Retinal Ganglion Cells in the Pacific Redfin, Tribolodon brandtii Dybowski, 1872: Morphology and Diversity
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
R E S EA R C H A R T I C L E
Retinal Ganglion Cells in the Pacific Redfin,
Tribolodon brandtii Dybowski, 1872: Morphology
and Diversity
Igor Pushchin1* and Yuriy Karetin2,3
1
Laboratory of Physiology, A.V. Zhirmunsky Institute of Marine Biology of the Far Eastern Branch of the Russian Academy of Scien-
ces, Vladivostok 690059, Russia
2
Laboratory of Embryology, A.V. Zhirmunsky Institute of Marine Biology of the Far Eastern Branch of the Russian Academy of Sci-
ences, Vladivostok 690059, Russia
3
Laboratory of Cell Biology, School of Natural Sciences, Far Eastern Federal University, Vladivostok 690950, Russia
ABSTRACT and dendrite stratification in the retina. Kruskal-Wallis
We studied the morphology and diversity of retinal gan- ANOVA-on-ranks with post hoc Mann-Whitney U tests
glion cells in the Pacific redfin, Tribolodon brandtii. showed significant pairwise between-cluster differences
These cells were retrogradely labeled with horseradish in two or more of the original variables. In total, eight
peroxidase and examined in retinal whole mounts. A cell types were discovered. The advantages and draw-
sample of 203 cells was drawn with a camera lucida. A backs of the methodology adopted are discussed. The
total of 19 structural parameters were estimated for present classification is compared with classifications
each cell, and a variety of clustering algorithms were proposed for other teleosts. J. Comp. Neurol.
used to classify the cells. The optimal solution was 522:1355–1372, 2014.
determined by using silhouette analysis. It was based
on three variables associated with dendritic field size C 2013 Wiley Periodicals, Inc.
V
INDEXING TERMS: retinal ganglion cell; classification; cluster analysis; structural heterogeneity; retrograde labeling;
teleost fish
To understand and model the functioning of the nerv- provides an adequate basis for classification (Masland
ous system, one has to identify its elementary blocks, and Raviola, 2000).
the neuron types. By a neuron type we mean a popula- The classification of or finding groups in data belongs
tion of cells of common origin, produced from the same to a more general exploratory framework known as
cell-fate decisions, and displaying high levels of struc- data mining. Data mining involves several stages, begin-
tural and functional similarity. It has long been sug- ning with obtaining a data sample and ending with the
gested that neuronal types should be discovered and interpretation and correction of the model. There are
not defined (Rodieck and Brening, 1983). A common certain requirements for the methods used at each
approach to the discovery of neuron types is to stage (Williams, 2011). Unfortunately, these require-
describe a group of neurons in a similar way and put ments are often violated or ignored in studies involving
neurons with similar features into the same group, neuron classification. Many authors base their systems
potentially representing a natural type (Cook, 1998). In
principle, any trait of a neuron might be used for classi-
fication purposes, and it would be advantageous to use
Grant sponsor: Russian Foundation for Basic Research; Grant num-
as many characteristics as possible. However, this ber: 12-04-00657-a; Grant sponsor: Far Eastern Branch of the Russian
Academy of Sciences; Grant numbers: 12-III-ff-06–094; KPFI12-06-019;
method of classifying neurons is technically demanding 12-I-P7-03.
and requires the simultaneous use of mutually exclusive *CORRESPONDENCE TO: I.I. Pushchin, Institute of Marine Biology, FEB
methods. For this reason, most approaches use a lim- RAS, 17 Palchevskogo str., Vladivostok 690059, Russia. E-mail:
ipushchin@gmail.com
ited set of traits. Particularly, the structure of a neuron
Received June 22, 2013; Revised October 11, 2013;
is closely associated with its functional properties and Accepted October 11, 2013.
DOI 10.1002/cne.23489
Published online November 1, 2013 in Wiley Online Library
C 2013 Wiley Periodicals, Inc.
V (wileyonlinelibrary.com)
The Journal of Comparative Neurology | Research in Systems Neuroscience 522:1355–1372 (2014) 1355I. Pushchin and Y. Karetin on few parameters or subjectively group neurons into MATERIALS AND METHODS types (Rodieck and Brening, 1983; Cook, 2003). How- Specimen preparation and light microscopy ever, even the use of multiple parameters and auto- Fish 37–41 cm long were caught in the Bay of Peter mated classification algorithms does not necessarily the Great (Sea of Japan) off Vladivostok in September– result in the accurate classification of neurons. First, October and kept in aerated water at 12–18 C under a the concept of neuron type is quite complex: decreas- natural light/dark cycle. The fish were deeply anesthe- ing the scale of analysis reveals more and more subtle tized with MS-222 (3-aminobenzoic acid ethyl ester, differences between any reasonable populations of neu- methanesulfonate salt; Sigma, St. Louis, MO; 0.01– rons, beginning with large classes, such as retinal rods 0.03% seawater solution) and kept alive in a holding and cones, and ending with individual cells, and it is tank by passing fresh oxygenated seawater over the often difficult to draw the line between between- and gills. The conjunctiva near the eye was incised, and the within-type variation (W€assle and Boycott, 1991; Bota eye was rotated to make the optic nerve accessible for and Swanson, 2007). Second, the choice of the appro- manipulation. The nerve was cut and small crystals of priate proximity measure, clustering features, horseradish peroxidase (Sigma type VI) were applied to dimensionality-reduction algorithms, and clustering algo- the lesioned nerve fibers. The conjunctiva was repaired rithms is inherently subjective and depends on the with a tiny drop of cyanoacrylate glue. The fish was per- expertise of the researcher (Xu and Wunsch, 2009). fused with water over the gills for 10–15 minutes and Third, even the most circumstantial and adequate maintained for 5–6 days under the same conditions as approaches will never be able to characterize fully or intact fish. The treated fish were subsequently dark represent perfectly between-type variation. For these adapted for 2 hours, deeply anesthetized with MS-222, reasons, neuron classification based on the simultane- and decapitated. The eyes were removed, and the reti- ous use of several parameters should be considered as nas were isolated and fixed. The samples were washed a working hypothesis rather than an ultimate solution in phosphate buffer, developed in diaminobenzidine (Rodieck and Brening, 1983; Bota and Swanson, 2007). solution, dehydrated through a series of increasing It may be improved in the future, e.g., through the com- concentrations of ethanol, cleared in xylene, and bination of morphometry with physiology and/or molec- whole mounted onto a slide. Four fish were used in ular phenotyping. these experiments. The fish were treated in strict accord- Ganglion cells (GCs) are a class of retinal neurons ance with the European Communities Council Directive. that integrate the information from the preceding nerve The authors certify that the formal approval to conduct cells and transmit it to the visual centers in the thala- the experiments described in the present study was mus, hypothalamus, and midbrain. To reveal homolo- obtained from the Animal Subjects Review Board of the gous GC types in different vertebrates and reconstruct A.V. Zhirmunsky Institute of Marine Biology, FEB RAS. the evolutionary changes of these cells, systematic The cells were observed under an Olympus BHS studies of GCs in a substantial number of species are microscope with a 3100/1.25 oil SPlan objective. In required. There are numerous studies on the morphol- total, 203 cells with well-stained dendritic arbors were ogy and classification of teleostean GCs (see, e.g., sampled for quantitative analysis of three whole Podugolnikova, 1985; Dunn-Meynell and Sharma, 1986; mounts. The completeness of dendrite staining was Hitchcock and Easter, 1986; Collin, 1989; Cook and confirmed by using a light microscope equipped with an Sharma, 1995; Cook et al., 1999; Mangrum et al., image-processing system that provided a reliable reso- 2002; Ott et al., 2007). However, only a few studies lution of approximately 0.25 lm (Karpenko, 1993). The meet the requirements described above. system facilitated an assessment of whether the label Here we used data mining approaches to examine had reached the dendrite terminal. The cells were the structural heterogeneity of retinal ganglion cells in drawn with an RA-6 drawing tube (Leningrad Optical a teleost fish, the Pacific redfin, Tribolodon brandtii. and Mechanical Company, St. Petersburg, Russia). The The reason for using this species is twofold. First, this drawings were then digitized and used for the estima- fish belongs to the order Cypriniformes, one of the tion of all numerical parameters. Relative dendritic most ancient and abundant teleostean branches, com- depths were measured through readings of a fine prising over 3,000 species (Saitoh et al., 2006; May- adjustment knob of the microscope (Harris, 1985). This den et al., 2009). Second, the Pacific redfin is a adjustment achieved a reproducible resolution of semianadromous fish, which spends a period of its approximately 1 lm, which was slightly more than the adult life in the sea and another period in freshwater; focal depth of the objective (0.68 lm). The inner plexi- therefore, it has to adapt to the changing visual form layer (IPL) boundaries were identified by unstained environment. profiles of cell bodies and processes visible with 1356 The Journal of Comparative Neurology | Research in Systems Neuroscience
Retinal ganglion cells in the pacific redfin
Nomarski differential contrast. All depths were recorded TABLE 1.
relative to the local depth of the ganglion cell layer Parameters Used to Characterize and Classify Tribolodon
(GCL) to compensate for retinal undulations. For several brandtii Retinal Ganglion Cells1
representative cells, a detailed radial view was recon- Parameter Abbreviation
structed from many individual depth measurements as Dendritic field area DFA
previously described (Cook and Sharma, 1995). Dendritic field perimeter DFP
Discovering GC types in the joint sample may be Dendritic field influence area DFIA
considered a data mining problem, specifically, the Dendritic field aspect ratio DFAR
Dendritic field compactness DFC
identification of the latent structure in the data. The Total dendrite length TDL
present analysis comprised the following steps: data Spatial density of dendrites SDD
accumulation and preprocessing, identification of latent Number of primary dendrites NPD
Number of branch points NBP
structure, estimation of model quality, correction of Average branch order BO
model design, and choice of the optimum model. Here, Average contraction CON
we briefly describe these steps. Average partition asymmetry PA
Average remote amplitude of bifurcation RBA
Spatial density of branch points BPD
Neuron sampling Box counting fractal dimension DFBC
A “good” sample of objects for classification should Mass-radius fractal dimension DFMR
be representative of the sampled population, i.e., Relative stratification range RSR
Sclerad relative stratification boundary SRSB
should reproduce well the structure of the sampled Vitread relative stratification boundary VRSB
population, and be large enough for the clustering algo- 1
See Materials and Methods for a detailed description of the
rithms to perform properly (Kaufman and Rousseeuw, parameters.
1990). In the present case, to minimize region-
dependent variation in the GC structure and avoid perimeter (DFP) was found as the polygon perimeter.
potentially immature cells from the retinal periphery, Dendritic field influence area (DFIA) was calculated
the cells were sampled only from the area between two through convolution of the dendrite boundary of the cell
rings with the radii of one-fourth and three-fourths of using a circular window with a fixed radius and meas-
the retinal radius. The GCs in area temporalis had uring the resulting area. The DFIA may be a more pre-
smaller dendritic fields and were more densely packed cise estimation of the effective spatial coverage
than their counterparts outside this region. Therefore, compared with the DFA, because the DFIA is more
these cells were also avoided. closely associated with the dendrite arborization pattern
Most clustering algorithms perform better when (Costa and Velte, 1999). Dendritic field aspect ratio
potential clusters are approximately equal in number. (DFAR) was calculated as the ratio of the axes of the
For this reason, we deliberately increased the propor- ellipse that best fit the dendritic field. Dendritic field
tion of medium-sized and large cells, which would be compactness (DFC) was calculated as (DFP2)/DFA.
poorly represented with random sampling.
Parameterization of neuron structure Parameters related to dendrite branching
Another crucial step is the choice of classification pattern and structural complexity
parameters. Ideally, the clustering model should be Total dendrite length (TDL) was measured as the sum
based on functionally relevant parameters reflecting dif- of the lengths of all dendrite segments. Spatial density
ferent aspects of neuronal structure. The frequency dis- of dendrites (SDD) was calculated as TDL/DFA. Num-
tribution of these parameters must be at least bimodal ber of primary dendrites (NPD) was found as a sum of
and preferably multimodal (Schweitzer and Renehan, the primary dendrites of the cell. Number of branch
1997). They must also not be mutually correlated, points (NBP) was calculated as the total number of
resulting in unnecessary data redundancy. It is there- branch points of the dendritic arborization. Average
fore reasonable to estimate a relatively large set of branch order (BO) was found by averaging the order of
parameters and select those that are most promising all dendrite branches relative to the soma (soma has
for classification. In the present study, in total 19 order 0). Contraction was calculated as a ratio of the
parameters were estimated for each cell (Table 1). Euclidean distance between two successive branch
points and the length of the dendrite segment connect-
Parameters related to dendritic field size ing these points. Average contraction (CON) was found
Dendritic field area (DFA) was calculated as the area of as the average of the ratios of all pairs of successive
a convex polygon circumscribing the cell. Dendritic field branch points. Partition asymmetry at the branch point
The Journal of Comparative Neurology | Research in Systems Neuroscience 1357I. Pushchin and Y. Karetin
was found as (jn1 2 n2j)/(n1 1 n2 2 2), where n1 and 2009; Parimala et al., 2011; Kriegel et al., 2012). Here
n2 are the numbers of the dendrite tips of the we tried all these approaches.
branches. Average partition asymmetry (PA) was calcu- There are a variety of approaches for parameter
lated as the average of the partition asymmetry at all choice, and their combined use yields far better results
branch points. Remote amplitude of bifurcation was than relying on a single method (Gan et al., 2007).
found as the angle formed by a branch point and the Here, we chose parameters using correlation, discrimi-
two adjacent higher-order branch points. Average nant function, and multimodality analyses. The quality
remote amplitude of bifurcation (RBA) was calculated of the resulting clustering models was also considered.
as the average of the angles for all branch points with The parameters were normalized to equalize their
adjacent higher-order branch points. Spatial density of impact as clustering variables. The multimodality index
branch points (BPD) was calculated as NBP/DFA. Frac- (MI) of each parameter was calculated (Schweitzer and
tal dimension may be used as a scale-dependent mea- Renehan, 1997). The value of the multimodality index
sure of the spatial expansion or complexity of dendritic indicates whether the distribution is mono-, bi-, or
arborizations (Jelinek and Fernandez, 1998). Box- multimodal.
counting and mass-radius fractal dimensions (DFBC, The correlation between parameters was measured
DFMR) were calculated in Benoit 1.3 software (TruSoft by Pearson’s linear correlation analysis. The scatterplots
International, St. Petersburg, FL). of pairwise correlations were also examined to reveal
spurious correlations resulting from outliers (Gordon,
1999). In each group of strongly and significantly corre-
Parameters related to dendrite stratification
lated parameters (R 5 0.8 at P < 0.05), only one param-
Let a and b be the absolute depths of inner plexiform
eter, maximum MI, was selected to ensure that aspects
layer (IPL)-GCL and IPL-inner nuclear layer (INL) boun-
of cell morphology are adequately, but nonredundantly,
daries, and let c and d be the absolute depths of the
represented (Schweitzer and Renehan, 1997).
vitread and sclerad boundaries of the stratification zone
Parameters with the lowest MIs and least discriminat-
of a cell, respectively. Relative stratification range (RSR)
ing power, as revealed by discriminant function analy-
was calculated as (c 2 d)/(a 2 b). Sclerad and vitread
sis, were successively excluded from the clustering
relative stratification boundaries (SRSB and VRSB) were
models as previously described (Pushchin and Karetin,
calculated as (a 2 c)/(a 2 b) and (a 2 d)/(a 2 b),
2009). We also used the automatic variable weighting
respectively.
(OVW) algorithm to improve the clustering quality (De
For descriptive purposes, we accepted the IPL divi-
Soete, 1986, 1998; Makarenkov and Legendre, 2001).
sion into three sublaminae as previously described
Starting with a clustering solution based on equal vari-
(Cook and Sharma, 1995; Cook et al., 1999). According
able weights, this algorithm changes the weights to
to this description, sublamina a comprises the sclerad-
maximize between-cluster variation.
most 40% of the IPL, sublamina b the next 40%, and
The factor analysis reduces the model dimensionality
sublamina c the vitreadmost 20%.
by describing the variance represented by the original
parameters in terms of a smaller number of latent vari-
Classification €
ables (factors; Uberla, 1997; Gordon, 1999). We used
One of the greatest problems in cluster analysis is R-mode factor analysis. The factors were extracted by
the so-called curse of dimensionality, where the cluster- using principal axis factoring and orthogonally rotated
ing quality rapidly decreases with increasing model by using varimax rotation. The number of significant
dimensionality (number of parameters; Gordon, 1999; factors was determined with the Kaiser criterion and
Xu and Wunsch, 2009). The common way to overcome the Cattell scree plot test and also considering the
the dimensionality curse is to reduce the number of €
interpretability of factors (Uberla, 1997; Costello and
parameters in the model through the exclusion of Osborne, 2005). Significant factors were then used as
parameters with the least discriminating power or using clustering variables.
linear transformation of the object-attribute matrix, To determine the statistical reliability of the cluster-
such as factorization or multidimensional scaling. Both ings, the significance of between-cluster differences
approaches may be useful, but they inevitably lead to was estimated by Kruskal-Wallis ANOVA-on-ranks with
information loss and deterioration of the clustering post hoc pairwise comparisons by the Mann-Whitney U
quality. A good alternative to these methods is the use test (Sheskin, 2000).
of recently developed density-based (subspace) cluster- Many different clustering algorithms have been pro-
ing algorithms that reveal the clusters in relevant sub- posed. All have specific advantages and drawbacks,
spaces of the original clustering space (M€uller et al., and their performance and efficiency depend on the
1358 The Journal of Comparative Neurology | Research in Systems NeuroscienceRetinal ganglion cells in the pacific redfin
nature of the data and structure of the sample. It is containing four or fewer clusters were considered inad-
therefore reasonable in each particular case to try dif- equate and excluded from further analysis. The upper
ferent algorithms and choose the most suitable ones limit of the cluster number was arbitrarily set to 20.
(Jain and Dubes, 1988; Gordon, 1999). In the present
study, eight clustering algorithms were used. Their
Software
detailed description may be found in recent handbooks
Four parameters (BO, CON, PA, and RBA) were meas-
on data mining (see, e.g., Sumathi and Sivanandam,
ured using L-measure, a software tool for the analysis
2006; Tan et al., 2006; Gan et al., 2007); therefore,
of neuronal morphology (Scorcioni et al., 2008). To do
these calculations are only briefly described here.
this, a digital reconstruction of each neuron was
Ward’s agglomerative clustering is a hierarchical clus-
obtained and saved as an SWC file in Neuromantic, a
tering algorithm. It starts with each object as a sepa-
program for reconstruction of neuronal morphology
rate cluster, merging similar objects into successively
(Myatt et al., 2006). In SWC format, a neuron is mod-
larger clusters. Analysis of variance is used at each
eled as a set of compartments defined by coordinates
cycle to minimize the within-cluster variance. In con-
in space and two radii. More details have been
trast to Ward’s clustering, divisive analysis clustering
described in the author’s manual available on the pro-
initially starts with all observations in a single cluster.
gram site (http://www.reading.ac.uk/neuromantic). BC
The clusters are subsequently divided until each cluster
and MR were calculated in Benoit 1.3 software (TruSoft
contains a single observation.
International). The other parameters were estimated in
k-Means and partitioning around medoids iteratively
ImageJ. The cluster analysis was performed in R
relocate the objects between clusters to minimize the
(http://www.r-project.org) with the extension packages
within-cluster variation. Both algorithms stop when no
clValid (Brock et al., 2008), mclust (Fraley and Raftery,
movement of an object from one cluster to another
1999), fpc (Hennig, 2007), and validator (http://cran.
reduces the within-cluster variation. Self-organizing map
r-project.org/web/packages/validator/validator.pdf).
is an artificial neural network that produces the low-
Automatic variable weighting was performed using the
dimensional representation of the input space. Self-
OVW program available free at http://www.bio.umon-
organizing tree clustering is another type of unsuper-
treal.ca/casgrain/en/labo/ovw.html. Some descriptive
vised network clustering. This algorithm constructs a
statistics were obtained using the data analysis module
binary tree (dendrogram) whose terminal nodes repre-
of MS Excel for Windows XP. Other procedures, includ-
sent the resulting clusters.
ing ad hoc data preparation and transformation, factor,
In model-based clustering, the data are modeled as a
discriminant, and correlation analyses, were performed
finite mixture of multivariate Gaussian distributions.
in the Statistica 6.0 package (Statsoft).
DBSCAN is a density-based clustering algorithm. It
starts by searching for core objects. The clusters are
subsequently constructed based on these core objects RESULTS
by joining neighboring objects within a given radius. Classification
Two distance measures were used: Euclidean and Man- The silhouette values of both nonweighted and OVW-
hattan distances. weighted solutions increased progressively as the num-
There are multiple clustering quality measures, with ber of parameters in the model decreased. Solutions
the vast majority related to within- or between-cluster obtained using Euclidean distance exceeded in quality
variance or both (Visvanathan et al., 2009; Mary and those based on the Manhattan distance (not shown).
Kumar, 2012). Here we used silhouette value as a mea- Solutions obtained with the OVW algorithm contained
sure of the distance from a point representing the cell less cohesive and isolated clusters and were character-
in the multidimensional space to the other points within ized by lower silhouette values than their nonweighted
the cluster vs. the points in the nearby cluster. The counterparts (not shown). The best solutions were
average silhouette of the clustering is calculated by based on a nonweighted set of three parameters, DFA,
averaging the silhouettes of individual cells. It is a good SRSB, and VRSB. The silhouette analysis showed that
measure of cluster cohesion and isolation (Kaufman, eight-cluster solutions were optimum in most cases
Rousseeuw, 1990). (Table 3). The solutions obtained using the DBSCAN
The available studies on the morphology and physiol- algorithm contained three clusters as maximum, with
ogy of teleostean GCs suggest that there are at least some cells classified as noise. These solutions were not
five and probably more types of these cells in the fish further analyzed as obviously inadequate (see Materials
retina (see, e.g., Maximova et al., 1971; Cohen et al., and Methods). The average silhouettes of the eight-
2002; Ott et al., 2007). For this reason, solutions cluster solutions exceeded 0.5 in most cases,
The Journal of Comparative Neurology | Research in Systems Neuroscience 1359I. Pushchin and Y. Karetin
TABLE 2.
Pairwise Correlations and Multimodality Indices of the Initial Parameters1
DFA DFP DFIA DFAR DFC TDL SDD NPD NBP BO CON PA RBA BPD DFBC DFMR RSR SRSB
2
DFP 0.95
DFIA 0.972 0.952
DFAR 20.18 20.26 20.18
DFC 0.06 0.16 0.03 20.72
TDL 0.722 0.762 0.782 20.10 20.07
SDD 20.49 20.51 20.43 0.12 20.15 0.01
NPD 0.12 0.13 0.15 0.04 20.08 0.18 20.03
NBP 0.29 0.34 0.36 0.06 20.21 0.702 0.24 0.18
BO 0.09 0.16 0.18 0.01 20.16 0.46 0.28 20.10 0.772
CON 20.01 0.01 0.01 20.01 20.06 20.01 20.09 0.04 0.06 0.10
PA 0.08 0.13 0.11 20.10 20.02 0.23 0.11 0.05 0.35 0.50 0.03
RBA 20.30 20.30 20.28 0.09 20.05 20.09 0.33 20.07 0.06 0.24 0.00 0.02
BPD 20.43 20.47 20.39 0.23 20.25 20.08 0.682 0.03 0.47 0.49 0.06 0.21 0.21
DFBC 20.08 20.01 0.00 20.02 20.09 0.22 0.33 0.15 0.39 0.50 0.01 0.24 0.18 0.36
DFMR 20.12 20.06 20.03 0.01 20.11 0.21 0.38 0.15 0.38 0.52 0.02 0.23 0.23 0.41 0.982
RSR 20.20 20.15 20.17 20.02 20.02 20.12 0.07 20.14 0.00 0.10 0.01 0.03 20.06 0.10 0.12 0.12
SRSB 20.13 20.01 20.08 20.09 0.04 0.14 0.12 0.15 0.27 0.24 20.01 0.24 0.06 0.11 0.27 0.25 0.37
VRSB 0.01 0.09 0.04 20.08 0.06 0.23 0.07 0.25 0.27 0.17 20.02 0.23 0.10 0.04 0.19 0.17 20.32 0.762
MI 0.75 0.63 0.69 0.44 0.58 0.61 0.42 0.38 0.60 0.50 0.31 0.39 0.29 0.42 0.39 0.37 0.55 0.62
1
MI, multimodality indices. See Table 1 for other abbreviations.
2
Strong (0.7) and significant (P 0.05).
TABLE 3.
Average Silhouettes of the Clusterings Obtained Using Different Algorithms1
Number of clusters Ward’s k-Means Diana SOM PAM SOTA MBC DBSCAN
5 0.5291 0.5306 0.4566 0.4812 0.4818 0.4117 0.5255 NA
6 0.4901 0.4998 0.4423 0.4998 0.4998 0.4234 0.4796 NA
7 0.5342 0.5344 0.4939 0.5244 0.5312 0.4436 0.5345 NA
8 0.5684 0.5682 0.5486 0.5412 0.5721 0.4413 0.5561 NA
9 0.4947 0.5046 0.4593 0.4859 0.5053 0.4258 0.4019 NA
10 0.4839 0.4927 0.4367 0.4661 0.4597 0.3931 0.4356 NA
11 0.4539 0.465 0.4048 0.4365 0.4577 0.4251 0.4156 NA
12 0.4255 0.4334 0.4297 0.4139 0.4232 0.3925 0.3816 NA
13 0.407 0.4149 0.4239 0.4417 0.4327 0.3805 0.4182 NA
14 0.405 0.4209 0.4226 0.4397 0.4327 0.386 0.4228 NA
15 0.4033 0.4117 0.4206 NA 0.4272 0.344 0.3943 NA
16 0.3808 0.3908 0.3937 0.3938 0.4106 0.3379 0.4028 NA
17 0.3844 0.3936 0.3904 NA 0.4103 0.3262 0.3934 NA
18 0.388 0.3984 0.387 NA 0.4028 0.3262 0.3763 NA
19 0.3854 0.3938 0.3771 NA 0.405 0.3134 0.3696 NA
20 0.3822 0.4067 0.3685 NA 0.3833 0.3074 0.3646 NA
1
Ward’s, Ward’s agglomerative clustering; k-means, k-means partitioning; DIANA, divisive analysis clustering; SOM, clustering using self-organizing
maps; PAM, partitioning around medoids; SOTA, self-organizing tree clustering; MBC, model-based clustering; DBSCAN, projected clustering using
DBSCAN algorithm. NA, no valid cluster structure was found. See Materials and Methods for further details on the algorithms.
suggesting a reasonable cluster structure. The pairwise inspection did not reveal a substantial increase in clus-
correspondence between the four best eight-cluster sol- ter homogeneity between the eight-cluster solutions
utions (Wards, k-means, Diana, and PAM; see Table 3 and those containing nine or more clusters. The statisti-
for definitions) was high, varying from 88% to 93%. In cal significance of the four optimal eight-cluster solu-
these solutions, the cells placed in the same cluster tions was confirmed using Kruskal-Wallis ANOVA-on-
were visually homogenous in terms of morphology and ranks, and post hoc Mann-Whitney U tests showed sig-
stratification. Solutions with seven or fewer clusters nificant pairwise between-cluster differences in one or
contained apparently heterogeneous clusters and were more of the clustering variables (P 5 0.05, corrected for
characterized by lower average silhouettes. The visual the number of comparisons; Table 4). In many cases,
1360 The Journal of Comparative Neurology | Research in Systems NeuroscienceTABLE 4.
Pairwise Differences Between Clusters of Tribolodon brandtii Retinal Ganglion Cells1
Cluster pairs
Parameters 1–2 1–3 1–4 1–5 1–6 1–7 1–8 2–3 2–4 2–5 2–6 2–7 2–8 3–4 3–5 3–6 3–7 3–8 4–5 4–6 4–7 4–8 5–6 5–7 5–8 6–7 6–8 7–8
Dendritic field area 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Dendritic field perimeter 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Dendritic field 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
influence area
Dendritic field
aspect ratio
Dendritic field
compactness
Total dendrite length 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Spatial density of 1 1 1 1 1 1 1 1 1 1 1 1
dendrites
Number of primary
dendrites
Number of branch points 1 1 1 1 1 1 1 1 1 1
Average branch order 1 1 1 1 1 1 1 1
Average contraction
Average partition
asymmetry
Average remote amplitude 1 1
of bifurcation
Spatial density of 1 1 1 1 1 1 1 1 1 1 1 1 1 1
branch points
Box counting 1 1 1 1 1 1 1
fractal dimension
Mass-radius fractal 1 1 1 1 1 1 1
dimension
Relative stratification 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
range
Sclerad relative 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
stratification boundary
Vitread relative 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
stratification boundary
1
1, Differences significant at P < 0.05 (Kruskal-Wallis ANOVA-on-ranks with post hoc pairwise Mann-Whitney U tests, corrected for the number of comparisons).
The Journal of Comparative Neurology | Research in Systems Neuroscience
Retinal ganglion cells in the pacific redfin
1361I. Pushchin and Y. Karetin
the pairwise between-cluster differences in noncluster-
ing variables were also significant at P 5 0.05 (cor-
rected for the number of comparisons; Table 4).
Solutions based on the factors or extracted from vari-
ous subsets of the original set of parameters displayed
a poor cluster structure and were characterized by low
silhouettes (not shown).
Correlations between parameters
Some parameters were significantly linearly corre-
lated (Table 2). All strong (R 5 0.7) correlations were
significant at P 5 0.05 (corrected for the number of
comparisons). To interpret the observed correlations
and exclude spurious ones, pairwise correlation scatter-
plots were also examined (not shown). Most strong cor-
relations were observed between the parameters
associated with the same aspect of cell morphology or
measuring it in different ways. There were several
groups of parameters associated with the dendritic field
size (DFA–DFP–DFIA), dendritic arborization complexity
(NBP–BO, DFBC–DFMR), and dendrite stratification
(SRSB–VRSB).
NBP was strongly correlated with TDL. However, TDL
was weakly correlated with SDD, suggesting that larger
cells, despite greater absolute numbers of branch
points, were relatively sparsely branched. BPD was
strongly correlated with SDD. However, SDD was
weakly correlated with CON or fractal measures (BFBC
and DFMR), suggesting that dendrite surface develop-
ment and space filling were achieved primarily through
increased branching profusion.
Description of the ganglion cell types
The following description of redfin GCs is based on
the eight-cluster solution obtained with PAM, because
its average silhouette exceeded the silhouettes of the
rest of the solutions. This solution is presented in Fig-
ure 1. Table 5 contains basic statistics for the clusters.
Figure 2 presents the means and 95% confidence levels
of the original parameters for each cluster. The scheme
of the arborization range of each cell type is shown in
Figure 3. Figures 4–11 present camera lucida drawings Figure 1. Scatterplots representing the optimal clustering from
different viewpoints. It can be seen that from at least one view-
of the representative cells of different types in their
point any two clusters are well separated in space. DFA, dendritic
projection on the whole mount and reconstructions of field area; SRSB, sclerad relative stratification boundary; VRSB,
their side views. A subsample of 22 GCs is shown in vitread relative stratification boundary.
Figures 12 and 13.
All cells exhibited rounded or irregularly shaped
somata situated within the GCL or displaced at various size and amount of dendrites. The somata of type 1
degrees to the IPL. An axon originated either from the cells were often displaced deep into the IPL or the IPL/
soma or from a primary dendrite. The dendritic field INL boundary, whereas those of type 2 cells were
shape and asymmetry varied regardless of the cell type always orthotopic. Several primary dendrites were
or location in the sampling area. Type 1 and 2 cells sparsely branched to form elliptical or polygonal arbors,
exceeded the remaining cells types in the dendritic field with rare en passant and terminal varicosities. The
1362 The Journal of Comparative Neurology | Research in Systems NeuroscienceTABLE 5.
Statistics for the Ganglion Cell Types Described Here1
DFA DFP DFIA DFAR DFC TDL SDD NPD NBP BO CON PA RBA BPD DFBC DFMR RSR SRSB VRSB
Type 1 (15 cells)
Mean 68,859 1,084.5 64,560 0.543 16.48 3,840.7 0.0559 4.40 87.80 5.12 0.852 0.564 71.48 0.00126 1.329 1.405 0.175 0.983 0.809
SEM 2,221 21.9 1,914 0.031 0.40 332.9 0.0048 0.24 13.35 0.35 0.007 0.016 2.10 0.00019 0.013 0.018 0.016 0.009 0.022
Type 2 (8 cells)
Mean 109,830 1,269.6 93,572 0.529 15.91 3,348.4 0.0307 3.50 39.13 3.74 0.850 0.494 66.17 0.00036 1.276 1.355 0.234 0.240 0.006
SEM 1,739 46.6 3,599 0.045 0.76 526.3 0.0050 0.19 11.86 0.55 0.011 0.022 3.64 0.00011 0.025 0.029 0.017 0.015 0.006
Type 3 (22 cells)
Mean 30,278 716.3 37,340 0.572 16.12 2,568.3 0.0894 4.09 83.41 7.23 0.853 0.579 82.29 0.00298 1.500 1.601 0.474 0.991 0.517
SEM 1,341 18.7 1,111 0.039 0.45 167.6 0.0077 0.23 8.80 0.51 0.006 0.015 2.27 0.00038 0.014 0.017 0.015 0.005 0.014
Type 4 (29 cells)
Mean 32,650 715.2 36,747 0.626 15.73 1,944.5 0.0609 3.48 46.76 4.61 0.855 0.517 76.33 0.00151 1.318 1.404 0.413 0.519 0.105
SEM 1,326 16.3 1,560 0.029 0.27 140.6 0.0048 0.19 6.14 0.37 0.004 0.014 2.06 0.00021 0.015 0.018 0.013 0.012 0.014
Type 5 (32 cells)
Mean 12,007 442.4 18,618 0.617 15.73 1,020.9 0.0877 3.16 36.13 4.37 0.853 0.522 74.99 0.00310 1.338 1.427 0.639 0.946 0.307
SEM 457 8.2 466 0.030 0.29 63.3 0.0052 0.22 3.43 0.29 0.004 0.012 2.44 0.00029 0.016 0.017 0.012 0.010 0.014
Type 6 (32 cells)
Mean 12,501 436.2 19,207 0.689 15.29 1,118.4 0.0903 3.22 38.53 4.68 0.861 0.499 80.76 0.00306 1.342 1.433 0.236 0.460 0.225
SEM 593 13.1 760 0.025 0.23 84.9 0.0059 0.20 4.98 0.43 0.004 0.015 2.05 0.00034 0.016 0.018 0.013 0.008 0.009
Type 7 (34 cells)
Mean 14,103 466.4 20,947 0.644 15.57 1,309.4 0.0914 3.79 43.41 4.75 0.850 0.543 82.79 0.00300 1.346 1.440 0.238 0.899 0.661
SEM 522 8.4 674 0.029 0.31 98.7 0.0049 0.18 4.58 0.32 0.004 0.014 1.90 0.00025 0.015 0.015 0.012 0.013 0.012
Type 8 (31 cells)
Mean 7,338 333.8 13,170 0.609 16.47 780.5 0.1098 3.74 27.48 3.43 0.844 0.506 83.04 0.00367 1.337 1.434 0.295 0.599 0.304
SEM 685 14.5 772 0.027 0.36 84.6 0.0072 0.24 4.32 0.28 0.007 0.018 2.06 0.00041 0.015 0.018 0.011 0.009 0.011
1
SEM, standard error of mean. See Table 1 for other abbreviations.
The Journal of Comparative Neurology | Research in Systems Neuroscience
Retinal ganglion cells in the pacific redfin
1363I. Pushchin and Y. Karetin
Figure 2. Plots of the means and 95% confidence intervals of original parameters for the cell types identified here.
dendritic coverage was not uniform, often containing stratified within IPL sublamina c and the vitreadmost
gaps. Type 2 arborizations were the sparsest among all part of sublamina b. Type 1 and 2 cells were rarely
types. Type 1 cells were stratified within the sclerad observed in the whole mounts. Type 3 and 4 cells were
half of IPL sublamina a, whereas type 2 cells were intermediate in the dendritic field size, and the somata
1364 The Journal of Comparative Neurology | Research in Systems NeuroscienceRetinal ganglion cells in the pacific redfin
Figure 3. Scheme of the arborization range of the cell types pres-
ently identified. IPL, inner plexiform layer. a, b, c, IPL sublaminae
according to Cook and Sharma (1995) and Cook et al. (1999). 1–
8, Cell types. The ganglion cell layer is shaded.
Figure 5. A type 2 cell. A: Drawing of the cell in plan view, as
observed in the whole mount. B: Reconstruction of the side view
of the cell. IPL, inner plexiform layer; GCL, ganglion cell layer.
Scale bars 5 100 lm in A; 10 lm in B.
Figure 4. Type 1 cell. A: Drawing of the cell in plan view, as
observed in the whole mount. B: Reconstruction of the side view
of the cell. Here and in the following figures, the terminal den-
drites are shown as thicker than they actually are for representa-
tion purposes. IPL, inner plexiform layer; GCL, ganglion cell layer.
Scale bars 5 100 lm in A; 10 lm in B.
were primarily orthotopic. The dendritic coverage was
more homogenous compared with type 1 and 2 cells.
Type 3 cells had numerous en passant varicosities, and
type 4 cells displayed a less pronounced microsculp-
ture. Type 3 cells displayed extraordinary structural
complexity, as reflected in the fractal measures. They
had more elaborate arbors compared with type 4 cells
in terms of branching profusion, branching order, and
coverage density. Type 3 cells were bistratified in the Figure 6. A type 3 cell. A: Drawing of the cell in plan view, as
sclerad half of sublamina a and at the boundary observed in the whole mount. B: Reconstruction of the side view
of the cell. IPL, inner plexiform layer; GCL, ganglion cell layer.
between IPL sublaminae a and b. Three type 3 cells dis- Scale bars 5 50 lm in A; 10 lm in B.
played diffuse branching within the sclerad IPL half.
Their dendrite structure resembled that of type 4 cells.
Type 4 cells arborized diffusely within IPL sublaminae b orthotopic or slightly displaced to the IPL. Relatively
and c. Type 5–7 cells had small arbors and were similar thin and wavy dendrites bore rare en passant varicos-
in dendrite course and branching, primarily differing in ities. Type 5 cells were bistratified in the middle zones
the arborization level in the retina. The somata were of IPL sublaminae a and b, except for four cells that
The Journal of Comparative Neurology | Research in Systems Neuroscience 1365I. Pushchin and Y. Karetin
Figure 9. A type 6 cell. A: Drawing of the cell in plan view, as
observed in the whole mount. B: Reconstruction of the side view
of the cell. IPL, inner plexiform layer; GCL, ganglion cell layer.
Figure 7. A type 4 cell. A: Drawing of the cell in plan view, as Scale bars 5 50 lm in A; 10 lm in B.
observed in the whole mount. B: Reconstruction of the side view
of the cell. IPL, inner plexiform layer; GCL, ganglion cell layer.
Scale bars 5 50 lm in A; 10 lm in B.
Figure 8. A type 5 cell. A: Drawing of the cell in plan view, as
observed in the whole mount. B: Reconstruction of the side view
of the cell. IPL, inner plexiform layer; GCL, ganglion cell layer.
Scale bars 5 50 lm in A; 10 lm in B.
branched diffusely within the sclerad 70% of the IPL.
Type 6 cells arborized within the vitread 75% of subla- Figure 10. A type 7 cell. A: Drawing of the cell in plan view, as
observed in the whole mount. B: Reconstruction of the side view
mina b, and type 7 cells arborized at the mid of subla- of the cell. IPL, inner plexiform layer; GCL, ganglion cell layer.
mina a. Type 8 cells were the smallest in dendritic Scale bars 5 50 lm in A; 10 lm in B.
arbor size and dendrite mass. Poorly branched
1366 The Journal of Comparative Neurology | Research in Systems NeuroscienceRetinal ganglion cells in the pacific redfin
variables and in other functionally important parame-
ters. The clusters were nearly cohesive. Some clusters
were not completely isolated from each other. However,
the clusters in the real data sets are typically not per-
fectly isolated because of noise and (residual) system-
atic variation (Gordon, 1999; Xu and Wunsch, 2009).
Cells comprising the same cluster appeared generally
similar in dendrite arborization and microsculpture. A
small percentage of cells (three type 3 cells and four
type 5 cells) differed in the stratification pattern from
their “type mates.” However, dendrite stratification may
vary to a certain degree within the same GC type (Cook
et al., 1999). Thus, the present classification seems to
be a good approximation of the “true” RGC typological
structure in the Pacific redfin. However, being poly-
thetic in nature, this analysis is best considered a work-
Figure 11. A type 8 cell. A: Drawing of the cell in plan view, as ing hypothesis for future acceptance or modification
observed in the whole mount. B: Reconstruction of the side view (Rodieck and Brening, 1983; Bota and Swanson, 2007).
of the cell. IPL, inner plexiform layer; GCL, ganglion cell layer.
This future analysis might be accomplished in various
Scale bars 5 50 lm in A; 10 lm in B.
ways. An efficient approach to discovering natural cell
types is to analyze the spatial arrangement of cells pre-
dendrites bore no prominent microsculpture. Type 8 sumptively assigned to different types (Cook, 1998). It
cells arborized within sublamina b. would also be fruitful to combine the morphological
data with other data, including cell physiology, neuro-
chemical profile, cell connections, and cell develop-
DISCUSSION ment, obtained using various modern techniques
Ganglion cell classification (Masland and Raviola, 2000).
We have classified a sample of 203 RGCs into eight
types. We applied a variety of clustering algorithms, Comparison with other teleosts
both traditional and recently developed ones. We also GC types similar and potentially homologous to redfin
used various data preprocessing procedures to improve GCs were identified in a number of teleosts (Table 6).
the classification quality and lift the curse of dimension- All available classifications are primarily or exclusively
ality. The successive exclusion of noninformative or based on the dendritic tree size and the level of den-
masking variables generally produced better results drite stratification. Both parameters are of high func-
than dimensionality reduction using factor analysis or tional importance. The area of the dendritic field is
optimal variable weighting. All clustering algorithms, related to that of the receptive field center (Peichl and
except for DBSCAN, produced clusterings of compara- W€assle, 1983; Nelson et al., 1993), and the level of
ble quality. DBSCAN did not provide reasonable solu- dendrite stratification determines the type of signals
tions, containing four or more clusters because the received by the cell (Famiglietti et al., 1977; Rodieck,
present data set exhibits large differences in density in 1998). We therefore used these characteristics as pri-
most discriminating subspaces and DBSCAN produces mary parameters and compared the present GC system
poor results with such data sets (Ester et al., 1996). with systems proposed in other studies. We also ana-
The present classification is based on the dendritic lyzed the dendrite course and arborization.
field size and stratification in the retina, which are func- There is an obvious correlation between large GCs in
tionally important parameters used in the vast majority the redfin (types 1 and 2) and those in other teleosts.
of morphological classifications of GCs (see, e.g., Hitch- In several teleosts, large GCs formed regular, spatially
cock and Easter, 1986; Kong et al., 1995; Chen and independent mosaics, providing strong evidence that
Naito, 1999; Mangrum et al., 2002). Most of the clus- these cells were natural GC types (Cook, 1998). In the
tering algorithms used produced similar solutions. The present case, the pattern of GC labeling was patchy,
silhouette analysis of the optimal solutions suggested preventing analysis of the spatial arrangement of cells
that a reasonable clustering structure was revealed. assigned to different types. However, the relative den-
ANOVA-on-ranks showed that different cell types dif- dritic field size, pattern of dendrite course, and stratifi-
fered significantly in one or more of the classifying cation of type 1 and 2 cells are similar to their
The Journal of Comparative Neurology | Research in Systems Neuroscience 1367I. Pushchin and Y. Karetin
Figure 12. Drawings of large-field cell types in projection to the retinal surface, all to the same scale. Scale bar 5 100 lm.
counterparts in other nonmammalian species (often (2002) reported that type II GCs in zebrafish display
named aa and ac, respectively), supporting the hypoth- numerous bead-like varicosities distinct from their
esis of symplesiomorphy and the potential homology of apparent homologs in other teleosts (see, e.g., Cook
large GCs in nonmammals (Cook and Noden, 1998). et al., 1992; Cook and Sharma, 1995; Pushchin et al.,
Our type 3 cells correspond to so-called aab cells, 2007).
described for several teleosts, in dendritic field size and Cell types 4–8 are closer in dendritic field size com-
dendrite stratification pattern. These cells differ from pared with other types. We are less certain about their
aab cells in other species in dendrite arborization pat- association with GC types revealed in other teleosts. In
tern and elaborated microsculpture. However, these dif- some cases, two or three GC types seemed to be
ferences are not large enough to rule out the homology equally similar to a cell type discovered here. Notably,
of these cells; both parameters might vary between the functional segregation of the IPL, particularly the
homologous GC types. For example, Mangrum et al. relative thickness of the ON and OFF sublaminae, may
1368 The Journal of Comparative Neurology | Research in Systems NeuroscienceRetinal ganglion cells in the pacific redfin
Figure 13. Drawings of small-field cell types in projection to the retinal surface, all to the same scale. Scale bar 5 100 lm.
vary between species (Rodieck, 1998; Connaughton circuitry may exhibit different stratification patterns.
and Nelson, 2000; Marc and Cameron, 2002). As a Studies concerning nonlarge GC types are much less
result, homologous GC types involved in the same common than those examining large GC types.
The Journal of Comparative Neurology | Research in Systems Neuroscience 1369I. Pushchin and Y. Karetin
Class 1
Therefore, it would be premature to speculate on the
2.4
S4
Nb
8
potential homologs of cell types 4–8.
Some GC types described from other teleosts were
not found in the present study. In particular, we have
not encountered so-called biplexiform cells, a GC type
Class 3
Wa Na
2.2
Ma
S1
with dendrites in both IPL and OPL (Mariani, 1982).
III
7
Biplexiform cells are found in many teleost species. In
some species, they are shown to form regular mosaics,
confirming their classification as natural GC types
Class 2
3.1 4.1
Wb Nb (Cook et al., 1996). The absence of these cells in our
S4
IV
6
whole mounts may reflect their capricious labeling
(Pushchin et al., 2003). However, biplexiform cells were
Retinal Ganglion Cell Types in Tribolodon brandtii and Their Potential Homologs in Other Teleosts
not described for the zebrafish with different GC stain-
ing techniques (Mangrum et al., 2007; Ott et al., 2007),
2.3 4.3 4.2
Class 4
suggesting that they may be absent in some species.
Wac
S3
5
The number of GC types found in Tribolodon brandtii
(eight) is generally lower than the numbers discovered
Cell types
in other teleosts and higher vertebrates (e.g., zebrafish:
11 [Mangrum et al., 2002]); goldfish: 15 [Hitchcock and
Class 6 class 7
Easter, 1986]; channel catfish: 11 [Dunn-Meynell and
3.1 4.1
Wb Wc
Sharma, 1986]; northern cutthroat eel: 10 [Hirt and
VIII
M5
S7
4
Wagner, 2005]; slider turtle, 21 [Kolb, 1982]; mouse:
12 [Badea and Nathans, 2004], 11 [Kong et al., 2005],
or 14 [Coombs et al., 2006]; rabbit: 11 [Rockhill et al.,
2002]). In addition, some studies have revealed even
TABLE 6.
Class 5
Inner a
inner a
fewer GC types than the present study (e.g., lamprey: 7
Wac
aab
aab
3.3
G3
ab
X
3
[Fletcher et al., 2013]; Florida garfish: 7 [Collin and
Northcutt, 1993]; tiger salamander: 5 [Toris et al.,
1995; Costa and Velte, 1999]; chick: 6 [Chen and
Naito, 1999; Naito and Chen, 2004]). These discrepan-
Class 8
Mb6
1.1
G2
Gb
ac
ac
ac
cies might reflect between-species differences, different
2
I
classification approaches, or the capricious labeling of
some GC types.
The present study is among the few to use quantita-
Outer a
outer a
G1 G4
tive analysis to identify GC types in the teleost fish ret-
Ma2
1.2
Ga
aa
aa
aa
1
II
ina. Further research is needed to obtain a
representative picture of the GC diversity in teleosts
and to reveal homologous GC lineages within and out-
These studies were restricted to large retinal ganglion cells.
side the infraclass.
Ictalurus punctatus (Dunn-Meynell and Sharma, 1995)
Synaphobranchus kaupi (Hirt and Wagner, 2005)
Oreochromis spilurus (Cook and Becker, 1991)1
Carassius auratus (Hitchcock and Easter, 1986)
ACKNOWLEDGMENTS
Ictalurus punctatus (Cook and Sharma, 1995)1
Pholidapus dybowskii (Pushchin et al., 2007)1
The authors are grateful to Dr. S.L. Kondrashev (A.V.
Bathymaster derjugini (Cook et al., 1999)1
Zhirmunsky Institute of Marine Biology, FEB RAS, Vladi-
Carassius auratus (Cook et al., 1992)1
Plectropomus leopardus (Collin, 1989)
vostok, Russia) and two anonymous reviewers for their
Danio rerio (Mangrum et al., 2002)
Tribolodon brandtii (present study)
valuable suggestions and comments on the manuscript.
Danio rerio (Ott et al., 2007)
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
Species (study)
ROLE OF AUTHORS
All authors had full access to all the data in the
study and take responsibility for the integrity of the
1
1370 The Journal of Comparative Neurology | Research in Systems NeuroscienceRetinal ganglion cells in the pacific redfin
data and the accuracy of the data analysis. Study con- Costa LD, Velte TJ. 1999. Automatic characterization and clas-
cept and design: IP. Acquisition of data: IP, YK. Analysis sification of ganglion cells from the salamander retina. J
Comp Neurol 404:33–51.
and interpretation of data: IP, YK. Drafting of the manu- Costello AB, Osborne LW. 2005. Best practices in exploratory
script: IP. Critical revision of the manuscript for impor- factor analysis: four recommendations for getting the
tant intellectual content: IP. Statistical analysis: IP. most from your analysis. Pract Assess Res Eval 10:1–9.
Obtained funding: IP. Administrative, technical, and De Soete G. 1986. Optimal variable weighting for ultrametric
and additive tree clustering. Quality Quantity 20:169–
material support: IP. Study supervision: IP. 180.
De Soete G. 1998. OVWTRE: a program for optimal variable
LITERATURE CITED weighting for ultrametric and additive tree fitting. J Class
5:101–104.
Badea TC, Nathans J. 2004. Quantitative analysis of neuronal Dunn-Meynell AA, Sharma SC. 1986. Visual system of the
morphologies in the mouse retina visualized by using a channel catfish (Ictalurus punctatus): I. Retinal ganglion
genetically directed reporter. J Comp Neurol 480:331–351. cell morphology. J Comp Neurol 247:32–55.
Bota M, Swanson LW. 2007. The neuron classification prob- Ester M, Kriegel HP, Sander J, Xu X. 1996. A density-based
lem. Brain Res Rev 56:79–88. algorithm for discovering clusters in large spatial data-
Brock G, Pihur V, Datta S, Datta S. 2008. clValid: an R pack- bases with noise. In: Proceedings of 2nd International
age for cluster validation. J Stat Software 25:1–22. Conference on Knowledge Discovery and Data Mining, p
Chen Y, Naito J. 1999. Morphological classification of ganglion 226–231.
cells in the central retina of chicks. J Vet Med Sci 61: Famiglietti EV, Kaneko A, Tachibana M. 1977. Neuronal archi-
537–542. tecture of on and off pathways to ganglion cells in carp
Cohen ED, Mangrum WI, Dowling JE. 2002. Ganglion cell types retina. Science 198:1267–1269.
in the zebrafish retina. Invest Ophthalmol Vis Sci 43: Fletcher LN, Coimbra JP, Rodger J, Potter IC, Gill HS, Dunlop
U650. SA, Collin AS. 2013. Classification of retinal ganglion
Collin SP. 1989. Topography and morphology of retinal gan- cells in the southern hemisphere lamprey Geotria aus-
glion cells in the coral trout Plectropoma leopardus (Ser- tralis (Cyclostomata). J Comp Neurol.
ranidae): a retrograde cobaltous-lysine study. J Comp Fraley C, Raftery A. 1999. Mclust: software for model-based
Neurol 281:143–158. cluster and discriminant analysis. Technical Report No.
Collin SP, Northcutt RG. 1993. The visual system of the Flor- 342. University of Washington.
ida garfish, Lepisosteus platyrhincus (Ginglymodi). 3. Reti- Gan G, Ma CH, Wu J. 2007. Data clustering: theory, algo-
nal ganglion cells. Brain Behav Evol 42:295–320. rithms, and applications. Philadelphia: SIAM, ASA.
Connaughton VP, Nelson R. 2000. Axonal stratification pat- Gordon AD. 1999. Classification. New York: Chapman & Hall/
terns and glutamate-gated conductance mechanisms in CRC.
zebrafish retinal bipolar cells. J Physiol 524:135–146. Harris RM. 1985. Light microscopic depth measurements of
Cook JE. 1998. Getting to grips with neuronal diversity. What thick sections. J Neurosci Methods 14:97–100.
is a neuronal type? In: Chalupa L, Finlay B, editors. Hennig C. 2007. The fpc Package. cran.r-project.org/web/
Development and organization of the retina. New York: packages/fpc/fpc.pdf.
Plenum Press. p 91–120. Hirt B, Wagner HJ. 2005. The organization of the inner retina
Cook JE. 2003. Spatial regularity among retinal neurons. In: in a pure-rod deep-sea fish. Brain Behav Evol 65:157–
Chalupa LM, Werner JS, editors. The visual neuroscien- 167.
ces. Cambridge, MA: MIT Press. p 463–479. Hitchcock PF, Easter SS. 1986. Retinal ganglion cells in gold-
Cook JE, Becker DL. 1991. Regular mosaics of large displaced fish: a qualitative classification into four morphological
and non-displaced ganglion cells in the retina of a cichlid types, and a quntitative study of the development of one
fish. J Comp Neurol 306:668–684. of them. J Neurosci 6:1037–1050.
Cook JE, Noden AJ. 1998. Somatic and dendritic mosaics Ito H, Murakami T. 1984. Retinal ganglion cells in two teleost
formed by large ganglion cells in the retina of the com- species, Sebastiscus marmoratus and Navodon modestus.
mon house gecko (Hemidactylus frenatus). Brain Behav J Comp Neur 229:80–96.
Evol 51:263–283. Jelinek HF, Fernandez E. 1998. Neurons and fractals: how reli-
Cook JE, Sharma SC. 1995. Large retinal ganglion cells in the able and useful are calculations of fractal dimensions? J
channel catfish (Ictalurus punctatus): three types with Neurosci Methods 81:9–18.
distinct dendritic stratification patterns form similar but Karpenko AA. 1993. The method and device for the registra-
independent mosaics. J Comp Neurol 362:331–349. tion of the TV signal of biological objects the device for
Cook JE, Becker DL, Kapila R. 1992. Independent mosaics of this goal. Patent of Russia N 1838893 A’3. Inform Byull
large inner- and outer-stratified ganglion cells in the gold- 32:46.
fish retina. J Comp Neurol 318:355–366. Kaufman L, Rousseeuw PJ. 1990. Finding groups in data: an
Cook JE, Kondrashev SL, Podugolnikova TA. 1996. Biplexiform introduction to cluster analysis. New York: John Wiley &
ganglion cells, characterized by dendrites in both outer Sons.
and inner plexiform layers, are regular, mosaic-forming Kolb H. 1982. The morphology of the bipolar cells, amacrine
elements of teleost fish retinae. Vis Neurosci 13:517– cells and ganglion cells in the retina of the turtle Pseu-
528. demys scripta elegans. Philos Trans R Soc Lond B Biol
Cook JE, Podugolnikova TA, Kondrashev SL. 1999. Species- Sci 298:355–393.
dependent variation in the dendritic stratification of Kong JH, Fish DR, Rockhill RL, Masland RH. 2005. Diversity of
apparently homologous retinal ganglion cell mosaics in ganglion cells in the mouse retina: unsupervised morpho-
two neoteleost fishes. Vis Res 39:2615–2631. logical classification and its limits. J Comp Neurol 489:
Coombs J, van der LD, Wang GY, Chalupa LM. 2006. Morpho- 293–310.
logical properties of mouse retinal ganglion cells. Neuro- Kriegel HP, Kr€oger P, Sander J, Zimek A. 2012. Density-based
science 140:123–136. clustering. WIREs Data Mining Knowl Disc 1:231–240.
The Journal of Comparative Neurology | Research in Systems Neuroscience 1371You can also read
