Abstract

Conclusions

Inter-Laboratory Comparisons of DNA and RNA Quality Assessment

Joanna Kerley-Hamilton 1, Stuart Levine 2, Charles Nicolet 3, Chris Wright 4, Savita Shanker 5, Jyothi Thimmapuram 6, Robert Lyons 7, Ariel Paulson 8, Anoja Perera 8, Marie Adams 9

1. Geisel School of Medicine at Dartmouth, 2. Mass. Inst. of Technology , 3. Univ. of Southern California , 4. Univ of Illinois, 5. Univ. of Florida, 6. Purdue Univ. , 7. Univ. of Michigan, 8. Stowers Inst. for Medical Research, 9. Univ. of Wisconsin

Survey Results

Experimental Design

Figure 1: Survey Results: The survey was sent to the entire ABRF mailing list, and completed by 44 individuals representing a variety of laboratories. Academic Core facilities represented almost 80% of respondents with the majority of those in mid sized facilities.

Quantifying RNA and DNA samples is one of the most basic methods used in all sequencing and microarray labs. A broad spectrum of instruments and techniques are used to ascertain nucleic acid quantity and quality, in preparation for more sophisticated analyses. Each laboratory tends to favor specific methods. However, beyond the manufacturer’s specifications, there are few standards to indicate how the instruments are performing relative to their optimal behavior, nor are there easy methods to directly compare platforms to examine relative benefits versus costs. The DSRG has surveyed over 40 core facilities to identify what equipment is currently being used. We have also directly measured the inter-lab reproducibility and inter- platform reproducibility of different DNA/RNA analyzers as well as their dynamic range using both RNA and DNA standards. Our updated results show that the Agilent BioAnalyzer 2100 remains the standard in the field, with the large majority of participating labs using the instrument at least once a week while newer instruments, such as the Agilent TapeStation and AATI Fragment Analyzer, are much less common among survey participants.

Quantitative methods are generally reproducible between labs and have modest variability (+/- 30%)

Analytical methods show poor quantification performance, have broad inter-lab variability, and modest reproducibility.

+/-20% specification is very difficult to achieve There was difficulty measuring accurately below

100pg/ul with all assays used.

Inter-Lab Experimental Results

Figure 5: IntraLab Variability. The data points within a lab are largely consistent within the range of each instrument suggesting that the serial dilutions were performed well. There is often a 2-4x difference seen between instruments. Each color represents a different QC method.

Figure 3: Dynamic Range for Analytical and Quantitative Methods. The range of concentrations that users perceive can be measured with each instrument varies widely and is often outside of the manufacturer’s supported range. The y-axis shows concentration and the width of the bar represents the number of respondents. Shaded boxes are the manufacturer’s supported range for analysis.

Figure 2: Instrument Usage for Analytical and Quantitative Methods: Survey results suggest that the bioanalyzerremains the most used analytical method. Nanodrop and Qubitare the predominant quantitative methods, with both pico- and ribo-green also widely used. This data suggests that labs are using multiple quantitation methods for sample QC.

Figure 4: Experimental DesignTwo DNA samples and 2 RNA samples were sent to 14 participating laboratories. Instructions were provided for performing serial dilutions and then measuring the quantity and quality of the samples on various quantitative and analytical instrumentation. Data was compiled by the DSRG.

DNA ‐ A

DNA ‐ B

RNA ‐ A

RNA ‐ B

NEB 50bp Ladder

Genomic Smear

High Quality RNA

Degraded RNA

Serial Dilute Samples (1:4)

Load on Mul ple Machines

Send Data to DSRG WORKSHEET Please create one worksheet per method.

METHOD DILUENT LAB DATE

Dilutiontotal mass 500bp mass 500bp size Det? total mass length of the peak Det? total mass RIN Det? total mass RIN Det?

12345678

DA (ladder) DB (smear) RA ‐ high quality RNA RB ‐ degraded RNA

comments/observations comments/observations comments/observations comments/observations

35

3 2 1 1

1 1 Academic core facility

Industry lab

Industry core facility

Academic lab

Diagnos c, academic and core facility

Government Core/Research

Hospital core facility

7

14 13

10 1 to 2 people

3 to 5 people

6 to 10 people

11+ people n=44

0 5 10 15 20 25 30 35 40 45

0 5

10 15 20 25 30 35 40 45

BioA

nalyzer

AATI

Fragm

ent

Analyzer

Calip

er Lab

Chip

Agile

nt Tap

e Sta

on

BioR

ad Exp

erion

Nan

odrop

Qub

it

RIBO

gree

n

Spec

trop

hotometer

Gem

ini Never

rarely

Monthly

Weekly

Daily

Mul ple mes per day

RNA

DNA

n=44

LAB1

Log(2)ob

served

/exp

ected LAB3 LAB2

Table 1: Participating Labs and Techniques Investigated. The participating labs provided data from at least 2 methods to be included in the study. The measured data was transformed to generate a factored concentration (ng/ul) based on the original dilution and then a LOG(2) value was used to generate the figures that are presented on this poster.

100

25

6.4

1.6

0.4

0.1

Measured length of 500bp band

ng loaded

BioA Frag.An. TapeStn

100

25

6.4

1.6

0.4

0.1

0.06

0.025

ng loaded

BioA Frag.An. TapeStn

RA RB

> >

Figure 6 (above): Variability Between Laboratories in both Quantitative and Analytical Methods. Data are represented for the Nanodrop, Qubit and Bioanalyzer, which are the 3 instruments with the most usage of the labs surveyed. The line graphs represent the variability of sample measurements for DNA samples, DA (light blue), and DB (dark blue), and for RNA samples, RA (light red) and RB (dark red). Box and Whisker plots are shown for the DA (50bp ladder) and RA (high quality RNA) only to show the intralabvariability.

Figure 9: RNA Quality. RNA Integrity Number (RIN) is a measure of RNA quality. Newer instruments tend to underestimate RIN scores. The BioAnalyzer pico kits also generate lower RIN scores.

Figure 8: Sizing of DNA. Data was reported from the analytical methods for the size of the 500bp band in sample DA (50bp ladder). The data shows that the Bio-Analyzer tends to underestimate size, compared with an overestimation for the newer instruments.

Agilent BioAnalyzer

Qubit

Nanodrop

$

AATI$Frag.An.$

Qubit$

Nanodrop$

n=35$ n=4$ n=21$ n=23$

BioAnalyzer$

10 1 100 10 1 100 10 1

pg /ul

ng/ul

ug/ul BioAnalyzer

n=37 n=5 n=26 n=29

AATI Frag.An.

Qubit

Nanodrop

DNA RNA

Caliper LabChip Agilent Tape Sta on AATI Fragment Analyzer

1

2

3

4

5

6

7

8

9

‐4

‐3

‐2

‐1

0

1

2

3

4

Nanodrop DA

100 25 6.4 1.6 0.4 0.1

1

2

3

4

5

6

7

8

9

‐4

‐3

‐2

‐1

0

1

2

3

4

Qubit DA

100 25 6.4 1.6 0.4 0.1

100 25 6.4 1.6 0.4 0.1

1

2

3

4

5

6

7

8

9

‐4

‐3

‐2

‐1

0

1

2

3

4

Nanodrop RA

100 25 6.4 1.6 0.4 0.1

1

2

3

4

5

6

7

8

9

‐4

‐3

‐2

‐1

0

1

2

3

4

Qubit RA

100 25 6.4 1.6 0.4 0.1

1

2

3

4

5

6

7

8

9

‐4

‐3

‐2

‐1

0

1

2

3

4

Bioanalyzer RA

100 25 6.4 1.6 0.4 0.1

DNA RNA

Figure 7 (left): Instrumentation with Limited Data. Data is shown for the Fragment Analyzer, Agilent Tape Station and Caliper LabChip instruments, which are some of the newer analytical methods available. Newer Caliper data is much more similar to AATI and BioAnalyzer data. Variability in RNA concentration is higher than for DNA samples in the analytical methods.

ng/ul

ng/ulng/ul

BioA AA TapeSt Caliper Nano QubitQuantiFluor RNaseP

A X XB X X XC X X X XD X X X XE XF X XG X X X XH X XI X X XJ X XK X XL X X X XM X X XN X X X X

Analytical & Quantitative Quantitative Only

100 25 6.4 1.6 0.4 0.1

Sample DA+B

0.025 100 25 6.4 1.6 0.4 0.1 0.0060.025

Sample DA+B

100 25 6.4 1.6 0.4 0.1 0.025

Sample RA+B

Sample DA+B

Sample RA+B

100 25 6.4 1.6 0.4 0.1 0.025

100 25 6.4 1.6 0.4 0.1 0.025 100 25 6.4 1.6 0.4 0.1 0.025

Sample DA+B

100 25 6.4 1.6 0.4 0.1 0.025

Sample RA+B

100 25 6.4 1.6 0.4 0.1 0.025

100 25 6.4 1.6 0.4 0.1 0.025ng/ul

100 25 6.4 1.6 0.4 0.1 0.025

ng/ul100 25 6.4 1.6 0.4 0.1 0.025

ng/ul100 25 6.4 1.6 0.4 0.1 0.025