Bull. Natl. Mus. Nat. Sci., Ser. B, 41(3), pp. 125–129, August 21, 2015

A Consistent Phenotyping of Floral Scent in Mitella (Saxifragaceae)
Using Solid-Phase Microextraction
Yudai Okuyama*
Department of Botany, National Museum of Nature and Science,
Amakubo 4–1–1, Tsukuba, Ibaraki 305–0005, Japan* E-mail: yokuyama@kahaku.go.jp(Received 19 May 2015; accepted 24 June 2015)

Abstract I established a simple method for quantitatively phenotyping ﬂoral scent traits in smallﬂowered plant species, especially Asian Mitella (Saxifragaceae). To test the reliability of themethod for comparative studies and examine if the scent traits are under the control of the plant
genotype, I used 29 individuals of the F2 generation of the hybrid of M. furusei and M. pauciﬂora.
The ﬂoral scent traits were examined for these plants in the ﬂowering seasons of both 2014 and2015. Accordingly, I conﬁrmed that the traits phenotyped in the present study were variable among
the F2 individuals but stable across the two ﬂowering seasons within each individual. These observations demonstrated that the study system is useful to carry on the genetic analysis of the ﬂoral
scent traits, which are likely controlling the reproductive isolation in Asian Mitella.
Key words : Heuchera group, genetics, pollination, quantitative trait, volatile.

Among ﬂoral traits that potentially determine
the speciﬁcity of plant-pollinator interactions, thescent might have been one of the most difﬁcult tomeasure. Nevertheless, it is crucial to establish astandard method for comparatively phenotype
the ﬂoral scents among the samples of different
plant origins, because only such a method
enables the comparative studies on phylogenetics
or genetics, which is necessary to clarify how thediversity of the ﬂoral scent has been shaped in an
evolutionary context.
The Heuchera group (Saxifragaceae) is amonophyletic group of small-ﬂowered (mostly5–10 mm in diameter) perennials that grow invarious habitats of North America and East Asia
(Soltis et al., 2001). Recently, this plant grouphas become the focus of the studies on ﬂoral
diversiﬁcation driven by plant-pollinator interactions (Okuyama et al., 2008; Thompson et al.,2013). More importantly, ﬂoral scents are considered to be the key traits that likely determine thespeciﬁcity observed therein (Friberg et al., 2013;

Okamoto et al., 2015).
Accordingly, here I apply a simple method
using solid-phase microextraction (SPME) to
quantitatively phenotype the ﬂoral scent traits inMitella, which is the genus included in the Heuchera group. In this study, I use 29 F2 hybrid
Mitella plants that are presumably variable in ﬂoral scent phenotypes because of different genetic
characteristics. By measuring the traits across
two ﬂowering years for these plants, I examine if
the measure obtained by the method is reproducible enough to allow genetic and/or phylogenetic
comparative studies.
Materials and Methods
Plant materials
For the present study, I used the F2 generationof the hybrid of M. furusei and M. pauciﬂora thathave been prepared for the genetic studies(Okuyama and Akashi, 2013). Twenty nine individuals out of the 222 F2s were randomly chosen

126

Yudai Okuyama

to examine the diversity of ﬂoral scent phenotypes and their stability across the two ﬂoweringseasons.
Sample preparation and sampling
A series of analytical standard (10, 50, 100 ,200, 300, 400, 500, and 1000 µg/µL) was prepared for (RS)-linalool (Wako, Tokyo, Japan)
using ethanol as the solvent.
Sampling of the ﬂoral scent was conducted in
the two ﬂowering seasons, namely, March–Aprilof 2014 and 2015. The plant individuals weretransferred from common garden to growth chamber (Biotron; NK System, Osaka, Japan) ＞14 h
before sampling. The growth chamber was set at
10°C with 80% moisture from 1600 to 1000 andat 20°C with 70% moisture from 1000 to 1600.
For ﬂoral scent sampling, 10 fully-bloomed
ﬂowers, if available, were removed from theﬂowering stems of an individual plant and placedin a 4 mL glass vial, the top of which was covered with aluminum foil. In the case only lessthan 10 fully bloomed ﬂowers were available,
three ﬂowers were sampled in the same way andplaced in a 1.5 mL silicon-capped glass vial (Shimadzu, Kyoto, Japan). For the analytical standard, 100 µL of each of them was placed in the1.5 mL silicon-capped glass vial, and samplings
and analyses were conducted with two replicatesfor each concentration.
SPMS needle was inserted in the glass vial
containing the ﬂowers, and the 100 µm
polydimethylsiloxane (PDMS) ﬁber (Supelco,Bellefonte PA, USA) was exposed to the headspace for 30 min. The sampling room temperature was set to 24–26°C. The sampled SPMS
ﬁber was immediately analyzed with gas chromatography (GC). As the control, ambient air
and the headspace of chopped vegetative parts ofthe plants were also sampled, while no GC signals were detected from these samples.
GC and data analyses
For analyses of the ﬂoral volatile compositions, GC was performed with Shimadzu GC17A (Shimadzu) equipped with a ﬂame ioniza-tion detector (FID) and an InertCap CHIRAMIX
capillary column (30 m×0.25 mm; ﬁlm thickness, 0.25 µm; GL science Inc., Tokyo, Japan).
Helium was used as the carrier gas at a velocityof 30 cm s−1, and the injector temperature was230°C. The injector was operated in the splitlessmode for 1 min. The oven temperature was programmed to the following sequence: 40°C for
5 min, followed by an increase of 2°C/min to180°C, at which the oven was held for 15 min.
Signals were detected by ﬂame ionization detector, and the peak area was used for quantiﬁcationof each signal.
The chromatogram peaks were identiﬁed bycomparing the chromatograms of the parental
species (Mitella furusei and M. pauciﬂora), of
which the proﬁles of the scent compounds werealready available (Okamoto et al., 2015). Whereavailable, the authentic compounds were also
examined ((RS)-linalool, (R)-linalool, andβ-caryophyllene; Wako, Tokyo, Japan or SigmaAldrich, Tokyo, Japan) using the GC with thesame settings to further conﬁrm the identity ofeach peak.
All statistical analyses were conducted using R(R Development Core Team, 2011).
Results
Contents of the ﬂoral scent
As expected, the ﬂoral scents of the F2 plants
were variable among samples but the contents
were only those observed in the parental species.
Linalool, β-caryophyllene, and lilac aldehyde(tentative identiﬁcation) are found to be the mostconstantly detected compounds across the samples. Because in the present study, a GC column
capable of resolving enantiomers was used, linalool was further dissected into (R)- and (S)-linalool. Accordingly, in the present study, I
selected only (R)- and (S)-linalool, lilac aldehyde, and β-caryophyllene as the target of thequantiﬁcation.

Phenotyping of Floral Scent in Mitella

Fig. 1. The relationship between the concentrations of the linalool analytical standards and theGC peak area values obtained by the presentmethod.

127

Variability and reproducibility of the ﬂoral scentphenotypes
Using the analytical standards, I obtained areference data for roughly quantifying theamount of linalool in the sample. As expected,
the peak areas were linearly correlated with thelinalool concentration of the analytical standards
up to 1000 µg/µL (Fig. 1). From this referencedata, a unit (1 µV·s) of the peak areas of (R)-linalool and (S)-linalool in the samples was roughly
calculated as equivalent to the headspace of theanalytical standards with 2.5×10−3 ng/µL inconcentration, respectively.
Floral scent phenotypes were variable acrossthe samples, ranging from zero (no signal) to94882 µV·s for (R)-linalool, from zero to382646 µV·s for (S)-linalool, from zero to10551 µV·s for lilac aldehyde, and from zero to193738 µV·s for β-caryophyllene (Table 1). The

Table 1. The ﬂoral scent phenotypes for 29 F2 individuals across two ﬂowering seasons in the four principal
volatile compounds (unit: µV·s)
F2 plantID

R-Linalool(2014)

R-Linalool(2015)

S-Linalool(2014)

S-Linalool(2015)

lilac
aldehyde*(2014)

lilac
aldehyde*(2015)

β-Caryophyllene(2014)

β-Caryophyllene(2015)

IF2-003
IF2-013
IF2-019
IF2-022
IF2-023
IF2-027
IF2-034
IF2-039
IF2-041
IF2-043
IF2-046
IF2-048
IF2-054
IF2-056
IF2-059
IF2-060
IF2-061
IF2-074
IF2-099
IF2-100
IF2-108
IF2-110
IF2-128
IF2-141
IF2-152
IF2-171
IF2-194
IF2-246
IF2-260

10513
15450
7306
19154
495417229
209
16629
47212
31089
60503
0
7763
12891
9900
246
6465
0
10055
0
30937
10225
6690
8000
9307
10138
12277
2579
349

6237
21863
16713
28499
5342
29704
0
7243
94882
19842
70101
0
12302
39347
11179
614
4829
767
13931
0
32457
8284
5593
26336
17020
6689
4431
10590
700

389
3983
2589
6725
1900
17235
2226
12296
222589154690
98391
1505
955
2526
4053
185071552405811727
0237146
103
3009
7487
0
9031
7311
3298
6968

376
5712
3085
11544
1864
41442
1095
9948
181838242879
246036
981
7715
11061
9351
25029002575925445
0382646
03743
4010
07742
5252
4547
13539

01364
2153
4020
977
1945
02088
00006811137
1712
00000000
4456
01816
272
256
2681

0
2376
2493
1598
712
548
227
1042
0000
568
785
1969
00
0
00000
2289
0
10551
4141
1434
6478

107275090023
1036
0
20571
54851
82934
00
75370
2937
1406
42059
52458
71831
149252
27143
97881
26838
66768
86870
98472
30656
193738
98304
0
16835
92453

1086590147725
2762
946
74555
10900554824
00103056
53754
0
64009
29309
165089104745120243
144352
166911
62957
12396281298
13774013216633912
0122181
94222*Tentative identiﬁcation based on the published data of the parental species (Okamoto et al., 2015)

128

Yudai Okuyama

Fig. 2. The patterns of correlations of the ﬂoral scent phenotypes between 2014 and 2015 in the four volatilecompounds. A: (R)-linalool, B: (S)-linalool, C: lilac aldehyde (tentative identiﬁcation), D: β-caryophyllene.
For A, B and D, the both axis are log-transformed and the zero values are plotted as 1.

ﬂoral scent phenotypes quantiﬁed in the two
ﬂowering seasons were modestly to strongly correlated with each other for all the four compounds (Fig. 2; Spearmanʼs rank correlation test;
p＜9.8×10−8, ρ＝0.81 for (R)-linalool, p＜
1.1×10−10, ρ＝0.89 for (S)-linalool, p＜8.4×10−11, ρ＝0.89 for lilac aldehyde, and p＜
9.2×10−4, ρ＝0.58 for β-caryophyllene). Simple
linear regression analyses (intercept was set tozero) were also all highly signiﬁcant (p＜0.001)
and showed that the scent phenotype quantiﬁed

in 2014 well predicts that quantiﬁed in 2015 forall the four compounds (y＝1.3x, r2＝0.86 for(R)-linalool, y＝1.3x, r2＝0.92 for (S)-linalool,
y＝1.1x, r2＝0.39 for lilac aldehyde, and
y＝1.1x, r2＝0.67 for β-caryophyllene).
Discussion
In this study, I successfully established a standard methodology for consistently and quantitatively phenotyping the ﬂoral scent traits ofPhenotyping of Floral Scent in Mitella

Mitella. The reference data using the analyticalstandards indicated that the values obtained withthe present methodology is proportional to theamount of volatiles in the headspace, at least forlinalool within the concentration range examinedin the present study. The obtained values for theindividual volatile compounds were variable
among F2 individuals but stable across the two
ﬂowering seasons within each individual (Fig. 2).
These observations indicate that the present phenotyping method is reliable enough to apply it togenetic analyses and any comparative analyses
on the scent traits. Moreover, the present studyled to another important conclusion, that is, thetraits studied in the present study are likely under
the strong control of the plant genotype alongwith the other phenotypic traits already studied
in the previous study (Okuyama and Akashi,2013). Because the parental species of the F2
hybrids were divergent in the ﬂoral scent traits
and associated pollinators they relied on (M.
furusei and M. pauciﬂora; Okamoto et al., 2015),
these ﬁndings would be the strong basis to pursue the genetic study identifying the genetic
basis of ﬂoral scent traits and associated reproductive isolation mechanisms in the plant lineage.
Acknowledgments
I thank Tomoko Okamoto for advising on the129

volatile analyses and comments on the earlier
draft of the manuscript.
References
Friberg, M., Schwind, C., Raguso, R. A. and Thompson,
J. N. 2013. Extreme divergence in ﬂoral scent among
woodland star species (Lithophragma spp.) pollinatedby ﬂoral parasites. Annals of Botany 111: 539–550.
Okamoto, T., Okuyama, Y., Goto, R., Tokoro, M. andKato, M. 2015. Parallel chemical switches underlying
pollinator isolation in Asian Mitella. Journal of Evolutionary Biology 28: 590–600.
Okuyama, Y., Pellmyr, O. and Kato, M. 2008. Parallel ﬂoral adaptations to pollination by fungus gnats within thegenus Mitella (Saxifragaceae). Molecular Phylogenetics and Evolution 46: 560–575.
Okuyama, Y. and Akashi, M. 2013. The genetic basis ofﬂower-related phenotypic differences between closelyrelated species of Asian Mitella (Saxifragaceae). Bulletin of the National Museum of Nature and Science.
Series B 39: 131–136.
R Development Core Team. 2011. R: A language andenvironment for statistical computing. R Foundationfor Statistical Computing, Vienna, Austria. ISBN3-900051-07-0, URL http://www.R-project.org/.
Soltis, D. E., Kuzoff, R. K., Mort, M. E., Zanis, M., Fishbein, M., Hufford, L., Koontz, J. and Arroyo, M. K.
2001. Elucidating deep-level phylogenetic relationships
in Saxifragaceae using sequences for six chloroplastic
and nuclear DNA regions. Annals of the Missouri
Botanical Garden 88: 669–693.
Thompson, J. N., Schwind, C., Guimarães, P. R. and Friberg, M. 2013. Diversiﬁcation through multitrait evolution in a coevolving interaction. Proceedings of theNational Academy of Sciences 110: 11487–11492.