Functional Diversity of Glycinergic Commissural Inhibitory Neurons in Larval Zebrafish

Article

Functional Diversity of Glycinergic Commissural Inhibitory Neurons in Larval Zebrafish Graphical Abstract

Authors Chie Satou, Takumi Sugioka, Yuto Uemura, Takashi Shimazaki, Pawel Zmarz, Yukiko Kimura, Shin-ichi Higashijima

Correspondence [email protected] (C.S.), [email protected] (Y.K.), [email protected] (S.-i.H.)

In Brief Satou et al. explore the functional properties dI6dmrt3a and V0d neurons in the spinal cord of larval zebrafish. Both populations provide inhibition to the swimming CPG and motor neurons on the contralateral side when the ipsilateral side is active. Ablation of dI6dmrt3a neurons confirms their contribution to left-right alternation.

Highlights d

dI6dmrt3a and V0d neurons are commissural inhibitory neurons in zebrafish

d

dI6dmrt3a and V0d neurons are cooperatively involved in leftright alternation

d

dI6dmrt3a and V0d neurons are recruited at different frequencies of swimming

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Ablation of dI6dmrt3a neurons impairs regular left-right alternation

Satou et al., 2020, Cell Reports 30, 3036–3050 March 3, 2020 ª 2020 The Author(s). https://doi.org/10.1016/j.celrep.2020.02.015

Cell Reports

Article Functional Diversity of Glycinergic Commissural Inhibitory Neurons in Larval Zebrafish Chie Satou,1,5,* Takumi Sugioka,1,2 Yuto Uemura,1,3 Takashi Shimazaki,1 Pawel Zmarz,4 Yukiko Kimura,1,2,* and Shin-ichi Higashijima1,2,6,* 1National

Institutes of Natural Sciences, Exploratory Research Center on Life and Living Systems, National Institute for Basic Biology, Okazaki, Aichi 444-8787, Japan 2Graduate University for Advanced Studies, Okazaki, Aichi 444-8787, Japan 3Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan 4Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA 5Present address: Friedrich Miescher Institute for Biomedical Research, Basel 4058, Switzerland 6Lead Contact *Correspondence: [email protected] (C.S.), [email protected] (Y.K.), [email protected] (S.-i.H.) https://doi.org/10.1016/j.celrep.2020.02.015

SUMMARY

Commissural inhibitory neurons in the spinal cord of aquatic vertebrates coordinate left-right body alternation during swimming. Their developmental origin, however, has been elusive. We investigate this by comparing the anatomy and function of two commissural inhibitory neuron types, dI6dmrt3a and V0d, derived from the pd6 and p0 progenitor domains, respectively. We find that both of these commissural neuron types have monosynaptic, inhibitory connections to neuronal populations active during fictive swimming, supporting their role in providing inhibition to the contralateral side. V0d neurons tend to fire during faster and stronger movements, while dI6dmrt3a neurons tend to fire more consistently during normal fictive swimming. Ablation of dI6dmrt3a neurons leads to an impairment of left-right alternating activity through abnormal co-activation of ventral root neurons on both sides of the spinal cord. Our results suggest that dI6dmrt3a and V0d commissural inhibitory neurons synergistically provide inhibition to the opposite side across different swimming behaviors. INTRODUCTION Central pattern generators (CPGs) in the spinal cord are neuronal networks that are capable of generating organized patterns of motor activity independently of sensory inputs (Delcomyn, 1980). Important advances in our understanding of neuronal components of mammalian CPGs have been achieved in the past 15 years by studying genetically identified neuronal classes in the spinal cord (Goulding, 2009; Goulding and Pfaff, 2005; Kiehn, 2006, 2011). These are identified during spinal cord development by the expression of specific transcription factors (Alaynick et al., 2011). Spinal progenitor cells are divided into 11 domains along the dorsoventral axis of the spinal cord. Dorsal progenitor domains give rise to six early classes of neurons (dI1–dI6), while ventral progenitor domains give rise to motor

neurons and four classes of interneurons (V0–V3). There has been considerable progress in the anatomical and functional characterization of each neuronal class (Goulding, 2009; Goulding and Pfaff, 2005; Kiehn, 2016). Locomotor CPGs have also been extensively studied in non-limbed vertebrates such as lamprey and frog tadpoles. Because of their relatively limited number of neurons and relatively simple motor behavior, locomotor CPGs for swimming were characterized in detail in non-limbed vertebrates before genetic cell identification methods became available (Grillner, 2003; Roberts et al., 1998). Since then, genetic methods have been applied using zebrafish as a model organism. For example, V1 and V2a (an excitatory subtype of V2 neurons) neurons in the zebrafish spinal cord were found to provide inhibition and excitation, respectively, to ipsilateral CPG neurons (Higashijima et al., 2004a; Kimura et al., 2006). Genetic identification of classes of spinal cord neurons provided two main advantages. First, the method enabled the detailed investigation of specific neuronal types (Ampatzis et al., 2014; McLean and Fetcho, 2008, 2009; Menelaou et al., 2014). Second, the method has provided a bridge between neuronal types in aquatic vertebrates and those of mammals, thereby making cross-species comparisons possible. Available data suggest that axonal trajectories and transmitter phenotypes of genetically defined neurons are largely conserved across vertebrate species (Goulding, 2009; Kiehn, 2011), suggesting that many of these neuronal classes were present in ancient vertebrates. In this sense, cross-species comparisons may provide insights into the evolution of spinal neuronal circuits in vertebrates. One of the most conspicuous features of swimming movements is left-right alternation of the body. This alternation is thought to be organized by glycinergic commissural inhibitory neurons (Grillner, 2003; Roberts et al., 1998). These neurons project to other CPG and motor neurons on the contralateral side and provide mid-cycle inhibition to prevent contralateral neurons from firing while ipsilateral motor neurons are active (Buchanan, 2001; Grillner, 2003; Roberts et al., 2008). Despite the importance of glycinergic commissural inhibitory neurons, however, their developmental origin has been elusive, hampering experimental access to this cell class through genetic methods. Consequently, fine-resolution analysis of this cell population has not been carried out in zebrafish.

3036 Cell Reports 30, 3036–3050, March 3, 2020 ª 2020 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

We also showed that both types of neurons generally fired in phase with nearby motor neurons located on the same side during fictive swimming. V0d neurons tended to fire during faster and stronger movements, while dI6dmrt3a neurons tended to fire more consistently during normal fictive swimming. These results suggest that V0d and dI6dmrt3a neurons are both involved in providing mid-cycle inhibition but are differentially recruited depending on swimming intensity and frequency. We further found that ablation of dI6dmrt3a neurons led to impairments of proper left-right alternation, indicating the importance of this population for swimming-related CPGs. Our results suggest that dI6dmrt3a and V0d commissural inhibitory neurons synergistically provide inhibition to the opposite side of the spinal cord across different swimming behaviors. RESULTS

Figure 1. Overall Distribution of dI6dmrt3a and V0d Neurons in Larval Zebrafish (A) Fluorescence image of a Tg[dmrt3a:GFP] fish. (B) Lateral view (maximum-intensity projection) of a stack of confocal images of Tg[dmrt3a:GFP] fish. (C) Cross-section view computed from confocal images of Tg[dmrt3a:GFP] fish. (D) Fluorescence image of a compound transgenic Tg[dbx1b:Cre]; Tg [glyt2:lRl-GFP] fish. RFP-positive neurons correspond to glyt2-positive neurons other than V0d neurons. GFP-positive neurons correspond to V0d neurons. (E) Lateral view (maximum-intensity projection) of a stack of confocal images of Tg[dbx1b:Cre]; Tg[glyt2:lRl-GFP] fish. Green channel. (F) Cross-section view computed from confocal images of Tg[dbx1b:Cre]; Tg [glyt2:lRl-GFP] fish. Green channel. (G) Distribution of dI6dmrt3a neurons in cross sections. The dotted line demarcates the spinal cord. Each circle represents the normalized location of a neuron’s soma (n = 165 from two fish). Histograms of the location of the neurons are shown at the top and to the right. Arrowheads indicate the average position along each axis. (H) Same as (G) but for V0d neurons. n = 161 from two fish. Scale bars: 500 mm for (A) and (D), 20 mm for (B) and (E), 10 mm for (C) and (F).

Here, we addressed this question by characterizing genetically defined classes of interneurons in the zebrafish spinal cord. We focused on V0 inhibitory neurons (hereafter called V0d neurons) and a dmrt3a-expressing subtypes of dI6 neurons (hereafter called dI6dmrt3a neurons; dmrt3a is a zebrafish homolog of mammalian dmrt3) because these cell populations have been implicated in commissural inhibitory pathways in mammalian CPGs (Andersson et al., 2012; Bellardita and Kiehn, 2015; Kiehn, 2016; Lanuza et al., 2004; Perry et al., 2019; Talpalar et al., 2013; Vallstedt and Kullander, 2013) and have been shown to be commissural neurons in zebrafish (Andersen, 2017; Satou et al., 2012). We revealed that both V0d and dI6dmrt3a neurons made monosynaptic inhibitory connections onto contralateral CPG and motor neurons.

dI6dmrt3a and V0d Neurons Are Predominantly Located in the Dorsal Region of the Spinal Cord in Larval Zebrafish As a first step in the characterization of dI6dmrt3a and V0d neurons, we examined their overall distribution in the spinal cord of mature larvae at 4 days post-fertilization (dpf). To visualize dI6dmrt3a neurons, we used a BAC Tg[dmrt3a:GFP] transgenic line (Figure 1A). To visualize V0d neurons, we used a combination of BAC Tg[dbx1b:Cre] transgenic (Satou et al., 2012) and knockin transgenic Tg[glyt2:loxP-RFP-loxP-GFP] fish (hereafter, loxP-RFP-loxP is referred to as lRl) (Figure 1D; note that GFPlabeled neurons are V0d neurons). It should be noted that both glycinergic and GABAergic neurons are included among V0d neurons (Satou et al., 2012). However, in comparison with glycinergic transmission, GABAergic transmission is thought to play only a minor role in locomotor regulation in zebrafish larvae (Buss and Drapeau, 2001; Fidelin and Wyart, 2014). Therefore, we consider only glycinergic (glyt2-positive) V0d neurons throughout this paper. The cell bodies of dI6dmrt3a and V0d neurons were predominantly located in the dorsal region of the spinal cord (Figures 1B, 1C, 1E, and 1F). We first quantified the numbers of GFP-positive dI6dmrt3a and V0d neurons relative to their location in the spinal cord. We found 24 ± 4 dI6dmrt3a neurons and 24 ± 5 V0d neurons per hemi-segment (mean ± SD; eight hemi-segments were examined for each type of neurons). Note that the number of V0d neurons is likely to be an underestimate, as Cre-mediated recombination was unlikely to occur in all dbx1b:Cre-expressing cells. We then mapped the locations of GFP-positive cells in several cross sections (Figures 1G and 1H). On average, dI6dmrt3a and V0d neurons were located in similar positions along the dorsoventral axis (normalized mean position from the ventral edge: 0.74 ± 0.013 for dI6dmrt3a neurons and 0.76 ± 0.009 for V0d neurons; p = 0.54). Along the medio-lateral axis, V0d neurons tended to be located close to the midline, while the location of dI6dmrt3a neurons became more lateral toward the ventral side of the spinal cord (normalized mean position from the midline: 0.31 ± 0.009 for dI6dmrt3a neurons and 0.18 ± 0.007 for V0d neurons; p = 0.20). Collectively, these results indicate that (1) similar numbers of dI6dmrt3a and V0d neurons

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are present in the spinal cord of 4 dpf zebrafish larvae, and (2) the spinal cord distributions of dI6dmrt3a and V0d neurons overlap, with a tendency for V0d neurons to be located more medially than dI6dmrt3a neurons. Morphology of dI6dmrt3a Neurons Next, we investigated the fine morphology of dI6dmrt3a neurons (the fine morphology of V0d neurons has been described previously in Satou et al. (2012). For this purpose, we used a transient stochastic labeling technique (see STAR Methods). We found that all GFP-labeled neurons had commissural axons (Andersen, 2017). Their morphology, however, was not homogeneous, leading us to classify dI6dmrt3a neurons into three types: A, B, and C (Figures 2A–2C). dI6dmrt3a type C neurons (n = 5) exhibited a distinctive morphology (Figures 2C1 and 2C2; spherical soma with a short dendrite, a thick short axon of 1–1.5 segments in length) that is a hallmark of CoLo neurons (Satou et al., 2009). CoLo neurons are present at one cell per hemi-segment and are known to be exclusively involved in escape behaviors. Thus, we revealed that CoLo neurons are among dI6dmrt3a neurons. The remaining neurons were classified into two types on the basis of the length of the primary dendrite. Neurons with primary dendrites extending >100 mm were classified as type A (n = 9), while all other neurons were classified as type B (n = 21). The plot of the length of the primary dendrites in type A and type B neurons revealed a boundary between the two groups (Figure 2E), suggesting a cutoff length of 100 mm. In the following, we describe the fine anatomy of these two neuronal types. Primary processes of dI6dmrt3a type A neurons extended ventrally, producing primary dendrites along the way. The dendrites exhibited extensive arborization (Figures 2A1 and 2A2). Axons on the contralateral side often bifurcated, with one ascending and the other descending (Figures 2A1 and 2A2). Descending axonal branches tended to be longer than ascending branches (4.6 ± 0.64 segments for descending axons and 2.3 ± 0.46 segments for ascending axons, n = 9, p = 0.012; Figure 2F). Primary processes of dI6dmrt3a type B neurons initially extended ventrally on the ipsilateral side, producing short dendrites along the way, and often bifurcated on the contralateral side (Figures 2B1 and 2B2). Overall axonal trajectories of type B neurons were similar to type A neurons. Descending axon branches were longer than ascending branches (6.5 ± 0.66 segments for descending axons and 2.0 ± 0.66 segments for ascending axons, n = 21, p < 0.01; Figure 2F). The morphology and axonal length of both dI6dmrt3a type A and dI6dmrt3a type B neurons were generally similar to V0d neurons (Figures 2A, 2B, and 2D; V0d data are reproduced from Satou et al. (2012), and all three types belonged to CoBL (commissural bifurcating longitudinal) neurons (Hale et al., 2001; Higashijima et al., 2004b; Liao and Fetcho, 2008). However, V0d neurons differed from dI6dmrt3a type A and dI6dmrt3a type B neurons in the ratio of descending and ascending axonal branch lengths, with V0d neurons exhibiting, on average, no difference between the branch lengths (2.7 ± 0.33 segments for descending axons and 3.0 ± 0.58 segments for ascending axons, n = 21, p = 0.67; Figure 2G).

3038 Cell Reports 30, 3036–3050, March 3, 2020

dI6dmrt3a and V0d Neurons Make Synaptic Connections onto Neurons that Are Active during Fictive Locomotion In frog tadpoles and adult lampreys, commissural inhibitory neurons are known to make monosynaptic connections onto CPG and motor neurons on the contralateral side to provide mid-cycle inhibition during swimming (Grillner, 2003; Roberts et al., 2008). We expected that similar monosynaptic connections were present between dI6dmrt3a/V0d neurons and CPG/motor neurons in zebrafish. To test this hypothesis, we prepared animals that expressed channelrhodopsin (ChR) in dI6dmrt3a or V0d neurons using the Gal4-UAS system. We then applied brief photo-stimulation to elicit action potentials in ChR-expressing neurons, while performing patch-clamp recordings to examine connectivity. We simultaneously recorded from the ventral root (VR) to examine the activity of the recorded neurons during fictive swimming. Fictive swimming was elicited either by changing the illumination or by electrically stimulating the fish. For dI6dmrt3a neurons, Tg [dmrt3a:Gal4] fish were used as driver fish, while for V0d neurons, Tg[dbx1b:Cre];Tg[glyt2:lRl-Gal4] compound transgenic fish were used (Figure 3A). Note that Gal4-UAS fish have been shown to often exhibit mosaic expression of a reporter gene (Goll et al., 2009). Therefore, random small subsets of dI6dmrt3a or V0d neurons expressed ChR (EYFP-fusion) in our experimental condition. We first examined whether brief photo-stimulation could elicit spikes in ChR-expressing neurons by performing patch-clamp recordings from ChR-EYFP-expressing neurons. We found that action potentials were elicited in dI6dmrt3a type A (4 of 5; Figure 3B), dI6dmrt3a type B (4 of 7; Figure 3C), and V0d neurons (4 of 12; Figure 3D). In the case of CoLo (dI6dmrt3a type C) neurons, however, action potentials could not be elicited (n = 5). This was consistent with our previous observation that a large amount of current was needed to elicit action potentials in CoLo neurons (Satou et al., 2009). Thus, in the current experiment, the contribution of CoLo activation through ChR was assumed to be minimal. We next performed random patch-clamp recordings from spinal cord neurons while stimulating either dI6dmrt3a or V0d neurons. Following the recordings, we identified neuronal types by morphological analysis. Twenty-two of 47 recorded neurons showed inhibitory postsynaptic current (IPSC) responses upon photo-stimulation of dI6dmrt3a neurons (Table 1). Considering the difficulty in eliciting spikes in dI6dmrt3a type C neurons upon photo-stimulation, the sources for these inhibitory inputs were likely to be dI6dmrt3a type A and/or dI6dmrt3a type B neurons. The neurons that exhibited IPSC responses included several different morphological types, such as motor neurons (Figure 3E), ipsilateral descending neurons (Figure 3F), and dI6dmrt3a neurons (Figure 3G; dI6dmrt3a neurons were identified by the presence of ChR-EYFP). Many of the responding neurons were active during fictive swimming (Figures 3E–3G, top panels), suggesting that dI6dmrt3a neurons provide inhibition onto contralateral CPG and motor neurons. Upon photo-stimulation of V0d neurons, 20 of 43 recorded neurons exhibited IPSC responses (Table 1). The neurons that exhibited IPSCs included several different morphological types, such as motor neurons (Figure 3H), ipsilateral ascending neurons

Figure 2. Morphological Analysis of GFP-Labeled dI6dmrt3a Neurons (A1) Confocal montage image of a dI6dmrt3a type A neuron expressing GFP. (A2) Depth code view of (A1). (B1) Same as (A1) but for a dI6dmrt3a type B neuron. (B2) Depth code view of (B1). (C1) Same as (A1) but for a dI6dmrt3a type C neuron. (C2) Depth code view of (C1). (D1) Same as (A1) but for a V0d neuron. (D2) Depth code view of (D1). (E) Distribution of primary dendrite lengths of dI6dmrt3a type A and dI6dmrt3a type B neurons. (F) Axonal length of dI6dmrt3a type A and dI6dmrt3a type B neurons. The axes indicate the number of body segments. (G) Axonal length of V0d neurons. Scale bars: 100 mm for (A1), (B1), (C1), and (D1).

(Figure 3I), and V0d neurons (Figure 3J; V0d neurons were identified by the presence of ChR-EYFP; note that the neuron exhibited large inward photo-currents upon photo-stimulation). Similar to dI6dmrt3a photo-stimulation experiments, many of these responding neurons were active during fictive swimming (Figures 3H–3J, top panels), suggesting that V0d neurons also provide inhibition onto contralateral CPG and motor neurons.

To exclude the possibility that intervening glutamatergic transmission was involved in the observed phenomena, we treated a subset of the animals with CNQX and AP5 to block glutamatergic transmission. In all the cases tested (n = 4 for dmrt3a-ChR fish and n = 2 for V0d-ChR fish), IPSC responses persisted (Figure S1). Collectively, our photo-stimulation experiments showed that dI6dmrt3a and V0d neurons make direct inhibitory synaptic

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Figure 3. dI6dmrt3a and V0d Neurons Make Synaptic Connections onto Neurons that Are Active during Fictive Swimming (A) Experimental scheme. Channelrhodopsin (ChR) was expressed in a random subset of dI6dmrt3a or V0d neurons using the Gal4-UAS system. A 20 ms pulse of blue light illumination was sequentially applied to three areas covering five or six segments of the fish spinal cord. (B) Whole-cell recordings from ChR-expressing dI6dmrt3a type A neurons. Blue light illumination was repeated five times (five traces are superimposed). (C) Same as (B) for dI6dmrt3a type B neurons. (D) Same as (B) for V0d neurons.

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Table 1. Fraction of Neurons that Exhibited IPSCs in ChR Experiments dmrt3a dI6 Neurons

V0d Neurons

Motor neurons

12/16

7/13

Sensory neuron (RB) or sensory interneurons

1/3

2/4

Ipsi ascending neurons

1/2

2/2

Ipsi descending neurons

2/5

3/4

YFP-positive neurons

5/18

4/14

YFP-negative commissural neurons

1/3

1/3

Unidentified neurons

0/0

1/3

Sum

22/47

20/43

connections onto neurons involved in fictive swimming. Because dI6dmrt3a and V0d neurons are commissural neurons, the observed IPSCs were likely to be elicited by the firing of dI6dmrt3a or V0d neurons located on the contralateral side to the recording location. dI6dmrt3a and V0d Neurons Are Active during Fictive Locomotion If dI6dmrt3a and V0d neurons provide mid-cycle inhibition onto contralateral CPG/motor neurons, these neurons should fire at the same time as nearby motor neurons located on the same side (Grillner, 2003; Roberts et al., 2008). To test this hypothesis, we recorded the activity of dI6dmrt3a and V0d neurons during fictive swimming. We labeled dI6dmrt3a or V0d neurons with GFP by combining the Cre-loxP and Gal4-UAS systems (see STAR Methods). Because of the mosaic expression nature of the Gal4-UAS system, GFP was expressed in relatively small numbers of dI6dmrt3a and V0d neurons (Figures 4A and 4B, respectively). This enabled us to observe dendritic morphology of GFP-labeled neurons and therefore distinguish dI6dmrt3a type A, dI6dmrt3a type B, and dI6dmrt3a type C neurons before electrophysiological recordings. Because electrophysiological properties of dI6dmrt3a type C (CoLo) neurons have been described previously (Satou et al., 2009), we did not analyze these neurons in the present study. We examined firing patterns of dI6dmrt3a type A, dI6dmrt3a type B, and V0d neurons during fictive swimming by performing whole-cell recordings from these neurons in conjunction with VR recordings from the ipsilateral side of the spinal cord (Figure 4C). Fictive swimming was elicited by changing illumination or by electrical stimulation. Fictive swimming also occurred

spontaneously. These data were pooled with experimentally initiated fictive swimming. All three types of neurons were active during fictive swimming (Figures 4D–4F; the left and right panels are examples of fictive swimming that occurred with [right] and without [left] electrical stimulation). Firing probability for each cycle of fictive swimming varied among different types of neurons (we return to this result below), but for all types of neurons, spiking activity generally occurred in phase with nearby VR activity. As revealed by circular plot analysis (Kjaerulff and Kiehn, 1996) shown in Figures 4G–4I, firing of all neuronal types was in phase with ipsilateral VR discharges during fictive swimming. These results support the notion that dI6dmrt3a type A, dI6dmrt3a type B, and V0d neurons contribute to the inhibition of contralateral CPG and motor neurons while ipsilateral motor neurons are active. As described above, we found that the probability of spiking activity for each cycle of VR activity varied among different types of neurons. dI6dmrt3a type A neurons fired consistently for each cycle of VR activity, while dI6dmrt3a type B and V0d neurons fired more sporadically (Figure 4J; VR bursts associated with escape movements were excluded from this analysis; see STAR Methods). We wondered whether these differences in firing probability during fictive swimming were due to differential recruitment of these neuronal types at different fictive swimming frequencies (Ampatzis et al., 2014; McLean et al., 2007, 2008; Menelaou and McLean, 2012). We addressed this issue by quantifying the frequency tuning of each neuronal type. We found that (1) dI6dmrt3a type A neurons were predominantly active at slower frequencies (peak at 27.5 Hz, similar to the median fictive swimming frequency of the fish; Figure S2B), (2) V0d neurons were predominantly active at faster frequencies (peak at 57.5 Hz), and (3) dI6dmrt3a type B neurons exhibited an intermediate frequency tuning profile (peak at 42.5 Hz; Figure 4K). Swimming frequency correlates with the strength of movements, such that faster swimming appears as stronger movements (McLean et al., 2007, 2008). In light of this, our results suggest that dI6dmrt3a type A neurons are predominantly active during weaker movements, while V0d neurons are predominantly active during stronger movements. dI6dmrt3a type B neurons exhibited an intermediate tendency. We found further support for this notion by quantifying differences in firing probabilities of each type of neuron during escape movements, one of the strongest movements generated by larval zebrafish (Budick and O’Malley, 2000). V0d neurons fired consistently during escape movements, while dI6dmrt3a type A neurons exhibited the lowest firing probability during escape movements (Figure 4L; note that escape movements

(E) Whole-cell recordings from a motor neuron. ChR was expressed in dI6 dmrt3a neurons. The upper subpanel shows the simultaneous recording between the cell (current clamp) and VR. The lower subpanel shows the response of the cell (voltage clamp) upon photo-stimulation (blue light was applied during the period indicated by the blue bar). Photo-stimulation was repeated five times, and the five traces are superimposed. (F) Same as (E) for an ipsilateral descending neuron. (G) Same as (E) for a dI6 dmrt3a neuron. (H) Same as (E) for a motor neuron. ChR was expressed in V0d neurons. (I) Same as (H) for an ipsilateral ascending neuron. (J) Same as (H) for a V0d neuron. In the experiments shown in (E)–(G), ChR was expressed in dI6drmt3a neurons. In the experiments shown in (H)–(J), ChR was expressed in V0d neurons.

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Figure 4. Activity of dI6dmrt3a Type A, dI6dmrt3a Type B, and V0d Neurons during Fictive Escape and Swimming (A) Confocal image of dI6dmrt3a neurons in the spinal cord of triple transgenic Tg[dmrt3a:Cre]; Tg[glyt2:lRl-Gal4]; Tg[UAS:GFP] fish. (B) Confocal image of V0d neurons in the spinal cord of triple transgenic Tg[dbx1b:Cre]; Tg[glyt2:lRl-Gal4]; Tg[UAS:GFP] fish.

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were defined by the first VR burst upon electrical stimulations, marked by ‘‘escape’’ in Figures 4D–4F, right panels). Taken together, our electrophysiological analyses revealed that dI6dmrt3a type A, dI6dmrt3a type B, and V0d neurons are differentially recruited during swimming depending on the frequency or strength of movements. For all types of neurons, spiking activity occurred generally in phase with nearby motor activity, suggesting that these neurons inhibit contralateral CPG neurons during periods of ipsilateral motor neuron activation. Genetic Ablation of dI6dmrt3a Neurons Impairs LeftRight Alteration during Swimming The results we have described are consistent with the idea that both dI6dmrt3a and V0d neurons are involved in providing mid-cycle inhibition to CPG and motor neurons on the contralateral side of the spinal cord. This would imply that the loss of these neurons should result in a decrease in mid-cycle inhibition, leading to impairments in left-right alternation during swimming. We tested this hypothesis by selectively ablating commissural inhibitory neurons in the spinal cord. We focused on dI6dmrt3a neurons because selective ablation of V0d neurons would have been technically more challenging (there are currently no known genes that are selectively expressed in postmitotic V0d neurons). In order to selectively ablate dI6dmrt3a neurons, we generated Tg[dmrt3a:lRl-DTA] (DTA [diphtheria toxin A subunit]). The fish were crossed to Tg[hoxa9a-30 enhancer:Cre] (hereafter called Tg[hox:Cre]) in which Cre recombinase expression is localized to the trunk (Kimura and Higashijima, 2019). This enabled targeting of DTA expression to the spinal cord. Successful ablation of dI6dmrt3a neurons (loss of GFP neurons) in the spinal cord of these compound fish was verified using a triple transgenic fish (a cross between Tg[dmrt3a:lRl-DTA]; Tg[hox:Cre]; Tg [dmrt3a:GFP] fish) (compare Figures 5A1 and 5A2). For phenotypic analysis, we first focused on regular (slow) fictive swimming, that is, fictive swimming that occurred without electrical stimulation. Typical swimming frequency of this type was around 32 Hz in control fish (Figures 5K1 and 5L). We investigated the effects of dI6dmrt3a neuron ablation on left and right motor activity during fictive swimming. During fictive swimming, the left and right VR activity in control fish exhibited alternating

bursts (Figure 5B), indicating normal left-right body alternation (Masino and Fetcho, 2005). In contrast, left-right alternation was markedly impaired in dmrt3a-DTA fish (Figures 5C and 5D). We first quantified the degree of impairment of left-right alternation by computing the cross-correlation function of left and right VR recordings in control and dmrt3a-DTA fish. In control fish, cross-correlation between left and right VR recordings was lowest at 0 ms lag time (Figures 5E1, 5E3, and 5F) and peaked at 16.0 ± 0.6 ms lag (Figures 5E1, 5E3, 5G, and 5H). This indicates that (1) left and right VR activity was strongly anti-phasic, and (2) left and right VR activity alternates with the interval of ~16 ms (corresponding to a preferred swimming frequency of ~32 Hz; Figures 5K1 and 5L). In dmrt3a-DTA fish, this phase relationship was less obvious (Figures 5E2 and 5E3). At a time lag of 0 ms, the anti-phase relationship between left and right VR activity that we observed in control fish was significantly weaker (0.49 ± 0.03, n = 3, for control; 0.26 ± 0.06, n = 4, for dmrt3a-DTA fish; p < 0.05; Figures 5E3 and 5F). In addition, peak cross-correlation was significantly lower (0.73 ± 0.02, n = 3, for control fish; 0.42 ± 0.32, n = 4, for dmrt3aDTA fish; p < 0.001; Figures 5E3 and 5G) and occurred at a shorter lag (16.0 ± 0.6 ms, n = 3, for control fish; 12.1 ± 0.7 ms, n = 4, for dmrt3a-DTA fish; p < 0.01; Figure 5H). To further examine the impairments of left-right alternation in dmrt3a-DTA fish, we next analyzed individual VR bursts. We found that the fraction of long bursts (defined as bursts lasting >20 ms) was significantly increased in dmrt3a-DTA fish compared with control fish (Figures S3). We also found that in contrast to control fish, VR bursts in dmrt3a-DTA fish exhibited long periods of left and right VR co-activation (see Figures 5C and 5D for examples of co-activation events, for the definition of co-activation, see STAR Methods). Quantification analysis clearly shows an increase in the number of co-activation events in dmrt3a-DTA fish compared with control fish (6.7% ± 2.0%, n = 3, for control fish; 50.8% ± 13.5%, n = 4, for dmrt3a-DTA fish; p < 0.001; Figure 5J). Thus, consistent with the impairments in leftright alternation revealed by our cross-correlation analysis, we found a phenotype of left-right VR co-activation in dI6dmrt3a neuron ablated fish. Instances of left-right VR co-activation that we observed in dmrt3a-DTA fish are consistent with a reduction of mid-cycle

(C) Experimental schematic: a stimulation electrode was placed on the ventral side of the fish near the end of the tail. Simultaneous recordings of VR and wholecell recordings were performed. (D) Simultaneous recordings from VR (top trace) and dI6dmrt3a type A neurons (bottom trace) during fictive swimming. Left: fictive swimming occurred spontaneously or was elicited by changing illumination. Right: fictive swimming (escape and swimming) was elicited by electrical stimulation. Electrical stimulation was applied at the time points indicated by the asterisk. Upon stimulation, VR activity occurred with short latency following the stimuli and most likely corresponded to escape events (indicated by ‘‘escape’’). (E) Same as (D) but for dI6dmrt3a type B neurons. (F) Same as (E) but for V0d neurons. (G) Circular plot showing spike timing of dI6dmrt3a-type A neurons (n = 7) relative to VR activity during fictive swimming. Dotted circle line marks the 5% significance level. (H) Same as (G) but for dI6dmrt3a type B neurons (n = 11). (I) Same as (H) but for V0d neurons (n = 7). (J) Firing probability of dI6dmrt3a type A (77.9% ± 4.5%, n = 9), dI6dmrt3a type B (23.0% ± 4.5%, n = 15), and V0d (12.0% ± 2.2%, n = 23) neurons for each swim cycle. Individual circles represent mean firing probabilities of individual neurons. Mean ± SEM; ***p < 0.001. (K) Frequency tuning profiles of dI6dmrt3a type A, dI6dmrt3a type B (green), and V0d (blue) neurons. Fictive swimming frequency was binned in eight 5 Hz bins. Same neurons as in (J). For each type of neuron, the thick line represents the average, while the shading represents SEM. (L) Mean firing probability of dI6dmrt3a type A (15.6% ± 0.11%, n = 9), dI6dmrt3a type B (36.1% ± 0.086%, n = 15), and V0d (72.8% ± 8.9%, n = 12) neurons during escape events. Same neurons as in (J). Mean ± SEM; *p < 0.05. All significance values were tested using unpaired two-sample Student’s t test (two tailed).

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Figure 5. Genetic Ablation of dI6dmrt3a Neurons Impairs Normal Left-Right Alternation during Swimming (A) Fluorescent images (green channel) of Tg[dmrt3a:GFP]; Tg[dmrt3a:loxP-RFP-loxP-DTA] compound transgenic fish without (A1) or with (A2) the cross to Tg [hox:Cre]. (B) Example VR recordings from left and right side of the spinal cord of control fish (CT). Top: raw traces of left (orange) and right (green) VR recordings. Middle: overlay of rectified and smoothed left and right VR traces. Bottom: binary representation of VR bursts (computed from rectified and smoothed versions of the VR traces; see Figure S2A). (C) Same as (B) but for dmrt3a-DTA fish (DT) exhibiting a severe phenotype. Overlapping left and right VR bursts (>10% co-activation) were defined as coactivation events (blacklines). (D) Same as (C) exhibiting a milder phenotype. (E) Left-right alteration is severely disrupted in dmrt3a-DTA fish. (E1) Distribution of cross-correlation functions (gray) of left and right recordings for individual VR bouts, computed from an example control fish (CT). Thick blue line represents the average cross-correlation function across all VR bouts. Blue shading represents SEM. The average peak cross-correlation coefficient was 0.77 at a time lag of 15.75 ms. (E2) Same as (E1) but for dmrt3a-DTA fish (thick magenta line represents

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inhibition in the absence of dI6dmrt3a neurons. To directly test this idea, we performed voltage-clamp recordings of small motor neurons during fictive swimming in order to measure mid-cycle inhibitory currents. We found that the amplitude of mid-cycle inhibition was indeed greatly reduced in dmrt3a-DTA fish compared with control fish (Figure S4). Although dI6dmrt3a neuron ablation significantly disrupted left-right VR coordination, as described above, we also observed instances of normal left-right VR alternation in dmrt3a-DTA fish (e.g., Figure 5D, blue bar; normal left-right VR alternation was defined as bursts exhibiting %10% co-activation). We examined whether instances of normal left-right VR alternation in dmrt3aDTA fish possessed different features from those in control fish. We quantified the cycle-frequency distribution of individual left and right VR recordings during normal left-right VR alternation and found that median cycle frequency was higher in dmrt3a-DTA fish than in control fish (31.7 ± 1.2 Hz, n = 6, for control fish; 35.8 ± 1.1 Hz, n = 8, for dmrt3a-DTA fish; median ± SEM; p = 0.026; Figures 5K and 5L). The increase in cycle frequency in dmrt3a-DTA fish was consistent with the difference of peak cross-correlation lag times between control and dmrt3a-DTA fish (Figure 5H). The impairments to left-right alternation in dmrt3a-DTA fish during regular (slow) swimming (Figure 5) were consistent with the preferential recruitment of dmrt3a neurons at slower swimming frequencies (Figure 4K). Consequently, we reasoned that this phenotype would be milder during faster swimming bouts for which dmrt3a neuron recruitment is reduced. To test this hypothesis, we next performed phenotypic analyses of dmrt3aDTA fish during faster phases of fictive swimming. In order to induce faster swimming phases, fictive swimming was elicited by applying an electrical stimulation to the tail. We analyzed VR activity within the first 100 ms following electrical stimulation to sample periods of fast swimming (100 ms window highlighted in blue in Figures 6A and 6B; note that the first VR burst following electrical stimulation corresponds to an escape event and was therefore excluded from this analysis). During this 100 ms period, swimming frequency in control fish ranged between 40 and 60 Hz (Figure 6I1), which was faster than during regular swimming (around 32 Hz; Figure 5K1). To compare these data with the phenotype during regular swimming, we computed the crosscorrelation function between left and right VR in control and dmrt3a-DTA fish during fast swimming (Figure 6C). For both groups of fish, cross-correlation was lowest at 0 ms lag time,

but unlike during regular swimming, dmrt3a-DTA fish exhibited an anti-phasic relationship more similar to control fish (0.54 ± 0.03, n = 4, for control; 0.51 ± 0.03, n = 4, for dmrt3a-DTA fish; p = 0.51; Figure 6D, compare with Figure 5F). Likewise, peak cross-correlation for the two groups of fish was more similar during fast swimming than regular swimming (0.56 ± 0.03, n = 4, for control; 0.42 ± 0.06, n = 4, for dmrt3a-DTA fish; p = 0.11; Figure 6E, compare with Figure 5G). We next quantified the number of co-activation events in both groups of fish and found a decrease of co-activation events in dmrt3a-DTA fish during fast swimming compared with regular swimming (11.3% ± 0.02%, n = 4, for control fish; 33.3% ± 0.1%, n = 4, for dmrt3a-DTA; p = 0.06; Figures 6G and 6H, compare with Figures 5I and 5J). These results suggest that the degree of impairment of left-right alternation in dmrt3a-DTA fish is milder during faster swimming. However, it should be noted that long-duration VR bursts (>20 ms), which rarely occurred in control fish, still more often occurred in dmrt3a-DTA fish (Figure S5, compare with Figure S3). As we observed during regular swimming, average swimming frequency was higher in dmrt3a-DTA fish than in control fish (46.5 ± 0.52 Hz, n = 4, for control fish; 58.9 ± 1.71 Hz, n = 4, for dmrt3a-DTA fish; p < 0.001; Figures 6I and 6J, compare with Figures 5K and 5L), consistent with the shorter peak cross-correlation lag times observed for dmrt3a-DTA fish (12.2 ± 0.22 ms, n = 4, for control fish; 8.8 ± 0.6 ms, n = 4, for dmrt3a-DTA fish; p < 0.05; Figure 6F, compare with Figure 5H). Collectively, our studies showed that dI6dmrt3a neuron ablation disrupted left-right motor neuron coordination, resulting in frequent co-activation of left and right motor neurons. Nonetheless, dmrt3a-DTA fish retained some capability to produce leftright alternation. The degree of swimming impairment appeared to be milder during faster phases of swimming. DISCUSSION In this study, we have performed morphological and physiological analyses of dI6dmrt3a neurons and of V0d neurons. Both types of neurons have many common features including the following: (1) both are commissural neurons (Figure 2), (2) both make inhibitory synaptic connections onto several types of neurons that are active during fictive swimming (Figure 3), and (3) both generally fire in phase with nearby VR activity (Figures 4D–4I). These results are consistent with the idea that both dI6dmrt3a and V0d neurons are involved in providing mid-cycle

average, magenta shading represents SEM). The average peak cross-correlation coefficient was 0.36 at a 11.25 ms lag. (E3) Distributions of average crosscorrelation coefficients across all fish (n = 3 for control fish [CT], n = 4 for dmrt3a-DTA fish [DT]). (F) Average cross-correlation coefficients at lag = 0 ms for all VR bouts for control (CT) versus dmrt3a-DTA fish (DT); *p < 0.05. (G) Average peak cross-correlation coefficients for all VR bouts for control (CT) versus dmrt3a-DTA fish (DT); ***p < 0.001. (H) Average time lag at peak cross-correlation for all VR bouts for control (CT) versus dmrt3a-DTA fish (DT); **p < 0.01. (I) dmrt3a-DTA fish exhibit increased left and right VR co-activation compared with control. (I1) Distribution of the fraction of time that VR co-activation occurred during individual VR bursts (percentage co-activation) for an example control fish. Bin size 10%. (I2) Same as (I1) for an example dmrt3a-DTA fish. For both plots, the dotted line indicates the threshold (>10% co-activation) used to determine co-activation events for individual bursts used in (J). (J) Average fraction of co-activation events (defined as bursts during which co-activation occurred >10% of the time) in control fish (CT) versus dmrt3a-DTA fish (DT); ***p < 0.001. (K) dmrt3a-DTA fish exhibit faster median fictive swimming frequencies. (K1) Distribution of fictive swimming frequencies for an example control fish. (K2) Same as (K1) for an example dmrt3a-DTA fish. (L) Median fictive swimming frequency for control (CT, n = 3) and dmrt3a-DTA fish (DT, n = 4); *p < 0.05. Data are presented as mean ± SEM. All significance values were tested using unpaired two-sample Student’s t test (two tailed).

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Figure 6. Phenotypic Analyses of dmrt3a-DTA Fish during Fast Fictive Swimming (A) Example VR recordings from left and right side of the spinal cord of control fish (CT) following electrical stimulation (marked by the asterisk). For the traces at the bottom, see the legends for Figures 5B and 5C. In (A) and (B), the 100 ms windows highlighted in blue were used for all subsequent analyses. (B) Same as (A) but for dmrt3a-DTA fish (DT). (C1) Distribution of cross-correlation functions (gray) of left and right recordings for individual VR bouts, computed from an example control fish (CT). Thick blue line represents the average cross-correlation function across all VR bouts. Blue shading represents SEM. The average peak cross-correlation coefficient was 0.59 at a time lag of 11.7 ms. (C2) Same as (C1) but for dmrt3a-DTA fish (thick magenta line represents average, magenta shading represents SEM). The average peak cross-correlation coefficient was 0.35 at a 9.9 ms lag. (C3) Distributions of average cross-correlation coefficients across all fish (n = 4 for control [CT] fish, n = 4 for dmrt3a-DTA fish [DT]). (D) Average cross-correlation coefficients at lag = 0 ms for all VR bouts for control fish (CT) versus dmrt3a-DTA fish (DT); p = 0.52. (E) Average peak cross-correlation coefficients for all VR bouts for control fish (CT) versus dmrt3a-DTA fish (DT); p = 0.11. (F) Average time lag at peak cross-correlation for all VR bouts for control fish (CT) versus dmrt3a-DTA fish (DT); *p < 0.05. (G) dmrt3a-DTA fish exhibit increased left and right VR co-activation compared with control fish. (G1) Distribution of the fraction of time that VR co-activation occurred during individual VR bursts (percentage co-activation) for an example control fish. Bin size 10%. (G2) Same as (G1) for an example dmrt3a-DTA fish. For both plots, the dotted line indicates the threshold (>10% co-activation) used to determine co-activation events for individual bursts used in (H). (H) Average fraction of co-activation events (defined as bursts during which co-activation occurred >10% of the time) in control fish (CT) versus dmrt3a-DTA fish (DT); p = 0.06. (I) dmrt3a-DTA fish exhibit higher median swimming frequencies during faster fictive swimming. (I1) Distribution of fictive swimming frequencies for an example control fish. (I2) Same as (I1) for an example dmrt3a-DTA fish. (J) Median fictive swimming frequency for control (CT, n = 4) and dmrt3a-DTA fish (DT, n = 4); ***p < 0.001. Data are presented as mean ± SEM. All significance values were tested using unpaired two-sample Student’s t test (two tailed).

inhibition to the contralateral side of the spinal cord. Our results also showed some differences between V0d and dI6dmrt3a neurons. V0d neurons tended to fire during faster and stronger movements, while dI6dmrt3a neurons tended to fire more

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consistently across normal fictive swimming (Figures 4J–4L). We further showed that ablation of dI6dmrt3a neurons led to impairments of proper left-right alternation (Figures 5 and 6), indicating the importance of this population in swimming CPGs.

Morphological Heterogeneity among dI6dmrt3a Neurons Our previous morphological analyses of V0d neurons (excluding GABAergic ones) showed that they are relatively homogeneous (Satou et al., 2012). In contrast, dI6dmrt3a neurons were more heterogeneous in their morphology (Figure 2). We classified them into three types: A, B, and C. Type C (CoLo) neurons were clearly distinct from the other types of dI6dmrt3a neurons. CoLo neurons are relatively sparse and typically present with only one cell per hemi-segment (Satou et al., 2009), indicating that the vast majority of dI6dmrt3a neurons fall into either the type A or the type B category. Type A and type B dI6dmrt3a neurons were categorized on the basis of their dendritic morphology (Figure 2E). We classified type A neurons as having extensive dendritic arborization, while the rest of the neurons with simpler dendritic morphology were classified as type B. It is unclear whether a definite boundary was present between the two types. Importantly, however, the morphological differences correlated with physiological differences, supporting the usefulness of the categorization. Synaptic Targets of dI6dmrt3a and V0d Neurons Our ChR-mediated photo stimulation experiments have shown that both dI6dmrt3a and V0d neurons make inhibitory synapses on motor neurons and other CPG neurons, including (1) ipsilateral ascending neurons (possibly V1 neurons), ipsilateral descending neurons (possibly V2a neurons), and commissural neurons (dI6dmrt3a and V0d neurons) (Figure 3; Table 1). The observed connections are consistent with the current CPG model for swimming (Grillner, 2003; Roberts et al., 1998). In addition, we observed synaptic connections from both dI6dmrt3a and V0d neurons to sensory-related neurons (Figure 3; Table 1). Although the physiological significance of these connections are unknown, synaptic connections from commissural inhibitory neurons onto sensory-related neurons are also noted in frog tadpoles (Li et al., 2007). Firing Patterns of dI6dmrt3a and V0d Neurons during Fictive Locomotion We have examined firing patterns of dI6dmrt3a (type A and type B) and V0d neurons during fictive locomotion. All three types of neurons were active during fictive swimming, but firing probabilities differed among different types of neurons (Figure 4J). dI6dmrt3a type A neurons fired most consistently for each fictive swim cycle, while firing probability of V0d neurons was the lowest. This phenomenon is a reflection of the frequency tuning preferences of each neuronal type (Figure 4K). dI6dmrt3a type A neurons were tuned to a slower (lower frequency; about 30 Hz) fictive swimming frequency, while V0d neurons were tuned to a faster (higher frequency; >40 Hz) fictive swimming frequency (preferential firing of V0d neurons at faster swimming frequency was also recently reported by Menelaou and McLean (2019). dI6dmrt3a type B exhibited an intermediate tendency. Because the most common swimming frequencies in our data tended to be lower frequencies (between 20 and 40 Hz; Figure S2B), dI6dmrt3a type A neurons exhibited the highest firing probability when all the swimming events were taken into account (Figure 4J).

Fast swimming is a stronger movement than slow swimming (McLean et al., 2008). Thus, the results described above suggest that dI6dmrt3a type A neurons preferentially fire during weaker movements, while V0d neurons preferentially fire during stronger movements. This notion is further supported by differences in the activity of these neurons during escape behaviors. V0d neurons fired most consistently during escape movements, while the firing probability of dI6dmrt3a type A neurons during escape movements was the lowest. The neuronal mechanism that underlies this frequency/strength tuning of dI6dmrt3a type A, dI6dmrt3a type B, and V0d neurons is currently not known, so uncovering this mechanism will be an important issue for future studies. Although V0d neurons tended to be involved in stronger movements than dI6dmrt3a neurons in general, it should be noted that dI6dmrt3a type C (CoLo) neurons are known to be involved only in escape movements (Satou et al., 2009). Therefore, it appears that dI6dmrt3a neurons include both escape-dedicated neurons (type C) and neurons that are tuned to steady-state slower swimming (type A and type B). Collectively, the present study reveals the heterogeneity and complexity of glycinergic commissural inhibitory neurons in larval zebrafish (Liao and Fetcho, 2008). The presence of a variety of commissural inhibitory neurons may be important for fine control of body movement during several behaviors of larval zebrafish (e.g., turning and prey capture; Budick and O’Malley, 2000). Phenotypes in Animals in which Spinal dI6dmrt3a Neurons Are Ablated Fictive swimming typically occurs with a frequency between 20 and 40 Hz (Figure S2B), which matches the frequency tuning range of dI6dmrt3a type A and type B neurons. Consequently, inactivation of dI6dmrt3a neurons would be expected to result in a weakening of mid-cycle inhibition, leading to impairment of proper left-right alteration. Our phenotypic analyses of dmrt3aDTA fish during regular swimming (Figure 5) supported this notion. The cross-correlation analysis revealed that left-right alternation was markedly disorganized in dmrt3a-DTA fish (Figures 5E–5H). In addition, longer VR bursts (>20 ms) were often observed, during which left and right VR activity often overlapped (Figures 5I and 5J). These results are consistent with the idea that dI6dmrt3a neurons play an important role for ensuring proper left-right alteration by providing mid-cycle inhibition. This notion was further strengthened by our observation that mid-cycle inhibition in regularly firing secondary motor neurons was greatly reduced in dmrt3a-DTA fish (Figures S4). Our study also showed that some degree of left-right alteration is maintained in dmrt3a-DTA fish. In addition to long-duration VR bursts, dmrt3a-DTA fish retained the capability of producing rhythmically alternating VR activities (Figure 5D). This suggests that commissural inhibition is not completely absent in dmrt3aDTA fish. One candidate neuronal type that could mediate this inhibition are V0d neurons. Although the firing probability of individual V0d neurons in each swimming cycle was low, as a population, V0d neurons may be capable of maintaining left-right alternation during the period when dmrt3a-DTA fish exhibited rhythmic swimming. Additionally, there is also a possibility that an as yet unidentified population of commissural inhibitory neurons exists, which may assist in maintaining left-right alteration.

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We also performed phenotypic analyses of dmrt3a-DTA fish at periods of fast swimming (the periods right after electrical stimulation; Figure 6). Across several parameters (trough and peak cross-correlation values, and occurrence of left-right co-activation events), the degree of change that occurred in dmrt3a-DTA fish was smaller than that obtained during regular slower swimming. These data suggest that the dmrt3a-DTA phenotype is milder during faster swimming. Given that V0d neurons tended to be more active during faster swimming (Figure 4), the results would be consistent with the idea that the relative importance of dI6dmrt3a and V0d neurons shifts with increasing swimming frequency. When dmrt3a-DTA fish exhibited rhythmic swimming, their frequency, on average, was higher than that of control fish. The phenomena were observed both for regular (Figure 5L) and faster swimming (Figure 6J). A likely explanation for this is that the absence of dI6dmrt3a neurons weakened mid-cycle inhibition (Figure S4), causing earlier activation of CPG and motor neurons on the contralateral side. If this is the case, our results are consistent with the idea that commissural inhibitory neurons are a part of swimming CPGs (Grillner, 2003; Roberts et al., 1998), with dI6dmrt3a neurons being one of the components.

seems different between the swimming CPG and walking CPG. In addition, the following point needs to be mentioned: in the walking CPG, both inhibitory (i.e., V0d) and excitatory (i.e., V0v) neurons are involved in left-right alternation. Excitatory neurons exert their control by activating local inhibitory neurons (Kjaerulff and Kiehn, 1997). Excitatory and inhibitory commissural neurons have been shown to work at different frequencies (Talpalar et al., 2013). In contrast, commissural inhibitory neurons of different classes provide direct inhibition to the contralateral side in a frequency dependent manner in zebrafish (this study). In addition to the phylogenetic issue discussed above, an ontogenetic issue needs to be considered. In this study, early larval stage was chosen for the analysis, but it is not guaranteed that what we have revealed here is completely applicable for adult zebrafish. For example, V0v neurons are active at higher frequencies in adult zebrafish while they are active at slower frequencies in larvae (Bjo¨rnfors and El Manira, 2016; McLean et al., 2008). Thus, functional characterization of dI6dmrt3a and V0d neurons in adult zebrafish will be an important issue in the future.

Evolutionary and Ontogenetic Considerations In mammalian spinal CPGs, both V0d and dI6 inhibitory neurons have been implicated in left-right coordination (Kiehn, 2016). V0d neurons are all commissural (Lanuza et al., 2004), while dI6 inhibitory neurons are divided into two types on the basis of the expression of either the dmrt3 or wt1 gene (Andersson et al., 2012; Vallstedt and Kullander, 2013). dmrt3 neurons have both contralateral and ipsilateral projections, but contralateral projections are predominant (Andersson et al., 2012; Perry et al., 2019). Recently, wt1 neurons were also characterized and shown to be commissural neurons (Haque et al., 2018). We have shown that V0d and dI6dmrt3a neurons in zebrafish are all commissural neurons (Satou et al., 2012, and the present study). Taken together, axonal projections of V0d and dI6 neurons are likely conserved across vertebrate species, suggesting that the commissural nature of V0d and dI6 neurons represents a primitive state in vertebrate evolution. The issue of whether wt1-expressing subtypes of dI6 neurons are present in zebrafish, and if so, what morphological features they possess, requires further studies. Our results suggest that dI6dmrt3a and V0d neurons have related functions, namely, providing mid-cycle inhibition during swimming. In mammals, CPGs for walking are more complex than swimming CPGs, and consequently, the functional relationship between dmrt3 and V0d neurons in mammals is less clear. This point awaits future studies. Our studies, in comparison with studies in mammals, have revealed several differences between swimming CPG and walking CPG regarding left-right alternation. Through the use of selective ablation of the V0d population in mice, these neurons were shown to play an important role in ensuring appropriate alternation of hindlimbs at low locomotor frequencies (Talpalar et al., 2013). In contrast, our data suggest that V0d neurons in larval zebrafish appear to play important roles during faster locomotion. Thus, the frequency tuning of V0d neurons

Detailed methods are provided in the online version of this paper and include the following:

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STAR+METHODS

d d d d

d d

KEY RESOURCES TABLE LEAD CONTACT AND MATERIALS AVAILABILITY EXPERIMENTAL MODEL AND SUBJECT DETAILS METHOD DETAILS B Transgenic zebrafish B Stochastic labeling of dI6dmrt3a neurons with GFP B Counting number of neurons and examination of soma location B Electrophysiology B ChR experiments B Measurement of mid-cycle inhibition QUANTIFICATION AND STATISTICAL ANALYSIS DATA AND CODE AVAILABILITY

SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j. celrep.2020.02.015. ACKNOWLEDGMENTS We thank Peter Rupprecht and Aya Takeoka for feedback. This work was supported in part by grants from the Ministry of Education, Culture, Sports, Science and Technology of Japan. AUTHOR CONTRIBUTIONS C.S. and S.H. conceived and designed the experiments. C.S., T. Sugioka, Y.U., T. Shimazaki, and Y.K. performed experiments. C.S. and P.Z. performed analysis. C.S., Y.K., and S.H. wrote the manuscript. DECLARATION OF INTERESTS The authors declare no competing interests.

Received: May 29, 2019 Revised: December 17, 2019 Accepted: February 4, 2020 Published: March 3, 2020 REFERENCES Alaynick, W.A., Jessell, T.M., and Pfaff, S.L. (2011). SnapShot: spinal cord development. Cell 146, 178–178.e1.

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Andersson, L.S., Larhammar, M., Memic, F., Wootz, H., Schwochow, D., Rubin, C.J., Patra, K., Arnason, T., Wellbring, L., Hja¨lm, G., et al. (2012). Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice. Nature 488, 642–646. Bellardita, C., and Kiehn, O. (2015). Phenotypic characterization of speedassociated gait changes in mice reveals modular organization of locomotor networks. Curr. Biol. 25, 1426–1436. Bjo¨rnfors, E.R., and El Manira, A. (2016). Functional diversity of excitatory commissural interneurons in adult zebrafish. eLife 5, e18579. Buchanan, J.T. (2001). Contributions of identifiable neurons and neuron classes to lamprey vertebrate neurobiology. Prog. Neurobiol. 63, 441–466. Budick, S.A., and O’Malley, D.M. (2000). Locomotor repertoire of the larval zebrafish: swimming, turning and prey capture. J. Exp. Biol. 203, 2565– 2579. Buss, R.R., and Drapeau, P. (2001). Synaptic drive to motoneurons during fictive swimming in the developing zebrafish. J. Neurophysiol. 86, 197–210. Delcomyn, F. (1980). Neural basis of rhythmic behavior in animals. Science 210, 492–498. Fidelin, K., and Wyart, C. (2014). Inhibition and motor control in the developing zebrafish spinal cord. Curr. Opin. Neurobiol. 26, 103–109.

Kjaerulff, O., and Kiehn, O. (1996). Distribution of networks generating and coordinating locomotor activity in the neonatal rat spinal cord in vitro: a lesion study. J. Neurosci. 16, 5777–5794. Kjaerulff, O., and Kiehn, O. (1997). Crossed rhythmic synaptic input to motoneurons during selective activation of the contralateral spinal locomotor network. J. Neurosci. 17, 9433–9447. Lanuza, G.M., Gosgnach, S., Pierani, A., Jessell, T.M., and Goulding, M. (2004). Genetic identification of spinal interneurons that coordinate leftright locomotor activity necessary for walking movements. Neuron 42, 375–386. Li, W.C., Cooke, T., Sautois, B., Soffe, S.R., Borisyuk, R., and Roberts, A. (2007). Axon and dendrite geography predict the specificity of synaptic connections in a functioning spinal cord network. Neural Dev. 2, 17. Liao, J.C., and Fetcho, J.R. (2008). Shared versus specialized glycinergic spinal interneurons in axial motor circuits of larval zebrafish. J. Neurosci. 28, 12982–12992. Masino, M.A., and Fetcho, J.R. (2005). Fictive swimming motor patterns in wild type and mutant larval zebrafish. J. Neurophysiol. 93, 3177–3188. McLean, D.L., and Fetcho, J.R. (2008). Using imaging and genetics in zebrafish to study developing spinal circuits in vivo. Dev. Neurobiol. 68, 817–834.

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Perry, S., Larhammar, M., Vieillard, J., Nagaraja, C., Hilscher, M.M., Tafreshiha, A., Rofo, F., Caixeta, F.V., and Kullander, K. (2019). Characterization of Dmrt3-derived neurons suggest a role within locomotor circuits. J. Neurosci. 39, 1771–1782.

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Roberts, A., Li, W.C., and Soffe, S.R. (2008). Roles for inhibition: studies on networks controlling swimming in young frog tadpoles. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 194, 185–193. Satou, C., Kimura, Y., Kohashi, T., Horikawa, K., Takeda, H., Oda, Y., and Higashijima, S. (2009). Functional role of a specialized class of spinal commissural inhibitory neurons during fast escapes in zebrafish. J. Neurosci. 29, 6780–6793. Satou, C., Kimura, Y., and Higashijima, S. (2012). Generation of multiple classes of V0 neurons in zebrafish spinal cord: progenitor heterogeneity and temporal control of neuronal diversity. J. Neurosci. 32, 1771–1783. Satou, C., Kimura, Y., Hirata, H., Suster, M.L., Kawakami, K., and Higashijima, S. (2013). Transgenic tools to characterize neuronal properties

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of discrete populations of zebrafish neurons. Development 140, 3927– 3931. Talpalar, A.E., Bouvier, J., Borgius, L., Fortin, G., Pierani, A., and Kiehn, O. (2013). Dual-mode operation of neuronal networks involved in left-right alternation. Nature 500, 85–88. Vallstedt, A., and Kullander, K. (2013). Dorsally derived spinal interneurons in locomotor circuits. Ann. N Y Acad. Sci. 1279, 32–42. Wang, H., Sugiyama, Y., Hikima, T., Sugano, E., Tomita, H., Takahashi, T., Ishizuka, T., and Yawo, H. (2009). Molecular determinants differentiating photocurrent properties of two channelrhodopsins from chlamydomonas. J. Biol. Chem. 284, 5685–5696.

STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Chemicals, Peptides, and Recombinant Proteins D-tubocurarine

Sigma Aldrich

T2379

CNQX

Wako

570-38321

D-AP5

Tocris

0106

Alexa Fluor 594 hydrazide

Thermo Fisher Scientific

A10438

Alexa Fluor 647 hydrazide

Thermo Fisher Scientific

A20502

Tg[glyt2:lRl-Gal4]

Higshijima lab

Satou et al., 2013

Tg[dbx1b:Cre]

Higshijima lab

Satou et al., 2012

Tg[UAS:GFP]

Higshijima lab

Kimura et al., 2013

Tg[UAS:ChR]

Higshijima lab

Kimura et al., 2013

Tg[hoxa9a-30 enhancer:Cre]

Higshijima lab

Kimura and Higashijima, 2019

Tg[dmrt3a:GFP]

Higshijima lab

In this study

Tg[dmrt3a:Gal4]

Higshijima lab

In this study

Tg[dmrt3a:Cre]

Higshijima lab

In this study

Tg[glyt2:lRl-GFP]

Higshijima lab

In this study

Tg[dmrt3a:loxP-DsRed-loxP-DTA (lRl-DTA)]

Higshijima lab

In this study

MATLAB

Mathworks

https://www.mathworks.com/products.html

IMARIS

Bitplane

http://www.bitplane.com/Default.aspx

pClamp

Molecular Device

https://www.moleculardevices.com

Experimental Models: Organisms/Strains

Software and Algorithms

LEAD CONTACT AND MATERIALS AVAILABILITY Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Shin-ichi Higashijima ([email protected]). Zebrafish lines generated in this study have been deposited to National BioResource Project in Japan. EXPERIMENTAL MODEL AND SUBJECT DETAILS Zebrafish adults, embryos and larvae were maintained at 28.5 C. Experiments were performed at room temperature (23-28 C). All procedures were performed in compliance with the guidelines approved by the animal care and use committees of the National Institutes of Natural Sciences. Animals were staged according to days post fertilization (dpf). The morphological and physiological experiments were performed using larvae at 4 dpf. METHOD DETAILS Transgenic zebrafish The following transgenic fish strains were described in previous studies: Tg[glyt2:lRl-Gal4] (Satou et al., 2013), Tg[dbx1b:Cre] (Satou et al., 2012), Tg[UAS:GFP] and Tg[UAS:ChR] (Kimura et al., 2013). For the dmrt3a gene, three BAC transgenic fish strains (Tg [dmrt3a:GFP], Tg[dmrt3a:Gal4] and Tg[dmrt3a:Cre) were generated using zC246F16 BAC. For all the dmrt3a BAC transgenic fish, the zebrafish hsp70 promoter was included in the constructs (Satou et al., 2012, 2013). Tg[glyt2:lRl-GFP] and Tg[dmrt3a:loxPDsRed-loxP-DTA (lRl-DTA)] transgenic fish were generated using the CRISPR/Cas9-mediated knock-in method with the hsp70 promoter (Kimura et al., 2014). Tg[hoxa9a-30 enhancer:Cre] was described in Kimura and Higashijima (2019). Tg[glyt2:lRl-GFP] and Tg[dmrt3a:Gal4] (without the hsp70 promoter) fish had been generated for an earlier study (Satou et al., 2013), but here we used a newly generated line as described above since the expression levels of reporter genes were stronger in these newly generated fish.

Cell Reports 30, 3036–3050.e1–e4, March 3, 2020 e1

Stochastic labeling of dI6dmrt3a neurons with GFP For stochastic expression of GFP in dI6dmrt3a neurons, DNA of glyt2:lRl-GFP was injected into one to four cell stage embryos of Tg [dmrt3a:Cre] stable transgenic fish. Larval fish expressing GFP in a small number of neurons were selected using a fluorescent dissecting microscope. GFP-labeled neurons were imaged in the living fish by confocal microscopy (Zeiss LSM510 or Leica SP8). Counting number of neurons and examination of soma location For labeling dI6dmrt3a neurons, Tg[dmrt3a:GFP] fish were used. For labeling V0d neurons, a combination of Tg[dbx1b:Cre] and Tg [glyt2:lRl-GFP] fish were used. GFP-labeled neurons were imaged in the living fish by confocal microscopy (SP8, Leica), which allowed us to work with spinal cord cross sections using Imaris (Bitplane). Neurons located between segment 12 and 13 were analyzed in this study. Data were collected from larval zebrafish at 4 dpf. To determine neuron location along the ventral-dorsal and lateralmedial axes, we carefully determined the edges of the spinal cord, and normalized the soma location relative to the edges (to fall within the range of 0 to 1 representing the full extent of the segment). Electrophysiology To obtain animals with GFP-labeled V0d or dI6dmrt3a neurons for electrophysiological studies, we combined Cre-loxP and Gal4UAS systems. For labeling V0d neurons, we used triple transgenic fish generated by crossing Tg[dbx1b:Cre], Tg[glyt2:lRl-Gal4], and Tg[UAS:GFP] fish. For labeling dmrt3a neurons, we used triple transgenic fish generated by crossing Tg[dmrt3a:Cre], Tg [glyt2:lRl-Gal4], and Tg[UAS:GFP] fish. Due to the mosaic expression nature of the Gal4-UAS system, GFP was expressed in a relatively small number ofV0d or dI6dmrt3a neurons. This enabled us to observe dendritic morphology of GFP-labeled neurons and therefore roughly identify dI6dmrt3a-type A, dI6dmrt3a-type B and dI6dmrt3a-type C neurons before electrophysiological recordings with the following criteria: Neurons possessing extensive dendritic arbors were considered as dI6dmrt3a-type A. Neurons with no dendrite in their primary processes were considered as dI6dmrt3a-type C (CoLo). The remaining neurons were considered as dI6dmrt3a-type B. Patch-clamp and ventral root (VR) recordings were performed as described previously (Kimura et al., 2006; Satou et al., 2009) with some modifications described below. Recordings were carried out using 4-dpf (4.0-4.9 dpf) larvae. Larvae were immobilized by soaking them in the neuromuscular blocker d-tubocurarine (0.1 mg/ml in distilled water; Sigma) for 5-15 min, and then pinned through the notochord to a Sylgard-coated, glass-bottomed dish with short pieces of fine tungsten pins. Animals were then covered with extracellular recording solution that contained (in mM): 134 NaCl, 2.9 KCl, 1.2 MgCl2, 2.1 CaCl2, 10 HEPES, 0.01 d-tubocurarine, and 10 glucose, adjusted to pH 7.8 with NaOH. The skin covering the midbody was removed with a pair of forceps. Then, muscle fibers of one or two segments were carefully manually removed with a tungsten needle. For all electrophysiology experiments, the preparations were monitored using a water immersion objective (40x; NA, 0.80; Olympus) on an upright microscope (BX51WI; Olympus) fitted with differential interference contrast (DIC) optics. dI6dmrt3a or V0d or neurons located in the midbody segments (segments 10-15) were targeted for recordings. VR recordings were made near the recording site of neurons. Electrodes for VR recordings (tip diameter, 20-40 mm) were filled with the extracellular recording solution. Patch electrodes (resistance, 12~20 MU) were filled with intracellular solution. We used two different patch solutions, one containing (in mM), 120.7 Kgluconate, 4.3 KCl, 2.4 MgCl2, 10 HEPES, 10 EGTA, and 4 Na2ATP, adjusted to pH 7.2 with KOH, and the other containing a lower concentration of chloride (in mM; 123 Kgluconate, 2 KCl, 2 MgCl2, 10 HEPES, 10 EGTA, and 4 Na2ATP, adjusted to pH 7.2 with KOH). The calculated chloride reversal potential of the former solution was 80 mV, whereas the latter was 70 mV. The calculated liquid junction potentials for which we corrected were 16 mV and 15 mV, respectively. Electrophysiological recordings were performed using MultiClamp700B amplifiers, and digitized with Digidata1440A (Molecular Devices). Neurons were labeled with 0.01% Alexa Fluor 633 or 647 hydrazide (Thermo Fisher Scientific) in the intracellular solution used for patching. In some of the recordings for examining spiking patterns of V0d neurons, we performed loose-patch technique instead of whole cell patch-clamp (Kimura and Higashijima, 2019). Fictive locomotion was elicited either by applying a brief electric shock near the tail (stimulus strength of 7-20 V for a duration of 0.2-1.0 ms) or by changing the illumination intensity. Fictive swimming also occurred spontaneously. The stimulation electrode was placed at the side contralateral to the recorded neurons. After the recordings, fluorescent images were acquired with a FV300 confocal unit with a 633 nm laser (Olympus). Simultaneous VR recordings on the left and right sides were essentially performed as described in Masino and Fetcho (2005). Briefly, skin of both sides of the body was removed, as described above. Then, fine tungsten wires were used to secure the fish in an upright posture on a Sylgard-coated, glass-bottomed dish. VR recordings were made as described above. ChR experiments Tg[UAS:ChR] (Kimura et al., 2013) was used to express a modified version of ChR, ChRWR (channelrhodopsin wide receiver; Wang et al., 2009) in dI6dmrt3a or V0d neurons. For dI6dmrt3a neurons, Tg[dmrt3a:Gal4] fish were used as driver fish, while for V0d neurons, compound transgenic fish of Tg[dbx1b:Cre]; Tg[glyt2:lRl-Gal4] were used. Gal4-UAS fish have been shown to often exhibit mosaic expression of a reporter gene (Goll et al., 2009). Consequently, random small subsets of dI6dmrt3a or V0d neurons expressed ChR (EYFP-fusion) in our experimental condition. The fish were mounted in the dish for electrophysiological studies as described above. Cells for patch recordings were randomly chosen in the midbody region. Recorded cells were held at 35~-50 mV in voltage clamp mode. A 20 ms pulse of blue light e2 Cell Reports 30, 3036–3050.e1–e4, March 3, 2020

illumination (power ~2 mW/mm2) was applied over an area covering 5~6 segments. Three locations were sequentially illuminated: (1) near the patched cell (center); (2) rostral to area (1); and (3) caudal to area (1) (Figure 3A). After the recording, the identity of the recorded cell was determined by morphological analysis. In some cases, the identity of the recorded cell was determined by the presence of ChR (EYFP-fusion) expression. To test whether blue-light illumination could elicit action potentials in a ChR-EYFP expressing neuron, we recorded from ChR-EYFP expressing neurons while stimulating them 5 times with blue-light illumination with an interstimulus interval of at least 1 s. If at least one action potential was elicited in any one of the five trials, the cell was considered responsive. In some of the experiments, we performed pharmacological treatment to eliminate the possible involvement of polysynaptic pathways. For this, the sample were perfused with an extracellular solution containing CNQX (20 mM; Wako) and AP5 (50 mM; Tocris) for 10 minutes. This pharmacological treatment abolished the animal’s capability of performing fictive swimming, suggesting that glutamatergic synaptic transmission was blocked with this treatment. After 10 minutes of the drug treatment, we again photo-stimulated the sample and measured responses of the recorded cells. In all cases tested (n = 4 for dmrt3a-ChR fish and n = 2 for V0d-ChR fish), IPSC-responses persisted. Measurement of mid-cycle inhibition Small motor neurons involved in regular fictive swimming were chosen for this experiment. To look for such neurons, we first performed simultaneous recordings of VR and loose-patch of possible target neurons located in a relatively ventral region of the spinal cord, in a motor-neuron rich area. After finding a neuron that consistently fired in most of the swim cycles during regular fictive swimming, we targeted the same neuron for whole-cell voltage-clamp recording. The voltage-clamp recordings for measuring inhibitory currents were performed as described in Kimura and Higashijima (2019). The intracellular solution contained (in mM) 140 CsMeSO4, 1 QX314-Cl, 1 TEA-Cl, 3 MgCl2, 10 HEPES, 1 EGTA, 4 Na2-ATP, and was adjusted to pH 7.2 with CsOH. The neurons were held at +10 mV. Additionally, neurons were labeled with 0.01% Alexa Fluor 594 hydrazide (Thermo Fisher Scientific) in the intracellular solution. After the recordings, identities of neuronal types (motor neurons) were verified by their torn axons at the lateral edge of the spinal cord (this occurred if their peripherally extending axons were torn during the muscle removal procedures) or their axons extended to the peripheral muscles. All recorded motor neurons had high input resistance (> 700 MU). QUANTIFICATION AND STATISTICAL ANALYSIS Electrophysiological data were analyzed with a custom-written program written in MATLAB (Mathworks). For the analysis of VR recordings (Figures 4, 5, and 6) we developed a semi-automated algorithm (Figure S2A). Raw VR recordings were first rectified, low-pass filtered at 500 Hz, smoothed with a 5 ms running-average window and median normalized. Onsets and offsets of VR bouts (corresponding to fictive swimming bouts) and individual bursts (corresponding to motor neuron activation events) were then detected by applying thresholds to the VR traces. For detecting VR bouts, a single threshold was used and a minimum inter bout interval constraint of 50 ms. For detecting individual VR bursts within a bout, a threshold was adapted to individual VR bouts (to account for any drift in the baseline), and minimum burst duration constraint of 5 ms as well as a minimum inter-burst interval constraint of 4 ms were applied. These burst constraints cap the maximum possible fictive swimming frequency at 111 Hz, however, this is out of range of larval zebrafish swimming and fictive swimming frequencies based on VR recordings (McLean et al., 2008, see Figures 2E and 5E). The results of the algorithm were always visually inspected for errors. VR activity associated with escape behaviors (Figure 4) were defined as those that occurred within 10 ms following electrical stimulations. All other VR activity were considered as fictive swimming. For determining the fictive swimming frequency (Figure 4K), the duration between mid-points of subsequent VR bursts were defined as a cycle period and fictive swimming frequency was defined as the inverse of the cycle period. Escape activity and the final VR burst in every bout were excluded from the frequency analysis of swimming. Circular plot analysis (Figures 4G–4I) was performed essentially as described previously (Kjaerulff and Kiehn, 1996) to provide a statistical measure of the coupling between neuronal firing and the phase of VR bursts. If the VR recording site was in the same segment as the neuronal recording, all the spikes (except for the ones associated with escapes) were subject to the analysis. If the VR recording site was in a different segment (rostral or caudal within 3 segments) to the site of neuronal recording, spikes that occurred in swim cycles whose frequency was between 25-40 Hz were subject to the analysis. In such cases, a spike timing adjustment was applied as described in the following; during swimming in the frequency range of 25-40 Hz, rostro-caudal delay of VR activity scales proportionally with cycle period when normalized to a single body segment. This leads to a constant rostro-caudal phase with mean rostro-caudal phase difference per segment of 2.6% (Masino and Fetcho, 2005). This value corresponds to 9.36 deg/ segment in a circular plot. Consequently, we used this value to adjust the phase of neuronal firing relative to VR activity. For example, if the recorded neuron was located 2 segments caudal to the VR recording site, spike plots were rotated counter clockwise by 18.72 (9.36 3 2) degrees. For the purpose of clear visualization, the 0-360 degree range was converted to a range between 0 and 1. For each neuron, 50 spikes were randomly selected, and their phases relative to two VR bursts surrounding the spikes were determined. Neurons that did not exhibit 50 spikes during recordings were not included in the circular plot analysis. To quantify average neuronal firing probabilities during fictive swim cycles (Figure 4J), we determined the fictive swimming frequency as described above. The presence/absence of at least one spike within an individual cycle was scored as active/silent, and average firing probabilities were calculated for each neuron. Each dot shows the average firing probability of each neuron.

Cell Reports 30, 3036–3050.e1–e4, March 3, 2020 e3

We then computed the average firing probability for a neuronal type (dI6dmrt3a-type A, dI6dmrt3a-type B, and V0d neurons) by averaging over individual average firing probabilities of each neuron. To quantify neuronal frequency tuning (Figure 4K), we constrained our analysis to periods of fictive swimming frequencies between 20 60 Hz. Data from all individually recorded neurons were pooled for each neuronal type. We binned the fictive swimming frequency in eight 5 Hz bins centered on 22.5 Hz, 27.5 Hz, ., 57.5 Hz. SEM was calculated over average frequency tuning profiles of individual neurons. To quantify average neuronal firing probabilities during escape (Figure 4L), the presence/absence of at least one spike within an individual escape event was scored as active/silent, and average firing probabilities were calculated for each neuron. Individual dots in the figure show the average firing probability of each neuron. We then computed the average firing probability for each neuronal type (dI6dmrt3a-type A, dI6dmrt3a-type B, and V0d neurons) by averaging over average firing probabilities of individual neurons. Phenotypic analyses of dmrt3a-DTA fish were performed using simultaneous VR recordings of the left and right sides. To analyze phenotypes during regular swimming (Figure 5), VR activity associated with swim bouts that occurred without electrical stimulations were analyzed. To analyze phenotypes during faster swimming (Figure 6), VR activity associated with swim bout elicited by electrical stimulation were analyzed. In order to sample periods of fast swimming, we restricted our analyses to a window of 100 ms following electrical stimulation (the very first VR burst that corresponds to an escape event was excluded from this analysis). To compute the cross-correlation function between left and right VR traces (Figures 5E–5H and 6C–6F), individual swimming bouts were analyzed and then averaged for each fish (16-202 bouts per fish during regular swimming; 19-38 bouts per fish during faster swimming). A positive cross-correlation value at time lag zero indicates that the two signals are relatively in phase, whereas a negative value indicates that they are anti-phasic. Since the VR traces were bound at zero, the minimum correlation value we could obtain was 0.5, which is indicative of a strong anti-phasic relationship. The lag time of the cross-correlation function indicates a shifting of one VR trace relative to the other. For phenotypic analysis shown in Figures 5I and 5J and Figures 6G and 6H, bursts during which co-activation of left and right VR activity occurred > 10% of the time were defined as co-activation events. To quantify the fictive swimming frequency (Figures 5K, 5L, 6I, and 6J), left-right VR burst events that were not counted as co-active were used. For the cross-correlation, phenotypic, and fictive swimming frequency analyses, both the left and right VR traces were analyzed independently and then pooled. For measuring mid-cycle inhibitory currents, the peak inhibitory currents at the 0.4-0.6 phase of a swim cycle (corresponding to mid-cycle phase) were measured. For dmrt3a-DTA fish, inhibitory currents during cycles of periodic VR activity were subject to analysis. The following criteria were used to define cycles of periodic VR activity; (1) absence of long-duration [> 20 ms] VR bursts, and (2) a relatively constant cycle frequency (defined for a given cycle as a < 20% change of frequency compared to the frequencies of the previous and following swim cycles). For each cell, at least 10 swim cycles were analyzed. All statistical comparisons were performed using the unpaired two-sample Student’s t test (two tailed). Results are presented as mean ± SEM (standard error of the mean) unless otherwise stated. DATA AND CODE AVAILABILITY The custom-written programs used in the current study (written in MATLAB) have not been deposited in a public repository because (1) MATLAB is not a freeware and because (2) the programs were written for a very specific analysis (not for general purpose). But the programs are available from the corresponding author (C.S.) on request.

e4 Cell Reports 30, 3036–3050.e1–e4, March 3, 2020