儿童与成人人气道上皮细胞内在差异 [激光捕获新鲜上皮细胞]

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148815

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

The airway epithelium is a key protective barrier whose integrity is preserved by the self-renewal and differentiation of basal progenitor cells. Epithelial cells are central to the pathogenesis of multiple chronic lung diseases for which age is a principle risk factor. Children are also less susceptible to SARS-CoV-2 infection, suffer less severe symptoms than adults and have a lower rate of mortality. Few studies have addressed differences between airway epithelial cells in children and adults. Here, we perform bulk RNA sequencing studies in laser-captured whole epithelium, FACS-sorted basal cells and cultured basal cells, as well as in vitro cell proliferation experiments, to address the intrinsic molecular differences between paediatric and adult airway basal cells. We find that, while the cellular composition of the paediatric and adult tracheal epithelium is broadly similar, in cell culture, paediatric airway epithelial cells displayed higher colony forming ability, better in vitro growth and outcompeted adult cells in competitive proliferation assays. Although recurring differences between airway epithelial gene expression were seen between samples from children and adults, RNA sequencing showed broad conservation of transcriptional programmes. Genes associated with SARS-CoV-2 infection were not differentially expressed between children and adults, although individuals showed some variability in their expression of viral infection-associated genes. Our results chart important cell intrinsic differences in transcriptional profile and regenerative capacity between tracheal epithelial cells of children and adults.

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。