Experimental Murine Model Generation
Experimental Murine Model Generation - Related Videos
02:21
Generation of a Murine Model with a Central Nervous System Infection
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2025
This video demonstrates the generation of a mouse model with a CNS infection. A suckling pup is injected with the virus both intracranially and intraperitoneally. The virus spreads, infecting the CNS causing neuronal cell death and inflammation. The pup is assessed for symptoms of infection as the disease progresses.
03:21
Murine Tail Lymphedema Model: An In Vivo Technique to Generate Sustained Lymphedema in Murine Tail
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2025
This video describes an in vivo technique to generate murine tail lymphedema, an abnormal fluid accumulation in the interstitial tissue space leading to swelling in the tail. The model is applied to study mechanisms and therapy for lymphedema.
07:20
Inducement and Evaluation of a Murine Model of Experimental Myopia
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Cited by 9 •
2019
In this protocol, we describe the full process of experimental myopia inducement in mice using newly designed eyeglasses and the technic needed for achieving stable and reproducible results in ocular parameter measurements.
03:16
Generation of Experimental Autoimmune Encephalomyelitis in a Mouse Model
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2025
This video demonstrates the method of inducing experimental autoimmune encephalomyelitis (EAE) by injecting myelin-derived peptides with an adjuvant to activate autoreactive T cells, which migrate to the brain. Pertussis toxin is administered to increase blood-brain barrier permeability, allowing these T cells and other immune cells to enter the brain. These immune cells cause demyelination, leading to impaired nerve transmission and development of EAE.
03:20
A Technique to Generate a Murine Model of Dextran Sulfate Sodium-Induced Colitis
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2025
This video demonstrates a technique to generate a chemical-induced murine model of colitis — an inflammatory bowel disease. The mouse is allowed to consume a dextran sodium sulfate (DSS) solution for an adequate duration. Upon consumption, DSS damages the mucus and epithelial layer barrier in the colon, allowing gut microbes to enter the underlying tissue — where they are sensed by the resident immune cells to mediate an exaggerated immune response, resulting in inflammation.
FAQs
Selection should follow the cancer question being investigated. A model designed to examine tumor progression may not reproduce the immune or stromal interactions needed for another study, while a system useful for therapy testing may capture only selected features of human disease. Matching model characteristics to the intended outcome makes conclusions more informative.
Experimental murine model generation can begin with tumor cells, patient-derived tissue, genetic alterations, or breeding strains with defined cancer-associated mutations. These routes create systems with different biological starting points, so they should not be treated as interchangeable. Comparing their represented features helps investigators choose a model that fits the mechanism, disease behavior, or treatment question under study.
The surrounding immune and stromal components can influence how a tumor develops and responds to treatment. Including these interactions in model evaluation helps investigators study cancer as a biological system rather than focusing only on tumor-cell behavior. This context is especially relevant when interpreting progression, candidate-therapy effects, or resistance.
A study generally begins by selecting a model strategy that matches the intended cancer question, such as introducing tumor cells or patient-derived tissue, establishing genetic alterations, or breeding defined mutant strains. Investigators then monitor tumor formation and progression under standardized conditions and characterize which disease features the resulting system reproduces.
After establishment, investigators monitor tumor formation and progression under standardized conditions and characterize what the model reproduces. This step links observed findings to the model's intended purpose and reveals which human-cancer features are represented. Without that characterization, results on biology or treatment response are harder to interpret across experimental systems.
These models support controlled studies of tumor biology, metastasis, candidate-therapy efficacy, and treatment resistance. Their value lies in allowing one system to be examined across defined experimental conditions while retaining selected disease features. Findings are most useful when researchers distinguish effects specific to the chosen model from conclusions intended to apply more broadly to cancer.