Subcapsular Sinus Macrophages Promote Melanoma Metastasis to the Sentinel Lymph Nodes via an IL1α-STAT3 Axis

During melanoma metastasis, tumor cells originating in the skin migrate via lymphatic vessels to the sentinel lymph node (sLN). This process facilitates tumor cell spread across the body. Here, we characterized the innate inflammatory response to melanoma in the metastatic microenvironment of the sLN. We found that macrophages located in the subcapsular sinus (SS) produced protumoral IL1α after recognition of tumoral antigens. Moreover, we confirmed that the elimination of LN macrophages or the administration of an IL1α-specific blocking antibody reduced metastatic spread. To understand the mechanism of action of IL1α in the context of the sLN microenvironment, we applied single-cell RNA sequencing to microdissected metastases obtained from animals treated with the IL1α-specific blocking antibody. Among the different pathways affected, we identified STAT3 as one of the main targets of IL1α signaling in metastatic tumor cells. Moreover, we found that the antitumoral effect of the anti-IL1α was not mediated by lymphocytes because Il1r1 knockout mice did not show significant differences in metastasis growth. Finally, we found a synergistic antimetastatic effect of the combination of IL1α blockade and STAT3 inhibition with stattic, highlighting a new immunotherapy approach to preventing melanoma metastasis.

CANCOL, a Computer-Assisted Annotation Tool to Facilitate Colocalization and Tracking of Immune Cells in Intravital Microscopy

Two-photon intravital microscopy (2P-IVM) has become a widely used technique to study cell-to-cell interactions in living organisms. Four-dimensional imaging data obtained via 2P-IVM are classically analyzed by performing automated cell tracking, a procedure that computes the trajectories followed by each cell. However, technical artifacts, such as brightness shifts, the presence of autofluorescent objects, and channel crosstalking, affect the specificity of imaging channels for the cells of interest, thus hampering cell detection. Recently, machine learning has been applied to overcome a variety of obstacles in biomedical imaging. However, existing methods are not tailored for the specific problems of intravital imaging of immune cells. Moreover, results are highly dependent on the quality of the annotations provided by the user. In this study, we developed CANCOL, a tool that facilitates the application of machine learning for automated tracking of immune cells in 2P-IVM. CANCOL guides the user during the annotation of specific objects that are problematic for cell tracking when not properly annotated. Then, it computes a virtual colocalization channel that is specific for the cells of interest. We validated the use of CANCOL on challenging 2P-IVM videos from murine organs, obtaining a significant improvement in the accuracy of automated tracking while reducing the time required for manual track curation.