Brain Research, 513 (1990) 225-236 Elsevier
225
BRES 15344
Peripheral mechanisms of olfactory discrimination of complex mixtures by the spiny lobster: no cell types for mixtures but different contributions of the cells to the across neuron patterns Marie-Nadia Girardot* and Charles D. Derby Department of Biology, Georgia State University, Atlanta, GA 30303 (U.S.A.) (Accepted 5 September 1989)
Key words: Chemoreception; Olfaction; Discrimination; Lobster; Neural coding; Cell type; Mixture
Toward understanding mechanisms of olfactory discrimination, we have examined the existence of cell types and the role of cells in the coding of odorant quality in the olfactory organ of the spiny lobster. The results consisted of responses of 30 antennular chemoreceptor cells to 8 behaviorally discriminable complex stimuli - - 4 natural extracts and 4 artificial mixtures, each at 3 concentrations. Multidimensional scaling and cluster analysis failed to identify unequivocal cell types, but rather suggested a continuum of cellular response profiles. The lack of cell types suggests that the code for the quality of natural odorants in this system is a population code. The distribution of cells along the response continuum was based on any of many features of their response profiles. The most effective stimulus (= best stimulus) and the least effective stimulus (= least stimulus), two features of the response profiles, could only partially explain the differences in response profiles of cells. Nonetheless, cells with different response profiles were shown to have different functions in odorant coding. Most cells contribute to some degree to the discrimination of any two stimuli, but a cell's contribution to the discrimination of two stimuli is usually disproportionally robust when those two stimuli produce very different responses in that cell. INTRODUCTION The role of individual neurons or types of neurons in the discrimination of the quality of chemical stimuli is important in understanding the cellular mechanisms of chemosensory discrimination. The two main theories of coding of stimulus quality differ in the emphasis that they place on the role of individual cells. The across-neuron pattern coding hypothesis holds that stimulus quality is coded by the pattern of activity across a population of neurons ( = across-neuron patterns, or ANPs) 12. The labeled line coding hypothesis specifies that any stimulus quality is coded by the activity of a particular class of relatively narrowly tuned cells 18. A n assumption of the labeled line hypothesis is that for each identifiable chemical quality, there must exist a cell type that codes that quality exclusively. In addition, all members of that cell type must have response spectra that are similar to each other but different from the members of other cell types. Thus, according to the labeled line coding scheme, the cells in the entire population have response profiles that differ in a discontinuous or clumped fashion rather than in a continuous fashion. A n important step in establishing the nature of neural
codes for sensory quality is the search for cell types. There is ample evidence that chemoreceptor cells, either gustatory or olfactory, peripheral or central, have differential sensitivities 17, but are there neuronal types? Multivariate statistics, such as cluster analysis, factor analysis, and multidimensional scaling, have become favored methods to examine the existence of neuronal types because they are based on quantitative, objective, and non-arbitrary criteria 2,14'2°'3°'31'36,4°A2. Searches for neuronal types in chemosensory systems using multivariate analyses are primarily based on responses of vertebrate gustatory neurons to single compounds, and the results suggest that cell types exist for some compounds in some species 11'18-2°'25'26'32'33"35-37'40, although this view has been challenged 13"14'42. Once cell types have been thus established, hypotheses concerning their function in chemosensory discrimination can be generated and tested. H o w gustatory cell types in hamsters may function in the discrimination of single compounds representing salty, sweet, and sour taste qualities has been proposed by Smith and colleagues 35-37. According to their scheme, each cell type plays an important and different role in the definition of ANPs. Only one cell type is necessary to establish the similarities
* Present address: Biomedical Design, Inc., Suite $204, 430 Tenth St., N.W., Atlanta, GA 30318, U.S.A. Correspondence: C. Derby, Department of Biology, Georgia State University, Atlanta, GA 30303, U.S.A. 0006-8993/90/$03.50 © 1990 Elsevier Science Publishers B.V. (Biomedical Division)
226 among A N P s generated by a group of similar tasting stimuli. For example, one cell type identified by cluster analysis and multidimensional scaling - - the S neurons (which are also the neurons that are most responsive to sucrose and other sweet-tasting compounds) - - are necessary to establish similarities among ANPs for sweet-tasting stimuli. Other cell types do not serve this function. However, neither the S neuron cell type nor any other type alone can establish ANPs that differentiate dissimilar tasting compounds. Such discrimination relies on contributions of more than one cell type to the ANPs. Although evidence for receptor cell types in mammalian olfactory systems is lacking 24'29'34, receptor cell types have been identified in several invertebrate olfactory systems (for reviews, see refs. 1, 27). This has been especially true in aquatic crustaceans 6"7"27. Multivariate statistical techniques have been applied to the olfactory receptor cells in the antennules of the spiny lobster Panulirus argus and have revealed that based on excitatory responses to 8 compounds each at one concentration, at least 5 types of narrowly tuned cells exist 6. Other types have since been identified. Derby and colleagues 6'9 suggested that each narrowly tuned cell type may function in discrimination as a labeled line, or, following the suggestion of Smith et al. 35-37, that each cell type may contribute disproportionately to the A N P for the compound to which it is narrowly tuned. More recently, the A N P code for stimulus quality in the olfactory system of spiny lobsters has been favored s'21'22. Factors other than analytical techniques that can have a critical impact on one's ability to identify cell types are the type and intensity of chemicals selected for study. The likelihood of finding cell types whose members respond to only one stimulus or group of similar stimuli is increased by selecting stimuli or groups of similar stimuli that markedly differ in structure, composition, or quality. Most studies of chemosensory coding of stimulus quality and the identification of cell types have dealt with single compounds. However, complex stimuli, such as chemical mixtures, probably represent the most biologically significant stimuli, and therefore should be considered for use in identifying cell types. Cellular response features such as mixture interactions, whose detection is dependent on testing mixtures, further affect the responsiveness of cells and therefore their categorization s . The search for such complex feature-detecting cells that selectively respond to unique complex stimuli has led to the identification of unique cell types, usually higherorder neurons, in such modalities as vision 2s and audition 3s. Intensity is another feature of stimuli that can have an impact on the identification of cell types. In most studies, cell types are identified from responses to one concen-
tration for each stimulus type. Defining cell types based on their responses to a range of stimulus concentrations rather than one concentration would be a more persuasive and powerful technique because it allows differentiation between cells based on more response characteristics. To examine further this issue of the existence of cell types in olfaction and their potential role in coding of odorant quality, we have explored responses of the spiny lobster's olfactory receptor cells to chemical mixtures. These mixtures are either potential food sources to the spiny lobster, extracted from tissues of crab, mullet, oyster, and shrimp, or artificial mixtures, synthesized using the chemical composition of the related extracts 3. Additionally, the classification of cell types was based on responses to three concentrations for each natural extract and artificial mixture, representing a concentration range of two log units. There are several advantages of using these mixtures and the spiny lobster's olfactory system in studies of cell types and olfactory coding. The mixtures are behaviorally relevant chemical stimuli to the spiny lobster, more similar than single compounds to what lobsters normally encounter in their natural environment. The mixtures have been shown to be behaviorally discriminable by spiny lobsters 415'16 and to establish unique ANPs in the spiny lobster's olfactory receptor cells 21"22. In fact, support for the A N P hypothesis for quality coding in this system comes from the fact that the degree of similarity in the ANPs for the mixtures parallels the animal's ability to discriminate these mixtures behaviorally. In addition, coding of odorant quality and coding of odorant quantity by this system are analytically separable 2~2z. MATERIALS AND METHODS Animals Spiny lobsters, Panulirus argus, collected in the Florida Keys, were held at room temperature in aquaria containing recirculating Instant Ocean. They were fed a diet of shrimp and squid. Experimental methods This section was described in detail by Girardot and Derby21, Briefly, extraceUular responses (number of spikes generated by the stimulus during the 5 s period following a noticeable increase) to complex behaviorally discriminable 4'15"16 stimuli were recorded from single receptor cells in the antennules, the olfactory organ. The stimuli were aqueous extracts of tissue of crab, mullet, oyster and shrimp, which are natural prey for the spiny lobster, and artificial mixtures of crab, mullet, oyster and shrimp synthesized according to the chemical composition of the related extracts 3. Each natural extract contains the same concentrations of the same compounds as the related artificial mixture, as well as other unidentified compounds of undetermined concentrations. The stimuli were presented at three different concentrations: 0.005, 0.05 and 0,5 mM serially diluted with artificial sea water from an initial concentration of 10 mM. Data analysis The responses (minus responses to the artificial sea water used as
227 a control) of each cell to stimuli were given in21. These responses were standardized by expressing each of them as a percentage of the highest response at each of the 3 concentrations. We have shown 2~'22 that this allows for discrimination of stimuli based on the differences of the pattern of responses produced by the population of neurons (ANP). Responses to natural extracts and to artificial mixtures w e r e analyzed separately. Search for cell types. A group of cells may be described as constituting a cell type if all cells of that group have response profiles across stimuli that are relatively similar to each other and relatively different from those produced by all other cells. Multidimensional scaling (MDS) and cluster analysis were used to derive these similarities and differences by evaluating clustering of cells. Clearly delineated clusters will indicate cell types; the absence of clearly delineated clusters will negate the possibility of cell types. Both MDS and cluster analysis were derived from a proximity matrix of squared Euclidean distances between any two cells, as determined by their responses to the various stimuli, using the SPSS-X statistical package. The Ward method of linkage was used in the cluster analysis. Scree analysis of stress and squared correlation values of each dimension for the various resolutions were used to determine the most appropriate MDS solution; scree diagrams were also used to determine the most appropriate number of clusters derived from cluster analysis2.
nates, as derived from MDS analysis of the ANPs produced by the cells in each group in response to all stimuli (see below for description Of DI). Necessity of each cell group for discrimination of any two stimulus types will be assessed by the DI resulting from its stimulus coordinates, as derived from MDS analysis of the ANPs produced by all cells except those in the concerned group in response to all stimuli. MDS analysis of ANPs was derived from a proximity matrix of squared Euclidean distances between the two elements of any pair of stimuli (ALSCAL, SPSS-X statistical package). Relevance of each dimension for coding was the criterion used for extracting the appropriate dimensionality of stimulus distribution (see refs. 22 for details). Determination of discrimination index values has been described in detail 22 and can be used to evaluate the discrimination of any stimulus from all other stimuli, of any type of stimulus from all other types of stimuli, or of any two types of stimuli. In the present study, the DI was used to evaluate the discrimination of any pair of stimulus types, taking into account the differences of ANPs within each of the two types of stimuli (that is, the differences produced by the three concentrations of each of the two stimulus types) and the differences of ANPs between the two types of stimuli. The Dis were derived from the Euclidean distances between the stimulus coordinates resulting from MDS using the following equation:
Search for the functional basis for the distribution of cells by MDS. The functional basis for the distribution of cells in the MDS space was investigated by superimposing upon the results of the MDS analysis grouping of cells. The groupings were based on either specific properties of the response profiles or the entire response profile. (1) Grouping cells according to two attributes of the response profile: the most effective stimulus and the least effective stimulus. Cells excited most by stimulus X are labeled stimulus X-best cells, and cells excited least by stimulus X are labeled stimulus X-least cells. The rationale of categorization of cells as stimulus X-best cells is derived from reported findings that in some chemosensory systems there is a high correlation between grouping of cells labeled as best-stimulus cells and grouping by multivariate analysis2°'35-37. This finding suggests that the best-stimulus of a cell is that cell's most important feature of its response profile, contributing disproportionately to the identity of that cell. This finding might lead to the conclusion that the code for chemical quality is a labeled line code. The rationale of categorization of cells as stimulus X-least cells is based on the reasoning that features of the response profile of a cell other than best-stimulus may also significantly contribute to the identity of that cell. Thus, the search for cell types based only on the stimulus X-best categorization may wrongly bias the results toward the labeled line theory. Indeed, if the code is a population code, cells that are not stimulus X-best cells should be as sufficient as the stimulus X-best cells to code for the quality of stimulus X. The labeled line code can be inferred only if the sti.mulus X-best cells are the only ones that are both sufficient and necessary for discrimination of stimulus X from other stimuli. The alternative finding that other cells, such as the stimulus X-least cells, are as sufficient and/or necessary to discriminate stimulus X from all other types of stimuli will support the conclusion that the code is a population code. (2) Grouping cells according to their entire response profiles. Cells labeled according to best- or least-stimulus will be grouped in the space resulting from MDS based on the clusters derived from cluster analysis of their entire response profiles. Comparison of the various grouping schemes allows evaluation of the importance of individual features of the response profiles and of the entire response profiles to the distribution of cells in the MDS space and therefore to their identity. Investigating the functional role for groups of cells. The functional role of the groups of cells as determined above will be investigated in terms of sufficiency anti necessity of each group for discrimination of any two types of stimuli. Sufficiency of each cell group for discrimination of any two types of stimulus types will be assessed by the discrimination index (DI) resulting from its stimulus coordi-
DIa 'b
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where a and b represent stimulus type a and stimulus type b, respectively, BDa, b (BD = between-types distance) is the mean of the Euclidean distances between the stimulus coordinates of the three concentrations of stimulus type a and the stimulus coordinates of the three concentrations of stimulus type b, WD. (WD = within-type distance) is the mean of the Euclidean distances between the stimulus coordinates of the three concentrations of stimulus type a, and W D b is the mean of the Euclidean distances between the stimulus coordinates of the three concentrations of stimulus type b. The DI can vary from -_1 to +1. At D I ~ 0 , there is no possible discrimination because the distances within each of the two types of stimuli are as great or greater than those between the two compared stimuli. Significance of discrimination is determined by comparing the distances between the three concentrations of stimulus a and the three concentrations of stimulus b to the distances between stimulus types a and b, using the Mann-Whitney U-test. Significance of discrimination is set at P < 0.05. RESULTS G e n e r a l r e s p o n s e f e a t u r e s , s u c h as r a t e o f s p o n t a n e o u s a c t i v i t y , r e s p o n s e s o f e a c h cell t o e a c h s t i m u l u s , m e a n r e s p o n s e s t o e a c h s t i m u l u s , as w e l l as m u l t i d i m e n s i o n a l scaling analysis of two features of the population sponses --
response
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re-
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n e u r o n p a t t e r n s ) a n d r e s p o n s e i n t e n s i t i e s (as r e v e a l e d b y absolute response magnitudes) -- have been described e l s e w h e r e 21'22. T h e a n a l y s i s in t h i s p a p e r will f o c u s o n s e a r c h i n g f o r cell t y p e s a n d i d e n t i f y i n g f u n c t i o n s o f cells.
Response profiles o f olfactory receptor cells P r o f i l e s o f t h e r e s p o n s e s o f e a c h o f t h e 30 r e c e p t o r cells t o t h e 4 n a t u r a l e x t r a c t s a n d 4 artificial m i x t u r e s a r e s h o w n in Fig. 1. F o r s i m p l i c i t y , r e s p o n s e s t o o n l y o n e o f t h e t h r e e c o n c e n t r a t i o n s (0.05 m M ) figure. Cells are ordered
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decreasing magnitude of response to one of the stimuli, crab extract. Broad tuning is evident, as described in ref. 21.
Search for cell types The issue addressed in this section is whether the cells fall into types based on excitatory response profiles. Cell types should exist if the mixtures are discriminated by a labeled line code. Multidimensional scaling. Multidimensional scaling (MDS) distributes the neurons into an n-dimensional space on the basis of differences in their response profiles. The distance between any two cells is directly proportional to the degree of dissimilarity in their response profiles. The presence of cell types (i.e. a discontinuum of response profiles) would be indicated by groups or clusters of cells clearly separated in space from other cells; l a c ~ o f cell types (i.e. a continuum of response profiles) would be indicated by a lack of clusters in the distribution, such as uniform or random distributions 43.
DIMENSION 1
Fig. 2. Spatial distribution of cells according to multidimensional scaling analysis of their response profiles. The location of each neuron is shown in a two-dimensional space derived from multidimensional scaling analysis (MDS) based on response profiles to (A) extracts or (B) mixtures. Data for all three concentrations were used. The neuron numbers correspond to the neurons whose response profiles are shown in Fig. 1. The results of M D S for natural extracts and artificial mixtures are shown in Fig. 2A and 2B, respectively. A two-dimensional solution is used in A and B because the amount of stress in the MDS solutions decreased markedly and the r sqdared values increased markedly from one to two dimensions but showed much smaller changes with three or more dimensions. For the one-, two-, and three-dimensional solutions for extracts, the stress values are 0.385, 0.160, and 0.110, respectively, and the r square values are 0.571, 0.879, and 0.926, respectively. For the one-, two-, and three-dimensional solutions for mixtures, the stress values are 0.391, 0.170, and 0.121, respectively, and the r square values are 0.574, 0.870, and 0.915, respectively. There is little indication of cell types based on their response profiles to natural extracts or artificial mixtures. Each MDS has one region with a high density of cells, but this locus is not segregated from the other cells. Each distribution is basically continuous and non-uniform, not clustered. Cluster analysis. Hierarchical cluster analysis provides a quantitative measure of the tendency of the response
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Fig. 3. Cluster analysis of response profiles of cells to (A) extracts and (B) mixtures. The number for each neuron corresponds to that in Fig. 1. Five clusters (I-V) are indicated in A for responses to extracts. Four clusters (I-IV) are indicated in B for responses to mixtures. profiles of the cells to fall into types or categories. The results of cluster analysis are depicted as dendrograms in Fig. 3A and 3B for natural extracts and artificial mixtures, respectively. Clustering distances indicate the degree of similarity between the response profiles of cells or groups of cells. The two most similar neuronal response profiles are separated by the least distance. Larger sets of neurons are interconnected at greater distances, indicating the greater degree of dissimilarity among their response profiles. Clusters are identified by an abrupt increase in the distance at which clusters are linked. Scree diagrams are conventionally used to identify these discontinuities in clustering and therefore indicate the number of clusters 2. Scree diagrams are shown for the extract data (Fig. 4A) and mixture data (Fig. 4B). The scree procedure involves moving from left to right on the curve until one finds a point such that it and all remaining points lie along a straight line 2. This point is often called the 'elbow', and represents the most appropriate number of clusters for the data. Analysis of our scree diagrams reveals that the elbow is not distinct, but probably is between 5 and 8 clusters for extract data and between 5 and 7 clusters for mixture data. Bieber and Smith 2 suggest that where distinct elbows do not exist, visual examination of the dendrogram may still reveal a logical organization to the data which would suggest the number of clusters, should clusters exist. For our data sets, cluster distances are 1 unit, usually much greater. Thus, the hierarchical cluster analysis leads us to conclude that, if clusters exist in our data sets, there are five clusters for the extract data and
five clusters for the mixture data. (Since the fifth cluster for the mixture data is composed of only one cell (cell 19), we have combined this cell with cluster II; thus, only 4 clusters are shown for the mixture data.) We stress, however, that the curvilinear nature and resultant lack of an elbow in the functions in the scree diagrams (Fig. 4) suggest that the response profiles of cells are not obviously clustered into groups and therefore cell types do not exist. This conclusion based on cluster analysis is consistent with and supports that from multidimensional scaling.
Functional basis for the distribution of cells by MDS Correlation of cell distributions using MDS and grouping by best-stimulus and least-stimulus. The distributions of response profiles for cells as indicated by MDS (Fig. 2) are basically continuous, not clustered. In an effort to identify on what basis the MDS distinguishes among the response profiles, we attempted to correlate response features with the distributions. Two features of response profiles that theoretically can be important in differentiating the profiles are the stimulus that best stimulates the cell (= best stimulus) and the stimulus that is least effective in stimulating the cell ( = least stimulus). The best-stimulus and least-stimulus identities of cells are given for natural extracts and artificial mixtures in Fig. 5. Extracts. Interestingly, for the extracts (Fig. 5A), differences in the distribution of response profiles for best-stimulus or least-stimulus cells along the continuous distribution of all cellular profiles are evident. Dotted lines were drawn in Fig. 5A encircling most or all of the best-stimulus (A-l) or least-stimulus (A-2) cells for each extract. Shrimp-best and shrimp-least cells are distributed along dimension 1. Oyster-best and oyster-least cells are also distributed along dimension 1, except in the reverse direction as for shrimp extract. Mullet-best and mulletleast cells are also distributed along dimension 1, although to a lesser extent than shrimp or oyster extract. Crab-best and crab-least cells are also distributed in a
230
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basically linear fashion, but along a line oblique to dimensions 1 and 2. Interestingly, shrimp-best cells are, in most cases, also oyster-least cells, and most of the mullet-best cells are also crab-least cells. These results suggest that the cells that are both shrimp-best and oyster-least discriminate c o m p o u n d ( s ) that are in high concentration in shrimp extract, low in oyster, and i n t e r m e d i a t e in crab and mullet, and the cells that are mullet-best and crab-least arte discriminating comp o u n d ( s ) that are in high concentration in mullet, low in crab, and i n t e r m e d i a t e in oyster and shrimp. Thus, although M D S analysis indicates that the profiles of responses to these four natural extracts for the entire p o p u l a t i o n of olfactory cells are continuous and t h e r e f o r e that cell types do not exist, the analysis reveals that the cells are a r r a n g e d along continua according to identifiable features of their response spectra. Mixtures. C o r r e l a t i o n of best- or least-mixture designation with cell distribution by M D S (Fig. 5B) generally did not provide much information about the nature of the distribution of response profiles to mixtures. H e n c e , lines delimiting m e m b e r s of best-stimulus or least-stimulus cell
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groups were not drawn for the mixture data. T h e r e is a group of cells in the lower region of the two-dimensional space that are all oyster-least and either shrimp-best of crab-best. The equivalence of shrimp and crab in this case is u n d e r s t a n d a b l e , given that shrimp and crab mixtures have the most similar compositions of the 4 mixtures 5,s. Since this group of cells includes only a fraction of all the oyster-least cells, shrimp-best cells, or crab-best cells, no delimiting lines were drawn. T h e oyster-least and either shrimp-best or crab-best cells are p r o b a b l y distinguishing the two mixtures based on c o m p o u n d ( s ) low in the concentration in oyster mixtures and high in concentration in crab and shrimp mixtures. The most obvious candidates are adenosine-5"-triphosphate, trimethylamine oxide, or L-lactate 5'~.
Correlation o f cell distributions based on cluster analysis, MDS, and best-stimulus and least-stimulus categorizations To show the relationship b e t w e e n the hierarchical cluster solution and the multidimensional scaling solution of these data, the 5-cluster solution for the extract data and the 4-cluster solution for the mixture data are e m b e d d e d in the two-dimensional M D S spaces for extract d a t a and mixture data, respectively (Fig. 6 A and 6B); d o t t e d lines encircle all cells belonging to a cluster.
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225
BRES 15344
Peripheral mechanisms of olfactory discrimination of complex mixtures by the spiny lobster: no cell types for mixtures but different contributions of the cells to the across neuron patterns Marie-Nadia Girardot* and Charles D. Derby Department of Biology, Georgia State University, Atlanta, GA 30303 (U.S.A.) (Accepted 5 September 1989)
Key words: Chemoreception; Olfaction; Discrimination; Lobster; Neural coding; Cell type; Mixture
Toward understanding mechanisms of olfactory discrimination, we have examined the existence of cell types and the role of cells in the coding of odorant quality in the olfactory organ of the spiny lobster. The results consisted of responses of 30 antennular chemoreceptor cells to 8 behaviorally discriminable complex stimuli - - 4 natural extracts and 4 artificial mixtures, each at 3 concentrations. Multidimensional scaling and cluster analysis failed to identify unequivocal cell types, but rather suggested a continuum of cellular response profiles. The lack of cell types suggests that the code for the quality of natural odorants in this system is a population code. The distribution of cells along the response continuum was based on any of many features of their response profiles. The most effective stimulus (= best stimulus) and the least effective stimulus (= least stimulus), two features of the response profiles, could only partially explain the differences in response profiles of cells. Nonetheless, cells with different response profiles were shown to have different functions in odorant coding. Most cells contribute to some degree to the discrimination of any two stimuli, but a cell's contribution to the discrimination of two stimuli is usually disproportionally robust when those two stimuli produce very different responses in that cell. INTRODUCTION The role of individual neurons or types of neurons in the discrimination of the quality of chemical stimuli is important in understanding the cellular mechanisms of chemosensory discrimination. The two main theories of coding of stimulus quality differ in the emphasis that they place on the role of individual cells. The across-neuron pattern coding hypothesis holds that stimulus quality is coded by the pattern of activity across a population of neurons ( = across-neuron patterns, or ANPs) 12. The labeled line coding hypothesis specifies that any stimulus quality is coded by the activity of a particular class of relatively narrowly tuned cells 18. A n assumption of the labeled line hypothesis is that for each identifiable chemical quality, there must exist a cell type that codes that quality exclusively. In addition, all members of that cell type must have response spectra that are similar to each other but different from the members of other cell types. Thus, according to the labeled line coding scheme, the cells in the entire population have response profiles that differ in a discontinuous or clumped fashion rather than in a continuous fashion. A n important step in establishing the nature of neural
codes for sensory quality is the search for cell types. There is ample evidence that chemoreceptor cells, either gustatory or olfactory, peripheral or central, have differential sensitivities 17, but are there neuronal types? Multivariate statistics, such as cluster analysis, factor analysis, and multidimensional scaling, have become favored methods to examine the existence of neuronal types because they are based on quantitative, objective, and non-arbitrary criteria 2,14'2°'3°'31'36,4°A2. Searches for neuronal types in chemosensory systems using multivariate analyses are primarily based on responses of vertebrate gustatory neurons to single compounds, and the results suggest that cell types exist for some compounds in some species 11'18-2°'25'26'32'33"35-37'40, although this view has been challenged 13"14'42. Once cell types have been thus established, hypotheses concerning their function in chemosensory discrimination can be generated and tested. H o w gustatory cell types in hamsters may function in the discrimination of single compounds representing salty, sweet, and sour taste qualities has been proposed by Smith and colleagues 35-37. According to their scheme, each cell type plays an important and different role in the definition of ANPs. Only one cell type is necessary to establish the similarities
* Present address: Biomedical Design, Inc., Suite $204, 430 Tenth St., N.W., Atlanta, GA 30318, U.S.A. Correspondence: C. Derby, Department of Biology, Georgia State University, Atlanta, GA 30303, U.S.A. 0006-8993/90/$03.50 © 1990 Elsevier Science Publishers B.V. (Biomedical Division)
226 among A N P s generated by a group of similar tasting stimuli. For example, one cell type identified by cluster analysis and multidimensional scaling - - the S neurons (which are also the neurons that are most responsive to sucrose and other sweet-tasting compounds) - - are necessary to establish similarities among ANPs for sweet-tasting stimuli. Other cell types do not serve this function. However, neither the S neuron cell type nor any other type alone can establish ANPs that differentiate dissimilar tasting compounds. Such discrimination relies on contributions of more than one cell type to the ANPs. Although evidence for receptor cell types in mammalian olfactory systems is lacking 24'29'34, receptor cell types have been identified in several invertebrate olfactory systems (for reviews, see refs. 1, 27). This has been especially true in aquatic crustaceans 6"7"27. Multivariate statistical techniques have been applied to the olfactory receptor cells in the antennules of the spiny lobster Panulirus argus and have revealed that based on excitatory responses to 8 compounds each at one concentration, at least 5 types of narrowly tuned cells exist 6. Other types have since been identified. Derby and colleagues 6'9 suggested that each narrowly tuned cell type may function in discrimination as a labeled line, or, following the suggestion of Smith et al. 35-37, that each cell type may contribute disproportionately to the A N P for the compound to which it is narrowly tuned. More recently, the A N P code for stimulus quality in the olfactory system of spiny lobsters has been favored s'21'22. Factors other than analytical techniques that can have a critical impact on one's ability to identify cell types are the type and intensity of chemicals selected for study. The likelihood of finding cell types whose members respond to only one stimulus or group of similar stimuli is increased by selecting stimuli or groups of similar stimuli that markedly differ in structure, composition, or quality. Most studies of chemosensory coding of stimulus quality and the identification of cell types have dealt with single compounds. However, complex stimuli, such as chemical mixtures, probably represent the most biologically significant stimuli, and therefore should be considered for use in identifying cell types. Cellular response features such as mixture interactions, whose detection is dependent on testing mixtures, further affect the responsiveness of cells and therefore their categorization s . The search for such complex feature-detecting cells that selectively respond to unique complex stimuli has led to the identification of unique cell types, usually higherorder neurons, in such modalities as vision 2s and audition 3s. Intensity is another feature of stimuli that can have an impact on the identification of cell types. In most studies, cell types are identified from responses to one concen-
tration for each stimulus type. Defining cell types based on their responses to a range of stimulus concentrations rather than one concentration would be a more persuasive and powerful technique because it allows differentiation between cells based on more response characteristics. To examine further this issue of the existence of cell types in olfaction and their potential role in coding of odorant quality, we have explored responses of the spiny lobster's olfactory receptor cells to chemical mixtures. These mixtures are either potential food sources to the spiny lobster, extracted from tissues of crab, mullet, oyster, and shrimp, or artificial mixtures, synthesized using the chemical composition of the related extracts 3. Additionally, the classification of cell types was based on responses to three concentrations for each natural extract and artificial mixture, representing a concentration range of two log units. There are several advantages of using these mixtures and the spiny lobster's olfactory system in studies of cell types and olfactory coding. The mixtures are behaviorally relevant chemical stimuli to the spiny lobster, more similar than single compounds to what lobsters normally encounter in their natural environment. The mixtures have been shown to be behaviorally discriminable by spiny lobsters 415'16 and to establish unique ANPs in the spiny lobster's olfactory receptor cells 21"22. In fact, support for the A N P hypothesis for quality coding in this system comes from the fact that the degree of similarity in the ANPs for the mixtures parallels the animal's ability to discriminate these mixtures behaviorally. In addition, coding of odorant quality and coding of odorant quantity by this system are analytically separable 2~2z. MATERIALS AND METHODS Animals Spiny lobsters, Panulirus argus, collected in the Florida Keys, were held at room temperature in aquaria containing recirculating Instant Ocean. They were fed a diet of shrimp and squid. Experimental methods This section was described in detail by Girardot and Derby21, Briefly, extraceUular responses (number of spikes generated by the stimulus during the 5 s period following a noticeable increase) to complex behaviorally discriminable 4'15"16 stimuli were recorded from single receptor cells in the antennules, the olfactory organ. The stimuli were aqueous extracts of tissue of crab, mullet, oyster and shrimp, which are natural prey for the spiny lobster, and artificial mixtures of crab, mullet, oyster and shrimp synthesized according to the chemical composition of the related extracts 3. Each natural extract contains the same concentrations of the same compounds as the related artificial mixture, as well as other unidentified compounds of undetermined concentrations. The stimuli were presented at three different concentrations: 0.005, 0.05 and 0,5 mM serially diluted with artificial sea water from an initial concentration of 10 mM. Data analysis The responses (minus responses to the artificial sea water used as
227 a control) of each cell to stimuli were given in21. These responses were standardized by expressing each of them as a percentage of the highest response at each of the 3 concentrations. We have shown 2~'22 that this allows for discrimination of stimuli based on the differences of the pattern of responses produced by the population of neurons (ANP). Responses to natural extracts and to artificial mixtures w e r e analyzed separately. Search for cell types. A group of cells may be described as constituting a cell type if all cells of that group have response profiles across stimuli that are relatively similar to each other and relatively different from those produced by all other cells. Multidimensional scaling (MDS) and cluster analysis were used to derive these similarities and differences by evaluating clustering of cells. Clearly delineated clusters will indicate cell types; the absence of clearly delineated clusters will negate the possibility of cell types. Both MDS and cluster analysis were derived from a proximity matrix of squared Euclidean distances between any two cells, as determined by their responses to the various stimuli, using the SPSS-X statistical package. The Ward method of linkage was used in the cluster analysis. Scree analysis of stress and squared correlation values of each dimension for the various resolutions were used to determine the most appropriate MDS solution; scree diagrams were also used to determine the most appropriate number of clusters derived from cluster analysis2.
nates, as derived from MDS analysis of the ANPs produced by the cells in each group in response to all stimuli (see below for description Of DI). Necessity of each cell group for discrimination of any two stimulus types will be assessed by the DI resulting from its stimulus coordinates, as derived from MDS analysis of the ANPs produced by all cells except those in the concerned group in response to all stimuli. MDS analysis of ANPs was derived from a proximity matrix of squared Euclidean distances between the two elements of any pair of stimuli (ALSCAL, SPSS-X statistical package). Relevance of each dimension for coding was the criterion used for extracting the appropriate dimensionality of stimulus distribution (see refs. 22 for details). Determination of discrimination index values has been described in detail 22 and can be used to evaluate the discrimination of any stimulus from all other stimuli, of any type of stimulus from all other types of stimuli, or of any two types of stimuli. In the present study, the DI was used to evaluate the discrimination of any pair of stimulus types, taking into account the differences of ANPs within each of the two types of stimuli (that is, the differences produced by the three concentrations of each of the two stimulus types) and the differences of ANPs between the two types of stimuli. The Dis were derived from the Euclidean distances between the stimulus coordinates resulting from MDS using the following equation:
Search for the functional basis for the distribution of cells by MDS. The functional basis for the distribution of cells in the MDS space was investigated by superimposing upon the results of the MDS analysis grouping of cells. The groupings were based on either specific properties of the response profiles or the entire response profile. (1) Grouping cells according to two attributes of the response profile: the most effective stimulus and the least effective stimulus. Cells excited most by stimulus X are labeled stimulus X-best cells, and cells excited least by stimulus X are labeled stimulus X-least cells. The rationale of categorization of cells as stimulus X-best cells is derived from reported findings that in some chemosensory systems there is a high correlation between grouping of cells labeled as best-stimulus cells and grouping by multivariate analysis2°'35-37. This finding suggests that the best-stimulus of a cell is that cell's most important feature of its response profile, contributing disproportionately to the identity of that cell. This finding might lead to the conclusion that the code for chemical quality is a labeled line code. The rationale of categorization of cells as stimulus X-least cells is based on the reasoning that features of the response profile of a cell other than best-stimulus may also significantly contribute to the identity of that cell. Thus, the search for cell types based only on the stimulus X-best categorization may wrongly bias the results toward the labeled line theory. Indeed, if the code is a population code, cells that are not stimulus X-best cells should be as sufficient as the stimulus X-best cells to code for the quality of stimulus X. The labeled line code can be inferred only if the sti.mulus X-best cells are the only ones that are both sufficient and necessary for discrimination of stimulus X from other stimuli. The alternative finding that other cells, such as the stimulus X-least cells, are as sufficient and/or necessary to discriminate stimulus X from all other types of stimuli will support the conclusion that the code is a population code. (2) Grouping cells according to their entire response profiles. Cells labeled according to best- or least-stimulus will be grouped in the space resulting from MDS based on the clusters derived from cluster analysis of their entire response profiles. Comparison of the various grouping schemes allows evaluation of the importance of individual features of the response profiles and of the entire response profiles to the distribution of cells in the MDS space and therefore to their identity. Investigating the functional role for groups of cells. The functional role of the groups of cells as determined above will be investigated in terms of sufficiency anti necessity of each group for discrimination of any two types of stimuli. Sufficiency of each cell group for discrimination of any two types of stimulus types will be assessed by the discrimination index (DI) resulting from its stimulus coordi-
DIa 'b
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where a and b represent stimulus type a and stimulus type b, respectively, BDa, b (BD = between-types distance) is the mean of the Euclidean distances between the stimulus coordinates of the three concentrations of stimulus type a and the stimulus coordinates of the three concentrations of stimulus type b, WD. (WD = within-type distance) is the mean of the Euclidean distances between the stimulus coordinates of the three concentrations of stimulus type a, and W D b is the mean of the Euclidean distances between the stimulus coordinates of the three concentrations of stimulus type b. The DI can vary from -_1 to +1. At D I ~ 0 , there is no possible discrimination because the distances within each of the two types of stimuli are as great or greater than those between the two compared stimuli. Significance of discrimination is determined by comparing the distances between the three concentrations of stimulus a and the three concentrations of stimulus b to the distances between stimulus types a and b, using the Mann-Whitney U-test. Significance of discrimination is set at P < 0.05. RESULTS G e n e r a l r e s p o n s e f e a t u r e s , s u c h as r a t e o f s p o n t a n e o u s a c t i v i t y , r e s p o n s e s o f e a c h cell t o e a c h s t i m u l u s , m e a n r e s p o n s e s t o e a c h s t i m u l u s , as w e l l as m u l t i d i m e n s i o n a l scaling analysis of two features of the population sponses --
response
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n e u r o n p a t t e r n s ) a n d r e s p o n s e i n t e n s i t i e s (as r e v e a l e d b y absolute response magnitudes) -- have been described e l s e w h e r e 21'22. T h e a n a l y s i s in t h i s p a p e r will f o c u s o n s e a r c h i n g f o r cell t y p e s a n d i d e n t i f y i n g f u n c t i o n s o f cells.
Response profiles o f olfactory receptor cells P r o f i l e s o f t h e r e s p o n s e s o f e a c h o f t h e 30 r e c e p t o r cells t o t h e 4 n a t u r a l e x t r a c t s a n d 4 artificial m i x t u r e s a r e s h o w n in Fig. 1. F o r s i m p l i c i t y , r e s p o n s e s t o o n l y o n e o f t h e t h r e e c o n c e n t r a t i o n s (0.05 m M ) figure. Cells are ordered
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decreasing magnitude of response to one of the stimuli, crab extract. Broad tuning is evident, as described in ref. 21.
Search for cell types The issue addressed in this section is whether the cells fall into types based on excitatory response profiles. Cell types should exist if the mixtures are discriminated by a labeled line code. Multidimensional scaling. Multidimensional scaling (MDS) distributes the neurons into an n-dimensional space on the basis of differences in their response profiles. The distance between any two cells is directly proportional to the degree of dissimilarity in their response profiles. The presence of cell types (i.e. a discontinuum of response profiles) would be indicated by groups or clusters of cells clearly separated in space from other cells; l a c ~ o f cell types (i.e. a continuum of response profiles) would be indicated by a lack of clusters in the distribution, such as uniform or random distributions 43.
DIMENSION 1
Fig. 2. Spatial distribution of cells according to multidimensional scaling analysis of their response profiles. The location of each neuron is shown in a two-dimensional space derived from multidimensional scaling analysis (MDS) based on response profiles to (A) extracts or (B) mixtures. Data for all three concentrations were used. The neuron numbers correspond to the neurons whose response profiles are shown in Fig. 1. The results of M D S for natural extracts and artificial mixtures are shown in Fig. 2A and 2B, respectively. A two-dimensional solution is used in A and B because the amount of stress in the MDS solutions decreased markedly and the r sqdared values increased markedly from one to two dimensions but showed much smaller changes with three or more dimensions. For the one-, two-, and three-dimensional solutions for extracts, the stress values are 0.385, 0.160, and 0.110, respectively, and the r square values are 0.571, 0.879, and 0.926, respectively. For the one-, two-, and three-dimensional solutions for mixtures, the stress values are 0.391, 0.170, and 0.121, respectively, and the r square values are 0.574, 0.870, and 0.915, respectively. There is little indication of cell types based on their response profiles to natural extracts or artificial mixtures. Each MDS has one region with a high density of cells, but this locus is not segregated from the other cells. Each distribution is basically continuous and non-uniform, not clustered. Cluster analysis. Hierarchical cluster analysis provides a quantitative measure of the tendency of the response
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Fig. 3. Cluster analysis of response profiles of cells to (A) extracts and (B) mixtures. The number for each neuron corresponds to that in Fig. 1. Five clusters (I-V) are indicated in A for responses to extracts. Four clusters (I-IV) are indicated in B for responses to mixtures. profiles of the cells to fall into types or categories. The results of cluster analysis are depicted as dendrograms in Fig. 3A and 3B for natural extracts and artificial mixtures, respectively. Clustering distances indicate the degree of similarity between the response profiles of cells or groups of cells. The two most similar neuronal response profiles are separated by the least distance. Larger sets of neurons are interconnected at greater distances, indicating the greater degree of dissimilarity among their response profiles. Clusters are identified by an abrupt increase in the distance at which clusters are linked. Scree diagrams are conventionally used to identify these discontinuities in clustering and therefore indicate the number of clusters 2. Scree diagrams are shown for the extract data (Fig. 4A) and mixture data (Fig. 4B). The scree procedure involves moving from left to right on the curve until one finds a point such that it and all remaining points lie along a straight line 2. This point is often called the 'elbow', and represents the most appropriate number of clusters for the data. Analysis of our scree diagrams reveals that the elbow is not distinct, but probably is between 5 and 8 clusters for extract data and between 5 and 7 clusters for mixture data. Bieber and Smith 2 suggest that where distinct elbows do not exist, visual examination of the dendrogram may still reveal a logical organization to the data which would suggest the number of clusters, should clusters exist. For our data sets, cluster distances are 1 unit, usually much greater. Thus, the hierarchical cluster analysis leads us to conclude that, if clusters exist in our data sets, there are five clusters for the extract data and
five clusters for the mixture data. (Since the fifth cluster for the mixture data is composed of only one cell (cell 19), we have combined this cell with cluster II; thus, only 4 clusters are shown for the mixture data.) We stress, however, that the curvilinear nature and resultant lack of an elbow in the functions in the scree diagrams (Fig. 4) suggest that the response profiles of cells are not obviously clustered into groups and therefore cell types do not exist. This conclusion based on cluster analysis is consistent with and supports that from multidimensional scaling.
Functional basis for the distribution of cells by MDS Correlation of cell distributions using MDS and grouping by best-stimulus and least-stimulus. The distributions of response profiles for cells as indicated by MDS (Fig. 2) are basically continuous, not clustered. In an effort to identify on what basis the MDS distinguishes among the response profiles, we attempted to correlate response features with the distributions. Two features of response profiles that theoretically can be important in differentiating the profiles are the stimulus that best stimulates the cell (= best stimulus) and the stimulus that is least effective in stimulating the cell ( = least stimulus). The best-stimulus and least-stimulus identities of cells are given for natural extracts and artificial mixtures in Fig. 5. Extracts. Interestingly, for the extracts (Fig. 5A), differences in the distribution of response profiles for best-stimulus or least-stimulus cells along the continuous distribution of all cellular profiles are evident. Dotted lines were drawn in Fig. 5A encircling most or all of the best-stimulus (A-l) or least-stimulus (A-2) cells for each extract. Shrimp-best and shrimp-least cells are distributed along dimension 1. Oyster-best and oyster-least cells are also distributed along dimension 1, except in the reverse direction as for shrimp extract. Mullet-best and mulletleast cells are also distributed along dimension 1, although to a lesser extent than shrimp or oyster extract. Crab-best and crab-least cells are also distributed in a
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basically linear fashion, but along a line oblique to dimensions 1 and 2. Interestingly, shrimp-best cells are, in most cases, also oyster-least cells, and most of the mullet-best cells are also crab-least cells. These results suggest that the cells that are both shrimp-best and oyster-least discriminate c o m p o u n d ( s ) that are in high concentration in shrimp extract, low in oyster, and i n t e r m e d i a t e in crab and mullet, and the cells that are mullet-best and crab-least arte discriminating comp o u n d ( s ) that are in high concentration in mullet, low in crab, and i n t e r m e d i a t e in oyster and shrimp. Thus, although M D S analysis indicates that the profiles of responses to these four natural extracts for the entire p o p u l a t i o n of olfactory cells are continuous and t h e r e f o r e that cell types do not exist, the analysis reveals that the cells are a r r a n g e d along continua according to identifiable features of their response spectra. Mixtures. C o r r e l a t i o n of best- or least-mixture designation with cell distribution by M D S (Fig. 5B) generally did not provide much information about the nature of the distribution of response profiles to mixtures. H e n c e , lines delimiting m e m b e r s of best-stimulus or least-stimulus cell
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groups were not drawn for the mixture data. T h e r e is a group of cells in the lower region of the two-dimensional space that are all oyster-least and either shrimp-best of crab-best. The equivalence of shrimp and crab in this case is u n d e r s t a n d a b l e , given that shrimp and crab mixtures have the most similar compositions of the 4 mixtures 5,s. Since this group of cells includes only a fraction of all the oyster-least cells, shrimp-best cells, or crab-best cells, no delimiting lines were drawn. T h e oyster-least and either shrimp-best or crab-best cells are p r o b a b l y distinguishing the two mixtures based on c o m p o u n d ( s ) low in the concentration in oyster mixtures and high in concentration in crab and shrimp mixtures. The most obvious candidates are adenosine-5"-triphosphate, trimethylamine oxide, or L-lactate 5'~.
Correlation o f cell distributions based on cluster analysis, MDS, and best-stimulus and least-stimulus categorizations To show the relationship b e t w e e n the hierarchical cluster solution and the multidimensional scaling solution of these data, the 5-cluster solution for the extract data and the 4-cluster solution for the mixture data are e m b e d d e d in the two-dimensional M D S spaces for extract d a t a and mixture data, respectively (Fig. 6 A and 6B); d o t t e d lines encircle all cells belonging to a cluster.
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