Published March 12, 2025 | Version v1

Single-cell transcriptomic analysis reveals the synergistic effect of gut microbiota and immunotherapy through modulating tumor microenvironment

Authors/Creators

Description

The gut microbiota crucially regulates the efficacy of immune checkpoint inhibitor (ICI) based immunotherapy, but the underlying mechanisms remains unclear at single cell resolution. In this study, we employed single-cell RNA sequencing and subsequent validations to investigate the synergistic effects of ICIs and gut microbiota through profiling the tumor microenvironment (TME) and elucidating critical cellular interactions in mouse models. Our results demonstrate that combination of intact gut microbiota and ICI treatment would synergistically increase the proportions of CD8+, CD4+, and γδ T cells, reduce their glycolysis metabolism, and reverse the exhausted CD8+ T cells to memory/effector CD8+ T cells, suggesting enhanced antitumor response. Moreover, the synergistic effect induces macrophage reprogramming from M2 polarization-related protumor Spp1+ tumor-associated macrophages (TAMs) to Cd74+ TAMs, which act as antigen-presenting cells (APCs). Depleting Spp1+ TAMs in Spp1 conditional knockout mice enhances ICI efficacy and promotes T cell infiltration regardless of gut microbiota clearance, underscoring the crucial negative role of Spp1+ TAMs and importance of macrophage reprogramming in immunotherapy outcomes. Mechanistically, we propose a γδ T cell-APC-CD8+ T cell axis, where gut microbiota and ICI enhance Cd40lg expression on γδ T cells, activating Cd40 overexpressed APCs (e.g., Cd74+ TAMs) through CD40-CD40L related NF-κB signaling, and boosting CD8+ T cell responses through CD86-CD28 interactions. These findings highlight the vital role of γδ T cells and SPP1¬-related macrophage reprogramming in activating CD8+ T cells and the synergistic effect of gut microbiota and ICIs, offering new insights into the cellular and molecular mechanisms that enhance immunotherapy efficacy.

Methods

Experiments were conducted in compliance with all relevant governmental and institutional guidelines and regulations. Female C57BL/6 mice, aged between 6 to 8 weeks, were bred and maintained in a specific pathogen-free (SPF) animal facility. Gut microbiota depletion was conducted through daily administering broad-spectrum antibiotic (ATBs), containing ampicillin (1 mg/ml), streptomycin (5 mg/ml), colistin (1 mg/ml), and vancomycin (0.25 mg/ml) from Shanghai Yuan Ye Bio-Technology. The ATBs were added to the sterile drinking water of the mice, with solution and bottle changes occurring thrice weekly. Antibiotic efficacy was verified by monitoring fecal microbial genome concentrations. Mice underwent two weeks of antibiotic treatment prior to tumor implantation and continuously throughout the MC38 sarcoma model experiment.

 

Subcutaneous MC38 tumors were carefully excised and immediately placed on ice. The harvested tissues were washed in Hanks’ Balanced Salt Solution (HBSS) to remove the residual blood and contaminants. Tissues were finely minced and sheared before undergoing enzymatic digestion. The digestion was performed at 37°C for 1 hour using a cocktail consisting of collagenase I (2 mg/ml; Gibco, catalog number 1710-0017), collagenase IV (1 mg/ml; Gibco, catalog number 1710-4019), and 0.25% pancreatic enzymes (Gibco, catalog number 25200-056). After digestion, the tissue lysate was filtered through a 40-µm cell strainer to remove undigested material and large cell clumps. The filtered cell suspensions were centrifuged at 500g for 5 minutes at 4°C. The resulting cell pellets were washed to remove residual enzymes and debris. Cell pellets were resuspended and subjected to erythrocyte lysis using 10 × RBC Lysis Buffer (Thermo Fisher Scientific, catalog number 00-4300-54). The cell suspensions were resuspended in HBSS supplemented with 0.04% bovine serum albumin (BSA). Cell viability and concentration were assessed using the Counting Star platform (Aber Instruments Ltd.). Subsequent to viability assessment, the remaining cells were pelleted again at 500g for 5 minutes at 4°C and stored at -80°C until further analysis. Single-cell suspensions were adjusted to a final concentration of approximately 5000 cells per milliliter. Single cell suspension preparation process was conducted by Novogene Co., Ltd.

Single-cell samples were prepared following the protocol laid out in the Chromium Single Cell 3' Reagents Kits v2 User Guide. Briefly, we utilized the Chromium Single Cell 3’ Library & Gel Bead Kit v2 (PN-120237), Chromium Single Cell 3’ Chip Kit v2 (PN-120236), and Chromium i7 Multiplex Kit (PN-120262). The single-cell suspension was washed twice using Phosphate-Buffered Saline (PBS) supplemented with 0.04% BSA. Cell quantity and concentration were verified using the TC20 Automated Cell Counter. Gel Beads in Emulsion (GEMs) were generated with a 10x Genomics Chromium Controller machine. Barcoded complementary DNAs (cDNAs) were prepared using the 10x Genomics Chromium Single Cell 3’ reagent kit (V2 chemistry), then purified and amplified for library construction.
   The quality and concentration of the cDNA libraries were assessed using an Agilent Bioanalyzer 2100. Libraries meeting quality control criteria were then subjected to PE150 sequencing on Illumina’s NovaSeq 6000 platform. These steps were conducted by Novogene Co., Ltd.

The raw data were processed using Cell Ranger (v3.0). A raw unique molecular identifier (UMI) count matrix was generated, which was then converted into a Seurat object using the R package Seurat. Low-quality cells, defined by cells with UMI numbers below 500, gene numbers below 200 or greater than 8,000, or mitochondrial-derived UMI counts of more than 15%, were filtered out. Additionally, potential doublets were identified and removed using Scrublet (v0.2) with expected doublet rate = 0.06. Cells passed quality control were left for subsequent analysis.

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