scFFPE-ATAC解锁FFPE组织单细胞染色质可及性

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE291155

标准获取流程：

1. 访问源站链接，注册/登录账号；
2. 进入数据集详情页，按页面指引申请权限或直接下载；
3. 部分平台提供 SDK，例如：
   • OpenDataLab: pip install openxlab 后 openxlab.dataset.get(...)
   • ModelScope: pip install modelscope 后 modelscope download
   • 天池: 登录后页面提供直链下载
     以源站「数据下载/使用文档」页面为准。

数据说明

Formalin-fixed paraffin-embedded (FFPE) samples are the gold standard for clinical tissue preservation. However, single-cell chromatin accessibility profiling technology, a powerful tool to understand epigenetic regulation in human disease, is not available for FFPE samples due to extensive DNA damage. To solve this technical challenge, we introduce scFFPE-ATAC by integrating uniquely designed DNA oligos on Tn5 transposase, split-and-pool ligation DNA barcoding, T7 promoter-mediated DNA damage rescue, and in vitro transcription—the first high-throughput single-cell chromatin accessibility assay for FFPE samples. scFFPE-ATAC enables the simultaneous profiling of up to 64 FFPE samples or spatial regions, with 884,736 cell barcodes in a single run. We successfully applied scFFPE-ATAC to clinical FFPE human lymph nodes stored for 8–15 years and identified distinct epigenetic regulators between tumor centers and invasive edge epithelial cells in human lung cancer. Additionally, we analyzed paired primary and relapsed follicular lymphoma (FL), as well as FL transformed into diffuse large B-cell lymphoma, from clinical samples archived for 2–7 years, revealing key epigenetic drivers of tumor progression. Overall, scFFPE-ATAC enables high-throughput, high-sensitivity chromatin accessibility profiling in long-term archived clinical specimens, paving the way for retrospective epigenetic studies and spatial epigenetic analysis. This technology provides a powerful tool for understanding tumor relapse and metastasis, with broad applications in basic research and personalized medicine.

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。