Descending control of swim posture by a midbrain nucleus in zebrafish.

Zebrafish lines

Zebrafish were raised and bred at 28°C on a 14 h light / 10 h dark cycle using standard techniques ( Westerfield, 1994 ). All animal procedures conformed to the guidelines of the University of California, San Francisco and the Max Planck Society. Transgenic lines were made in the TLN background, which is based on the Tüpfel long-fin (TL) wildtype strain carrying mutations in mitfa ( nacre, N ). We used Tg(UAS-E1b:Kaede)s1999t , Tg(UAS:ChR2(H134R)-mCherry)s1986t , Tg(UAS:GFP)mpn100 , Tg(UAS:GCaMP6s)mpn101, and Et(-0.6hsp70l:Gal4-VP16) s1171t . The Gal4 s1171t line was established from a Tol2 enhancer-trap screen (Scott et al., 2007). Linker-mediated cloning established that Gal4 s1171t is inserted in the first intron of the sim1a gene (T. Thiele, unpublished result). Tg(UAS:GCaMP6s)mpn101 was constructed by first cutting the GCaMP6s open reading frame out of pGP-CMVGCaMP6s (Addgene no. 40753) and cloning it into a pTol2-14xUAS vector. This construct was then injected with transposase mRNA into one-cell-stage Gal4 s1171t embryos. Transgenic lines were maintained in either the TL or the TLN background. Designations of mutant and transgenic lines adhered to nomenclature rules set according to http://zfin.org . Immunohistochemistry, backfills and confocal imaging Gal4 s1171t /UAS:GFP larvae (5dpf) were fixed in 4% PFA and processed for antibody staining according to published protocols ( Xiao and Baier, 2007 ). A mouse anti-GFP antibody (GTX13970, Genetex) was used at a concentration of 1:1000 and a goat anti-choline acetyltransferase antibody (AB144p, Millipore) was used at a concentration of 1:200.

Alexa dye conjugated secondary antibodies

(Invitrogen) were used at 1:1000 dilutions. Backfills were performed as described previously ( Gahtan et al., 2005 ). Briefly, a 50% (w/v) solution of Texas Red dextran (10,000 MW, Invitrogen) was pressure injected into the spinal cord of 5-7dpf larvae anesthetized with 0.02% tricaine in Danieau’s solution. Confocal imaging was performed on either a Zeiss LSM700 or LSM780 microscope. Image processing was done using FIJI ( Schindelin et al., 2012 ). Electroporations Gal4 s1171t /UAS:GFP fish (5dpf) were embedded in 2% low melting point agarose (Invitrogen) and immersed in extracellular physiological saline containing 0.02% tricaine. Patch pipettes (8-9 MΩ) were filled with intracellular saline containing 15% tetramethylrhodamine dextran (3000 MW). For MeS labeling, small GFP-positive somas dorsal to the MeL neurons were visually targeted using a 40x water immersion objective (Olympus, 0.8NA). Upon cell contact, light suction was applied, and a voltage train (1.5 second duration, 150 Hz, 1.5 ms pulse width, 2-7 volts) was applied using an Axon Axioporator (Molecular Devices). Cell morphologies were then imaged on a Zeiss LSM780 confocal microscope.

Calcium imaging nMLF

Gal4 s1171t /UAS:GCaMP6s zebrafish (6dpf) were head-embedded in 2% low melting point agarose (Invitrogen). Agarose around the tail was dissected away using a scalpel blade. Fish were allowed to recover from the mounting procedure for several hours. Calcium responses were imaged using a customized moveable objective microscope (MOM, Sutter Instruments) and a 20x objective (Olympus XLUMP, 0.95NA). Scan control and image acquisition were controlled using ScanImage software ( Pologruto et al., 2003 ). GCaMP6s was excited by 920 nm light (Chameleon Ultra, Coherent). Scan rates were 5.92 frames/second (256 × 256 pixels). Tail kinematics were simultaneously imaged at 100 Hz using an infrared ring light and an IR sensitive high speed CMOS camera (Photonfocus, MV1-D1312l-160-CL-12). Frame acquisition was controlled using StreamPix software (Norpix Inc). Data streams were synched using a custom Python script. Tail kinematics were scored manually and confirmed by independent observers. Data were analyzed using Igor Pro software (Wavemetrics). A threshold of 0.2 delta F/ F was used to define a calcium response. Hypaxial muscle Gal4 s1171t /UAS:GCaMP6s/UAS:ChR2(H134R)-mCherry larval zebrafish (6dpf) were head-embedded in 2% low melting point agarose. Imaging was performed using a water immersion objective (10x, 0.3NA) on a LSM780 Zeiss confocal microscope, controlled with ZEN software. Scan rates were 10 frames/second (128 × 128 pixels). Muscles were imaged using an Argon 488 nm laser with 0.9% power before and after unilateral nMLF stimulation. The nMLF was stimulated using a region bleaching scan mode with 40-50% laser power for 200 ms. The minimum laser power required to produce a repeatable tail deflection was used. Muscles were not imaged during the stimulation period.

ChR2 stimulation

Gal4 s1171t /UAS ChR2(H134R)-mCherry larval zebrafish (7dpf) were head-embedded in 2% low melting point agarose. Agarose around the tail was dissected away using a scalpel blade. Fish were allowed to recover from the mounting procedure for several hours. Laser light (473 nm) was delivered to the fish’s head, using low numerical aperture multimode optic fibers (10 μm or 105 μm; HPSC10 or AFS105/125Y, Thorlabs). Optic fibers were prepared as described previously ( Arrenberg et al., 2009 ). The position of the optic fiber was controlled using a micromanipulator (MC1000e, Siskiyou Corporation). A 473 nm direct diode laser (LuxX 80mW, Omicron) and 405 nm direct diode laser (LuxX 60mW, Omicron) were mounted within a laser beam combiner (Lighthub, Omicron) and coupled to the optic fiber. Light intensities were controlled by sending an analog voltage signal to the laser. Light intensities between 0.5 and 2 mW/mm 2 measured at the fiber tip were used for ChR2 activation. Tail kinematics were imaged at 250 frames per second (390×390 pixels) using a high-speed camera (Pike F032B, Allied Vision Technologies) and StreamPix software (Norpix Inc). The camera was coupled to a boom-mounted stereomicroscope (SMZ800, Nikon) with a C-mount adapter. Stimulation and imaging were synchronized using custom scripts written in LabVIEW. Muscle stimulation experiments were conducted in the same manner except a 105 μm diameter fiber was used. For Kaede conversion experiments, the laser line was switched to 405 nm (1.8 mW) for 2 min. The light dosage for conversion experiments was therefore 60-90 times greater (intensity x duration) than that used for ChR2 experiments. Given this large difference in light exposure and the increased scattering at 405nm, our conversion experiments are likely to be an upperbound size estimate for ChR2 stimulation sites ( Arrenberg et al., 2009 ). Neuronal ablations Neurons were located by position and GFP expression in Gal4 s1171t /UAS:GFP fish. Imaging and ablations were performed using the same two-photon microscope used for calcium imaging. Neurons were killed by scanning a focused 850 nm femtosecond pulsed laser beam for ~200 ms over a ~1 μm square in the center of soma. Laser power after the objective was ~270 mW/mm 2 . Behaviors were assessed before ablations and 8-12 hours after surgery. For complete unilateral nMLF ablations, we targeted all visible MeS and MeL neurons (6-8 dorsal MeS neurons, MeS1, MeS2, MeLc, MeLr and MeLm in each animal). For MeS-only ablations, we targeted 6-8 neurons MeS neurons and MeS1 and MeS2. For MeL-only ablations, we targeted MeLc, MeLr and MeLm. The completeness of ablations was determined by imaging the ablated brain region after behavioral experiments. Data from animals with incomplete ablations were discarded.

Optomotor assay

Fish larvae (7-8dpf) were head-embedded in the same manner as for ChR2 stimulation except they were allowed to recover from mounting for 8-12 hours. We found this time-delay improved the responsiveness of mounted larvae to visual stimulation. The experimental arena and control software used for the optomotor assay were described previously ( Schoonheim et al., 2010 ). Briefly, fish were placed in the middle of an arena surrounded by three LCD screens (5.5 × 7.5 cm), one in front of the fish and one on each side. Caudal-to-rostral drifting gratings were displayed on the two side screens to evoke a forward optomotor response. Rotating gratings were displayed on all three screens to evoke turn responses. Fish tail kinematics were imaged at 250 frames per second (390×390 pixels) using a high-speed camera (Pike F032B, Allied Vision Technologies) and StreamPix software (Norpix Inc). Grating presentation and speed were controlled using a custom script written in LabVIEW. At the start of an experiment, an ideal grating speed was determined for each fish. These speeds ranged from 16-28 o /second for forward gratings and 20-30 o /second for rotating gratings. The same grating speeds were used before and after ablations. In the text ipsiversive refers to gratings rotating toward the ablation site whereas contraversive refers to gratings drifting away from the ablation site.

Data analysis

Tail motions were tracked using custom software written in Python. Tail tracking software used OpenCV to load videos and then implemented a tracking algorithm, which returns a series of midpoints along the tail in each frame. The algorithm is seeded by a user-selected point near the base of the zebrafish larvae tail and then iterates towards the end of the tail. At each point, the tail’s lateral midpoint was located by taking a cross-section of the tail and convolving with a function representing the luminosity of a prototypical tail cross-section. The maximum of this convolution was used as the tail midpoint. This procedure is then repeated along the length of the tail returning ~40 points for each video frame. Tail angle was calculated by measuring the angle between the first midpoint near the tail base and the mean position of three midpoints at the end of the tail (to reduce noise). The detected tail midpoints were normalized to correct for small variations in the baseline position of the tail. Another custom Python script was used to segment the tracked tail movements into separate behavioral bouts. The bout detection algorithm operates by comparing the smoothed absolute value of the first derivative of the tail angle over time to a threshold. Data were further compiled and visualized using Igor Pro (Wavemetrics).

Statistics

Statistical analysis was performed in Python using the following libraries: Pandas for data structures, Scipy and Statsmodels for statistics. Welch’s t -test was used for pairwise comparisons. The family wise error rate was controlled with the Bonferonni correction. Normality and homoscedasticity were inspected visually (Q-Q plots) and using tests: Shapiro-Wilk for normality, and Bartlett’s and Levene’s for equality of variance. Individual tests were as follows: Steer angles ( Figure 5C ) ANOVA, tail beat frequency ( Figure 5E ) Box Cox transform and ANOVA, steer rise time ( Figure S6 ) linear regression, swim duration ( Figure S6 ) Box Cox transform and ANOVA, ablations ( Figure 6 , 7 and S7 ) paired t -test.

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