T cell exhaustion is a major impediment to antitumor immunity. However, it remains elusive how other immune cells in the tumor microenvironment (TME) contribute to this dysfunctional state. Here, we show that the biology of tumor-associated macrophages (TAMs) and exhausted T cells (T_ex) in the TME are extensively linked. We demonstrate that in vivo depletion of TAMs reduces exhaustion programs in tumor-infiltrating CD8⁺ T cells and reinvigorates their effector potential. Reciprocally, transcriptional and epigenetic profiling reveals that Tex express factors that actively recruit monocytes to the TME and shape their differentiation. Using lattice light sheet microscopy, we show that TAM and CD8⁺ T cells engage in unique, long-lasting, antigen-specific synaptic interactions that fail to activate T cells but prime them for exhaustion, which is then accelerated in hypoxic conditions. Spatially resolved sequencing supports a spatiotemporal self-enforcing positive feedback circuit that is aligned to protect rather than destroy a tumor.

ZipSeq

ZipSeq spatial transcriptomics was performed as described previously (Hu et al., 2020). Briefly, B78ChOVA cells were injected subcutaneously as described above and were harvested on day 16 post-injection. Tumors were sectioned while live using a compresstome (Precisionary Instruments VFZ-310-0Z) to generate ~160 µm sections. The sectioning, imaging, spatial barcoding, tumor dissociation, sorting, 10X encapsulation, and library construction were identical to the methods described in (Hu et al., 2020). The targeted number of cells for loading was 5000. With this in mind, we aimed for 30,000 reads per cell during sequencing on an Illumina S4 flowcell with a 1:10 molar ratio of Zipcode reads to gene expression reads. Resulting fastq files were processed using the CellRanger 4.0.0 pipeline, aligning to the GRCm38 Mus musculus assembly. CellRanger output thus resulted in ~359k reads for the gene expression library and ~40k reads for the Zipcode library.

Analysis of scRNA-Seq

The raw feature-barcode matrix generated by 10X CellRanger was loaded into Seurat (Satija et al., 2015). Cells with mitochondrial read % over 20% and those with less than 500 genes detected were excluded from analysis. Zipcode read counts from CellRanger were also loaded into Seurat as a separate ‘ADT’ assay and using CLR normalized counts, cells with either too few Zipcode reads or mixed Zipcode reads were also excluded from analysis. Following built-in Seurat methods for gene expression normalization and variance stabilization (Single Cell Transform (Hafemeister and Satija, 2019)), cells underwent one more round of clean-up, removing a small cluster of contaminating CD45– cells and another small cluster dominated by mitochondrial and ribosomal genes. This yielded 2,765 cells. At this stage, the mean # of UMI’s and # of detected genes was: (25,566 and 4,199 respectively) while for the antibody-derived Zipcode tags the mean UMI was 1,891 reads. At this stage, we also determined cluster identities using Seurat’s FindAllMarkers function performed on the log-normalized read counts which by default uses the Wilcoxon Rank Sum test. For CellChat analysis, this cleaned object was fed into the CellChat workflow, using the built-in mouse ligand-receptor database, and a tri-mean thresholding for significance of interaction. The Seurat object was split into 3 sub-objects based on regional assignment and these 3 objects were separately analyzed using the CellChat workflow for multiple datasets and then merged. For signature score generation, we used Seurat’s builtin AddModuleScore function with gene lists for Glycolysis (Argüello et al., 2020), T cell exhaustion (Wherry et al al., 2007), and Antigen Presentation (GO term 0048002) using 50 control features. Full gene lists can be found in the Extended Data. For pseudotime analysis, the monocyte/macrophage sub-object was passed into Monocle v3 without any changes to the UMAP dimensional reduction.

• Image files can be opened using any image analysis software such as Fiji or Imaris
• .rds file can be opened using readRDS function in R. Seurat v3 object.