Impaired coordination between signaling pathways is revealed in human colorectal cancer using single-cell mass cytometry of archival tissue blocks

摘要

Cellular heterogeneity poses a significant challenge to understanding tissue level phenotypes and confounds conventional bulk analyses. To facilitate the analysis of signaling at the single-cell level in human tissues, we applied mass cytometry using CyTOF (Cytometry Time-of-Flight) to formalin-fixed paraffin-embedded (FFPE) normal and diseased intestinal specimens. We developed and validated a technique called FFPE-DISSECT (Disaggregation for Intracellular Signaling in Single Epithelial Cells from Tissue), a single-cell approach for characterizing native signaling states from embedded solid tissue samples. We applied FFPE-DISSECT coupled to mass cytometry and found differential signaling by tumor necrosis factor α (TNF-α) in intestinal enterocytes, goblet cells and enteroendocrine cells, implicating the role of the downstream RAS-RAF-MEK-ERK signaling pathway in dictating goblet cell identity. In addition, application of FFPE-DISSECT, mass cytometry, and data-driven computational analyses to human colon specimens confirmed reduced differentiation in colorectal cancer (CRC) compared to normal colon, and revealed quantitative increases in inter- and intra-tissue heterogeneity in CRC with regards to the modular regulation of signaling pathways. Specifically, modular co-regulation of the kinases P38 and ERK, the translation regulator 4EBP1, and the transcription factor CREB in the proliferative compartment of the normal colon was loss in CRC, as evidenced by their impaired coordination over samplings of single cells in tissue. Our data suggest that this single-cell approach, applied in conjunction with genomic annotation, such as microsatellite instability and mutations in KRAS and BRAF, allows rapid and detailed characterization of cellular heterogeneity from clinical repositories of embedded human tissues. FFPE-DISSECT coupled of mass cytometry can be used for deriving cellular landscapes from archived patient samples, beyond CRC, and as a high resolution tool for disease characterization and subtyping.