Journal of Chemical Neuroanatomy, Vol. 4:343-353 (1991)

Recent Trends in Receptor Analysis Techniques and Instrumentation J. M . Palacios*t, G. Mengod*, M . T. Filar~* and P.

Ramm~

*Preclinical Research, Sandoz Pharma Ltd, 4002 Basel, Switzerland SDepartment of Psychology, Brock University, St Catharines, Ontario, Canada L2S 3A 1 ABSTRACT Receptor autoradiography allows visualization of receptor binding sites at the regional or light microscopic level. Receptor autoradiography is a mature methodology, in widespread use. It is also a dynamic and expanding methodology, benefiting constantly from the introduction of new techniques and instrumentation. In particular, receptor autoradiography has taken advantage of image analysis instrumentation to provide efficient spatial mapping of receptor populations and their pharmacological characteristics. A major contribution to the understanding of receptors has come from the recent cloning of the genes coding for many of these receptors. This has allowed the use of in situ hybridization to demonstrate the cells expressing mRNA coding for specific receptor subtypes. The result is that many receptor populations, previously thought to be homogeneous, are shown to be composed of several subtypes. As a consequence, the distribution of many receptors requires re-examination, which is aided by the development of new and more selective ligands. With the incorporation of techniques from molecular biology into receptor autoradiography, the demands upon image analysis instruments have expanded. Over the past decade, densitometric image analysers have attained a high level of sophistication for classical receptor autoradiography. However, to serve the needs of today's receptor laboratory, an image analyser must be equally capable in regional densitometry, in counting and spatial mapping of grain and/or cell locations at the microscopic level, and in analysing electrophoresis gels. Advances in image analysis hardware and software are keeping pace with the requirements of receptor laboratories. KEYWORDS" Neurotransmitter receptors Receptor autoradiography analysis INTRODUCTION Receptor autoradiography visualizes, at the regional or light microscopic level, radiolabelled receptor binding sites. Like its parent technique, high affinity binding analysis, receptor autoradiography is well established as an essential research methodology, and is widely used in both pure research, and in the evaluation of compounds for potential clinical efficacy. Affinity binding studies deal primarily with pharmacological and functional aspects of receptors, in that their spatial resolution is very low. Immunohistochemistry, in contrast, is primarily oriented towards spatial localization of receptor populations. Full quantification of immunohistochemical label has not been demonstrated. Therefore, classical receptor autoradiography (CRA) is a powerful complement to these methods. When used with image analysis, CRA provides spatial mapping of receptor location at moderately high resolutions ,Author for correspondence. 0891-0618/91/050343-11 $05.50 © 1991 by John Wiley and Sons Ltd

In situ hybridization Image

(regions > 100 I~m), in combination with full quantitation of ligand affinity and/or receptor densities, However useful it is, CRA has suffered from a number of limitations. A major problem is discrimination of sites of origin for receptors. Which cells are actually synthesizing the receptors? Receptor autoradiography, like immunohistochemistry, can be used to demonstrate the final location of receptor polypeptides. These receptor proteins are synthesized within the soma, but are generally transported to dendrites or axons, distant from the cell bodies where they originated. Thus, demonstration of a receptor population by CRA or immunohistochemistry is insufficient to discriminate which perikarya synthesize the receptors. Demonstration that a given receptor originates in a specific cell population could only be made indirectly. For example, lesions could be placed within a nucleus, and the effects upon receptor population in a remote site evaluated. Another major limitation of CRA is not inherent to the technique itself, but arises from the lack of selectivity of many ligands. We now know that

344 J.M. Palacios et al. ligands previously considered as selective for a receptor population do, in fact, bind to up to five different subtypes of the same receptor class. Fortunately a new battery of tools has appeared, which greatly enhance our ability to perform precisely localized and very selective receptor analyses. New developments in receptor analysis result primarily from advances in the understanding of the molecular biology of receptors. In recent years, the genes coding for a large and ever-increasing number of receptor molecules have been cloned. Knowledge of the sequence of the mRNA coding for receptor proteins has made possible the development of nucleic acid probes. These can be used in combination with the technique of in situ hybridization to visualize perikarya that express the mRNA for a particular receptor with unprecedented selectivity. Thus, in situ hybridization becomes an important adjunct to CRA and immunohistochemistry. It allows the direct demonstration of sites of active synthesis of receptor proteins. Further, the combination of in situ hybridization and receptor autoradiography with highly selective ligands permits a new level of precision in characterizing receptor populations.

IMAGING RECEPTORS: COMBINATION OF LIGAND BINDING AND I N S I T U HYBRIDIZATION

Methodological considerations The combination of in situ hybridization with receptor autoradiography is favored by the similarity and compatibility of tissue manipulation requirements for both techniques. As starting material, frozen brain sections can be used in both cases, thus allowing the visualization of mRNA and receptor proteins in consecutive sections of the same animal. Furthermore, both techniques can produce the same kind of visual support for data, i.e. film autoradiograms, which are amenable to the same kind of image analysis and display, i.e. densitometry, macro photography, etc. In addition, a higher level of cellular resolution can easily be obtained with in situ hybridization if hybridized sections are dipped into liquid photographic emulsion and viewed and analysed with the help of the light microscope. Several kinds of probes can be used in in situ hybridization studies to detect the mRNA of interest. We favor the use of oligonucleotide probes for many reasons (see Vilar6 et al., 1991b). Perhaps the greatest advantage of their use is the possibility of obtaining probes highly specific for the mRNA of interest. This is especially important when multiple subtypes of receptors exist for a given neurotransmitter, since synthetic oligonucleotides can be designed to hybridize with regions of the mRNA which share very little or no identity among the different subtypes. In this way, probes which will

recognize only one of the mRNA subtypes can be obtained and used to map the expression of the various subtypes, even of those for which neither selective ligands nor specific antibodies exist yet. As mentioned above, in situ hybridization permits the visualization of the perikarya which synthesize receptor mRNAs, whereas receptor autoradiography reveals the final localization of the receptor polypeptides themselves. When combining both techniques, this fact can result in an overlap of the signal patterns when receptors found in a given brain nucleus are synthesized by cells intrinsic to this nucleus. In contrast, if receptors visualized in one nucleus are synthesized by afferents to this nucleus whose perikarya are located in distant brain areas, the patterns of signal will be complementary rather than overlapping. Like receptor autoradiography, in situ hybridization is not devoid of limitations. One of them is related to the fact that it gives an idea of the abundance of transcripts for a receptor but not of the actual number of receptor molecules themselves. Translation rates may vary for the different mRNAs, as well as turnover rates for the different receptor proteins, and, therefore, a low number of transcripts could result in a high number of receptor proteins and vice versa. In addition, an exact quantification of transcripts in terms of, for example, 'number of mRNA molecules per cell' has yet to be achieved. However, relative quantitative estimates can be obtained by microdensitometrical measurements of the autoradiograms or by grain counting in emulsion-dipped tissue sections. In situ hybridization pinpoints areas of subtype-

selective receptor expression Receptor autoradiography has been instrumental in identifying subtypes of receptors because it added essential anatomical information to the pharmacological differences by identifying regions where one rather than another subtype was expressed. A paradigm of this kind of research has been the identification of subtypes of serotonin receptors, which are particularly enriched in relatively small tissues or areas of the brain like the choroid plexus, the substantia nigra or the dorsal subiculum. Receptor cloning has shown that receptor multiplicity goes beyond what even the most daring pharmacologists had dreamed. In this section we want to illustrate how the combination of in situ hybridization with receptor autoradiography can contribute to increase our understanding of receptor subtype distribution and also help in the delineation of assay conditions for the selective visualization of receptor subpopulations with non-selective radioligands. We are currently applying this combined approach to the study of dopaminergic (Mengod et al., 1989, 1991a,b), serotonergic (Mengod et al. 1990a,b; Pompeiano et al., submitted) and muscarinic cholinergic receptors (Vilar6 et al., 1990, 1991a; Vilar6

Recent Trends in Receptor Imaging et al., submitted). As an example, we will illustrate

here some of our results with muscarinic receptors. Multiplicity of muscarinic receptors was detected with antagonists such as pirenzepine or AF-DX 116 which, unlike classical antagonists, detected subclasses of the receptors. Thus, a first classification into M l and M 2 receptors was proposed (Hammer and Giachetti, 1982) and subsequently extended to Ml, M2 and M 3(Hammer et al., 1986; de Jonge et al., 1986). Very shortly after these proposals, and in a period as short as 2 years, several reports appeared on the molecular cloning of a family of five (m l-m5) distinct muscarinic receptor subtypes (reviewed in Bonner, 1989). As a consequence, the pharmacological classification of muscarinic receptors lagged behind the molecular classification, a situation which has become quite common in the field of neurotransmitter receptors. Several in situ hybridization studies (Buckley et al., 1988; Weiner and Brann, 1989; Palacios et al., 1990; Vilar6 et al., 1990, 1991a) have shown that all five receptor subtype mRNAs are expressed in brain. From these studies, three different categories of brain areas appear to emerge. First, those structures (e.g. cerebral cortex, hippocampal formation) which express most, if not all, of the subtypes. Second, regions where a subtype appears to be expressed predominantly although lesser populations of other subtypes are also present. This is, for example, the case of m4 receptors in the striatum and olfactory tubercle. Finally, a few nuclei which seem to express only one of the subtypes cloned so far (e.g. m5 receptors in the ventral tegmental area and substantia nigra, or m2 receptors in certain cranial nerve nuclei such as the facial or hypoglossus). It is therefore evident that the muscarinic receptor system in the brain is much more complex than thought only a few years ago. As a consequence, previous autoradiographic studies may have given an incomplete or oversimplified picture of the situation, especially given the fact that many of the ligands previously thought to be selective for one pharmacological subtype present in fact similar high affinities for more than one cloned subtype. Current work in our laboratory is aimed at establishing a detailed picture of the distribution of muscarinic receptor subtypes in brain by combining information obtained from in situ hybridization studies with data derived from receptor autoradiography with several ligands and radioligands. We will illustrate here two rather different and extreme situations: the facial nucleus, where only m2 mRNA is detected, and the hippocampal formation, which expresses mRNA for all five subtypes. In the facial nucleus, transcripts for only one of the five subtypes, the m2, have been detected (Fig. 1A). In good agreement with this, the facial nucleus is labelled by non-selective radioligands such as [3H]NMS (Fig. 1B) and also by radioligands reported to be selective for M 2 receptors such as [3H]Oxotremorine-M (Fig. 1C) or [3H]AF-DX 384 (Fig. 1D). In contrast, radioligands selective for

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other than M 2 receptors do not label this nucleus. This is the case of ['H]4-DAMP (Fig. 1E), reported to be selective for MI-M 3 receptors (Doods et al., 1987) and [3H]pirenzepine (Fig. 1F). Binding studies in cell lines expressing each of the five subtypes of cloned receptors have shown that the selectivity of most ligands is not as pronounced as previously suggested (Buckley et al., 1989; Novotny and Brann, 1989; Drrje et al., 1991). Thus, many 'M2-selective' ligands seem to recognize with high affinity both m2 and m4 receptors. Similarly, pirenzepine, the key antagonist in defining M~ receptors, appears to prefer both m 1 and m4 receptors over the other subtypes, and 4-DAMP has similar high affinities for ml, m3, m4 and m5 receptors. It is therefore becoming evident that, with the available radioligands, it is not possible to label exclusively one of the subtypes if the radioligand is used alone. However, as will be illustrated below, preferential labelling of one subtype can be achieved if the appropriate unlabelled ligands are used to block the 'second preferences' of the radioligands. The data obtained from in situ hybridization experiments are proving invaluable in determining areas which can serve as models of a given receptor population. This is, for example, the case of the facial nucleus in establishing the best ligand combination to label preferentially m2 receptors, or the case of the substantia nigra when trying to label m5 receptors. As shown in Fig. 2, the hippocampus illustrates a situation radically different from that observed in the facial nucleus, since it expresses mRNAs for the five muscarinic receptor subtypes, ml transcripts (Fig. 2B) are abundant in the pyramidal cells of all fields of Ammon's horn, as well as in the granule cells of the dentate gyrus. No apparent gradient of expression is observed for this subtype, m2 mRNA (Fig. 2C) is not homogeneously distributed. The highest levels are observed in the pyramidal cells of the very rostral tip of the hippocampus, whereas proceeding caudally, the intensity of the signal decreases rapidly, reaching very low or undetectable levels over the pyramidal cells of the posterior hippocampus. The pyramidal cells of field CA3 close to and extending into the hilar region show intermediate levels of signal rostrally, whereas no signal is detected more caudally. No signal is observed over the granule cells of the dentate gyrus, m3 transcripts (Fig. 2D) are abundant in the pyramidal cells of Ammon's horn, with lower levels in a small region which could correspond to field CA2. Lower levels are also detected in the granule cells of the dentate gyrus, m4 mRNA (Fig. 2E) is more abundant in pyramidal cells of fields CA1, CA2 and in the zone of CA3 (CA3c) close to and extending into the hilar region. The rest of CA3 shows lower levels of signal, whereas no signal is detected in the granule cells of the dentate gyrus. Finally, m5 transcripts (Fig. 2F) are detected in fields CA1 and CA2, with the levels being very low rostrally and increasing caudally. Although an

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Fig. 2. Distribution ofmRNAs for the five subtypes of muscarinic receptors in the rat hippocampus. (A) Cresyl violet staining showing the pyramidal cells of Ammon's horn (Py) and the granular cells of the dentate gyrus (GrDG). (B-F) Distribution o f m l (B), m2 (C), m3 (D), m4 (E) and m5 (F) mRNAs. Sections in (C) and (F) are respectively more rostral and more caudal than the rest. Arrows in (C) indicate the very low levels of m2 mRNA detected in the pyramidal cells of CA 1, CA2 and part of CA3. Abbreviations: CA 1, CA2, CA3, fields of Ammon's horn; Hil, hilus of the dentate gyrus. Bar = 1 ram.

devoid of labelling. Much lower densities of receptors are labelled in the Ammon's horn by [3H]Oxotremorine-M (Fig. 3C) or [3H]AF-DX 384 (Fig. 3D). In the dentate gyrus, low densities of

receptors are detected by [3H]AF-DX 384 and even lower by [3H]Oxotremorine-M. [3H]4-DAMP (Fig. 3E) and ['H]pirenzepine (Fig. 3F) show patterns of labelling similar to that of [3H]NMS.

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Fig. 3. Distribution of muscarinic receptors in the rat hippocampus visualizedwith several radioligands. (A) Acetylcholinesterasestaining of a section close to the ones used for receptor autoradiography. (B-F) Distribution of muscarinic binding sites visualized with [3H]NMS (B), [3H]Oxotremorine-M(C), [3H]AF-DX384 (D), [3H]4-DAMP(E) and [3H]pirenzepine(F). Abbreviations: Gr, granule cells of dentate gyrus; LMol, estratum lacunosum moleculare; Mol, molecular layer of dentate gyrus; Or, stratum oriens; Py, pyramidal cell layer; Rad, stratum radiatum. Bar = 1 mm. C o m p a r i s o n o f the distribution o f receptor subtype m R N A s (Fig. 2) with that o f radioligand binding sites (Fig. 3) allows a series o f conclusions to be drawn. First, hippocampal muscarinic receptors

synthesized by pyramidal and granule cells appear to be located in the dendritic arborizations o f these cells rather than in their perikarya. Second, none o f the patterns o f labelling observed with the 'selective'

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and receptor autoradiography is a new challenge to image analysis. We will now refer to some recent advances in image analysis that should facilitate this kind of correlation. IMAGE ANALYSIS REQUIREMENTS FOR RECEPTOR A U T O R A D I O G R A P H Y

t*

Fig. 4. Preferentiallabellingof m3 receptorsin the hippocampus by [~H]4-DAMPin the presenceof pirenzepine. (A) Total binding; (B) binding in the presenceof 50 n~ unlabelledpirenzepine. Note how the labellingin the dentategyrus(longarrow)is much moresensitiveto pirenzepinethan the labellingin Ammon'shorn (short arrows).Bar = 1mm. radioligands can be fully explained by the distribution of mRNA for only one particular subtype. Thus, for example, the pattern of [3H]4-DAMP labelling (Fig. 3E) in the Ammon's horn is somewhat comparable to the distribution of m3 (Fig. 2D) or m4 (Fig. 2E) mRNAs. In contrast, in the dentate gyms, the high levels of radioligand binding seem to be paralleled much closer by the high levels of ml mRNA (Fig. 2B) than by the much lower or undetectable levels of m3 and m4 mRNAs respectively. This situation can in fact be explained by the similar high affinities that this antagonist displays for cloned ml, m3 and, to a lower extent, m4 receptors (Drrje et al., 1991). In such cases, other unlabelled ligand/s with different patterns of selectivity can be used in order to block mainly one of the receptor populations recognized by the radioligand and to visualize preferentially the other population/ s. This is illustrated in Fig. 4, which shows how the addition of pirenzepine, which has higher affinities for ml and m4 receptors (Drrje et al., 1991) results in a much more pronounced decrease of [3H]4DAMP labelling in the dentate gyms than in the Ammon's horn (Fig. 4B). The receptor distribution thus obtained is now much more comparable to m3 mRNA distribution (Fig. 2D). The establishment of spatial and quantitative correlations between results from in situ hybridization

Image analysis of receptor data has been most commonly performed at the regional level, using magnifications typical of macro photography. That is, radiolabelled tissue sections are exposed to film. Each film is also exposed to standards containing known amounts of isotope, cross-calibrated to tissue-equivalent ligand concentration. The image analyser is calibrated to the standards, and performs interpolation to read values lying between those of the film standards. Once the system is calibrated, the autoradiographs are digitized. Regions of interest, such as brain nuclei, are defined on the autoradiographs. The image analyser reports integrated optical densities directly in units of ligand concentration. Given the methodological simplicity of analysing film data, many commercial and non-commercial bioscience image analysers (BIAs) have been applied to analysis of receptor data from films. Only a few of these BIAs have survived to become widely used, as film analysis was poorly implemented on most. The majority of BIAs were spun off from general purpose image processing tools, or were developed for morphometric applications. They lacked a user interface or functions designed for efficient film analysis. In contrast, BIAs succeeding in autoradiography applications are tailored specifically for rapid analysis of films, and this tailoring allows large quantities of autoradiographic data to be generated in short periods of time. Many receptor laboratories have been working with image analysis for some time, and have become very sophisticated in their application of the technology. In the most active laboratories, initial satisfaction with the ability to capture images, read calibrated density data, and display images in enhanced form soon wanes. Expectations increase, and demands are made which drive continual improvement of the image analyser. Some of the BIAs used for film autoradiography have been under development for more than 5 years, and have attained a very high level of sophistication for this purpose. The major demands of experienced users are summarized below.

Highly developedapplicationsoftware BIAs fall into two major software classes. Turnkey systems are delivered with very complete software, directed specifically at receptor analysis and other applications. No programming is required for most users, though the instruments are programmable if

350 J.M. Palacios et al. there is need. In contrast, macro-based systems require some user programming to function in any but the simplest applications. User programming is the rule, via a simplified programming environment. The mini-programs generated are called macros. The majority of installed receptor analysis systems are of the turnkey kind. The majority of BIAs on the market are macro-based. Life scientists appear to prefer the turnkey approach. Given the end-user preference for turnkey software, why are most BIAs macro-based? The answer is that the computer scientists and engineers who create most image analysers lack a deep understanding of end-user requirements. Therefore, creation of a system that performs accurate and efficient receptor analysis is beyond them. However, they can easily market multifunctional image analysis instruments with a simplified programming language (a macro creation system). These devices are sold as 'flexible' (user programmable) and 'easy to use' (the programming process is simplified). With such a system, ultimate responsibility for software design and system performance is taken away from the manufacturer and placed upon the researcher. In the hands of those with the simplest requirements, or if the types of image analysis being performed are unique, the macro-based system can be very successful. It can also be lower in cost than a turnkey system. However, few receptor scientists have the time, need, or inclination to create their own software. Rather, they require rapid, accurate, and efficient analysis of receptor data, from a system using proven hardware and software. Researchers are wary of macro-based software that has not been extensively validated, and prefer turnkey systems that operate in exactly the same way in every laboratory. Therefore, experienced users demand highly developed receptor analysis software and turnkey systems. More efficient user interface

In most receptor laboratories, analysis of the autoradiographs is performed by temporary staff (e.g. post-doctoral fellows) or by research assistants without training in imaging. Therefore, it is important that operation of the image analysis system be easily learned, and easily transferred from user to user. The earliest BIAs used character-based menuing interfaces. Some of these systems functioned very well, but were difficult to learn. The newest generation of BIA software uses standard graphical user interfaces (GUIs) to simplify the process of learning and using software. Users of GUI-based software are finding that, not only is the product easier to use, but skills learned in mastering one software package are readily transferred to all software running under the GUI. This allows the receptor analysis system to interact easily with word processing, desk-top publishing, graphics and spreadsheet programs.

The need for standardization There is a growing demand, especially a m o n g laboratories within pharmaceutical companies, for the

standardization o fimage analysis procedures, image formats, and instrumentation. Like any complex instrument, an image analyser can yield inaccurate data if used under inappropriate conditions or without regard to its inherent limitations. Standardization of procedures and instruments would increase confidence that a particular data set is valid, and would facilitate the comparison and transfer of receptor data from site to site. There is no formal body of standard procedures for the imaging of receptor data. Rather, each laboratory must learn to apply a BIA within its own context. Similarly, no single BIA is universally accepted as the standard instrument for receptor analysis. Each laboratory evaluates the offerings of BIA suppliers, and makes purchases that accord with its budget and inclinations. Some laboratories have installed BIAs whose performance in receptor applications is not well characterized. Fortunately, a very few BIAs have been widely adopted for receptor analysis. The finest such instruments are in place at hundreds of sites, have been used to publish many papers, and could be regarded as standards for the field. There is a growing trend for corporate and institutional researchers to demand that one of the standard systems be used for receptor analysis or that, at least, any nonstandard system be validated against and interchange image data with standard systems. The demand for new applications

The rapid proliferation of techniques from molecular biology requires that a cost-efficient receptor BIA includes an expanded functional repertoire. Receptor laboratories typically use a combination of film and emulsion (micro) autoradiography. Therefore, efficient functions for cellular-level analysis should be part of the BIA. (i) Fully manual tracing or counting of target data is always possible. However, this is tedious, and most BIAs offer segmentation algorithms to assist in automatically discriminating grains or cells containing reaction product from background. The segmentation process may require gray level or color thresholding, accompanied by preprocessing of the image, using tuned filters or morphological operators. (ii) Segmentation is imperfect, and good facilities for the editing of target data (deleting invalid targets and adding missed targets) are essential. (iii) A tedious aspect of microscope-level image analysis is placement of the tissue. The BIA should include facilities for motor stage control, to allow field-by-field or incremental movement of a microscope stage, and tracking of target XY position. In addition to this basic stage control, there should be

Recent Trends in Receptor Imaging functions for defining fields of view under low power, and then scanning those fields under high power. This allows automatic positioning of the specimen high under magnification. There should be provision for creating schematics showing target distribution across large areas of tissue. Receptor laboratories are also making increasing use of hybridization to blots of DNA or RNA obtained after gel electrophoresis. The data from these studies are presented as stained blots (if nonradioactive probes are used) or film autoradiograms (in the case of radiolabelled probes). Basic functions for image analysis of these data include: (i) non-linear distance calibration, for use with molecular weight standards; (ii) non-linear warping to match spatially distorted lanes to a control; (iii) graphic display of transept lines across lanes; (iv) processing of transept lines to remove baseline variation and deconvolve peaks; (v) sampling of data from the transept line to yield parameters such as per cent of total line area occupied by a specific peak. RECENT DEVELOPMENTS IN IMAGE ANALYSER TECHNOLOGY Most receptor analysis systems have used rather simple, low-cost imaging hardware. Spatial resolution is about 500 x 500 pixels, with image memory sufficient to store multiple images at that resolution. Density resolution is 8 bits (256 gray levels) with video input being almost universal. The price of a complete BIA built around such equipment has stabilized at $25 000-$50 000. This level of BIA has been very successfully applied to receptor analysis with, in recent years, the most major advances Coming in the area of software quality. Some applications require more advanced imaging capabilities than any of today's low-cost systems can provide. Common requirements for advanced receptor analysis are detailed below.

High-resolution image capture and display Capture and display of images at resolutions of 1024 x 1024 or 1280 x 1024 pixels allow the viewing of a much larger specimen while maintaining high resolution. This is useful with large format film specimens, such as whole body or human brain autoradiographs. A particular advantage is that the digital image of an entire specimen may be archived, with confidence that even fine detail is rendered accurately in the large image. For example, a coronal section of a human brain is about 200 mm wide. On a 512~2) imaging system, each pixel would represent about 400 I~m of tissue. Sampling of density data with adequate contrast transfer requires that

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features be at least 3--4 pixels wide (see Ramm, 1990 for discussion), so reading of details smaller than about 12001am across would result in a serious degradation of density accuracy. This resolution is insufficient for most purposes. Therefore, increased optical magnification would be used, with a corresponding decrease in field of view. Imaging of the same specimen on a system with resolution of 1280x1024 pixels yields about 160~tm/pixel. Accurate density data could be read from details about 500 ~tm across, without resorting to increased optical magnification. High resolution is also an advantage in grain or cell analysis, where standard video systems capture only the central portion of the microscope field of view. Thus, many fields must be scanned to provide adequate coverage. A 1280 x 1024 pixel system can digitize five times the area of a 512 x 512 system, covering most of the field of view at the same spatial resolution.

Powerful real-time image processing Segmentation of grains or histochemically labelled cells from background can require a considerable amount of image processing. To be fast, this processing should be implemented in hardware, particularly in high-resolution systems where large quantities of image data are present.

Flexible input device selection The 8-bit density resolution of video image analysers can be restrictive. Typical applications which require greater bit density are: analysis of autoradiographs with accurate rendition of both total binding and low levels of non-specific binding; gel analysis, in which the density range can easily span 2D units; histofluorescence imaging, in which a wide range of fluorescence intensities must be imaged simultaneously. Sensors which offer high bit densities are becoming more readily available, and should be matched to BIAs with hardware and software supporting this feature. Researchers have long recognized the benefits of greater image processing power, higher resolution, and increased input flexibility. However, the cost of image processing hardware beyond the basic level was prohibitive. Until very recently, BIAs built around more capable hardware cost well in excess of $100 000, and were beyond the budgets of most laboratories. In 1990, a new generation of powerful image processing hardware appeared from a number of board manufacturers. This hardware is now being incorporated into BIAs which use increased imaging power to greatly enhance the performance of receptor analysis. The cost of such devices (approximately $70 000) is about double that of a basic image analyser, but far below that of older BIAs offering similar performance.

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Characteristics of today's most powerful receptor analysis systems Modular construction

A modular system is composed of a number of discrete imaging components (e.g. image memories, convolution processors), each on its own circuit board. The imaging components do not communicate with each other via the host computer. Rather, they achieve very high rates of data transfer using a special data path, the imaging bus. The modular system has two major advantages. Image data can be manipulated without being slowed by going through the host computer, and additional components may be added to the imaging bus at any time. Broad bus bandwidth

The bandwidth of the imaging bus sets the data manipulation limit of the system. Typical 512(2) imaging systems have a bus bandwidth of about 7 mHz, sufficient to deal with data from medium resolution imaging. However, at least a 20-mHz bus is required to handle high resolution imaging at reasonable speeds. The more advanced imaging systems have a bus bandwidth of 30-40 mHz or more. Large image memories with flexible configuration

Image memory ranges from 8 MB to virtually any size. Memory configuration is user-selectable. For example, 8MB could be configured as eight 1024(2)x 8 bit/pixel images, four 1024(2)x 16 bit/ pixel images, eight 512(2)x 32 bit/pixel images (for true color + overlays), thirty-two 512(2) x 8 bit/pixel images, etc. High-speed digitizers and display controllers

Digitizers are capable of filling a 1024(2) x 8 bit/pixel image memory at 10--30 frames/s. Display controllers can display at least 1000 lines of image data, usually at a 60 Hz non-interlaced frame rate to minimize flicker. The more advanced systems can digitize and display 24-bit, true color images at rates up to 30 frames/s. Special-purpose processors operate on lO00-1ine images in real time

There are hardware accelerators for various types of common image processing operations. Arithmetic logic units perform simple individual pixel combinations, such as image addition or subtraction in real time. Neighborhood processors are specialized for morphology and statistical operations. Array processors rapidly perform spatial convolution, image rotation and geometric distortion. Flexible input devices

Monochrome and color video cameras, highresolution array and line-scan cameras, integrating cooled cameras, scanning electron microscopes, and other devices can be interfaced directly to the

imaging system. It is critical that the BIA manufacturer has implemented the functions to control such input devices.

The immediate future of receptor image analysis The first laboratory-built autoradiographic image analysers appeared in the early 1980s. Commercial receptor analysis systems appeared about 1985. The very limited systems available at that time have matured to the point that, with the best commercial systems, the imaging of receptor data is routine. It need be no more difficult to apply a BIA to receptor analysis than to apply a computer to word processing. The trend towards standardization, improved ease of use, and increased functionality of commercial software will continue. The ability to communicate data from the BIA will be enhanced, as the integration of BIAs into standard desk-top computers progresses. For example, the rapid transfer of numerical and image data from the BIA to spreadsheet, statistics and desk-top publishing programs can save a great deal of time in preclinical evaluation of a new compound, or in the creation of publishable reports. Communication of data will also benefit from advances in graphics hardware and software, which will facilitate the use of threedimensional renderings of receptor data. Both surface and volume renderings will be far lower in cost and much more easily produced than is possible today. As always in a rapidly expanding field of computing science, digital hardware will develop more rapidly than analog hardware and software. It will be early 1992 before the latest generation of highperformance hardware is fully integrated into BIAs with sophisticated receptor analysis functions. The routine use of commercial, 1000-line image analysers will expand rapidly at that time. Before mid-decade, the standard BIA will offer image acquisition, processing and display at resolutions of 1024(2)or higher, all at moderate cost. The most advanced systems will be working with data at resolutions greater than 2000 lines, though the cost of analog instruments (cameras, monitors) capable of acquiring and displaying such images will remain relatively high. Above all, it is to be hoped that the tendency for image analysis and three-dimensional graphics to lose their patina of mystery will continue. Commercial suppliers of BIAs must develop software which makes even the most powerful imaging and graphics display functions readily accessible to life scientists with minimal computer expertise. The researchers can then concentrate on using and advancing the science of receptor analysis, with little effort expended on mastering the tools of the trade. REFERENCES Bonner, T. I. (1989). The molecular basis of muscarinic receptor diversity. Trends Neurosci. 12, 148-151.

Recent Trends in Receptor Imaging Buckley, N. J., Bonner, T. I. and Brann, M. R. (1988). Localization of a family of muscarinic receptor mRNAs in rat brain. J. Neurosci. 8, 4646-4652. Buckley, N. J., Bonner, T. I., Buckley, C. M. and Brann, M. R. (1989). Antagonist binding properties of five cloned muscarinic receptors expressed in CHO-KI cells. Mol. Pharmacol. 35, 469-476. de Jonge, A., Doods, H. N., Riesbos, J. and van Zwieten, P. A. (1986). Heterogeneity of muscarinic binding sites in rat brain, submandibular gland and atrium. Br. J. Pharmacol. 89, Suppl. 551P. Doods, H. N., Mathy, M. -J., Davidesko, D., van Charldorp, K., de Jonge, A. and van Zwieten, P. A. (1987). Selectivity of muscarinic antagonists in radioligand and in vivo experiments for the putative M~, M z and M 3 receptors. J. Pharmacol. Exp. Ther. 242, 257-262. D6rje, F., Wess, J., Lambrecht, G., Tacke, R., Mutschler, E. and Brann, M. R. (1991). Antagonist binding profiles of five cloned human muscarinic receptor subtypes. J. Pharmacol. Exp. Ther. 256, 727-733. Hammer, R. and Giachetti, A. (1982). Muscarinic receptor subtypes: M 1 and M2. Biochemical and functional characterization. Life Sci. 31, 2991-2998. Hammer, R., Giraldo, E., Schiavi, G. B., Monferini, E. and Ladinsky, H. (1986). Binding profile of a novel cardioselective muscarine receptor antagonist, AF-DX 116, to membranes of peripheral tissues and brain in the rat. Life Sci. 38, 1653-1662. Mengod, G., Martinez-Mir, M. I., Vilar6, M. T. and Palacios, J. M. (1989). Localization of the mRNA for dopamine D2 receptor in the rat brain by in situ hybridization histochemistry. Proc. Natl. Acad. Sci. USA 86, 8560-8564. Mengod, G., Nguyen, H., Le, H., Waeber, C., Liibbert, H. and Palacios, J. M. (1990a). The distribution and cellular localization of the serotonin IC receptor mRNA in the rodent brain examined by in situ hybridization histochemistry. Comparison with receptor binding distribution. Neuroscience 35, 577-59 I. Mengod, G., Pompeiano, M., Martinez-Mir, M. I. and Palacios, J. M. (1990b). Localization of the mRNA for the 5-HT2 receptor by in situ hybridization histochemistry. Correlation with the distribution of receptor sites. Brain Res. 524, 139-143.

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Mengod, G., Vilar6, M. T., Niznik, H. B., Sunahara, R. K., Seeman, P., O'Dowd, B. F. and Palacios, J. M. (1991a). Visualization of a dopamine D1 receptor mRNA in human and rat brain. Mol. Brain Res. 10, 185-191. Mengod, G., Vilar6, M. T., Landwehrmeyer, G. B., Martinez-Mir, M. I., Niznik, H. B., Sunahara, R. K., Seeman, P., O'Dowd, B. F., Probst, A. and Palacios, J. M. (1991 b). Visualization of dopamine Dl, D2, and D3 receptor mRNAs in human and rat brain. Neurochem. Int, in press. Novotny, E. A. and Brann, M. R. (1989). Agonist pharmacology of cloned muscarinic receptors. Trends Pharmacol. Sci. Supplement: Subtypes of Muscarinic Receptors IV. Abstract no. 69. Palacios, J. M., Mengod, G., Vilar6, M. T., Wiederhold, K. H., Boddeke, H., Alvarez, F. J., Chinaglia, G. and Probst, A. (1990). Cholinergic receptors in the rat and human brain. Microscopic visualization. Progr. Brain Res. 84, 343-353. Ramm, P. (1990). Microcomputer-based image analysis for the neurosciences. Computerized Medical Imaging and Graphics 14, 287-306. Vilar6, M. T., Palacios, J. M. and Mengod, G. (1990). Localization of m5 muscarinic receptor mRNA in rat brain examined by in situ hybridization histochemistry. Neurosci. Lett. 114, 154-159. Vilar6, M. T., Wiederhold, K. -H., Palacios, J. M. and Mengod, G. (1991a). Muscarinic cholinergic receptors in the rat caudate put amen and olfactory tubercle belong predominantly to the m4 class: in situ hybridization and receptor autoradiography evidence. Neuroscience 40, 159-167. Vilar6, M. T., Martinez-Mir, M. I., Sarasa, M., Pompeiano, M., Palacios, J. M. and Mengod, G. (1991 b). Molecular neuroanatomy of neurotransmitter receptors: the use of in situ hybridization histochemistry for the study of their anatomical and cellular localization. In Current Aspects of the Neurosciences, Vol. 3 (ed. Osborne, N. N.), pp. 1-36. Macmillan Press, London. Weiner, D. M. and Brann, M. R. (1989). Distribution of ml-m5 muscarinic receptor mRNAs in rat brain. Trends Pharmacol. Sci. Supplement: Subtypes of Muscarinic Receptors IV. Abstract no. 67.

Accepted 1 July 1991

Recent trends in receptor analysis techniques and instrumentation.

Receptor autoradiography allows visualization of receptor binding sites at the regional or light microscopic level. Receptor autoradiography is a matu...
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