Predicting recovery trajectories and injury severity following partial crush spinal cord injury in mice

The partial crush spinal cord injury (SCI) model enables preclinical testing of experimental therapies in mice, but substantial inter-animal variability in recovery outcomes confounds efficacy assessments. Here, we used open field behavioral data collected during the first 3 days post partial thoracic SCI to generate an Acute Functional Score (AFS) that defined three subgroups with divergent recovery trajectories. Applying latent class growth analysis and growth mixture modeling to open field and grid walk testing data, we demonstrated 83-92% prediction accuracy for AFS-defined recovery trajectories.

The three subgroups differed significantly in treadmill kinematics and histological assessments of lesion size and astrocyte bridging. Applying the recovery trajectory framework to mice receiving saline or biomaterial vehicle injections at 3 days post-SCI revealed robust predictive accuracy while exposing disproportionate injury severity distributions between experimental groups. The approach enables individualized post-SCI recovery characterization that can neutralize procedural bias, minimize animal numbers, and provide a probabilistic basis for evaluating whether interventions enhance or suppress wound repair processes.

Our findings establish a foundation for improving preclinical SCI study design and accelerating identification of effective therapies. We thank the Micro and Nano Imaging (MNI) core in the Biomedical Engineering Core Facilities at Boston University for microscope support. This research was supported by funding from: Boston University Start-Up Funds (T.M.O.), Boston University’s Undergraduate Research Opportunities Program (UROP) (H.P.), Boston University BUNano PhD Fellowship (P.S.W.), Paralyzed Veterans of America Research Foundation (T.M.O.), Wings for Life (T.M.O.), Ruth L.

Kirschstein Predoctoral Individual National Research Service Award NIH NINDS (F31 NS145754 to L.F.H), and Maximizing Investigators’ Research Award (MIRA) NIH NIGMS (R35GM154942 to T.M.O.) (the content in this manuscript is solely the responsibility of the authors and does not reflect the official views of the NIH). These authors contributed equally: Kunyu Li, Laboni F. Department of Biomedical Engineering, Boston University, Boston, MA, USA Kunyu Li, Laboni F.

Hassan, Himagowri Prasad, Paige S. O’Shea Graduate Program for Neuroscience, Boston University, Boston, MA, USA The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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