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Biology subjects

Weber, R. Z.

Publications and source records attributed to Weber, R. Z..

2 recordsLinked to original sources

Xeno-free induced pluripotent stem cell-derived neural progenitor cells for in vivo applications

Cell-based therapies are a promising treatment paradigm for neurodegenerative diseases and other brain injuries. Despite recent advances in stem cell technology, major concerns have been raised regarding the feasibility and safety of cell therapies for clinical applications. Here, we generate good manufacturing practice (GMP)-compatible neural progenitor cells (NPCs) from transgene- and xeno-free induced pluripotent stem cells (iPSCs) that can be smoothly adapted for clinical applications. The produced NPCs have a stable gene-expression over at least 15 passages and can be scaled for up to 1018 cells per initially seeded 106 cells. To ensure a pure NPC population for in vivo applications, we reduce risks of iPSC contamination using micro RNA-switch technology as a safety checkpoint. Using lentiviral transduction with a fluorescent and bioluminescent dual-reporter construct, combined with non-invasive in vivo bioluminescent imaging, we longitudinally tracked the grafted cells in healthy wild-type and genetically immunosuppressed mice as well as in a mouse model of ischemic stroke. Long term in-depth characterization revealed that transplanted cells have the capability to survive and spontaneously differentiate into functional and mature neurons throughout a time course of a month.

neuroscience↗

Deep learning based behavioral profiling of rodent stroke recovery

Stroke research heavily relies on rodent behavior when assessing underlying disease mechanisms and treatment efficacy. Although functional motor recovery is considered the primary targeted outcome, tests in rodents are still poorly reproducible, and often unsuitable for unraveling the complex behavior after injury. Here, we provide a comprehensive 3D gait analysis of mice after focal cerebral ischemia based on the new deep learning-based software (DeepLabCut, DLC) that only requires basic behavioral equipment. We demonstrate a high precision 3D tracking of 10 body parts (including all relevant joints and reference landmarks) in several mouse strains with an accuracy of 99.4%. Building on this rigor motion tracking, a comprehensive post-analysis (with >100 parameters) unveils biologically relevant differences in locomotor profiles after a stroke over a time course of three weeks. We further refine the widely used ladder rung test using deep learning and compare its performance to human annotators. The generated DLC-assisted tests were then benchmarked to five widely used conventional behavioral set-ups (neurological scoring, rotarod, ladder rung walk, cylinder test, and single-pellet grasping) regarding sensitivity, accuracy, time use and costs. We conclude that deep learning-based motion tracking with comprehensive post-analysis provides accurate and sensitive data to describe the complex recovery of rodents following a stroke. The experimental set-up and analysis can also benefit a range of other neurological injuries that affect locomotion.

neuroscience↗