ReviewNature protocols2025
Massively parallel in vivo Perturb-seq screening.
Review in Nature protocols, 2025. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Characterizing and mitigating cross-library PCR chimeras in Perturb-seq using Perturb-Audit.Blood science (Baltimore, Md.) · 2026Article
- Article
- Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer's disease.Nature communications · 2026Article
- In Vivo T-Cell Engineering: Revolution in Delivery Strategies and Clinical Translation.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2026Review
- Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq.bioRxiv : the preprint server for biology · 2026Article
- Article
- A Multispecies, Modality-Agnostic Scalable In Vivo Mosaic Screening Platform for Therapeutic Target Discovery.bioRxiv : the preprint server for biology · 2026Article
- Probing neuropsychiatric disorders through in vivo CRISPR screening.Current opinion in genetics & development · 2026Review
- FGF-FGFR Signaling in Parkinson's Disease: Mechanistic Links to Ferroptosis and Neuroprotection.Brain sciences · 2026Review
- Dissecting cellular ecosystem with single-cell CRISPR screens.Blood science (Baltimore, Md.) · 2025Article
- Cutting-edge technologies in neural regeneration.Cell regeneration (London, England) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Advances in genomics have identified thousands of risk genes impacting human health and diseases, but the functions of these genes and their mechanistic contribution to disease are often unclear. Moving beyond identification to actionable biological pathways requires dissecting risk gene function and cell type-specific action in intact tissues. This gap can in part be addressed by in vivo Perturb-seq, a method that combines state-of-the-art gene editing tools for programmable perturbation of genes with high-content, high-resolution single-cell genomic assays as phenotypic readouts. Here we describe a detailed protocol to perform massively parallel in vivo Perturb-seq using several versatile adeno-associated virus (AAV) vectors and provide guidance for conducting successful downstream analyses. Expertise in mouse work, AAV production and single-cell genomics is required. We discuss key parameters for designing in vivo Perturb-seq experiments across diverse biological questions and contexts. We further detail the step-by-step procedure, from designing a perturbation library to producing and administering AAV, highlighting where quality control checks can offer critical go-no-go points for this time- and cost-expensive method. Finally, we discuss data analysis options and available software. In vivo Perturb-seq has the potential to greatly accelerate functional genomics studies in mammalian systems, and this protocol will help others adopt it to answer a broad array of biological questions. From guide RNA design to tissue collection and data collection, this protocol is expected to take 9-15 weeks to complete, followed by data analysis.
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What OpenQuestion holds
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.