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• W2016397514 abstract "We describe recent progress toward defining neuronal cell types in the mouse retina and attempt to extract lessons that may be generally useful in the mammalian brain. Achieving a comprehensive catalog of retinal cell types now appears within reach, because researchers have achieved consensus concerning two fundamental challenges. The first is accuracy—defining pure cell types rather than settling for neuronal classes that are mixtures of types. The second is completeness—developing methods guaranteed to eventually identify all cell types, as well as criteria for determining when all types have been found. Case studies illustrate how these two challenges are handled by combining state-of-the-art molecular, anatomical, and physiological techniques. Progress is also being made in observing and modeling connectivity between cell types. Scaling up to larger brain regions, such as the cortex, will require not only technical advances but also careful consideration of the challenges of accuracy and completeness. We describe recent progress toward defining neuronal cell types in the mouse retina and attempt to extract lessons that may be generally useful in the mammalian brain. Achieving a comprehensive catalog of retinal cell types now appears within reach, because researchers have achieved consensus concerning two fundamental challenges. The first is accuracy—defining pure cell types rather than settling for neuronal classes that are mixtures of types. The second is completeness—developing methods guaranteed to eventually identify all cell types, as well as criteria for determining when all types have been found. Case studies illustrate how these two challenges are handled by combining state-of-the-art molecular, anatomical, and physiological techniques. Progress is also being made in observing and modeling connectivity between cell types. Scaling up to larger brain regions, such as the cortex, will require not only technical advances but also careful consideration of the challenges of accuracy and completeness. When President Obama announced his BRAIN Initiative, the NIH enlisted a “dream team” of prominent neuroscientists to formulate a plan. In its final report, these advisors proclaimed that “[i]t is within reach to characterize all cell types in the nervous system” and named this as the number one goal out of seven for the BRAIN Initiative (http://www.nih.gov/science/brain/2025/index.htm). In this perspective piece, we describe methods currently being used to identify cell types and discuss the prospects of extending them to catalog all cell types in the nervous system. We will restrict ourselves to neuronal cell types, though nonneuronal cell types are important too. The term “cell type” will refer to classification at the finest granularity, analogous to “species” in biological taxonomy (Masland, 2004Masland R.H. Neuronal cell types.Curr. Biol. 2004; 14: R497-R500Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar), and accordingly, the term “subtype” will be avoided. Ideally, one would also define higher ranks for neuronal taxonomy, analogous to “genus” and so on. In the absence of accepted terminology, we will use “class” to refer to any level of the hierarchy above cell type (Masland, 2004Masland R.H. Neuronal cell types.Curr. Biol. 2004; 14: R497-R500Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar). Our exposition focuses on the example of the retina, a region of the mammalian central nervous system in which cell types have been intensively investigated for well over a century (Cajal, 1893Cajal S.R.y. La rétine des vertébrés.Cellule. 1893; 9: 17-257Google Scholar). We will discuss only the mouse retina, which has emerged as an important model system due to the power of mouse genetics. While mice have low visual acuity, they exhibit interesting visually guided behaviors, and visual regions of the mouse brain are being explored by many researchers (Huberman and Niell, 2011Huberman A.D. Niell C.M. What can mice tell us about how vision works?.Trends Neurosci. 2011; 34: 464-473Abstract Full Text Full Text PDF PubMed Scopus (206) Google Scholar). The advances to be reviewed here build on previous work with rabbits, cats, monkeys, and nonmammalian species, but space does not permit inclusion of this previous literature. Progress has been hindered not only by technical limitations but also by two fundamental difficulties of methodology. The first is accuracy: how do we know when we have found a true cell type? The second is completeness: how can we identify all cell types, and how will we know when we are done? As will be explained below, provisional answers to these two questions have emerged for the retina, so finishing the catalog of retinal cell types does truly seem within reach. We will conclude this piece by speculating about whether and how impending success in the retina will generalize to the brain. The retina is composed of three layers of cell bodies and two layers of neurites (Figure 1). You could imagine it as a club sandwich with somata as bread and neurites as meat. The three bread layers are the outer nuclear layer (ONL), inner nuclear layer (INL), and ganglion cell layer (GCL). The two meat layers are the outer plexiform layer (OPL) and inner plexiform layer (IPL). The retina contains five classes of neurons: photoreceptor, horizontal, bipolar, amacrine, and ganglion cells. Photoreceptor and horizontal cells are divided into just a handful of types and will not be discussed here. The challenges of defining retinal cell types and determining their connectivity mainly involve the bipolar, amacrine, and ganglion cells, which synapse with each other in the IPL (Masland, 2012Masland R.H. The neuronal organization of the retina.Neuron. 2012; 76: 266-280Abstract Full Text Full Text PDF PubMed Scopus (602) Google Scholar). The intricate structure of the IPL depends on depth, which is measured along the axis perpendicular to the retina. IPL depths 0 and 1 are conventionally placed at the IPL borders adjacent to the INL and GCL, respectively. The IPL depth is divided at roughly the halfway mark into Off and On zones that are adjacent to the INL and GCL respectively. The IPL depth is more finely divided into five (Cajal, 1893Cajal S.R.y. La rétine des vertébrés.Cellule. 1893; 9: 17-257Google Scholar) or ten (Roska and Werblin, 2001Roska B. Werblin F. Vertical interactions across ten parallel, stacked representations in the mammalian retina.Nature. 2001; 410: 583-587Crossref PubMed Scopus (403) Google Scholar) “strata.” A recent study has shown that the precision of IPL structure is even finer than these conventional divisions (Sümbül et al., 2014Sümbül U. Song S. McCulloch K. Becker M. Lin B. Sanes J.R. Masland R.H. Seung H.S. A genetic and computational approach to structurally classify neuronal types.Nat. Commun. 2014; 5: 3512Crossref PubMed Scopus (114) Google Scholar). Roughly speaking, a cell type is defined as a population of cells with similar molecular, anatomical, and physiological properties. These three kinds of definition are illustrated by the starburst amacrine cell (SAC), a class of retinal neuron that comes in On and Off types (Figure 2):•The SAC is molecularly defined as the only cholinergic neuron in the mammalian retina (Masland and Tauchi, 1986Masland R.H. Tauchi M. The cholinergic amacrine cell.Trends Neurosci. 1986; 9: 218-223Abstract Full Text PDF Scopus (106) Google Scholar). On SACs express semaphorin 6A (Sema6A), while Off SACs do not (Sun et al., 2013Sun L.O. Jiang Z. Rivlin-Etzion M. Hand R. Brady C.M. Matsuoka R.L. Yau K.-W. Feller M.B. Kolodkin A.L. On and off retinal circuit assembly by divergent molecular mechanisms.Science. 2013; 342: 1241974Crossref PubMed Scopus (100) Google Scholar).•Anatomists define SACs as neurons that are almost planar, with dendrites extending roughly radially and symmetrically about the soma (Famiglietti, 1983Famiglietti Jr., E.V. ‘Starburst’ amacrine cells and cholinergic neurons: mirror-symmetric on and off amacrine cells of rabbit retina.Brain Res. 1983; 261: 138-144Crossref PubMed Scopus (223) Google Scholar). Off and On types of SACs arborize in strata located at roughly 1/3 and 2/3 of the IPL depth, respectively.•On SACs are activated when light turns on, while Off SACs are activated when light turns off. Physiologists have discovered that a SAC dendrite is more activated by outward motion than inward motion. Here, “outward” refers to visual stimuli that move from the soma toward the tip of the dendrite (Euler et al., 2002Euler T. Detwiler P.B. Denk W. Directionally selective calcium signals in dendrites of starburst amacrine cells.Nature. 2002; 418: 845-852Crossref PubMed Scopus (432) Google Scholar, Hausselt et al., 2007Hausselt S.E. Euler T. Detwiler P.B. Denk W. A dendrite-autonomous mechanism for direction selectivity in retinal starburst amacrine cells.PLoS Biol. 2007; 5: e185Crossref PubMed Scopus (110) Google Scholar). The physiological definition is interesting because it is closely related to the function of SACs, suggesting that SACs are involved in the visual detection of motion. Indeed, ablation of SACs results in loss of the optokinetic reflex (Yoshida et al., 2001Yoshida K. Watanabe D. Ishikane H. Tachibana M. Pastan I. Nakanishi S. A key role of starburst amacrine cells in originating retinal directional selectivity and optokinetic eye movement.Neuron. 2001; 30: 771-780Abstract Full Text Full Text PDF PubMed Scopus (278) Google Scholar). However, it is difficult to arrive at the physiological definition without means of visualizing, recognizing, and manipulating SACs. This is where the molecular and anatomical definitions come in. The molecular definition of SACs is useful for visualization by antibody staining. SACs are selectively stained by antibodies against choline acetyltransferase (ChAT), the enzyme that synthesizes acetylcholine, or against vesicular acetylcholine transporter (VAChT). The molecular definition is also useful for genetic manipulations, for example, through transgenic mouse lines that express Cre in ChAT-positive cells (Yonehara et al., 2011Yonehara K. Balint K. Noda M. Nagel G. Bamberg E. Roska B. Spatially asymmetric reorganization of inhibition establishes a motion-sensitive circuit.Nature. 2011; 469: 407-410Crossref PubMed Scopus (122) Google Scholar, Duan et al., 2014Duan X. Krishnaswamy A. De la Huerta I. Sanes J.R. Type ii cadherins guide assembly of a direction-selective retinal circuit.Cell. 2014; 158: 793-807Abstract Full Text Full Text PDF PubMed Scopus (144) Google Scholar). The ablation of SACs mentioned above was accomplished by genetic targeting of SACs based on their expression of the mGluR2 receptor, another molecule characteristic of SACs (Yoshida et al., 2001Yoshida K. Watanabe D. Ishikane H. Tachibana M. Pastan I. Nakanishi S. A key role of starburst amacrine cells in originating retinal directional selectivity and optokinetic eye movement.Neuron. 2001; 30: 771-780Abstract Full Text Full Text PDF PubMed Scopus (278) Google Scholar). In principle, a cell type is defined by its entire transcriptomic or proteomic state. In practice, just a few molecules are expected to be sufficient to distinguish any given cell type from other cell types in its vicinity. For example, as explained above, acetylcholine and sema6A appear to be sufficient to distinguish On and Off SACs from other retinal cell types. The anatomical definition of SACs was used to recognize them after intracellular dye fills, so that they could be targeted for the physiological experiments that revealed direction selectivity of SAC dendrites (Euler et al., 2002Euler T. Detwiler P.B. Denk W. Directionally selective calcium signals in dendrites of starburst amacrine cells.Nature. 2002; 418: 845-852Crossref PubMed Scopus (432) Google Scholar, Hausselt et al., 2007Hausselt S.E. Euler T. Detwiler P.B. Denk W. A dendrite-autonomous mechanism for direction selectivity in retinal starburst amacrine cells.PLoS Biol. 2007; 5: e185Crossref PubMed Scopus (110) Google Scholar). In our opinion, it is important to cross-validate these three definitions. We can be most sure of the validity of a cell type if it has three independent definitions based on molecular properties only, anatomical properties only, and physiological properties only and these three definitions agree with each other. This ideal situation has been partially achieved for SACs. A SAC can currently be identified by purely molecular criteria or purely anatomical criteria. The physiological property of direction selectivity is not specific enough to stand on its own as an independent definition of SACs, but one can imagine that further research could yield a larger set of physiological properties that fully define SACs. Why do we expect that these definitions should agree for a genuine cell type? If a cell type serves a function, then we expect evolution to adapt the properties of the cell type to serve the function. It was already noted that the physiological property of direction selectivity appears to serve visual behaviors like the optokinetic reflex. Likewise, the anatomical properties of SACs support their role in visual function. To understand this point, it is helpful to consider the relation of SACs with another class of retinal neuron, On-Off direction-selective ganglion cells (ooDSGCs). Each ooDSGC is bistratified, meaning that its dendrites stratify at two IPL depths. These depths correspond exactly to those of SACs, so that ooDSGCs costratify with On and Off SACs (Famiglietti, 1992Famiglietti E.V. Dendritic co-stratification of ON and ON-OFF directionally selective ganglion cells with starburst amacrine cells in rabbit retina.J. Comp. Neurol. 1992; 324: 322-335Crossref PubMed Scopus (124) Google Scholar). Costratification means that contact is possible, and contact is a necessary (though not sufficient) condition for synaptic coupling. Indeed, it turns out that SACs provide synaptic input to ooDSGCs (Fried et al., 2002Fried S.I. Münch T.A. Werblin F.S. Mechanisms and circuitry underlying directional selectivity in the retina.Nature. 2002; 420: 411-414Crossref PubMed Scopus (298) Google Scholar), and the direction selectivity of ooDSGCs is thought to be inherited from their SAC inputs (Fried et al., 2002Fried S.I. Münch T.A. Werblin F.S. Mechanisms and circuitry underlying directional selectivity in the retina.Nature. 2002; 420: 411-414Crossref PubMed Scopus (298) Google Scholar, Briggman et al., 2011Briggman K.L. Helmstaedter M. Denk W. Wiring specificity in the direction-selectivity circuit of the retina.Nature. 2011; 471: 183-188Crossref PubMed Scopus (576) Google Scholar). More generally, stratification depth is an important constraint on the connectivity of retinal circuits, which in turn is an important determinant of visual function (Masland, 2004Masland R.H. Neuronal cell types.Curr. Biol. 2004; 14: R497-R500Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar). Therefore, it makes sense for stratification depth to be crucial for the anatomical definition of not only SACs but also virtually every type of retinal neuron. The anatomical properties of a neuron not only influence its function by constraining its connectivity to inputs and outputs but also by shaping the single-neuron biophysics underlying input-output relations. Additional distinctive anatomical properties not mentioned above—the existence of both input and output synapses on SAC dendrites, the lack of an axon, and the small diameter of SAC dendrites (suggesting weak electrical coupling)—were used as the basis for speculation that SAC dendrites function independently (Miller and Bloomfield, 1983Miller R.F. Bloomfield S.A. Electroanatomy of a unique amacrine cell in the rabbit retina.Proc. Natl. Acad. Sci. USA. 1983; 80: 3069-3073Crossref PubMed Scopus (90) Google Scholar), as was later confirmed by two-photon calcium imaging (Euler et al., 2002Euler T. Detwiler P.B. Denk W. Directionally selective calcium signals in dendrites of starburst amacrine cells.Nature. 2002; 418: 845-852Crossref PubMed Scopus (432) Google Scholar, Hausselt et al., 2007Hausselt S.E. Euler T. Detwiler P.B. Denk W. A dendrite-autonomous mechanism for direction selectivity in retinal starburst amacrine cells.PLoS Biol. 2007; 5: e185Crossref PubMed Scopus (110) Google Scholar). Molecules expressed in SACs support their anatomical properties by participating in developmental processes. MEGF10 is important for the regular spacing of SAC cell bodies across the retina (Kay et al., 2012Kay J.N. Chu M.W. Sanes J.R. MEGF10 and MEGF11 mediate homotypic interactions required for mosaic spacing of retinal neurons.Nature. 2012; 483: 465-469Crossref PubMed Scopus (129) Google Scholar). Protocadherins are important for SAC self-avoidance, the apparent repulsion between the dendrites of a single SAC that is important for its characteristic “starburst” shape (Lefebvre et al., 2012Lefebvre J.L. Kostadinov D. Chen W.V. Maniatis T. Sanes J.R. Protocadherins mediate dendritic self-avoidance in the mammalian nervous system.Nature. 2012; 488: 517-521Crossref PubMed Scopus (304) Google Scholar). Sema6A, mentioned above, is important for SAC stratification at 1/3 and 2/3 of the IPL depth (Sun et al., 2013Sun L.O. Jiang Z. Rivlin-Etzion M. Hand R. Brady C.M. Matsuoka R.L. Yau K.-W. Feller M.B. Kolodkin A.L. On and off retinal circuit assembly by divergent molecular mechanisms.Science. 2013; 342: 1241974Crossref PubMed Scopus (100) Google Scholar). SAC molecules also support the intrinsic and synaptic physiology of these cells. While this is the case, the link to function is often nonobvious. Cholinergic transmission in SACs is thought to exert excitatory influences on other cells, but its role in SAC function has not been explained. Most accounts of direction selectivity instead emphasize inhibitory GABAergic transmission by SACs. The mGluR2 receptor is expressed in SACs, a fact used for the genetically targeted ablation mentioned earlier (Yoshida et al., 2001Yoshida K. Watanabe D. Ishikane H. Tachibana M. Pastan I. Nakanishi S. A key role of starburst amacrine cells in originating retinal directional selectivity and optokinetic eye movement.Neuron. 2001; 30: 771-780Abstract Full Text Full Text PDF PubMed Scopus (278) Google Scholar). Pharmacological manipulation of mGluR2 receptors appears to have an effect on direction selectivity, but the mechanism and relevance for function are not altogether clear (Jensen, 2006Jensen R.J. Activation of group II metabotropic glutamate receptors reduces directional selectivity in retinal ganglion cells.Brain Res. 2006; 1122: 86-92Crossref PubMed Scopus (15) Google Scholar). The physiological definition of SACs given above is based on responses to visual stimuli. There is also considerable research on the intrinsic and synaptic physiology of retinal neurons, and such properties could potentially be distinctive enough to be used in cell type definitions. This approach is popular in the physiological definition of cortical cell types (Markram et al., 2004Markram H. Toledo-Rodriguez M. Wang Y. Gupta A. Silberberg G. Wu C. Interneurons of the neocortical inhibitory system.Nat. Rev. Neurosci. 2004; 5: 793-807Crossref PubMed Scopus (2110) Google Scholar, Ascoli et al., 2008Ascoli G.A. Alonso-Nanclares L. Anderson S.A. Barrionuevo G. Benavides-Piccione R. Burkhalter A. Buzsáki G. Cauli B. Defelipe J. Fairén A. et al.Petilla Interneuron Nomenclature GroupPetilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.Nat. Rev. Neurosci. 2008; 9: 557-568Crossref PubMed Scopus (1050) Google Scholar). Identifying cell types might be denigrated as a tedious exercise in “descriptive” neuroscience, akin to stamp collecting. Indeed, many publications on this subject have been relegated to “archival” journals with low impact factors. Why then have cell types become so prominent a topic, as evidenced by the priorities of BRAIN Initiative? One reason is the recent revolution in applying genetic methods to systems neuroscience. Identifying a cell type is now a prelude to manipulating it, enabling experiments that rely on visualizing neurons with fluorescent reporters or controlling their activity with optogenetics. A second reason is conceptual rather than technological. Without knowledge of cell types, one cannot even frame many important questions of neuroscience. Ganglion cells are the outputs of the retina, the only class of neuron that extends axons from the eye to the brain. According to textbook accounts, a ganglion cell is the output of a computation that is well approximated by a linear filter with center-surround structure. If this simple picture were accurate, it would be hard to understand why ganglion cells come in at least 20 types. It seems more plausible that each ganglion cell type is the output of a distinct visual computation performed by the retina (Gollisch and Meister, 2010Gollisch T. Meister M. Eye smarter than scientists believed: neural computations in circuits of the retina.Neuron. 2010; 65: 150-164Abstract Full Text Full Text PDF PubMed Scopus (436) Google Scholar). Once a cell type is defined, then retinal physiologists can set to work characterizing the visual computation that it serves. Before the cell type is defined, it would be difficult to even formulate the question. Similarly, SACs must be anatomically defined before developmental neuroscientists can find molecules that are important for establishing the starburst shape (Lefebvre et al., 2012Lefebvre J.L. Kostadinov D. Chen W.V. Maniatis T. Sanes J.R. Protocadherins mediate dendritic self-avoidance in the mammalian nervous system.Nature. 2012; 488: 517-521Crossref PubMed Scopus (304) Google Scholar) and stratification depth (Sun et al., 2013Sun L.O. Jiang Z. Rivlin-Etzion M. Hand R. Brady C.M. Matsuoka R.L. Yau K.-W. Feller M.B. Kolodkin A.L. On and off retinal circuit assembly by divergent molecular mechanisms.Science. 2013; 342: 1241974Crossref PubMed Scopus (100) Google Scholar). Third, neurodegenerative disorders may not affect all cell types uniformly. For example, glaucoma researchers have long known that ganglion cells degenerate in the disease, while other classes of retinal neurons remain unaffected. A recent study on a mouse model of the disease suggests that individual ganglion cell types may be affected differentially (Della Santina et al., 2013Della Santina L. Inman D.M. Lupien C.B. Horner P.J. Wong R.O.L. Differential progression of structural and functional alterations in distinct retinal ganglion cell types in a mouse model of glaucoma.J. Neurosci. 2013; 33: 17444-17457Crossref PubMed Scopus (174) Google Scholar), while a previous study of a different mouse model failed to detect differences across types (Jakobs et al., 2005Jakobs T.C. Libby R.T. Ben Y. John S.W.M. Masland R.H. Retinal ganglion cell degeneration is topological but not cell type specific in DBA/2J mice.J. Cell Biol. 2005; 171: 313-325Crossref PubMed Scopus (305) Google Scholar). Fourth, future therapies for diseases may depend on genetic targeting of specific cell types. For example, optogenetic control of On bipolar cells (BCs) (Lagali et al., 2008Lagali P.S. Balya D. Awatramani G.B. Münch T.A. Kim D.S. Busskamp V. Cepko C.L. Roska B. Light-activated channels targeted to ON bipolar cells restore visual function in retinal degeneration.Nat. Neurosci. 2008; 11: 667-675Crossref PubMed Scopus (438) Google Scholar) has been explored as a means of restoring visual function in a mouse model of retinal degeneration. BC bodies are located in the INL. Their dendrites extend into the OPL and axons into the IPL. BCs are the only conduit from the OPL to the IPL and, therefore, the only way for signals to travel from photoreceptors to ganglion and amacrine cells. BCs are functionally classified into On and Off classes, which are activated by light and dark stimuli, respectively. While BCs have been studied in many species, much recent work has focused on the mouse due to the availability of genetic methods. The classification of mouse BCs seems very close to complete, with about a dozen types (Euler et al., 2014Euler T. Haverkamp S. Schubert T. Baden T. Retinal bipolar cells: elementary building blocks of vision.Nat. Rev. Neurosci. 2014; 15: 507-519Crossref PubMed Scopus (271) Google Scholar). There is a single rod BC type, which receives its input from rod photoreceptors. Cone BC types are more numerous. Ghosh et al., 2004Ghosh K.K. Bujan S. Haverkamp S. Feigenspan A. Wässle H. Types of bipolar cells in the mouse retina.J. Comp. Neurol. 2004; 469: 70-82Crossref PubMed Scopus (313) Google Scholar defined nine cone BC types in the mouse retina based on anatomical criteria. They filled BCs by microinjection, imaged them with light microscopy (LM), and examined the IPL depth at which the axons stratified. Roughly speaking, Types 1 through 9 stratify progressively deeper in the IPL (Figure 1). Wässle et al., 2009Wässle H. Puller C. Müller F. Haverkamp S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina.J. Neurosci. 2009; 29: 106-117Crossref PubMed Scopus (313) Google Scholar defined 11 cone BC types by the presence and absence of certain molecules, as determined by a combination of antibody staining and transgenic mouse lines. For validation, cells were imaged using LM to reveal their anatomical properties. This molecular classification mostly matched the anatomical classification of Ghosh et al., 2004Ghosh K.K. Bujan S. Haverkamp S. Feigenspan A. Wässle H. Types of bipolar cells in the mouse retina.J. Comp. Neurol. 2004; 469: 70-82Crossref PubMed Scopus (313) Google Scholar. However, two of the anatomical types were further subdivided, as explained below. Ghosh et al., 2004Ghosh K.K. Bujan S. Haverkamp S. Feigenspan A. Wässle H. Types of bipolar cells in the mouse retina.J. Comp. Neurol. 2004; 469: 70-82Crossref PubMed Scopus (313) Google Scholar lumped Types 3a and 3b together in Type 3 because they stratify in the same IPL sublayer. Wässle et al., 2009Wässle H. Puller C. Müller F. Haverkamp S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina.J. Neurosci. 2009; 29: 106-117Crossref PubMed Scopus (313) Google Scholar distinguished between Types 3a and 3b using antibodies against HCN4 and PKARIIb, following the lead of Mataruga et al., 2007Mataruga A. Kremmer E. Müller F. Type 3a and type 3b OFF cone bipolar cells provide for the alternative rod pathway in the mouse retina.J. Comp. Neurol. 2007; 502: 1123-1137Crossref PubMed Scopus (95) Google Scholar. Wässle et al., 2009Wässle H. Puller C. Müller F. Haverkamp S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina.J. Neurosci. 2009; 29: 106-117Crossref PubMed Scopus (313) Google Scholar also argued that Type 5 should be divided into Types 5a and 5b, but they provided no molecular criteria by which the distinction could be made. Then they declared victory, saying that “our proposed catalog of 11 cone BCs and one rod BC is complete, and all major BC types of the mouse retina appear to have been discovered.” This brings up an important methodological question. When classifying cell types, how do we know when homogeneity is achieved? For example, how do we know that Types 3a and 3b are the final answer? Perhaps they are also mixtures of types, and should be further subdivided. It has become accepted that the axons of a BC type cover the area of the retina with little overlap, a property known as “tiling” (Figure 3). Wässle et al., 2009Wässle H. Puller C. Müller F. Haverkamp S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina.J. Neurosci. 2009; 29: 106-117Crossref PubMed Scopus (313) Google Scholar showed that the axons of Type 3 BCs overlap heavily, violating the tiling property. They also showed that Type 3a axons tile the retina, and the same is true for Type 3b. Therefore, they concluded that Types 3a and 3b are pure types rather than mixtures and should not be subdivided further. Similarly, Wässle et al., 2009Wässle H. Puller C. Müller F. Haverkamp S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina.J. Neurosci. 2009; 29: 106-117Crossref PubMed Scopus (313) Google Scholar showed that Type 5 cells overlap excessively and, on this basis, proposed that they should be divided into Types 5a and 5b. Ghosh et al., 2004Ghosh K.K. Bujan S. Haverkamp S. Feigenspan A. Wässle H. Types of bipolar cells in the mouse retina.J. Comp. Neurol. 2004; 469: 70-82Crossref PubMed Scopus (313) Google Scholar stained BCs by microinjection, while Wässle et al., 2009Wässle H. Puller C. Müller F. Haverkamp S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina.J. Neurosci. 2009; 29: 106-117Crossref PubMed Scopus (313) Google Scholar used antibody staining and transgenic mouse lines. These techniques could yield a biased sample of BCs, causing some types to be missed. To test for this possibility, Wässle et al., 2009Wässle H. Puller C. Müller F. Haverkamp S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina.J. Neurosci. 2009; 29: 106-117Crossref PubMed Scopus (313) Google Scholar computed the density of each BC type (i.e., cell bodies per square millimeter). When they added the densities together, they found good agreement with the total density of all BC types (Jeon et al., 1998Jeon C.J. Strettoi E. Masland R.H. The major cell populations of the mouse retina.J. Neurosci. 1998; 18: 8936-8946Crossref PubMed Google Scholar) and concluded that they had identified all “major BC types.” In other words, any hypothetical missing type would have to be rare. To summarize, declaring victory in cell type classification involves two claims, one of accuracy and the other of completeness. Accuracy means that" @default.
• W2016397514 created "2016-06-24" @default.
• W2016397514 creator A5044273339 @default.
• W2016397514 creator A5079933409 @default.
• W2016397514 date "2014-09-01" @default.
• W2016397514 modified "2023-10-18" @default.
• W2016397514 title "Neuronal Cell Types and Connectivity: Lessons from the Retina" @default.
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• W2016397514 doi "https://doi.org/10.1016/j.neuron.2014.08.054" @default.
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