Data underlying the publication: Image-based phenotypic sorting of synthetic cells

Data underlying the publication: Van den Brink, M., Stam, M., Claassens, N.J. and Danelon, C. (2025) Image-based phenotypic sorting of synthetic cells. bioRxiv, 10.1101/2025.09.25.678515.<br>This dataset contains data collected during experiments as part of Marijn van den Brink's PhD project. The data was collected from 2022-2025.<br>The raw data consist of microscopy data, gel electrophoresis data, flow cytometry data, quantitative PCR data, DNA sequencing data. All data processing and analysis steps are described in detail in the Methods section of the publication.<br>The data are grouped in folders based on three levels. Primary, by the data type. Secondary, by the figure number in the publication. Tertiary, within each figure folder, data are divided into raw and processed subfolders. The publication figures are stored in a separate folder.<br>Large microscopy data collections (e.g., &gt; 2 GB), generated during automated screening and photoactivation, are stored in separate folders, so they can be downloaded separately.<br>Code or other files used to process the raw data are stored in the "processed data" folders.<br>Generalized code for use and adaptation by others is stored in the folder "automated screening and photo activation scripts" and are also published on GitHub (https://github.com/DanelonLab).

本数据集关联的已发表研究成果：Van den Brink, M.、Stam, M.、Claassens, N.J. 与 Danelon, C. (2025) 《基于图像的合成细胞表型分选》（Image-based phenotypic sorting of synthetic cells）。bioRxiv, 10.1101/2025.09.25.678515。
本数据集包含Marijn van den Brink博士学位课题实验中采集的全部实验数据，数据采集周期为2022年至2025年。
原始数据涵盖显微镜成像数据、凝胶电泳数据、流式细胞术数据、定量聚合酶链式反应（quantitative PCR, qPCR）数据以及DNA测序数据。所有数据处理与分析的完整步骤均已在该发表论文的方法章节中详细阐述。
数据集采用三级文件夹结构进行组织：一级分类依据为数据类型；二级分类依据为发表论文中的对应图号；三级分类则是在每个图文件夹内，进一步划分为原始数据与处理后数据两个子文件夹。发表论文所用的全部插图均存储于单独的专属文件夹中。
针对自动化筛选与光激活实验中生成的大型显微镜成像数据集（如单文件体积大于2 GB的数据），将单独创建专属文件夹存储，方便用户单独下载。
用于处理原始数据的代码及其他相关辅助文件均存储于“处理后数据”文件夹内。
可供其他研究者复用与改造的通用化代码存储于“自动化筛选与光激活脚本”文件夹中，同时该代码也已上传至GitHub平台（https://github.com/DanelonLab）。