Exploring MPS for RNA profiling. Titia Sijen

Transcription

1 Exploring MPS for RNA profiling Titia Sijen

2 When cells have a specialized function, there must be special molecules: Messenger RNA Micro RNA Epigenetic Proteome biochemical Microbial colonization 210 cell types in a human body That may be used as specific forensic markers

3 Pro s: Multiple candidates, high numbers per cell, mixture analysis, no consumption DNA extract Messenger RNA Micro RNA Epigenetic Proteome biochemical Microbial colonization I m V e r y S p e c i a l Cons: Less stable than DNA, variable expression, spurious signals, DNA/RNA protocols

4 Tissue-specific mrna expression 2% of the genome translated 85% transcribed (also non-coding) 22,000 coding genes / 300,000 mrnas per cell with average half life time 10 h Gene function determines whether expression is cell type specific or general PRM1 Testis GFAP Brain RRNAD1 General

5 Forensic relevance & classic body fluids Blood Type Forensic relevance Violence, human-specific assay Body fluids Semen, fertile Semen, sterile Saliva Vaginal mucosa Sexual assault, confirmation sampled area Sexual assault, confirmation sampled area Sexual assault e.g. licking, kissing or inoffensive stain Sexual assault, confirmation sampled area Menstrual secretion Sexual assault or inoffensive alternative scenario Touch Skin Confirmation sampled area

6 Forensic relevance & other secretions Type Expirated blood Nasal blood Forensic relevance Violence, confirmation bloodstain pattern analysis Thump on the nose or inoffensive alternative scenario Other secretions Nasal secretion Sweat Urine Tears Breast milk Inoffensive alternative scenario Confirmation witness report Confirmation sampled area Possible inoffensive scenario if cross-reactive Possible inoffensive scenario if cross-reactive Vomit Faeces Contains saliva and stomach content, inoffensive scenario Anal sexual assault

7 Forensic relevance & organ typing Type Forensic relevance Organs Brain Heart, lung Kidney, liver Head injury Chest injury Abdominal injury

8 RNA NFI: two distinct CE-based assays Body Celtypering fluids: 19-plex DNA: Who? RNA: What? Organs: 18-plex

9 Casework NFI: Since 2010, RNA typing applied in 161 cases: 129 cell typing: most samples are mixtures 32 organ typing: most samples are single-source 3 exposures to judiciary: 1 examining magistrate, 1 inquisitorial court, 1 adversarial court 8 cases with RNA research in published court decisions: For 2 cases the RNA results are not specifically relevant: blood on bullet point, blood on truncheon: match victim For 6 cases the RNA results were important: Three sexual assaults: vaginal on fingers suspect, vaginal on underpants suspect, menstrual/vaginal on penile swab suspect Three violent deaths: human brain on shirt, brain on coat-vicinity suspect, blood & nasal on trousers-vicinity suspect

10 DNA: Two copies per somatic cell Human-specific quant Balanced profile Reference profile Weight of evidence RNA: Amounts total RNA differ per cell type Amounts individual RNAs differ in a cell mrna expression is enriched not restricted Expression may change with environment / physiological state No human-specific RNA quant Mixture interpretation complicated from expression differences Mr. DNA Mrs. RNA

11 Accommodating complications of RNA analysis 1. Stability: Small amplicons - cdna-specific 2. Lack of quantification system: Serial input approach 3. Variation mrna expression: Multiple markers per cell type 4. Background/spurious expression: Replicates & Interpretation rule RNA fixed input cdna variable input multiplex PCR 0.01µL cdna 0.2µL cdna 1.0µL cdna Background signals of low height: detection 150 RFU

12 Accommodating complications of RNA analysis 1. Stability: Small amplicons - cdna-specific 2. Lack of quantification system: Serial input approach 3. Variation mrna expression: Multiple markers per cell type 4. Background/spurious expression: Replicates & Interpretation rule # of detected peaks / # possible peaks (through markers per cell type X # replicates) Alike consensus rule low-template DNA profiles Especially important for challenging samples If 0% not observed If 50% observed If between 0-50% sporadically observed not reliable Co-expressed markers (blood/vaginal with menstrual) not present as such

13 Combining DNA & RNA-profiles DNA: Who? RNA: What?

14 Combining DNA & RNA-profiles One donor can give multiple cell types! One cell type can be given by multiple donors!! DNA profiling and RNA profiling are not equally sensitive The major in the DNA profile may or may not give the highest RNA peaks

15 DNA profiling more sensitive: RNA profiling more sensitive: RNA: marker detec on % RNA: marker detec on % RNA: marker detec on % 100% Variable results for markers/donors: 80% 60% 40% 20% 0% 0% 20% 40% 60% 80% 100% 100% 80% 60% 40% 20% 100% 80% 60% 40% 20% Semen DNA: % detected alleles Skin 0% 0% 20% 40% 60% 80% 100% DNA: % detected alleles Vaginal mucosa 0% 0% 20% 40% 60% 80% 100% DNA: % detected alleles SEMG1 PRM1 KLK3 CDSN LCE1C MYOZ1 CYP2B7P1 MUC4 DNA: D1 D2 RNA: D1 D Blood Saliva Major DNA = Major RNA DNA: D1 D2 RNA: D1 D Menstrual Blood Major DNA Major RNA

16 Risk: association fallacy when using peak heights to associate results of DNA & RNA profiling Exception: gender specific body fluids and 2 donors of different gender

17 MPS & Forensic RNA analysis: two goals 1 Finding RNA markers for specific cell types / body fluids / organs 2 Platform for RNA analysis of evidentiary samples

18 MPS to Find RNA markers for specific cell types

19 MPS for RNA analysis of evidentiary samples 1 Sequencing RT-PCR products 2 RNA-Seq or whole transcriptome shotgun deep-sequencing Technique Advantage Disadvantage RT-PCR High accuracy Necessary to know the sequences Manageable data Build on knowledge from CE-based assays For instance regarding interpretation of mixtures Amplification bias PCR artifacts RNA-seq Also non-human information Also non-human / housekeeping information Unbiased sequencing More individual-specific variation Digital sequencing: counts the random tags not the barcodes Bioinformatician needed for analysis Higher costs Target enrichment

20 Sequencing RT-PCR products: first MiSeq FGx runs 1 Cell-typer (19-plex) & Organ-typer (18-plex) 2 All target fluids & organs 3 Various donors 4 For now only high cdna inputs, lower inputs to follow FDSTools to generate graphical output Specific signals! Variation in expression per donor A-specific signals: as known Sequence variants!

21 Saliva: two donors: different expression levels

22 Semen: two variants SEMG1

23 Vaginal: low read numbers for non-target genes alike CE

24 Similarities MPS and CE-based RNA profiles 1 Primer concentrations to balance for expression differences With CE also different strengths for distinct fluorescent labels GTEXportal µm 0.05µM 0.25µM 393 whole blood samples Median RPKM Ratio to CD x 14x 1x 2 Preferred signal strength / read coverage: eg >500 rfu / >1000 reads Accommodate expression differences Easier to discern artifacts

25 Advantages MPS vs CE-based RNA profiles 1 Wider dynamic range, not dependent on fluorescent signals No need for serial input range Still a need for a detection threshold (cut-off value) 2 Better suited for larger PCR multiplexes (some more primerdimers etc) More markers per cell type (if identified, can be low expressed) More cell types (combine Cell and Organ typer) 3 No spacing issues (bleed-through signals / trailing issues) Small amplicons that can have overlapping sizes 4 SNPs Associate donor and cell type through SNPs? Determine DNA sequence reference samples

26 Cell type A Variant GG Donor A Variant GG Cell type B Variant AA Donor B Variant AA Heterozygocity will complicate interpretation

27 5 -UTRs may carry more sequence variation: For instance: MUC7 (saliva marker) Donor MUC7 Read # ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 1 REF 5939 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 2 REF ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 3 REF 17G>- 21C>T ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA 1635 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 4 REF 1799 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 5 17G>- 21C>T 2343 ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA REF 2030 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 6 REF 6053 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 6 REF 8692 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 7 REF 5908 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 8 REF 3331 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 9 REF 2017 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA 17G>- 21C>T 1258 ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA Donor 10 REF 6344 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 11 17G>- 21C>T 1148 ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA REF 357 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 12 17G>- 21C>T 150 ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA Donor 13 17GAG>- 21C>T ATTCACACTGCACCAG ---ATATCAGAAAGAATGAAAA 17G>- 21C>T ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA Donor 14 REF 2005 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 15 REF 2424 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 16 REF ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA 17G>- 21C>T 2389 ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA Donor 17 REF 6096 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA 17G>- 21C>T 1920 ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA Donor 18 REF 2938 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA Donor 19 REF 6301 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA

28 5 -UTRs may carry more sequence variation: Possibly: Effects on expression level? Role of 5 -UTR! Donor 13 17GAG>- 21C>T ATTCACACTGCACCAG ---ATATCAGAAAGAATGAAAA 17G>- 21C>T ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA Donor 16 REF ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA 17G>- 21C>T 2389 ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA Donor 17 REF 6096 ATTCACACTGCACCAG GAGACATCAGAAAGAATGAAAA 17G>- 21C>T 1920 ATTCACACTGCACCAG -AGATATCAGAAAGAATGAAAA

29 Combine DNA & RNA profiling at the MPS stage Evidentiary sample DNA RNA RNA (DNase treated) cdna PCR STRs PCR cell types MPS

30 Combine DNA & RNA profiling all-through the assay 1 Reverse transcription in the presence of DNA 2 Simultaneous amplification of STRs and mrna markers Thorough validation (court purposes) Technical challenge? Evidentiary sample all nucleic acids cdna PCR STRs & cell types MPS

31 Interpretation & software tools For both: Developments as we go Build on knowledge from CE-based assays Compare to CE-based results Acceptance in court Many thanks to: Questions: Kris van der Gaag Margreet van den Berge Jerry Hoogenboom Illumina & you!