A new quantitative framework called colocatome analysis has been developed to compare spatial characteristics and cell-cell interactions between engineered assembloids, derived from cancer cells and fibroblasts, and human lung adenocarcinoma tissue specimens.
The study presents colocatome analysis, which catalogs and quantifies normalized cell-cell colocalizations. This framework is utilized to examine spatial features of lung adenocarcinoma organoids and fibroblasts, aiming to bridge comparisons with human tumor specimens. The research was conducted by authors associated with Stanford University and their collaborators.
While the publication date is not specified explicitly, it reflects contemporary advancements with methodologies and responses to treatments detailed during the study. Research was carried out at Stanford University, involving human tumor samples and engineered organoid systems. There is a need for standardized methods to compare spatial features across different biological models and pathology specimens to improve cancer research and treatment strategies.
The colocatome framework uses multiplexed immunofluorescence imaging, cell segmentation, statistical colocalization measures, and spatial randomization to assess significant cell interactions. The analysis revealed unique spatial organizations between cancer-associated fibroblasts and lung adenocarcinoma cells, indicating potential relationships with drug resistance.
"Our data show the assembloids recapitulate human LUAD tumor-stroma spatial organization, justifying their use as a tool for investigating the spatial biology of human disease,” the authors explained.
Using the colocatome framework, the researchers identified which drug-resistant and drug-sensitive cell-pairs colocalizations from the in-vitro model are present in treatment-naïve clinical samples. They provided insights by stating, "Through our colocatome composite analysis, we identified additional cancer–fibroblast spatial features potentially associated with drug resistance.”
This innovative framework offers opportunities to create standardized cataloging of spatial features linked to various cancer processes, enhancing the scientific community's ability to explore new therapeutic avenues and improve patient outcomes.