Fig 1.
Experimental design and verification of glial and fibrotic scar formation after spinal cord injury (SCI).
(A) Experimental schematic: Wild-type mice were subjected to SCI at day 0, and spinal cord tissue was analyzed at 42 dpi using IHC. Hargreaves and von Frey behavioral assays were assessed at baseline (BL) (7 days prior to SCI) and every 7 days for 6 weeks. Blackbox was assessed at BL, 1 dpi and 7 day intervals for 6 weeks. (B) Blackbox system: Allows simultaneous bottom-up video recording of up to four animals in individual chambers. The recordings are then analyzed by the Blackbox system to produce skeleton-based motion tracking data and luminance-based paw pressure data. (C) SCI induces astrocyte and fibroblast activation, leading to glial and fibrotic scar formation. Representative transverse spinal cord sections stained with GFAP (green, astrocytes), PDGFRβ (red, fibroblasts), and DAPI (blue, nuclei). Sham-operated mice exhibit preserved spinal cord structure, while SCI sections demonstrate a prominent lesion core marked by increased GFAP and PDGFRβ expression. Scale bar = 100 µm. (D) Quantitative analysis of the PDGFRβ+ injury area (µm2) indicates a significantly larger lesion size in the SCI group compared to sham controls (****p < 0.0001, two-tailed unpaired t-test, n = 4 per group). Data corresponds to the largest PDGFRb area such as the representative SCI image in (C). (E) Higher magnification views of the lesion microenvironment. In SCI conditions, GFAP-positive astrocytes (green) form a glial scar encasing the lesion site, and PDGFRβ-positive fibroblasts (red) are enriched within the lesion core, contributing to fibrotic scar formation. The dashed line outlines the lesion boundary. DAPI (blue) highlights the distribution of nuclei. Scale bar = 50 µm.
Fig 2.
Changes in animal posture and locomotion following SCI.
(A) Representative skeletal visualizations of animal posture at baseline (BL), 1-day post-injury (dpi), and 28 dpi in sham and SCI-treated animals. Skeletal representations illustrate limb positioning and coordination changes over time. (B) Hierarchical heatmap of the mean z-score for all detected features automatically provided in the standard summary metrics output of the Blackbox Palmreader software utilizing pose estimation skeleton tracking. Significance is represented as *** = p < 0.001, ** = p < 0.01, and * = p < 0.05. A sample of the significantly different postural and motor parameters are: (C) Distance between forepaws; (D) Distance between hindpaws; (E, F) Right and Left Hindpaw angles, measured as the angle between the intersection of a vector from the tail base to the sacrum and a vector from the heel to the center of the paw; (G) Femur width, (H) Total distance traveled during the recording session, representing overall locomotor activity. Tracking data was also used to analyze additional parameters: (I) Occupied bins counts reflecting exploratory behavior and movement patterns; (J) Average speed in SCI animals over time. A two-way ANOVA is used in (B). A two-tailed unpaired Student’s t-test for comparisons between sham and SCI at specific timepoints is used in (C-J). Abbreviations: Left and Right Forepaw (LFP, RFP), Left and Right Hindpaw (LHP, RHP).
Fig 3.
Changes in paw pressure following SCI.
(A) Representative paw pressure luminance in Sham and SCI-treated animals at baseline (BL), 1-day post-injury (dpi), and 28 dpi, illustrating the distribution of weight-bearing across all four paws, with changes in intensity reflecting shifts in pressure distribution. (B) Hierarchical heatmap of the mean z-score for all detected features automatically provided in the standard summary metrics output of the Blackbox Palmreader software using luminance detection. Significance is represented as *** = p < 0.001, ** = p < 0.01, and * = p < 0.05. Several luminance values were assessed during locomotion: (C) Average fore-to-hindpaw luminance ratio over time. This ratio represents the relative pressure applied to the forepaws compared to the hindpaws. (D-E) Luminance (arbitrary units (A.U.)/pixel^2) of the right and left forepaws over time, measured as an indicator of weight-bearing on each forelimb. (F-G) Luminance of the right and left hindpaws over time, reflecting changes in hindlimb weight support. Data are shown as mean ± SEM for n = 6 Sham animals and n = 8 SCI animals. Two outliers in the Sham group over three standard deviations from the mean were removed because of urine interfering with the luminance signal. Statistical significances using a two-way ANOVA are shown for (B) and using two-tailed unpaired Student’s t-test for (C-G) comparisons between Sham and SCI groups at specific time points.
Fig 4.
Principal Component Analysis (PCA) of locomotor and behavioral features following SCI.
(A) PCA plot of selected significant features from individual mice across all time points. Each point represents a single mouse, with numbers and colors indicating the experimental group and time post-injury (see legend). The x-axis represents Principal Component 1 (PC1) and the y-axis represents Principal Component 2 (PC2), capturing the primary dimensions of variance in the dataset. Ellipses illustrate grouping trends among sham and SCI animals across different time points. (B) Scree plot showing the percentage of variance explained by the first six principal components. The x-axis represents the principal component number, and the y-axis represents the percentage of variance explained, indicating the relative contribution of each component to the overall variance in the dataset. Data are presented for n = 8 animals per group. Statistical significances using MANOVA are indicated between sham and SCI groups across time points.
Fig 5.
Keypoint MoSeq analysis reveals behavioral motifs and motor function patterns following SCI.
(A) Frequency of selected behavioral motifs (syllables) in control, 1 dpi, and 7–42 dpi groups. The x-axis represents individual syllables corresponding to distinct movement patterns, while the y-axis represents the frequency of each syllable occurrence. (B) Syllable trajectory plots and syllable-to-behavior mapping. The left panel shows visualized movement trajectories associated with key syllables, depicting characteristic motion patterns. Different colored points represent different body parts, with orange indicating mouse head, red indicating central body parts and tail, and yellow indicating paws. The right panel presents a table mapping each syllable to its corresponding behavioral classification. Data are presented for n = 8 animals per group. Statistical significances using a two-way ANOVA with Tukey post hoc correction are shown for comparisons between sham Control and SCI groups at specific time points.
Fig 6.
Blackbox Data Analysis App enables detailed, multifaceted behavioral data analysis.
(A) Time series speed plot showing frame-by-frame analysis of animal speed over time. The x-axis represents time (s), and the y-axis represents speed (cm/s). (B) Time series feature plot displaying the progression of a selected behavioral feature across frames. The x-axis represents time (s), and the y-axis represents the feature value. (C) Mouse trajectory plot, visualizing movement paths within the testing environment. The x- and y-axes represent spatial dimensions (box length and width in cm), while color denotes the recording frame (blue = early in session, red = later in session). (D) Cage occupancy heatmap, showing spatial distribution of time spent in different areas of the enclosure. The x- and y-axes represent cage dimensions (cm), and the color scale represents occupancy density (blue = less time, red = more time). (E) Graphical user interface (GUI) of the Blackbox Data Analysis App, illustrating user input options for data selection, metric extraction, and visualization tools. This figure highlights the functionality of the Blackbox app (https://helenlailab.shinyapps.io/BlackboxDataAnalysisPlots/) for in-depth movement tracking and behavioral quantification.
Fig 7.
Recovery of Thermal and Mechanical Sensitivity Following SCI.
(A, B, C) Thermal sensitivity recovery, measured by withdrawal latency (seconds) using the Hargreaves assay. (A) Average withdrawal latency, (B) left paw withdrawal latency, and (C) right paw withdrawal latency are shown over the recovery period. The x-axis represents days post-injury (dpi), and the y-axis represents latency in seconds. (D, E, F) Mechanical sensitivity recovery, measured by withdrawal thresholds (grams) using the simplified up-down (SUDO) von Frey method. (D) Average withdrawal threshold, (E) left paw withdrawal threshold, and (F) right paw withdrawal threshold are shown over the recovery period. The x-axis represents days post-injury (dpi), and the y-axis represents withdrawal threshold in grams. Data represent n = 8 animals per group, with mean ± SEM displayed. Statistical significances using two-tailed unpaired Student’s t-tests are shown for comparisons between Sham and SCI groups at specific time points.
Fig 8.
Workflow schematic outlining the analytical pipeline from Blackbox video acquisition to interpretation of motor recovery.
Raw locomotor recordings were collected using the Blackbox system with two synchronized imaging modalities: FTIR (frustrated total internal reflection) for paw-contact pressure signals and transillumination for whole-body pose tracking. FTIR and transillumination videos were processed in Blackbox Palmreader to generate video outputs (paw-pressure heatmaps and pose-tracking skeleton videos) and data outputs (summary .csv files and .HDF5 files). Summary (.csv) files were used to obtain standard metrics automatically generated by the software (Figs 2B and 3B). HDF5 tracking files were processed through custom R scripts to extract additional locomotor features (e.g., speed, fore-to-hindpaw speed ratio, acceleration, and occupied bin measurements) and syllable identification and frequency quantification with Keypoint MoSeq. All data outputs can be used as inputs for a custom web app that generates raster plots, occupancy heatmaps, PCA visualizations, and extracts time-window data (Figs 4 and 6). All extracted metrics were integrated for statistical analysis to interpret motor recovery patterns following spinal cord injury. Abbreviations: FTIR, frustrated total internal reflection.