PO.MCB08.04 · 分子与细胞生物学

光学混合CRISPR筛选:结合多重gRNA检测与单细胞空间多组学

Optical pooled CRISPR screening coupling multiplexed guide RNA detection and single-cell spatial multi-omics

海报缩略图:光学混合CRISPR筛选:结合多重gRNA检测与单细胞空间多组学
编号 5923 展板 11 时间 4/21 02:00–05:00 区域 Section 21 主讲 Shanshan He, MD;PhD
分会场 Genetic and Transcriptomic Dissection of Cancer Evolution
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作者与单位 Authors & Affiliations

Yi Cui1, Marena I. Trinidad2, Nurel Arriaran2, Isabel Lee1, Chia-Ying Lee1, Shanshan He1, Timothy Riordan1, Joseph Beechem1, Alexander E. Ehrenberg2, Hanqin Li2

1Bruker Spatial Biology, Seattle, WA,2Innovative Genomics Institute, Berkeley, CA

摘要 Abstract

中文摘要
光学混合筛选(OPS)能够直接原位高通量测量细胞对遗传扰动的反应。然而,大多数基于合成测序(SBS)的OPS方法仍局限于单基因扰动读出或狭窄的表型面板,限制了其解析扰动驱动细胞状态全部复杂性的能力。相比之下,基于多重原位杂交的平台提供了一种正交检测策略,能够在同一细胞内同时测量多个gRNA和丰富的多组学表型。我们建立了一个使用CosMx空间分子成像仪(SMI)进行大规模混合CRISPR筛选的框架,能够实现空间解析的单细胞全转录组图谱分析,并整合多重gRNA检测。我们评估了表达条形码化gRNA的多种载体架构,以最小化引导-条形码解偶联(混合光学筛选中的一个主要障碍)。使用基于报告基因的功能测定,我们鉴定出一种优化的载体设计,可在保留Cas9活性的同时,实现对原位条形码化gRNA的灵敏、准确、高保真检测。我们进一步展示了在标准CosMx工作流程中,gRNA检测与亚细胞分辨率下高维RNA和蛋白质图谱分析的无缝结合。这为具有组学水平表型的混合光学组合CRISPR筛选提供了一条实用且可扩展的途径。通过利用全转录组空间读出,该方法捕获了使用靶向表型分析或解离单细胞方法无法检测到的全局基因表达变化、涌现的细胞状态、微环境依赖性表型以及空间协调的转录反应。重要的是,空间解析的全转录组OPS能够在其原生结构背景下解读基因敲除,揭示遗传扰动如何影响细胞间通讯、信号级联、邻域形成和配体-受体相互作用。这些能力使CosMx支持的混合CRISPR OPS成为癌症研究的变革性平台。它使研究人员能够在复杂的体外和离体模型中定位因果遗传机制,发现仅在空间组织环境中显现的隐匿表型,并鉴定常规混合筛选无法发现的治疗靶点。总之,这些结果为下一代多模态空间CRISPR筛选奠定了坚实基础。以无偏、空间解析的全转录组读出进行混合扰动的能力,将加速癌症相关通路的发现,支持作用机制和耐药性研究,并促进空间感知功能基因组学图谱和AI驱动的肿瘤生物学预测模型的开发。
查看英文原文 English abstract
Optical pooled screening (OPS) enables high-throughput measurement of cellular responses to genetic perturbations directly in situ. However, most sequencing-by-synthesis (SBS)-based OPS approaches remain limited to single-gene perturbation readouts or narrow phenotypic panels, restricting their ability to resolve the full complexity of perturbation-driven cellular states. In contrast, multiplexed in situ hybridization-based platforms offer an orthogonal detection strategy capable of simultaneously measuring multiple gRNAs and rich multiomic phenotypes within the same cell. We establish a framework for large-scale pooled CRISPR screening using the CosMxⓇ Spatial Molecular Imager (SMI), enabling spatially resolved, single-cell whole-transcriptome profiling integrated with multiplexed gRNA detection. We evaluated diverse vector architectures expressing barcoded gRNAs to minimize guide-barcode decoupling, a major obstacle in pooled optical screens. Using a reporter-based functional assay, we identified an optimized vector design that preserves Cas9 activity while enabling sensitive, accurate, high-fidelity detection of barcoded gRNAs in situ. We further demonstrate seamless coupling of gRNA detection with high-dimensional RNA and protein profiling at subcellular resolution within the standard CosMx workflow. This provides a practical and scalable path to pooled optical combinatorial CRISPR screening with omics-level phenotypes. By leveraging whole-transcriptome spatial readouts, this approach captures global gene expression changes, emergent cell states, microenvironment-dependent phenotypes, and spatially coordinated transcriptional responses not detected using targeted phenotyping or dissociated single-cell methods. Importantly, spatially resolved whole-transcriptome OPS enables knockouts to be interpreted in their native architectural context, revealing how genetic perturbations influence cell-cell communication, signaling cascades, neighborhood formation, and ligand-receptor interactions. These capabilities position CosMx-enabled pooled CRISPR OPS as a transformative platform for cancer research. It allows investigators to map causal genetic mechanisms within complex in vitro and ex vivo models, uncover cryptic phenotypes that manifest only in spatially organized environments, and identify therapeutic targets invisible to conventional pooled screens. Together, these results establish a strong foundation for next-generation multimodal spatial CRISPR screening. The ability to perform pooled perturbations with unbiased, spatially resolved whole-transcriptome readouts will accelerate discovery of cancer-relevant pathways, support mechanism-of-action and resistance studies, and enable development of spatially aware functional genomics atlases and AI-driven predictive models of tumor biology.
利益披露 Disclosure
Y. Cui, None.. M. I. Trinidad, None.. N. Arriaran, None.. I. Lee, None.. C. Lee, None.. S. He, None.. T. Riordan, None.. J. Beechem, None.. A. E. Ehrenberg, None.. H. Li, None.

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