Defined mixes of synthetic microRNAs: prototypes for controls for miR profiling assays

Karol Thompson and Barry Rosenzweig, DARS/OCP/CDER/FDA Scott Pine, NIST

ERCC v2.0 Workshop

Stanford, CA July 10-11, 2014

This presentation reflects the views of the presenter and does not represent FDA’s views or policies.

• Why needed: The inherent properties of microRNA (small size, sequence similarity, etc) challenge their accurate measure in multiplex assays. Different technological approaches for profiling microRNAs can vary significantly in specificity and sensitivity (results of miRQC et al).

• Project goal: Prototype a synthetic microRNA mixture for use as a spike-in control for microRNA profiling platforms

• Design: Heterologous calibration curves that span the dynamic range of a platform. Plots of expression measurement versus input concentration generate a standard curve. Similar to some designs used for testing the ERCC v1 controls

Controls for microRNA profiling platforms

Controls for microRNA profiling platforms • Selection of miRs was based on the following

– Use on common fixed-content commercial platforms • Filtered for presence on human and rodent TaqCards and Exiqon

PCR arrays, and Affymetrix microRNA microarrays (platforms available for in-house testing of prototype)

– Use in samples of preclinical or clinical origin • Filtered for identical sequence in human, rat, and mouse

– Use as a spike-in into serum/plasma samples • Filtered against high expression in blood cells (Pritchard et al., 2012)

– Limit to 32 analytes (8 input concentrations in quadruplicate) – Other filters

• Filtered against propensity to form stable secondary structures (based on Minimum Free Energy calculated using RNAfold)

• Filtered against sequence homology with other candidates

Controls for microRNA profiling platforms

• Formulation of prototype microRNA controls – Stocks of 32 selected microRNAs were synthesized by IDT

• 100 nmole scale synthesis, 5’-phosphorylated, RNase-free HPLC purified • Dried oligos were reconstituted to a nominal concentration of 80 μM in TE pH 8 • Quantitated by NanoDrop (using RNA setting, based on μg/OD260 = 40) • Diluted to 40 μM based on NanoDrop OD260

– Prepare 8 pools from the stocks of 32 miRs • Portion 32 miRs into 8 pools (A-H) that are equimolar (nominal 10 μM) for 4 microRNAs • Re-calculate actual concentration of each microRNA in the pools based on μg/OD260 values

supplied by IDT for each miR

– Combine pools into 8 (modified) Latin Square mixes • Each Latin Square Mix (LSM) contains all 8 pools • The LSMs were made in a background of 0.5 ng/uL (0.3 nM) bacteriophage MS2 RNA (Roche) in

0.8x TE pH8 • Within each LSM, the pools differ by 5-fold increments (except two at 1:1) and span 4 logs • Across each LSM, each pool spans 4 logs at 5-fold increments (except two at 1:1)

Pool Name Sequence (FASTA)miR-125a-5p UCCCUGAGACCCUUUAACCUGUGAmiR-323a-3p CACAUUACACGGUCGACCUCUmiR-451a AAACCGUUACCAUUACUGAGUUmiR-98-5p UGAGGUAGUAAGUUGUAUUGUUmiR-10a-5p UACCCUGUAGAUCCGAAUUUGUGmiR-129-2-3p AAGCCCUUACCCCAAAAAGCAUmiR-187-3p UCGUGUCUUGUGUUGCAGCCGGmiR-218-5p UUGUGCUUGAUCUAACCAUGUmiR-137 UUAUUGCUUAAGAAUACGCGUAGmiR-150-5p UCUCCCAACCCUUGUACCAGUGmiR-361-5p UUAUCAGAAUCUCCAGGGGUACmiR-499a-5p UUAAGACUUGCAGUGAUGUUUmiR-129-5p CUUUUUGCGGUCUGGGCUUGCmiR-183-5p UAUGGCACUGGUAGAAUUCACUmiR-26b-5p UUCAAGUAAUUCAGGAUAGGUmiR-495-3p AAACAAACAUGGUGCACUUCUUmiR-152 UCAGUGCAUGACAGAACUUGGmiR-204-5p UUCCCUUUGUCAUCCUAUGCCUmiR-342-3p UCUCACACAGAAAUCGCACCCGUmiR-382-5p GAAGUUGUUCGUGGUGGAUUCGmiR-132-3p UAACAGUCUACAGCCAUGGUCGmiR-195-5p UAGCAGCACAGAAAUAUUGGCmiR-23a-3p AUCACAUUGCCAGGGAUUUCCmiR-335-5p UCAAGAGCAAUAACGAAAAAUGUmiR-128 UCACAGUGAACCGGUCUCUUUmiR-340-5p UUAUAAAGCAAUGAGACUGAUUmiR-369-5p AGAUCGACCGUGUUAUAUUCGCmiR-375 UUUGUUCGUUCGGCUCGCGUGAmiR-191-5p CAACGGAAUCCCAAAAGCAGCUGmiR-192-5p CUGACCUAUGAAUUGACAGCCmiR-365a-3p UAAUGCCCCUAAAAAUCCUUAUmiR-9-5p UCUUUGGUUAUCUAGCUGUAUGA

F

G

H

A

B

C

D

E

Prototype of a microRNA profiling standard

• Hypothesis tested with Latin Square mixes • Intra-platform heterologous calibration curves are comparable to inter-platform

homologous calibration curves for assessing platform performance

• Test 8 Latin Square Mixes on: ABI TaqMan Rodent A v2.0 PCR arrays (384-well TaqCards) ABI TaqMan Custom PCR Arrays (4 in a 384-well format) Exiqon Rodent Panel I v3 PCR arrays (384-well plates) Affymetrix GeneChip miRNA 3.0 arrays • ABI TaqMan Human PCR arrays in complex background (human serum & plasma)

Pool MIX1 MIX2 MIX3 MIX4 MIX5 MIX6 MIX7 MIX8

A 125 25 5 1 1 0.2 0.04 0.008

B 25 5 1 1 0.2 0.04 0.008 125

C 5 1 1 0.2 0.04 0.008 125 25

D 1 1 0.2 0.04 0.008 125 25 5

E 1 0.2 0.04 0.008 125 25 5 1

F 0.2 0.04 0.008 125 25 5 1 1

G 0.04 0.008 125 25 5 1 1 0.2

H 0.008 125 25 5 1 1 0.2 0.04

125 nM

25 nM

5 nM

1 nM

0.2 nM

40 pM

8 pM

1 nM

25 nM 5 nM 1 nM 1 nM 0.2 nM 40 pM 8 pM

Example of “standard” calibration curve with LSM data (Exiqon Rodent Panel I v3 PCR arrays)

Example of heterologous calibration curve with LSM data (Exiqon Rodent Panel I v3 PCR arrays)

Similar results independent of position of specific analytes in mixes

Example of heterologous miR calibration curves minus (top row) or plus (bottom row) complex background

LSM1 in Human Serum

-20 -15 -10 -5 010

20

30

40

Slope = -0.83r2 = 0.68

log nM concentration

Ct

LSM1 in Human Plasma

-20 -15 -10 -5 010

20

30

40

Slope = -0.88r2 = 0.69

log nM concentration

Ct

LSM1 on TaqMan Rodent A Arrays

-16 -12 -8 -4 0 410

20

30

40

miR-125a

miR-323

miR-451miR-98

miR-10amiR-129-2

miR-187

miR-218

miR-137

miR-150

miR-361miR-499

miR-129miR-183

miR-26b

miR-495miR-152

miR-204miR-342

miR-382

miR-132

miR-195

miR-23a

miR-335

miR-128

miR-340

miR-369

miR-375miR-191

miR-192miR-365

miR-9

Slope: -0.954r2 = 0.78

Log2 nM Concentration

Ct

Summary of results from testing LSM samples

• Calibration curve pools of miRs appeared comparable to homogeneous calibration curves for assessing miR platform performance, based on testing to date on qRT-PCR platforms. Not yet known if same design is suitable for NGS platforms

• 32 analytes (or fewer) may be an adequate number for this type of control

• Latin Square datasets were produced that can help inform the development of community miR controls

• The spike-in miR controls could measure: – Platform dynamic range and linear range – Assay reproducibility – Relative abundance of analytes across dynamic range – False positive (FP) rates and effects of thresholds on FPs – Differences between miRs in reverse transcription efficiency that impact

absolute quantitation – Ability to discriminate between closely related miRs