* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Cautionary Observations on Preparing and Interpreting Brain
Survey
Document related concepts
Cognitive neuroscience wikipedia , lookup
Synaptogenesis wikipedia , lookup
Signal transduction wikipedia , lookup
Haemodynamic response wikipedia , lookup
Development of the nervous system wikipedia , lookup
Clinical neurochemistry wikipedia , lookup
Subventricular zone wikipedia , lookup
Neurogenomics wikipedia , lookup
Metastability in the brain wikipedia , lookup
Optogenetics wikipedia , lookup
Feature detection (nervous system) wikipedia , lookup
Neuropsychopharmacology wikipedia , lookup
Transcript
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, 171 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. 172 K. ITO ET AL. 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 173 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. 174 K. ITO ET AL. 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- 176 K. ITO ET AL. 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) MOLECULAR BIOLOGY-BASED BRAIN STAINING 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). 178 K. ITO ET AL. 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 179 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 180 K. ITO ET AL. 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 MOLECULAR BIOLOGY-BASED BRAIN STAINING 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- 182 K. ITO ET AL. 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. MOLECULAR BIOLOGY-BASED BRAIN STAINING 183 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 184 K. ITO ET AL. 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 MOLECULAR BIOLOGY-BASED BRAIN STAINING 185 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. REFERENCES Baird GS, Zacharias DA, Tsien RY. 2000. Biochemistry, mutagenesis, and oligomerization of DsRed, a red fluorescent protein from coral. Proc Natl Acad Sci USA 97:11984 –11989. Baumann CT, Lim CS, Hager GL. 1998. Simultaneous visualization of the yellow and green forms of the green fluorescent protein in living cells. J Histochem Cytochem 46:1073–1076. Bolam JP. 1992. Experimental neuroanatomy: the practical approach series, Volume 114. Oxford: IRL Press. 273 p. Brand AH, Dormand EL. 1995. The GAL4 system as a tool for unraveling the mysteries of the Drosophila nervous system. Curr Opin Neurobiol 5:572–578. 186 K. ITO ET AL. Brand AH, Perrimon N. 1993. Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Development 118:401– 415. Callahan CA, Thomas JB. 1994. Tau-beta-galactosidase, an axontargeted fusion protein. Proc Natl Acad Sci USA 91:5972–5976. Chen W, Burgess S, Golling G, Amsterdam A, Hopkins N. 2002. High-throughput selection of retrovirus producer cell lines leads to markedly improved efficiency of germ line-transmissible insertions in zebra fish. J Virol 76:2192–2198. Cheng L, Fu J, Tsukamoto A, Hawley RG. 1996. Use of green fluorescent protein variants to monitor gene transfer and expression in mammalian cells. Nat Biotechnol 14:606 – 609. Connolly JB, Roberts IJH, Armstrong JD, Kaiser K, Forte M, Tully T, O’Kane CJ. 1996. Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies. Science 274:2104 –2107. Dunin-Borkowski OM, Brown NH. 1995. Mammalian CD2 is an effective heterologous marker of the cell surface in Drosophila. Dev Biol 168:689 – 693. Estes PS, Ho GL, Narayanan R, Ramaswami M. 2000. Synaptic localization and restricted diffusion of a Drosophila neuronal synaptobrevin-green fluorescent protein chimera in vivo. J Neurogenet 13:233–255. Feng G, Mellor RH, Bernstein M, Keller-Peck C, Nguyen QT, Wallace M, Nerbonne JM, Lichtman JW, Sanes JR. 2000. Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron 28:41–51. Fiala A, Spall T, Diegelmann S, Eisermann B, Sachse S, Devaud JM, Buchner E, Galizia CG. 2002. Genetically expressed chameleon in Drosophila melanogaster is used to visualize olfactory information in projection neurons. Curr Biol 12:1877–1884. Fischer JA, Ginigar E, Maniatis T, Ptashne M. 1988. GAL4 activates transcription in Drosophila. Nature 332:853– 856. Giniger E, Wells W, Jan LY, Jan YN. 1993. Tracing neurons with a kinesin beta-galactosidase fusion protein. Rouxs Arch Dev Biol 202:112–122. Grenningloh G, Rehm EJ, Goodman CS. 1991. Genetic analysis of growth cone guidance in Drosophila: fasciclin II functions as a neuronal recognition molecule. Cell 67:45–57. Hayashi S, Ito K, Sado Y, Taniguchi M, Akimoto A, Takeuchi H, Aigaki T, Matsuzaki F, Nakagoshi H, Tanimura T, Ueda R, Uemura T, Yoshihara M, Goto S. 2002. GETDB, a database compiling expression patterns and molecular locations of a collection of Gal4 enhancer traps. Genesis 34:58 – 61. Hockfield S, Carlson S, Evans C, Levitt P, Pintar J, Silberstein L. 1993. Selected methods for antibody and nucleic acid probes. Cold Spring Harbor: Cold Spring Harbor Press. 679 p. Ito K, Urban J, Technau GM. 1995. Distribution, classification, and development of Drosophila glial cells in the late embryonic and early larval ventral nerve cord. Roux Arch Dev Biol 204:284 –307. Ito K, Awano W, Suzuki K, Hiromi Y, Yamamoto D. 1997a. The Drosophila mushroom body is a quadruple structure of clonal units each of which contains a virtually identical set of neurones and glial cells. Development 124:761–771. Ito K, Sass H, Urban J, Hofbauer A, Schneuwly S. 1997b. GAL4responsive UAS-tau as a tool for studying the anatomy and development of the Drosophila central nervous system. Cell Tissue Res 290 1:1–10. Ito K, Suzuki K, Estes P, Ramaswami M, Yamamoto D, Strausfeld NJ. 1998. The organization of extrinsic neurons and their implications regarding the functional roles of the mushroom bodies in Drosophila melanogaster Meigen. Learn Mem 5:52–77. Joyner AL. 1991. Gene targeting and gene trap screens using embryonic stem cells: new approaches to mammalian development. Bioessays 13:649 – 656. Kido A, Ito K. 2002. Mushroom bodies are not required for courtship behavior by normal and sexually mosaic Drosophila. J Neurobiol 52:302–311. Lee T, Luo L. 1999. Mosaic analysis with a repressible neurotechnique cell marker for studies of gene function in neuronal morphogenesis. Neuron 22:451– 461. Lehman R, Tautz D. 1994. In situ hybridization to RNA. Methods Cell Biol 44:575–598. Lin DM, Goodman CS. 1994. Ectopic and increased expression of Fasciclin II alters motoneuron growth cone guidance. Neuron 13: 507–523. Lukacsovich T, Yamamoto D. 2001. Trap a gene and find out its function: toward functional genomics in Drosophila. J Neurogenet 15:147–168. Lukacsovich T, Asztalos Z, Awano W, Baba K, Kondo S, Niwa S, Yamamoto D. 2001. Dual-tagging gene trap of novel genes in Drosophila melanogaster. Genetics 157:727–742. Marin EC, Jefferis GS, Komiyama T, Zhu H, Luo L. 2002. Representation of the glomerular olfactory map in the Drosophila brain. Cell 109:243–255. Miller DM 3rd, Desai NS, Hardin DC, Piston DW, Patterson GH, Fleenor J, Xu S, Fire A. 1999. Two-color GFP expression system for C. elegans. Biotechniques 26:914 –921. Murray MJ, Merritt DJ, Brand AH, Whitington PM. 1998. In vivo dynamics of axon pathfinding in the Drosophila CNS: a time-lapse study of an identified motorneuron. J Neurobiol 37:607– 621. Nichtl A, Buchner J, Jaenicke R, Rudolph R, Scheibel T. 1998. Folding and association of beta-galactosidase. J Mol Biol 282:1083–1091. O’Kane CJ. 1998. Enhancer traps. In: Roberts DB, editor. Drosophila, a practical approach, 2nd edition. Oxford: Oxford University Press. p 131–178. O’Kane CJ, Gehring WJ. 1987. Detection in situ of genomic regulatory elements in Drosophila. Proc Natn Acad Sci USA. 84:9123–9127. Prasher DC, Eckenrode VK, Ward WW, Prendergast FG, Cormier MJ. 1992. Primary structure of the Aequorea victoria green-fluorescent protein. Gene 111:229 –233. Rodan AR, Kiger JA Jr, Heberlein U. 2002. Functional dissection of neuroanatomical loci regulating ethanol sensitivity in Drosophila. J Neurosci 22:9490 –9501. Schulz RA, Chromey C, Lu MF, Zhao B, Olson EN. 1996. Expression of the D-MEF2 transcription in the Drosophila brain suggests a role in neuronal cell differentiation. Oncogene 12:1827–1831. Skarnes WC, Auerbach BA, Joyner AL. 1992. A gene trap approach in mouse embryonic stem cells: the lacZ reported is activated by splicing, reflects endogenous gene expression, and is mutagenic in mice. Genes Dev 6:903–918. Strausfeld NJ. 1976. Atlas of an insect brain. New York: SpringerVerlag. 214 p. Technau G, Heisenberg M. 1982. Neural reorganization during metamorphosis of the corpora pedunculata in Drosophila melanogaster. Nature 295:405– 407. Tsien RY. 1998. The green fluorescent protein. Annu Rev Biochem 67:509 –544. Verkhusha VV, Otsuna H, Awasaki T, Oda H, Tsukita S, Ito K. 2001. An enhanced mutant of red fluorescent protein DsRed for double labeling and developmental timer of neural fiber bundle formation. J Biol Chem 276:29621–29624. Vosshall LB, Wong AM, Axel R. 2000. An olfactory sensory map in the fly brain. Cell 102:147–159. Williams DW, Tyrer M, Shepherd D. 2000. Tau and tau reporters disrupt central projections of sensory neurons in Drosophila. J Comp Neurol 428:630 – 640. Wong AM, Wang JW, Axel R. 2002. Spatial representation of the glomerular map in the Drosophila protocerebrum. Cell 109:229 – 241. Yang MY, Armstrong JD, Vilinsky I, Strausfeld NJ, Kaiser K. 1995. Subdivision of the Drosophila mushroom bodies by enhancer-trap expression patterns. Neuron 15:45–54. Yoshihara M, Ito K. 2000. Improved Gal4 screening kit for large-scale generation of enhancer-trap strains. Dros Inf Service 83:199 –202. Yuste R, Lanni F, Konnerth A. 2000. Imaging neurons: a laboratory manual. Cold Spring Harbor NY: Cold Spring Harbor Press. 836 p. Zars T, Fischer M, Schulz R, Heisenberg M. 2000. Localization of a short-term memory in Drosophila. Science 288:672– 675.