DOI: 10.7272/q6qz287f
title: Spatiotemporal co-dependency between macrophages and exhausted CD8 + T cells in cancer

description: 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 (Tex)
 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.
description: 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.
description: 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.