2018 Mitchell Max Awardee:
Assistant Professor, University of Pennsylvania
Ishmail Abdus-Saboor was born and raised in Philadelphia, PA. He received his bachelor’s degree in Animal Science from North Carolina A&T University in 2006 and earned his PhD in Cell and Molecular Biology in 2012 with Meera Sundaram at the University of Pennsylvania. His PhD thesis work was supported by an NIH T32 training grant and recognized with the Tom Kadesch Prize in Genetic Research. He completed postdoctoral training with Benjamin Shykind at Weill Cornell Medical College and Wenqin Luo at the University of Pennsylvania. As a postdoctoral fellow his research was supported by several grants from the NIH and Burroughs Welcome Fund including, a K12 IRACDA grant from NIGMS, a Postdoctoral Enrichment Program grant, and a K99/R00 Pathway to Independence grant from NIDCR. In July of 2018 Ishmail began as an Assistant Professor in the Biology Department at the University of Pennsylvania.
A Mouse Pain Scale: Assessment of Pain Sensation in Mice Using Sub-second Behavioral Mapping and Statistical Modeling
Ishmail Abdus-Saboor1, Nathan T. Fried1, Mark Lay3, Peter Dong1, Justin Burdge1, Ming Lu2, Minghong Ma1, Xinzhong Dong3, Long Ding1, Wenqin Luo1
1 Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, PA
2 StatConfidence. LLC, Philadelphia, PA
3 Howard Hughes Medical Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD
Rodents are the main model systems for pain research, but determining their pain state is very challenging. To improve upon the use of the paw withdrawal reflex to objectively assess pain sensation in mice, we developed a “behavior-centered” approach that integrates multiple parameters of the withdrawal behavior, which were precisely measured with highspeed videography, into a single index via statistical modeling and machine learning.
We first verified through in vivo calcium imaging in the dorsal root ganglion of whole animals, the quality (noxious or innocuous) of the natural mechanical stimuli we used in the study. We then performed detailed analyses of sub-second mouse responses to identify behavioral features that differentiate between innocuous and noxious mechanical stimulation. With statistical modeling and machine learning techniques, we integrated these features onto a graded index, which could
indicate “pain severity” or the probability a particular withdrawal was “pain-like” on a single trial basis. In contrast to the withdrawal frequency, this score correlates very well with stimulus intensity. Finally, we performed two proof-of-principle experiments to test the utility of our new methodology: one with a natural stimulus, von Frey hairs (VFHs), and the other with transdermal optogenetic stimulation of two transgenic lines that target almost all nociceptors (TRPV1-ChR2) or a smaller population of C-fiber nociceptors (MRGPRD-ChR2). Under all scenarios, our platform reliably indicates the sensation experienced by the mouse, highlighting the precision of our approach.
We strongly believe this “pain scale” method will greatly improve rigor and reproducibility of using mice as a model system to study pain mechanisms or perform drug screening, and may potentially change how the pain research field will perform, analyze, and interpret rodent reflexive behavioral assays in the future.