Unbiased niche labelling identifies Kupffer cells as the architects of metastatic niche formation in colorectal cancer
Abstract
Metastasis is the primary cause of cancer-related mortality, yet it is a highly inefficient process in which only a small fraction of disseminated tumor cells (DTCs) survives and establishes overt secondary tumors. Successful metastatic progression critically depends on the ability of DTCs to interact with, adapt to, and remodel the tumor microenvironment (TME) at distant organs. These reciprocal tumor–host interactions determine whether disseminated cells are eliminated, enter dormancy, or ultimately progress to macro-metastatic disease. Among metastatic cancers, colorectal cancer (CRC) represents a particularly aggressive clinical entity, with a strong propensity to metastasize to the liver. Upon arrival in the liver, CRC cells encounter a diverse array of resident and recruited host cell populations with which they must establish pro-tumorigenic communication networks to enable metastatic outgrowth. While substantial progress has been made in characterizing tumor-intrinsic programs and the cellular composition of established macro-metastatic lesions, our understanding of the earliest stages of metastatic colonization remains limited. In particular, it is still unclear which host cell populations first engage newly arrived metastatic cells, how these initial interactions are organized in space and time, and how they ultimately drive the transition from micro- metastatic persistence to overt metastatic outgrowth. This knowledge gap largely stems from the lack of sufficiently sensitive tools capable of detecting and characterizing these rare and transient microenvironments at single-cell resolution. Although recent advances such as single-cell and spatial transcriptomics, as well as proximity-labeling approaches, have greatly expanded our ability to study complex tissues, technical limitations have hindered a comprehensive analysis of early metastatic niches. To overcome these challenges, this thesis describes the development and application of EnviroTag, a proximity-guided labeling strategy based on receptor-mediated fluorophore transfer that enables highly sensitive and efficient identification of the immediate cellular environment of any cell of interest in vivo. EnviroTag was applied to study the niche requirements of early CRC metastasis by introducing the system into mouse CRC organoids and transplanting them into recipient mice. In combination with single-cell RNA sequencing, EnviroTag enabled the identification of niche-specific liver-resident and recruited immune populations, fibroblasts, endothelial cells, and parenchymal liver cells. Using EnviroTag-based labeling, we mapped the dynamic co-evolution of tumor and niche cells from single-cell seeding through micro-metastatic stages to macro-metastatic outgrowth. The high sensitivity of the system identified Kupffer cells as the primary niche cells during early liver micro-metastasis. Strikingly, genetic or pharmacological disruption of tumor- Kupffer cell interactions, or transient blockade of Kupffer cell–derived IL-1β signaling during early metastatic stages, completely prevented subsequent niche evolution. Resulting lesions lacked fibroblast expansion, failed to recruit monocyte populations, did not develop an immunosuppressive microenvironment, and were ultimately cleared by immunotherapy. These findings identify Kupffer cells as key architects of the evolutionary trajectory of CRC liver metastases. Together, this work establishes EnviroTag as a powerful and versatile platform for dissecting cell-cell interactions in vivo across diverse biological contexts. By enabling unbiased identification of rare and transient cellular niches, EnviroTag provides a framework to uncover previously inaccessible mechanisms of tissue organization, disease progression, and therapeutic vulnerability.
Fields of Study & Subjects
Cancer Cells and Metastasis
Immune cells in cancer
Mechanisms of cancer metastasis
Publication details
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Thesis