bioRxivCell BiologyPreprint
SPACE: multimodal spatial CRISPR screening with whole-transcriptome readout at subcellular resolution in 3D models
Mengwei HuiD, Yi CuiiD, Qianhui HuangiD, Khoi Chu, Sierra Mckinzie, Michael J. Patrick, Sharanya Iyengar, Maerjianghan AbuduliiD, Marianne L. SpatziD, Nandita Joshi, Brendan Miller, Shamsudheen Karuthedath VellarikkaliD, Timothy Riordan, Danny A. BittoniD, Jan Lubojacky, Iya G. Khalil, Federica PiccioniiD, Michael Rhodes, Alex M. Tamburino, Shanshan HeiD, Joseph BeechemiD, Vanessa M. Peterson
Abstract
Current spatial CRISPR screening technologies are limited by targeted readouts and high costs, restricting the scope of biological discovery. Here we present SPAtial Cell Exploration (SPACE), a spatial CRISPR screening platform that integrates whole-transcriptome profiling ([~]18,000 genes), multiplexed protein detection ([~]68 markers), and CRISPR perturbation mapping at subcellular resolution. SPACE significantly reduces whole-transcriptome profiling costs compared to sequencing methods while preserving spatial context. We demonstrate SPACE by screening 43 CRISPR knockouts (KOs) across [~]100,000 cells in hundreds of cancer-associated fibroblast (CAF)-tumor spheroids, obtaining whole-transcriptome and multiplexed protein readout from the same exact cells. SPACE revealed previously unknown regulatory mechanisms on tumor extracellular matrix (ECM) remodeling, and identified spatially-resolved ligand-receptor interactions and perturbation-specific spatial gene signatures that are not detectable with dissociation-based methods. This scalable, cost-effective platform provides a transformative framework for high-throughput spatial perturbation studies in complex tissue models.
- organism: human
- assay: spatial transcriptomics
- assay: proteomics
- assay: CRISPR screen
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- research focus: perturbation response prediction
- approach: experimental assay
- approach: deep learning
- approach: statistical model
- Citations
- 4
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