A computational approach to quantifying axon regeneration in the presence of mesenchymal stem cells (MSCs)

Lam, K P; Dempsy, K P; Smith, W A; Wright, K T; Masri, W E; Richardson, J B

Abstract

Transplantation of bone marrow stromal cells has shown to encourage functional recovery in animal models of spinal cord injury (SCI) and recent clinical trials suggest possible recovery also in humans. However, two fundamental barriers to the development of new and improved MSC-based treatments exist: (1) the general lack of cost-effective strategies to boost the number of MSCs in vitro in order to meet clinical and research demands; and, relevantly, (2) the absence of understanding of the mechanism and condition for these improvements. To overcome these barriers, novel computational toolsets are required to quantitatively assess and characterize spinal cord motor neurite interactions with human bone marrow stromal cells (MSCs) in an in vitro SCI model [16]. These analyses may begin to unlock the mechanisms responsible for the growth and regeneration of neurons, which are believed to be responsible for the functional improvements noted after cell transplantation for treating lesions to the central nervous system. Since phase contrast (PC) microscopy is the primary imaging technique for the long-term monitoring of the spinal motor neurite outgrowth, an accurate and robust method for its evaluation is a crucial prerequisite for implementing such toolsets.

Citation

Lam, K. P., Dempsy, K. P., Smith, W. A., Wright, K. T., Masri, W. E., & Richardson, J. B. (2013, November). A computational approach to quantifying axon regeneration in the presence of mesenchymal stem cells (MSCs). Presented at 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), San Diego, CA, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)
• Start Date: Nov 6, 2013
• End Date: Nov 8, 2013
• Publication Date: 2013-11
• Deposit Date: Jun 7, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• DOI: https://doi.org/10.1109/ner.2013.6696240
• Public URL: https://keele-repository.worktribe.com/output/442946