emerging technologies in molecular pathology

Emerging Technologies in Molecular Pathology

Edward C. Stack, Ph.D.

January 5, 2012

Molecular techniques can offer tremendous insight into disease processes. However, indiscriminate use of these techniques, without a clear understanding of underlying pathology, can lead to false assumptions.

• How pathology guides molecular analysis• Dissection methods and consequences

How pathology guides molecular analysis

Dissection methods and consequences

Microdissection Macrodissection

Dissection methods and consequences

RNA Profiles

Robot Row in CMOP

Nucleic Acid Extraction

Fres

hFF

PE

• NanoString for limited transcriptome analysis• Affy ST microarrays for more complete transcriptome coverage• RNAseq for whole transcriptome coverage

• Fresh v. FFPE• RNA species differences• analysis issues and biocomputational evolution

Emerging tools in the molecular pathology armory

As new tools avail themselves, it is important to understand those currently used and how new assays, such as nanoString or RNAseq, compare to those currently considered the ‘gold’ standard, such as TaqMan qPCR.


qPCR

A technical advancement over standard PCR, as developed by Mullis and Faloona.

PCR allows for exponential amplification of DNA sequences, when the genomic sequence at each end of the target is known.

Importantly, quantification (the q in qPCR) of expressed transcripts is not possible with standard PCR, which is more qualitative and at best, semi-quantitative. Why?

It is essential to understand standard PCR in order to make effective use of qPCR.


qPCRStandard

PCRStrand Separation Primer Hybridization DNA synthesis


qPCRThe RT portion – can be performed as

either a one- or two-step process


qPCRUsing TaqMan Reagents (Applied Biosciences)

http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/real-time-pcr/taqman-and-sybr-green-chemistries.html


qPCR

qPCR allows reactions to be characterized by the point in time during cycling when amplification of a PCR product achieves a fixed level of fluorescence.

In the initial PCR cycles (baseline), fluorescence is negligible. Software then calculates the Rn (normalized reporter), with an algorithm determining the point on the amplification plot at which the ∆Rn crosses the threshold. The cycle at which this occurs is defined as CT.


qPCR In practice - interrogating miR 146a (with RNU48 snoRNA control).


NanoStringThis novel transcription analysis was developed within the last 5

years, and is proving to be an attractive alternative to qPCR for comprehensive, though still small, transcriptome studies.

Developed by Geiss and colleagues -


NanoStringThrough use of a capture and reporter pairing, this assay is capable of

detecting ~16K individual transcripts. How it works:

From Geiss, et al (2008)


NanoStringAnalysis of spiked-in controls provided demonstrates linearity and

reproducibility of the assay



NanoStringScatterplot analysis of normalized NanoString signal shows very tight R2. GEP

alterations also observed.



NanoStringComparison between NanoString and an Affy array demonstrated higher

transcript detection in the Nanostring platform.



NanoStringFurther comparisons of fold-changes between NanoString and an Affy array

demonstrated fairly good agreement (R2 = 0.788).


For 202 transcripts significantly regulated in both the NanoString and Affy platforms, the two platforms demonstrated significant agreement (Correlation coefficient of 0.788). .


NanoStringAdditional validation of detected fold changes between NanoString and Affy

using TaqMan revealed very high concordance (more so than Affy). Furthermore, using human reference RNA, the concordance between NanoString and TaqMan persists (R2 = 0.945). What does this mean?



NanoStringSo how well does it work on FFPE?

NanoString qPCR

Pearson Coefficient r = 0.59

Pearson Coefficient r = 0.90 Pearson Coefficient r = 0.50

Samples are FFPE oral carcinomas


Affy ST microarray

The Affy 1.0 ST Array interrogates 28,869 genes using 764,885 unique probes (~40/gene), based on more than a 3’-based interrogation. Why might this be important in FFPE?

Total RNA

whole transcriptome amplification

cDNA

Figure courtesy of Neil Martin, MD


Affy ST microarray How does the array work?


Affy ST microarray Does the array work well in FFPE? • Pilot data from sample 1175.

Log2 ratios between paired fresh and FFPECorrelation trending correctly.


Affy ST microarray Does the array work well in FFPE?

Normalized data clusters based on tissue type – squamous cell carcinoma or adenocarcinoma. Putative p63

signature (correlative and anti-correlative changes) between SCC and AC.

From Hall, et al, 2011


Affy ST microarray

Post-assay considerations:

• Bioinformatics required for parsing of the data.

• Unlike the NanoString, calls of gene up/down regulation need to be verified by an independent method – typically TaqMan.


RNAseqShorthand for R N A Sequencing.

Developed in the late 1990’s by Cambridge scientists Shankar Balasubramanian and David Klenerman, in part through their work on fluoroescently labeled captured DNA molecules.

Allows for the sequencing of ALL RNA species, providing an unparalleled view of the entire transcriptome.


RNAseqOverview.

Transcriptome reconstruction

Expression quantification

From Wang et al, 2009

FreshTissu

eCores

RNA from FreshTissu

e

RNA Isolation Typical RNA Profile from FFPE TruSeq Library Prep Blunt End°°

Adenylate°°

Ligate Adapter

°°Enrich Library

Typical TruSeq Library Profile from FFPE

1st Strand

2nd Strand


RNAseqLibrary Construction – Fresh RNA

FFPE Tissu

eCores

RNA from FFPE Tissu

e

RNA Isolation Typical RNA Profile from FFPE TruSeq Library Prep Blunt End°°

Adenylate°°

Ligate Adapter

°°Enrich LibraryRNA

from FFPE

Tissue

1st Strand

2nd Strand


RNAseqLibrary Construction – FFPE RNA

Typical TruSeq Library Profile from FFPE


RNAseqData Generated – Fresh RNA

Reads / Aligned Reads

Chromosome Map


RNAseqData Generated – Feasibility in FFPE RNA

Reads / Aligned / Not / Multi

Chromosome Map

Typical visualization of expression (PTEN) in replicate samples from FFPE blocks containing prostate cancer tissue.

While expression is not restricted to exons, there is a tight segregation of reads within identified gene regions, and a general absence of reads outside of identified transcripts.


RNAseqData Visualized – Feasibility in FFPE RNA

0200

400600

8001000

12001400

16001800

20000

200400600800

100012001400160018002000

Gene FPKM Concordance

R1, FPKM

R2, F

PKM

Concordance over 10,000 gene transcripts detected by RNAseq in replicate FFPE PrCa samples. R2 = 0.9838

0 10 20 30 40 50 60 70 80 90 1000

20

40

60

80

100

Concordance over 7000 transcription starts sites (TSS) detected by RNAseq in replicate FFPE PrCa samples. R2 = 0.9989

TSS FPKM Concordance

R2, F

PKM

R1, FPKM

A Sample of PrCa-relevant genes detected include:MMP10, SMTN, ERG, TMPRSS2, BIRC5, UBE2C, CDKN3, VEGF, and TK1.


RNAseqData Visualized – Feasibility in FFPE RNA


RNAseqFeasibility in FFPE RNA

A fairly robust read depth via single end sequencing results in ~ 40% alignment to hg18 sufficient to support interrogation of the transcriptome.

Inter-assay reproducibility is demonstrated by high concordant replicates, for both gene transcripts and transition start site sequences, demonstrating high assay fidelity.

Lack of poly-A selection offers more transcriptome coverage, providing a more complete snapshot of the RNA environment, including mRNA, ncRNA, etc..

Does this mean RNAseq is ready for research?


RNAseqIssues with FFPE RNA

Bioinformatic challenges include:

Data handling and storage (sequencing leads to massive data outputs).

Contig assembly and genomic alignment (we currently use TopHat, though there are others). Complex genomes though have problems with:

exon spanning sequences (junction library);sequence reads that map to multiple genomic locations (proportional assignment

based on neighboring unique sequences).

Cost versus coverage and depth – real world concerns.



Mapping aligned reads to UCSC Genome browser demonstrate marked differences between fresh and FFPE RNAseq samples in the housekeeping gene transcript ALDOA - aldolase A, fructose-bisphosphate.

From Dr. Svitlana Tyekucheva



A similar trend was observed when mapping aligned reads to UCSC Genome browser demonstrate marked differences between fresh and FFPE RNAseq samples in: the Pca specific gene transcript KLK3 - kallikrein-related peptidase 3 (or PSA); or the housekeeping gene transcript GAPDH - glyceraldehyde-3-phosphate dehydrogenase.


GAPDH

KLK3



A similar trend was observed when mapping aligned reads to UCSC Genome browser demonstrate marked differences between fresh and FFPE RNAseq samples in: the housekeeping gene transcript ACTNB- Actin B; or the housekeeping gene transcript RPL32- ribosomal protein L321.

While the apparent loss of aligned signal is less than ideal, the overall picture suggests that the development of novel bioinformatic approaches to FFPE RNAseq data will lead to a new era in transcriptome analyses.


RPL32

ACTNB


RNAseqFFPE small RNA – miRRNAseq in practice!

Small RNAseq (smRNAseq), where the assay can detect miRNA snoRNA, scRNA, snRNA, as well at tRNA. Mt-tRNA, rRNA, introninc, exonic, and more…



Unsupervised cluster analysis of miR expression calls between RCC and normal as assessed by RNAseq in both fresh and FFPE samples demonstrates the ability of each substrate to classify tumor from normal.

Notice anything unusual?

From Weng et al, 2010



Correlational analysis of miR expression calls between RCC and normal as assessed by both RNAseq and microarray in fresh samples demonstrates high correlation of expression alterations (R=~0.8). Same is true for differentially expressed miR’s (R=0.77).

Data demonstrate practical suitability for large-scale smRNAseq.




Correlational analysis of miR expression calls between RCC and normal as assessed by RNAseq in fresh and FFPE samples also demonstrates high correlation of expression alterations (R=~0.96). Differentially expressed miR’s show similar concordance as before (R=0.79).

Anything unusual about this approach?




Specific miR expression levels by RNAseq, microarray, and qPCR in fresh samples

Does the data presented leave any impression?


Emerging Technologies in Molecular PathologyReference List:

Geiss GK, et al.. (2008) Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 26:317-325.

Hall JS, Leong HS, Armenoult LS, Newton GE, Valentine HR, Irlam JJ, Möller-Levet C, Sikand KA, Pepper SD, Miller CJ, West CM. (2011) Exon-array profiling unlocks clinically and biologically relevant gene signatures from formalin-fixed paraffin-embedded tumour samples. Br J Cancer 104:971-981.

Mullis KB, Faloona FA. (1987) Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods Enzymol 155:335-350.

Osborne MA, Furey WS, Klenerman D, Balasubramanian S.(2000) Single-molecule analysis of DNA immobilized on microspheres. Anal Chem 72:3678-3681.

Reis PP, Waldron L, Goswami RS, Xu W, Xuan Y, Perez-Ordonez B, Gullane P, Irish J, Jurisica I, Kamel-Reid S. (2011) mRNA transcript quantification in archival samples using multiplexed, color-coded probes. BMC Biotechnol 11:46-56.

Weng L, Wu X, Gao H, Mu B, Li X, Wang JH, Guo C, Jin JM, Chen Z, Covarrubias M, Yuan YC, Weiss LM, Wu H. (2010) MicroRNA profiling of clear cell renal cell carcinoma by whole-genome small RNA deep sequencing of paired frozen and formalin-fixed, paraffin-embedded tissue specimens. J Pathol 222:41-51.

Wang Z, Gerstein M, Snyder M. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57-63.

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