Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery.

Abstract

Arabidopsis thaliana is a useful model organism for deciphering the genetic determinants of seed size; however the small size of its seeds makes measurements difficult. Bulk seed weights are often used as an indicator of average seed size, but details of individual seed is obscured. Analysis of seed images is possible but issues arise from variations in seed pigmentation and shadowing making analysis laborious. We therefore investigated the use of a consumer level scanner to facilitate seed size measurements in conjunction with open source image-processing software.
By using the transmitted light from the slide scanning function of a flatbed scanner and particle analysis of the resulting images, we have developed a method for the rapid and high throughput analysis of seed size and seed size distribution. The technical variation due to the approach was negligible enabling us to identify aspects of maternal plant growth that contribute to biological variation in seed size. By controlling for these factors, differences in seed size caused by altered parental genome dosage and mutation were easily detected. The method has high reproducibility and sensitivity, such that a mutant with a 10% reduction in seed size was identified in a screen of endosperm-expressed genes. Our study also generated average seed size data for 91 Arabidopsis accessions and identified a number of quantitative trait loci from two recombinant inbred line populations, generated from Cape Verde Islands and Burren accessions crossed with Columbia.
This study describes a sensitive, high-throughput approach for measuring seed size and seed size distribution. The method provides a low cost and robust solution that can be easily implemented into the workflow of studies relating to various aspects of seed development.

Full text

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METHODOLOGYOpen Access
Rapid analysis of seed size in Arabidopsis for
mutant and QTL discovery
Rowan P Herridge1, Robert C Day1†, Samantha Baldwin2, Richard C Macknight1*†
Abstract
Background: Arabidopsis thaliana is a useful model organism for deciphering the genetic determinants of seed
size; however the small size of its seeds makes measurements difficult. Bulk seed weights are often used as an
indicator of average seed size, but details of individual seed is obscured. Analysis of seed images is possible but
issues arise from variations in seed pigmentation and shadowing making analysis laborious. We therefore
investigated the use of a consumer level scanner to facilitate seed size measurements in conjunction with open
source image-processing software.
Results: By using the transmitted light from the slide scanning function of a flatbed scanner and particle analysis
of the resulting images, we have developed a method for the rapid and high throughput analysis of seed size and
seed size distribution. The technical variation due to the approach was negligible enabling us to identify aspects of
maternal plant growth that contribute to biological variation in seed size. By controlling for these factors,
differences in seed size caused by altered parental genome dosage and mutation were easily detected. The
method has high reproducibility and sensitivity, such that a mutant with a 10% reduction in seed size was
identified in a screen of endosperm-expressed genes. Our study also generated average seed size data for 91
Arabidopsis accessions and identified a number of quantitative trait loci from two recombinant inbred line
populations, generated from Cape Verde Islands and Burren accessions crossed with Columbia.
Conclusions: This study describes a sensitive, high-throughput approach for measuring seed size and seed size
distribution. The method provides a low cost and robust solution that can be easily implemented into the
workflow of studies relating to various aspects of seed development.
Background
More food will need to be produced during the next
50 years than in the entire history of humankind. There-
fore, increasing crop yields is a major challenge for the
21stcentury. Since most of the world’s food calories
come from seed, one way to meet this challenge is to
create plants with more and larger seeds. Arabidopsis
thaliana is a useful model organism for studying seed
development due to its ease of cultivation and extensive
genetic and community resources available. Thus far,
only a handful of genes are known to be directly
involved in determining Arabidopsis seed size [1-8].
Genes that regulate seed size can be discovered by
screening for mutants or by quantitative trait loci (QTL)
analysis to identify the genes that underlie the natural
variation in seed size between different accessions.
Alonso-Blanco et al. [9] performed a QTL analysis on
recombinant inbred lines (RILs) from crosses between
the small seeded Landsberg erecta (Ler) accession and
the large seeded Cape Verde Islands (Cvi) accession.
Seed weight and length QTL were mapped as well as
those affecting maternal factors that contribute to seed
size (such as seed number and leaf size). Six QTL affect-
ing seed size, without significant effects on the maternal
plant, were identified [9]. The genes/alleles underlying
these QTL have not been determined, which is an
important step if they are to be applied in a biotechno-
logical context. Only a few Arabidopsis mutants have
been identified that directly affect seed size. These
mutants reveal that both endosperm and integument
* Correspondence: richard.macknight@otago.ac.nz
† Contributed equally
1Department of Biochemistry, University of Otago, PO Box 56, Dunedin 9054,
New Zealand
Full list of author information is available at the end of the article
Herridge et al. Plant Methods 2011, 7:3
http://www.plantmethods.com/content/7/1/3
PLANT METHODS
© 2011 Herridge et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

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growth are involved in seed size determination in Arabi-
dopsis [10]. HAIKU1, 2 (IKU1 and 2) and MINISEED3
(MINI3) act in the same pathway to control early endo-
sperm proliferation and subsequent seed size at maturity
[2,4]. MINI3 and IKU2 are in close proximity to two
quantitative trait loci (QTL) discovered by Alonso-
Blanco et al. [9] suggesting that they may play an
important role in the natural variation observed between
accessions [4]. SHORT HYPOCOTYL UNDER BLUE1
(SHB1) binds the promoters of IKU2 and MINI3. How-
ever, the shb1 mutant only has a minor effect on seed
size [7], suggesting that there may be other regulators of
seed size upstream of this pathway. The AUXIN
RESPONSE FACTOR2 (ARF2) and TRANSPARENT
TESTA GLABRA2 (TTG2) genes affect seed size via
integument cell elongation [5,11]. Crossing ttg2 and
iku2 mutants reveals that, although the genes operate in
independent pathways, cross-talk occurs between the
integument and endosperm to determine final seed
size [10].
Parental genome dosage can also affect seed size. In
interploidy crosses, a seed size effect is observed when
the ratio of maternal to paternal genomes is altered in
the endosperm, with over-representation of paternal
genomes resulting in larger seeds whereas the opposite
is true for maternal genomes [12]. A similar phenom-
enon is found when reciprocal crosses of met1 mutants
are performed, suggesting that DNA methylation plays
an important role in seed size determination, via the
action of imprinted genes in the endosperm and also
hypomethylation in the integuments [13,14].
A major aim of our laboratory is to discover the mole-
cular mechanisms that regulate seed size. We have
developed a sensitive, high-throughput method of mea-
suring seed size using a scanner and particle analysis
software. Furthermore, by identifying and taking into
account certain maternal factors that contribute to seed
size we were able to reduce variation, enabling routine
detection of subtle differences in size. We show that the
approach is capable of identifying differences in seed
size due to mutation, parental genome dosage, natural
variation and detection of a number of novel seed
size QTL.
Results
A document scanner and open source image analysis
software provide a low cost and reliable means of rapidly
measuring seed size
We investigated whether a document scanner could
provide a means of rapidly screening a large number of
Arabidopsis plants for seed size phenotypes with a high
degree of sensitivity. Using a commercially available
scanner with a resolution of 1200 dpi combined with
image analysis software we were able to quickly obtain
accurate measurements of seed size. The slide-holder
included with the scanner enabled 24 samples to be
scanned simultaneously, reducing the amount of labour
required (Figure 1A). A major factor with regard to
the ease of processing was the use of transmitted light.
?
B B
A
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D
Seed size (?m2)
Figure 1 Reliable measurement of seed size is achieved using
transmitted light. (A) Scanner with slide-holder insert allows
simultaneous scanning of 24 samples. (B) Images are generated by
emitting light from the lid, through the frame and onto the scanner
bed; the seeds cast shadows which are detected by the scanner
bed. (C) The resulting images are converted to solid black and
white images using the threshold function of ImageJ prior to
particle analysis. (D) Ten measurements were made on 54 seeds
from a single silique, reorienting the seeds between measurements
(circles = outliers > 1.5 IQR from median).
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This enabled us to avoid potential complications due to
seed colour or variation in the white background that
may be occur when using reflective imaging (Figure 1B).
This enabled us to quickly process the images using the
“threshold” function of ImageJ and ensured that the
resulting black and white images were accurate repre-
sentations of the seeds (Figure 1C).
Due to the fact that seeds are ellipsoid it is possible
that their orientation on the scanner bed will affect the
measurement. We tested the ability of the scanner to
generate reproducible results from a large number of
seeds by measuring the seeds from a single silique mul-
tiple times and reorienting the seeds between measure-
ments. The distribution of seed sizes generated by the
scanner remained relatively constant (Figure 1D), indi-
cating that the effect of seed orientation is negligible
when measuring multiple seeds.
The biological variation in seed size can be reduced by
controlling the growth of the maternal plant
When performing a mutant screen or QTL analysis, var-
iation in seed size must be reduced as much as possible
to increase the sensitivity of the screen. More consistent
results will allow detection of more subtle phenotypes
which may otherwise be overlooked, thus controlling this
variation is of great importance. The maternal plant plays
an important role in seed size determination; therefore to
reduce biological variation in seed size the growth of the
maternal plant must be kept constant. We investigated a
number of easily controlled factors of the maternal plant
that may contribute to variation in seed size.
To test whether the position of the silique on the
main bolt had an effect on seed size, every silique was
taken from the main bolt and the seeds measured three
times. The first three siliques show a marked increase in
seed size, after the fourth silique there is a small
decrease in average seed size up the shoot (Figure 2A).
The likely cause of the increase in seed size in the first
three siliques was the fact that these siliques contained
fewer seeds (16, 32 and 45, for silique 1, 2 and 3,
respectively, compared with an average of 56 for siliques
4-20). To investigate if the number of seeds in a silique
affects seed size, seed number was varied by allowing
plants to self-pollinate or emasculating flowers and par-
tially pollinating or fully pollinating the stigma. As the
number of seeds in a silique decreases there is some evi-
dence that the average seed size increases, however this
is accompanied by an increase in the variation of seed
size (Figure 2B). Once a silique contains ~50 seeds this
variability is reduced. Thus, fully extended siliques
should be selected for seed size measurement to reduce
variation. To investigate if the availability of maternal
resources affects seed size, the total number of siliques
on the plant was altered by trimming auxiliary buds and
flowers to allow only a set number of siliques to form.
It was found that the more siliques on a plant the smal-
ler the average seed size (Figure 2C). However the stan-
dard deviation within treatments was similar between
treatments, indicating that there is not an optimum
number of siliques for improving accuracy (Figure 2C).
The flowering time of a plant may have an effect on
seed size due to increased vegetative growth before seed
production. To determine the extent to which flowering
time affected seed size, plants were grown in short day
conditions to delay flowering. Plants were moved into
long day conditions once they began to flower to ensure
a comparable amount of light was available while
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Figure 2 Factors affecting seed size. (A) Average seed sizes from
siliques in different positions on the main bolt (Error bars = S.D. of
biological replicates). (B) Average seed sizes from siliques containing
different numbers of seeds after partial, full or self-pollination.
(C) Average seed sizes of plants with different numbers of siliques
(Wild-type, untrimmed; error bars = S.D. of biological replicates).
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producing seeds. The increase in vegetative growth
caused by the delayed flowering led to a minor but sig-
nificant increase in seed size (~10%, p < 0.01, student’s
T-test; data not shown). Vernalization often reduces the
variation in flowering time between different accessions
and therefore may be a useful way of increase the likeli-
hood of identifying QTL specifically affecting seed size
rather than QTL that influence seed size by affecting
maternal resource availability.
Overall, our data highlights the influence maternal fac-
tors have on seed size. We concluded that the simplest
way to obtain accurate seed size measurements is to
remove auxiliary buds to reduce variability in total sili-
que number and take only fully extended siliques from
between the fourth and tenth position on the main bolt.
Validation of the seed size measurements using
interploidy crosses and known mutants
Interploidy crosses using C24 and Ler accessions of Ara-
bidopsis produce seeds of variable size, depending on
whether there is an excess of maternal or paternal gen-
omes [12]. The average weight of seeds from a 2 × 4×
and 4 × 2× cross was ~2.5× and ~0.75× that of a 2×2×
cross respectively for both C24 and Ler accessions [12].
To confirm these results using the Col-0 accession, and
to demonstrate the utility of our seed size assay, we per-
formed crosses with diploid and tetraploid Col-0 plants.
Surprisingly, we found average seed size to be similar
between seeds from a 2×4× and 2×2× cross (See addi-
tional file 1: Figure S1.pdf). Similar results were found
in interploidy crosses of the Ler accession (See addi-
tional file 1: Figure S1.pdf). It was apparent that a large
number of seeds were aborting in the 2×4× cross, thus
reducing the average seed size. To better illustrate the
differences in seed size between these crosses, measure-
ments from the interploidy crosses were normalized
relative to the mean of a balanced cross and plotted on
a box and whisker diagram (Figure 3). This showed that
although a large proportion of seeds from the 2×4×
cross were aborting, there were some that were ~2 fold
larger than 2×2× seeds. In addition, although the aver-
age size of seeds from a 4×2× cross was significantly
less than those of a 2×2× cross, some seeds were still
similar in size to those from a balanced cross (Figure 3).
To validate the method as a tool for detecting novel
seed size mutants we examined a number of known
seed size mutants. We grew several seed size mutants
under controlled conditions (without auxiliary buds) and
measured the seeds in triplicate. Figure 4A shows the
average seed size of the iku2-1, arf2-9, APETALA1 pro-
moter driving expression of the ARF coding sequence
(AP1-ARF in an arf2 mutant background) and fis2-1
mutants. The iku2-1 mutant is known to have smaller
seeds [2], whereas the arf2-9 and AP1-ARF (arf2 mutant
background) mutants have larger seeds [5,15]. The AP1-
ARF construct rescues the arf2-9 mutation in the flow-
ers resulting in improved fertility and a reduced seed
size compared to arf2-9 mutants [15]. The fis2-1 mutant
has a 50% rate of seed abortion, where the aborted
seeds appear smaller than the viable ones [16]. All
mutants were detected with a high level of significance
including the subtle difference between arf2-9 and AP1-
ARF mutants (Figure 4A), indicating that the scanner is
capable of detecting these known mutants. It is also pos-
sible that a mutation may result in an altered distribu-
tion of seed sizes within a silique, which can be
observed using a histogram. The fis2-1 mutant is a
model for such a phenotype; seeds of a wild-type silique
and a fis2-1 mutant silique were measured three times,
measurements were normalized by dividing by the aver-
age seed area and plotted on histograms (Figure 4B).
The 50% seed abortion phenotype is clearly shown in
the histogram and suggests that the scanner is capable
of identifying mutants based on differences in the distri-
bution of seed sizes in a silique.
To identify novel genes with effects on seed size and
demonstrate the effectiveness of the scanner in a high-
throughput application we performed a mutant screen.
137 homozygous T-DNA insertion lines from the SALK
collection [17] corresponding to 119 endosperm-
expressed genes identified by Day et al. [18] were
selected for the screen. Average seed size for each line
was calculated and compared to the average of all other
lines grown simultaneously, resulting in a relative
increase/decrease in seed size for each line (See
Figure 3 Parental dosage affects seed size. Reciprocal crosses
were made between diploid and tetraploid Col-0 plants causing a
genomic imbalance in the endosperm causing alterations in seed
size depending on the direction of the cross. Seeds were measured
from the crosses and each measurement was divided by the mean
of the balanced cross (2×2×) and plotted on a box and whisker
diagram (circles = outliers > 1.5 IQR from median).
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additional file 2: Table S1.pdf). Lines that showed a sig-
nificant (p < 0.05) change in seed size greater than 10%
were confirmed by growing alongside wild-type Col-0
plants (data not shown). One line (SALK_147417C)
showed a reproducible 10% reduction in seed size (Fig-
ure 4C). The identification of a novel mutant as part of
a high-throughput screen demonstrates the effectiveness
of this method in performing large-scale analysis of seed
size phenotypes.
Variation in seed size between different Arabidopsis
accessions
A large amount of genetic diversity is present between
different accessions of Arabidopsis. This genetic diversity
can be used in a QTL analysis to discover new loci that
regulate seed size. Accessions with the greatest difference
in seed size are most informative in a QTL analysis as
they are more likely to contain alleles with large and
easily detectable effects on seed size. We aimed to detect
differences in seed sizes between different accessions of
Arabidopsis with a view of performing a QTL analysis.
Plants were vernalized at 4°C for 3 weeks in an attempt
to reduce any differences in seed size caused by flowering
time and were grown in controlled conditions (without
auxiliary buds). Significant differences in average seed
size between accessions were observed (Figure 5). To
further validate our method, we also determined the aver-
age seed weight of these accessions by weighing a specific
number of seeds (~200 for each accession, counted using
the scanner and particle analysis software). The average
seed weight showed a strong correlation with average
seed area (See additional file 3: Figure S2.pdf). Seeds of
the Bur accession were clearly the largest, while Bay-0,
Ler, Eil and Sha accessions had the smallest seeds.
Although we attempted to reduce biological variation by
vernalization and trimming of the auxiliary buds, it is
likely that maternal resource allocation still had some
influence on the differences we observed. We also exam-
ined the seed size of 80 different Arabidopsis accessions
from the 1001 genomes project [19]. Seeds obtained from
the stock center were measured directly on the scanner;
the size of seeds from these accessions varied from
~77,000-155,000 μm2, a difference of 100% (See addi-
tional file 4: Figure S3.pdf). These results provide a basis
for analyzing the natural variation in seed size found
between accessions.
Identification of QTL affecting seed size
Based on the differences in seed sizes between acces-
sions (Figure 5), two core populations of 164 RILs
resulting from crosses between BurxCol and CvixCol
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Figure 4 Known seed size mutants can be detected. (A) Seeds
were measured from iku2, arf2, AP1-ARF (arf2 mutant background)
and fis2. Average seed size was determined and compared to Col-0
and Ler (Error bars = S.E.M. of biological replicates). (B) Three
measurements of seeds from a wild-type (left) and a fis2-1 mutant
silique (right) were normalized and plotted on a histogram,
respectively. (C) Average seed size of SALK_147417C compared to
wild-type Col-0 (Error bars = S.E.M. of biological replicates).
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Arabidopsis accessions
Figure 5 Variation between accessions can be detected. Seeds
from various accessions were vernalized at 4°C for 3 weeks and final
seed size was measured (Error bars = S.E.M. of biological replicates).
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were obtained from INRA [20]. These RILs were
selected as Bur had a much larger seed size than Col,
and seed size QTL in LerxCvi RILs had been mapped
previously [9], allowing some comparison with our
results. Seeds obtained from the stock center were mea-
sured directly using the scanner.
Regions of interest were first explored using basic sin-
gle marker analysis, which revealed significant associa-
tions (log of odds (LOD) > 3) with markers on
chromosomes 1 and 4 in CvixCol and 1, 4 and 5 for the
BurxCol RIL populations (Figure 6). To better define
and identify putative QTLs, interval mapping was car-
ried out using the EM algorithm. A 5% significance
threshold was calculated as LOD 2.44 and 2.35 for Cvix-
Col and BurxCol, respectively, using 1000 permutations.
Based on this threshold, interval mapping identified sig-
nificant QTL on chromosome 1, 2, 3, 4 and 5 for Cvix-
Col and 1, 4 and 5 for BurxCol (Figure 6). Similar
results were obtained using Haley-Knott [21] and the
extended Haley-Knott analysis [22] (data not shown).
Given that multiple QTL were identified further analysis
was used that could better identify and model the effects
of multiple QTL segregating in the population. There-
fore a 2 QTL analysis (scantwo) was used to compare
each chromosome for likely additive effect and or inter-
acting (the full model) QTL. The significance of the
association was determined using 1000 permutations.
For CvixCol the highest LOD scores were for 2 QTL
on chromosomes 1 and 4 with a slightly higher LOD if
interactions were allowed (LOD score for the full model
of 10.32 versus the additive of 10.28) and the LOD
scores were higher for the 2 QTL models versus the sin-
gle QTL model (5.67). This was followed by an interac-
tion of QTL on chromosomes 2 and 4 (9.28 for 2 QTL
model compared to 4.87 for single). There was also
weaker evidence for QTL on 3 and 5 based on the full
Figure 6 QTL associated with average seed size (AvSS) for BurxCol (BC) and CvixCol (CC) RIL populations. Linkage maps are shown for
each chromosome with CC on the left and BC on the right. The markers significantly associated with average seed size from single marker
analysis are indicated (*). The LOD profiles from interval mapping are shown for each significant QTL including the permuted 5% significance
thresholds (dashed lines). QTL identified using multiple QTL modelling are shown by bars and end where the LOD drops by 1.5 for both CC
(diagonal) and BC (filled).
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model LOD scores (6.46 compared to 3.6 for just 1
QTL).
For BurxCol the highest LOD scores were also for 2
QTL on chromosomes 1 and 4 (20.11) the full model
that included an interaction between the loci was only
slightly better than the purely additive model (18.61).
There was some evidence of a QTL on 5 with a signifi-
cant result for the full model (5.61) when compared to
chromosome 3 (a chromosome with no large QTL
effects). Given this evidence and the results from the
linkage mapping a chromosome 5 QTL was also
included in the subsequent multiple QTL modeling.
Multiple QTL modeling
To further interrogate and to estimate QTL effects, mul-
tiple QTL modeling was undertaken. Each putative QTL
that had been identified from the interval or 2 QTL ana-
lysis was used to develop a multiple QTL model. This
was used to determine the amount of variation in seed
size explained for both the individual and combined
QTL. Various models were tested including all of the
QTL identified for the CvixCol and BurxCol crosses,
including the two possible QTL on chromosome 4 (as
shown by the multiple peaks in Figure 6 from the inter-
val mapping results), individually modeling each of the
different positions for the QTL on chromosome 4, and
possible interactions identified from the 2 QTL analysis.
For CvixCol the model was maximized (LOD 20 and
43% variance explained) when 1 QTL on all chromo-
somes was tested and the position of the QTL on chro-
mosome 4 was 62.7 cM. The variance explained by the
model was 43% with 5 QTL ranging from individual
variance of 4% (CC_AvSS3) - 14% (CC_AvSS4). For the
BurxCol data the model was slightly improved when a
QTL on 5 was included in the model along with the
QTL on 1 and 4 (LOD 21 and 44% variance explained).
The variance explained by each of the QTL varied from
3.4 (BCAvSS_5) to 15.3% (BCAvSS_4). The variation in
average seed size for each genotype at the closest mar-
kers to the QTL for both BurxCol and CvixCol RIL
populations are shown in Figure 7.
To summarize, in both populations there were major
QTLs on chromosomes 1 and 4 with only the QTL on
4 overlapping. Looking at the LOD profiles from the
interval mapping using the BurxCol data it is possible
that there are actually two regions on chromosome 1
that are important but that the effect of the locus that
would overlap CC_AvSS1a is being masked by the
effects of the QTL BC_AvSS1b.
Discussion
Seed size is a trait of considerable importance. However,
the small size of seeds and high levels of biological var-
iation hinder its study in Arabidopsis. Here, we describe
a rapid method of measuring seed size that is capable of
detect subtle differences. This method offers advantages
over weighing large numbers of seeds to determine seed
size, as it avoids the need to count individual seeds and,
as every seed is measured individually, alterations in the
distribution of seed sizes is easily identified. We demon-
strate the utility of this method by successfully using it
to identify a seed size mutant and seed size QTL.
One important determinant of seed size is the rate
and duration of endosperm proliferation during the
early stages of seed development. This has been demon-
strated in crosses between Arabidopsis plants of differ-
ent ploidies [12]. Crosses between a diploid seed parent
and a tetraploid pollen parent produces seeds that are
more than double the weight of seeds from 2×2×
crosses and over 40% heavier than those from 4×4×
crosses; these large seeds also contain large embryos
[12]. Seed development in these crosses is characterized
by an increase in the rate and duration of division in
peripheral endosperm, delayed endosperm cellularization
and an increase in the size of chalazal endosperm [12].
Our analysis of interploidy crosses using the Col-0
accession gave a slightly different result whereby seeds
from 2×4× crosses had a tendency to abort - as was
seen in 2×6× crosses using the C24 accession [12].
However, in addition to the aborting seeds several seeds
were ~2-fold larger than the average 2×2× seed, a result
which is similar to those reported by Scott et al. [12].
Additionally, we identified the presence of some seeds
resulting from a 4×2× cross which were similar in size
to 2×2× seeds (Figure 3). A more recent study [23] has
shown that seed abortion in interploidy crosses is
dependent on the accession of Arabidopsis used, and
that when Col-0 is used as the male parent in a 2×4×
cross seed abortion occurs at a high rate, consistent
with the results we obtained. Parental genome dosage
effects, including the dosage of imprinted genes, have
been implicated in seed viability of interploidy crosses
[24]; however, it is known that the TTG2 gene,
expressed in the maternal sporophytic tissues, plays a
role in seed viability of these crosses and that the Col
allele of TTG2 has a negative impact on seed viability in
interploidy crosses [23].
The role of the endosperm in determining seed size
was also revealed by three mutants that function in the
same genetic pathway, HAIKU1 (IKU1), IKU2 and
MINISEED3 (MINI3) [2,4]. The small seed size of these
mutants is the result of reduced growth and early cellu-
larization of the endosperm. As a first step to identify
additional genes involved in endosperm development,
we obtained the endosperm transcriptome from laser
dissected proliferating endosperm tissue [18]. We identi-
fied 800 genes that were preferentially expressed during
early endosperm development. To investigate if any of
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these genes affect endosperm proliferation, and there-
fore final seed size, we screened through homozygous
SALK T-DNA insertion lines in these genes [17]. Using
the scanner and image software allowed us to rapidly
measure the seed size of 137 T-DNA lines. This identi-
fied a mutant with ~10% increase in seed size, which
had an insertion in exon 3 of the At2g01810 locus
(Figure 4C; Additional file 2: Table S1.pdf). Two other
T-DNA insertion lines were also analyzed for this gene,
SALK_099086C in exon 3 and a segregating line,
SALK_079456, in exon 1. However, these lines did not
show any effect on seed size (See additional file 2: Table
S1.pdf), suggesting that the seed size effect seen in
SALK_147417C was possibly due to another mutation.
Further analysis of this mutant indicated a chromosomal
translocation had occurred which may be causing the
phenotype (data not shown), so a map-based cloning
approach will be needed to isolate the mutation. None-
theless, the discovery of this seed size mutant demon-
strates that our method is effective way of screening for
novel mutants.
Another approach for identifying genes involved in
determining seed size is to utilize the considerable
natural variation in the size of seeds from different
Arabidopsis accessions. We grew 11 accessions under
controlled conditions and measured the seed size, iden-
tifying the Bur accession as having the largest seeds
(Figure 5). An additional 80 accessions with genome
sequences available from the 1001 genomes project [19]
were measured directly using seeds obtained from the
stock center (See additional file 4: Figure S3.pdf). The
results obtained in our study are similar to those

B A
C
D E F
G H I
Figure 7 Boxplots of the average seed size for each QTL marker genotype. (A-D) A is homozygous for the Col allele, B is homozygous for
the Bur allele and H is heterozygous. (E-I) A is homozygous for the Col allele, B is homozygous for the Cvi allele and H is heterozygous.
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reported by de Jong et al. [25] who measured average
seed weight of 24 accessions. Accessions with large dif-
ferences in seed size offer an ideal resource for identify-
ing the underlying genetics. To determine if our method
for measuring seed size could be used to identify QTL,
we obtained two RIL populations generated using Col
and the large seed size accessions Cvi and Bur. As we
were interested in discovering QTL with a major affect
on seed size, we limited our analysis to the core popula-
tions of 164 RIL lines, described in Simon et al. [20].
Five QTL were identified from the CvixCol RIL popula-
tion and four QTL from the BurxCol RIL population.
These QTL explained ~44% of the variation in seed size.
The QTL with the largest effect (explaining ~15% of the
seed size variation) mapped to a similar location on
chromosome 4 in both RIL populations. A major QTL
in this position was also identified by Alonso-Blanco
et al. [9] in CvixLer RILs. Interestingly, this QTL region
does not encompass the chromosome 4 genes, AINTE-
GUMENTA or APETALA2, known to affect seed size
[1,3]. The CvixLer QTL analysis of Alonso-Blanco et al.
[9] showed that the entire lower arm of chromosome 4
had an effect on seed size, suggesting a number of genes
with the potential to affect seed size might be located
within this region. The QTL region of chromosome 5
segregating in the CvixCol RIL population contains the
gene for ARF2, raising the possibility that this gene may
be causing differences in seed size between the Cvi and
Col accessions. A QTL in a similar position on chromo-
some 5 was also detected by Alonso-Blanco et al. [9]
suggesting that the Cvi allele in this position is likely
to have a strong effect on seed size. However, we did
not detect the major QTL on chromosome 1 identified
by Alonso-Blanco et al. [9], and thought to correspond
to MINI3 [4], suggesting that the Col, Bur and Cvi
alleles of this QTL have a similar effect on seed size.
We are currently backcrossing a number of the RILs
with Col to generate near isogenic lines to facilitate
the cloning of the major QTL using a map-based
approach. The fact that the only the chromosome 4
QTL mapped to similar region in the two RIL popula-
tions, highlights the importance of using multiple par-
ents to identify different alleles capable of affecting
seed size. With an increasing number of RIL popula-
tions becoming available [20,26,27], our method for
rapidly and accurately determining the seed size of the
individual RILs should help facilitate the discovery of
novel QTL.
Conclusions
We have developed a simple, inexpensive and rapid
method for measuring seed size that offers significant
advantages over measuring seed weight. Using this
method, we identified a mutant with smaller seeds and
discovered a number of seed size QTL, thus proving its
utility in high-throughput and large scale applications.
Methods
Plant material and growth conditions
All wild-type diploid Arabidopsis accessions were
obtained from the Arabidopsis Biological Resource Cen-
tre (Ohio State University, USA). The iku2-1 [2] and
fis2-1 [16] mutants were provided by Dr Ming Luo
(CSIRO, Black Mountain, AUS) and the arf2 [5], AP1-
ARF (in arf2 background) mutants [15] and tetraploid
Col-0 were supplied by Professor Rod Scott (University
of Bath, UK). Plants were sown on 1/2 MS media grown
at 20°C with a 16 h photoperiod (8 h for short day
plants) with light levels of ~100 μE.m2.s-1. Seedlings
were transferred to potting mix after ~2 weeks on 1/2
MS media.
Seed size measurement
Siliques were harvested once they had turned completely
brown but before they had dropped seeds. Siliques were
allowed to dry in open microcentrifuge tubes for at least
three days before measurement. Dried silique material
was removed using forceps and the seeds were spread
onto the scanner bed (Microtek Scanmaker i800) ensur-
ing that no seeds were touching. Images were taken of
each individual silique at a resolution of 1200 dpi using
transmitted light. ImageJ particle analysis software was
used to measure seed area [28]. Images were processed
using the “threshold” feature of ImageJ (to an arbitrary
value of 162 on the greyscale) and seed size was mea-
sured using the “particle analysis” feature, with a lower
limit 30,000 μm2to exclude any non-seed material. Data
was analyzed using Microsoft Excel and SPSS statistical
analysis software.
Manual pollination
Flowers were emasculated using fine-tipped forceps tak-
ing care not to damage the ovary. Two days after emas-
culation, pollen from the appropriate male parent was
applied to the tip of the stigma.
QTL analysis
Linkage maps were reconstructed for both populations
using marker and recombination data from VNAT [29].
QTL analysis was carried out with the R/qtl package
[30,31] and executed in R [32]. The linkage maps were
initially assessed for associations with average seed size
using single marker analysis and by then interval map-
ping using the EM algorithm. LOD thresholds were cal-
culated using 1000 permutations for a significance level
of 5%. A two-QTL genome scan using scantwo analysis
was then used to identify QTL with additive or interac-
tive effects (referred to as the full model) and
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significance thresholds were determined using 1000 per-
mutations. The results from these analyses were used to
develop multiple QTL models that were compared using
the makeqtl and fitqtl functions. The best model was
chosen based on LOD score, %variance explained and
simplicity. This model was then used to determine
improved estimates of the QTL locations using refineqtl.
The 1.5 LOD intervals were then calculated for each
individual QTL. Each QTL was given a name based on
the population CC (CvixCol) or BC (BurxCol), AvSS
(for average seed size), chromosome (number) and indi-
vidual QTL identifier (letter) where the position along
the chromosome was different. The linkage maps and
QTL summary were constructed using MapChart v2.2
[33].
Additional material
Additional file 1: Average seed sizes of interploidy crosses of
Columbia and Landsberg erecta accessions.
Additional file 2: Relative seed size of T-DNA insertion lines.
Additional file 3: Correlation between average seed area and
average seed weight for various accessions of Arabidopsis.
Additional file 4: Average seed sizes of 80 accessions from the 1001
genomes project.
Acknowledgements and Funding
RPH was funded by a Top Achiever Doctoral Scholarship from the Tertiary
Education Commission of New Zealand. RCD and RCM were funded by the
Marsden Fund.
Author details
1Department of Biochemistry, University of Otago, PO Box 56, Dunedin 9054,
New Zealand.2New Zealand Institute for Plant and Food Research Ltd,
Lincoln, Private Bag 4704, Christchurch 8140, New Zealand.
Authors’ contributions
RCD instigated the use of transmitted light for particle analysis of seed size
and both RCD and RCM advised RPH on study design. RPH developed the
approach and carried out the validation experiments. SB performed the QTL
analysis. RPH wrote the manuscript with contributions from RCD, SB and
RCM. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 11 December 2010 Accepted: 8 February 2011
Published: 8 February 2011
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doi:10.1186/1746-4811-7-3
Cite this article as: Herridge et al.: Rapid analysis of seed size in
Arabidopsis for mutant and QTL discovery. Plant Methods 2011 7:3.
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