ABSTRACT Neutrophils are a highly heterogenous cellular population. However, a thorough examination of the different transcriptional neutrophil states, between health and malignancy, has not been performed. We utilised single-cell RNA-sequencing of human and murine datasets, both publicly available and independently generated, to identify neutrophil transcriptomic subtypes and their developmental lineages in health and malignancy. Datasets of lung, breast and colorectal cancer (CRC) were integrated to establish and validate the reproducibility of neutrophil gene-signatures. Pseudo-time analysis was used to identify genes driving neutrophil development from health to cancer. Finally, ligand-receptor interactions and signalling pathways between neutrophils and other immune cell populations in primary CRC and metastatic CRC were investigated. We define two main neutrophil subtypes in primary tumours: an activated subtype sharing the transcriptomic signatures of healthy neutrophils; and a tumour-specific subtype. This signature is conserved in murine and human cancer, across different tumour types. In CRC metastases, neutrophils are more heterogenous, exhibiting additional transcriptomic subtypes. Pseudo-time analysis implicates an IL1B/CXCL8/CXCR2 axis in the progression of neutrophils from health to cancer and metastasis, with effects on T-cell effector function. Assessment of global communication signalling identified CD4+ T-cells and macrophages as dominant regulators of the immunosuppressive, metastatic niche, whereas CD8+ T-cells are receivers of signals from other immune cells. We propose that the emergence of metastatic-specific neutrophil subtypes is driven by an IL1/CXCL8/CXCR2 axis, with the evolution of different transcriptomic signals that impair T-cell function at the metastatic site. Thus, a better understanding of the neutrophil transcriptomic programming could optimise immunotherapeutic interventions into early and late interventions, targeting different neutrophil subtypes.

Characterising neutrophil subtypes in cancer using human and murine single-cell RNA sequencing datasets