Embryonic and postnatal neurogenesis produce functionally distinct subclasses of dopaminergic neuron

Abstract

Most neurogenesis in the mammalian brain is completed embryonically, but in certain areas the production of neurons continues throughout postnatal life. The functional properties of mature postnatally-generated neurons often match those of their embryonically-produced counterparts. However, we show here that in the olfactory bulb (OB), embryonic and postnatal neurogenesis produce functionally distinct subpopulations of dopaminergic (DA) neurons. We define two subclasses of OB DA neuron by the presence or absence of a key subcellular specialisation: the axon initial segment (AIS). Large AIS-positive axon-bearing DA neurons are exclusively produced during early embryonic stages, leaving small anaxonic AIS-negative cells as the only DA subtype generated via adult neurogenesis. These populations are functionally distinct: large DA cells are more excitable, yet display weaker and more broadly-tuned responses to odorant stimuli. Embryonic and postnatal neurogenesis can therefore generate distinct neuronal subclasses, placing important constraints on the functional roles of adult-born neurons in sensory processing.

Introduction

The adult central nervous system has been long believed to be incapable of self-regeneration. Five decades ago, however, pioneering studies revealed the existence of well-defined neurogenic niches in the brain of adult rodents (Altman, 1962). Such neurogenic zones are small, spatially defined and give rise to only two broad neuronal populations: hippocampal dentate granule cells, and interneurons in the olfactory bulb (OB) (Lledo et al., 2006). These newly-generated cells are believed to bring unique properties to existing networks, largely by virtue of the specialised functional and plastic features associated with their transient immature status (e.g., (Schmidt-Hieber et al., 2004, Ge et al., 2007, Gu et al., 2012, Marin-Burgin and Schinder, 2012, Carleton et al., 2003, Nissant et al., 2009, Livneh et al., 2014), but see (Sailor et al., 2016)). Once fully mature, though, the functional properties of adult-generated neurons in both the hippocampus and olfactory bulb often closely match those of their developmentally-generated neighbours (e.g., (Carleton et al., 2003, Laplagne et al., 2006, Marin-Burgin and Schinder, 2012, Grubb et al., 2008, Nissant et al., 2009), but see (Valley et al., 2013)). Does this mean, then, that within broad classes of neuron – for example, dentate granule cells, or OB granule cells – embryonic and postnatal neurogenesis always produce fundamentally similar cell types?

Among the wider population of adult-generated OB cells is a heterogeneous group of inhibitory neurons situated in the structure’s glomerular layer, whose main role is to modulate the earliest stages of sensory information processing (Alonso et al., 2012, Livneh et al., 2014, Livneh and Mizrahi, 2012, Grubb et al., 2008, Fukunaga et al., 2014). Different subclasses of glomerular layer interneuron can be identified by their specific expression of calcium-binding proteins (Kosaka and Kosaka, 2011), while another major subclass is identified by its ability to co-release both GABA and dopamine (Borisovska et al., 2013, Vaaga et al., 2017). These dopaminergic (DA) interneurons can be generated via adult neurogenesis (De Marchis et al., 2007, Adam and Mizrahi, 2011, Bonzano et al., 2016), and the survival of postnatally-generated DA cells is activity-dependent (Bonzano et al., 2014). Moreover, adult-born DA cells have been shown to contribute to specific olfactory behaviours (Lazarini et al., 2014).

In recent years the manner in which resident and adult-generated DA neurons contribute to olfactory processing has been widely studied, and there is now an accumulating - and sometimes contrasting - body of evidence on the role played by DA cells in glomerular circuits. A wide spectrum of functions has been proposed for these cells, involving either local or broadly-distributed actions within the glomerular layer, and roles as diverse as modulating release from olfactory sensory neuron terminals, signal normalisation, contrast enhancement, and temporal decorrelation (Banerjee et al., 2015, Cavarretta et al., 2016, Economo et al., 2016, Liu et al., 2013, Mainland et al., 2014, Pignatelli and Belluzzi, 2017, Roland et al., 2016, Vaaga et al., 2017). The complexity and sometimes mutually exclusive nature of such functions – especially in relation to spatial connectivity – make it unlikely that a single class of interneuron could perform them all. Indeed, morphological variability has been demonstrated among OB DA neurons, and has been linked to their time of birth (Halasz et al., 1981, McLean and Shipley, 1988, Pignatelli and Belluzzi, 2017, Pignatelli et al., 2005, Kiyokage et al., 2010, Kosaka and Kosaka, 2007, Kosaka et al., 2008, Kosaka and Kosaka, 2011, Kosaka and Kosaka, 2016, Kosaka and Kosaka, 2009). However, no discrete features demarcating distinct OB DA subpopulations have yet been identified. More importantly, nothing is currently known regarding the functional properties of putative OB DA subtypes. Are there physiological differences between embryonically-generated and adult-born DA cells? And might such differences start to account for the various functional roles ascribed to this cell type in sensory processing?

Here, we build on previous work in vitro (Chand et al., 2015), to show that different classes of OB DA neuron in vivo can be clearly distinguished based on the presence or absence of an axon and its key subcellular specialisation, the axon initial segment (AIS). AIS-positive DA cells are larger, with broader dendritic arborisations, and are exclusively born in early embryonic development. Postnatally-generated DA cells, in contrast, are all small and anaxonic. Crucially, these morphological and ontological distinctions also map onto clear functional differences in both cellular excitability and odorant response properties in vivo, strongly constraining the potential role of adult-born DA cells in sensory processing.

Results

The axon initial segment is only present in a distinct subset of DA cells

To investigate the presence of an axon initial segment (AIS) in DA cells we performed immunohistochemistry on fixed slices of the olfactory bulb of juvenile (P28) wild-type C57Bl6 mice. We identified DA cells by labelling them with an antibody against tyrosine hydroxylase (TH), the rate-limiting enzyme in the biosynthesis of dopamine. For AIS identification we stained for ankyrin-G, the master AIS organising molecule (AnkG, Fig.1A) (Hedstrom et al., 2008, Jenkins et al., 2015, Zhou et al., 1998). While for most TH-positive cells we could not detect AnkG label on any of their processes, we identified a subset of large DA neurons that possessed a clear AnkG-positive AIS (Fig. 1A, middle and right panel).

Figure 1. Two subtypes of DA neuron can be characterised based on size, location and presence of an AIS.

A. Left: example image of olfactory bulb stained with an anti-TH antibody (blue). Dashed lines indicate subregions of the glomerular layer (GL). The asterisk indicates an AlS-positive DA cell. Middle, right: zoomed image of the aster-isked cell from the left panel, co-stained for TH (blue) and the AIS marker ankyrin-G (AnkG, magenta or greyscale). The solid line indicates the emergence of the axonal process from the soma; arrows indicate AIS start and end positions.

B. Frequency plots showing soma area of the general DA population (blue, filled area, n=519, N=3), and of the subset of DA neurons that possesses an AIS (magenta line, n=271, N=6).

C. Locations in the GL for both the general DA population (blue, mean±sem; n=888, N=6) and AlS-positive DA neurons (magenta, mean±sem; n=127, N=6)

Soma size quantification revealed these AIS-containing DA neurons to be morphologically distinct. As shown in Figure 1B, the soma area distribution of the general bulbar TH+ population is clearly not unimodal (blue distribution): most DA cells are relatively small (peak 55 μm²), but there is a distinct minority that are significantly larger (peak 140 μm²) (Kosaka and Kosaka, 2007, Pignatelli et al., 2005, McLean and Shipley, 1988). In contrast, performing a similar analysis solely for TH+/AnkG+ cells (i.e. DA neurons with an AIS) produced a unimodal distribution centred on the large-cell peak of the full population curve (Fig. 1B, magenta line; stats). Large AIS-positive cells therefore represent a distinct sub-population of OB DA neurons.

These large, AIS-positive DA neurons are also located in a specific sub-region of the glomerular layer (GL). Dividing the GL into four sub-laminae (Fig. 1A; see Methods) revealed the overall TH-positive population to be concentrated in the mid-GL (Fig. 1C). AIS-positive DA neurons, however, were mostly found in the lower portions of the GL towards the external plexiform layer (EPL) border, with very little presence in the upper or mid-GL (Fig. 1C; (Liberia et al., 2012)).

AIS-lacking DA neurons are anaxonic

The AIS is crucial for the maintenance of axo-dendritic neuronal polarity (van Beuningen et al., 2015), and is often employed as an indicator of axonal identity (e.g. (Watanabe et al., 2012)), so does the absence of an AIS in the majority of small DA neurons mean that these cells do not possess an axon? Addressing this question required us to be able to identify and follow all of a given cell’s individual processes. We therefore achieved sparse label of individual OB DA neurons, either by injecting floxed GFP-encoding viruses (either AAV or RV::dio) in embryos or neonates from VGAT-^Cre or DAT-^Cre reporter lines, or by electroporating GFP-encoding plasmid DNA in wild-type neonates (see Methods). The dopaminergic phenotype of the infected neurons was confirmed by immunohistochemical label for TH. We then adopted a dual strategy for axon identification.

First – as a positive control – we confirmed that while the AnkG-positive processes of large AIS-containing DA cells co-localised with the axonal marker TRIM-46 (Fig2A) (van Beuningen et al., 2015), this axonal marker was entirely absent from the processes of small OB DA neurons (Fig. 2B; n=10, N=3, average soma area 58 μm²). Second – as a negative control – we analysed the expression of the dendritic marker MAP-2 (van Beuningen et al., 2015, Kosik and Finch, 1987, Rolls and Jegla, 2015). DA cells with an AIS express MAP-2 in all processes, even in the axon; however this axonal MAP-2 expression fades where AnkG expression begins (Fig. 2C). Conversely, AIS-negative DA neurons express MAP-2 along the entire length of all their processes (Fig. 2D; n=10, N=3, average soma area 49 μm²). These data strongly suggest that the presence of an AIS is indicative of axonal identity in OB DA cells, and that the small TH-positive neurons that lack an AIS are truly anaxonic.

Figure 2. DA marker MAP-2. urons that lack an AIS also lack the axonal marker TRIM-46, and all their processes co-stain with the dendritic

A. Example image of a DA cell in a wild-type mouse stained for TH (blue}, AnkG (magenta} and the axonal marker TRIM-46 (green}. Asterisks ind icate soma positi on; li nes indicate the emergence of the axonal process from the soma; tr iangles ind icate AIS star t and end positions.

B. Example image of an anaxonic DA cell in a DAT-Cre mouse injected at E12 with rv::dio-GFP, stained for TH (blue} and TRIM -46 (magenta}. Asterisks indicate soma position; triangle shows a TRIM -4 6- and TH-positive process belonging to a neighbouring, non-GFP-expressing cell.

C. Example image of an AIS-containing DA cell in a DAT-Cre mouse injected at E12 with rv::dio-GFP, stained for TH (blue), MAP-2 (orange) and AnkG (grey). Asterisks indicate soma position; triangles indicate AIS start and end positions; numbers and arrows indicate the three main processes emerging from the soma. Note that MAP-2 fluorescence in the axon (process 1) ends when AnkG fluorescence begins.

D. Left: Maximum intensity projection image of an anaxonic DA cell in a wild-type mouse electroporated with GFP at Pl, stained for TH (blue} and MAP-2 (orange). The asterisks indicates soma position; numbers and arrows indicate the three main processes emerging from the soma. Right: panels 1-3 show single z-plane images of each dendriti c process,visualised with GFP plus MAP-2 label (top} or TH plus M AP-2 label (bottom}. Note that all processes are positive for all three markers.

Broader dendritic branching in AIS-positive DA neurons

Sparse labelling of individual OB DA neurons also allowed us to investigate their dendritic morphology (Fig. 3A), and this again revealed clear differences between AIS-positive and AIS-negative subtypes. Small, anaxonic DA neurons had limited dendritic arborisations that ramified across a small region of the glomerular layer (Fig. 3B, C, G). By contrast, the dendrites of large, axon-bearing DA cells were much more broadly spread (Fig. 3D, H). Despite considerable cell-to-cell morphological variability within each sub-class (Fig. 3G, H), these differences were highly significant (Fig. 3F; furthest branch distance, AIS-negative, mean ± SEM 77 ± 8 μm, n=14; AIS-positive, 148 ± 14 μm, n = 9; t₂₁ = 4.66, p = 0.0001). This is all the more striking given the thin OB slices necessary for AnkG label, and the likely resulting underestimation of glomerular layer ramification by AIS-positive DA neurons.

Figure 3. AlS-positive DA cells have more widely ramified dendritic morphology

A. Schematic representation of the experimental strategy adopted to achieve sparse labelling of DA cells: Pl-2 neonates or E12 embryos from VGAT-Cre or DAT-Cre lines were injected with floxed AAV-YFP or rv:: dio-GFP viruses. Tissue was collected for analysis at P28.

B-C. Example images of OB DA cells sparsely labelled with GFP (green), co-stained for TH (blue). GL, glomerular layer; EPL, exter-nal plexiform layer. AlS-negative DA cells ramify their dendritesnarrowly.

D.Example image of a GFP-labelled, AlS-positive DA cell which ramifies more broadly, co-stained for TH (blue) and AnkG (magenta). Right: zoomed insets showing GFP, TH and/or AnkG label; line indicates axon start; triangles show AIS start and end positions. E. Soma area of sparsely labelled DA neurons without (black) and with (magenta) an AIS. Circles represent individual cells; barplots show mean ± SEM (n=29, N=6); ^**, p < 0.01.

F. Furthest intersection with Sholl circles of processes belonging to reconstructed DA neurons without (black) and with (magenta) an AIS. Circles represent individual cells; bar plots show mean ± SEM (n=23, N=6); ^***, p < 0.001.

G. Morphological reconstructions of 23 sufficiently sparsely-labelled DA neurons without (top) and with (bottom) an AIS. Approximate AIS location is indicated with a magenta square.

AIS-positive DA neurons are exclusively born during early embryonic development, but anaxonic DA cells continue to undergo postnatal and adult neurogenesis

Glomerular layer interneurons in the OB, including TH-positive DA neurons, belong to the highly restricted group of neuronal types capable of regenerating throughout life via adult neurogenesis (Betarbet et al., 1996, De Marchis et al., 2007, Winner et al., 2002, Bonzano et al., 2016). This prolonged neurogenic capacity is often considered to be a universal and hallmark feature of these inhibitory interneurons (e.g. (Liu et al., 2013)). However, data from birthdating experiments suggest that – at least on the basis of soma size – OB DA neurons are not homogeneous in their time of generation (Kosaka and Kosaka, 2009). This prompted us to ask whether the two morphological subtypes of AIS-positive and AIS-negative OB DA neuron also differ developmentally.

To address this question we performed classic pulse-chase birthdating experiments. We injected pregnant mice with the thymidine analogue bromodeoxyuridine (BrdU) at different gestational days starting from embryonic day (E) 11, when the nascent olfactory bulb has begun to appear. We then collected tissue from the progeny once they reached one month of age, and from the mothers themselves to analyse adult-generated neurons (Fig. 4A). We also labelled neonatally-generated bulbar interneurons via postnatal electroporation of GFP-encoding plasmid DNA injected into the lateral ventricles at P1 (Fig. 4A). Cells were then immunostained for BrdU or GFP, along with TH and – because of the histological processing necessary for BrdU detection – a more robust but as yet mysterious marker of the AIS: the unidentified microtubule-associated protein labelled by the ‘pIκBα’ antibody (Buffington et al., 2012). For each injection time point we analysed all cells that were both BrdU and TH positive, measuring soma area, and noting the presence or absence of an AIS. The results, presented in Fig. 4B-H, clearly show that with increasing birth age the soma area distributions lose their right-end tail, indicating that the large DA neurons are mostly born during early development (Kosaka and Kosaka, 2009). The developmental distinction between AIS-positive and –negative DA cells, however, was even clearer. AIS-positive DA neurons were exclusively born in embryonic development, with a clear peak in their generation at E11-12 (Fig. 4B,C,H) and only very few being produced between E13 and E18 (Fig. 4D-F,H). We did not find a single neonatal-or adult-born OB DA neuron that possessed an AIS (Fig. 4G,H).

Figure 4. AlS-positive DA cells are generated exclusively in early embryonic development.

A. Schematic representation of the experimental strategy for birthdating experiments:pregnant wild-type mice were injected with a single dose of BrdU at different gestationaldays. Tissue was collected from their offspring at 1 month of age (P28), and also from theinjected mothers which constitute the "adult" group. We also labelled neonatally-generated cells by electroporating GFP-encoding plasmid DNA injected into the lateralventricles at PI.

B-H. Left: example images from OB slices stained with antibodies against BrdU (green), TH(blue) and the AIS markers pkba or AnkG (magenta). Asterisks indicate BrdU+/TH+ cells; triangles indicate AIS start and end positions; in E-H AlSes are indicated in BrDU-cells forcomparison. Right: soma area distribution of BrdU+/TH+ DA cells (for each BrdU time pointn=100, N=3; for PI electroporation n=68, N=3; for adult mothers n=70, N=2). Circlesindicate AlS-positive cells (BrdU+/TH+/plKba+) at their respective soma size value.

I. Summary graph indicating the percentage of AlS-positive cells (BrdU+/TH+/plKba+)generated at each developmental time point (mean ± SEM). No AlS-positive DA cells wereborn after E18.

To fully convince ourselves that adult-born DA neurons never possess an AIS, we had to rule out the possibility that these cells took longer than 1.5 months to fully mature. We therefore collected tissue from adult BrdU-injected mice after a prolonged chase period of 4 months (Fig. 5A). We found very few double-labelled BrdU+/TH+ cells (Fig. 5B), and all were AIS-negative. The absence of an AIS in adult-generated cells is therefore not due to insufficient maturation time, and instead reflects a fundamental characteristic of this OB DA subtype.

Figure 5. Fully mature adult-born DA neurons are small and never possess an AIS

A. Schematic representation of the experimental strategy: pregnant wild-type mice were injected with a singledose of BrdU, and their tissue was collected 4 months later to allow full maturation of adult-born neurons.

B. Example image of adult tissue perfused 4 months post BrdU injection, stained with antibodies against BrdU (green), TH (blue) and the AIS marker plKba (magenta). Asterisk indicates a BrdU+ DA cell (TH+/ plKba-), trianglesindicate AIS start and end positions; the arrow indicates a neighbouring large AIS+ DA neuron that is BrdU-negative.

These data strongly suggest that prolonged neurogenic capabilities are not a widespread property of bulbar DA neurons. Instead, adult neurogenesis is restricted to the axonless subpopulation, while large AIS-bearing cells are only born during early developmental stages. This finding raised two immediate questions concerning the production and longevity of AIS-positive neurons: 1) is there a preponderance of large AIS-positive DA cells in the neonate? And 2) do embryonically-generated AIS-positive neurons persist throughout life? To address the first question we collected tissue from newborn pups (P0) and quantified the soma area of TH-labelled DA neurons (Fig. 6A). We indeed found a right-shifted distribution of larger DA neurons at this early postnatal timepoint (Fig. 6B, Kolmogorov-Smirnov test D = 0.3581, p < 0.01; (McLean and Shipley, 1988)). In addition, we found that DA neurons in newborn mice can already possess an AnkG-positive AIS (Fig. 6C), and those AIS-positive neurons are big (soma area mean ± SEM 98 ± 5 μm², n=16, N=2; Fig. 6D).

Figure 6. In neonates there is a preponderance of large DA neurons that can already possess an AIS.

A.Low magnification example images of tissue from PO and P28 wild-type mice stained with an antibodyagainst TH (blue). Arrows indicate cells with a soma area bigger than 100).im²; "GL" indicates glomerular layer, "EPL" indicates external plexiform layer.

B.Soma area distribution of TH+ DA cells in PO mice (teal, n=781, N=3), overlaid on the soma area distri-bution of the general DA cell population at P28 (blue filled line; see Fig. IB); Kolmogorov-Smirnov test between the two distributions **, D= 0.3581, p<0.01.

C. High magnification example image of tissue from a PO mouse stained with antibodies against TH (blue) and AnkG (magenta). Asterisk indicates the soma of an AlS-positive cell; dashed lines show the inset area magnified below; solid line shows axon start; triangles indicate AIS start and end positions.

D.Soma area of TH+/AnkG+ DA cells in PO mice. Empty circles represent individual cells, full circle shows mean ± SEM (110 ± 10 mm2; n=16, N=2).

To address the second question concerning the longevity of AIS-positive DA neurons, we employed a prolonged pulse-chase birthdating protocol. Mice were injected with BrdU at E12, then left until six months of age, when we collected tissue and looked for AIS-positive DA neurons (Fig. 7A). As shown in Fig. 7B, we were still able to find DA neurons born at E12 in the bulb of these fully adult mice. All these neurons were large and the overwhelming majority carried an AIS. When we compared these adult animals to littermates that had also been injected at E12 but perfused at P28, we found that E12-born DA neurons in adult animals were larger (91 ± 4 μm²; Fig 7C,D). Moreover, while in one-month-old animals TH+/E12-BrdU+ cells were relatively abundant but only 25% of them were AIS-positive, in adult mice TH+/E12-BrdU+ cells were rare but over 90% of them possessed an AIS (Fig. 7E). These findings strongly suggest that, of the mixed pool of DA neurons born at E12, the initially abundant AIS-negative cells are turned over at some point before 6 months of age, to be substituted by postnatally-born AIS-negative neurons. In contrast, embryonically-generated, large AIS-positive cells can persist throughout adult life.

Figure 7. AlS-positive cells form the vast majority of embryonically-generated DA neurons that persist through-out adult life.

A. Schematic representation of the experimental strategy: pregnant wild-type mice were injected with a singledose of BrdU at E12. Tissue was collected from their offspring when they reached 6 months of age and compared with data collected from littermates perfused at 1 month of age (data shown in Fig 6C).

B.Left: Low magnification example image of 6-month-old tissue stained with antibodies against BrdU (green), TH (blue) and the AIS marker pkBa (magenta). GL, glomerular layer; EPL, external plexiform layer; asterisk indicates an E12-6mo BrdU+/TH+ DA cell; dashed line indicates the inset magnified on the right. Right: magnified example image of an E12-6mo BrdU+/TH+/plKBa+ cell; solid line indicates axon start; arrows show AIS start and end positions.

C. Soma area distribution of E12-6mo BrdU+/TH+ DA cells (dark red, n=78, N=4), overlaid on the soma area distri-bution of E12-lmo BrdU+/TH+ DA cells (dashed light red line; see Fig. 4C).

D. Soma area of E12-6mo BrdU+/TH+/plKBa+ DA cells. Empty circles represent individual AlS-positive neurons, dark red lines show mean ± SEM (102 ± 4 j.im²; n=71, N=4).

E. Mean ± SEM percentage of AlS-positive E12-BrdU+/TH+ DA cells in tissue from 1-month-old (light red, 25%; n=100, N=3) and 6-month-old (dark red, 91%; n=78, N=4) mice.

AIS-positive DA neurons possess distinct intrinsic functional properties

Our morphological and developmental analyses revealed a clear distinction between - on the one hand – embryonically-born, large and widely-branching DA neurons with an axon and AIS, and – on the other – lifelong-generated, small and locally-ramifying anaxonic DA cells that do not have an AIS. But do these marked ontological and structural differences also translate into functional heterogeneity? To test this hypothesis we first performed whole-cell current-clamp electrophysiological recordings on DA neurons in acute ex vivo OB slices.

We visualised DA neurons by crossing the dopaminergic reporter line DAT-Cre (Backman et al., 2006) with a floxed tdTomato (tdT) reporter line (Madisen et al., 2010). In the resulting DAT-tdT mice the majority of TH+ DA cells also expressed tdT (90 ± 4 % of all TH+ neurons were also tdT+; n=369, N=3; Fig. 8A). The rare TH+ neurons that lacked tdT fluorescence tended to be large (Fig. 8B), suggesting that while our genetic labelling strategy comprehensively identified small, anaxonic DA neurons, it under-represented the large, AIS-positive DA subtype. Indeed, co-label for AnkG revealed that only 33% of TH+/AIS+ DA neurons also expressed tdT (Fig. 8B,C). However, tdT expression did not appear to reveal any further subdivision amongst the large, AIS-positive DA cells, because we found no difference in soma size between TH+/AIS+/tdT+ and TH+/AIS+/tdT-neurons (tdT+, mean ± SEM 118 ± 10 μm²; tdT-, 126 ± 5 μm²; Welch’s corrected t_21.12 = 0.66, p = 0.52). So, although DAT-tdT mice do not comprehensively reveal all bulbar DA neurons, visually targeting tdT-positive cells for electrophysiological recordings (Fig. 8C) still enables functional comparisons to be made between AIS-positive and –negative DA cell types.

Figure 8. AIS-positive DA neurons have greater intrinsicexcitability

A. Example image of a fixed, 50 μm OB slice from a P28 DAT-tdT (red) mouse, immunostained with an anti-TH antibody (blue). While most TH-positive neurons exhibit red tdT fluorescence, some are tdT-negative (arrows).

B. Soma size distributions of all DAT-tdT-positive cells (red), and of DA neurons that are DAT-tdT-negative but TH-positive (blue). Inset: percentages of AlS-positive/TH-positive DA cells that are either tdT-positive or —negative (n=50, N=5).

C. Example image of a DAT-tdT-labelled DA cell (red) stained with the axonal marker TRIM-46 (green) and the AIS marker AnkG (greyscale). Asterisks indicate the soma; line indicates axon start; arrows indicate start and end position of the TRIM (green) and AnkG (white) label.

D. Schematic representation of the experimental strategy for whole-cell recordings: acute 300 μm OB slices were obtained from P21-35 DAT-tdT mice, and tdT-positive DA cells of either subtype were targeted for whole-cell patch-clamp recording.

E. Example current-clamp traces of single APs fired by monophasic (AIS-negative, black, n=15) and biphasic (AIS-positive, magenta, n=11) DAT-tdTomato neurons. Left: action potentials fired to threshold 10 ms somatic current injection. Right: phase plane plots of the spikes shown on the left. Arrow points to the AIS-dependent first action potential phase.

F. Quanfification of soma area (t-test; ***, p = 0.0006), current threshold (Welch-corrected t-test;, p = 0.017), and onset rapidness (Welch-corrected t-test;, p = 0.035) ¡n monophasic and biphasic cells. Empty circles show values from individual cells, filled circles show mean ± SEM.

G. Top: Example current-clamp traces of multiple APs fired ¡n response to a 300pA I 500 ms somatic current injection in monophasic and biphasic cells. Bottom: input-output curve of injected current density versus mean ±SEM spike number for each group.

H. Quantification of input-output slope (t-test; *, p=O.044), and of the maximum number of action potentials fired by each cell over the whole range of injected current intensities (t-test;, p = 0.0092). Empty circles show values from individual cells; filled circles show mean ± SEM.

I. Classification of DAT-tdT neurons based on values obtained from whole-cell recordings. Each circle shows one cell, plotted accord¡ng to its primary and secondary PCA component scores (these components accounted for 92% and 7% of the variance ¡n the data, respectively). Filled circles show cells correctly classified by k-means analysis; open circles show the few cells (3/26 overall) that were incorrectly classified.

Post-recording survival of bulbar DA cells for morphological or immunohistochemical analysis is notoriously difficult (A. Pignatelli, personal communication); this meant that we could not classify our recorded neurons as AIS-positive or -negative on the basis of AnkG staining. Instead, we relied on a functional indicator of AIS presence: non-somatic action potential (AP) initiation. In phase-plane plots of single spikes fired in response to 10 ms somatic current injection, the site of AP generation can be inferred from the shape of the initial, rising component of the spike waveform (Khaliq et al., 2003, Chand et al., 2015, Coombs et al., 1957, Jenerick, 1963, Shu et al., 2007, Bean, 2007). While a smooth, monophasic phase plane plot is indicative of AP initiation at the somatic recording site, cells that initiate spikes at a distance from the electrode location – almost always at the AIS (Coombs et al., 1957, Foust et al., 2010, Palmer and Stuart, 2006, Kole et al., 2007, Bender and Trussell, 2012) - display a distinctive biphasic, or ‘double-bumped’ phase plane plot waveform (Fig. 8E). We therefore divided our recorded DAT-tdT+ cells into monophasic and biphasic groups, which should be largely representative of AIS-negative and AIS-positive DA neurons, respectively. Indeed, we found that biphasic neurons were significantly larger than their monophasic counterparts (Fig. 8F; Table 1; (Chand et al., 2015)).

Table 1. Intrinsic electrophysiological properties of DAT-tdTomato neurons.

Mean values ± SEM of passive, action potential and repetitive firing properties for monophasic (putative AIS-negative, n=15) and biphasic (putative AlS-positive, n=ll) DAT-tdTomato cells. Statistical differences between groups (monophasic vs biphasic) were calculated with a Student’s t test for normally-distributed data ("t") or with a Mann-Whitney test for non-normally distributed data ("MW"). Grey shading indicates statistically different measures, for which individual data points and example traces are presented in Figure 5.

We also identified several differences in intrinsic excitability between monophasic and biphasic DAT-tdT cells. Biphasic neurons generated single APs in response to lower-amplitude somatic current injection, and initiated those APs more rapidly (Fig. 8F; Table 1). When induced to fire repeatedly in response to longer-lasting 500 ms somatic current injections of increasing intensity, biphasic cells displayed a linear input-output curve. Conversely, monophasic cells could not produce such a linear increase in spike number and soon reached a firing plateau (Fig. 8G; Table 1). This resulted in monophasic cells having a significantly lower slope of their input-output curve, and a significantly lower maximum number of fired APs (Fig. 8H; Table 1). While these differences in intrinsic excitability are certainly consistent with reported functional characteristics of AIS-positive versus AIS-negative neurons (Chand et al., 2015, Zonta et al., 2011, Zhou et al., 1998), we cannot rule out contributions from other, non-AIS-dependent factors (Eyal et al., 2014, Baranauskas et al., 2013, Pignatelli et al., 2009). Nevertheless, and regardless of their underlying cause, these physiological differences point to significantly greater intrinsic excitability in the biphasic, presumptive AIS-possessing DA subpopulation.

Finally, we asked whether the above measures from our whole-cell recordings could be reliably used to classify DAT-tdT neurons as belonging to either the biphasic/AIS-positive or the monophasic/AIS-negative subtype. Applying principal component analysis (PCA; see Methods) to the five variables that differed significantly between mono- and biphasic DA cells generated primary and secondary component scores for each neuron that, when plotted against each other, revealed clear clustering by cell type (Fig. 8I). Furthermore, using a k-means classification approach with the same data (see Methods) we were able to assign our recorded cells to either the mono- or biphasic group with 85% accuracy. This suggests that, although there is considerable overlap in the functional properties of different subclasses of OB DA neuron, when taken together those properties reveal a significant distinction between AIS-positive and AIS-negative cell types.

Large, putative AIS⁺ DA neurons respond more weakly yet are more broadly tuned to odour stimuli

We next asked whether the morphological and physiological differences between the two subtypes of OB DA neuron are associated with distinct sensory response properties in vivo. Do different types of OB DA cell respond differently to olfactory stimuli in the intact animal?

To address this question, we employed a conditional mouse line in which the Cre-dependent Ca²⁺ indicator GCamP6s was selectively expressed in OB DA neurons under the control of the DAT promoter (see Methods). We could then characterise the sensory response properties of these cells by monitoring changes in GCaMP fluorescence while animals were presented with a panel of eight odour stimuli (see Methods; Fig. 9A; (Kapoor et al., 2016)). Given the current lack of a reliable in vivo live AIS marker, we classified DA neurons in these experiments using the proxy measure of soma size – this can be readily measured in live neurons, and is consistently associated with AIS-positive or-negative identity across multiple datasets (Figs. 1, 3, 8). Our upper bound for cells classed as ‘small’ was 70 μm², taken from the mean soma size of confirmed AIS-positive DA cells (Fig. 1B, magenta distribution) minus two standard deviations (i.e. 136.7 – 2 x 33.2 μm²). Under an assumption of normality, this cutoff excludes all but the smallest 2.5 % of AIS-containing neurons. Similarly, our lower bound for cells classed as ‘big’ was 99 μm², taken from the mean soma area of confirmed AIS-negative DA neurons labelled via GFP electroporation at P1 (Fig. 4H) plus two standard deviations (i.e. 65.6 + 2 x 16.6 μm²). Again assuming normality, this cutoff excludes all but the biggest 2.5 % of AIS-lacking cells.

Figure 9. Large putative AlS-positive DA cells respond less strongly to odours and are more broadly tuned.

A. Schematic representation of the experimental strategy for in vivo recordings: adult DAT-GCaMP6s mice (anaesthetised with ketamine/xylazine) were presented with a panel of eight odours. Resulting changes in GCaMP fluorescence in DA neurons were imaged through a cranial window positioned over the OB.

B.Example field of view of the deeper part of the glomerular layer used for image acquisition (sum intensity projection of time axis, enhanced contrast). Fields of view were selected so as to contain both ‘big’ (soma area > 99 μm², putatively AlS-positive; magenta arrow) and ‘small’ (soma area < 70 (μm², putatively AlS-negative; green arrow) DA neuron types.

C.Representative examples of the three categories of Af/f GCaMP responses that we observed in the dataset. Deflections from baseline were considered events when they exceeded a threshold (horizontal dashed line) set at 3 times the baseline standard deviation. Excitatory responses occurred either quickly after odour presentation (early excitatory responses,[T]; recorded in 52% of cells) or on a later timeframe (late excitatory responses, [/"]; recorded in 22% of cells). The early/late cut-off value was 6.25s (vertical dashed line; see Methods). Approximately a fifth of all cells also showed supra threshold negative deflection from baseline (inhibitory responses [i]; present in 25% of cells). Green circle indicates absolute maximum or minimum value (peak) and green line shows the mean of the 3s around the peak; yellow line indicates the 3 sec of baseline prior to odour presentation (blue box).

D. Example Af/f GCaMP responses to the eight odours (rows, 3s stimulus timing is indicated by blue shaded bars) for three big and three small example cells (columns; soma areas are indicated below the responses) imaged in the same mouse. Significant responses are indicated as: t early excitatory, s’ late excitatory, i inhibitory.

E. Mean values of odour tuning indices for early excitatory (left), late excitatory (middle) and inhibitory responses (right) measured in small (n = 611) and big (n = 639) cells. Coloured dots indicate mean values for each cell type from each of the 13 imaged mice. The grayscale colour of the connecting lines indicates the number of recorded cells for each mouse (scale below). Cell-type effect in multilevel ANOVA; n.s., non significant; **, p < 0.01; * **, p < 0.0001.

F. Similar to E. Median values of odour peak intensity for early excitatory (left), late excitatory (middle) and inhibitory responses (right). Cell-type effect in multilevel ANOVA; *, p <0.05; p <0.0001.

We then analysed the odorant response properties of small/putative AIS-negative (n = 611) and big/putative AIS-positive (n = 639) GCaMP+ cells imaged in 13 mice. It immediately became apparent that different forms of odour-evoked responses could occur in these neurons (see Methods; Fig. 9B). In many cases a given odorant stimulus produced a relatively rapid increase in GCaMP fluorescence that then decayed back towards baseline – these responses, which we termed ‘early excitatory’ events, were the most prevalent form of odour-evoked signal in our DAT-GCaMP neurons (at least one early excitatory response was observed in 652/1250 = 52 % of cells). In other cases, stimuli produced an increase in GCaMP intensity that had a delayed onset and peaked late in a given recording sweep – these ‘late excitatory’ events were readily and objectively distinguishable from early excitatory events (see Methods; Fig. 9C) and were less frequent in our sample (272/1250 = 22 % of cells had at least one late excitatory response). Finally, we observed reasonably common examples of decreased GCaMP fluorescence upon odorant presentation. These ‘inhibitory’ events (at least one seen in 318/2150 = 25 % of cells) usually had delayed onset, and were perhaps detectable because of the characteristically high spontaneous activity levels in OB DA neurons (Pignatelli et al., 2005, Chand et al., 2015, Puopolo et al., 2005). Most of these response types occurred in isolation, although we did see some examples of combined early excitatory-inhibitory responses (at least one seen in 84/1250 = 7 % of cells). Overall, 846/1250 = 68 % of imaged DAT-GCaMP+ cells displayed at least one of these response types evoked by at least one odour stimulus.

All forms of odorant-evoked GCaMP response were observed in both big and small OB DA cell types (Fig. 9D). There were, however, some significant differences in their relative prevalence in the two neuronal populations. Paired, within-animal comparisons for the three major response types across the 13 mice in our sample revealed no significant differences in the proportions of small vs big DA neurons that displayed at least one odorant-evoked fast excitatory (small cells, mean ± SEM 46 ± 7 %; big cells 50 ± 6 %; paired t-test, t₁₂ = 1.05, p = 0.31), or slow excitatory (small cells, 19 ± 3 %; big cells 25 ± 4 %; paired t-test, t₁₂ = 1.37, p = 0.19) response. However, we did see a significantly higher proportion of inhibitory-responding neurons amongst the big cell population (small cells, 16 ± 4 %; big cells 27 ± 6 %; Wilcoxon test, W₁₃ = 62, p = 0.012).

To interrogate sensory stimulus selectivity further, we calculated a simple ‘tuning index’ (TI) for each response type in each cell, from the sum of all stimuli producing a significant change in GCaMP fluorescence (see Methods). Cells with higher TI values responded to more odorants in our 8-stimulus panel. Although we acknowledge that this cannot represent a comprehensive description of tuning across all of odour space, this measure nevertheless allowed us to detect differences in response selectivity to a select group of odorant stimuli known to activate broad regions of the dorsal OB (Livneh et al., 2014, Rokni et al., 2014). In line with previous observations (Banerjee et al., 2015), we observed broad representations of odours in the responses of OB DA neurons (Fig 9E). The mean TI value for all excitatory responses (early+late combined) was 1.99 across all neurons in our sample, rising to 3.26 within the subset of neurons that displayed at least one excitatory response. Overall, this broad tuning was shared by both big and small OB DA sub-populations. However, we did observe significant cell-type-dependent differences in odour selectivity for particular response types. Importantly, we found only very weak correlations between TI measures calculated for the three major forms of odour-evoked response (early excitatory vs late excitatory, Spearman r = 0.018, p = 0.52; early excitatory vs inhibitory, r = 0.12, p < 0.0001; late excitatory vs inhibitory, r = 0.069, p = 0.014; n = 1250 in all cases), suggesting that TI values for early excitatory, late excitatory and inhibitory events represent rather independent measures of tuning for distinct types of response produced by glomerular layer circuitry. To compare these TI measures between cell types, we needed powerful statistical tests that could leverage the large numbers of imaged neurons in our dataset whilst accounting for significant across-animal variability (see Methods; Fig. 9E). We therefore employed multilevel ANOVA analyses, where TI values from individual cells were compared between small versus big cell populations nested in animal subjects (see Methods; (Aarts et al., 2014)). Using this approach, we found no effect of cell type on early excitatory TI values (Fig. 9E; fixed effect of cell-type in multilevel ANOVA, F_1,1248 = 0.07, p = 0.79). For both late excitatory and inhibitory response types, though, the effect of cell type on TI was highly significant (Fig. 9E; late excitatory, F_1,1248 = 7.18, p = 0.007; inhibitory, F_1,1248 = 12.46, p < 0.0001), with big cells possessing consistently larger TI values on a mouse-by-mouse basis. When responding to odorant stimuli with late excitatory or inhibitory events, therefore, big OB DA cells are significantly more broadly tuned than their small-soma neighbours.

Could this broader tuning in big OB DA neurons be explained by larger, more readily detectable odour-evoked responses in this cell type? Actually, measures of event amplitudes revealed the opposite to be the case: big cells had significantly weaker responses to odorant stimuli, a highly significant effect that held across all response types (Fig. 9F; early excitatory, fixed effect of cell-type in multilevel ANOVA, F_1,649 = 6.44, p = 0.011; late excitatory, F_1,270 = 4.67, p = 0.032; inhibitory, F_1,317 = 21.13, p < 0.0001). Despite their higher intrinsic excitability (Fig. 8), big, putative AIS+ DA neurons therefore do not display stronger responses to sensory stimuli in vivo. This unexpected effect may be because of fundamental differences between sensory stimulation in vivo versus direct electrical stimulation in vitro, or it may be due to cell-type differences in synaptic connectivity, or in the modulation of intrinsic properties in the intact OB. Additionally, it could be related to another feature of in vivo GCaMP activity: baseline fluorescence. Resting fluorescence was significantly higher in big cells (fixed effect of cell-type in multilevel ANOVA, F_1,626 = 12.04, p = 0.001), while baseline noise was significantly lower in this cell type (F_1,1245 = 32.68, p < 0.0001), and both measures, especially noise, correlated strongly with all response amplitude measures (baseline fluorescence vs early excitatory amplitude, Spearman r = -0.49, p < 0.0001, n = 632; vs late excitatory amplitude, r = -0.41, p < 0.0001, n = 261; vs inhibitory amplitude, r = -0.55, p < 0.0001, n = 303; baseline noise vs early excitatory amplitude, r = 0.66, p < 0.0001, n = 652; vs late excitatory amplitude, r = 0.69, p < 0.0001, n = 272; vs inhibitory amplitude, r = 0.83, p < 0.0001, n = 318). The increased buffering capacity associated with higher resting GCaMP levels (Svoboda et al., 1999) could therefore lead to dampened response amplitudes in big cells. Additionally, lower spontaneous fluctuations in resting activity could allow big OB DA neurons to significantly respond to odorant stimuli with lower amplitude events. However, these cell-type distinctions in baseline activity cannot account for the differences in response selectivity between big and small cell populations (Fig. 9E). Not only did we see identical big versus small cell selectivity for early excitatory events when baseline differences might be expected to influence tuning across all response types, we also observed only weak and inconsistent correlations with the different TI measures for both baseline fluorescence and noise (baseline fluorescence vs early excitatory TI, Spearman r = -0.04, p = 0.16; vs late excitatory TI, r = 0.15, p < 0.0001; vs inhibitory TI, r = 0.14, p < 0.0001; n = 1197 in all cases; baseline noise vs early excitatory TI, r = 0.081, p = 0.004; vs late excitatory TI, r = -0.17, p < 0.0001; vs inhibitory TI, r = -0.14, p < 0.0001; n = 1250 in all cases).

Overall in terms of odorant response properties, therefore, big/putative AIS-positive and small/putative AIS-negative OB DA neurons differ significantly in: 1) the broader selectivity of big cells for specific odour-evoked response types; and 2) the higher resting fluorescence, lower baseline noise, and smaller response amplitudes of big cells. Moreover, these two major functional features appear to be largely independent of each other.

Discussion

Our results demonstrate the existence of two subtypes of OB DA neurons with distinct morphological, developmental and – crucially – functional characteristics. The majority of DA cells are small, locally-projecting, anaxonic neurons which fire low numbers of somatic action potentials; they are continuously generated and turned over throughout life, and have stronger odorant-evoked responses that for certain event types are more narrowly tuned. Conversely, a minority of DA cells are large, wide-branching and equipped with an axon and an AIS, from which they generate high frequency discharges of action potentials. These AIS-positive DA cells are born exclusively during early embryonic development and persist throughout life; their high excitability is nevertheless associated with weaker sensory-evoked responses in vivo, some types of which are more broadly tuned to odorant stimuli.

Cell-type identity and functional diversity in neuronal circuits

An absolutely crucial step in understanding information processing in any neuronal network is to build an accurate classification of its component parts (Zeng and Sanes, 2017). Cell-type identity – as determined by ontology, gene expression, morphology, connectivity, and/or physiology – is intimately linked to the functional role that any neuron can play in a given circuit. It is therefore no surprise that in recent attempts to model realistic network operations, a great deal of effort has been spent delineating just how many component parts those networks contain. In different regions of the mammalian brain, we now have comprehensive descriptions of cell-type diversity with regards to, for instance, gene expression (e.g. (Romanov et al., 2017, Tasic et al., 2016, Zeisel et al., 2015), neuronal morphology (e.g. (Cerminara et al., 2015, Parekh and Ascoli, 2015)), synaptic connectivity (e.g. (Morgan et al., 2016)), and sensory response properties (e.g.(Baden et al., 2016)), as well as combinatorial cellular-level identification schemes that multiplex several levels of description (e.g. (Fuzik et al., 2016, Markram et al., 2015, Sanes and Masland, 2015)). These studies show that broad cell-type distinctions must be supplemented by fine-scale subdivisions within different cell types in order to fully understand network function. Such classification schemes are no less vital in our understanding of information processing in olfactory bulb circuits, where a uniquely modular topographic organisation of sensory inputs, coupled with the constant remodelling associated with both peripheral and central adult neurogenesis, promises novel insight into the way the brain interprets and adapts to the outside world.

However, our current understanding of functional diversity amongst neuronal populations in the olfactory bulb is far from complete. In glomerular layer circuits –the first networks to process sensory information arriving from the periphery – there is at least broad consensus on the division of juxtaglomerular neurons into excitatory and inhibitory cell types: glutamatergic, vGlut-expressing external tufted cells are, on the whole, readily distinguished from their GABA-positive interneuron neighbours ((Hayar et al., 2004), but see (Tatti et al., 2014)). Furthermore, amongst those GABAergic interneurons are three neurochemically-distinct subpopulations, distinguishable (at least in mouse) by their non-overlapping expression of calretinin, calbindin, and tyrosine hydroxylase (Kosaka and Kosaka, 2007). However, although it has long been recognised that this latter group of TH-positive OB DA neurons are highly heterogeneous (Halasz et al., 1981, Pignatelli et al., 2005, Davis and Macrides, 1983, Kosaka and Kosaka, 2007), there has been significant disagreement as to the precise nature of cell sub-type identity within this population. There is as yet no definitive classification of OB DA neurons, even though such a scheme is vital for our understanding of sensory processing functions in glomerular circuits.

Two major differing approaches to classifying OB DA neurons are currently under dispute. In the first, morphological considerations, especially the fact that many OB DA neurons spread their processes across more than one glomerulus (but see (Bywalez et al., 2016)), were used to label all of these neurons as superficial ‘short-axon’ cells (SACs;(Kiyokage et al., 2010)). This DA SAC population was then further subdivided into ‘oligoglomerular’ and ‘polyglomerular’ subtypes based on the extent of ramification across the glomerular layer (Kiyokage et al., 2010). In contrast, the second approach argues that classic morphological descriptions of superficial SACs report a complete lack of glomerular arborisation, and that the term ‘SAC’ should not be used to describe any OB DA neurons (Kosaka and Kosaka, 2011, Kosaka and Kosaka, 2016). Instead, according to this scheme, small-soma DA neurons form a subset of true periglomerular cells (DA-PGCs), while large-soma DA cells that project long distances across the glomerular layer are termed ‘inhibitory juxtaglomerular association neurons’, or IJGAs (Kosaka and Kosaka, 2011, Kosaka and Kosaka, 2016).

This lack of agreement has led to some studies simply grouping all DA neurons into a single neurochemically- or genetically-defined class (e.g. (Banerjee et al., 2015)). While we agree that the dopaminergic-GABAergic phenotype of these cells is one of their most striking characteristics, and thus defines them as a distinct population of OB interneurons, failing to identify important DA subclasses can produce issues in the interpretation of their functional roles within OB networks. The division we observe here may help to clarify matters substantially, and actually appears to fit reasonably well with both of the alternative schemes already proposed. On the one hand, AIS-positive, large OB DA neurons share many features with the ‘polyglomerular’ (Kiyokage et al., 2010) and ‘IJGA’ (Kosaka and Kosaka, 2011, Kosaka and Kosaka, 2016) classes. On the other hand, AIS-negative, small OB DA cells have much in common with the ‘oligoglomerular’ (Kiyokage et al., 2010) and ‘DA-PGC’ (Kosaka and Kosaka, 2011, Kosaka and Kosaka, 2016) subtypes. The AIS-negative class also shares important morphological features with a population of DAT-expressing ‘clasping SACs’ identified by recent live imaging of intracellular fills in acute OB slices (Bywalez et al., 2016), whose distinct dendritic architecture and predominantly juxtaglomerular arborisations would appear to separate them from classically-defined PGCs (Pinching and Powell, 1971, Kosaka and Kosaka, 2016). Most importantly, while soma size and dendritic spread are continuous variables that do not permit simple sub-group identification, the presence or absence of an axon is a discrete feature that should allow for cleaner classification. Indeed, segregating OB DA neurons based on axonal criteria has enabled important functional distinctions to be identified between subgroups (Fig. 8; (Chand et al., 2015)) that were not evident from previous divisions based on continuous measures (Pignatelli et al., 2005, Pignatelli and Belluzzi, 2017). Finally, in terms of nomenclature, we certainly feel that ‘SAC’ is a misleading term for all OB DA neurons, unless it is acknowledged that in some cells the axon in question is so short as to be non-existent. Perhaps a simple distinction between ‘axonic’ and ‘anaxonic’ OB DA neurons will prove both clear and useful, although whether those subgroups represent forms of classically-defined PGC, SAC or other cell types can remain a matter for debate.

Functional roles of axonic vs anaxonic DA neurons in sensory processing networks

The existence of two clear, functionally-distinct subgroups of DA neurons raises the obvious question: how do these two subpopulations contribute to sensory processing? As part of the wider population of GABAergic glomerular layer interneurons, and as a neurochemically-defined single subtype, DA neurons have been proposed to serve a plethora of functions within OB circuits. These include signal normalisation, gain control, contrast enhancement and temporal decorrelation of olfactory sensory information arriving from the periphery (Vaaga et al., 2017, Cavarretta et al., 2016, Economo et al., 2016, Banerjee et al., 2015, Roland et al., 2016, Liu et al., 2013, Mainland et al., 2014, Korshunov et al., 2017, Pignatelli and Belluzzi, 2017). Recognising a functional distinction between axonic and anaxonic DA cells has the potential to simplify this picture somewhat. It also underscores the importance of accurately delineating cell-type identity before attempting to understand circuit function.

Based on their axon-bearing identity – unique amongst glomerular layer interneurons – large, deeplying, highly excitable AIS-positive OB DA neurons are a prime candidate for mediating interglomerular inhibition. Given the distribution of odorant information across glomeruli in a spatial map for odour identity (Murthy, 2011), lateral inhibition between glomeruli might be predicted to enhance contrast between individual stimulus representations, and therefore aid odorant identification and/or discrimination (e.g. (Uchida et al., 2000, Urban, 2002, Linster and Cleland, 2004)). Indeed, the broader dendritic spread of axonic DA neurons (Fig. 3F) and their broad tuning (Fig. 9E; (Banerjee et al., 2015)) might enable them to distribute relatively non-specific output to target glomeruli, setting an inhibitory tone that only distinct, large-amplitude incoming signals will be able to overcome. This lateral signal could be distributed via well-described long-range glomerular layer GABAergic monosynaptic connections onto external tufted cells (Whitesell et al., 2013, Liu et al., 2013, Banerjee et al., 2015), with the resulting inhibition counteracting the important intraglomerular signal amplification performed by that excitatory cell type (Gire and Schoppa, 2009). Connectivity is crucial to the precise mechanisms in operation here, since odour-evoked inhibition, known to depend upon GL interneuron activity (Fukunaga et al., 2014), shows a glomerulus-specific rather than broad blanket tuning signature (Economo et al., 2016). Broadly-tuned information carried between glomeruli by AIS-positive DA neurons could still be glomerulus-specific, though, if the axonic DA neurons receiving input from a given set of glomeruli all send their axons to the same set of distal target glomeruli. This anatomical prediction needs testing. Alternatively, lateral projections from AIS-positive DA neurons may set a truly diffuse inhibitory tone that is dependent upon overall levels of broad glomerular input. This could then produce an overall enhancement of contrast in odorant identification, with odour-specific inhibition being generated instead by local intraglomerular circuitry (see below).

A full understanding of lateral inhibition via axonic OB DA neurons also needs to take into account the complex odour-evoked response properties of these cells. Their especially broad tuning for late, long-latency responses might reflect functionally predominant input from polysynaptic olfactory sensory neurons (OSN)-input-stimulated pathways (Kiyokage et al., 2010, Kiyokage et al., 2017) via widely-ramifying dendrites that mainly target juxtaglomerular rather than ON-targeted glomerular neuropil (Bywalez et al., 2016, Pinching and Powell, 1971), and could have an especially strong impact on delayed components of mitral/tufted cell responses (Bundschuh et al., 2012, Patterson et al., 2013, Chaudhury et al., 2010, Markopoulos et al., 2012). Likewise, the rather common odour-evoked inhibitory events observed in this cell type could arise via local intra- or juxtaglomerular polysynaptic GABAergic signalling (Murphy et al., 2005, Parsa et al., 2015, Shao et al., 2009, Bywalez et al., 2016) and have the potential to produce broadly-tuned lateral disinhibition of ETCs under particular stimulus circumstances. Finally, in addition to their GABAergic hyperpolarisation of distal ETCs, the interglomerular projections of (AIS-positive) OB DAs can induce rebound excitation via dopaminergic D1-receptor activation (Liu et al., 2013). Together with broadly-tuned late excitatory and delayed inhibitory responses to odorant stimuli (Fig. 9), this DA-mediated effect could contribute to the complex modulation of interglomerular dynamics, especially in the later stages of stimulus processing.

Conversely, the morphological and functional features of small, narrowly-tuned, anaxonic OB DA cells suggest that they might contribute significantly to intraglomerular inhibitory signalling. Indeed, if the anatomical findings from large EPL-residing DA neurons in rats (Liberia et al., 2012) apply to all deep-lying axonic OB DA cells to the extent that this cell type cannot effect dendritic neurotransmitter release, all of the intraglomerular inhibition produced by OB DA neurons will have to arise from the AIS-negative, anaxonic subclass. Within individual glomeruli, the release of GABA and/or dopamine can have an inhibitory effect on release probability at OSN presynaptic terminals via the activation of GABA_B and D₂ receptors, respectively (Hsia et al., 1999, Ennis et al., 2001, Korshunov et al., 2017, McGann, 2013, Vaaga et al., 2017). Local GABA release can also provide a brake on recurrent excitatory glomerular networks (Gire and Schoppa, 2009, Murphy et al., 2005, Najac et al., 2011), as well as effecting auto-disinhibition at high input strengths (Parsa et al., 2015). Potentially parallel inhibitory effects of intraglomerular dopamine release on projection neuron dendrites, however, are yet to be demonstrated outside of dissociated culture systems (Davila et al., 2003, Brunig et al., 1999, Vaaga et al., 2017). Such intraglomerular inhibition acting at the levels of both input terminals and projection neuron dendrites has been proposed to subserve highly local gain control, potentially acting as a high-pass temporal and contrast filter to facilitate the detection of strong odorant stimuli (e.g. (Gire and Schoppa, 2009, Cavarretta et al., 2016, Banerjee et al., 2015, Korshunov et al., 2017, Cleland and Sethupathy, 2006). Indeed, we suggest that the in vivo effects on OB input-output gain control observed after ablation of local DA neurons (Banerjee et al., 2015) might be largely carried by the loss of the small anaxonic DA sub-population.

Finally, there might be a significant developmental component to the relative functional contributions of axonic vs anaxonic OB DA neurons. Early in postnatal development, when neuronal activity contributes to the refinement of both OSN terminals (Zou et al., 2004, Yu et al., 2004) and projection neuron dendrites (Matsutani and Yamamoto, 2000, Lin et al., 2000) to individual glomeruli, the large, interglomerular-projecting AIS-positive DA cell type is relatively more numerous (Fig. 6B). Perhaps these inhibitory interneurons play a crucial role in co-ordinating odour-evoked and/or spontaneous activity across the glomerular layer at these early ages, allowing distinct activity patterns to drive anatomical segregation at the individual glomerulus level.

A distinct role for functional plasticity in postnatally-generated neurons?

Perhaps the most remarkable difference between the two axonic and anaxonic DA subtypes is that only the latter is generated throughout adult life. This observation is in agreement with a more general trend of adult-born neurons in the olfactory bulb, where neither PGCs nor granule cells possess an axon (Lledo et al., 2006). A recently observed small cohort of adult-born cortical neurons is also anaxonic (Le Magueresse et al., 2011). In fact, with the notable exception of hippocampal dentate granule cells, it appears that all CNS neurons constitutively born during adulthood are anaxonic, contributing purely to local network activity by releasing neurotransmitter from their dendrites. Indeed, one may speculate that it is simpler for a newly-generated neuron to insert itself in a pre-existing network without having to extend and connect a far-reaching axonal process. Accordingly, the large, axonic OB DA cells that do need to form such extensive connections are born only during early development at the same time that other projection neurons are populating the bulb (Treloar et al., 2010).

What is the evolutionary advantage for maintaining continuous neurogenesis of anaxonic local interneurons? The answer to this question is highly dependent on understanding the exact role of these small local neurons in olfactory processing. Immature adult-born neurons are distinguished by their heightened potential for activity-dependent plasticity (Livneh and Mizrahi, 2012). We might hypothesise, then, that a local intraglomerular gain control mechanism that can be readily modified by experience allows for broader behavioral flexibility, and permits rapid adaptation to new conditions in the external world (Rochefort et al., 2002, Lazarini et al., 2014, Livneh and Mizrahi, 2012). Moreover, if adult neurogenesis can be seen as an extreme form of structural plasticity that only one subtype of DA cells is capable of performing, it is probably reasonable to assume that more standard and less dramatic forms of plasticity are also differentially expressed by anaxonic and axonic cells. By definition, cells that do not have an AIS cannot undergo AIS plasticity, while in vitro evidence suggests that large axonic OB DA cells are capable of regulating the length and position of their AISes in an activity-dependent manner (Chand et al., 2015). But does this happen in vivo, and if so, does it have an impact on the cell’s processing of olfactory inputs? Additionally, are other forms of activity-dependent plasticity in OB DA neurons (Banerjee et al., 2013, Bonzano et al., 2016, Coppola, 2012, Hsia et al., 1999, Mizrahi, 2007, Wang et al., 2017) specific to individual axonic versus anaxonic subclasses? Future studies will need to elucidate if other forms of neuronal plasticity can be induced in both DA cell subtypes in response to perturbations in sensory experience, and if so, how they impact on olfactory behaviour (Tillerson et al., 2006, Taylor et al., 2009).

Materials and methods

Animals

Unless otherwise stated we used mice of either gender, and housed them under a 12-h light-dark cycle in an environmentally controlled room with free access to water and food. Wild-type C57Bl6 mice (Charles River) were used either as experimental animals, or to back-cross each generation of transgenic animals. The founders of our transgenic mouse lines – DAT-Cre (B6.SJL-Slc6a3^{tm1.1(cre)Bkmn/J}, Jax stock 006660), VGAT-Cre (Slc32a1^tm2(cre)Lowl/J, Jax stock 016962), flex-tdTomato (B6.Cg– Gt(ROSA)26Sor^{tm9(CAG-tdTomato)Hze}, Jax stock 007909), and flex-GCaMP6s animals (Ai96; B6;129S6-Gt(ROSA)26Sor^{tm96(CAG-GCaMP6s)Hze}/J, Jax stock 024106) – were purchased from Jackson Laboratories. If not stated otherwise, all experiments were performed at postnatal day (P) 28. All experiments were performed under the auspices of UK Home Office personal and project licences held by the authors, or were within institutional (Harvard University Institutional Animal Care and Use Committee) and USA national guidelines.

Immunohistochemistry

Mice were anesthetised with an overdose of pentobarbital and then perfused with 20 mL PBS with heparin (20 units.mL⁻¹), followed by 20mL of 1% paraformaldehyde (PFA; TAAB Laboratories; in 3% sucrose, 60 mM PIPES, 25 mM HEPES, 5 mM EGTA, and 1 mM MgCl₂). The olfactory bulbs were dissected and post-fixed in 1 % PFA for 2-7 d, then embedded in 5 % agarose and sliced at 50 μm using a vibratome (VT1000S, Leica). Free-floating slices were washed with PBS and incubated in 5 % normal goat serum (NGS) in PBS/Triton/azide (0.25% triton, 0.02% azide) for 2 h at room temperature. They were then incubated in primary antibody solution (in PBS/Triton/azide) for 2 d at 4°C. The primary antibodies used and their respective concentrations are indicated in Table 2.

Table 2.

Details of primary antibodies

Slices were then washed three times for 5 minutes with PBS, before being incubated in secondary antibody solution (species-appropriate Life Technologies Alexa Fluor-conjugated; 1:1000 in PBS/Triton/azide) for 3 h at room temperature. After washing in PBS, slices were incubated in 0.2% sudan black in 70% ethanol at room temperature for 3 min to minimise autofluorescence, and then mounted on glass slides (Menzel-Gläser) with MOWIOL-488 (Calbiochem). Unless stated otherwise all reagents were purchased from Sigma.

Birth-dating and sparse labelling

To birth-date neurons we injected mice with a saline-based solution containing 50 mM bromodeoxyuridine (BrdU, Sigma) and 17.5mM NaOH. Pregnant C57BL6 female mice received one single intraperitoneal injection of this solution (0.075 ml/g) on the relevant gestational day; pregnancy start date (E0) was investigated twice daily and confirmed by the presence of a vaginal plug. Injected mothers and offspring were transcardially perfused on the relevant day, as detailed above. To permit BrdU detection, slices were first incubated in 2 M HCl for 30min at 37°C, washed thoroughly and then processed for immunohistochemistry as described above.

Sparse morphological labelling was achieved by injecting 2 μl of AAV9.EF1a.ChR2-YFP ^lox/lox virus (AV-9-PV1522, Penn Vector Core, USA) in the lateral ventricle of P1-2 DAT^Cre or VGAT^Cre neonatal mice. A combination of birth-dating and sparse labelling was accomplished by either electroporating 2 μl of EGFP in the lateral ventricle of P1 C57BL6 mice, or by injecting floxed rv::dio-GFP^lox/lox retrovirus (Ciceri et al., 2013) in the lateral ventricle of E12 DAT^Cre embryos. All invasive surgery was performed under isoflurane anesthesia, with Fast Green (0.3 mg/ml) co-injected to visually confirm positional accuracy.

Injections in embryos were performed with an injector and a 30.5 ga needle through the uterine wall into one of the lateral ventricles of the embryos. The uterine horns were then returned into the abdominal cavity, the wall and the skin were sutured, and embryos were allowed to continue their normal development.

Injections in neonates were performed in a semi-stereotaxic frame using a Hamilton syringe and a borosilicate glass capillary (GC100-15, Harvard Apparatus). For electroporation, after the injection of 2 μl of GFP in the lateral ventricle, five 50ms-0.15 A electrical pulses were delivered at 1Hz with plate electrodes (10mm diameter, Nepagene, Japan) oriented in such a way to drive the current dorso-ventrally.

Fixed-tissue imaging and analysis

All images were acquired with a laser scanning confocal microscope (Zeiss LSM 710) using appropriate excitation and emission filters, a pinhole of 1 AU and a 40x oil immersion objective. Laser power and gain were set to either prevent signal saturation in channels imaged for localisation analyses, or to permit clear delineation of neuronal processes in channels imaged for neurite identification (e.g. TH, GFP).

In ex vivo tissue, for branching patterns and reconstructions, images were taken with a 1x zoom (0.415 μm/pixel), 512x512 pixels, and in z-stacks with 1 μm steps. For AIS identification, images were taken with 3x zoom, 512x512 pixels (0.138 μm/pixel) and in z-stacks with 0.45 μm steps. All quantitative analysis was performed with Fiji (Image J). Cell position in the glomerular layer (GL) was classified as: a) ‘upper’ if the soma bordered with both the GL and the olfactory nerve layer; b) ‘middle’ if the soma was fully embedded in the GL; c) ‘lower’ if the soma bordered with both the GL and external plexiform layer (EPL); and d) ‘EPL border’ if the soma did not border at all with the GL. Soma area was measured at the cell’s maximum diameter. Morphological reconstructions of neurons that were sufficiently sparsely and brightly labelled were obtained with the auto-tracing Neuron 2.0 function in Vaa3D-3.20. Sholl analysis was performed on traced images using the automated Image J function, with a fixed first circle radius of 24 μm, and 12 μm increments for the following concentric circles.

Acute slice electrophysiology

P21-35 DAT^Cre-tdTomato mice were decapitated under isoflurane anaesthesia and the OB was removed and transferred into ice-cold slicing medium containing (in mM): 240 sucrose, 5 KCl, 1.25 Na₂HPO₄, 2 MgSO₄, 1 CaCl₂, 26 NaHCO₃ and 10 D-Glucose, bubbled with 95%O₂ and 5% CO₂. Horizontal slices (300 μm thick) of the olfactory bulb were cut using a vibratome (VT1000S, Leica) and maintained in ACSF containing (in mM): 124 NaCl, 5 KCl, 1.25 Na₂HPO₄, 2 MgSO₄, 2 CaCl₂, 26 NaHCO₃ and 20 D-Glucose, bubbled with 95% O₂ and 5% CO₂ for >1 hr before experiments began.

Whole-cell patch-clamp recordings were performed using an Axopatch amplifier 700B (Molecular Devices, Union City, CA, USA) at physiologically-relevant temperature (32-34°C) with an in-line heater (TC-344B, Warner Instruments). Signals were digitised (Digidata 1550, Molecular Devices) and Bessel-filtered at 3 kHz. Recordings were excluded if series (RS) or input (RI) resistances (assessed by -10 mV voltage steps following each test pulse, acquisition rate 20KHz) were respectively bigger than 30 Mω or smaller than 100 Mω, or if they varied by > 20% over the course of the experiment. Fast capacitance was compensated in the on-cell configuration and slow capacitance was compensated after rupture. Recording electrodes (GT100T-10, Harvard Apparatus) were pulled with a vertical puller (PC-10, Narishige) and filled with an intracellular solution containing (in mM): 124 K-Gluconate, 9 KCl, 10 KOH, 4 NaCl, 10 HEPES, 28.5 Sucrose, 4 Na₂ATP, 0.4 Na₃GTP (pH 7.25-7.35;290 MOsm) and Alexa 488 (1:150). DA cells were visualised using an upright microscope (Axioskop Eclipse FN1 Nikon, Tokyo, Japan) equipped with a 40X water immersion objective, and tdT / Alexa 488 fluorescence was revealed by LED (CoolLED pE-100) excitation. Post-patch fill with Alexa 488 was used both to confirm tdT-positive cell identity, and to measure soma area (ImageJ) in live images captured via a SciCam camera (Scientifica).

In current-clamp mode, evoked spikes were measured with V_hold set to -60 ± 3 mV. For action potential waveform measures, we injected 10-ms-duration current steps of increasing amplitude until we reached the current threshold at which the neuron reliably fired an action potential (V_m > 0 mV; acquisition rate 200KHz). For multiple spiking measures, we injected 500-ms-duration current steps from 0pA of increasing amplitude (δ2pA) until the neuron passed its maximum firing frequency (acquisition rate 50KHz).

Exported traces were analysed using either ClampFit (pClam10, Molecular Devices) or custom-written routines in MATLAB (Mathworks). Before differentiation for dV/dt and associated phase plane plot analyses, recordings at high temporal resolution (5 μs sample interval) were smoothed using a 20 point (100 μs) sliding filter. Voltage threshold was taken as the potential at which dV/dt first passed 10 V/s. Onset rapidness was taken from the slope of a linear fit to the phase plane plot at voltage threshold. Monophasic versus biphasic phase plane plots were visually determined independently by EG and MSG, and only cells with identical classification were included in the analysis. Spike width was measured at the midpoint between voltage threshold and maximum voltage. Rheobase and afterhyperpolarisation values were both measured from responses to 500 ms current injection, the latter from the local voltage minimum after the first spike fired at rheobase. Input-output curves were constructed by simply counting the number of spikes fired at each level of injected current density.

In vivo imaging

Thirteen DAT^Cre-GCaMP6s mice (either gender, age 4–10 months) were anaesthetised with a mixture of ketamine (100 mg/kg) and xylazine (10 mg/kg), placed in a stereotaxic apparatus and equipped with a cranial window over the olfactory bulbs using a sterile 3 mm biopsy punch (Integra Miltex). A custom-built titanium head plate was secured to their skull with adhesive luting cement (C&B Metabond, Parkell). A coverslip (3 mm, Warner Instruments) was placed over the cranial window and tissue adhesive (3M Vetbond) was used to secure the coverslip to the bone. The mice were allowed a minimum of a week to recover from surgery before the first imaging session. Prior to each imaging session the mice were newly anaesthetised with a mixture of ketamine (100 mg/kg) and xylazine (10 mg/kg) and secured in a custom-built microscope as described previously (Kapoor et al., 2016). Mice were given a maximum of one booster injection of anaesthesia per session, and were never imaged on consecutive days. GCaMP was excited and imaged via a water immersion objective (20x, 0.95 NA, Olympus; sterile saline was used as the fluid for the immersion objective) at 927 nm using a Ti:sapphire laser (Chameleon Ultra, Coherent) with 140 fs pulse width and 80 MHz repetition rate. Image acquisition, scanning, and stimulus delivery were controlled by custom-written software in LabVIEW (National Instruments). Eight odors were individually delivered via a custom-built olfactometer (Kapoor et al., 2016). The odour panel included: methyl propionate (Sigma, 81988), methyl butyrate (Sigma, 246093), ethyl valerate (Sigma, 290866), hexanal (Sigma, 115606), methyl tiglate (Penta, 13-73400), valeraldehyde (Sigma, 110132), propyl acetate (Tokyo Chemical Industry, A0044), and pentyl acetate (Sigma, 109549). All odours were diluted in diethyl phthalate solvent (Sigma-Aldrich) at 2% v/v.

Single-plane images of 300 x 300 pixel fields of view were acquired at 4 Hz during odour stimulation trials. Trial temporal structure consisted of: 7.5 s baseline, 3 s odour delivery, 7.5 s post-odour acquisition (18 s total, with 10 s inter-trial interval). All eight odours were probed sequentially, and then the entire block was repeated two more times, for a total of 24 odour trials for each field of view (3 repetitions per odour). Cell soma selection was performed manually in Image J, using both the timecourse and the maximum intensity projections of each odour trial, and stored in a ROI mask for each field of view. Soma area and mean intensity values were then extracted for each ROI with a custom-written ImageJ macro, and saved as .xls and .txt files respectively. Data were then analysed with custom scripts in Matlab (Mathworks). Mean response traces were calculated across 3 individual stimulus presentations of each odour for each cell, before bleach correction was carried out by extrapolation and subtraction of a single exponential function fitted to the 7.5 s pre-stimulus baseline. Mean bleach-corrected baseline fluorescence over a 3 s window immediately preceding odorant presentation (f) was then used to generate δf/f traces. In some analyses comparing baseline fluorescence values across different animals and imaging sessions (but not when calculating δf/f values for all other measures), this baseline F value, averaged across all stimulus presentations, was normalised by the mean value for all small DA cells in a given field of view. The standard deviation of δf/f values within the 3 s pre-stimulus period, averaged across all stimulus presentations, was taken as a measure of each cell’s baseline noise. For each cell and each odorant we then detected the point of maximum (for excitatory events) and minimum (for inhibitory events) δf/f after stimulus onset, and took response amplitude as mean δf/f over a 3 s window centred on this peak timepoint. Responses were classed as significant if this amplitude value was ≥ 3 x the baseline noise for that trace. Excitatory responses displayed a clear bimodal distribution of peak timepoints, so we used unbiased k-means clustering on this parameter to set a threshold timepoint at 6.25 s after stimulus onset – all significant excitatory responses which peaked before this timepoint were classed as ‘early’, and all significant excitatory responses which peaked at or after this timepoint were classed as ‘late’. Tuning index (TI) values were calculated by summing the number of stimuli producing significant responses of a given type for each cell. These values were often zero, and many cells had zero TI values for all response types. These non-responding cells were included in all reported TI analyses, but results were identical in terms of significance if analyses were restricted only to those cells that displayed at least one significant response of any type to at least one odour. Amplitude measures were calculated for each cell as the mean across all odours that produced significant responses, but cell-type effects were also consistent if this was calculated as the maximum amplitude across all significant responses instead.

Statistical analysis

Statistical analysis was carried out using Prism (Graphpad), Matlab (Mathworks) or SPSS (IBM). Sample distributions were assessed for normality with the D’Agostino and Pearson omnibus test, and parametric or non-parametric tests carried out accordingly. α values were set to 0.05, and all comparisons were two-tailed. Principal component analysis (PCA) and k-means classification on electrophysiological data were performed (Matlab functions ‘pca.m’ and ‘kmeans_lpo.m’, respectively) on the 5 variables that differed significantly between monophasic and biphasic DAT-tdT neurons. All were normally distributed except onset rapidness, which was rendered normal by logarithmic transform. Results of the k-means analysis were validated with a ‘leave-one-out’ protocol, which revealed cell-type classification to be robust to the removal of any one cell from the dataset. For multilevel analyses of in vivo GCaMP data, distributions of baseline noise and response amplitude measures were rendered normal by logarithmic transform, and outliers – defined as any value with an absolute z-score > 3 – were removed (Aarts et al., 2014); a single outlier was removed from each dataset, representing < 0.5 % of each sample). These parameters were then analysed using linear mixed models (SPSS) with mouse as the subject variable. Tuning index data could not be rendered normal by any standard transforms, so were analysed using generalised linear mixed models with a negative binomial target distribution (accounting for > 90 % of sample variance; SPSS) and mouse as the subject variable. Dummy variable analysis revealed significant intracluster correlations in all cases, stressing the importance of nesting cell-by-cell data on individual mouse subjects (Aarts et al., 2014). Due to the non-normal nature of tuning index distributions, and the rarity of observing multiple different response types in any given single neuron, PCA was not attempted on our GCaMP data.

Author Contributions

EG, VNM and MSG designed experiments. EG, MB, ANC and DJB performed experiments. EG, EF, MB, DJB and MSG analysed data. EG, EF, VNM and MSG discussed the results. EG and MSG wrote the paper.

Acknowledgements

This work was supported by a Sir Henry Wellcome fellowship (103044) to EG, a Wellcome Trust Career Development Fellowship (088301) and ERC Consolidator Grant (725729; FUNCOPLAN) to MSG, a Medical Research Council 4 year PhD studentship to DJB, and an NIH grant (DC013329) to VNM. We wish to thank Casper Hoogenraad and Phillip Gordon-Weeks for antibodies, Mackenzie Mathis for DAT-GCaMP6s animals, Guilherme Neves and Lynette Lym for technical assistance with viral injections, and Vikrant Kapoor and Joseph Zak for guidance with in vivo imaging. Gordon Shepherd, Pierre-Marie Lledo, Oscar Marin, Juan Burrone, and all members of the Grubb and Murthy laboratories provided helpful discussions, while Alex Fleischmann and Ian Thompson made invaluable comments on the manuscript.