DOI: 10.5061/dryad.vq83bk47n
title: Disrupting usp14-mediated PARP1 dynamics reinstates mic-a/b-driven antigen-independent CD8⁺ t-cell killing in glioma

description: Antigen loss is a major mechanism of resistance to immunotherapy. MIC-A/B
 are stress-inducible ligands expressed by tumour cells that activate NKG2D
 on cytotoxic immune cells and mediate NKG2D-dependent, antigen-independent
 tumour cell killing, yet the mechanisms underlying their reduced
 expression in glioma remain unclear. Using single-cell RNA sequencing and
 spatial transcriptomics, we investigated ectopic MIC-A/B in mouse glioma
 and identified USP14 as a key regulator through deubiquitinase screening.
 Proteomic, coimmunoprecipitation, chromatin immunoprecipitation,
 immunofluorescence and ubiquitination assays characterized the
 interactions between USP14, PARP1 and NFIL3, while an intracranial tumour
 model combined USP14 inhibition and immunotherapy to evaluate effects on
 tumourigenesis and antitumour immunity. We found that MIC-A/B increased
 CD8⁺ T-cell infiltration and reversed exhaustion, and that USP14
 stabilized PARP1 via K63-linked deubiquitination at lysine 653, reducing
 NFIL3 binding to the MIC-A/B promoter through poly(ADP-ribosyl)ation.
 Inhibition of USP14 activated CD8⁺ T cells in a MIC-A/B–NKG2D-dependent,
 antigen-independent manner, and synergized with PD1 blockade to prolong
 survival and enhance antitumour immunity. Clinical glioma specimens showed
 USP14 overexpression was correlated with PARP1 and dysfunctional CD8⁺
 T-cell infiltration. Collectively, these results demonstrate that USP14
 inhibition restores MIC-A/B-mediated CD8⁺ T-cell activation, reverses
 immune exhaustion and represents a promising strategy to enhance glioma
 immunotherapy.
description: # Disrupting usp14-mediated PARP1 dynamics reinstates mic-a/b-driven
 antigen-independent CD8⁺ t-cell killing in glioma Dataset DOI:
 [10.5061/dryad.vq83bk47n](https://doi.org/10.5061/dryad.vq83bk47n) This
 dataset contains processed data to reproduce key transcriptomic analyses
 testing the hypothesis that restoring stress-ligand signaling
 (MIC-A/B–NKG2D axis) and/or inhibiting the deubiquitinase USP14 (IU1) can
 reshape the immune microenvironment in the GL261 mouse intracranial glioma
 model, consistent with enhanced cytotoxic immune programs. Data are
 provided as per-sample normalized expression matrices to facilitate
 downstream reuse. ## Description of the data and file structure **The
 submission comprises three coordinated modules:** (1) CD45⁺ scRNA-seq from
 MICAB overexpression (MICAB-OE) and matched control tumors (per-sample
 normalized matrices), enabling immune-cell clustering/annotation and
 comparison of CD8⁺ T-cell state distributions (e.g., effector vs exhausted
 programs); (2) spatial transcriptomics from MICAB-OE and control tumors,
 including per-sample normalized spot-level matrices and an
 integrated/combined matrix, supporting analyses that relate
 condition-associated transcriptional programs to tumor architecture and
 inflammatory niches;  (3) CD45⁺ scRNA-seq from IU1-treated and matched
 vehicle/control tumors (per-sample normalized matrices), enabling
 assessment of treatment-associated changes in immune composition, inferred
 intercellular communication, and CD8⁺ T-cell activation/exhaustion
 programs. This submission contains **three compressed archives (.tar)**,
 each bundling processed/normalized expression matrices derived from the
 GL261 mouse intracranial glioma model. Within each archive, data are
 organized as standard 10x-style matrix bundles for each sample, consisting
 of three files: barcodes.tsv.gz (cell/spot identifiers), features.tsv.gz
 (gene/feature identifiers), and matrix.mtx.gz (sparse gene-by-cell/spot
 matrix in Matrix Market format). Group membership is encoded by the folder
 structure inside each archive. ### Files and variables #### File:
 scRNAseq_matrix_of_MICAB_overexpression_tumours.tar **Description:** This
 archive contains scRNA-seq matrices for CD45+ tumor-infiltrating
 leukocytes from MICAB overexpression (MICAB) tumors and matched controls
 (CTRL). It includes 8 sample folders in total: 4 CTRL sample folders and 4
 MICAB sample folders. Each sample folder contains barcodes.tsv.gz,
 features.tsv.gz, and matrix.mtx.gz, representing the normalized expression
 matrix for that individual biological sample (one tumor from one mouse).
 In these scRNA-seq bundles, barcodes correspond to single cells and
 features correspond to genes. #### File:
 Spatial_transcriptome_integrated_data_and_matrix_of_MICABoe_tumours.tar
 **Description:** This archive contains spatial transcriptomics
 (spot-level) matrices from MICAB overexpression versus control tumors,
 plus an integrated object. It includes two matrix folders: Ctrl_bc_matrix
 (control) and MIC-AB_bc_matrix (MICAB overexpression). Each folder
 contains barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz, representing
 normalized spot-level expression values. In addition, the archive includes
 brain.merge.rdata, which stores integrated/merged data across
 sections/samples (including slice-level information) to facilitate
 cross-sample spatial analyses. For spatial data, barcodes correspond to
 spatial capture spots (not single cells). #### File:
 scRNAseq_matrix_of_IU1_treated_tumours.tar **Description:** This archive
 contains scRNA-seq matrices for CD45+ tumor-infiltrating leukocytes from
 tumors treated with the USP14 inhibitor IU1 and matched controls. It
 includes 6 sample folders in total: 3 DMSO control sample folders and 3
 IU1-treated sample folders. As in Archive 1, each sample folder contains
 barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz, representing the
 normalized expression matrix for that individual biological sample.
 **Abbreviations used.** MICAB: MICA/B overexpression group. MICAB-OE:
 MICA/B overexpression (used synonymously with MICAB where applicable).
 CTRL: control group for the MICAB perturbation. IU1: treatment with the
 USP14 inhibitor IU1. DMSO: vehicle control group for IU1 treatment.
 scRNA-seq: single-cell RNA sequencing (here, CD45+ tumor-infiltrating
 leukocytes). Spatial transcriptomics (ST): spot-level gene expression
 profiling from tumor tissue sections. bc_matrix: barcode matrix bundle in
 standard 10x format (barcodes.tsv.gz, features.tsv.gz, matrix.mtx.gz).
 barcodes.tsv.gz: list of cell/spot identifiers. features.tsv.gz: list of
 gene/feature identifiers. matrix.mtx.gz: sparse expression matrix in
 Matrix Market format. brain.merge.rdata: an R data object containing
 integrated/merged spatial data and slice-level information derived from
 the spatial matrices. ## Access information L**inks to other publicly
 accessible locations of the data:** * Not applicable. All
 processed/normalized matrices are available in this Dryad submission.
 **Data was derived from the following sources:** * Not applicable.
 Matrices were generated from the authors’ sequencing experiments and
 processed using standard pipelines as described in the associated
 manuscript. ## Code/Software All analyses were performed in **R (version
 4.4.1)**. The scRNA-seq and spatial transcriptomics data are provided
 primarily in standard 10x-style Matrix Market format (barcodes.tsv.gz,
 features.tsv.gz, matrix.mtx.gz), which can be imported into R and analyzed
 using Seurat-based workflows. The integrated spatial object
 (brain.merge.rdata) can be loaded directly in R for inspection and
 downstream analysis, depending on the object type stored in the file.
 **Key** **R packages** used for data processing, integration,
 visualization, and downstream analyses include: Seurat, SeuratObject,
 SeuratData, tidyverse (including dplyr, tibble, tidyr, readr, stringr,
 ggplot2, tidyselect), harmony, glmGamPoi, DoubletFinder, zellkonverter,
 reshape2, RColorBrewer, scRNAtoolVis, COSG, ComplexHeatmap, cols4all,
 patchwork, jsonlite, hdf5r, clusterProfiler, org.Mm.eg.db, and enrichplot,
 as well as trajectory-related packages monocle2 and monocle3. These
 packages support typical single-cell/spatial workflows such as quality
 control, normalization, batch correction/integration, clustering and
 cell-state annotation, differential expression, pathway enrichment,
 visualization, and trajectory inference.