Biology subjects

Tuffaha, S.

Publications and source records attributed to Tuffaha, S..

3 recordsLinked to original sources

bioRxiv2026

Reinnervation of Muscle Targets Enhances the Separability of Motor Unit Signals Following Peripheral Nerve Transfers

After amputation, advanced prosthetic limbs offer a promising means of restoring motor function. However, state-of-the-art prostheses often rely on aggregate electromyogram (EMG) signals to decode motor intention, which limits their ability to replicate natural limb movements. Decomposing EMG signals into individual motor unit components has shown potential for more natural control, but distinguishing between individual units can be challenging when nearby signals overlap. This study demonstrates that muscle target reinnervation surgeries can naturally increase physical separation between motor unit signals, thereby mitigating this overlap. Reinnervation of individual motor units is evaluated in a rodent hindlimb model after direct nerve-to-muscle implantation. Histological and electrophysiological analyses reveal that structural changes following reinnervation surgery result in beneficial motor unit signal changes, particularly improving spatial separation between motor unit signals compared to those in intact muscle. This spatial separation contributed to fewer instances of complex, overlapping signals in reinnervated muscle recordings. Motor unit signals were leveraged to provide a proof-of-concept of precise control of a virtual prosthesis for the first time after direct nerve-to-muscle implantation surgery. These findings highlight the potential of reinnervated muscle targets as key biological interfaces that facilitate motor unit separation, reducing the burden on decomposition algorithms and improving prosthetic control.

bioengineering↗

bioRxiv2025

Lack of mRNA Methylation in Schwann Cells Results in Demyelination and Regenerative Failure

Schwann cells are essential for peripheral nerve myelination and regeneration. N6-methyladenosine (m6A) RNA methylation, regulated by methyltransferase-like 14 (Mettl14), is a critical post-transcriptional modification, but its role in Schwann cell biology remains unclear. Using a conditional knockout (cKO) mouse model, we investigated the impact of Mettl14-mediated m6A methylation on Schwann cells. Mice born with Schwann cell-specific genetic deletion of Mettl14 developed normally but starting in young adulthood exhibited progressive motor deficits, severe demyelination, and axonal degeneration, confirmed by behavioral assessments and histological analyses. Mettl14-deficient Schwann cells displayed impaired proliferation and mitochondrial dysfunction in vitro. Following sciatic nerve injury, Mettl14 cKO mice showed defective macrophage recruitment, slowed axonal degeneration, and impaired regeneration. These findings suggest that Mettl14-mediated m6A methylation is critical for Schwann cell maintenance but not development. Given that Mettl14 cKO mice developed a demyelinating polyneuropathy, it is possible that manipulation of m6A methylation in Schwann cells is a promising therapeutic strategy targeting peripheral nerve repair and myelination.

neuroscience↗

bioRxiv2021

A simple and robust method for automating analysis of naïve and regenerating peripheral nerves

BackgroundManual axon histomorphometry (AH) is time- and resource-intensive, which has inspired many attempts at automation. However, there has been little investigation on implementation of automated programs for widespread use. Ideally such a program should be able to perform AH across imaging modalities and nerve states. AxonDeepSeg (ADS) is an open source deep learning program that has previously been validated in electron microscopy. We evaluated the robustness of ADS for peripheral nerve axonal histomorphometry in light micrographs prepared using two different methods. MethodsAxon histomorphometry using ADS and manual analysis (gold-standard) was performed on light micrographs of naive or regenerating rat median nerve cross-sections prepared with either toluidine-resin or osmium-paraffin embedding protocols. The parameters of interest included axon count, axon diameter, myelin thickness, and g-ratio. ResultsManual and automatic ADS axon counts demonstrated good agreement in naive nerves and moderate agreement on regenerating nerves. There were small but consistent differences in measured axon diameter, myelin thickness and g-ratio; however, absolute differences were small. Both methods appropriately identified differences between naive and regenerating nerves. ADS was faster than manual axon analysis. ConclusionsWithout any algorithm retraining, ADS was able to appropriately identify critical differences between naive and regenerating nerves and work with different sample preparation methods of peripheral nerve light micrographs. While there were differences between absolute values between manual and ADS, ADS performed consistently and required much less time. ADS is an accessible and robust tool for AH that can provide consistent analysis across protocols and nerve states.

neuroscience↗