The design of the optical strategy had eight criteria: (1) that somatosensory stimuli are delivered non-invasively without touching or approaching the mice; (2) localization of stimuli are spatially precise and accurate (<10 μm); (3) freely moving mice can be targeted anywhere within a relatively large (400 cm²) arena; (4) stimuli can be controlled with a computer interface from outside the behavior room; (5) stimulation patterns, lines, and points are generated by rapidly scanning the stimuli between predefined locations; (6) stimulation size can be controlled down to ≥150 μm diameter; (7) stimuli are temporally precise to control individual action potentials using sub-millisecond time-locked pulses; and (8) behavioral responses are recorded at high speed at the stimulated site and across the whole body simultaneously. An optical system was assembled to meet these specific criteria (Figure 1B and C).

The stimulation path uses two mirror galvanometers to remotely target the laser stimulation to any location on a large glass stimulation floor. A series of lenses expands the beam and then focuses it down to 0.018 mm² (150 μm beam diameter) at the surface of this floor. This was defocused to provide a range of calibrated stimulation spot sizes up to 2.307 mm², with separable increments that were stable over long periods of time (Figure 1—figure supplement 1A). The optical power density could be kept equal between these different stimulation spot sizes. The glass floor was far (400 mm) from the galvanometers, resulting in a maximum focal length variability of <1.5% (see Materials and methods). This design yielded a spatial targeting resolution of 6.2 μm while minimizing variability in laser stimulation spot sizes across the large stimulation plane (coefficient of variation ≤0.1, Figure 1—figure supplement 1B). The beam ellipticity was 74.3% ± 14.3% (median± MAD, range of 36–99%) for all spot sizes. The optical power was uniform across the stimulation plane (Figure 1—figure supplement 1C). The galvanometers allow rapid small angle step (300 µs) responses to scan the laser beam between adjacent positions and shape stimulation patterns using brief laser pulses (diode laser rise and fall time: 2.5 ns). Custom software (see Materials and methods) was developed to remotely control the laser stimulation position, trigger laser pulses, synchronize galvanometer jumps, and trigger the camera acquisition (Figure 1—figure supplement 2).

The camera acquisition path was used to manually target the location of the laser stimulation pulse(s); the path was descanned through the galvanometers so that the alignment between the laser and camera is fixed (Figure 1B). The camera feed is displayed in the user interface and enables the operator to use this image to target the laser to the desired location. High signal-to-noise recordings were obtained using near-infrared frustrated total internal reflection (NIR-FTIR) in the glass stimulation floor (Roberson, D. P. et al., manuscript submitted). If a medium (skin, hair, tail, etc.) is within a few hundred microns of the glass, it causes reflection of the evanescent wave and this signal decreases non-linearly with distance from the glass such that very minor movements of the paw can be detected. The acquisition camera acquired the NIR-FTIR signal in high-speed (up to 1000 frames/s) with a pixel size of 110 μm. A second camera was used to record the entire arena and capture behaviors involving the whole body before and after stimulation. Offline quantification was carried out using custom analysis code combined with markerless tracking tools (Mathis et al., 2018).

To validate the strategy, we first crossed Trpv1-IRES-Cre (TRPV1^Cre) and R26-CAG-LSL-ChR2-tdTomato mice to obtain a line (TRPV1^Cre::ChR2) in which ChR2 is selectively expressed in a broad class of nociceptors innervating glabrous skin (Browne et al., 2017). These mice were allowed to freely explore individual chambers placed on the stimulation plane. When mice were idle (still and awake), a time-locked laser pulse was targeted to the hind paw. Stimuli could be controlled remotely from outside the behavior room. We recorded paw withdrawal dynamics with millisecond resolution. For example, a single, small 1 ms laser pulse initiated a behavioral response at 29 ms, progressing to complete removal of the hind paw from the glass floor just 5 ms later (Figure 2A and Figure 2—video 1). The stimulus used for this protocol was S₆, 0.577 mm² in area, which corresponds to less than 1% of the glabrous paw area and highlights the sensitivity of the nociceptive system. Motion energy, individual pixel latencies, and response dynamics could be extracted from these high-speed recordings (Figure 2B and C).

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Time courses of paw movement recorded at 1000 frames/s with stimuli that vary in duration and size.

Stimuli (40 mW/mm²) were delivered at 0 ms. Data are from TRPV1^Cre::ChR2 mice and littermate controls.

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Time courses of paw movement recorded at 500 frames/s with single point and patterned stimuli.

Stimuli (1 ms, 40 mW/mm²) were delivered at 0 ms. Data are from TRPV1^Cre::ChR2 mice and littermate controls.

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We probed multiple sites across the plantar surface and digits and found that the hind paw heel gave the most robust responses (Figure 2—figure supplement 1). This region was targeted in all subsequent experiments. Littermates that did not express the Cre recombinase allele confirmed that the laser stimulation did not produce non-specific responses. These mice did not show any behavioral responses, even with the largest stimuli (spot size S₈, 30 ms pulse, Figure 2—figure supplement 2). We next provide some examples of the utility of the strategy by examining the relationship between nociceptive input and protective behaviors.

Fast protective withdrawal behaviors can be triggered by the first action potential arriving at the spinal cord from cutaneous nociceptors. A brief optogenetic stimulus generates just a single-action potential in each nociceptor activated (Browne et al., 2017). This is due to the rapid closing rate of ChR2 relative to the longer minimal interspike interval of nociceptors. The same transient optogenetic stimulus (Browne et al., 2017), or a pinprick stimulus (Arcourt et al., 2017), initiates behavior before a second action potential would have time to arrive at the spinal cord. That the first action potential can drive protective behaviors places constraints on how stimulus intensity can be encoded, suggesting that the total population of nociceptors firing a single-action potential can provide information as a "Boolean array." The consequences of this have not been investigated previously as precise control of specific nociceptive input had not been possible. We predicted that the relative number of nociceptors firing a single-action potential determines the features of the behavioral response.

Varying the pulse duration with nanosecond precision influences the probability of each nociceptor generating a single-action potential within the stimulation site. A pulse as short as 300 μs elicited behavioral responses but with relatively low probability (Figure 2D). This probability increased with pulse duration until it approached unity, closely matching the on-kinetics of the ChR2 used (τ = 1.9 ms; Lin, 2011). We next controlled the spatial, rather than temporal, properties of the stimulation in two further experiments. Firstly, we find that the total area of stimulated skin determines the behavioral response probability, such that the larger the nociceptive input the larger the response probability (Figure 2E). Secondly, we generated different stimulation patterns. We find that sub-threshold stimulations are additive (Figure 2F). Specifically, seven spatially displaced small sub-threshold stimulations could reproduce the response probability of a single large stimulation that was approximately seven times their size. This could not be achieved by repeated application of the small stimulations to the same site (Figure 2F).

Time-locking the stimulus enabled us to examine the hind paw responses with high temporal resolution. The nociceptive input size influenced the behavioral response latency: for example, a 3 ms pulse resulted in response latencies of 27 ± 1 ms, 30 ± 2 ms, 33 ± 5 ms, and 112 ± 46 ms for spot sizes S₈, S₇, S₆, and S₅, respectively (Figure 3A and B). The shorter latencies are consistent with medium-conduction velocity Aδ-fibers that arrive at the spinal cord before slower C-fiber action potentials (>35 ms) (Browne et al., 2017). The rank order of response latencies follows the nociceptive input size for both pulse durations, and they fit well with log-log regressions (3 ms pulse R² = 0.87, 1 ms pulse R² = 0.90). Once a hind limb motor response was initiated, it developed rapidly, lifting from the glass with rise times that show the vigor of the motor response was also dependent on nociceptive input size (Figure 3C). These responses, in >65% of cases, proceeded to full withdrawal. However, in a fraction of trials the paw moved but did not withdraw (Figure 3D), highlighting the sensitivity of the acquisition system. Even the smallest of nociceptive inputs still produced a large fraction of full withdrawal responses, despite decreases in response probability (Figure 3E). The fraction of full withdrawal responses increased with the size of nociceptive input. The onset latency of both full and partial responses decreased as nociceptive input increased (Figure 3F).

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Pain-related responses are not limited to the affected limb but involve simultaneous movement of other parts of the body (Blivis et al., 2017; Browne et al., 2017). These non-local behaviors theoretically serve several protective purposes: to investigate and identify the potential source of danger, move the entire body away from this danger, attend to the affected area of the body (Huang et al., 2019) and to maintain balance (Sherrington, 1910). Whole-body movements were quantified as motion energy (Figure 4—figure supplement 1A) and high-speed recordings show this initiated with a mean response latency of 30 ± 1 ms, with the first movement bout displaying a mean duration of 136 ± 14 ms (80 trials from 10 mice) (Figure 4—figure supplement 2). The magnitude of whole-body movement increased with the stimulation spot size (Figure 4—figure supplement 1B). Peak motion energy had a lognormal relationship with nociceptive input size (R² = 0.99). This indicates that global behaviors are also proportional to the relative size of the nociceptive input; the recruited nociceptors firing a single-action potential (Figure 4—figure supplement 1B).

Most behaviors arise from the complex coordination of discrete body parts, which can be tracked individually. To dissect specific components of these behaviors, we implemented DeepLabCut (Mathis et al., 2018) by training a network using frames from the high-speed (400 frames/s) videos to track 18 user-defined body parts across the mouse (for details, refer to Materials and methods, Global behaviors during optogenetic stimulation). The high-speed video recordings of stimulation trials were analyzed using this network. Specific nociceptive input at the hind paw (S₈, 2.307 mm², 10 ms pulse) causes behavior that initiates simultaneously across the body. Inspection of the movements of each body part relative to the baseline pose (Figure 4A) shows fast outward movement of the stimulated and contralateral hind paws, and concomitant initiation of head orientation (two example responses in Figure 4B). Based on these observations, we examined the behavioral trajectories in the first 115 ms across the population of 80 trials. The first three principal components (PCs) were fit using six body part x and y values at 115 ms after the stimulus onset. These PCs explain 88.8% of the variance (50.4, 26.5, and 11.9% for PC1, PC2, and PC3, respectively). PC1 is dominated by hind paw translation, PC2 by head and body movement, and PC3 by head orientation (Figure 4C). Projecting the entire time course onto these same PCs can explain 78.1% of the variance (37.1, 24.3, and 16.7% for PC1, PC2, and PC3, respectively). The response trajectories revealed that movements occur largely in same direction within PC space with a circular standard deviation of 52.9° (Figure 4D and E). Shuffling body parts on each trial gave non-directional trajectories with a circular standard deviation of 126.8° (Figure 4—figure supplement 3). Behavioral trajectories also show that the response magnitude in PC space can be partly explained by initial PC1 and PC2 values (Figure 4F and G). This suggests that the initial pose influences these fast behavioral responses.

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Whole-body motion energy recorded at 40 frames/s with different size stimuli.

Stimuli (3 ms, 40 mW/mm²) were delivered at 0 ms. Data are from TRPV1^Cre::ChR2 mice.

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Time courses for coordinates of six tracked body parts recorded at 400 frames/s.

Stimuli (10 ms, 40 mW/mm², laser spot size S₈ = 2.307 mm²) were delivered at 0 ms and body parts tracked with DeepLabCut. Data are from TRPV1^Cre::ChR2 mice.

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Examining specific features of these behaviors over a slightly longer period (300 ms) provides further insights. Displacement of each body part relative to their baseline position reveals the response timing, extent, and coordination (Figure 4H). The stimulated paw started moving at 29 ± 1 ms, the contralateral hind paw at 34 ± 4 ms, and the nose at 33 ± 2 ms (80 trials from 10 mice). With this intense stimulus, only in 6% of trials did the hind paws or single body parts move alone, although the magnitude of the head movement varied between trials. The distance traveled by the nose positively correlates with the distance for the stimulated paw (Pearson’s r = 0.64, n = 80 trials from 10 mice). Examining the relative distance between the nose and stimulated hind paw shows a reliably short latency (Figure 4I), indicating that these responses are driven by Aδ-nociceptor input rather than more slowly conducting C-fibers. A diversity of responses was observed: the head and stimulated paw move closer together in some trials and in others moved further apart (Figure 4I and J). This could result from the head moving towards or away from the stimulated paw but also the stimulated paw moving backwards as the body rotates. Indeed, consistent with initial observations (Figure 4A and B) and principal component analysis (PCA; Figure 4C–G), we find that the head selectively and rapidly orients to the stimulated side (Figure 4K). The presence of head orientation suggests that a brief nociceptive input can rapidly generate a coordinated spatially organized behavioral response. This is likely integral to protective pain-related behaviors and might function to gather sensory information about the stimulus or its consequences, and potentially provides coping strategies. Protective behaviors can be statistically categorized (Abdus-Saboor et al., 2019) and computational discrimination of high-speed hind paw responses used as a score of pain (Jones et al., 2020). We have shown that the analysis can easily be customized to incorporate computational tools that facilitate quantification and reveal insights into complex behavioral responses.

The vesicular glutamate transporter-1 (Vglut1) is a known marker of Aβ-LTMRs (Alvarez et al., 2004; Brumovsky et al., 2007). To demonstrate the utility of the system in the broader context of somatosensation, we crossed Slc17a7-IRES2-Cre-D (Vglut1^Cre) mice with R26-CAG-LSL-ChR2-tdTomato mice to generate a line (Vglut1^Cre::ChR2) that express ChR2 in LTMRs (Harris et al., 2014). A recent detailed anatomical and physiological characterization of Vglut1^Cre::ChR2 mice further confirmed that in DRG neurons, ChR2 is restricted to broad class of myelinated Aβ-LTMRs (Chamessian et al., 2019). Here, we find that a single 3 ms stimulus (S₇ = 1.155 mm²) precisely delivered to the hind paw of these mice rarely elicited hind paw responses (mean paw withdrawal probability = 0.10 ± 0.03 SEM, 99 trials from n = 11 mice), with the earliest response occurring at 206 ms after stimulation (Figure 5A and B), which is an order of magnitude slower than we observed in TRPV1^Cre::ChR2 mice (fastest response: 19 ms). Trains of five pulses, however, frequently elicited responses, showing mean paw withdrawal probabilities of 0.31 ± 0.09 (SEM, 108 trials from n = 12 mice) for 5 Hz and 0.40 ± 0.10 (SEM, 117 trials from n = 12 mice) for 10 Hz trains (Figure 5C). Increasing stimulation frequency to 20 Hz did not result in higher withdrawal probabilities, which may reflect ChR2 desensitization, rather than a physiological process (Lin, 2011). While the responses at first seem to be frequency-dependent (Figure 5D, left), inspection of recordings indicated that these occurred after the second or third pulse in most trials, regardless of stimulation frequency (Figure 5A). We find that the response distributions superimpose when withdrawal latencies are normalized to the interstimulus interval (pulse-matched latencies in Figure 5D, right). This observation suggests that response probability is likely driven by pulse summation, rather than by stimulation frequency. Indeed, we find that the probabilities and latencies can be explained by the probability sum rule, using the values for a single pulse to predict the values for five pulses (Figure 5C and D).

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Time courses of paw movement recorded at 1000 frames/s with stimuli that vary in frequency.

Stimuli (3 ms, laser spot size S₇ = 1.181 mm², 40 mW/mm²) were delivered at 0 ms. Data are from Vglut1^Cre::ChR2 mice and littermate controls.

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Whole-body motion energy recorded at 400 frames/s with stimuli that vary in frequency.

Stimuli (3 ms, laser spot size S₇ = 1.181 mm², 40 mW/mm²) were delivered at 0 ms. Data are from Vglut1^Cre::ChR2 mice.

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The magnitude of whole-body motion was not altered by increasing frequencies (Figure 5—figure supplement 1). In contrast to the TRPV1^Cre::ChR2 line, whole-body behaviors in response to optogenetic stimulation of Vglut1^Cre::ChR2 mice were subtle: visual inspection of high-speed whole-body behavior videos revealed that responses were mostly limited to small hind paw lifts or shifts towards the center of the body in cases where the stimulated paw was initially further away from the body. In most instances, these movements did not disturb balance or alter the animal’s posture. Interestingly, we observed that whisking and, to a lesser extent, circular movements of the upheld forepaws would precede hind paw responses and initiate as early as the first pulse, even in trials that would not proceed to withdrawal. We speculate that mice may perceive the stimulation early on, but only act on this after a delay.

Optical elements, optomechanical components, mirror galvanometers, the diode laser, LEDs, controllers, machine vision cameras, and structural parts for the optical platform are listed in the table in Supplementary file 1. These components were assembled on an aluminum breadboard as shown in the Solidworks rendering in Figure 1C. The laser was aligned to the center of all lenses and exiting the midpoint of the mirror galvanometer housing aperture when the mirrors were set to the center of their working range. A series of lenses (L1–L3) expanded the beam before focusing it on to the glass stimulation plane, on which mice are placed during experiments. The glass stimulation platform was constructed of 5-mm-thick borosilicate glass framed by aluminum extrusions. NIR-FTIR was achieved by embedding an infrared LED ribbon inside the aluminum frame adjacent to the glass edges (Roberson, D. P. et al., manuscript submitted). The non-rotating L1 lens housing was calibrated to obtain eight defined laser spot sizes, ranging from 0.0185 mm² to 2.307 mm², by translating this lens along the beam path at set points to defocus the laser spot at the 200 mm × 200 mm stimulation plane. The beam size can be altered manually using this rotating lens tube per design, but this is modular and could be altered by the user. To ensure a relatively flat field in the stimulation plane, the galvanometer housing aperture was placed at a distance of 400 mm from its center. In this configuration, the corners of the stimulation plane were at a distance of 424 mm from the galvanometer housing aperture and variability of the focal length was below 1.5%.

Optical power density was kept constant by altering the laser power according to the laser spot area. Neutral density (ND) filters were used so that the power at the laser aperture was above a minimum working value (≥8 mW) and to minimize potential changes in the beam profile at the stimulation plane. The laser and mirror galvanometers were controlled through a multifunction DAQ (National Instruments, USB-6211) using custom software written in LabVIEW. The software displays the NIR-FTIR camera feed, whose path through the mirror galvanometers is shared with the laser beam, so that they are always in alignment with one another. Computationally adjusting mirror galvanometer angles causes identical shifts in both the descanned NIR-FTIR image field of view and intended laser stimulation site, so that the laser can be targeted to user-identified locations. Shaped stimulation patterns were achieved by programmatically scaling the mirror galvanometer angles to the glass stimulation plane using a calibration grid array (Thorlabs, R1L3S3P). The timings of laser pulse trains were synchronized with the mirror galvanometers to computationally implement predefined shapes and lines using small angle steps that could be as short as 300 µs. The custom software also synchronized image acquisition from the two cameras, so that time-locked high-speed local paw responses were recorded (camera 1: 160 pixels × 160 pixels, 250–1000 frames/s depending on the experiment). Time-locked global whole-body responses were recorded above video-frame rate (camera 2: 664 pixels × 660 pixels, 40 frames/s) or at high speed (camera 2: 560 pixels × 540 pixels, 400 frames/s) across the entire stimulation platform.

To calibrate the L1 lens housing and ensure consistency of laser spot sizes across the glass stimulation platform, we designed a 13.90 ± 0.05 mm thick aluminum alignment mask. This flat aluminum mask was used to replace the glass stimulation platform and was combined with custom acrylic plates that align the aperture of a rotating scanning-slit optical beam profiler (Thorlabs, BP209-VIS/M) to nine defined coordinates at different locations covering the stimulation plane. The laser power was set to a value that approximates powers used in behavioral experiments (40 mW). The laser power was then attenuated with an ND filter to match the operating range of the beam profiler. Using Thorlabs Beam Software, Gaussian fits were used to determine x-axis and y-axis 1/e² diameters and ellipticities for each laser spot size over three replicates at all nine coordinates. The averages of replicates were used to calculate the area of the eight different laser spot sizes that were measured in each of the nine coordinates (Figure 1—figure supplement 1A) and then fitted with a two-dimensional polynomial equation in MATLAB to create heatmaps (Figure 1—figure supplement 1 B).

The average values over the nine coordinates were defined for each laser spot size: S₁ = 0.0185 mm², S₂ = 0.0416 mm², S₃ = 0.0898 mm², S₄ = 0.176 mm², S₅ = 0.308 mm², S₆ = 0.577 mm², S₇ = 1.155 mm², S₈ = 2.307 mm². These measurements were repeated 6 months after extensive use of the optical system to ensure stability over time (Figure 1—figure supplement 1A). In addition, the uniformity of laser power was assessed by measuring optical power at five positions of the experimental platform with a power meter (Thorlabs, PM100D) (Figure 1—figure supplement 1C).

Experiments were performed using mice on a C57BL/6j background. Targeted expression of ChR2-tdTomato in broad-class cutaneous nociceptors was achieved by breeding mice homozygous for Cre-dependent ChR2(H134R)-tdTomato at the Rosa26 locus (RRID:IMSR_JAX:012567, R26-CAG-LSL-hChR2(H134R)-tdTomato, Ai27D; Madisen et al., 2012) with mice that have Cre recombinase inserted downstream of the Trpv1 gene in one allele (RRID:IMSR_JAX:017769, Trpv1-IRES-Cre, TRPV1^Cre; Cavanaugh et al., 2011). Aβ-LTMRs were selectively stimulated by breeding homozygous Ai27D mice with mice in which Cre recombinase is targeted to cells expressing the vesicular glutamate transporter 1 (RRID:IMSR_JAX: 023527, Slc17a7-IRES2-Cre-D, Vglut1^Cre; Harris et al., 2014). Resultant mice were heterozygous for both transgenes and were housed with control littermates that do not encode Cre recombinase but do encode Cre-dependent ChR2-tdTomato. Adult (2–4 months old) male and female mice were used in experiments. Mice were given ad libitum access to food and water and were housed in 21°C ± 2°C, 55% relative humidity and a 12 hr light:12 hr dark cycle. Experiments were carried out on at least two separate cohorts of mice, each cohort contained 4–6 mice. Experiments were spaced by at least one day in the case where the same cohort of mice was used in different experiments. All animal procedures were approved by University College London ethical review committees and conformed to UK Home Office regulations.

Prior to the first experimental day, mice underwent two habituation sessions during which each mouse was individually placed in a plexiglass chamber (100 mm × 100 mm, 130 mm tall) on a mesh wire floor for 1 hr, then on a glass platform for another hour. On the experimental day, mice were again placed on the mesh floor for 1 hr, then up to six mice were transferred to six enclosures (95 mm × 60 mm, 75 mm tall) positioned on the 200 mm × 200 mm glass stimulation platform. Mice were allowed to settle down and care was taken to stimulate mice that were calm, still, and awake in an ‘idle’ state. The laser was remotely targeted to the hind paw glabrous skin using the descanned NIR-FTIR image feed. The laser spot size was manually set using the calibrated L1 housing, while laser power and neutral density filters were used to achieve a power density of 40 mW/mm² regardless of spot size. The software was then employed to trigger a laser pulse of defined duration (between 100 μs and 30 ms) and simultaneously acquire high-speed (1000, 500, or 250 frames/s depending on experiment) NIR-FTIR recordings of the stimulated paw, as well as a global view of the mice with a second camera (400 frames/s or 40 frames/s) (Figure 1C). Recordings of stimulations of TRPV1^Cre::ChR2 mice were 1500 ms in duration, with the laser pulse initiated at 500 ms. For each stimulation protocol, six pulses, three on each hind paw, spaced by at least 1 min were delivered to eight mice, split into two cohorts. For experiments involving Vglut1^Cre::ChR2 mice, we used a single stimulation spot size (S₇ = 1.155 mm²) and duration (3 ms). In addition to the single-pulse stimulation, these mice received a train of five pulses applied at 5, 10, or 20 Hz. The recording time for each trial was extended to 2000 ms to accommodate for the longer stimulation period. For each protocol, Vglut1^Cre::ChR2 mice were stimulated in 10 trials, split equally between the two hind paws. Data was collected from 12 Vglut1^Cre::ChR2 mice and 8 littermate controls lacking Cre recombinase split into five cohorts. In all experiments, the behavioral withdrawal of the stimulated hind paw was also manually recorded by the experimenter.

TRPV1^Cre::ChR2 mice were stimulated on the heel of the hind paw with each of the following protocols: (1) a single 1 ms pulse with spot size S₇ (1.155 mm²); (2) a single 1 ms pulse with spot size S₄ (0.176 mm²); (3) seven 1 ms pulses with spot size S₄, superimposed on the same stimulation site and spaced by 500 μs intervals; (4) seven 1 ms pulses with spot size S₄, spaced by 500 μs intervals and spatially displacing stimuli with 0.3791 mm jumps such as to draw a small hexagon; (5) seven 1 ms pulses with spot size S₄, spaced by 500 μs intervals and spatially displacing stimuli with 0.5687 mm jumps such as to draw a hexagon expanded by 50% compared to the previous shape; and (6) seven 1 ms pulses with spot size S₄, spaced by 500 μs intervals and spatially displacing stimuli with 0.3791 mm jumps such as to draw a straight line. The power density of the stimulations was kept constant at 40 mW/mm² as before. Seven mice, split into two cohorts, received 10 stimulations per protocol (five on each hind paw) after a baseline epoch of 500 ms. An additional cohort of four littermates lacking Cre recombinase were stimulated in the same way and served as negative controls. Finally, three TRPV1^Cre::ChR2 mice were stimulated (spot size S₈, 10 ms pulse duration) with a single pulse adjacent to the hind paw, five times on each side, in order to control for potential off-target effects. The NIR-FTIR signal was recorded at 500 frames/s.

To obtain recordings optimized for markerless tracking with DeepLabCut, a single acrylic chamber (100 mm × 100 mm, 150 mm tall) was centered on the glass stimulation platform of the system. Rapid movements were recorded at 400 frames/s using a below-view camera (FLIR, BFS-U3-04S2M-CS). Two white and two infrared LED panels illuminated the sides of the behavioral chamber in order to optimize lighting for these short exposure times and achieve high contrast images. NIR-FTIR was not used in this configuration. TRPV1^Cre::ChR2 mice received between 10 and 20 single-shot laser pulse stimulations of 10 ms each, at least 1 min apart and equally split between right and left hind paw and using spot size S₈ (2.31 mm²). The first 10 trials that exceeded quality control were used (see Markerless tracking of millisecond-timescale global behaviors, Data processing). Each trial consisted of a 500 ms baseline and 4000 ms after-stimulus recording epoch.

High-speed NIR-FTIR recordings were saved as uncompressed AVI files. A Python script was implemented in Fiji to verify the integrity of the high-speed NIR-FTIR recordings and extract average 8-bit intensity values from all frames within a circular region of interest on the stimulation site (60 pixels diameter). This output was then fed into RStudio to calculate the average intensity and associated standard deviation of the baseline recording (first 500 ms). A hind paw response was defined as a drop of intensity equal to or below the mean of the baseline minus five times its standard deviation. Paw response latency was defined as time between the start of the pulse and the time at which a hind paw response was first detected. For purposes of quality control, only recordings with a baseline NIR-FTIR intensity mean ≥ 3 and a standard deviation/mean of the baseline ratio ≥23 were retained for analysis. Another criterion was that response latencies are not 10 ms or shorter since this would be too short to be generated by the stimulus itself. Only one trial out of 2369 trials did not meet this criterion (spot size S₆, 1 ms pulse, 8 ms response latency). In addition to this two-step workflow using Fiji/Python to process AVI files and then RStudio to analyze the resulting output, alternative code was written in Python 3, which combines both steps and also computes individual pixel latencies and motion energy using NumPy and Pandas packages. A median filter (radius = 2 pixels) was applied to the NIR-FTIR recordings used to create the representative time series in Figure 2A and Figure 2—video 1. For raster plots of hind paw response dynamics in Figure 4A, NIR-FTIR intensity values were normalized to the average baseline value. For the patterned stimulation experiments in Figure 2F and Vglut1^Cre::ChR2 experiments in Figure 5A–D, trials were analyzed as stated to compute local response probabilities, but an additional rule was introduced to further minimize the risk of false positives. A response required the signal to fall by 20% and exceed a threshold of four times the standard deviation of baseline. Compared to the performance of an experimenter manually processing the videos with Fiji, the automated analysis pipeline was substantially faster for similar accuracy. For example, it took an experimenter two working days to analyze 127 videos, whereas the Fiji/Python pipeline generated the identical output within 90 s.

Videos of the entire stimulation platform were cropped into individual mouse chambers (200 × 315 pixels) and then analyzed using RStudio to quantify the amount of whole-body movements, including those stemming from the response of the stimulated limb, herein referred to as global behavior (GB). GB was approximated as the binarized motion energy: the summed number of pixels changing by more than five 8-bit values between two subsequent frames (Pixel Change). Briefly, for each pixel_n (n = 63,000 pixels/frame), the 8-bit value at a given frame (F_n) was subtracted from the corresponding pixel_n at the previous frame (F_n-1). If the resulting absolute value was ≤5, 0 would be assigned to the pixel. If the absolute resulting value was >5, 1 would be assigned to the pixel. The threshold was chosen to discard background noise from the recording. The pixel binary values were then summed for each frame pair to obtain binarized motion energy. Normalized binarized motion energy was calculated by subtracting each post-stimulus frame binarized motion energy from the average baseline binarized motion energy. As an alternative to this analysis strategy, we have developed code in Python that processes the video files and calculates motion energy. The peak normalized binarized motion energy was determined and only trials displaying a peak response ≥5 standard deviations of the baseline mean were retained for further analysis and plotting. For TRPV1^Cre::ChR2 mice, the analysis was restricted to a time window of 100 ms after stimulus onset (first three frame pairs proceeding the stimulus frame) to enable time-locking to the stimulus. Between 41 and 47 videos from eight mice were analyzed per spot size. For experiments with Vglut1^Cre::ChR2 mice, the peak normalized binary motion energy exceeding five standard deviations of the baseline mean was determined for the entire 1.5 s recording epoch proceeding stimulus onset. Between 51 and 80 trials from 11 to 12 mice were analyzed per stimulation frequency.

GB was analyzed within a 1 ms time frame following stimulation by computing the binarized motion energy relative to a baseline reference frame 5 ms prior to stimulation as described above. Here, the threshold for pixel change was set to seven 8-bit values. The binarized motion energy (sum of pixel binaries) of a given frame was normalized to the total number of pixels within that frame after removing those frames that had been affected by the stimulation laser pulse. The global response latency of movement initiation was determined as the time when binarized motion energy was greater than 10 times the standard deviation at baseline. Termination of movement was determined as the time point when binarized motion energy returned below 10 times standard deviation from baseline following the first movement bout.

Data was analyzed in RStudio 1.2.5019, Python 3.6.8, ImageJ/Fiji 2.0.0 and Prism 7 and visualized using Seaborn, Prism 7, and Adobe Illustrator 24.0. In all experiments, repeated measurements were taken from multiple mice. Paw responses to patterned stimulation were reported as mean probabilities ± standard error of the mean (SEM) and analyzed using Friedman’s non-parametric test for within-subject repeated measures followed by Dunn’s signed-rank test for multiple comparisons (Figure 2F). In this experiment, one of the seven TRPV1^Cre::ChR2 mice was removed from the dataset because it displayed saturating responses to Protocol 3 preventing comparison of values across a dynamic range. Response latencies, response rise times, and response durations were computed using a hierarchical bootstrap procedure (Saravanan et al., 2020) modified to acquire bootstrap estimates of the median with balanced resampling. Briefly, mice are sampled with replacement for the number of times that there are mice. For each mouse within this sample, its trials were sampled with replacement, but the number of selected trials was balanced, ensuring each mouse contributes equally to the number of trials in the sample. The median was taken for this resampled population and this entire process was repeated 10,000 times. Bootstrap estimates from 1000 simulated experiments show that an additional 1.6–3.1% of values fall within 1% of the population median for seven mice with between 2 and 6 responses. Values provided are the mean bootstrap estimate of the median ± the standard error of this estimate. The median bias was small due to the resampled population size from hierarchically nested data and only moderate distribution skew. Global peak motion energy (Figure 4B) was examined in a similar way, except the mean of resampled populations was used as it represents a better estimator of the population mean. In this case, we report the mean bootstrap estimate of the mean ± the standard error of this estimate. Pearson’s correlation coefficients were determined to compare maximum distances moved from baseline for each body part (Figure 4F). Experimental units and n values are indicated in the figure legends.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We are grateful to Dr Mehmet Fisek and Dr Adam M Packer for initial advice on the optical system, and thank Dr David P Roberson and the Woolf lab for sharing the NIR-FTIR technology. We gratefully acknowledge feedback on the manuscript from Dr Adam M Packer and Professor John N Wood. This work was supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (109372/Z/15/Z).

All animal procedures were approved by University College London ethical review committees and conformed to UK Home Office regulations.

1. Preprint posted: August 10, 2020 (view preprint)
2. Received: August 11, 2020
3. Accepted: July 28, 2021
4. Accepted Manuscript published: July 29, 2021 (version 1)
5. Version of Record published: September 2, 2021 (version 2)

© 2021, Schorscher-Petcu et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.