To capture sub-second behavioral ethograms during somatosensory behaviors in freely behaving mice, we recorded mice at 2000 fps, with a particular focus on the stimulated paw. We reasoned that we could develop a pipeline where we first performed behavior experiments, followed by automated paw tracking and automated pain scoring, and lastly statistical modeling to transform multidimensional datasets into a single dimension that separated touch from pain (Figure 1A). To begin, we used 10 mice from seven commonly used inbred lines and on separate days to avoid sensitization to the stimuli, we applied to one hind paw a static innocuous stimulus (cotton swab), a moving innocuous stimulus (dynamic brush), a weak noxious stimulus (light application of a pinprick), and an intense noxious stimulus (heavy application of a pinprick). We used pinprick instead of von Frey hair filaments (VFHs) based on our prior studies showing that pinprick stimuli activate nociceptors and consistently evoke painful responses, whereas VFHs were more variable depending upon the force applied (Abdus-Saboor et al., 2019). Using traditional pain scoring, we noticed that the paw withdrawal frequencies to these four stimuli varied widely across these seven strains. For example, Balb/cJ, DBA1/J, and CBA/J displayed high paw withdrawal rates to all mechanical stimuli, both the innocuous and the two noxious (Figure 1B). Conversely, C57BL/6J and AKR/J displayed high paw withdrawal rates to both the noxious stimuli and the innocuous dynamic brush, but not to a cotton swab (Figure 1B). Finally, A/J and 129S1 mice displayed high rates of paw lifting to both pinprick stimuli and low withdrawal rates to the two touch stimuli (Figure 1B). Since mice will move their paw to both innocuous and noxious stimuli, it is hard to determine if these differences in withdrawal frequencies are driven by genetic differences in susceptibility to pain. What these data likely reveal using the common traditional approach is that this test in isolation may be an inadequate measurement of pain at baseline states.

Next, we turned to automated tracking with the seven mouse strains to determine the X, Y coordinates of the paw across approximately 5000 frames – recording at 2000 fps with total behavior time from stimulus application to paw lift and return to the floor being approximately 2–3 s. Using machine learning algorithms embedded within the ProAnalyst motion tracking software, we manually labeled the center of the stimulated paw in each video and the machine automatically tracked the paw throughout each additional frame (Figure 2A–B') (see Figure 2—video 1). While observing the automated paw trajectory patterns, we noticed that stereotyped motor sequences defined the movement away from the four stimuli, regardless of strain background (Figure 2A–B'). For example, the responses to the two innocuous stimuli were typically up-down C-shaped movements (Figure 2A–B'). Conversely, the responses to the two noxious stimuli were typically more elaborate movements, often accompanied by an orbital tightening of the eye, which is a known facial feature of intense pain (Figure 2A–B'; Langford et al., 2010). Only the 129S1 strain showed responses to pinprick that were devoid of sinusoidal irregular paw trajectory patterns (Figure 2Y–B'). In the majority of tested strains, we also noticed that the paw trajectory pattern in response to a weakly painful stimulus (light pinprick) often resulted in a figure-eight like sequence that may point toward a shared sensorimotor neural circuit that governs how animals respond to weak and painful stimuli given their body posture and space constraints (Figure 2A–B').

Next, we randomly chose one strain (AKR/J) and we used a deep learning-based pose tracking algorithm called SLEAP (in preparation, based on Pereira et al., 2019), to predict mouse toe and mid-paw positions during somatosensory behaviors recorded at high speed (Figure 2C′-F′). Our mouse paw-tracking model was generated from a small training set of video frames collected from the four assays (~9.5% of video frames per assay). In general, the paw trajectory patterns with SLEAP resemble those of ProAnalyst, and the software package we describe below is compatible with automated tracking data from either tool or even others. Taken together, our ability to detect clear qualitative distinctions in paw movements with automated tracking approaches gave us confidence that we could use spatiotemporal data of paw position to automatically extract features that may be useful in determining the mouse pain state. Additionally, these data demonstrate that the nature of the response when an animal withdrawals its paw might be a more reliable indicator of pain state than the number of times the animal lifts its paws to a given stimulus. In other words, paw withdrawal frequencies revealed great variation across the seven strains (Figure 1), whereas paw trajectory patterns to a given stimulus showed quite stereotyped responses among these strains (Figure 2).

We developed software to systematically quantify the seven pain-relevant features of the paw position time series based on existing measurements in the literature (Abdus-Saboor et al., 2019). Maximum paw height, lateral velocity, vertical velocity, and the total distance traveled by the paw, were all computed based on a polynomial smoothing (Savitsky-Golay filter of order 3) of the original paw position time series. These four features were computed for two different windows of the paw trajectory time series: the time leading up to the initial peak in paw height (time t* in Figure 3A), and the time following the initial paw peak, which we refer to as pre-peak and post-peak, respectively. The t* designation reflects a biological designation: behavior before t* corresponds to a reflexive rapid withdrawal of the paw, whereas behavior following t* includes lingering paw attending behaviors that require some degree of conscious perception. In the post-peak time window, we also identified periods of ‘shaking’ and ‘guarding’ based on a threshold displacement along the principal axis of the paw movement (Figure 3B and C). We used this delineation to quantify the total duration spent shaking or guarding as well as the total number of paw shakes across shaking periods. We refer to this software package as PAWS).

With software generated to automatically measure paw movement features related to the mouse pain state, we plotted and analyzed the data across the seven mouse strains with the four mechanical stimuli described above. For plotting the individual behavioral features, we separated the paw distance traveled measurement into pre-peak and post-peak distances. To standardize across varying units and to appreciate the individual deviation from the median, we transformed the raw output measurements into Z-scores (Figure 4). The first readily apparent feature we noticed was that all measurements across these seven strains were interspersed without clear separation among strains, despite the vast differences in paw withdrawal frequencies to these same stimuli (Figure 1B). These data suggest that these seven strains are phenotypically quite similar in their acute responses to mechanical stimuli despite the genetic variation that distinguishes these mouse lines. In regards to pre-paw peak behavioral features, we observed a statistical separation in the stimulated paw’s velocity on both the X and Y axes between touch (CS, DB) and pain stimuli (LP, HP) as the paw withdrew upwards to reach its first highest peak (Figure 4A–C). The mean max height that the paw reached in its first peak was statistically different in the innocuous versus painful stimuli (Figure 4A).

Next, we plotted the pain behavioral features that occur after the paw has reached its highest first peak and before the animal places its paw back to the surface. Similar to our observations with the four pre-paw peak pain behaviors, the four post-paw peak behaviors also show statistical separation between innocuous and pain stimuli (Figure 4E–D). Although we are not the first research group to observe paw shaking and guarding behaviors in rodents, this is one of the first technologies to automatically score the number and duration of these paw movement dynamics. Taken together, these automated measurements extracted from paw time series data are sufficient to objectively separate touch from pain in genetically diverse mice. Additionally, each one of our seven behavioral features was sufficient to independently separate touch versus pain during baseline conditions (Figure 4). Therefore, researchers can opt to choose any of these measurements to score the mouse pain state, and it’s possible that some of these features will become more or less important depending upon the model used and the basal state of the mouse (Figure 4—source data 1).

The seven behavioral features we extracted from video each provide more useful information than paw withdrawal frequency alone. Nevertheless, the simplest way to score behavior is using a univariate pain scale derived from these behavioral features. To accomplish this, we used ordinal logistic regression to identify a univariate linear subspace of the seven behavioral features that best separates the four somatosensory stimuli: CS, DB, LP, and HP. We did this for two cases: first, restricting to just four features quantifiable in the pre-peak period (t < t*), and then for all seven features quantified during the post-peak period (Figure 4H and I, respectively). For both the pre- and post-peak paw features, the two no-pain stimuli (CS and DB) cover the same parts of the subspace and are largely indistinguishable. By contrast, the low and high pain conditions (LP and HP) clearly separate from the no-pain stimuli and also separate from each other in this univariate pain score.

The relative importance of each standardized (mean subtracted, variance scaled) behavioral feature was quantified as the loading of that feature on the univariate pain scale identified by ordinal logistic regression. The feature importance for both pre- and post-peak features (Figure 4—figure supplement 1) indicate that relatively simple properties of the paw trajectory, such as the maximum height of the paw and the total distance traveled before the paw returns to resting position, contribute substantially to separating pain from no-pain. The max y velocity and the number of paw shakes further contributes to the separation of low versus high pain categories in the post-peak paw features (low and high pain classification is slightly improved by post- rather than pre-peak features, Figure 4—figure supplement 2).

Next, we used the PAWS system to quantify pain-related paw movement in mice treated with a common inflammatory pain agent, the complete Freund’s adjuvant (CFA). To accomplish this, we used 20 mice (5 each of the C57BL/6, 129S1, Balb/c, and A/J strains chosen randomly) and applied the innocuous dynamic brush and the 4 g VFH filament before and 48 hr after unilateral hind paw injection of CFA. Since the dynamic brush is not painful and 4 g VFH lies close to the boundary separating painful and non-painful responses, we reasoned that we would observe a mechanical allodynia phenotype with increased PAWS values (Abdus-Saboor et al., 2019; Alhadeff et al., 2018). However, we did not observe a significant increase in the PAWS measurements following CFA (Figure 4—figure supplement 3). In fact, following CFA the responses to dynamic brush were slightly decreased, and the responses to 4 g VFH, although greater than to dynamic brush, nevertheless fell near the threshold separating touch and pain at baseline (Figure 4—figure supplement 3; Figure 4—source data 2). While performing these assays, we noted that the CFA-injected paw was red, inflamed, and swollen and the mice tried not to move the injured paw whatsoever. Thus, for peripheral manipulations that cause animals to drag heavy and swollen limbs, PAWS may not be a suitable system to detect pain hypersensitivity. Measuring thermal hyperalgesia with the Hargreaves assay, or detecting changes in mechanical threshold with VFHs, may be the most suitable method of quantifying CFA-induced pain hypersensitivity (Petrus et al., 2007; Dhandapani et al., 2018).

The performance of the univariate pain score was evaluated by leave-one-out (LOO) cross-validation for individual mice (Figure 5A), and by leave-one-strain-out for strains (Figure 5B). We compared the pre- and post-peak paw univariate pain scale classifications to a null model that assigns pain classes according to their probability in the training data, without reference to measured behavioral features. For prediction, CS and DB were treated as a single ‘no-pain’ class, while LP and HP were treated as a single ‘pain’ class, resulting in a binary classification: no-pain or pain (Figure 5—figure supplement 1). For predicting the pain state of a given mouse, or all the mice in a strain (generalizing across strains), post-peak paw features consistently performed best. In particular, the univariate pain score based on post-peak features was able to correctly identify the stimulus as painful or non-painful with 83.5% accuracy (cross-validated by leave-out one mouse); and it provided 81.3% accuracy when predicting the stimulus for a mouse of a novel strain (cross-validated by leave-out one strain). Nonparametric bootstrap 95% confidence intervals were [79.1%, 87.8%] and [76.9%, 85.9%], respectively.

After testing the seven inbred lines described above, we next tested an additional strain (SJL) that other labs have used in various experimental contexts. The SJL mice have not been studied in the context of pain, but some researchers use these mice to study aggression, as these mice display strong bouts of aggression and are prone to fight (Lumley et al., 2004; Tellegen and Horn, 1972). We reasoned that this strain would make a good test case for our new platform.

Using traditional scoring with the SJL strain, we noticed the response rate mirrored the canonical C57BL/6 strain (Figure 1), with low responsiveness to a cotton swab, and high responsiveness to the other stimuli (Figure 6A). Using this metric alone, we might have concluded that the SJL strain is no different than the most commonly used wildtype strain C57BL/6. However, when using high-speed videography and PAWS we find that SJL appears hypersensitive to mechanical pain – responding to the soft dynamic brush as if it were pinprick and having even more elaborate paw withdrawal stimuli (Figure 6B). The outlier pattern of this strain is observed when scoring the seven individual pain behavioral features. Consistent with this finding, our confidence in predicting the stimulus the animal received based on its response using our univariate pain scale is significantly reduced in the SJL strain (significant difference in mean LOO accuracy between DBA1 and SJL; one-sided Welch two-sample t-test, t = 2.9405, df = 36.894, p=0.003), reflecting the nature of the outlier phenotype in these mice (Figure 6C). These data reveal that our automated pain assessment platform is capable of detecting individual strain differences in susceptibility to mechanical pain and that this platform can provide new information that would not have been predicted based on simple paw withdrawal frequencies.

Finally, we sought to directly test whether our separation of pre- versus post-paw peak features (denoted by t*) could be functionally separated or attributed to nociception in supraspinal circuits for affective-motivational behaviors rather than circuits that might influence the sensitivity of the pre-peak reflexives. To accomplish this reasoned that we could not use a standard inflammatory or neuropathic pain model, as those assays would alter the pain circuit from periphery to the brain, and thus the question we sought to address in relation to separating pre- versus post-paw peak behaviors would be obscured. Therefore, we focused our attention on manipulating pain circuits in the BLA (Corder et al., 2019). Thus, we hypothesized that the post-paw peak features could be increased by selectively driving hyperactivity in a recently identified basolateral amygdala nociceptive ensemble. Briefly, we used the activity-dependent transgenic TRAP2 mice (Fos-FOS-p2A-iCre-ERT2) to gain viral-genetic access to BLA neurons that are responsive to a noxious pinprick to the left hind paw. The transgenic mice in combination with an AAV expressing an excitatory DREADD (AAV5-hSyn-FLEx-hM3q-mCherry) allow the specific expression of hM3 only in neurons responsive to the noxious pinprick stimulus (painTRAP2^hM3; Figure 7A). We confirmed bilateral hM3-mCherry expression that was restricted to only the BLA and not within the neighboring central nucleus (Figure 7—figure supplement 1). We first performed our analysis on Cre-negative control animals and observed no behavioral effects of hM3-agonist CNO (i.p., 3.0 mg/kg). Next, we performed our behavioral analysis on painTRAP2^hM3 mice at baseline (-CNO) and after activation of the BLA pain ensemble (+CNO) (Figure 7). Because activation of the BLA pain ensemble resulted in unilateral spontaneous guarding pain behaviors, we waited until mice were calm, stood still , and had all four paws on the surface before applying our sensory stimuli. We applied a cotton swab and dynamic brush on one day, and a light and heavy pinprick on a second day. We observed that the PAWS measurements at baseline (-CNO) mirrored those that we observed with the seven strains described above, showing separation between innocuous and noxious stimuli (Figure 7B–H). Conversely, with activation of the BLA nociceptive ensemble in painTRAP2^hM3 mice (+CNO), we noticed increased separation with the post-paw peak pain measurements when delivering the heavy pinprick stimuli, most noticeable in the combined shaking/guarding measurement (Figure 7H). When we used ordinal logistic regression for statistical separation that combined our seven behavioral features, we observed an emergent behavioral representation of the post-paw peak features that was highly separated from controls, indicating that selective activation of supraspinal amygdalar ensembles shifted the internal state of the animals to engage in heightened nociceptive behaviors (Figure 7I,J). Specifically, after CNO administration all pain scale values with heavy pinprick applications are greater than every other stimulus (Figure 7J). Thus, PAWS automatically measures increased mechanical hyperalgesia to noxious stimuli that was influenced by top-down processes in the absence of peripheral or spinal sensitization. These data suggest that our pre- versus post-peak designations have biological relevance because we can genetically increase the gain on the more temporally-delayed behavioral representations, which may reflect the aversiveness of the pain experience (Figure 7J; Figure 7—source data 1).

Mice for behavior testing were maintained in a barrier animal facility in the Carolyn Lynch or Translational Research Laboratory (TRL) buildings at the University of Pennsylvania. The Lynch and TRL vivariums are temperature controlled and maintained under a 12 hr light/dark cycle (7 am/7 pm) at 70 degrees Fahrenheit with ad lib access to food (Purina LabDiet 5001) and tap water. The feed compartment on the wire box lid of the cage was kept at a minimum of 1/3 full at all times. All cages were provided with nestlets to provide enrichment for mice. All procedures were conducted according to animal protocols approved by the university Institutional Animal Care and Use Committee (IACUC) and in accordance with the National Institutes of Health (NIH) guidelines. C57BL/6J, A/J, 129S1/SvlmJ, Balb/cJ, DBA/1J, AKR/J, CBA/J, SJL/J, and TRAP2 mice (Fos-FOS-2A-iCre-ERT2, stock no. 030323) mice were all purchased from Jackson Laboratories. All animals were habituated to our facility for 2 weeks after delivery before beginning behavioral experiments described below. All inbred strains of mice tested were male with 10 mice used per strain used unless otherwise noted in the figure or figure legend, while six female TRAP2 mice tested. The choice to use female mice in the TRAP2 experiments was random based on availability of mice. All mice were adults between 2 and 4 months. Animals were co-housed with 4–5 mice per cage in a large holding room containing approximately 500 cages of mice. For the SJL/J strain, some aggression was observed, and several mice had to be removed from testing and separated due to fighting.

Mouse behaviors were recorded at 2000 fps with a high-speed camera (Photron FastCAM Mini AX 50 170 K-M-32GB - Monochrome 170K with 32 GB memory) and attached lens (Zeiss 2/100M ZF.2-mount). Mice performed behavior in rectangular plexiglass chambers on an elevated mesh platform. The camera was placed at a ~45° angle at ~1–2 feet away from the Plexiglas holding chambers on a tripod with geared head for Photron AX 50. CMVision IP65 infrared lights that mice cannot detect were used to adequately illuminate the paw for subsequent tracking in ProAnalyst. All data were collected on a Dell laptop computer with Photron FastCAM Analysis software. Each cohort of mice (n = 40) used roughly 80 GB of video storage space, (average size of video file = ~2 GB), which amounts to a total of ~160 GB for all eight inbred strains. The format for tracking data = . csv and. txt output (which amounted to ~5.4 MB for the eight inbred lines).

In all behavioral experiments, we used a sample size of 6–10 mice per strain, as these numbers of consistent with studies of this kind in the literature to reach statistically significant conclusions. For experiments with inbred mice, animals were not ear-tagged, so we had no source of identification, and thus animals within a given strain were randomly tested. For experiments with fos-trap mice, both the experimenter and evaluator of the data were blind to which animals were in the -CNO versus +CNO experimental groups. In general, all mice were habituated for a minimum of 5 days, for one hour each day, in the Plexiglas holding chambers before testing commenced. During baseline, mice were tested in groups of five and chambers were placed in a row with barriers preventing mice from seeing each other. On testing day, mice were habituated for an additional ~10 min before stimulation and tested one at a time. Stimuli were applied through the mesh to the hind paw proximal to the camera. Testing only occurred when the camera’s view of the paw was unobstructed. Mice only received one stimulus on a given testing day (cs, db, lp, or hp) and were given at least 24 hr between each stimulus session. Stimuli were tested from least painful to most: cotton swab, dynamic brush, light pinprick, and heavy pinprick. Cotton swab tests consisted of contact between the cotton Q-tip and the hind paw until paw withdrawal. Dynamic brush tests were performed by wiping a concealer makeup brush (L'Oréal Paris Infallible Concealer Brush, item model number 3760228170158) across the hind paw from back to front. Light pinprick tests were performed by touching a pin (Austerlitz Insect Pins) to the hind paw of the mouse. The pin was withdrawn as soon as contact was observed. Heavy pinprick tests were performed by sharply pressing this pin into the paw so that it was pushed upward, without the breaking the skin barrier. The pin was withdrawn as soon as approximately 1/3 of the pin’s length had passed through the mesh. For the application of von Frey hairs (VFHs, Stoelting Company, 58011), we used 4 g of force. As previously described, the VFH was directed at the center of the plantar paw and pressed upward until the filament bent (Abdus-Saboor et al., 2019). For inducing inflammatory pain, approximately 10 μL of Complete Freund’s Adjuvant (CFA, Sigma, F5881) was injected into the plantar surface of 3% isoflurane-anesthetized mice as previously published (Abdus-Saboor et al., 2019).

We used ProAnalyst software to automatically track hind paw movements following stimulus application. This software allowed us to integrate automated and manually scored data, possible through the ‘interpolation’ feature within ProAnalyst. We were able to define specific regions of interest (paw), track, and generate data containing ‘x’ and ‘y’ coordinates of the paw through time, as well as velocity, speed, and acceleration information. In a subset of videos, additional manual annotation was performed for increased accuracy. For deep learning-based paw tracking with the SLEAP algorithm, we pseudo-randomly chose a small set of training frames from each video and hand-labeled the paw and toe. We trained SLEAP to predict toe and paw positions in unlabeled video frames (>~90% of total video frames). To generate trajectories, we overlaid the inferred x, y positions of the toe and paw in each video frame on a single still image corresponding to the apex of the mouse’s paw during the assay.

Behavioral features were extracted from raw paw position time series in an automated and standardized procedure. First, the start and end of paw movement (paw at rest on the ground) were identified, and analysis was restricted to this time window. Peaks in paw height were then determined based on Savitsky-Golay smoothed estimates of paw velocity, and the first peak identified. The time of the first peak (designated t*) was used to separate pre-peak behavioral feature calculations from post-peak calculations. To differentiate shaking from guarding in the post-peak period, we constructed a moving reference frame based on the principal axis of paw displacement across a sliding window (0.04 s in duration) for each time point, and identified periods of consecutive displacements above a specified threshold (35% of maximum paw height) as periods of shaking. Note that in the construction of the moving reference frame the principal axes of variation were recovered via principal component analyses, which is not invariant to the sign of the recovered axes. Since displacement is measured over time it is sensitive to reversals in sign along the axis we measure it. We therefore ensured consistency by using the axis direction minimizing the angular deviation from the axis recovered at the previous time step. PAWS is open source and freely available at https://github.com/crtwomey/paws.

4-hydroxytamoxifen (Hello Bio, #HB2508) prepared in Kolliphor EL (Sigma, #27963), Clozapine-N-oxide (Hello Bio, #HB6149), and 0.9% sodium chloride (Sigma, #S3014).

For chemogenetic manipulation of BLA pain-active neurons, we intracranially injected 200 nL of AAV5-hSyn-DIO-hM3D(G_q)-mCherry (Addgene, titer: 7 × 10¹²) into both the left and right BLA at coordinates AP: −1.4 mm, ML:±3.1 mm, DV: −4.2.

We conducted surgeries under aseptic conditions using a small stereotaxic instrument (World Precision Instruments). We anesthetized mice with isofluorane (5% induction, 1–2% maintenance) during the entire surgery and maintained body temperature using a heating block. We injected mice with a beveled 33G needle attached to a 10 µL syringe (Nanofil, WPI) for delivery of 200 nL of viral reagent at a rate of 40 nL/min. After viral injection, the needle remained at the injection depth for 10 min before slow withdrawal over 2 min. After surgery, we maintained the animal’s body temperature using a heating pad.

We utilized female Fos^2A-iCreERT2 (TRAP2) mice, Jackson Laboratory, stock #030323 aged P46-73. We bilaterally transduced an AAV (AAV9-hSyn-DIO-hM3D(Gq)-mCherry) into the BLA. Two weeks after injection, mice were stimulated with a noxious pinprick on the left hind paw every 30 s for 10 min. One hour later, we injected mice with 4-hydroxytamoxifen (4-OHT) (20 mg/kg in 0.2 mL vehicle; subcutaneous) to induce genetic recombination. Eight weeks following 4-OHT administration, we examined mouse behavior. We examined behavior 30 min after injection of CNO (3 mg/kg in 0.2 mL vehicle; subcutaneous) or vehicle. Mice were euthanized via transcardial perfusion 4 months after viral injections. Formalin-fixed brains were collected and sectioned at 50 µm on a cryostat. Tissue was mounted and imaged on a fluorescent Keyence microscope.