SPACE: spatially resolved multiomic analysis for high-throughput CRISPR screening in 3D models

Hu, M.; Cui, Y.; Huang, Q.; Chu, K.; McKinzie, S.; Patrick, M.; Iyengar, S.; Abuduli, M.; Spatz, M.; Joshi, N.; Miller, B.; Vellarikkal, S.; Riordan, T.; Bitton, D.; Lubojacky, J.; Khalil, I.; Piccioni, F.; Rhodes, M.; Tamburino, A.; He, S.; Beechem, J.; Peterson, V.

doi:10.1101/2025.09.14.675819

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bioRxiv · 10.1101/2025.09.14.675819

SPACE: spatially resolved multiomic analysis for high-throughput CRISPR screening in 3D models

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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.

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Hu, M., Cui, Y., Huang, Q., Chu, K., McKinzie, S., Patrick, M., Iyengar, S., Abuduli, M., Spatz, M., Joshi, N., Miller, B., Vellarikkal, S., Riordan, T., Bitton, D., Lubojacky, J., Khalil, I., Piccioni, F., Rhodes, M., Tamburino, A., He, S., Beechem, J., Peterson, V.. 2025-09-17. SPACE: spatially resolved multiomic analysis for high-throughput CRISPR screening in 3D models. https://doi.org/10.1101/2025.09.14.675819

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