Unexpected System-Specific Periodicity in qPCR Data and its Impact on Quantitation

Andrej-Nikolai SpiessDepartment of Andrology

University Hospital Hamburg-Eppendorf

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The infamous Ruijter et al. (2013) 384-replicate Dataset (Linear model baselined data)

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Plot of Cycle 10 Fluorescence ValuesThroughout all 379 Samples

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Plot of Cycle 40 Fluorescence

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379 Samples at Fq = 1000

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We have seen that there seems to be some sort of pattern in Fluorescence values as well as Cq values in a technical replicate dataset.

qPCR data is the result of a time-dependent process (Cycling !)

Hence, methods of „time series analysis“ should be feasible for analysing qPCR data and for revealing inherent structural features.

One of these methods is: Autocorrelation analysis

How can we reveal structure in qPCR data ?

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Autocorrelation:Correlation againsta „shifted itself“

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Autocorrelation analysis of Cq values

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1. Take Cq values of all samples

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2. Fit a linear/quadratic model

3. Subtract trend and use residuals for autocorrelation analysis

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Autocorrelation analysis of Cq values=> There is systemic pattern !

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24 sample period

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CFX96, VIM, 96 technical replicates,Single-Channel Pipette

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Andrej Spiess, Hamburg

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Mapping the CFX96 Cq value residualsto the MTP positions (Heatmap)

Autocorrelation on Efficiency FCq/FCq-1 @ Fq = 1000

Not only periodicity in Cq,but also in E, when estimated at Fq !

Why? Fq = 1000. Cq is periodic. F(Cq – 1) is periodic. Fq/F(Cq – 1) is periodic !

Good for Jan:LinReg removes periodicity in E

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Ok, how do the „mechanistic“ models perform?

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CM3:

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Period

icities in C

q values take

nfrom

the d

iffere

nt meth

ods in

Ruijte

r et al. (2

013

) (Supple

ments)

Another way to destroy periodicity in Cq values:Normalization to [0, 1] (Larionov et al., 2005)

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Sinus scaling factor

Cq @ SDM Cq @ F(thresh) = 1

Scaling the plateau phase results in periodicCq values, that can be compensated

qPCR curve

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σymax = 33.14

σCq = 0.045

σCq = 0

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We can create plateau dispersionby assuming error in E !

N = N0 * E1 * E2 * E3 * ...

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In HRM, periodicity of Tm valuesis even more extreme !

iQ5 CFX96

When mapping TM residuals to theirMTP position, interesting things appear...

=> Do we see uneven thermal block profiles?

Summary • We can observe periodic Cq values in many qPCR systems.

• We can effectively apply time series analysis methods to make them visible.

• We see this in all cycles starting from the exponential region.

• It‘s unlikely a result of multichannel pipettors.

• Mapping of periodic data to MTP positions suggests block effects.

• The periodicity propagates from Cq to E, if estimated there.

• There is no Cq periodicity when using methods based on FDM, SDM.

• Cq periodicity is driven by plateau phase periodicity. If we remove this (normalization), we can remove Cq periodicity.

• Plateau phase periodicity may be a result of cycle-to-cycle noise in E.

• There is even more dramatic Tm periodicity in HRM technology.

• Solution? Fingerprinting a qPCR system, remove fingerprint from Cq/Tm data.