Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego, La Jolla, CA, USA.
Nat Methods. 2020 Jun;17(6):636-642. doi: 10.1038/s41592-020-0826-8. Epub 2020 May 11.
Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and are currently impossible to screen with pooled methods. Here we combine pooled CRISPR-Cas9 screening with microraft array technology and high-content imaging to screen image-based phenotypes (CRaft-ID; CRISPR-based microRaft followed by guide RNA identification). By isolating microrafts that contain genetic clones harboring individual guide RNAs (gRNA), we identify RNA-binding proteins (RBPs) that influence the formation of stress granules, the punctate protein-RNA assemblies that form during stress. To automate hit identification, we developed a machine-learning model trained on nuclear morphology to remove unhealthy cells or imaging artifacts. In doing so, we identified and validated previously uncharacterized RBPs that modulate stress granule abundance, highlighting the applicability of our approach to facilitate image-based pooled CRISPR screens.
使用基于 CRISPR 的 pooled 方法进行遗传筛选具有可扩展性和经济性，但仅限于标准读数，包括存活、增殖和可分选标记。然而，许多与生物学相关的细胞状态涉及细胞和亚细胞变化，只能通过显微镜可视化来检测，目前无法通过 pooled 方法进行筛选。在这里，我们将 pooled CRISPR-Cas9 筛选与微筏阵列技术和高内涵成像相结合，以筛选基于图像的表型（CRaft-ID；基于 CRISPR 的微筏 followed by guide RNA identification）。通过分离含有单个 guide RNA（gRNA）的遗传克隆的微筏，我们鉴定了影响应激颗粒形成的 RNA 结合蛋白（RBPs），应激颗粒是应激过程中形成的点状蛋白-RNA 组装体。为了自动识别命中，我们开发了一种基于核形态学的机器学习模型来去除不健康的细胞或成像伪影。通过这样做，我们鉴定并验证了以前未表征的调节应激颗粒丰度的 RBPs，突出了我们的方法在促进基于图像的 pooled CRISPR 筛选中的适用性。
