As a tumour develops, genomic alterations accumulate, and different cancer cell states emerge; this results in a heterogeneous cancer cell population that enables adaptation to different environmental conditions. It is challenging to explore how this heterogeneity is established and maintained at the local scale, which is precisely where distinct environmental conditions can drive selection. To tackle this challenge, we developed EpicMIBI, a technique that employs epitopes for imaging using combinatorial tagging (EpicTags) visualized by multiplex ion beam imaging (MIBI), a highly multiplexed technology that is capable of reading ~40 antibodies in tissue sections at subcellular resolution. EpicMIBI allows in situ tracking of barcoded cells to provide information about the cell type, state and neighbourhood, enabling the spatial study of cancer cell clonality. To model cancer cell heterogeneity, we transplanted a neuroendocrine small cell lung cancer (SCLC) cell line that contained a fraction of non-neuroendocrine cells into mice. Using this model, we found that non-neuroendocrine cells, although fewer, formed larger clonal patches than neuroendocrine cells. In addition, we found that areas within the same tumour had diverse structures; some areas contained larger clonal cell patches, while in other areas the cell patches were smaller and dispersed. This highlights the emergence of clonal spatial heterogeneity across the tumour in addition to cellular heterogeneity.
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