Background & Aims

Mechanical sensitivity assays are widely used in preclinical pain testing to assess nociception and pain. Manual application of von Frey filaments with subsequent assessment of pain by withdrawal frequency or threshold has remained somewhat of a gold standard in the field. This assay requires extensive training, gives a limited redoubt, and produces reproducibility issues due to human error and the effect of the researcher’s proximity on the mouse. We aimed to address these limitations by developing the automated reproducible mechano-stimulator (ARM), a device that can remotely give mechanical stimuli in a customizable and reproducible manner. We then proceeded to validate the ARM and determine the effects of the experimenter’s presence and stimulus delivery variability on the mouse response.

Methods

Experiments were conducted with the assistance of internal and external testers with experience in stimulus delivery to compare the ARM’s accuracy, stimulus consistency, and experiment speed to manual stimulus delivery. Mouse withdrawal behavior was analyzed with a videography and machine learning pipeline of automated paw tracking followed by behavioral quantification with PAWS software (Jones 2020). The ARM was linked to the highspeed camera to measure withdrawal latency. The effect of an experimenter’s presence on the mouse response was measured by using the ARM to apply either a cotton swab (touch) or pinprick (pain) stimulus remotely (n=10). During these experiments either no researcher was present or a male/female researcher was near the mice. The effect of variability in stimulus delivery on the mouse response was measured by using the ARM to deliver the same pinprick to a cohort of mice (n=15) for a consistent amount of time with 9 intensities of variable max paw depression an

Results

The ARM was found to be more accurate compared to manual stimulus delivery in terms of both aim and stimulus intensity/timing variability. This included the ARM decreasing off-target stimuli to a stationary target by 92% and variability in von Frey stimulus application to a force sensor by 79% when compared to data from individual researchers. It was found that the ARM was 2.4x faster than manual testers at completing von Frey experiments in behaving mice.
Remote delivery experiments found that a researcher’s presence led to elevated responses in male mice compared to females in responses to touch and pain stimuli. Further, male mice were found to show elevated responses when a female researcher was present that disappeared when the researcher was removed. Linear regression revealed that stimulus intensity significantly influenced various measures of mouse paw withdrawal including paw withdrawal latency, max Y Velocity, distance traveled, and max paw height.

Conclusions

These results indicate that the ARM represents a significant improvement to both the scientific rigor and logistics of rodent mechanosensory assays. Manual stimulus delivery was highly variable compared to ARM-delivered stimuli, and when isolated comparable variability in the delivery of a pinprick stimulus led to significant differences in the mouse behavioral response. Experimenter presence was found to have a significant effect on the mouse behavioral response leading to significant baseline differences in touch and pain behavioral responses that disappeared when that experimenter was removed. These results build upon previous findings from the Mogil lab (Chesler 2002) and others. Finally, these findings indicate that the ARM could increase accessibility to mechanosensory assays by reducing physical impediments and the training/time needed while eliminating many of the logistical impediments to longer-course chronic pain experiments by making results experimenter-agnostic.

References

Abdus-Saboor, I., Fried, N. T., Lay, M., Burdge, J., Swanson, K., Fischer, R., Jones, J., Dong, P., Cai, W., Guo, X., Tao, Y. X., Bethea, J., Ma, M., Dong, X., Ding, L., & Luo, W. (2019). Development of a Mouse Pain Scale Using Sub-second Behavioral Mapping and Statistical Modeling. Cell Reports. https://doi.org/10.1016/j.celrep.2019.07.017

Bohic M, Pattison L, Jhumka A, Rossi H, Thackray J, Ricci M, Foster W, Ogundare S, Twomey C, Hilton H, Arnold J, Mossazghi N, Yttri E, Tischfield, MA, Smith ESJ, Abdus-Saboor I., Abraira V. (2023). Mapping the neuroethological signatures of pain, analgesia, and recovery in mice. Neuron

Burdge, J., Fried, N. T., & Abdus-Saboor, I. (2021). Protocol Using high-speed videography for objective and reproducible pain measurement on a mouse pain scale. STAR Protocols, 2(1), 100322. https://doi.org/10.1016/j.xpro.2021.100322

Chesler, E.J., Wilson, S.G., Lariviere, W.R., Rodriguez-Zas, S.L., and Mogil, J.S. (2002). Influences of laboratory environment on behavior. Nature Neuroscience, 5, 1101-1102. DOI: 10.1038/nn1102-1101

Jones, J. M., Foster, W., Twomey, C. R., Burdge, J., Ahmed, O. M., Pereira, T. D., Wojick, J. A., Corder, G., Plotkin, J. B., & Abdus-Saboor, I. (2020). A machine-vision approach for automated pain measurement at millisecond timescales. ELife. https://doi.org/10.7554/ELIFE.57258

Mao, J. (2012). Current challenges in translational pain research. Trends in pharmacological sciences, 33(11), 568-573.

Mogil, J.S., Sorge, R.E., LaCroix-Fralish, M.L., Smith, S.B., Fortin, A., Sotocinal, S.G., Ritchie, J., Austin, J.-S., Schorscher-Petcu, A., Melmed, K., Czerminski, J., Bittong, R.A., Mokris, J.B., Neubert, J.K., Campbell, C.M., Edwards, R.R., Campbell, J.N., Crawley, J.N., Lariviere, W.R., Wallace, M.R., Sternberg, W.F., Balaban, C.D., Belfer, I., and Fillingim, R.B. (2011). Pain sensitivity and vasopressin analgesia are mediated by a gene-sex-environment interaction. Nature Neuroscience, 14, 1569-1573. DOI: 10.1038/nn.2941

Sadler, K. E., Mogil, J. S., & Stucky, C. L. (2022). Innovations and advances in modelling and measuring pain in animals. Nature Reviews Neuroscience, 23(2), 70-85.

Presenting Author

Justin Burdge

Poster Authors

Justin Burdge

BSc

Columbia University Abdus-Sabooor Lab

Lead Author

Simon Ogundare

Lead Author

Nicholas Baer

Lead Author

Anissa Jhumka

Lead Author

Leah Yadessa

Lead Author

Ashar Khan

Lead Author

Topics

  • Assessment and Diagnosis