ReviewNeural regeneration research2023
Cre-recombinase systems for induction of neuron-specific knockout models: a guide for biomedical researchers.
Review in Neural regeneration research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Generation of a New Immunodeficient Rat Model of Retinal Degeneration With LSL TdTomato Reporter and TdTomato-Pcp2 Expression.Translational vision science & technology · 2026Article
- Schizophrenia-associated glycoprotein Adamtsl3 regulates perineuronal net formation, maintenance, and adult cortical plasticity.Molecular psychiatry · 2026Article
- Olfactory epithelium regeneration and homeostasis: cellular and molecular mechanisms and novel methodological advances.Cell regeneration (London, England) · 2026Review
- Programmable large-cargo integration: Overcoming size constraints for next-generation gene therapy.Synthetic and systems biotechnology · 2026Review
- Generation of a new immunodeficient rat model of retinal degeneration with LSL TdTomato reporter and TdTomato-Pcp2 expression.bioRxiv : the preprint server for biology · 2026Article
- Cardiac fibroblast heterogeneity in cardiac fibrosis implication for cell-type-specific treatment.Frontiers in physiology · 2026Review
- A rapid and efficient red-light-activated Cre recombinase system for genome engineering in mammalian cells and transgenic mice.Nucleic acids research · 2025Article
- Genetic ablation ofLife science alliance · 2024Article
- Insights into the Multifaceted Roles of Thioredoxin-1 System: Exploring Knockout Murine Models.Biology · 2024Review
- Need of orthogonal approaches in neurological disease modeling in mouse.Frontiers in molecular neuroscience · 2024Review
- Cell-Type-Specific Neuroproteomics of Synapses.Biomolecules · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene deletion has been a valuable tool for unraveling the mysteries of molecular biology. Early approaches included gene trapping and gene targetting to disrupt or delete a gene randomly or at a specific location, respectively. Using these technologies in mouse embryos led to the generation of mouse knockout models and many scientific discoveries. The efficacy and specificity of these approaches have significantly increased with the advent of new technology such as clustered regularly interspaced short palindromic repeats for targetted gene deletion. However, several limitations including unwanted off-target gene deletion have hindered their widespread use in the field. Cre-recombinase technology has provided additional capacity for cell-specific gene deletion. In this review, we provide a summary of currently available literature on the application of this system for targetted deletion of neuronal genes. This article has been constructed to provide some background information for the new trainees on the mechanism and to provide necessary information for the design, and application of the Cre-recombinase system through reviewing the most frequent promoters that are currently available for genetic manipulation of neurons. We additionally will provide a summary of the latest technological developments that can be used for targeting neurons. This may also serve as a general guide for the selection of appropriate models for biomedical research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.