Transcriptomics

Jiri Zavadil, PhDMolecular Mechanisms and Biomarkers

International Agency for Research on Cancer, Lyon

Transcriptomics - Definitions

Transcriptome - the complete set of RNA transcripts produced by the genome at a given time

Transcriptome is highly dynamic and complex in comparison to the relatively stable genome

Transcriptomics - the global study of gene expression at the RNA level

- can include genes for ncRNAs (microRNAs etc)

Blood spots Cord bloodWhole bloodGenetic, epigenetic, transcriptomic analyses (nucleic acids)Proteomic analysis, serological and chemical analyses

UrineChemical, proteomic and nucleic acid analysis

Tumor cells, tissues

Biospecimens in I4C Mother-Child and Infant-Child Cohorts

Case for Integrated Omics Analyses

DNA methylation

Histone modification

The prospective biospecimen collection and retrospective case analysis will yield interconnected results

Epigenetics gene regulation RNA and protein markers

Studied by transcriptomics

Transcriptomics – Applications for I4C

Specific gene expression Genes and signatures determined by particular genetic, epigenetic regulatory factors, environmental exposures

Exploratory approachesNot hypothesis driven, e.g global gene expression in tumors versus healthy tissues, differential responses to distinct environmental exposures

Disease etiology and classification Patterns/signatures rather than single markers can improve knowledge about etiology and diagnosis

DNA Microarray Platforms

Affymetrix GeneChip

Illumina BeadArray

WorkflowReverse transcription, IVT with labeled nucleotides, array hybridization, staining, washing scanning

Pros/ConsRapid and streamlined protocols, standardized analysis; biased target collection, levels but limited sequence information

miRNA TLDA Array

742 total target miRs

Quantile Normalization

ABI 7900 SDS HT

MicroRNA - TaqMan Low Density Array

Total RNASample

Pros/ConsQuantitative abundance analysis; biased target collection

Stratton, MR. Science 331, 1553 (2011)

Cancer Genome SequencingIntegrated Molecular Profiling By MPS Massively Parallel Sequencing (MPS) - powerful nucleic acid analysis tool providing base-pair resolution information at the genome scale

emPCR

Massively Parallel Sequencing

Accuracy < 99.99% Throughput/Day <10–15 GbThroughput/Run <90 Gb or >1.4 B reads (paired-end or mate-paired runs)

Samples/Run• 1 genome• 12 exomes• 6 transcriptomes

ABI SOLiD 5500

Bridge amplification, clonal expansion

Massively Parallel Sequencing

6 human genomes at 30x64 transcriptomes at 20M mapped reads/sample

Illumina HiSeq2000/2500

mRNA Abundance AnalysisRPKM (Reads Per Kilobase per Million mapped reads)

FPKM (Fragments Per Kilobase per Million mapped reads)

Methods of quantifying gene expression levels from RNA-seq data by normalizing for total read length and the number of sequencing reads or fragments (PE reads).

Equivalent distribution Identical distribution (spread, range and median)

Unnormalized data Scaling Normalization Quantile Normalization

log 2(R

PKM

)

-4 -2

0 2

4

-4 -2

0 2

4

-4 -2

0 2

4

A1 A2 A3 A1 A2 A3 A1 A2 A3

Differential mRNA Abundance Analysis

ACSL5 – normalized differential abundance ratio = 8.4

Non-syn SNV/mutation identified at both DNA/RNA levels

DNA

RNA

Single Nucleotide Variant Analysis

Acceptor Splice Sites Mutated in UUCExon N Exon N+1GU------A-----AG5’ 3’

Tumor RNA

Tumor DNA

Normal DNA

mRNA Splicing Aberrations

Patient ID MutationMutant Allele Frequency (17,000-50,000x coverage)

    Diagnosis Relapse719515 p.R238W 0.01% 27%

737185 p.R238W 0 18%

761159 p.R238W 0 31%

756421 p.R367Q 0.02% 25%

716996 p.K404KD 0 55%

763368 p.S408R 0 50%

769886 p. S445F 0 25%

728610 p. L626F 0 49%

726584 p. E274Q 0 19%

728610 p. S171I 0 43%

728610 p. M244L 0.38% 47%

726584 p. A53V 0 20%

6 matched diagnosis/relapse pediatric ALL samples (n=12) RNA-seq to discover novel mutations specific to relapse disease Targeted amplicon resequencing at ultra-deep coverage

Stage-Specific RNA Aberrations in ALL

Microarray and RNA-seq Transcriptome Profiling

•Possible with >10 picograms total RNA•Degraded samples, RIN scores >2.0•Formalin-fixed, paraffin-embedded (FFPE) samples •Whole blood•Direct cell lysate from the equivalent of a single or a few cells

microRNA Profiling•megaplex amplification protocols - 1-350 ng total RNA•non- amplification based for 350 – 1000 ng total RNA

Solutions for Low Yield Samples

MPS cost goes down, technologies become more advanced and powerful, platforms develop rapidly – a strong case for transcriptomics within integrated omics approaches applied to large cohorts such as I4C.

The Future of MPS-based OMICS

The Economist, 2011

• Low yield samples (blood spots, extracellular microRNAs) might require application of amplification methods

• Tissue and cell specificity of gene expression (e.g. cord blood vs leukemic clone) – need for carefully matched controls

• Only genes and RNAs expressed at the time of sampling are detected

• Depth of coverage needs for RNAseq affect cost-related decisions

• Specific disease progression stages might mask etiology-associated aberrations

• Bioinformatics – limited standards for complex data processing and analysis (RNAseq), more benchmarking studies needed using data from consortia-like efforts (FDA’s SEQC). Data storage and access solutions.

Considerations for I4C Transcriptomics

Thank you….