02187nas a2200349   4500000000100000000000100001008004100002260001500043653001800058653003100076653002400107653002400131100001800155700001700173700001900190700001300209700002400222700001600246700001800262700002200280700001500302700002100317700002100338700001200359700002400371245014000395856005500535300000900590490000700599520121700606022001401823       2025                            d  c2025-02-0610aCancer models10aNon-small-cell lung cancer10aSingle-cell imaging10aStatistical methods1 aGina Bouchard1 aWeiruo Zhang1 aIlayda Ilerten1 aIrene Li1 aAsmita Bhattacharya1 aYuanyuan Li1 aWinston Trope1 aJoseph B. Shrager1 aCalvin Kuo1 aMichael G. Ozawa1 aAmato J. Giaccia1 aLu Tian1 aSylvia K. Plevritis00aA quantitative spatial cell-cell colocalizations framework enabling comparisons between in vitro assembloids and pathological specimens  uhttps://www.nature.com/articles/s41467-024-55129-6  a13920 v163 aSpatial 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.  a2041-1723