In contrast to many cancers, a high infiltration of macrophages in colorectal cancer (CRC) has been associated with improved prognosis for patients. Cytokines and other stimuli from the tumor microenvironment affect monocyte to macrophage maturation and subsequent phenotype and function. Heterogeneous myeloid populations were identified using a novel flow cytometry panel in both tumor and paired non-tumor bowel (NTB) from CRC patients. The frequency of macrophage subsets with a gut-conditioned phenotype was lower in tumor compared with NTB. We used an in vitro system to show that two of the macrophage populations represented pro-inflammatory and anti-inflammatory phenotypes. Conditioned media that contained high levels of interleukin-6 promoted and maintained an anti-inflammatory phenotype in vitro. This study demonstrates the plasticity and heterogeneity of macrophage subtypes in human CRC, and the feasibility of studying complex populations. Ex vivo experiments demonstrate that macrophage subsets are influenced by the tumor microenvironment.

The immune response to colorectal cancer has proven to be a reliable measure of patient outcome in several studies. However, the complexity of the immune response in this disease is not well understood, particularly the interactions between tumour-associated cells and cells of the innate and adaptive immune system. This review will discuss the relationship between cancer associated fibroblasts and macrophages, as well as between macrophages and T cells, and demonstrate how each population may support or prevent tumour growth in a different immune environment.

High‐dimensional cytometry represents an exciting new era of immunology research, enabling the discovery of new cells and prediction of patient responses to therapy. A plethora of analysis and visualization tools and programs are now available for both new and experienced users; however, the transition from low‐ to high‐dimensional cytometry requires a change in the way users think about experimental design and data analysis. Data from high‐dimensional cytometry experiments are often underutilized, because of both the size of the data and the number of possible combinations of markers, as well as to a lack of understanding of the processes required to generate meaningful data. In this article, we explain the concepts behind designing high‐dimensional cytometry experiments and provide considerations for new and experienced users to design and carry out high‐dimensional experiments to maximize quality data collection.

T cell infiltration of tumors plays an important role in determining colorectal cancer disease progression and has been incorporated into the Immunoscore prognostic tool. In this study, mass cytometry was used to demonstrate a significant increase in the frequency of both conventional CD25+FOXP3+CD127lo regulatory T cells (Tregs) as well as BLIMP-1+ Tregs in the tumor compared with nontumor bowel (NTB) of the same patients. Network cluster analyses using SCAFFoLD, VorteX, and CITRUS revealed that an increase in BLIMP-1+ Tregs was a single distinguishing feature of the tumor tissue compared with NTB. BLIMP-1+ Tregs represented the most significantly enriched T cell population in the tumor compared with NTB. The enrichment of ICOS, CD45RO, PD-1, PDL-1, LAG-3, CTLA-4, and TIM-3 on BLIMP-1+ Tregs suggests that BLIMP-1+ Tregs have a more activated phenotype than conventional Tregs and may play a role in antitumor immune responses.

Background: The advent of mass cytometry has dramatically increased the parameter limit for immunological analysis. New approaches to analysing high parameter cytometry data have been developed to ease analysis of these complex datasets. Many of these methods assign cells into population clusters based on protein expression similarity. Results: Here we introduce an additional method, termed Brick plots, to visualize these cluster phenotypes in a simplified and intuitive manner. The Brick plot method generates a two-dimensional barcode that displays the phenotype of each cluster in relation to the entire dataset. We show that Brick plots can be used to visualize complex mass cytometry data, both from fundamental research and clinical trials, as well as flow cytometry data. Conclusion: Brick plots represent a new approach to visualize complex immunological data in an intuitive manner.