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Sensitivity of the Erythrocyte Micronucleus Assay: Dependence

on Number of Cells Scored and Inter-animal Variability

Grace E. Kissling1,*, Stephen Dertinger2, Makoto Hayashi3, and James T. MacGregor4

1National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709

2Litron Laboratories, Rochester, NY 14623

3National Institute of Health Sciences, Tokyo 158-8501, Japan

4Toxicology Consulting Services, Arnold, MD 21012

Abstract

Until recently, the in vivo erythrocyte micronucleus assay has been scored using microscopy. Because

the frequency of micronucleated cells is typically low, cell counts are subject to substantial binomial

counting error. Counting error, along with inter-animal variability, limit the sensitivity of this assay.

Recently, flow cytometric methods have been developed for scoring micronucleated erythrocytes

and these methods enable many more cells to be evaluated than is possible with microscopic scoring.

Using typical spontaneous micronucleus frequencies reported in mice, rats and dogs, we calculate

the counting error associated with the frequency of micronucleated reticulocytes as a function of the

number reticulocytes scored. We compare this counting error with the inter-animal variability

determined by flow cytometric scoring of sufficient numbers of cells to assure that the counting error

is less than the inter-animal variability, and calculate the minimum increases in micronucleus

frequency that can be detected as a function of the number of cells scored. The data show that current

regulatory guidelines allow low power of the test when spontaneous frequencies are low (e.g., ≤

0.1%). Tables and formulas are presented that provide the necessary numbers of cells that must be

scored to meet the recommendation of the International Working Group on Genotoxicity Testing

that sufficient cells be scored to reduce counting error to less than the inter-animal variability, thereby

maintaining a more uniform power of detection of increased micronucleus frequencies within each

species across the range of spontaneous frequencies observed in these three species.

Keywords

Erythrocyte micronucleus assay; flow cytometry; binomial counting error; inter-animal variability;

power calculation

1. Introduction

The ability of the in vivo erythrocyte micronucleus assay to detect small increases in the

spontaneous background frequency of micronucleated cells in a group of animals (or study

subjects) is limited by either the binomial counting error, when the number of cells scored gives

small numbers of scored events (micronucleated cells) [1–4], or by inter-animal variation, when

that variation is so large that it obscures a small, but real, increase. Furthermore, the sensitivity

* Corresponding author: Dr. Grace E. Kissling, (919) 541-1756 (phone), (919) 541-4311(FAX), kissling@niehs.nih.gov.

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Author Manuscript

Mutat Res. Author manuscript; available in PMC 2007 December 12.

Published in final edited form as:

Mutat Res. 2007 December 1; 634(1-2): 235–240.

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to detect small increases in the micronucleus frequency in an individual animal is limited at

small micronucleus counts by the binomial counting error and at higher counts by the

spontaneous variability in that individual animal over the time span in which measurements

are made. Recognizing these facts, the working group on the in vivo micronucleus assay

organized by the International Workshops on Genotoxicity Testing (IWGT) has recommended

that, whenever possible, sufficient cells should be scored to reduce the counting error to less

than the variability in MN frequency between individual animals (for comparison of values in

different treated groups) [5].

Prior to the development of flow cytometric scoring methods, the number of cells scored was

generally limited by the practical consideration of the number of cells that could be scored in

a reasonable period of time by a microscopist, and therefore the minimum number of cells

recommended to be scored in current regulatory guidelines is generally less than that required

to discern differences between individual animals. Flow cytometric methodologies now make

it practical to reduce the counting error to very small values [6–8], allowing, for the first time,

reliable determination of the inter- and intra-animal variation in the spontaneous micronucleus

frequency.

We summarize below experimentally determined mean and variability among animals in the

spontaneous frequency of micronucleated reticulocytes (MN-RETs) in peripheral blood

reticulocytes in the Sprague-Dawley rat, CD-1 mouse, and beagle dog, and in bone marrow

reticulocytes in the Sprague-Dawley rat, and compare this inter-animal variability with the

microscopic counting error associated with the current regulatory recommendations for scoring

bone marrow or peripheral blood reticulocytes in these species. From these values, we

determine the minimum increase in group mean frequencies of MN-RETs that can be detected

in these species, tabulate the minimum increases that can be detected as a function of the number

of RETs scored, and identify the numbers of cells that need to be scored to meet the IWGT

recommendation that sufficient cells should be scored such that the error in individual animal

MN-RET frequencies is less than the inter-animal variability.

2. Inter-Animal Variability

The inter-animal variability of the percentage of MN-RETs among RETs (#MN-RET/#RETs

scored × 100) in the peripheral blood of Sprague-Dawley rats, CD-1 mice, and purpose-bred

beagle dogs, and also in the bone marrow of Sprague-Dawley rats after removal of nucleated

cells on a cellulose mini-column as described by Romagna [9] and Weiner et al. [10], was

estimated by scoring 20,000 reticulocytes using the flow cytometric method described by

Dertinger et al. [11–13]. These data are summarized in Table 1. The data are taken from

previously reported studies in these species [14,15]. Details of the experimental methodology

are reported in the previous publications. As is discussed below, scoring 20,000 RETs results

in a sufficient number of events (MN-RETs) that the error associated with individual animals

does not exceed approximately 50% of the inter-animal variability of spontaneous MN-RET

frequencies in the respective species. The inter-animal % coefficient of variation (%CV =

Standard Deviation ÷ Mean × 100%) of the MN-RET frequencies was 41% for the rat, 35%

for the mouse, and 30% for the dog.

Table 2 presents the binomial error in the count of MN cells in an individual animal obtained

by scoring 2000, 4000, 8000, or 20,000 RETs as a function of the spontaneous frequency of

MN-RETs. It should be noted that the spontaneous frequency in rodent bone marrow or

peripheral blood reported by different experienced laboratories has ranged from 0.05% in rat

(see individual laboratory values in [14]) to a mean value of 0.2% in the mouse [15,16] and

0.31% in the beagle dog. Since the counting error depends on the background rate and the

number of cells scored, we have tabulated values over the range of spontaneous frequencies

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commonly reported in rodents and recently observed in the beagle dog (manuscript in

preparation). As can be seen in Table 2, when 2000 cells are scored (the recommended number

in the current OECD, FDA, and EPA regulatory guidelines [17–19] the error in the counts

observed in individual animals is substantially greater than the variation between animals

(Table 1). When the spontaneous frequency is 0.1%, approximately 6000 cells would need to

be scored to reduce the error in the individual animal count to less than the inter-animal

variability observed in the rat.

3. Sensitivity to Increases above the Spontaneous Frequency

Table 3 summarizes the minimum increases above the spontaneous frequency that can be

detected in groups of five animals (the minimum currently recommended in OECD, FDA, and

EPA guidelines [17–19] as a function of the number of target cells scored (in this case RETs)

and observed spontaneous frequency (in this case %MN-RETs among RETs). Minimum

detectable increases in MN-RET frequencies at p ≤ 0.05 or p ≤ 0.01, with 90% or 95% power

were determined using Monte Carlo simulations. Specifically, to reflect inter-animal

variability, five binomial probabilities were randomly selected from a normal distribution with

the following mean, μ0, and standard deviation, σ, combinations: (μ0, σ) = (0.05%, 0.02%),

(0.10%, 0.045%), (0.20%, 0.070%), or (0.30%, 0.092%). For a given fold-increase, f, a second

set of five binomial probabilities were randomly selected from a normal distribution with mean,

μ1 = μ0 × f, and the same σ given above. Using the 5 binomial probabilities from the spontaneous

mean group, five MN-RETs frequencies were randomly generated from binomial distributions,

with n = number of RETs scored, 2000, 4000, or 20,000. Such selection from a binomial

distribution introduces the binomial counting error. Five MN-RET frequencies were similarly

generated using the 5 binomial probabilities from the increased mean group. A one-tailed

Mann-Whitney test was then performed on these 10 counts, comparing the spontaneous group

to the increased group, and the p-value was noted as to whether it was 0.05 or less and/or 0.01

or less. This was repeated 3000 times and the percentages of the 3000 ‘samples’ for which the

p-value was 0.05 or less and 0.01 or less were calculated. The process was repeated over a

series of increases, f, at increments of 0.1, to determine the first point at which the power

exceeded 90% or 95%. We obtained very similar results (not shown) by generating the 5

binomial probabilities from beta distributions having the above combinations of μ0, μ1 and σ.

For the line labeled “∞” in Table 3, there is no counting error; rather, the variability in

frequencies is due to inter-animal variation alone. If we assume that inter-animal variation is

normally distributed, the minimum difference between μ1 and μ0, δ = μ1 − μ0, detectable using

5 animals per group with significance level α and power 1 − β is

δ = (tα+ tβ)σ

2

5

[20]. Here, tα and tβ are the critical values from the 5 + 5 − 2 = 8 degree of freedom t-distribution

having upper tail probabilities α and β, respectively. The minimum detectable fold-increase

over the spontaneous group is then

f =

μ1

μ0

=

μ0+ δ

μ0

= 1 +

δ

μ0

.

While spontaneous MN-RET frequencies determined from counting 2000 RETs from different

animals are not often normally distributed, it has been our experience that spontaneous

frequencies determined from counting 20,000 RETs from different animals are approximately

normally distributed. Therefore, the assumptions of normality that we made above are most

likely reasonable.

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It should be noted that even if the counting error of the MN-RET frequency in each individual

animal could be eliminated, the sensitivity of detection of changes in the observed mean group

frequency would still be limited by the inter-animal variability (represented in Table 3 by the

line in which an infinite number of cells is scored). It is clear that the regulatory assay as

currently conducted is relatively insensitive to changes in the spontaneous frequency,

especially when the spontaneous frequency is low. For example, when the spontaneous

frequency is 0.05% and only 2000 RETs are scored, even a 6.8-fold increase would fail to be

detected at a confidence level of p ≤ 0.01 in 10% of experiments conducted. Even at the more

commonly-reported spontaneous frequency of 0.1% a 4.8-fold increase would fail to be

detected 10% of the time at this same confidence level. The use of flow cytometric scoring to

achieve a sufficient cell count to allow individual animal frequencies with adequate certainty

(i.e., certainty of the individual value relative to the inter-animal variation) would increase the

sensitivity such that a doubling of a spontaneous frequency of 0.1% among 20,000 RETs scored

would be detected nearly 90% of the time at a confidence level of p ≤ 0.05. It should also be

noted that, regardless of the spontaneous frequency, the sensitivity achieved by scoring 20,000

RETs is close to the optimal sensitivity that could be achieved if no counting error were present.

4. Number of Reticulocytes Required to be Scored to Reduce Counting Error

to Less than Inter-Animal Variability

Table 4 summarizes the number of cells required to be scored to reduce the counting error of

individual animal values (Table 2, %CV) to the observed inter-animal variation or less (Table

1, %CV). These numbers were calculated by setting a multiple (m = 1.0, 0.5, or 0.2) of the

inter-animal %CV equal to the binomial counting error %CV and solving for the required

sample size, n. Mathematically, if p is the percent of MN-RETs among all RETs within an

animal, then

% CVbinomial error=

p(1 − p)

n

p

= m × % CVinter_animal.

Solving for n, we get

n =

1 − p

p(m × % CVinter_animal)2.

The numbers of RETs required are prohibitively laborious to obtain by conventional

microscopic scoring, but are easily achieved by automated procedures such as flow cytometry.

Acknowledgements

We thank Ronald Fiedler of Pfizer, Inc. for providing the data on spontaneous frequencies and inter-animal variability

of the frequency of micronucleated reticulocytes in the bone marrow of Sprague-Dawley rats. This research was

supported in part by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences.

References

1. Margolin BH, Collings BJ, Mason JM. Statistical Analysis and Sample-Size Determinations for

Mutagenicity Experiments With Binomial Responses. Environ Mutagen 1983;5:705–716. [PubMed:

6617600]

2. Margolin, BH.; Risko, KJ. The statistical analysis of in vivo genotoxicity data: case studies of the rat

hepatocyte UDS and mouse bone marrow micronucleus assays. In: Ashby, J.; de Serres, FJ.; Shelby,

MD.; Margolin, BH.; Ishidate, M., Jr; Becking, GC., editors. Evaluation of Short-term Tests for

Carcinogens: Report of the International Programme on Chemical Safety’s Collaborative Study on In

Kissling et al. Page 4

Mutat Res. Author manuscript; available in PMC 2007 December 12.

NIH-PA Author Manuscript

NIH-PA Author Manuscript

NIH-PA Author Manuscript

Page 5

Vivo Assays. 1. Cambridge University Press on behalf of WHO; Cambridge and New York, NY: 1988.

p. 1.29-1.42.

3. Kastenbaum MA, Bowman KO. Tables for determining the statistical significance of mutation

frequencies. Mutat Res 1970;9:527–549. [PubMed: 5424720]

4. Hayashi M, Yoshimura I, Sofuni T, Ishidate M Jr. A procedure for data analysis of the rodent

micronucleus test involving a historical control. Environ Mol Mutagen 1989;13:347–359. [PubMed:

2737186]

5. Hayashi M, MacGregor JT, Gatehouse DG, Blakey DH, Dertinger SD, Abramsson-Zetterberg L,

Krishna G, Morita T, Russo A, Asano N, Suzuki H, Ohyama W, Gibson D. In vivo erythrocyte

micronucleus assay: III. Validation and regulatory acceptance of automated scoring and the use of rat

peripheral blood reticulocytes, with discussion of non-hematopoietic target cells and a single dose-

level limit test. Mutat Res 2007;627:10–30. [PubMed: 17157053]

6. Asano N, Torous D, Tometsko C, Dertinger S, Morita T, Hayashi M. Practical threshold for

micronucleated reticulocyte induction for low doses of mitomycin C, Ara-C and colchicine.

Mutagenesis 2006;21:15–20. [PubMed: 16364928]

7. Grawe J, Abramsson-Zetterberg L, Zetterberg G. Low dose effects of chemicals as assessed by the

flow cytometric in vivo micronucleus assay. Mutat Res 1998;405:199–208. [PubMed: 9748577]

8. Torous D, Asano N, Tometsko C, Sugunan S, Dertinger S, Morita T. Performance of flow cytometric

analysis for the micronucleus assay - a reconstruction model using serial dilutions of malaria-infected

cells with normal mouse peripheral blood. Mutagenesis 2006;21:11–13. [PubMed: 16188876]

9. Romagna F. Current issues in mutagenesis and carcinogenesis; fractionation of a pure PE and NE

population from rodent bone marrow. Mutat Res 1988;206:307–309. [PubMed: 3200254]

10. Weiner SK, Fiedler RD, Schuler MJ. Development and Evaluation of a Flow Cytometric Method for

the Analysis of Micronuclei in Rat Bone Marrow In Vivo. Environ Molec Mutagen 2004;43:236.

11. Dertinger SD, Torous DK, Tometsko K. Simple and reliable enumeration of micronucleated

reticulocytes with a single-laser flow cytometer. Mutat Res 1996;371:283–292. [PubMed: 9008730]

12. Dertinger SD, Torous DK, Hall NE, Tometsko CR, Gasiewicz TA. Malaria-infected erythrocytes

serve as biological standards to ensure reliable and consistent scoring of micronucleated erythrocytes

by flow cytometry. Mutat Res 2000;464:195–200. [PubMed: 10648906]

13. Dertinger SD, Camphausen K, MacGregor JT, Bishop ME, Torous DK, Avlasevich S, Cairns S,

Tometsko CR, Menard C, Muanza T, Chen Y, Miller RK, Cederbrant K, Sandelin K, Ponten I,

Bolcsfoldi G. Three-color labeling method for flow cytometric measurement of cytogenetic damage

in rodent and human blood. Environ Mol Mutagen 2004;44:427–35. [PubMed: 15517570]

14. MacGregor JT, Bishop ME, McNamee JP, Hayashi M, Asano N, Wakata A, Nakajima M, Aidoo A,

Moore MM, Dertinger SD. Flow cytometric analysis of micronuclei in peripheral blood

reticulocytes:II. An efficient method of monitoring chromosomal damage in the rat. Toxicol Sci

2006;94:92–107. [PubMed: 16888079]

15. Torous DK, Hall NE, Illi-Love AH, Diehl MS, Cederbrant K, Sandelin K, Pontén I, Bolcsfoldi G,

Ferguson LR, Pearson A, Majeska JB, Tarca JP, Hynes GM, Lynch AM, McNamee JP, Bellier PV,

Parenteau M, Blakey D, Bayley J, van der Leede BM, Vanparys P, Harbach PR, Zhao S, Filipunas

AL, Johnson CW, Tometsko CR, Dertinger SD. Interlaboratory validation of a CD71-based flow

cytometric method (MicroFlow®) for the scoring of micronucleated reticulocytes in mouse

peripheral blood. Environ Mol Mutagen 2005;45:44–55. [PubMed: 15605355]

16. Heddle JA, Salamone MF, Hite M, Kirkhart B, Mavournin K, MacGregor JT, Newell GW. The

induction of micronuclei as a measure of genotoxicity. Mutat Res 1983;123:61–118. [PubMed:

6888413]

17. OECD, Guideline for the testing of chemicals. Mammalian erythrocyte micronucleus test. Guideline

474, July, 1997.

18. FDA (U.S. Food and Drug Administration). Office of Food Additive Safety, Redbook 2000,

Toxicological principles for safety assessment of food ingredients. Updated November 2003,

www.cfsan.fda.gov/~redbook/red-toca.html

19. EPA (U.S. Environmental Protection Agency) Health Effects Test Guidelines OPPTS 870.5395.

Mammalian Erythrocyte Micronucleus Test, Office of Prevention, Pesticides and Toxic Substances

Kissling et al.Page 5

Mutat Res. Author manuscript; available in PMC 2007 December 12.

NIH-PA Author Manuscript

NIH-PA Author Manuscript

NIH-PA Author Manuscript

Page 6

(7101) EPA 712–C–98–226. Aug. 1998 www.epa.gov/opptsfrs/publications/OPPTS_Harmonized/

870_Health_Effects_Test_Guidelines/Series/870–5395.pdf

20. Snedecor, GW.; Cochran, WG. Statistical Methods. 6. The Iowa State University Press; Ames, IA:

1976. p. 113-116.

21. Fiedler, R. Pfizer, Inc, Personal communication. 2007.

22. Beutler E, Gelbart T. The mechanism of removal of leukocytes by cellulose columns. Blood Cells

1986;12:57–64. [PubMed: 3790738]

Kissling et al. Page 6

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Table 1

Mean and Inter-Animal Variation of the Micronucleated Reticulocyte Frequency in the Peripheral Blood (PB) and Bone Marrow (BM) of Sprague-Dawley

Rats, Swiss Mice, and Beagle Dogs.

Species

Strain/

Breed

Tissue

Mean %MN-

RET

Std. Dev. of %

MN-RET

Inter-animal %

CVb

# Animals

# Expts.

Reference

Rat

SD

PB

0.11

0.045

41%

15

3

[14]

Rat

SD

BM

0.23a

0.059a

26%

190

38

[21] R. Fiedler, Personal

communication

Mouse

CD-1

PB

0.20

0.070

35%

79

9

[15]

Dog

Beagle

PB

0.31

0.092

30%

22

4

Manuscript In Preparation

aBone marrow %MN-RET values were determined by separation of nucleated cells on a mini-cellulose column [9,22], with subsequent scoring of the MN-RET frequency among 20,000 RETs by

the same flow cytometric procedure used for analysis of peripheral blood.

b%CV = Std. Dev./Mean ×100%

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Table 2

Counting Error (Standard Deviation (S.D.) and Coefficient of Variation) of Individual Animal Values of MN-RET Frequency as a Function of True

Spontaneous Frequency and Number of RETs Scored.

True %MN-RET

# RETs Scored per Animal

S.D. of Count

Counting Error %CV

0.05

2000

0.050

100%

4000

0.035

71%

8000

0.025

50%

20,000

0.016

32%

0.10

2000

0.071

71%

4000

0.050

50%

8000

0.035

35%

20,000

0.022

22%

0.20

2000

0.100

50%

4000

0.071

35%

8000

0.050

25%

20,000

0.032

16%

0.30

2000

0.122

41%

4000

0.086

29%

8000

0.061

20%

20,000

0.039

13%

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Table 3

Minimum Detectable Increases in MN-RET Frequency in Groups of Five Animals as a Function of Spontaneous Frequency and Number of RETs Scoreda

Minimum Detectable Fold Increase in Spontaneous Frequency

With 90% Probability

With 95% Probability

Spontaneous Frequency (%MN-RET)

Number of RETs

Scored

at p ≤ 0.05

at p ≤ 0.01

at p ≤ 0.05

at p ≤ 0.01

0.05

2000

4.5

6.8

5.6

9.3

S.D. = 0.020

4000

3.5

5.5

4.0

6.3

20,000

2.3

3.1

2.4

3.4

∞

1.8

2.1

1.9

2.2

0.10

2000

3.3

4.8

4.1

6.4

S.D. = 0.045 (rat PB)

4000

2.9

4.2

3.2

4.7

20,000

2.2

3.0

2.4

3.2

∞

1.8

2.0

2.1

2.4

0.20

2000

2.7

3.9

3.0

4.5

S.D. = 0.070 (mouse PB)

4000

2.3

3.2

2.4

3.5

20,000

1.9

2.5

2.2

2.7

∞

1.7

2.0

1.8

2.1

0.20

2000

2.7

3.9

2.9

4.4

σ = 0.059 (rat BM)

4000

2.2

3.1

2.4

3.3

20,000

1.8

2.3

1.9

2.5

∞

1.6

1.8

1.7

1.9

0.30

2000

2.4

3.4

2.6

3.7

S.D. = 0.092 (dog PB)

4000

2.1

2.8

2.2

3.0

20,000

1.8

2.3

1.9

2.4

∞

1.6

1.8

1.7

1.9

aValues for the cases of infinite cell counts are calculated based on the observed inter-animal variability (standard deviation from Table 1) for the species stated, assuming no counting error; the

inter-animal variability for frequency 0.05% is assumed to be 0.02%. The detectable increase depends on the relative magnitudes of both the counting error and the inter-animal variability. Although

counting error can be reduced by scoring more RETs, the minimum detectable increase cannot go below a bound determined by the inter-animal variability (i.e., the value given in the infinite cellcount rows). Species entries correspond to the approximate spontaneous frequency and associated inter-animal standard deviation in the species specified in Table 1.

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Table 4

Number of Reticulocytes Required to be Scored to Reduce Counting Error to Less Than the Observed Inter-Animal Coefficient of Variation.

Number of RETs to be scored to reduce counting error %CV to:

Spontaneous

Frequency (% MN-

RET)

Species/Tissue With this Appx.Spontaneous Freq,

Inter-Animal %

CVa

Equal the inter-animal %

CV

50% of the inter-animal %

CV

20% of the inter-animal %

CV

0.05

Rat BM & PB(Microscopy, some

reports)

NAb

NA

NA

NA

0.10

Rat PB (Data cited above)

41%

5943

23,772

148,573

0.20

Mouse BM & PB

35%

4074

16,294

101,837

0.30

Dog

30%

3693

14,770

92,315

aExperimentally-determined inter-animal %CV by flow cytometric scoring of 20,000 peripheral blood RETs, at the approximate spontaneous frequency tabulated.

bInter-animal %CV has not been determined at the spontaneous frequency of 0.05%; no reported experiments have scored sufficient cells to determine the inter-animal variability.

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