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Science: How to Perform “Genetic Screening” on Astrocytes? In Vivo Single-Cell Library Screening Technology

　　Introduction

　　The integration of single-cell transcriptomics (scRNA-seq) with CRISPR‑based screening—known as Perturb‑seq—has revolutionized how we dissect gene function. However, in the context of intact, living tissues, efficiently and at scale investigating gain-of-function (GOF) effects remains fraught with technical challenges.

　　Recently, a study published in Science, titled “Mapping transcription factor functions in astrocytes using in vivo gain-of-function Perturb-seq,” has unveiled a novel strategy—iGOF-Perturb-seq (in vivo gain-of-function Perturb-seq). Leveraging this platform, the research team successfully constructed a functional atlas of astrocyte transcription factors (TFs) in the living mouse brain, encompassing nearly a thousand TFs. This work not only elucidated gene regulatory networks under physiological conditions but also enabled the identification of promising therapeutic targets in disease models.

　　

 

　　iGOF–Perturb-seq experimental workflow diagram

　　Today, we will provide an in-depth analysis of this paper, focusing on how its most pivotal in vivo screening technique—iGOF‑Perturb‑seq—is designed and implemented.

　　01 Why is it necessary to screen for gain-of-function (GOF) mutations that confer functional activity in vivo?

　　Before delving into the specific technologies, let’s first clarify two concepts:

　　Why conduct it “in vivo”?

　　Although in vitro cell culture is straightforward, it cannot replicate the complex microenvironments found in living tissues—such as the blood–brain barrier, immune cell infiltration, and intercellular communication. The functions of astrocytes are highly dependent on their microenvironment; when removed from the brain, the clinical relevance of many research findings is significantly diminished.

　　Why study “gain-of-function” (GOF)?

　　Currently, mainstream in vivo CRISPR screens predominantly focus on gene knockout (loss-of-function, LOF). However, in cancer and neurodegenerative diseases, gene overexpression often serves as a critical driver of disease pathogenesis. Moreover, identifying therapeutic targets with protective effects typically also requires gain-of-function (GOF) strategies.

　　Choose a platform

　　The research team’s greatest challenge was ensuring that, with a limited number of animal models and cells, every gene in the extensive library was adequately represented while minimizing false positives. They successfully developed the iGOF‑Perturb‑seq platform through the following key steps:

　　01 Design and Construction of a High-Fidelity Library

　　Streamlined Targeted Library: Given the packaging limitations of AAV, researchers opted not to use a whole-genome approach but instead carefully selected approximately 1,200 mouse transcription factor (TF) open reading frames (ORFs) each shorter than 2 kb.

　　Barcode one-to-one correspondence: Each TF ORF is assigned a unique 16-bp barcode.

　　Rigorous quality control in synthesis and assembly: To avoid chimeric mismatches introduced by PCR amplification, the researchers directly synthesized TF ORFs with homology arms and inserted them into the AAV plasmid backbone using seamless cloning.

　　Quality Control: Following assembly, the plasmid library was validated using PacBio long-read sequencing to ensure that barcode–TF matching accuracy exceeded 80%, ultimately confirming 1,089 high-quality TFs.

　　

 

　　Viral plasmid backbone design and quality control of plasmid matching accuracy

　　02 Precise in vivo delivery and MOI control

　　Precise Targeting Across the Blood–Brain Barrier: The study employed the AAV-PHP.eB capsid variant. This variant can cross the blood–brain barrier via a simple retro-orbital injection, efficiently transducing cells throughout the brain.

　　Cell-specific expression: In the AAV vector, a glial fibrillary acidic protein–specific short fragment (sGFAP) promoter was employed, and ZsGreen green fluorescent protein was tandemly linked for subsequent cell sorting.

　　Multiplicity of infection (MOI): This is critical to the success of in vivo screening. If a single cell is infected by multiple viruses (MOI > 1), it becomes impossible to determine which gene is responsible for the observed phenotypic changes. The research team conducted preliminary experiments to precisely titrate the viral dose, establishing an injection volume of 2.5 × 10¹¹ viral genomes per animal, which resulted in infection of approximately 10% of astrocytes throughout the brain. This approach enabled them, during data analysis, to maximize the identification of single-cell data corresponding to an MOI of 1.

　　

 

　　Validation of Cell-Targeting Specificity

　　03 Single-Cell Sequencing and Directed Amplification Using Barcodes

　　Cell collection: Three weeks after viral injection, mouse brain tissue was dissociated, and ZsGreen‑expressing astrocytes were sorted using flow cytometry (FACS).

　　10x single-cell sequencing with targeted capture: In conventional single-cell sequencing, the detection efficiency of exogenous genes is often very low. To ensure accurate identification of which TF each cell has been infected with, researchers designed specific primers after the 10x Genomics whole-transcriptome amplification (WTA) step to selectively amplify both the TF barcode and the cell barcode.

　　Data matching: The measured single-cell transcriptomic data are matched against the targeted TF barcode library. Only cells in which a single TF barcode is unambiguously detected (i.e., MOI = 1) are included in the final analysis.

　　

 

　　Monitoring Gene Length and Loss Rate, and a Preliminary Exploration of Single-Cell Sequencing Data

　　

 

　　03 Analyzing gene regulatory networks to identify and validate disease-relevant therapeutic targets

　　Research Findings: From Atlas Construction to Disease Treatment—Using the rigorous methods described above, the research team generated a high-quality single-cell atlas of astrocytes comprising more than 130,000 cells.

　　Gene regulatory landscape under physiological conditions:

　　Using non-negative matrix factorization (cNMF), the researchers identified five TF modules that function in a coordinated manner—regulating cellular identity, immune responses, and other processes. This not only validated the functions of well-known TFs but also successfully predicted the roles of previously understudied TFs, such as Maff and Lef1, in astrocytes.

　　

 

　　Unbiased Clustering of Transcription Factors Based on Gene Expression Programs

　　Secondary screening of therapeutic targets in disease states:

　　This is also the most exciting aspect of the study. Leveraging their earlier analytical findings, the researchers selected 39 transcription factors (TFs) potentially implicated in neurodegenerative diseases and constructed a sub‑library. They injected this sub‑library into mice and induced neuroinflammation with lipopolysaccharide (LPS) to mimic a pathological state. Through a secondary screening under these pathological conditions, they discovered that overexpression of the transcription factor Ferd3l markedly suppresses the activation of neurotoxic astrocytes.

　　

 

　　Screening for therapeutic transcription factors under inflammatory conditions using iGOF–Perturb-seq

　　In vivo efficacy validation:

　　In the 5XFAD Alzheimer’s disease mouse model, brain-wide, astrocyte-specific overexpression of Ferd3l not only suppressed inflammatory responses but also markedly alleviated cognitive deficits, demonstrating the substantial potential of iGOF-Perturb-seq for identifying therapeutic targets.

　　

 

　　Whole-brain overexpression of the Ferd3l gene in astrocytes alleviates Alzheimer’s disease (AD) symptoms in 5XFAD mice.

　　04 High-throughput screening drives the discovery of novel drug targets, with end-to-end solutions empowering cutting-edge research.

　　This study published in Science not only maps an exhaustive landscape of transcription factors in astrocytes, but more importantly, it offers a comprehensive framework for large-scale functional genomic studies within complex living tissues.

　　The flexibility of the iGOF‑Perturb‑seq platform means it can be applied not only to transcription factors but also extended to other protein families and even noncoding RNAs. It is foreseeable that, as viral delivery technologies and single‑cell sequencing costs continue to improve, in vivo high‑throughput screening will become a central driving force for deciphering the mechanisms of complex diseases and identifying novel drug targets.

　　After reading the groundbreaking study published in Science, do you also want to conduct high‑throughput in vivo screening in your own research area—such as oncology, metabolism, or neuroscience? Ailinfei is dedicated to developing and applying cutting‑edge CRISPR‑based in vivo library‑screening technologies, offering end‑to‑end solutions that span target prediction, library design, efficient delivery, and validation in complex animal models. Feel free to leave us a message or contact us to discuss your next breakthrough!

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