Análise cinemática da marcha de sagital em camundongos C57BL/6 sujeitado a MOG35-55 induzido encefalomielite auto-imune Experimental

The overall goal of this procedure is to measure functional motor impairments in the experimental autoimmune encephalomyelitis or EAE, mouse model multiple sclerosis using kinematic gait analysis. The application of kinematic gait analysis to mouse-walking behavior has been previously established and described by others. Neurological deficits in an EAE result from neural inflammation and sporadic white matter loss throughout the spinal cord and the cerebellum.

Traditionally, motor stability in EAE mice has been assessed using clinical scoring systems, in which mice are assigned a clinical score based on the observer's impression of the severity of motor deficits. Data from clinical scores are ordinal and do not correlate well with the spinal cord histopathology. Kinematic gait analysis has recently been shown to be a better behavioral correlate of white matter loss than clinical scores, in addition to providing an objective description of walking deficits in the EAE mice.

This technique involves placing reflective markers on the high limbs of mice and allowing them to walk on a treadmill while being recorded with a high-speed camera. Kinematic parameters are then extracted from the video using motion analysis software. The first step is to make the markers that will be placed on the animal's hind leg.

The reflection of light off of these markers allows the coordinates of anatomical points on the leg to be extracted from the videos. To begin, punch the desired number of circles from the sheet of reflective paper. Each mouse requires five markers per recording, two large and three small.

Using fine scissors, make a cut extending from the perimeter to the center of the circle. Peel off the paper backing to reveal the adhesive surface using fine forceps. Grip the marker with forceps and using your finger, roll the marker in on itself to form a cone.

To make a small marker, make a longer cut and curl the cone more tightly. To make a large marker, make a shorter cut and curl the cone more loosely. Using a hot glue gun, fill the inside of the cone with glue and adhere it to a piece of cardboard.

It is important to fully fill the marker with glue to prevent the marker from collapsing when handling during recording. Once the glue is dry, cut the marker off using a scalpel. Ensure that you cut away from your body.

The next step is to prepare the animal for recording. To do this, the reflective markers must be adhered to the hind limb of the mouse at the proper anatomical locations. This is conducted under light anesthesia.

Place the mouse in an induction chamber and anesthetize with 2.5%isoflurane. Once unconscious, transfer the mouse from the induction chamber to a nose cone or a recirculating water heating pad. Apply a topical lubricant to both eyes.

Shave the desired hind limb extending from the ankle to the spine and the bottom of the ribs. In this demonstration, we are recoding movements of the right hind limb, but either or both limbs may be used. Locate the iliac crest by bringing both knees together with your thumb and forefinger and palpating just below the ribs.

Mark this location using a permanent marker. Locate the hip by extending the leg and moving it back and forth. Place a marker over the hip joint, which is the point of articulation between the head of the femur and the pelvis.

Using a flexible ruler, measure and record the length of the tibia or shank of the mouse. Measure and record the length of the femur or thigh as well. To isolate the fourth toe for marker placement, tape down the rest of the foot.

Grasp a small marker with forceps and dip the flat end in glue. Place the small marker on the tip of the fourth toe. Place another small marker on the metatarsophalangeal joint.

Place the last small marker on the ankle. Place a large marker on the hip joint directly over the mark on the skin. Place the second large marker on the mark over the iliac crest.

Remove the tape from the foot. Place the mouse in a recovery cage and transport immediately to the gait recording room. The next step is recording the mouse walking on a treadmill.

This picture is of our treadmill recording setup, showing the light, high-speed camera, and treadmill. Prior to recording the mouse's gait, take a picture of a calibration block with known dimensions on the treadmill. This will allow the pixels in the video to be converted to real measurements.

It is critical that the camera angle and position at which the calibration picture is taken remains the same during the recording of walking behavior. Place the mouse in the treadmill. Increase the treadmill's speed gradually to orient the mouse in the correct direction.

Slowly accelerate to 20 centimeters per second, which is the ideal running speed for obtaining recordings of steady gait. For accurate analysis, it's best to record eight to 12 step cycles of steady walking behavior. This is an example of consistent walking.

The following is the same video shown at half speed. Because mice walk so quickly, it can be useful to view the videos at slower speeds to count step cycles and to better appreciate the gait pattern. This is one example of an EAE mouse walking on the treadmill.

This mouse is unable to support its body weight as its pelvis is very low to the ground. Additionally, it is having difficulty lifting its foot off the ground during the swing phase. This is an example of another EAE mouse that has less severe motor deficits.

The EAE mouse is walking on its toes with reduced movement at the ankle joint. This gives the mouse an uneven gait. The following are examples of behaviors that will compound analysis.

Recordings, including these behaviors, should not be used. Lagging occurs when the mouse stops walking and travels to the back of the treadmill, but then resumes walking. This may occur in any mouse, but will occur more frequently at lower speeds.

Rearing occurs when the mouse shifts its weight on its back hind limbs and raises its head and upper body. This behavior is common in anxious mice. The following are examples of poor lighting that will lead to issues with analysis of the markers'position in time and space.

If the lighting is too dim, markers may not reflect enough light to be recognized by the computer software. In this video, the toe markers are not easily visible due to insufficient lighting. If the lighting is too bright, objects other than the markers may reflect too much light and be recognized as a marker.

In this video, the toe markers appear to be merging with the reflective parts of the treadmill. Data from the reflective markers on mice walking can be used to create stick models of the leg from which kinematic parameters can be extracted. This is a recording of a mouse walking with a stick model of the leg superimposed.

Note that in this video, there is a sixth marker on the knee, which is not necessary because the location of the knee can be triangulated from the positions of the hip and ankle joints, and the measured lengths of the femur and tibia. Because markers over the knee joint are often inaccurate due to skin slippage, triangulation is the preferred method. A mouse's step cycle can be divided into two main phases.

The stance phase and the swing phase. This stick diagram can be used to qualitatively illustrate the movement of the hind limb in time and space. An example of this could be to assess the movement of the hind limb at different times points throughout the study.

This example shows that the hind leg is compressed during the stance phase, indicating that the mouse has difficulty supporting its body weight. This is reflected by increased flexion of the knee and ankle joints. At the later time point, this has partially recovered.

This video illustrates the relationship between the walking behavior and joint angles over time. The hip, knee, and ankle angle waveforms can be extracted from each recording. The joint angles can then be averaged over eight to 12 consecutive step cycles, resulting in an average step cycle that can be used for further analysis.

This graph represents a knee joint waveform averaging from 10 consecutive step cycles. The data has been normalized, so that the length of the stance and swing phases are 100 frames respectively. The clear background represents the swing phase and the green background represents the stance phase.

For this mouse, the walking behavior from week to week was very consistent and the step-cycle waveforms from each week overlapped considerably. However, there can also be substantial variability in gait in healthy mice as seen in this graph. The degree of variability shown here is acceptable and within the range of what might be expected from a mouse.

This graph represents the knee step cycle from the EAE mouse recorded on three consecutive weeks. There is a slight change in the shape of the step cycle at week two and a substantial deviation by week three, which the mouse's knee is much more flexed and does not extend during walking. There are numerous parameters that can be measured using this technique.

We'll briefly describe three. The average angle is obtained by averaging all angles throughout the normalized step cycle. In this instance, the average angle decreases throughout the study, suggesting that the mice are not extending their knees as much as normal.

The range of motion is obtained by subtracting the smallest angle from the largest angle in the normalized step cycle. This parameter can give you insight into joint flexibility, rigidity, or weakness. In this example, the knee range of motion decreases throughout the study indicating that the mice are not able to move their knee normally, possibly due to muscle weakness.

Root-Mean-Square difference is a method used to measure deviation of step-cycle waveforms from the baseline recording. This parameter tells you how much deviation there is from the initial recording. Kinematic gait analysis is a valuable technique that can be used to sensitively detect and describe changes in gait.

The application of kinematic gait analysis to EAE studies may be a valuable tool in understanding the functional consequences of spinal cord pathology in this model. It may facilitate discovery of novel treatments for multiple sclerosis. Additionally, kinematic gait analysis is not limited to the context of EAE.

This technique has previously been used in mouse models with spinal cord injury, amyotrophic lateral sclerosis, Huntington's disease and stroke, and can be applied to other mouse models with neurological disorders, including Parkinson's disease.