Type 2 diabetes Reprograms Bone Marrow Hematopoiesis and Dysregulates Immune Signaling in Response to Stroke

Data Repository for scRNA-seq, GeoMx digital spatial profiling (DSP) and Nanostring nCounter Analysis in the bone marrow in T2DM Stroke Model This repository contains the raw transcriptomic data and Seurat objects generated for analyzing hematopoiesis and immune cell regulation of bone marrow in non-diabetes (db/+) and diabetes (db/db) after ischemic stroke. Data was generated using single-cell RNA sequencing (scRNA-seq) for bone marrow cell annotation, cell-cell communication and gene expression. Geomx DSP and nCounter analysis validated the gene expression in monocytes and neutrophils in the bone marrow. This study aims to reveal how diabetes reprograms bone marrow hematopoiesis after ischemic stroke, driving maladaptive immune regulation. Repository Structure Root Files README.md: This file, describing the repository and data files. sc RNA seq_raw_data: Raw scRNA-seq data files. GeoMx-analysis_BM_raw data: Raw GeoMx DSP data files. ncounter_analysis-BM_raw data: Raw nCounter data files. Seurat object: Contains Seurat objects for clustering and visualization of whole bone marrow. CellChat object: Contains RDA files for CellChat analysis. File Descriptions sc RNA seq_raw_data/ dbdb-MCAO.zip: Raw scRNA-seq data for db/db mice post-distal middle cerebral artery occlusion (dbdb.Stroke). dbdb-sham.zip: Raw scRNA-seq data for db/db mice with sham surgery (dbdb). db_pos-sham.zip: Raw scRNA-seq data for db/+ mice with sham surgery (control). db_pos-MCAO.zip: Raw scRNA-seq data for db/+ mice post-DMCAO (db+. Stroke). Methods: scRNA-seq was performed using 10X Genomics GemCode Technology. Data were processed with Cell Ranger (v1.3), and differential gene expression analysis was conducted in Seurat with normalization based on UMI counts. Seurat object/ bonemarrow_resolved.rda: Seurat object containing processed scRNA-seq data of bone marrow cells from both diabetic and normoglycemic mice under stroke and sham conditions. Contains cell clusters annotated using SingleR and the Tambula Muris database. Methods: Filtering was performed for cells with fewer than 500 detected genes, and data were normalized using log-normalization. Clustering was carried out with PCA and visualized with UMAP. CellChat object/ T2DM.Stroke_cellchat.rda: CellChat object for the db/db mice with ischemic stroke. Analysis was conducted to identify cell-cell communication patterns altered due to stroke in diabetic conditions. T2DM_cellchat.rda: CellChat object for db/db control (no stroke) mice. Provides baseline data for diabetic conditions. Stroke_cellchat.rda: CellChat object for the db/+ (normoglycemic) mice with ischemic stroke, representing the stroke model in non-diabetic conditions. Ctrl_cellchat.rda: CellChat object for db/+ control (no stroke) mice. Baseline for non-diabetic, non-stroke conditions. Methods: CellChat analysis identifies ligand-receptor interactions to reveal cross-talk between bone marrow cells, especially focusing on hematopoietic precursor cells (HPC1) and monocytes. These data provide insights into immune dysregulation through identified signaling pathway activation. GeoMx-analysis_BM_raw data/ annotation: annotation files contain metadata describing the experimental design and spatial regions that were analyzed, including sample identifiers, region of interest (ROI) labels, cell segment, experimental and group conditions, and other relevant sample-level annotations. dccs: DCC files contain the raw digital counts for each target measured by the GeoMx DSP. Each DCC file corresponds to a single region of interest and reports the number of detected barcode counts for each gene or protein target prior to normalization. pkcs: PKC files contain information related to probe performance and quality control, including counts for individual probes targeting the same gene or protein. Methods: GeoMx samples were sequenced on an Illumina NovaSeq 6000 platform. GeoMx RNA expression data were analyzed in R using the GeomxTools package for quality control, filtering, normalization, and dimensionality reduction with UMAP. The GeoMx object was then converted into a Seurat object for differential gene expression and Gene Ontology (GO) enrichment analysis. ncounter_analysis-BM_raw data/ .RCC: .RCC files are the primary raw data output generated by the NanoString nCounter system. Each RCC file corresponds to a single biological sample and contains the digital counts for all targets measured in the assay. Methods: Gene expression profiling was performed using the NanoString nCounter® Myeloid Innate Immunity Panel v2. Data normalization and pathway analysis were conducted using nSolver 4.0 software. Gene expression boxplots were generated in R. License Data and scripts are available for non-commercial use under a Creative Commons License.T2DM卒中模型骨髓组织单细胞RNA测序、GeoMx数字空间分析（GeoMx digital spatial profiling, DSP）及Nanostring nCounter分析数据集仓库 本仓库包含用于分析非糖尿病（db/+）与糖尿病（db/db）小鼠缺血性卒中后骨髓造血功能与免疫细胞调控的原始转录组数据及Seurat对象（Seurat）。数据通过骨髓细胞注释、细胞间通讯分析及基因表达研究所需的单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）生成；GeoMx DSP与nCounter分析则对骨髓单核细胞与中性粒细胞的基因表达进行了验证。本研究旨在揭示糖尿病如何在缺血性卒中后重编程骨髓造血功能，进而驱动适应性不良的免疫调控过程。 ## 仓库结构 ### 根目录文件 README.md：本文件，用于说明仓库概况及数据文件详情。 sc RNA seq_raw_data：原始单细胞RNA测序数据文件。 GeoMx-analysis_BM_raw data：原始GeoMx DSP数据文件。 ncounter_analysis-BM_raw data：原始nCounter数据文件。 Seurat object：包含用于全骨髓细胞聚类与可视化的Seurat对象（Seurat）。 CellChat object：包含用于CellChat分析（CellChat）的RDA文件。 ## 文件详情 ### sc RNA seq_raw_data/ dbdb-MCAO.zip：db/db小鼠远端大脑中动脉闭塞（middle cerebral artery occlusion, MCAO）后（dbdb.Stroke）的原始单细胞RNA测序数据。 dbdb-sham.zip：db/db小鼠假手术组（dbdb）的原始单细胞RNA测序数据。 db_pos-sham.zip：db/+小鼠假手术组（对照组）的原始单细胞RNA测序数据。 db_pos-MCAO.zip：db/+小鼠远端大脑中动脉闭塞（middle cerebral artery occlusion, MCAO）后（db+. Stroke）的原始单细胞RNA测序数据。 方法：采用10X Genomics GemCode技术完成单细胞RNA测序，使用Cell Ranger（v1.3）处理数据，并基于UMI计数进行标准化后，通过Seurat开展差异基因表达分析。 ### Seurat object/ bonemarrow_resolved.rda：包含糖尿病与血糖正常小鼠在卒中及假手术条件下的骨髓细胞测序数据处理后的Seurat对象（Seurat），其中细胞簇通过SingleR及小鼠器官图谱（Tambula Muris）数据库完成注释。 方法：对检测基因数少于500的细胞进行过滤，采用对数标准化处理数据，通过主成分分析（PCA）完成聚类，并使用UMAP进行可视化。 ### CellChat object/ T2DM.Stroke_cellchat.rda：缺血性卒中db/db小鼠的CellChat对象（CellChat），用于识别糖尿病条件下卒中引发的细胞间通讯模式改变。 T2DM_cellchat.rda：db/db对照组（无卒中）小鼠的CellChat对象（CellChat），提供糖尿病状态下的基线数据。 Stroke_cellchat.rda：db/+（血糖正常）缺血性卒中小鼠的CellChat对象（CellChat），代表非糖尿病条件下的卒中模型。 Ctrl_cellchat.rda：db/+对照组（无卒中）小鼠的CellChat对象（CellChat），为非糖尿病、非卒中状态提供基线参考。 方法：CellChat分析（CellChat）通过识别配体-受体相互作用，揭示骨髓细胞间的信号串扰，尤其聚焦于造血前体细胞（hematopoietic precursor cells, HPC1）与单核细胞。本数据集通过已鉴定的信号通路激活情况，为免疫失调机制提供研究视角。 ### GeoMx-analysis_BM_raw data/ annotation：注释文件包含实验设计与分析区域的元数据，涵盖样本标识符、感兴趣区域（ROI）标签、细胞片段、实验及分组条件，以及其他相关样本级注释信息。 dccs：DCC文件包含GeoMx DSP检测的每个靶标的原始数字计数，每个DCC文件对应单个感兴趣区域，报告标准化前每个基因或蛋白靶标的检测条形码计数。 pkcs：PKC文件包含与探针性能及质量控制相关的信息，包括靶向同一基因或蛋白的单个探针的计数。 方法：GeoMx样本在Illumina NovaSeq 6000平台完成测序，使用GeomxTools工具包在R语言中对GeoMx RNA表达数据开展质量控制、过滤、标准化及UMAP降维分析，随后将GeoMx对象转换为Seurat对象（Seurat）以进行差异基因表达及基因本体（Gene Ontology, GO）富集分析。 ### ncounter_analysis-BM_raw data/ .RCC：.RCC文件是NanoString nCounter系统产出的主要原始数据，每个RCC文件对应单个生物学样本，包含实验中检测的所有靶标的数字计数。 方法：采用NanoString nCounter® 髓系先天免疫组V2试剂盒完成基因表达谱分析，使用nSolver 4.0软件开展数据标准化及通路分析，基因表达箱线图通过R语言生成。 ## 许可证 本数据及脚本采用知识共享许可协议，仅可用于非商业用途。