Special Focus Issue – Regular Paper
Position-Specific Gene Expression Analysis Using a Microgram
Dissection Method Combined with On-Bead cDNA Library
Construction
Tomoharu Kajiyama1,*, Akihiko Fujii1, Kouji Arikawa1, Toru Habu1, Nobuyoshi Mochizuki2,
Akira Nagatani2 and Hideki Kambara1,*
1
Central Research Laboratory, Hitachi, Ltd., Tokyo, 185-8601, Japan
Graduate School of Science, Kyoto University, Kyoto, 606-8502 Japan
2
*Corresponding author: Hideki Kambara, E-mail, [email protected]; Tomoharu Kajiyama,
E-mail, [email protected]; Fax, +81-42-327-7784.
(Received December 4, 2014; Accepted May 26, 2015)
Editor-in-Chief’s Choice
Gene expression analysis is a key technology that is used to
understand living systems. Multicellular organisms, including plants, are composed of various tissues and cell types,
each of which exhibits a unique gene expression pattern.
However, because of their rigid cell walls, plant cells are
difficult to isolate from the whole plant. Although laser
dissection has been used to circumvent this problem, the
plant sample needs to be fixed beforehand, which presents
several problems. In the present study, we developed
an alternative method to conduct highly reliable gene
expression profiling. First, we assembled a dissection
apparatus that used a narrow, sharpened needle to dissect
out a microsample of fresh plant tissue (0.1–0.2 mm on each
side) automatically from a target site within a short time
frame. Then, we optimized a protocol to synthesize a highquality cDNA library on magnetic beads using a single
microsample. The cDNA library was amplified and subjected
to high-throughput sequencing. In this way, a stable and
reliable system was developed to conduct gene expression
profiling in small regions of a plant. The system was used
to analyze the gene expression patterns at successive
50 mm intervals in the shoot apex of a 4-day-old
Arabidopsis seedling. Clustering analysis of the data demonstrated that two small, adjacent domains, the shoot apical
meristem and the leaf primordia, were clearly distinguishable. This system should be broadly applicable in the
investigation of the spatial organization of gene expression
in various contexts.
Keywords: Dissection needle Microdissection Organspecific transcriptome Position-specific gene expression Subnanogram RNA Whole-genome transcriptome.
Abbreviations: CV, coefficient of variation; GO, gene ontology; ID, inner diameter; LED, light emitting diode; OD, outer
diameter; PBS, phosphate-buffered saline; q-PCR, quantitative
real-time PCR; RNA-seq, high-throughput sequencing of
cDNA libraries; RPKM, reads per kilobase of exon per million
mapped reads; TE, Tris-EDTA buffer.
Introduction
Gene expression analysis is a key technology that is used to
understand living systems, and it is usually carried out using
DNA probe arrays, which consume a large amount of sample.
Recently, gene expression analysis via the high-throughput
sequencing of cDNA libraries (RNA-seq) has been demonstrated. More recently, technology that can analyze the gene
expression profiles of single cells has been reported by several
groups (Tang et al. 2009, Ramsköld et al. 2012, Sasagawa et al.
2013), including ours (Huang et al. 2014, Matsunaga et al. 2015).
Such technology enables gene expression analysis using a small
amount of cells or a small piece of tissue. However, these techniques have not been fully explored in plants.
Unlike animal cells, plant cells are closely packed and tightly
connected to each other with rigid cell walls. Consequently, it is
more difficult to isolate a single cell or cluster of cells from a
plant. Although protoplasts are available in some cases
(Bargmann and Bimbaum 2010), their preparation is timeconsuming and cumbersome. Moreover, their gene expression
profile might change during preparation. Additionally, a large
proportion of the plant cell contains the vacuole, which stores
various enzymes involved in degradation (Hara-Nishimura and
Hatsugai 2011). Hence, great care should be taken in the preparation of high-quality RNA samples from a small quantity of
plant tissue.
The importance of cell type- and tissue-specific gene expression profiling in understanding various biological phenomena
in plants has been widely recognized (Nelson et al. 2008, Rogers
et al. 2012). Laser dissection provides a way to generate celltype-specific gene expression profiles in plants (Kerk et al. 2003,
Nakazono et al. 2003). It has been used in combination with
DNA microarray technology to conduct genome-wide analyses
of various tissues and cell types (Nelson et al. 2008). More
recently, the method has been combined with the RNA-seq
technique (Schmid et al. 2012, Osaka et al. 2013).
The spatial resolution of laser dissection is virtually unlimited; one can even use it to isolate a single cell from a tissue slice.
Plant Cell Physiol. 56(7): 1320–1328 (2015) doi:10.1093/pcp/pcv078, Advance Access publication on 18 June 2015,
available FREE online at www.pcp.oxfordjournals.org
! The Author 2015. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.
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Plant Cell Physiol. 56(7): 1320–1328 (2015) doi:10.1093/pcp/pcv078
This technical advantage has been successfully used to analyze
small internal domains of plants such as the shoot apex (Brooks
et al. 2009, Torti et al. 2012). However, the plant sample needs
to be fixed prior to dissection, which is time-consuming and
cumbersome, and the fixation method needs to be optimized
for each application (Takahashi et al. 2010). Even after taking
great care, the mRNA recovery rate from the dissected sample is
often low. In addition, there are concerns about altering the
sample’s mRNA composition during the fixation and extraction
processes.
The shoot apex is a microstructure from which all of the
major aerial organs such as the stem, leaves and flowers are
generated (Barton 2010). At the center of the shoot apex is the
shoot apical meristem, which is between approximately 0.05
and 0.1 mm in size and consists of undifferentiated, dividing
cells. Leaf primordia are successively formed peripherally to
the central zone of the apex. A number of functionally important genes that are specifically expressed in certain parts of the
shoot apex are known (Barton 2010). Laser dissection combined with microarray analysis has been employed to search
for such genes in different domains of the maize shoot apex
(Brooks 2009). More recently, laser dissection and RNA-seq
have been used to analyze transcriptomic changes during the
floral transition in the Arabidopsis shoot apex (Torti et al. 2012)
As an alternative to laser dissection, we have developed a
system to produce a high-quality cDNA library from a small,
precisely located sample of fresh tissue in a short period of time
without changing its gene expression pattern. With this system,
a microsample of plant tissue (0.1–0.2 mm on each side, 1–2 mg
FW) was dissected out using a narrow sampling needle and
immediately frozen in sampling buffer. The sample was then
homogenized and used to synthesize a cDNA library on magnetic beads. Then, gene expression profiling was performed
using RNA-seq. To evaluate the system, microsamples were
collected at short intervals along the shoot apical region of
Arabidopsis seedlings, and genes that were preferentially expressed in either the shoot apical meristem or the leaf primordia were successfully distinguished.
Results and Discussion
Microsampling from a 4-day-old Arabidopsis
seedling
The dissection system (Fig. 1) equipped with a narrow sampling needle (Fig. 2) was used to study 4-day-old Arabidopsis
seedlings. Microsamples were collected from the shoot apical
region, the cotyledon blade and the middle of the hypocotyl
(Fig. 3). The dissected microsamples were cylindrical and
had a 130 mm diameter, which was equivalent to the inner
diameter (ID) of the sampling needle (Fig. 3). The sample
sizes varied slightly, even if the sizes of the needles used were
the same (relative SD of 9.7%, Supplementary Table S1).
However, in this study, the variation in sample size is not a
cause for concern because the gene expression levels are
represented as reads per kilobase of exon per million mapped
reads (RPKM).
Fig. 1 A photograph of the dissection system. The system consists of a
video microscope (1) with a ring-shaped 510 nm LED light (2), a 2-D
moving stage (3) equipped with a dissection device containing a
sampling needle (4), a tube holder (5) for sample collection, a washing
stage (6) for the sampling needle, a sampling stage (7) and a control
system (8).
Fig. 2 A photograph of the needle-based sampling device. (a) The
needle was made from a 31-gauge stainless steel pipe (260 mm OD and
130 mm ID). The plant microsample captured inside the needle was
pushed out with a plunger. (b) A magnified view of the needle tip,
which has four tips for dissecting a tissue sample.
Although the system had a mechanical positioning accuracy
of 1 mm, a sample placed on the gel plate sometimes moved
when touched with the sampling needle. Because the sample
moved up to 10 mm in some cases, the actual spatial accuracy
was considered to be approximately 10 mm. With this accuracy,
we could reproducibly prepare shoot apex microsamples that
were centered on the shoot apical meristem at a high rate
(>90%) (Fig. 3). The accuracy of the positioning was further
confirmed by the expression patterns of the marker genes for
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T. Kajiyama et al. | Position-specific gene expression analysis
Fig. 3 Photographs of microsamples dissected from a 4-day-old Arabidopsis seedling. Black bar = 100 mm. (a) The upper part of the seedling,
from which the shoot apex (A), cotyledon blade (C) and hypocotyl (H) microsamples were collected. (b) A side view of a cotyledon microsample.
(c) A side view of a hypocotyl microsample. (d) Front and side views of a shoot apex microsample. (e) The shoot apical region before and after the
dissection. (f) A clear view of the shoot apical region. The red circle indicates the size of the microsample (diameter = 130 mm).
leaf primordium and shoot apical meristem (see below). Taken
together, the accuracy and reproducibility of the system was
high enough to examine the gene expression patterns in a small
region of the plant body such as the shoot apex (see below).
mRNA decomposition during preparation
In the present study, a needle-based device was used to collect a
microsample of fresh tissue rapidly (Fig. 2). The dissected single
sample was transferred into a droplet of sampling solution and
frozen with liquid nitrogen within 15 s. The sample was then
homogenized to extract the mRNA, and the mRNA was used to
synthesize cDNA on magnetic beads (Jost et al. 2007). Because
the size of the droplet of sampling solution considerably affected the cDNA recovery rate, this parameter was experimentally optimized (see the Materials and Methods).
A large proportion of a plant cell’s volume is occupied by
vacuoles, which contain various enzymes that are involved in
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degradation (Hara-Nishimura and Hatsugai 2011). Hence, RNA
degradation might occur during dissection and homogenization. To investigate this issue, we examined the effect of the lag
time between dissection and homogenization on cDNA recovery (Fig. 4). Cotyledon microsamples were incubated in the
sampling solution for a certain period of time at room temperature and then subjected to cDNA synthesis. Then, the expression of a housekeeping gene, TUB2 (AT5G62690), was
analyzed by quantitative real-time PCR (q-PCR) without
cDNA amplification (Fig. 4). The amount of TUB2 recovered
from single microsamples decreased exponentially with prolonged incubation. The half-life of TUB2 mRNA was calculated
to be approximately 5 min.
We further examined how the lag time affected the gene
expression profile. cDNA libraries were prepared from microsamples that had been processed with or without a lag time of
5 min, and the libraries were subjected to RNA-seq analysis. A
Plant Cell Physiol. 56(7): 1320–1328 (2015) doi:10.1093/pcp/pcv078
Fig. 4 The lag time dependence of TUB2 cDNA recovery from a single
microsample. The cotyledon microsamples were incubated at room
temperature, and TUB2 (AT5G62690) cDNA was measured via q-PCR
without cDNA amplification. The recovery of TUB2 (AT5G62690)
decreased with increased lag time between sample dissection and
freezing, with a half-life of approximately 5 min. n = 5.
scatter plot of the gene expression levels in the two samples is
shown in Fig. 5. The plot was not symmetric relative to the
diagonal line, indicating that prolonged incubation altered the
gene expression profile. Therefore, obtaining the true gene expression profile requires rapid sample processing. Using our
system, the sampling process can be completed within 15 s,
which is short enough to maintain the profile.
The reproducibility of the gene expression profiles
To obtain a reliable cDNA library, it is very important to remove
impurities in the sample. Therefore, the cDNA libraries were
produced on magnetic beads. This technology has been successfully used in animal cells, and its reproducibility for single
animal cells, as well as for pooled cell samples, has been confirmed (Taniguchi et al. 2009). To validate the reproducibility of
the gene expression profiles obtained from the plant microsamples, cDNA libraries were prepared from 10 independent
samples each from the shoot apical region, hypocotyl and cotyledons of 4-day-old Arabidopsis seedlings (30 samples in total)
(Fig. 3), and then they were subjected to RNA-seq analysis.
A detailed comparison of the data revealed that the expression levels of certain genes varied substantially from sample to
sample, even if the samples were prepared from the same position. In general, it is very difficult to validate the correctness of
the gene expression profiles of microsamples because of possible microheterogeneities among them. To confirm that the
variation was due to the samples themselves and not the sampling protocol, we searched for genes that were stably expressed
at all three positions. The coefficients of variation (CVs; calculated as SD/mean) were calculated for the RPKM values across
all positions (in total, 30 samples), and 278 genes were identified as having a low CV (0.3) (Supplementary Table S2).
Fig. 5 A scatter plot showing the average expression levels of 11,288
genes in microsamples that were prepared after different lag times.
Condition 1 (y-axis): the samples were frozen 5 min after dissection
(n = 4). Condition 2 (x-axis), the samples were frozen immediately
after dissection (n = 4). Both of the samples were from cotyledons.
Then, the expression levels of these genes in a newly prepared cotyledon microsample were determined and compared
with the average values calculated for the previous 30 samples
(Fig. 6). If the gene expression analysis had generated errors in
the new measurements, the plots would be dispersed. As shown
in Fig. 6, in the sample frozen immediately after dissection, the
dispersion was small (the deviation was <50% for 87% of the
genes), indicating that the expression was reproducibly detected. In contrast, the dispersion was much larger when the
microsamples were incubated for 5 min prior to homogenization. Because the deviations were so large, the average of five
samples was plotted. Nevertheless, the deviation was <50%
only for 35% of the genes.
We also compared the number of genes with a low CV
(0.3) between the 10 cotyledon standard samples and the
five cotyledon samples incubated for 5 min prior to the extraction. The number was dramatically reduced from 2,125 to 171
by the incubation. Hence, incubation altered not only the average RPKM values but also the deviation of gene expression
levels in individual samples. Taken together, immediate freezing
was important to obtain a reliable gene expression profile.
Some genes are assumed to be stably expressed in different
tissues. For example, 19 genes, including ACT2 (AT3G18780),
UBQ10 (AT4G05320), UBC9 (AT4G27960), EF-1 (AT1G07920,
AT5G60390) and TUB2 (AT5G62690), have been selected as
superior reference genes in Arabidopsis (Hong et al. 2010).
However, only two of these genes, YSL8 (AT5G08290) and
AT4G33380, were found in our set of 278. Among the remaining 17 genes, five were expressed at only very low levels
(RPKM < 10) and exhibited large CVs. However, the expression
of the others was relatively stable (CV < 0.5 in most cases)
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T. Kajiyama et al. | Position-specific gene expression analysis
Fig. 6 The reproducibility of the gene expression levels of the 278
stably expressed genes. The expression levels determined for newly
prepared samples (y-axis) were plotted against the average expression
levels in the standard 30 samples (y-axis). A new cDNA sample prepared without a lag time (red), and five new cDNA samples prepared
with a lag time of 5 min before freezing (blue), were analyzed. In the
latter case, only the average values were plotted.
(Supplementary Table S3). Furthermore, the CVs calculated
for genes within a given position were even lower (CV < 0.3) in
many cases. Hence, in the present experiment, the stable expression of known reference genes was confirmed.
Applicability to other types of materials
To examine whether the present method is applicable to other
tissue and organ types, three microsamples from the root tips
of young seedlings and from mature rosette leaves were analyzed, and 16,235 and 17,995 genes were detected, respectively.
Of these, 2,658 and 5,128 genes, respectively, exhibited reproducible expression (RPKM > 20, CV 0.4).
Of these genes, those that were expressed at especially high
levels (RPKM > 400) were selected (Supplementary Table S4)
and subjected to gene ontology (GO) functional categorization
analysis through TAIR (https://www.arabidopsis.org/)
(Supplementary Fig. S1). The root tip genes were enriched
in ‘cell wall (Cellular Component)’ and ‘ribosome (Cellular
Component)’ genes, which probably reflected the high cell division and elongation activity in this region. In contrast, the
rosette leaf blade was characterized by the terms ‘plastid
(Cellular Component)’ and ‘electron transport or energy pathway (Biological Process)’, reflecting the development of chloroplasts and the high photosynthetic activity in this organ
(Supplementary Fig. S1). In addition, for unknown reasons,
the term ‘ribosome (Cellular Component)’ was enriched to
some extent.
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Fig. 7 The sampling positions in the shoot apical region used for the
spatial gene expression analysis. Position 3 was centered on the shoot
apical meristem (position A in Fig. 3). Positions 1 (0.1 mm above), 2
(0.05 mm above), 4 (0.05 mm below), 5 (0.1 mm below) and 6 (0.2 mm
below) were serially defined relative to position 3. Significant overlap
(approximately 50%) existed between adjacent positions spaced at
0.05 mm intervals. In addition, positions 7 (position H in Fig. 3) and
8 (position C in Fig. 3) were defined.
Although use of the present system for microsampling was
convenient and effective, it might not be suitable for certain
other purposes. For example, inner tissues cannot be directly
collected with the sampling needle. However, the sample could
be sliced prior to sampling with the needle. In addition, the
needle can be detached from the apparatus (Fig. 2) and used on
its own, thereby enabling flexible sampling. Indeed, the present
method was successfully used to examine the expression of
certain genes in the palisade and spongy tissues of
Saintpaulia (Ohnishi et al. 2015). Therefore, this method
should be applicable to a wide range of plant materials not
only in Arabidopsis but also in other plant species.
Spatial gene expression analysis in the shoot
apical region
Hand sectioning of fresh plant samples makes it difficult to
investigate position-specific gene expression in a small region
of a plant because the size of the dissected tissue is rather large
(0.5 mm square). Although the samples can be prepared at
higher spatial resolution using laser-capture microdissection,
the sample needs to be fixed prior to dissection (Kerk et al.
2003, Nakazono et al. 2003). Hence, the method is cumbersome,
may distort the mRNA composition and often reduces mRNA
recovery. In contrast, the present system enabled the rapid and
reproducible collection of microsamples of fresh plant tissue
(typically in a cylindrical shape with a diameter of 0.1 mm and
a depth of 0.1–0.2 mm, Fig. 3), from which highly reliable cDNA
libraries could be produced (Fig. 6).
To evaluate the system, five successive positions (numbered
1–6) at 50 or 100 mm intervals were defined in the uppermost
part of the Arabidopsis hypocotyl (Fig. 7). Position 3 was centered on the shoot apical meristem (position A in Fig. 3).
In addition, position 7, in the middle of the hypocotyl (position
Plant Cell Physiol. 56(7): 1320–1328 (2015) doi:10.1093/pcp/pcv078
The characteristics of positions 5, 6 and 7
(hypocotyl)
Fig. 8 gCLUTO-based clustering of 3,795 genes based on their behavior in the dissected samples. The sampling positions are explained in
Fig. 7. Each column represents the expression levels in an individual
microsample.
H in Fig. 3), and position 8, on the cotyledon blade (position C
in Fig. 3), were defined. Between six and 10 microsamples were
collected from each position and were independently analyzed
by RNA-seq.
The gene clustering analysis
The mean expression levels and CVs were calculated for each
position, and 3,795 genes that were expressed robustly and
stably (RPKM > 20, CV 0.4) in at least one of the eight positions were selected. Then, gCLUTO (Rasmussen and Karypis
2004) was used to cluster the genes based on their spatial expression patterns, and they clustered into six groups (Fig. 8;
Supplementary Table S5). The results of GO functional
categorization via TAIR (https://www.arabidopsis.org/)
(Supplementary Fig. S2) and GO term enrichment analysis
via g:Profiler (http://biit.cs.ut.ee/gprofiler/index.cgi) (Reimand
et al. 2011) (Supplementary Table S6) on each cluster are
shown. The averaged expression profiles across the eight positions are shown (Supplementary Fig. S3).
The characteristics of position 8 (cotyledons)
Position 8 (cotyledons) was characterized by the high expression of cluster 2 genes (Fig. 8). GO functional categorization
(Supplementary Fig. S2) revealed that this cluster was highly
enriched for ‘chloroplast (Cellular Component)’, ‘plastid
(Cellular Component)’ and ‘electron transport or energy pathways (Biological Process)’ genes. In addition, g:Profiler analysis
indicated enrichment for GO terms related to photosynthesis,
such as ‘PSII associated light-harvesting complex II catabolic
process’ (Supplementary Table S6). These observations are
consistent with the greater photosynthetic activity at this
position.
Morphologically, these positions represented the ‘true’ hypocotyl, which consists of concentric layers of tissue (Figs. 3, 7).
The characteristic gene clusters of this domain were 1 and 4
(Fig. 8). The cluster 1 genes were expressed at higher levels at
position 7, which represented a more developed part of the
hypocotyl. GO functional categorization analysis indicated that
the terms ‘cell wall (Cellular Component)’, ‘extracellular
(Cellular Component)’ and ‘Golgi apparatus (Cellular
Component)’ were enriched in this cluster (Supplementary
Fig. S2), probably reflecting the cell wall expansion activity at
this position. In addition, consistent with the transport activity
of the hypocotyl, the ‘transport (Biological Process)’ term was
enriched.
In the hypocotyl, the cluster 4 genes were expressed more
uniformly than the cluster 1 genes (Fig. 8; Supplementary Fig.
S3) and their expression levels gradually increased along the
hypocotyl axis from position 3 to 7. GO functional categorization analysis indicated that the terms ‘cell wall’, ‘Golgi apparatus’, ‘transporter activity’ and ‘transport (Biological Process)’
were enriched (Supplementary Fig. S2). With respect to
g:Profiler analysis, both clusters were enriched for several GO
terms related to cell expansion and transport (Supplementary
Table S6). It is intriguing that the spatial expression patterns of
these two clusters were clearly distinguishable despite their
high functional similarity.
The characteristics of position 3 (central apex)
The cluster 6 genes, whose expression peaked at position 3,
characterized this position (Fig. 8; Supplementary Fig. S3).
GO functional categorization revealed no particular enrichment related to the apex’s function (Supplementary Fig. S2).
However, GO terms such as ‘meristem maintenance’, ‘determination of bilateral symmetry’, ‘cellular response to auxin
stimulus’ and ‘flower morphogenesis’ were enriched in this cluster (Supplementary Table S6). Importantly, the cluster 6 genes
included known shoot apical meristem marker genes such as
STM (AT1G62360) (see below for details).
The characteristics of positions 1 and 2 (upper
apex)
These three positions are often collectively recognized as the
‘shoot apex’. However, positions 1 and 2 consisted mainly of leaf
primordia, whereas position 3 was centered on the shoot apical
meristem (Fig. 3). Positions 1 and 2 were characterized by the
cluster 3 genes (Fig. 8; Supplementary Fig. S3), which were
enriched for the GO functional categorization terms ‘ribosome
(Cellular Component)’, ‘structural molecule activity (Molecular
Function)’ and, to a lesser extent, ‘DNA or RNA metabolism
(Biological Process)’ (Supplementary Fig. S2). These terms may
reflect the proliferative activity of this domain. Indeed, GO
terms such as ‘mitotic cell cycle’, ‘cell proliferation’ and ‘cell
division’ were enriched in this cluster (Supplementary Table
S6). In addition, ‘leaf morphogenesis’ genes such as PHB
(AT2G34710) were enriched (see below for details).
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T. Kajiyama et al. | Position-specific gene expression analysis
Fig. 9 The Mountain Visualization of relationships between the different positions. The clustering analysis and visualization were performed using gCLUTO. The samples were clustered into P1–P6
groups. The number of samples involved in the group/total number
of the samples for different positions are shown. The distance between
a pair of peaks represents the similarity of clusters, whereas the height
and color of a peak represent the internal similarity and standard
deviation, respectively (Rasmussen and Karypis 2004).
The characteristics of position 4 (lower apex) and
gene cluster 5
Morphologically, this position corresponded to the lower part
of the shoot apex and consisted of the vascular junction and the
lower part of the apical meristem (Fig. 3). Both the hypocotyl
genes (clusters 1 and 4) and the apex genes (clusters 3 and 6)
were moderately expressed in this position (Fig. 8;
Supplementary Fig. S3). Hence, this position might represent
the transition zone between the shoot apex and the ‘true’
hypocotyl.
As discussed above, five out of the six gene clusters were
connected to particular positions or domains. The exception
was the cluster 5 genes, whose expression slightly decreased
towards the basal part of the hypocotyl (Fig. 8;
Supplementary Fig. S3). No particular GO terms were clearly
enriched in this cluster, although a weak enrichment for ‘plastid
(Cellular Component)’ was observed (Supplementary Fig. S2).
The GO terms enriched in this cluster were related to diverse
biological functions (Supplementary Table S6).
The sample clustering analysis
To evaluate the adequateness of sampling, the samples were
clustered on the basis of their gene expression patterns using
gCLUTO (Rasmussen and Karypis 2004). The result is displayed
in the Mountain Visualization, with labels indicating the
numbers of samples from different positions included in the
clusters (P1–P6) (Fig. 9). In addition, a hierarchical tree generated by additional agglomerative clustering is shown in
Supplementary Fig. S4.
In the Mountain Visualization, each peak represents a single
cluster. The distance between a pair of peaks represents the
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similarity of clusters (Rasmussen and Karypis 2004). The height
and color of a peak represent the internal similarity and standard deviation, respectively. As shown in the visualization
(Fig. 9), samples from positions 8 (cotyledons) and 7 (lower
hypocotyl) exclusively constituted the P4 and P6 clusters, respectively. In addition, P6 was connected to P5, which mainly
consisted of the samples from positions 5 and 6 (upper hypocotyl) and a half of the samples from position 4 (lower apex).
Hence, samples from positions 5–8 were clustered as expected
from their morphological nature.
The samples from the topmost part of the hypocotyl were
clustered in a little more ambiguous way, which probably reflected the difficulty in collecting morphologically equivalent
samples (Fig. 9). Nevertheless, the cluster P1 was dominated by
position 1 and 2 samples (upper apex), whereas P2 and P3
mainly consisted of position 3 (central apex) and position 4
(lower apex) samples, respectively. Hence, the present method
was accurate enough to distinguish small domains of 100 mm in
size (Fig. 3).
The spatial expression patterns of marker genes in
the shoot apical region
The shoot apical region is structurally organized to produce
new stem tissue below the tip and new leaves laterally
(Barton 2010). Because the genes involved in the organization
of the shoot apex are well characterized, we analyzed their expression in our experiment. Among the genes involved in shoot
apical meristem function (Barton 2010), five genes, CLV1
(AT1G75820), STM (AT1G62360), KNAT2 (AT1G70510), ICU4
(AT1G52150) and BOP2 (AT2G41370), were robustly and stably
expressed. They were all classified as part of cluster 6
(Supplementary Table S5), and their expression peaked at
the meristematic region in position 3 (Supplementary Fig.
S5). It should be noted that the two adjacent positions overlapped position 3 by 50% (Fig. 7). Hence, even if a certain gene
were expressed exclusively in position 3, its expression would
inevitably be detected in the adjacent positions. Thus, this
result indicates that the five genes were expressed almost exclusively in position 3.
In contrast to the shoot apical meristem genes, five genes
implicated in leaf primordium development, AMP1
(AT3G54720), MP (AT1G19850), PHYB (AT2G34710), PHYV
(AT1G30490) and YAB2 (AT1G08465), were classified as part
of cluster 3 (Supplementary Table S5). Accordingly, their expression peaked at position 2, although YAB2 was expressed in
positions 1 and 3 as well (Supplementary Fig. S5). Hence, it
was further confirmed that the shoot apical meristem and the
basal part of the leaf primordia could be successfully distinguished despite their small size and close proximity (Fig. 3).
Expression patterns of epidermis-specific genes
It should be noted that the microsamples included several different tissues such as epidermis (Fig. 3). So, we examined how
the wax-related genes, which are specifically expressed in epidermis, were expressed in our samples. Among 20 genes listed
in the literature (Bernard and Joubés 2013), nine were expressed
robustly and stably (RPKM > 20, CV 0.4) in at least one of the
Plant Cell Physiol. 56(7): 1320–1328 (2015) doi:10.1093/pcp/pcv078
eight positions. Among them, the CER2 (AT4G24510), CER5
(AT1G51500) and CER6 (AT1G68530) genes were classified as
part of cluster 1, indicating that they were expressed highly in
the mid-hypocotyl (Fig. 8). In addition, CER3 (AT5G57800) was
expressed more uniformly in the hypocotyl (cluster 4). Three
other genes, LACS2 (AT1G49430), WBC11 (AT1G17840) and
CER9 (AT4G34100), were expressed ubiquitously throughout
the positions (cluster 5). Hence, epidermis appeared to be represented in all the samples, probably at higher rates in the
hypocotyl samples. Exceptionally, CER8 (AT2G47240) and
LTPG1 (AT1G27950) were expressed preferentially in the leaf
primordia (cluster 3) and the shoot apical meristem (cluster 6),
respectively. Further analysis of those genes would help to estimate the ratio of epidermis in each sample.
Conclusion
The present method enabled us to obtain reliable transcriptomic data from a single microsample of fresh plant tissue (0.1–
0.2 mm on each side, 1–2 mg FW). The sampling needle worked
effectively to collect samples in a short period of time. By taking
advantage of this system, we could discriminate the gene expression patterns between closely spaced domains within the
shoot apical region. This system could be broadly applied for
different purposes. For example, differences in light responses in
the shoot apex and the cotyledons were successfully compared
using the present method (Nito et al. 2015). This method was
also used to analyze the expression of certain genes in palisade
and mesophyll tissues that had been dissected from fresh leaf
slices (Ohnishi et al. 2015). Thus, this method will provide a new
approach to analyze the spatial structure of gene expression at
high resolution in various biological contexts.
Materials and Methods
Plant materials
The Arabidopsis thaliana ecotype used in this study was Columbia-0. Seeds
were surface sterilized and sown on 0.8% (w/v) agar plates containing
Murashige and Skoog (MS) medium supplemented with 0.5% (w/v) sucrose
at pH 5.6. The plates were cultured in the dark at 4 C for 48 h and subsequently
transferred to a growth chamber for 8 h/16 h (day/night) periods at 23 C.
Development of the dissection system
The dissection system (Fig. 1) consists of a video microscope with a ring-shaped
510 nm light-emitting diode (LED) (GR200BCM2, SDS-H and GRD-76/50 G,
Shodensha, Inc.), a 2-D moving stage equipped with a sampling needle, a
tube holder for collecting samples with space for 12 PCR tubes, a washing
stage for the needle, a stage to hold a plant sample (whole seedling, dissected
organ. etc.) from which the microsamples are collected, and a control system.
The sampling needle (Fig. 2) was made of stainless steel pipe {SUS304, 31
gauge [outer diameter (OD) of 0.26 mm; ID of 0.13 mm] or 21 gauge (OD of
0.41 mm; ID of 0.21 mm), Teshima Co.}. Four tips were cut into the needle and
then sharpened to allow the dissection of a microsample of fresh plant tissue.
All of the system’s movements (i.e. positioning of the 2-D stage, pushing down/
pulling up the sampling needle and pushing/pulling the sampling plunger) were
controlled with a programmable controller (KV-5000, Keyence Co.).
sampling position was determined, a microsample of tissue was captured
inside the needle by pushing the needle into a plant. The microsample was
then pushed out from the needle with a 0.1 mm diameter tungsten plunger
placed inside the sampling needle. The sample was placed into a 0.5 ml droplet
of sampling solution [phosphate-buffered saline (PBS), Tris-EDTA buffer (TE),
Buffer RLT (QIAGEN), or Buffer RLC (QIAGEN)] in a PCR tube. Because of its
small volume, the sample solution was easily and precisely placed on the inner
surface of the PCR tube, facilitating the easy transfer of the sample into the
droplet. The dissected microsample and sampling solution were immediately
frozen.
Because 12 PCR tubes can be mounted on the tube holder, 12 different
samples can be processed at a time. Three different solutions in 1.5 ml washing
tubes were used sequentially to wash the needle [PBS with 1% Tween-20, RNase
Zap (Life Technologies Co.), and PBS]. We confirmed that the residual rate of
mRNA was as low as 0.03 ± 0.01% (n = 5) on the basis of TUB2 (AT5G62690)
cDNA levels. The whole process, from setting the plant sample on the stage to
washing the needle, took between 2 and 3 min. Hence, multiple samples could
be handled within a relatively short time frame.
cDNA library construction
The frozen microsample was homogenized with a pestle. The tube containing
the homogenized sample was then centrifuged together with the pestle to
recover the sample solution. Nuclease inhibitors were added to the sampling
buffer to protect the mRNA from digestion. A cDNA library was constructed in
each tube using magnetic beads. The detailed protocol for producing the cDNA
library is described in the Supplementary protocol, but its basic technology
has been reported previously (Taniguchi et al. 2009, Huang et al. in 2014,
Matsunaga et al. 2015). DNA sequencing of the amplified cDNA library was
TM
carried out with an Ion-Torrent PGM with a 318 Chip (Life Technologies Co.).
First, the amplified cDNA was fragmented into approximately 200 bp fragments
TM
according to a standard protocol (Ion Xpress Plus gDNA Fragment Library
Preparation). The sequencing data were downloaded in FASTQ format and
annotated to the Arabidopsis thaliana database (TAIR Ver. 10) using Tophat
(Ver. 2.0.5). The gene expression levels were expressed on an RPKM scale using
Cufflinks (Ver. 2.0.0).
Optimization of sampling buffer
The selected sampling buffer must minimize mRNA degradation during the
homogenization process at room temperature. Usually, PBS or TE buffer is used
for reverse transcription reactions. However, high RNase activity is expected in
plant extracts (Hara-Nishimura and Hatsugai 2011). Hence, we compared the
RNA recovery rates using different sampling buffers, including Buffer RLT and
Buffer RLC (RNeasy Plant Mini Kit, QIAGEN). Although the exact compositions
of Buffer RLT and RLC are confidential, Buffer RLT is known to contain a high
concentration of guanidine isothiocycanate, whereas Buffer RLC contains high
concentrations of guanidine hydrochloride.
The amount of TUB2 (AT5G62690) cDNA was determined to clarify the
effects of the different extraction buffers on cDNA production
(Supplementary Fig. S6). The microsamples examined were from the cotyledons of 4-day-old seedlings (approximately 1 mg FW). As a control, a standard
total RNA sample prepared from cotyledons using a conventional method
(RNeasy Plant Mini Kit, QIAGEN) was used (2 ng of RNA, corresponding to
4 mg of tissue). The volume of the sampling buffer was 0.5 ml.
In the standard sample, the cDNA production rate with PBS was higher
than the rates obtained with Buffers RLC or RLT, indicating that the reverse
transcription reaction was slightly inhibited by the use of these buffers.
However, the amount of TUB2 (AT5G62690) cDNA produced from a single
microsample was much larger using Buffers RLC or RLT than PBS or TE. Hence, a
considerable amount of RNase, which extensively degraded the mRNA when
using PBS, existed in the microsamples, but the inhibitory effect of Buffer RLT
and Buffer RLC on RNase suppressed it
Optimization of the sampling buffer volume
Microsample preparation
Plant samples, such as a seedling or a young rosette leaf, were placed on a gel
plate (2%, 2 mm thick) and checked with a video microscope. After the
Buffers RLT and RLC affected the cDNA recovery rate in two opposite ways:
they suppressed RNA degradation and inhibited reverse transcription. Hence,
the buffer volume needed to be carefully optimized. The amount of TUB2
1327
T. Kajiyama et al. | Position-specific gene expression analysis
(AT5G62690) cDNA produced from a single microsample using larger volumes
of sampling buffer is shown (Supplementary Fig. S7). Although the recovery
rate increased substantially using 0.5 ml of Buffer RLC or RLT, 1.0 ml was much
less effective, and 1.5 ml was inhibitory. Because 0.5 ml was the minimum practical droplet size, a volume of 0.5 ml was chosen.
Reagents
An RNeasy Plant Mini Kit was obtained from QIAGEN GmbH, Germany. Buffer
RLT and Buffer RLC were included in this kit. A SuperScript III CellsDirect cDNA
Synthesis Kit and Dynabeads MyOne Streptavidin C1 beads were obtained from
Life Technologies Corporation, USA. Axygen(R) 0.2 ml MAXYmum Recovery(R)
Thin Wall PCR Tubes (Product #PCR-02-L-C) were obtained from Corning
Incorporated, USA.
Supplementary data
Supplementary data are available at PCP online.
Funding
This work was supported by the Ministry of Education, Culture,
Sports, Science and Technology (MEXT) [KAKENHI to H.K.
(grant No. 22120008) and A.N. (grant No. 22120002)]; the
Global COE Program ‘Formation of a strategic base for biodiversity and evolutionary research: from genome to ecosystem’
[a Grant-in-Aid (A06) to A.N.].
Acknowledgments
We thank Dr. H. Matsunaga, Ms. M. Goto and Ms. K. Taniguchi
for their helpful suggestions about single-cell analysis.
Disclosures
The authors have no conflicts of interest to declare.
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