Abstract 1214: SPACE: Spatially resolved multiomic analysis for high-throughput CRISPR screening in 3D models.

Abstract High-content single-cell perturbation screens are pivotal for elucidating gene functions and uncovering novel biology, yet conventional methods necessitate cell dissociation, forfeiting critical spatial information essential for dissecting cell-cell interactions and tissue architecture in complex microenvironments. While spatial CRISPR screening mitigates this partially, existing technologies are constrained by hypothesis-driven phenotyping panels limited to sparse RNA or protein coverage, curtailing comprehensive gene function assessment and discovery breadth.To overcome these barriers, we developed SPACE (SPAtial Cell Exploration), a pioneering platform that fuses whole-transcriptome profiling, CRISPR perturbations, and multiplexed protein detection at single-cell resolution within intact 3D tissue contexts. SPACE delivers unbiased, transcriptome-wide readouts alongside compatibility for up to 76 protein markers, vastly expanding phenotypic landscapes in spatial screens. As the highest-plex multimodal spatial CRISPR assay to date, SPACE achieves this at unprecedented scale and affordability and largely outperforms sequencing-based alternatives in efficiency.We applied SPACE across 42 gene perturbations in cancer-associated fibroblasts (CAFs) co-cultured with tumor cells in 3D spheroids, yielding multidimensional multiomic datasets from hundreds of spheroids. High-confidence guide RNA detection was coupled with robust endogenous mRNA characterization. Unbiased analyses uncovered new insights on CAF-tumor dynamics: extracellular matrix (ECM) remodeling, spatially resolved ligand-receptor interactions, and perturbation-specific gene variability.Notably, ISG20 knockout in CAFs profoundly suppressed multiple matrix metalloproteinases - an unreported link validated orthogonally - implicating ISG20 in novel ECM regulation and tumor progression. Some perturbations were further revealed to reshape intercellular signaling, revealing knockout-dependent spatial ligand-receptor shifts and coordinated expression signatures that underscore microenvironmental crosstalk in tumor phenotypes.Culminating in a landmark demonstration, SPACE simultaneously captured whole transcriptomes, CRISPR identities, and 68 protein markers on one slide, enabling holistic perturbation phenotyping. This transformative technology propels spatial CRISPR screening into translational realms, facilitating target and biomarker identification in multicellular models mirroring human tissue intricacy. By merging high-throughput perturbations with spatially resolved multiomics at transcriptome scale, SPACE catalyzes discovery in heterogeneous tissues. SPACE datasets will fuel generative AI models for causal biology inference, accelerating drug discovery and precision medicine. Citation Format: Mengwei Hu, Yi Cui, Qianhui Huang, Khoi Chu, Sierra McKinzie, Michael Patrick, Sharanya Iyengar, Maerjianghan Abuduli, Marianne Spatz, Nandita Joshi, Brendan Miller, Shams Vellarikkal, Timothy Riordan, Danny Bitton, Jan Lubojacky, Iya Khalil, Federica Piccioni, Michael Rhodes, Alex Tamburino, Shanshan He, Joseph Beechem, Vanessa Peterson. SPACE: Spatially resolved multiomic analysis for high-throughput CRISPR screening in 3D models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1214.