Download Cautionary Observations on Preparing and Interpreting Brain

MICROSCOPY RESEARCH AND TECHNIQUE 62:170 –186 (2003)
Cautionary Observations on Preparing and Interpreting
Brain Images Using Molecular Biology-Based
Staining Techniques
KEI ITO,1,2,3* RYUICHI OKADA,1,2,3 NOBUAKI K. TANAKA,1,2,5
AND
TAKESHI AWASAKI1,2,4
1
Institute of Molecular and Cellular Biosciences, University of Tokyo, Tokyo 113-0032, Japan
National Institute for Basic Biology, Okazaki 444-8585, Japan
Institute for Bioinformatics Research and Development (BIRD), Japan Science and Technology Corporation (JST),
Tokyo 102-0081, Japan
4
Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Corporation (JST),
Kawaguchi 332-0012, Japan
5
The Graduate University for Advanced Studies, Hayama 240-0193, Japan
2
3
KEY WORDS
brain; neural circuit; staining; GAL4; enhancer trap; Drosophila
ABSTRACT
Though molecular biology-based visualization techniques such as antibody staining, in situ hybridization, and induction of reporter gene expression have become routine procedures for analyzing the structures of the brain, precautions to prevent misinterpretation have not
always been taken when preparing and interpreting images. For example, sigmoidal development
of the chemical processes in staining might exaggerate the specificity of a label. Or, adjustment of
exposure for bright fluorescent signals might result in overlooking weak signals. Furthermore,
documentation of a staining pattern is affected easily by recognized organized features in the image
while other parts interpreted as “disorganized” may be ignored or discounted. Also, a higher
intensity of a label per cell can often be confused with a higher percentage of labeled cells among
a population. The quality, and hence interpretability, of the three-dimensional reconstruction with
confocal microscopy can be affected by the attenuation of fluorescence during the scan, the refraction
between the immersion and mounting media, and the choice of the reconstruction algorithm. Additionally, visualization of neurons with the induced expression of reporter genes can suffer because of the low
specificity and low ubiquity of the expression drivers. The morphology and even the number of labeled
cells can differ considerably depending on the reporters and antibodies used for detection. These aspects
might affect the reliability of the experiments that involves induced expression of effector genes to
perturb cellular functions. Examples of these potential pitfalls are discussed here using staining of
Drosophila brain. Microsc. Res. Tech. 62:170 –186, 2003. © 2003 Wiley-Liss, Inc.
INTRODUCTION
To understand how a complicated neural circuit in
the brain develops and functions, it is important to
obtain detailed knowledge about the overall structure
of the brain as well as the innervation patterns of
individual neurons or specific subsets of them. A large
variety of staining methods has been developed to address these questions (Bolam, 1992; Strausfeld, 1976).
Histochemical techniques such as hematoxylin-eosin
staining, silver staining, and Golgi impregnations, as
well as dye-filling methods using cobalt, Lucifer yellow,
horse radish peroxidase (HRP), DiI, and other tracers,
have successfully revealed important aspects of brain
structures.
The advent and development of molecular biology–
based staining techniques greatly enhanced the way
biologists can visualize the brain (Hockfield et al.,
1993; O’Kane, 1998; Yuste et al., 2000). There are three
major categories of this type of technique: antibody
staining, in situ RNA hybridization, and induction of
reporter gene expression. These techniques not only
provide more versatile tools for addressing the questions described above, but also make it possible to
study expression patterns of genes as well as distribu©
2003 WILEY-LISS, INC.
tion of proteins and other cellular components that are
relevant to brain function and development.
The preparation and interpretation of brain images
stained using these techniques, however, are not always straightforward. Most organs of the animal body
consist of relatively simple set of cell types. Cells in a
particular subpart of the organ often share similar
properties. Though the brain essentially consists of
only two types of cells, neurons and glia, these have
countless subtypes with diverse morphological and biochemical properties. Even a small area of the brain
contains numerous subtypes of such cells. Unlike in
other organs, both neurons and glial cells in the brain
send long fibers and processes that project long dis-
*Correspondence to: Kei Ito, Institute of Molecular and Cellular Biosciences,
University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-0032, Japan.
E-mail: [email protected]
Received 30 December 2002; accepted in revised form 12 March 2003
Contract grant sponsor: BIRD/JST; Contract grant sponsor: Human Frontier
Science Program; Contract grant number: RG0134/1999-B; Contract grant sponsor: PRESTO/JST; Contract grant sponsor: Ministry of Education, Culture,
Sports, Science and Technology of Japan.
DOI 10.1002/jemt.10369
Published online in Wiley InterScience (www.interscience.wiley.com).
MOLECULAR BIOLOGY-BASED BRAIN STAINING
tances and crisscross with each other, making it even
more difficult to identify their positions and spatial
relationships.
Thanks to the rapid development of genomic analysis
and research on the molecular mechanisms underlying
brain function and development, there are an increasing number of people working on the visualization of
multifarious aspects of brain organization. However,
documenting any part of the brain requires much
greater care than taking a picture of an electrophoresis
gel or a plate of bacteria. Discriminating signal from
background is not as easy as locating a band in the
blotting filters. Though such a difference appears obvious and is universally understood in principle, it is
unfortunately also true that not enough care is taken
in practice when preparing and interpreting the brain
images. This can lead to misinterpretation of otherwise
important findings. In this article, we discuss where
and how such misinterpretations could occur and how
best they might be avoided. Though the examples are
taken from staining the brain of Drosophila melanogaster, most issues discussed here should also be applicable to all other organisms.
MATERIALS AND METHODS
Drosophila Strains
The MZ series GAL4 enhancer-trap strains were
generated by Joachim Urban and co-workers of G. M.
Technau’s group at University of Mainz (Ito et al.,
1995). The NP series strains were generated by the
“NP consortium”, a joint venture of eight Drosophila
laboratories in Japan (T. Aigaki, S. Hayashi, S. Goto,
K. Ito, F. Matsuzaki, H. Nakagoshi, T. Tanimura, R.
Ueda, T. Uemura, and M. Yoshihara) (Hayashi et al.,
2002; Yoshihara and Ito, 2000). Other GAL4 enhancertrap strains used are: C155 (elav) (Lin and Goodman,
1994), c739 and 201y (Yang et al., 1995), OK107 (Connolly et al., 1996) and MB247 (Schulz et al., 1996; Zars
et al., 2000). UAS-lacZ (Brand and Perrimon, 1993),
UAS-GFP::S65T (strains T10 and T2, gift from B. Dickson) (Ito et al., 1997a) and UAS-mCD8::GFP (strain
LL5) (Lee and Luo, 1999) were used for visualizing the
general morphology of the GAL4-expressing cells, and
UAS-NlacZ (gift from Y. Hiromi) (Ito et al., 1998) for
visualizing the positions of the nuclei.
X-gal Staining
The brains were dissected in phosphate-buffered saline (PBS) from the head of 4 –7-day-old females, fixed
in 1% glutaraldehyde/PBS (10 min at RT), washed in
PBS, and incubated at 37°C for 20 minutes to 2 hours
in a staining solution (10 mM phosphate buffer, pH 7.2;
150 mM NaCl; 1 mM MgCl2; 3.1 mM K4[FeII(CN)6];
3.1 mM K3[FeIII(CN)6]; 0.3% Triton X-100; 2% gelatine;
20 ␮l of 10% X-Gal in DMSO per 1 ml solution).
Antibody Staining
The dissected brains were fixed in 4% formaldehyde
in PEM (100 mM PIPES, 2 mM EGTA, 1 mM MgSO4,
50 min at RT), washed in PBT (0.1% TritonX in PBS)
and incubated with primary and secondary antibodies
in 10% normal goat serum (Vectastain, USA) in PBT.
The following antibodies were used: anti mouse-mCD8
␣-subunit (1:100, Caltag, USA) for visualizing
mCD8::GFP, anti-GFP (mouse monoclonal 1:300,
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Roche Diagnostics, USA; and rabbit polyclonal 1:1,000,
Molecular Probes, USA) for enhancing the GFP signal
(see Figs. 14 and 15), and anti-␤-galactosidase (mouse
monoclonal 1:300, Promega, USA; and rabbit polyclonal 1:3000, Cappel, USA) for visualizing the NlacZ
reporter. For secondary antibodies, alexa 488-conjugated anti-mouse, anti-rat, and anti-rabbit IgG (1:300,
Molecular Probes) and Cy3-conjugated anti-rat and
anti-mouse IgG (1:600, Jackson Laboratories, USA)
were used. The specificity of the antibodies is checked
in situ by confirming the lack of staining in the brain of
the animals that do not express the reporter genes
(mCD8::GFP, GFP, and NlacZ).
In Situ Hybridization
Dissected brains were fixed in 4% paraformaldehyde
in PBS (50 min at RT), treated with 20 ␮g/ml proteinase K and 2 mg/ml glycine in PBS-TW (0.1% Tween
20 in PBS) for 4 and 2 minutes, respectively, and postfixed in 4% paraformaldehyde in PBS for 20 minutes
(Lehmann and Tautz, 1994). Prehybridization and hybridization were performed in hybridization buffer
[50% formamide, 5⫻ saline sodium citrate (SSC), 0.1%
Tween 20, 100 ␮g/ml of salmon sperm DNA, 50 ␮g/ml
Heparin] for 60 minutes and overnight (at least
18 hours) at 45°C, the latter with DIG-labeled singlestrand DNA probes generated using PCR. After washing in a series of hybridization wash buffer (50% formamide, 5 ⫻ SSC, 0.1% Tween20) in PBS-TW, the labels
were detected with alkaline phosphatase-conjugated
anti-DIG antibody (1:2,000, Cappel) at 4°C overnight
and visualized with 1/50 5-bromo,4-chloro,3-indolylphosphate/nitroblue tetrazolium (BCIP/NBT) solution in the staining buffer (100 mM NaCl, 50 mM
MgCl2, 100 mM Tris-HCl, pH 9.5, 0.1% Tween20) for
20 –180 minutes at RT in the dark.
Confocal Microscopy and Three-dimensional
Reconstruction
Preparations were kept in 50% glycerol in PBS for
2 hours to overnight and mounted in 50 or 80% glycerol
in PBS; 160 to 200 confocal serial optical sections at
0.5- to 1.3-␮m intervals were taken with LSM
510 confocal microscopes (Carl Zeiss, Germany)
equipped with 40⫻ or 63⫻ water-immersion apochromat objectives. To compensate the attenuation of signals at deeper focusing plane (see Fig. 8), the transmission rate of the AOTF (acousto optical tunable filter) is
increased from around 5% to around 20% (in some
cases from 0.5% to up to 100%), the detector gain from
around 700 to around 900 during the scanning. The
amplifier offset is also slightly decreased as the background noise increases at higher detector gain.
Three-dimensional reconstruction was performed
with Imaris 2.7 software (BitPlane , Switzerland) running on Octane 2 workstations (Silicon Graphics, USA).
The ray-tracing algorithm with transparency parameter between 70 and 99% were used for the reconstruction.
RESULTS AND DISCUSSION
Brief Introduction of the Staining Techniques
We briefly review the three major systems of molecular biology-based staining to visualize neurons and
other types of cells.
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Antibody Staining. Antibodies label cells that possess matching epitopes: a particular protein or other
cellular component. Within cells, antibodies ideally reveal the subcellular localization of the corresponding
antigen. Although this is the advantage of the method
for certain purposes, it can in some cases be a disadvantage for visualizing the whole morphology of the
labeled neurons. To reveal the entire cellular morphology, the antibody should recognize an antigen that is
distributed evenly throughout all parts of the labeled
cells. But this is often not the case. For example, the
anti-Fas II antibody, which recognizes cell adhesion
molecule Fasciclin II (Drosophila homologue of NCAM;
Grenningloh et al., 1991), nicely labels neural fibers of
specific cells but not their cell bodies, making it impossible to trace the origin of the labeled fibers. Many
other cell-specific antibodies label only cell bodies but
not their fibers, since many proteins, such as transcription factors, exist only in the nuclei or within the cytoplasm of the cell bodies. Antibodies recognizing such
antigens cannot visualize the overall structure of the
labeled cells.
In situ RNA Hybridization. The in situ RNA hybridization (ISH) technique visualizes cells that possess a particular messenger RNA. It is, thus, a powerful tool for directly detecting gene expression. Until
recently, probes for hybridization were generated only
after particular genes were cloned. The advancement of
genome biology has greatly expanded the source of
such probes: large sets of expression sequence tags
(ESTs) are now publicly available for various organisms. By locating the genes from the genome DNA
sequence and ordering the respective EST clones, it has
become relatively easy to obtain probes for any gene of
interest. Even if ESTs are not available, one can generate a synthetic DNA probe for any gene using sequence information from the genome database.
One disadvantage of in situ RNA hybridization is,
again, that it cannot label the whole cell. Since most
messenger RNAs exist in the cell bodies, it can only
visualize the position of their cell bodies and the projection patterns of the neural fibers cannot be studied
with this technique.
Induction of Reporter Gene Expression. The
third technique is to express a certain gene or genes
specifically in cells of interest. These genes are called
“reporter genes” whose expression is detected either
directly by the fluorescence of the reporter protein or
indirectly by staining with antibodies that recognize
the reporter. There are four ways for inducing such
expression. (1) Transformation of animals with the reporter gene fused under the control of a particular
promoter sequence (Fig. 1A). This technique is widely
used for the organisms with which permanent or
trangent introduction of recombinant DNA is possible,
e.g., mice, zebra fish, Drosophila, nematode, and Xenopus. (2) Substitute the whole or a part of the endogenous gene with the reporter gene using the method of
homologous recombination (Fig. 1B). This method is
most feasible in animals where embryonic stem cells
(ES cells) are available, since it is much more difficult
to perform homologous recombination without ES cells.
(3) Insert the reporter gene without the promoter somewhere in the genome, and let the reporter be expressed
under the control of the nearby promoter (“gene trap,”
Fig. 1. Various methods to drive expression of the transgene (gene
X). A: The transgene is fused with appropriate promoter sequence and
introduced into the genome. B: The transgene is inserted into the
position of the target gene by homologous recombination. C: The
transgene is introduced into genome randomly using transposon or by
injection. If the transgene is inserted into the endogenous gene, it will
be expressed under the control of the endogenous promoter. Many
transformant strains are generated and screened for useful expression pattern. D: The transgene with a minimum promoter is introduced into the genome. Since it has its own promoter, the transgene
will be expressed under the influence of nearby enhancer. Many
strains are screened for useful expression pattern. A2: Variant of A.
The transgene is indirectly activated by the promoter via GAL4-UAS
system. A single GAL4 strain can be used to drive expression of many
transgenes linked with UAS. D2: Variant of D. An enhancer-trap
strain can drive the expression of various UAS-linked transgenes.
MOLECULAR BIOLOGY-BASED BRAIN STAINING
Fig. 1C). This is widely used in mice and is being
applied to various other organisms like the zebra fish
and Drosophila (Chen et al., 2002; Joyner, 1991;
Lukacsovich et al., 2001; Lukacsovich and Yamamoto,
2001; Skarnes et al., 1992). (4) Insert the reporter gene
with a minimum promoter somewhere in the genome,
and let the reporter be expressed under the control of
the nearby enhancer sequence (“enhancer trap”, Fig.
1D). This technique has routinely been used in Drosophila (O’Kane and Gehring, 1987; O’Kane, 1998). To
efficiently insert the reporter gene into various
genomic sites, transposons such as Drosophila P elements are used for mobilizing the construct. A similar
transposon-based approach is being developed for other
organisms such as the zebra fish.
For the reporter, the lacZ gene has for a long time
been the first choice. Cells expressing lacZ can be detected with the X-gal activity staining or using anti-␤galactosidase antibody. Recently, the green fluorescent
protein (GFP) (Cheng et al., 1996; Tsien, 1998) and its
variants (CFP, YFP, DsRed, etc.) (Baird et al., 2000;
Baumann et al., 1998; Feng et al., 2000; Miller et al.,
1999; Verkhusha et al., 2001) are quickly replacing
X-gal staining or immunocytology. Unlike lacZ, fluorescence of the reporter protein is detectable without
staining and even in living organisms. When required,
GFP is detectable by anti-GFP antibodies, which is
useful for enhancing fluorescence as well as for making
histochemical preparations for bright field and Nomarski microscopy.
Unlike antibody staining against endogenous antigens, or in situ RNA hybridization, reporter expression
systems can visualize various aspects of labeled cells by
choosing appropriate reporters. Both ␤-galactosidase
and GFP distribute relatively evenly in the cytoplasm,
revealing the whole morphology of the labeled cells. To
label specific subcomponents of the cells, various proteins and localization signals are fused with the reporter. For example, ␤-galactosidase and GFP fused
with nuclear localization signal condense within the
nuclei. Those fused with microtubule-associated proteins and membrane-bound proteins distribute actively
to the neural fibers and cell surfaces, respectively.
A powerful improvement of the reporter expression
system was the substitution of the reporter gene with
the yeast-derived transcription factor GAL4, which activates the expression of any gene under control of the
“upstream activation sequence” (UAS, Fig. 1, A2 and
D2) (Brand and Dormand, 1995; Brand and Perrimon,
1993; Fischer et al., 1988). When an animal carrying
the GAL4 is crossed with an animal carrying the transgene under control of the UAS, the transgene is specifically expressed in the GAL4-expressing cells of the
first filial generation (F1). Various UAS-linked reporter genes for visualizing the labeled cells have been
developed and are widely available. The advantage of
the GAL4-UAS system is that it can separate the process of generating the expression driver from the reporter gene to be expressed. Once a GAL4 line with a
useful expression pattern is developed, it can be used
for driving not only the collection of UAS-linked transgenes that are already available but also transgenes
that will be developed in the future. Newly developed
reporter systems include those that can monitor neuronal activity by detecting fluorescence whose intensity
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change reports intracellular calcium concentration
(Fiala et al., 2002) and those that can visualize individual cells out of the population of the GAL4-expressing cells (Wong et al., 2002). These reporters can, in
principle, be combined with any one of the thousands of
GAL4 driver strains already available.
The induced expression system is also useful for driving expression of genes, called “effectors,” with important biological functions. By expressing toxin genes or
developmental fate determination genes, for example,
one can kill, block, or alter cell function, or change the
developmental fate of GAL4-expressing cells (O’Kane,
1998). This approach is also widely used for examining
the function of newly identified genes.
Staining Could Look More Specific
Than It Actually Is
The following sections discuss phenomena that
might give rise to misinterpretation of signals. The first
is that staining could look more specific than it actually
is: namely, weaker signals tend to be ignored or mistaken for background.
Sigmoidal Nature of the Staining Process. Figure 2A and B shows the staining of the same GAL4
enhancer-trap strain visualized with two different
methods. Figure 2A reveals much fewer labeled cells
than Figure 2B. If the observer analyzes only the
former image, the expression pattern would appear
very specific. In fact, there are many other cells that
express the reporter at a weaker level. Why do these
weak signals not appear at all in Figure 2A?
Figure 2B is the direct detection of the fluorescence
emitted from GFP. In this case, the intensity of the
fluorescence signal is essentially in proportion to the
amount of protein present (Fig. 2C). Figure 2A, on the
other hand, is the X-gal activity staining of ␤-galactosidase. Staining with anti-␤-galactosidase antibody
yields similarly specific staining (not shown). These
methods involve one or more steps of chemical reaction,
in which the intensity of resulting signal vs. the
amount of molecules follows approximately a sigmoidal
relationship. Signals from strongly labeled cells would
appear disproportionally more intense than weakly labeled cells. When staining is attained with multiple
reaction steps, this effect will be amplified. Compared
to the antibody staining with directly-conjugated fluorescent secondary antibody, staining featuring peroxidase or alkaline phosphatase, and especially those with
intensifying processes such as avidin-biotin complex
(ABC) and tyramide signal amplification (TSA), have
the inherent drawback that strong signals are exaggerated whereas weak signals are obscured among the
background.
Another factor that affects the specificity of labeling
is the time course of color development. For making
X-gal staining, as well as histochemical antibody staining and in situ hybridization visualized with peroxidase and alkaline phosphatase, a common procedure is
that the researcher observes the specimen under the
microscope to check the development of colorization at
the final step of staining, and stops the reaction when
enough signal is detected. Though this is helpful for
controlling the intensity of staining, it can mislead,
especially when strong and weak signals coexist in the
same specimen.
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Fig. 2. Artifactually exaggerated specificity of staining. In all the following figures, the scale bar (in ␮m)
and the names of the strains (expression driver strain
and the reporter strain) are shown. A: Drosophila adult
brain expressing lacZ gene under control of UAS, driven
with the GAL4 enhancer-trap strain MZ1135. The product of the lacZ gene, ␤-galactosidase, is detected by the
X-gal activity staining. Montage of Nomarski optical
sections of the anterior half of the whole-mount brain to
collect regions of labeled cells into one image. AL: antennal lobe. B: Expression of green fluorescent protein
(GFP) driven by the same GAL4 strain. Expression is
detected directly by the fluorescence of GFP. Threedimensional reconstruction of confocal images (anterior
half of the brain). Cells and structures stained in B but
not in A are indicated by arrows. C: Schematic showing
the relation between the signal intensity and the
amount of label (product of the reporter gene). D: Schematic showing the relation between the signal intensity
and the time of incubation for the development of colorization.
The time course of colorization is also sigmoidal (Fig.
2D). In the strongly labeled cells, the staining darkens
rapidly. At the time colorization in these cells is close to
saturation, colorization in the weakly labeled cells has
just started to progress. Signals in these cells become
detectable only when the specimen is incubated long
enough. If incubation is terminated too early, only the
strongly labeled cells are visualized.
To avoid these problems, it is wise to perform two
batches of staining. For the first batch, the colorization
will be monitored under the microscope and terminated
right after the specific label is observed. For the second
batch, colorization should be continued for a much longer
time regardless of the development of strong staining.
This will monitor the existence of weaker signals, especially if the region with weaker signals is spatially separated from the strongly labeled regions. Note that weaker
signals might still be obscured, if cells with strong label
are scattered throughout the specimen or if processes of
such cells surround weakly labeled cells.
The “Full-Moon Effect”. In the case of fluorescence
microscopy, not only antibody staining and in situ hybridization but also the direct detection of GFP signals
present another potential problem that can lead to the
observer overlooking weaker signals.
At full moon, only a few stars are visible (Fig. 3A).
This is because the sensitivity of our eyes is decreased to accommodate the reflected brightness of
the full moon. A similar situation occurs when a
researcher observes a fluorescent-labeled preparation. The exposure level is usually adjusted so that
the detail of the brightly labeled objects will be
clearly recorded (Fig. 3B). There is, however, the
possibility that cells with weaker signals might remain below detection level. When the same specimen
is photographed with much higher sensitivity (Fig.
3C), there appear many other labeled cells that were
not visible in Figure 3B. The dynamic ranges of the
CCD digital cameras and photomultipliers (PMTs) of
the confocal microscopes are much narrower than
that of the conventional photographic film. When
imaging fluorescent preparations using such an apparatus, it is important to check the existence of
weak signals by taking images at two exposure levels: one with an exposure adequate for the strongly
labeled regions and the other with an overexposure.
Although this will eliminate any resolution within
regions having the stronger signals, it will reveal
structures, such as isolated cell bodies or processes,
that have extremely weak signals.
Discrimination of Weak, Ubiquitous Signals
From Backgrounds. Another crucial issue is the specimen in which strong expression in specific cells coexists with weak expression in the majority of the remaining cells. This is especially problematic when examining whole-mount preparations. In such case,
signals with weak expression appear ubiquitous and
are often disregarded as mere background. Careful
comparison between staining and a control section in
thin paraffin or plastic, or by detailed examination of
optical sections generated with a confocal microscope,
might reveal that the weak label indeed reveals a specific distribution of the signal.
MOLECULAR BIOLOGY-BASED BRAIN STAINING
175
Fig. 3. Full moon effect. A: No stars can be seen around the bright full moon. B: Brain of the GAL4
enhancer-trap strain expressing GFP. 3-D reconstruction. Neurons of the mushroom body (MB) and a
small subset of neurons in the antennal lobe (AL) are clearly labeled and emit strong fluorescence. C: By
overexposing the same preparation, expression in many other cells is revealed (arrows).
Description of the Staining Pattern Might Be
Affected by Misinterpretation and Illusion
As discussed above, careful staining and image recording procedures are the prerequisite for the correct
analysis of the staining pattern. The next step is its
documentation. Since documentation is a subjective
process, it inevitably involves interpretation by the
observer. In this section, we discuss what problems
could arise during this step.
Staining in Organized Structures Might Appear More Prominent. The human visual system is
tuned for detecting organized structures from random
backgrounds. Coherent structures such as straight
lines, lattices, arrays, and radial or circular arrange-
Fig. 4. “Organized” and “disorganized” structures. A, B: A plate of spaghetti with two green
noodles in an organized array and in a disorganized manner. C, D: GAL4 enhancer-trap strains
showing expression in various neurons in the
brain. 3-D reconstruction. Only the cells in the
organized array-like structures can be identified
easily although there are many other labeled cells.
AL: antennal lobe; MB: mushroom body; fb: fanshaped body; eb: ellipsoid body; m bdl: median
bundle.
ments of fibers catch the eye of the observer. These
features, however, might all bias documentation.
Figure 4A and B shows a dish of spaghetti in which
many strands run parallel across it. Amongst these
strands are two green noodles. These two parallel elements in the dish attract the attention of the observer.
Though the same number of green noodles exists in
dish B, these are less obvious and harder to describe
than those in dish A because they do geometrically
conform to the parallel strands of spaghetti. The same
phenomenon, that of cryptic structure, occurs when
examining staining patterns in the brain. In Figure 4C
and D, fluorescent labeling in the so-called “organized
structures” in the Drosophila brain, such as the fan-
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Fig. 5. Intensity of staining and the density of labeled cells. A: In
the low-magnification picture (center), weak expression in the majority of cells (top left) and the strong expression in only a few cells
(top right) both appear as similarly weak staining. Medium level of
expression in all the cells (bottom left) and strong expression in
many cells (bottom right) also result in similarly strong staining. B:
Low-magnification in situ hybridization picture of the Drosophila
optic lobe, showing unlabeled, strongly labeled, and weakly labeled
regions. Montage of Nomarski optical sections. C: High-magnification
picture of the rectangle shown in B. When single cells are resolved,
the strong and weak staining actually corresponds largely to the
density of labeled cells.
shaped body (fb), ellipsoid body (eb), median bundle (m
bdl), antennal lobe (AL), and mushroom bodies (MB) all
appear prominent. Note, however, that the number of
labeled cells in other, apparently “disorganized” brain
regions, actually exceeds the number of those in the
“organized” structures. Documenting the results by referring only to the “organized” structures would inadequately and misleadingly describe the actual pattern
of staining.
is different for each probe. The label in Figure 6C
appears the most intense, whereas that in Figure 6A
appears the weakest. When the number of labeled and
unlabeled cells are actually counted, however, the percentage of positive cells in Figure 6B is by far the
highest, whereas those in Figure 6A and C are about
the same.
In order to discuss the intensity of staining, it is thus
important to examine the preparation at high magnification (with 63⫻ or 100⫻ objectives) so that single
cells can be resolved. It is most unfortunate, from this
point of view, that many papers provide only low-magnification figures of stained structures.
How to Describe the Region of Labeled Cells.
Documenting a labeled region of the brain is not so
straightforward as might be thought, even when the
staining itself seems to be a model of clarity. In principle, any brain region consists of three categories of
neurons: local neurons, input (afferent) neurons, and
projection or output (efferent) neurons. In the vertebrate brain, the afferent and efferent neurons can easily be distinguished by checking whether the cell bodies
and dendrites reside within or outside the region of
interest. The distinction is more difficult in invertebrates, since the cell bodies of many neurons lie far
from its dendrites, and the structural differences between axon terminal and dendiritic arborization are
not obvious. In such a case, the term “extrinsic” neurons have been used to refer to both afferent and efferent neurons.
In certain brain regions, local neurons dominate the
structure, and afferent and efferent neurons comprise
a minority contribution to it. For example, most of the
mushroom body’s volume is occupied by the processes
of local neurons (Kenyon cells). In comparison, the
contribution of these three categories of neurons to the
antennal lobe (AL) is almost equal (Fig. 7A–C). Thus, a
sentence saying “the antennal lobe is labeled” cannot
Difference Between “Strong Staining” and
“Staining in Many Cells”
One concern is that two completely different aspects
of staining, “signal intensity” and the “percentage of
labeled cells,” are often confused and carelessly
treated. The sentence “strong labeling was observed in
brain region A” often appears in papers and conference
presentations. In many cases, however, such a sentence actually means that the percentage of positive
cells in brain region A is higher than in other regions,
but with no difference in the signal intensity in each
labeled cell.
Figure 5A explains this situation. Unless single cells
are resolved with high-resolution microscopy, both
weak signals distributed evenly in many cells (Fig. 5A,
top left) and strong signals in only a few scattered cells
(Fig. 5A, top right) appear similar. They both appear as
a weak label. Figure 5B and C show one such example.
The image of an in situ hybridization at low magnification (Fig. 5B) shows weakly and strongly labeled
regions (black box). A high-magnification picture of this
region, however, reveals that not only the signal intensity per cell but also the percentage (density) of positive
cells is different.
A quantitative example is provided in Figure 6. Figure 6A–C shows the brain labeled with three different
in situ hybridization probes. The intensity of staining
in the dorsolateral corner of the antennal lobe (arrows)
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177
Fig. 6. Intensity of staining at low resolution and the number of
labeled cells. A–C: In situ hybridization of the Drosophila brain with
three different probes. Montage of Nomarski optical sections. AL:
antennal lobe. Graph: Percentage of labeled cells in the area of the
rectangle in A. The number of labeled and unlabeled cells is counted
throughout the thickness of the cortex. The percentage of labeled cells
(mean and standard deviation of three preparations) is shown above
each bar. The total number of cells counted is shown at the bottom.
be informative unless it explains which types of neurons are labeled. Admittedly, this distinction can be
difficult if the relevant antibodies label only arborization (axon terminals or dendrites) but not the axons or
cell bodies.
While such cautionary remarks might seem to be
self-evident to physiologists and anatomists, they are
not so for those who work primarily in other fields of
neurobiology. But thanks to recent advances in the
analysis of olfactory systems (Marin et al., 2002;
Vosshall et al., 2000; Wong et al., 2002), far fewer
people now confuse axon terminals of the olfactory
sensory neurons with the dendrites of projection neurons. Only a couple of years ago this was not the case
and the same deficit still applies to the descriptions of
other brain regions. This is illustrated by the many
queries we receive about whether we can provide GAL4
enhancer-trap strains that label particular brain regions. Only a few such queries precisely inform us
whether what is required are local, afferent, or efferent
components.
Gene Expression: The Law of Low Specificity
and the Law of Low Ubiquity. One crucial point is
that few antibodies and in situ hybridizations appear
to specifically and ubiquitously label cells belonging to
a certain brain region or a certain cell type. While
certain staining patterns are specific in the sense that
a fixed subset of cells are labeled reproducibly among
individuals, in most cases, however, staining is observed in various cells in various brain regions. Neu-
Fig. 7. Three types of neurons that together compose the antennal
lobe (AL). 3-D reconstruction of GFP-labeled preparations. A: Local
interneurons of the AL. The neural fibers do not go out of the AL
neuropil. B: Terminal arborization of the input (afferent) neurons.
Olfactory sensory neurons in the maxillary palpus send axons via the
labial nerve to the AL. C: Dendrites of the projection (efferent) neurons. The fibers project towards second-order processing sites: the
mushroom body (MB) and the lateral horn (LH).
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rons and glial cells can be categorized in many ways: by
their location within or between neuropils, by their
detailed morphology, by their supposed functions, and
so on. But the only features that usually match a pattern of stained neurons are those that reflect certain
biochemical properties of those cells, usually the presence of certain enzymes that should correspond to the
expression of a particular gene. For example, cells that
can be categorized as “cholinergic neurons” can be specifically and ubiquitously labeled by the antibody
against choline acetyltransferase or the probe for its
gene. In contrast, it seems to be very difficult or even
impossible to find antibodies or in situ probes that
specifically label cells that belong to a structural category (such as “intrinsic neurons of the mushroom body”
or “motor neurons”) without labeling cells of other
structural categories (the law of low specificity). Nor is
it easy to find an expression pattern that is ubiquitous
for all cells that belong to a particular structural category (the law of low ubiquity).
Sentences such as “gene A is expressed specifically in
brain region B” are not uncommon in papers and presentations. Considering the situation described above,
however, such a claim should better be interpreted as
“gene A is expressed in many, but not all, cells within
brain region B, as well as in several other unidentified
cells in other hitherto undescribed parts of the brain.”
However, gene expression in those regions of the brain
that are irrelevant to the experiment, or that are tangential to the point being made, are often not mentioned in the paper simply because the editors of many
leading journals have a fear of papers becoming overdescriptive. Thus, unless a paper clearly affirms that
there is no expression in other brain regions and states
that all precautions have been taken to determine this,
it is safer to assume otherwise: namely, that there is
indeed expression in other brain regions even though
the paper in question avoids mentioning this.
The low ubiquity of expression is the cause of another problem. When several genes are reported to be
expressed in the same brain region, the probability
that they are really co-expressed in the same cells may
not be high, since each gene is actually expressed in
different subsets of cells that may or may not overlap.
This is different from the situation in other body organs, as well as during embryogenesis. In such cases,
both the pattern of gene expression and the complexity
of cell types within a particular region are simpler than
in the brain, making the specificity and ubiquity of
gene expression more common.
Problems Associated With Confocal Microscopy
and Three-Dimensional Reconstruction
The brain is a voluminous organ in which neural
fibers crisscross three-dimensionally. To understand
its structure, examination of serial sections and 3-D
reconstruction gives us indispensable information. Recording and reconstruction processes, however, can
lead to other misleading artifacts.
Compensation of the Light Attenuation. To make
a confocal dataset for 3-D reconstruction, the specimen
is scanned from the top surface to the bottom. The
thickness of a Drosophila brain is about 200 ␮m, a
distance that a conventional single-photon confocal microscope can accommodate. (A two-photon confocal mi-
Fig. 8. Compensation of signal attenuation during confocal serial
scanning. A,B: Side view of the microscope objective and the specimen
showing the attenuation of laser light and fluorescent emission due to
longer distance traveled in the specimen. C: 3-D reconstruction of the
datasets rotated by 90 degrees. The top of the image corresponds to
the optical section closest to the cover glass. The bottom corresponds
to the deepest region in the specimen. Since no compensation was
applied during scanning, signals deeper in the specimen are not
recorded. D: Preparation of the same enhancer-trap strain recorded
with compensation.
croscope would be required to penetrate more than
250 ␮m into the specimen.) As the focal plane is lowered through the tissue, both the excitation laser light
and the emitted fluorescence must travel longer distances through the specimen. This results in considerable attenuation of the signal intensity (Fig. 8A and B).
The fluorescence also gradually fades (bleaches) as
multiple scanning is performed. If attenuation and fading are left uncompensated, signals in the deeper region of the specimen cannot be adequately recorded
(Fig. 8C).
This attenuation can be compensated by gradually
increasing laser intensity and detector sensitivity during the serial scan (Fig. 8D). At first, both the sensitivity of the light detector (voltage charged on the photomultiplier) and the intensity of the laser light should be
kept at a minimum. Since it damages the laser tube to
change the laser power dynamically during recording,
it is better to use the controllable attenuator such as an
AOTF (acousto optical tunable filter). To maintain laser light stability, the laser’s power should best be kept
at near maximum.
As the scan goes deeper, the observer should gradually increase the detector sensitivity and the laser intensity (AOTF transmission rate). The AOTF transmission rate should be increased geometrically rather
than arithmetically. The effect of increasing the rate
from 1 to 2% is the same as increasing it from 50 to
MOLECULAR BIOLOGY-BASED BRAIN STAINING
Fig. 9. Compensation of refraction. A,B: Side section view of the
microscope, showing the relation of the movement of the stage and
that of the focal plane. C–F: 3-D reconstruction of the datasets rotated
by 90 degrees. Without compensation, the preparations appear flattened.
100%, not from 50 to 51%. On the other hand, the
increase of the detector’s sensitivity should be arithmetical, since a higher voltage in the photomultiplier
exponentially collects more signals. As raising the detector sensitivity increases background noise, the
threshold or amplifier offset of the photomultiplier
should also be adjusted. In some extreme cases, the
gain of the amplifier or even the scanning speed should
be adjusted to get higher sensitivity at the deeper levels of the specimen.
There is a trade-off between the detector sensitivity
and the laser intensity. Brighter laser results in lower
detector gain, hence less noise, but it causes quicker
fading of the fluorescence. Avoiding fading requires
lower laser power, which results in higher detector
gain and more noise. The decision depends on the intensity and distribution of the signals as well as the
purpose of the study.
Compensation of the Refraction Index. In many
papers and conference presentations, pictures of 3-D
reconstruction appear somewhat flattened. This is because people ignore the effect of light refraction. Raising the stage by one micron does not mean that the
focal plane shifts at the same distance. It is a shame
that few confocal microscope manufacturers explicitly
tell users about this problem.
As shown in Figure 9A and B, the light from the
objective lens is refracted at the interface between the
immersion medium (or air in case of a dry lens) and the
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mountant. The distance of the focal plane movement in
relation to the movement of the stage is the function of
the optical refraction indices of the immersion and
mounting media. A first-order approximation shows
that the focal plane movement is as much as 47%
longer than the stage movement when a glycerolmount preparation is recorded by a dry lens. If the
compensation is ignored, the reconstructed volume
would be flattened by two thirds (Fig. 9C). The effect is
smaller but not negligible in water- and oil-immersion
lenses (Fig. 9D). For reconstructing with high fidelity,
it is advisable to compensate this either in the control
program of the confocal microscope or in the 3-D reconstruction software, which usually has the option to set
the size of the pixel (x and y value of the voxel) and the
interval of the sections (z value of the voxel).
Artifactual Effects of Different Reconstruction
Algorithms. Serious problems can occur when the confocal dataset is subjected to reconstruction. The final
reconstructed image can vary significantly according to
the different reconstruction algorithms applied. The
most popular algorithm is the so-called “maximum intensity” method. With this algorithm, the software
compares the intensity value of each point of the dataset along the viewing axis of each pixel of the reconstructed image, and the highest value is selected for
the intensity of that pixel. Though simple and fast, this
algorithm has an inherent danger. If the signal intensity of an object close to the viewing point is weaker
than those behind it, the object gets “erased” from the
reconstructed image, since the intensity value of the
object behind it is higher and taken as the value of that
pixel. This, however, contradicts our experience in realworld vision, where a darker object obstructs but is not
completely erased by the brighter object lying behind
it.
The “ray tracing” or “emission-absorption” method
solves this problem. With this algorithm, each point of
the dataset both emits light and obstructs the light
from behind. The degree of obstruction can be userdefined as a “transparency” parameter. If the obstruction is set to zero (100% transparency), the calculation
becomes the same as maximum intensity. The lower
the transparency, the more dark objects in front of
brighter ones remain in the final image. Figure 10
compares the results of the two algorithms using the
same dataset. With maximum intensity, bright structures deep in the specimen are visible. Cells and neural
fibers with weaker label are completely erased if there
are brighter signals behind them (Fig. 10A–D). In the
region where two fiber bundles intersect, maximumintensity reconstruction causes the illusion as if the
brighter bundle further from the viewpoint was running in front of the bundle that is actually closer (compare arrows in Fig. 10E,F). Reconstruction with a ray
tracing algorithm visualizes these objects much more
faithfully.
The resulting image, however, might appear very
different according to the transparency parameter set
by the user. Careful trial and error would be required
to find the best parameter for the reconstruction that is
most faithful to the spatial relationship of the labeled
objects. Detailed examination of the spatial distribution of the signals should thus be performed with the
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Fig. 10. Effect of different 3-D reconstruction algorithms. The same confocal dataset of the developing compound eye in late pupae (A B: oblique view), posterior
medial region of the pupal brain (C,D: posterior view),
and tracts in the larval brain (E,F: oblique view) are
reconstructed with simple maximum intensity algorithm
(A, C, E) and more complicated ray-tracing algorithm (B,
D, F). Arrows indicate the regions where objects closer to
the point of view are erased in the maximum intensity
reconstruction by brighter objects lying behind. Transparency parameter set for each ray-tracing reconstruction is 86% for B, 80% for D, and 90% for F.
raw dataset of serial sections rather than with the
reconstructed images.
Potential Pitfalls When Using The Induced
Expression System
In the last two sections, we discuss issues concerning
the reporter expression system. This versatile technique has become a routine procedure for visualizing
the brain of organisms like Drosophila, the nematode
Caenorhabditis elegans, the zebrafish, and the mouse.
Efforts in many other animals are also succeeding. The
system, however, still has its intrinsic limitations. Underestimating these results can lead to oversimplified
and misleading interpretation.
Difficulty to Find Specific and Ubiquitous
Expression Driver
It is attractive to think that the gene of interest could
be expressed specifically in any desired sets of cells by
choosing an appropriate driver. We are often asked
whether there is a driver strain that can “specifically
label the cells in the neurons of the central brain but
not in the peripheral nervous system,” or “label all the
neurons in a specific brain region,” or “label all the
neurons that fall in certain category,” etc. Apparently,
many seem to believe that there are expression patterns that correspond to specific brain regions or cell
types. As discussed earlier, this is unlikely to be so.
A convenient approach for identifying useful drivers
is to screen gene-trap or enhancer-trap strains. We
performed a large-scale screening of GAL4 enhancertrap strains in the adult Drosophila brain. The 3,939
strains screened so far cover about 2,000 loci in the
chromosome, which is about one seventh of the estimated 13,600 genomic sites (Hayashi et al., 2002).
Among these lines, more than 98% showed expression
in some cells of the brain. Essentially all of them express GAL4 in many other cells of various body parts.
Thus, the expression is in most cases not specific to the
brain. We found many strains that show expression in
a vast majority of brain cells. Careful examination
reveals at least some cells in the brain that do not
express GAL4, however. Expression is thus not ubiquitous in the brain.
The situation is the same at the level of substructures within the brain. Even in strains that show expression in only a few cells, it is very rare that the
position of the labeled cells are restricted within a
particular brain region (low specificity). It is also very
rare that all the cells in a particular brain region are
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181
Fig. 11. Specificity and ubiquity of the expression of the GAL4 drivers in the MB. Large pictures of
A–D are 3-D reconstructions of the lobe region viewed from the front. Smaller pictures are cross-sections
of the medial lobe (␤, ␤’, and ␥: white bar in D). E: Reconstruction of the same line as in A from above.
Reconstruction from overexposed dataset reveals faint staining in the ellipsoid bodies (eb).
labeled (low ubiquity). When neurons are classified
according to their morphology, position, or their assumed function, there seldom exists a strain that
shows expression specifically in all the cells of that
class.
Figure 11 describes an example of the low specificity
and ubiquity. The Drosophila mushroom body (MB)
consists of about 3,000 neurons (Technau and Heisenberg, 1982). Since the MB is thought to play important
roles in associative learning in insects, extensive
screening has been performed to identify driver stains
that can specifically label this structure (Ito et al.,
1997a; Yang et al., 1995). Our current screening added
more than 1,000 GAL4 enhancer-trap lines that label
various subsets of MB neurons. Yet, there is not a
single line that specifically and ubiquitously labels the
MB.
The most specific driver strain identified so far,
MB247 (Schulz et al., 1996; Zars et al., 2000), labels
only a few other neurons outside the MB and is thus
relatively specific (Fig. 11A). Even in this case, however, some glial cells at the brain surface are labeled.
Detection of expression at very high sensitivity also
reveals expression in neurons contributing the ellipsoid body (Fig. 11E). Thus, although there are strong
differences in expression levels, even this driver is not
completely specific to the MB. Within the MB, expression is limited only to a relatively small subset of its
intrinsic neurons (inset in Fig. 11A, showing a cross
section). This line, thus, cannot drive ubiquitous expression among MB neurons. The secondmost specific
driver, 201Y (Yang et al., 1995), labels several neurons
in various other brain regions. Its expression in the MB
is likewise limited to a small subset.
Compared to these lines, the expression driven by
the line OK107 (Connolly et al., 1996) is more widespread in the MB (Fig. 11C). Though a large part of MB
intrinsic neurons are labeled, there are still many un-
labeled cells. Strongly labeled cells contributing various other brain regions are also observed. The expression is thus, again, neither specific nor ubiquitous. The
only strain that really drives expression in all the MB
neurons, hence a true ubiquitous driver, is the line
C155 (Lin and Goodman, 1994) (Fig. 11D). This line,
however, drives expression in all neurons in the brain
(this is the only line of this kind that we found so far.)
The expression is, therefore, not specific at all.
We also screened for lines that label other brain
regions or neural cell types such as antennal lobe projection neurons, central complex neurons, optic lobe
neurons, etc. We found no lines that show specific and
ubiquitous expression in these cell classes.
Such low specificity and lack of ubiquity might be
partially due to the fact that the enhancer-trap system
does not faithfully detect the expression pattern of
genes, which is controlled not only by enhancers but
also by promoters and post-transcriptional processes.
Also, GAL4 expression might be influenced by several
enhancers at once, each of which, if successfully separated, could drive expression in a more specific manner. Considering the fact that most enhancer-trap
strains fail to drive expression in all the cells of a
certain category, however, raising specificity by introducing promoter control or by the division of enhancers
would not result in an expression driver that is ubiquitous to a specific population of cells.
Different Appearance of the Cells Visualized
With Different Reporters. Various reporters that employ induction of expression have been developed to
visualize cells. Some reporters, such as lacZ (␤-galactosidase) fused with the nuclear localization signal
(NlacZ) (Ito et al., 1998) and GFP fused with neuronal
synaptobrevin that is actively transported to the region
of presynapses (nsyb::GFP)(Estes et al., 2000; Ito et al.,
1998), are designed to label specific subcellular components. Others are intended to visualize the whole cel-
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lular structure. There are three types of reporters for
this purpose: cytoplasmic, cytoskeleton-bound, and
membrane-bound.
The first type includes ␤-galactosidase and GFP
themselves, which diffuse relatively freely within the
cytoplasm. Among them, GFP labels long fibers and
small branches much better than ␤-galactosidase, since
GFP is a small molecule and exists as a monomer at
ordinary concentration (Prasher et al., 1992), whereas
␤-galactosidase is large and forms a tetramer (Nichtl et
al., 1998).
The examples of the cytoskeleton-bound reporters
are the lacZ and GFP fused with kinesin or tau, which
actively transport the reporter towards the furthest
end of the neural fibers, e.g., kinesin::lacZ (Giniger et
al., 1993; Ito et al., 1995), tau::lacZ (Callahan and
Thomas, 1994), and tau::GFP (Murray et al., 1998).
Tau itself is also used as a reporter (Ito et al., 1997b),
since the antibody against bovine Tau does not cross
react with endogenous protein of Drosophila. Though
these reporters are useful, their overexpression sometimes causes abnormal cellular morphology, perturbation of synaptic vesicle transportation, and in some
cases lethality (Williams et al., 2000). Special care
should thus be taken when using this type of reporters.
The membrane-bound type includes GFP fused with
mammalian membrane-bound proteins such as
mCD8::GFP (Lee and Luo, 1999), or membrane-bound
protein itself such as CD2 (Dunin-Borkowski and
Brown, 1995). The reporter molecules diffuse along the
cellular membrane. Unlike tau or kinesin, induced expression of CD2 or mCD8 does not seem to cause noticeable change in neuronal structure.
Thus, to visualize whole cell morphology, either cytoplasmic GFP or a membrane bound reporter such as
CD2 or mCD8::GFP is the safest choice. The appearance of cells with these two reporter types, however, is
considerably different (Fig. 12).
The intensity of the reporter signal corresponds to
the amount of reporter molecules. This is in relation to
the volume of the cytoplasm in the case of a cytoplasmic
reporter, and to the membrane density in the case of a
membrane-bound reporter. In fine branches and thin
fibers, the amount of membrane is relatively high compared to the amount of cytoplasm. As the diameter of a
fiber or the volume of the cell increases, the surface
area of the membrane increases as a power of two,
whereas the cytoplasmic volume increases as a power
of three. The surface to volume ratio decreases as the
fiber gets thicker and the cell gets bigger.
Thus, the signal intensity of the cytoplasmic reporter
is most sensitive to the diameter of the labeled fiber.
Whereas membrane-bound reporter labels fine dendritic branches intensely (Fig. 12D), cytoplasmic reporter visualizes thick trunks more prominently (Fig.
12G). Though a membrane-bound reporter nicely labels
fine branches in the axon terminal (Fig. 12E), the terminal’s swellings and varicosities are better visualized
with the cytoplasmic reporter (Fig. 12H). Such differences should be taken into account when selecting reporters and interpreting obtained images.
Different Level of the Signals Due to Different
Antibodies and Reporters. Except for GFP and its
variants, the distribution of reporter molecules is detected by antibody staining. Even in the case of GFP,
anti-GFP antibodies are used frequently to boost signal
intensity, since the fluorescence of GFP decreases tremendously in fixed preparations. The sensitivity of reporter detection, however, is affected significantly by the
types of antibodies used for staining. Figure 13 shows one
such example. Here, specimens dissected and fixed at the
same time were stained with different types of anti-GFP
antibodies. The sensitivity to antibodies can be so different that many cells detected with one antibody are not
revealed at all with the other antibody.
The types of reporters used also affect signal levels.
When the same driver is used for expressing cytoplasmic and nuclear-localized reporters, the latter sometimes reveals significantly more cells than the former
(Fig. 14). The intensity of the signal is affected by the
density of the reporter molecule. When the reporter is
confined to a subcellular component, such as nuclei, the
signal would be stronger than that of the reporter that
diffuses into the whole cytoplasm.
Consideration of these two factors is very important
when assessing any expression pattern. If inappropriate antibodies and reporters are used, the pattern
might appear much more specific than it actually is
(compare Figs. 13A and 14B).
Background Expression of the Reporter. Another
factor to be considered, especially in case of the GAL4-UAS
mediated expression induction system, is the background
expression of the reporter. The UAS-linked reporter gene
has a minimum promoter in its construct. Thus, the reporter gene itself can function as an enhancer-trap system,
showing endogenous expression according to the activity of
nearby enhancer even without any GAL4.
When developing a new UAS-linked reporter system,
researchers usually generate several transformant
strains with different insertion loci and select a line that
does not show endogenous expression. It is difficult, however, to cover all the organs and developmental stages for
the expression test. Reporter strains that do not show
noticeable endogenous expression in some developmental
stages might show significant expression in another.
One of the widely available GFP strains, UAS-GFP
(T10), shows strong expression in the mushroom bodies
even without GAL4 (Fig. 15A, E). Another strain, UASGFP (T2), does not show such staining in equivalent
detection conditions (Fig. 15B, F). The line is useful
enough for detecting the GAL4 activity in many cases
(Figs. 2–14). When endogenous expression is checked
at very high sensitivity, however, the line actually
shows weak but characteristic expression in a subset of
glial cells (Fig. 15C,G). Expression is somewhat stronger in larvae than in the adult. The strain of the membrane-bound UAS-mCD8::GFP does not show expression even at this high sensitivity (Fig. 15D,H).
To reveal any faint expression that might be hidden
by the intense signal, it is advisable to take overexposed images of the specimen (Figs. 3C, 11E). This
procedure, however, might also reveal the faint endogenous expression of the reporter. To distinguish them,
it is important to check background endogenous expression of the reporter with very high sensitivity in
the same organ and the developmental stage as the
scope of the particular study.
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Fig. 12. Different appearance of labeled cells visualized with cytoplasmic and membrane-bound reporters. A, B: Schematics of the
signal intensity in relation to the volume and surface area of the
labeled objects. C–H: Antennal lobe projection neurons visualized
with membrane-bound (C–E) and cytoplasmic (F–H) reporters. C, F:
Overview. D, G: Higher magnification view of the dendrites in the AL
(single section). E, H: Higher magnification view of the terminals in
the MB (reconstruction).
Precautions for Designing and Interpreting
Experiments That Use Induction of
Effector Gene Expression
As discussed in the previous section, it is in practice
very difficult to drive expression with clear specificity
and ubiquity. This may not pose a critical problem
when the reporter expression system is used only to
reveal the morphology of the labeled cells. When the
system is applied for driving the expression of “effector”
genes, which block or alter cellular functions, two aspects should be considered in order to avoid misleading
experimental design and interpretation of the results.
First, the level of expression that is sufficient for causing the envisaged phenotype of the effector gene may not
be the same as the level of expression necessary for the
visualization by the reporters. Whereas numerous molecules of ␤-galactosidase or GFP are required for visualizing a single cell, much smaller number of molecules
would be enough for toxins and transcription factors to
attain their function. Cells that are not visualized, or only
very weakly labeled by the reporters, might well be affected significantly by the expression of such effector
genes. In principle, the sufficient level of expression for
the particular effector gene should be assessed carefully
in regard to the intensity of driver activity revealed by the
reporters. Such assessment, however, would be very difficult in practice. Careful design of the control experiment
would thus be important.
Second, the low specificity and ubiquity of the driver
expression means that a significant number of cells in
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Fig. 13. Expression of UAS-linked-GFP driven
by the same GAL4 strain. A: GFP is detected by
mouse monoclonal anti-GFP antibody from Roche.
B: Staining with rabbit polyclonal anti-GFP antibody from Molecular Probes.
Fig. 14. Sensitivity difference between cytoplasmic and nuclearlocalized reporters. UAS-GFP and UAS-NlacZ are driven in the same
animal and visualized with rabbit polyclonal anti-GFP and mouse
monoclonal anti-␤-galactosidase antibodies. A–C: Overall brain (3-D
reconstruction, frontal view). D–F: Higher magnification view of the
AL (single confocal section). A, D: Signal of the UAS-GFP (green). B,
E: UAS-NlacZ (magenta), C, F: merged picture. Labeled cells detected
only with UAS-NlacZ appear magenta. Those that are labeled by both
reporters appear white (magenta plus green).
other regions of the brain also express the effector gene
at similar intensity. Whether the effect of such expression can safely be ignored depends on the type of experiment. For developmental studies such as cell-fate
determination and axon path finding, effector expression in other cells that are beyond the range of immediate intercellular interaction are unlikely to affect the
phenotype of the cells under investigation. On the
other hand, for studies such as the control of animal
behavior and integrative brain function like learning
and memory, any expression in other brain regions
cannot be ignored with complete safety, since the perturbation of cellular functions in those cells might affect the synergistic performance of the brain. For example, ectopic expression of the feminization gene
transformer in mushroom body neurons using the
rather specific driver strain 201y (Fig. 11b) induces
bisexual courtship behavior (Kido and Ito, 2002). Ectopic expression of cAMP-dependent protein kinase A
(PKA) using the same strain also significantly affects
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Fig. 15. Endogenous expression of UAS-linked reporters without
GAL4. A–D: Brain of the late third-instar larvae. E–H: Adult brain. A,
E: UAS-GFP (T10) with constitutive expression in the MB. B, F:
UAS-GFP (T2) strain, which is used throughout this article, shows
very faint background expression. C, G: With extreme overexposure,
these labeled cells turned out to be a subset of glial cells. D, H:
Membrane-bound UAS-mCD8::GFP (LL5) strain shows few background expression even with overexposure.
the ethanol sensitivity of fruitflies (Rodan et al., 2002).
These phenotypes, however, are actually due to the
effector expression in other seemingly obscure cells,
since the ablation of the mushroom bodies in these flies
does not affect the phenotype. The prominent expression in the mushroom bodies had essentially nothing to
do with the induced phenotypes in these cases.
Disturbingly, the expression of effectors in cells other
than those that are under study are often not rigorously described. This omission can be charitably interpreted as due to the need for brevity. But if there is any
possibility that effector gene expression in other brain
regions might affect the investigated phenotype, then
special care should be taken when describing and interpreting any results.
The induced expression of effector genes may not be
as specific as most researchers would wish. There is,
however, no alternative technique that is so noninvasive and reproducible for perturbing the function of
defined set of cells. Careful experimental design and
cautious interpretation of the results should overcome
its potential shortcomings.
Molecular biology-based visualization techniques
provide powerful tools for revealing the detailed structure of the nervous system as well as addressing its
functions. Whereas many procedures in molecular biology are becoming automated and computerized, interpretation of stained images still relies solely on the
knowledge and caution of the observer. The three-dimensional complexity and diversity of neurons, and the
low specificity and ubiquity of expression pattern of
many genes and drivers all pose difficulties when planning experiments and assessing their results. Topics
discussed in this article should help to avoid potential
pitfalls. On the other hand, if one would strictly follow
all the precautions described here, it might become
practically impossible to draw any conclusion from an
experimental data set. A fine balance is needed between oversimplified interpretation of the staining results and excessive caution that might paralyze an
objective and inclusive analysis.
ACKNOWLEDGMENTS
We thank J. Urban and G. Technau for the MZ series
of enhancer-trap strains, the member of the NP Consortium for the NP series strains, and Bloomington
stock center for UAS-reporter strains, We are grateful
to A. Hattori, N. Nishimura, and A. Hoshino for technical assistance, and H. Otsuna, K. Sugimura, and T.
Uemura for helpful suggestions. This work was supported by a BIRD/JST grant and Human Frontier Science Program (RG0134/1999-B) to K.I., PRESTO/JST
grant to T.A., and Grant-in-Aid for Scientific Research
from the Ministry of Education, Culture, Sports, Science and Technology of Japan to K.I.
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