Imaging mass cytometry reveals early β-cell dysfunction and changes in immune signatures during type 1 diabetes progression in human pancreata
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T1D IMC Analysis Deposited Data Single-cell Data: Single Cell objects are provided separately for both Islet + Immune panel. spe_Islet_full.rds + spe_Immune_full.rds Please note that for reproducibility purposes these data include 7 Pancreatitis samples, which have not been analyzed in the Manuscript. Raw IMC Data raw: Raw data is too large to fit into this repo and is therefore deposited in the following zenodo repositories. Immune Panel: I: 10.5281/zenodo.15074971 II: 10.5281/zenodo.15079318 III: 10.5281/zenodo.15082431 Islet Panel: I: 10.5281/zenodo.15084757 II: 10.5281/zenodo.15087441 III: 10.5281/zenodo.15088286 Processed images (.tiffs): Data is available in the following repositories: Immune panel: https://zenodo.org/records/15971803 Islet panel: https://zenodo.org/records/15919088 Pre-processing List of subfolders: masks_cells: cell segmentation masks, generated with the`03_CellSegmentation.ipynb` notebook using the *whole-cell* segmentation type. masks_islets: islet segmentation masks, generated with the`02_IsletSegmentation.ipynb` notebook. The model used for islet segmentation isalso provided (`islet_segmentation_model.pt`). spillcomp: measurements of spillover compensation slides. One slide wasgenerated for each panel (Islet and Immune), with 45 spots spiked with the 45antibodies in the panel. Each slide was measured once per batch, generating 45.txt files that are used in the second script of the data analysis workflow(02_SpilloverCompensation_cells_*.Rmd). The spillover matrices generated bythat script are also provided as .csv files in the `spillcomp` folder. Running T1D_preprocesing (see publication-associated GitHub) on the raw data will further generate the following subfolders: data_cells: single cell data. These .csv files are used as input fordownstream analysis in R. The data include marker intensities (`intensities`),cell neighbors (`neighbors`), and spatial properties (`regionprops`) andwere generated with the `04_Measurements.ipynb` notebook. data_islets: islet-level data, including marker intensities(intensities), and spatial properties (regionprops). These data weregenerated with the `04_Measurements.ipynb` notebook. img: multichannel IMC images formatted as .tiff files and generated bythe `01_Preprocessing.ipynb` notebook. In addition, metadata files are provided. These include: Antibody panels, as `panel_Islet.csv` and `panel_Immune.csv`. These panelfiles are used as input to the `01_Preprocessing.ipynb` notebook. The `cases.csv` file contains clinical metadata about pancreas donors. It isused for downstream analysis in R. The `cases_full.csv' file contains further clinical metadata, as provided by nPOD. The `order.csv` indicates in which order samples should be plotted, accordingto their clinical T1D stage. This file is also used for downstream analysis inR. # Pilot study: SCEs and Images of the PIlot study can be found at: https://zenodo.org/uploads/15072596
T1D IMC 分析 ## 存档数据集 ### 单细胞数据： 胰岛面板与免疫面板的单细胞数据集对象均单独提供，对应文件为`spe_Islet_full.rds`与`spe_Immune_full.rds`。 请注意，为保障实验可复现性，本数据集包含7例胰腺炎样本，该类样本未在本论文中开展分析。 ### 原始IMC数据： 原始IMC数据体积过大，无法存入本仓库，故存档于以下Zenodo仓储： #### 免疫面板： Ⅰ：10.5281/zenodo.15074971 Ⅱ：10.5281/zenodo.15079318 Ⅲ：10.5281/zenodo.15082431 #### 胰岛面板： Ⅰ：10.5281/zenodo.15084757 Ⅱ：10.5281/zenodo.15087441 Ⅲ：10.5281/zenodo.15088286 ## 处理后图像（.tiffs）： 相关数据存档于以下仓储： - 免疫面板：https://zenodo.org/records/15971803 - 胰岛面板：https://zenodo.org/records/15919088 ## 预处理流程： 子文件夹列表如下： 1. `masks_cells`：细胞分割掩码，由`03_CellSegmentation.ipynb` Jupyter笔记本 (Jupyter Notebook)生成，采用全细胞分割类型。 2. `masks_islets`：胰岛分割掩码，由`02_IsletSegmentation.ipynb` Jupyter笔记本 (Jupyter Notebook)生成，配套的胰岛分割模型`islet_segmentation_model.pt`亦已提供。 3. `spillcomp`：溢流补偿玻片测量数据集。每个面板（胰岛与免疫）各制备一张溢流补偿玻片，玻片上设有45个检测位点，分别加入对应面板中的45种抗体。每批次对每张玻片进行一次检测，生成45个.txt文件，用于数据分析工作流的第二个脚本`02_SpilloverCompensation_cells_*.Rmd`。该脚本生成的溢流补偿矩阵亦以.csv格式保存于`spillcomp`文件夹中。 在原始数据上运行`T1D_preprocesing`（详见论文关联的GitHub仓库）可进一步生成以下子文件夹： 1. `data_cells`：单细胞数据。这些.csv文件可作为R语言下游分析的输入文件，数据集包含标志物强度（`intensities`）、细胞邻域（`neighbors`）与空间属性（`regionprops`），由`04_Measurements.ipynb` Jupyter笔记本 (Jupyter Notebook)生成。 2. `data_islets`：胰岛级数据，包含标志物强度（`intensities`）与空间属性（`regionprops`），由`04_Measurements.ipynb` Jupyter笔记本 (Jupyter Notebook)生成。 3. `img`：多通道IMC图像，格式为.tiff文件，由`01_Preprocessing.ipynb` Jupyter笔记本 (Jupyter Notebook)生成。 此外，本数据集还提供了元数据文件，具体如下： - 抗体面板文件：`panel_Islet.csv`与`panel_Immune.csv`。此类文件可作为`01_Preprocessing.ipynb` Jupyter笔记本 (Jupyter Notebook)的输入文件。 - `cases.csv`：包含胰腺供者的临床元数据，用于R语言下游分析。 - `cases_full.csv`：包含由nPOD提供的更多临床元数据。 - `order.csv`：指示样本按照临床T1D分期进行绘图的顺序，该文件同样用于R语言下游分析。 # 先导研究： 先导研究的单细胞实验 (Single Cell Experiments, SCEs）与图像可从以下仓储获取： https://zenodo.org/uploads/15072596
