A quantitative spatial cell-cell colocalizations framework enabling comparisons between in vitro assembloids and pathological specimens

Gina Bouchard, Weiruo Zhang, Ilayda Ilerten, Irene Li, Asmita Bhattacharya, Yuanyuan Li, Winston Trope, Joseph B. Shrager, Calvin Kuo, Michael G. Ozawa, Amato J. Giaccia, Lu Tian and Sylvia K. Plevritis ()
Additional contact information
Gina Bouchard: Stanford University
Weiruo Zhang: Stanford University
Ilayda Ilerten: Stanford University
Irene Li: Stanford University
Asmita Bhattacharya: Stanford University
Yuanyuan Li: Stanford University
Winston Trope: Stanford University
Joseph B. Shrager: Stanford University
Calvin Kuo: Stanford University
Michael G. Ozawa: Stanford University
Amato J. Giaccia: Stanford University
Lu Tian: Stanford University
Sylvia K. Plevritis: Stanford University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Spatial omics is enabling unprecedented tissue characterization, but the ability to adequately compare spatial features across samples under different conditions is lacking. We propose a quantitative framework that catalogs significant, normalized, colocalizations between pairs of cell subpopulations, enabling comparisons among a variety of biological samples. We perform cell-pair colocalization analysis on multiplexed immunofluorescence images of assembloids constructed with lung adenocarcinoma (LUAD) organoids and cancer-associated fibroblasts derived from human tumors. Our data show that assembloids recapitulate human LUAD tumor-stroma spatial organization, justifying their use as a tool for investigating the spatial biology of human disease. Intriguingly, drug-perturbation studies identify drug-induced spatial rearrangements that also appear in treatment-naïve human tumor samples, suggesting potential directions for characterizing spatial (re)-organization related to drug resistance. Moreover, our work provides an opportunity to quantify spatial data across different samples, with the common goal of building catalogs of spatial features associated with disease processes and drug response.

Date: 2025
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DOI: 10.1038/s41467-024-55129-6

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