAGE was ideal forthat. For his work, microarrayswerent even an option (there are nochicken-based microarrays avail-able), but SAGE can be used onnon-mainstream organisms. In hischicken studies, Gandrillon usesthe method to see which genes havetheir expression levels changebetween differentiating and self-renewing cells, and he says its reli-able. It really gives you a good ideaof whats inside the cell.

You can use it on any species,even where you have no sequencedata available, says Parkin, whostudies low temperature responsivegenes in Arabidopsis, but is alsointerested in applying that work tocrop plants such as Canola, forwhich genomic data may not beavailable.

But having theArabidopsis genome

at hand for her analyses allows herto use SAGE data in a more effectiveway, she says. In her study of lowtemperature responsive genes inArabidopsis, Parkin compared herSAGE data to what had already beenuncovered using microarrays. Ofthe low temperature responsivegenes she found, 40 percent hadalready been identified. The restwere novel. When she comparedbetween the microarray analyses,she found wide variation in the

genes they identified. I think

SAGE, if you have enough depth, it s

very good for looking at both lowabundant and high abundant tran-scripts, Parkin says. Microarraystend to saturate the high abundanttranscripts so it is more difficult totry to tell differences in expression.

In addition to identifying tran-scripts, SAGE can also be quantita-tive. Every time you capture a tag,you are effectively counting thenumber of times that transcript ispresent. In that way, you can workout the abundance of that particu-lar transcript within an RNA pool,Parkin notes.

One downside to using SAGEmight be that it is teetering on theedge of being high throughput.Creating the libraries can be time-c on su mi ng a nd p ro bl em at ic ,Parkin says. [When] we firststarted, the library constructionwas a little bit tricky and, at thetime, there was no kit available fordeveloping these libraries. But we

got quite efficient atthat, so that was fine,she says.

The number of stepsinvolved in SAGE, how-ever, has been reduced.Gandrillon remembersthe way it was beforen e xt -g e n s e qu e nc in gwas used, when SAGEhad the extra step of cre-

ating concatemers that were closed

in bacteria before being sequenced.I would say those cons are nowcompletely relieved by Solexasequencing technology because theconstruction is much lighter, hesays. You can reproduce yourexperiments within [a s ing lesequencing run], if you can paral-lelize, which we are trying to do.

Like other next-gen sequencing-based approaches, SAGE spits out alot of data. Parkin and her groupwrote their own software to visual-

ize the data they were generating,

especially since they wanted to

look at it a bit differently than otherSAGE users generally do. In SAGEpeople tend to look at a particulartag and say, OK, that tag haschanged in expression, but youllfind that the same gene may pro-duce more than one tag becauseyou can get alternative transcriptsand anti-sense, things like that. Wewanted to see, for a particulargene, all the tags for that particulargene and what each of those tagswas doing, she says. It was a chal-lenge, but in a way it was fun andwe got a lot more out of the data bydoing that.

RNA-seq

The newest method for studyingthe transcriptome takes a sweepingapproach by casting a net for all ofthe RNA. With RNA-seq, all theRNA is turned into cDNA andsequenced it can be f ragmented

at either the RNA or the cDNAstep. The bottom line is you takeRNA, drop it into cDNA, andsequence that very, very deeply,

Yales Snyder says.This method only came about due

to the influx of next-gen machines.With the new sequencing tech-nologies that came along, wethought wed give that a try and see what we can see from thesequencing, Snyder says. His

group tried RNA-seq on yeast anddefined the transcribed regions atthe highest resolution yet seen, andit showed something unexpectedabout yeast genes: They overlap andhave heterogeneity at their 3 ends.

RNA-seq has the potential to getdown into the transcriptome andcharacterize its structure. It reallyhas huge potential to be extremelyuseful, particularly for identifyingregions of spliced boundaries,identifying novelly transcribed

regions, potentially for exon-spe-

I think SAGE, if you have

enough depth, its very

good for looking at both

low abundant and high

abundant transcripts.


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about what splice forms are pres-

ent, Marioni says.Furthermore, RNA-seq is quanti-

tative. You really have genes whichhave only one sequence read andgenes which would have 2,000sequence reads. You see that themore sequence reads, the more ofthe transcript you cover, naturally,

Yaspo says.RNA-seq is just much more

quantitative than microarrays,Snyder adds. It has an 8,000-folddynamic range. Microarrays can beabout, at best, 100-fold.

Despite its strengths, RNA-seqdoes have its weaknesses: the tech-nology is still in its infancy, andmight turn out to be less quantita-tive than it looks.

Har va rd s J on at ha n S ei dm anthinks RNA-seq might not be thebest tool for quantitative work.There are, he says, an average of300,000 RNAs per cell and theaverage mRNA is 3,000 base pairs

long. If the most abundant RNA isexpressed 105 times more than theleast abundant RNA, then youdhave to sequence at least 10 mil-lion nucleotides and do that atsome depth to make sure yousee everything. That would begood for looking at splicing, butits not going to be so good formaking distributional, differentnumbers of RNA in a cell, Seid-man says.

Since RNA-seq is a youngmethod, Marioni says that anyinherent biases havent yet beenexplored. I think its probably sim-ilar to the early days of microarrayswhen, for example, all sorts ofbiases were discovered over thefirst few years microarrays weredeveloped. The technologies wereadapted accordingly and wereprobably at a very similar stage withsequencing data, he says. So itlltake a little while until we really

understand all of the biases that are

going on and I suspect

that experimental proto-cols will continue tochange. Marioni is par-ticularly interested inhow uniformly the tran-s c r ip t s a r e c ove redacross the sequence.Yales Snyder is hoping

for some longer reads.Short reads, he says, cantell you where a single splice junc-tion is, but cannot give you theentire splicing pattern. It would benice to be able to do more novel dis-covery. Its hard to do with shortreads, he says. Longer reads willhelp us find junctions withnovel transcribed regions better.

Meanwhile, the promise of RNA-seq is unlikely to be an immediatethreat to arrays for transcriptomicswork. Today, Lyons Olivier Gan-drillon says, most projects start outusing arrays and move on fromthere. Eventually, though, theres

going to be less and less arrays andmore and more sequencing-basedtranscriptomics, he says.

Others think that there willalways be a spot for microarrays theyre currently cheaper, for onething. They are not going to dis-appear. I think they are relativelyeasy to use and they dont usemuch tissue, Seidman says. Fors o m e p u r p o s e s , t h e y a r eabsolutely great.

The change

The field of transcriptomics is juststarting to take off. The field istransitioning and is going to reallymap out transcriptomes at a resolu-tion that no ones ever seen beforeand at an accuracy that no ones everseen before, Snyder says.As that happens, researchers can

explore the complexity of the tran-scriptome even further. We really

have tools to look within the cell or

the tissue. We can see, for instance,what is the complexity of the tran-scriptome in the cell, and I thinkthat is very important, Yaspo says.We apply that to different contexts.For instance, we work on Downsyndrome expression profiling,also on several strains of mice ofgenetically regulated exons whichcan promote differential alternativesplicing so a slew of projects forwhich this is essential.

But as researchers dive furtherinto the complexity of the transcip-tome, more and more data will pile

up. I think we are going to havemammoth amounts of databecause of all this next-generations eq ue nc in g t ec hn ol og y. T ha tseems to be moving so fast ... theyare getting more and more readsper run and you are getting longertags per run, Parkin says. Thenthe question is having enoughcomputing power to be able to store your data, archive it, and ana-lyze it, which is going to become

the problem.All this data, Yaspo says, will helpresearchers properly annotate thegenome, or at least help them learnmore about regulatory regions andtranscribed regions. I believe therewill be a lot of data that will accu-mulate on context: cell and organ-isms, man and mouse and fromthat, a lot of work to put together[how] exactly the genome isdecoded into a transcriptome.Thats what I see for the next com-

ing years.

The field is transitioning andis going to really map out

transcriptomes at a resolution

that no ones ever seen before

and at an accuracy that no

ones ever seen before.


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