Spatial Transcriptomics Data and Computational Tools for Integrated Super-Resolution In Situ Sequencing and Protein Imaging for 3D Characterization of Intact Human Brain Organoids

This repository provides the complete set of data and analysis scripts supporting the study titled:“Integrated Super-Resolution In Situ Sequencing and Protein Imaging for 3D Characterization of Intact Human Brain Organoids” The dataset includes processed CSV files containing 3D spatial coordinates (x, y, z) and gene identities for individual RNA puncta detected in expanded human brain organoids. These organoids were generated from induced pluripotent stem cells (iPSCs) derived from patients carrying the iSTXBP1 mutation and their sex-matched healthy family members. The data were acquired using two complementary techniques: Expansion Sequencing (ExSeq): A super-resolution, in situ RNA sequencing method that allows nanoscale localization of transcripts within intact, physically expanded organoids. Ex situ RNA sequencing: Conventional transcriptomic profiling performed outside the tissue context to provide additional validation and complementary expression data. Together, these methods enabled a high-resolution, spatially informed characterization of gene expression patterns in disease-relevant and control brain organoids. In addition to the spatial transcriptomics data, the repository includes Python scripts designed for quantitative spatial analysis and visualization. These scripts perform: 3D segmentation of nuclei or cell boundaries, Distance-based analyses (e.g., distances to centroids or edges), Spatial statistics such as Moran’s I for assessing local spatial autocorrelation, The combination of ExSeq and Ex situ data, along with this computational toolkit, allows researchers to explore the spatial organization of gene expression at subcellular resolution and identify potential spatial signatures associated with neurodevelopmental disorders. Please refer to the included README.md file for detailed descriptions of file structure, dependencies, and analysis steps.本仓库提供了支撑题为《整合超分辨率原位测序与蛋白质成像，用于完整人类脑类器官的三维表征》的研究的全套数据与分析脚本。 该数据集包含经过处理的CSV文件，其中记录了物理扩增后的人类脑类器官中检测到的单个RNA斑点的三维空间坐标（x、y、z）与基因标识。此类脑类器官由携带iSTXBP1突变的患者及其性别匹配的健康家庭成员的诱导多能干细胞（induced pluripotent stem cells, iPSCs）构建获得。本数据通过两种互补技术获取： 扩增测序（Expansion Sequencing, ExSeq）：一种超分辨率原位RNA测序方法，可在经过物理扩增的完整类器官中实现转录本的纳米级定位。 异位RNA测序（Ex situ RNA sequencing）：在脱离组织原生环境的条件下开展的常规转录组分析，用于提供额外验证数据与互补的表达谱信息。 上述两种技术共同实现了疾病相关与对照脑类器官中基因表达模式的高分辨率、空间信息导向的表征。 除空间转录组数据外，本仓库还配备了用于定量空间分析与可视化的Python脚本，可实现以下功能： 1. 细胞核或细胞边界的三维分割； 2. 基于距离的分析（例如，到细胞质心或组织边缘的距离计算）； 3. 空间统计分析，例如用于评估局部空间自相关性的Moran’s I指数。 结合扩增测序（ExSeq）与异位RNA测序的数据，搭配该计算工具包，研究人员可探索亚细胞分辨率下的基因表达空间组织模式，并识别与神经发育障碍相关的潜在空间特征。 有关文件结构、依赖项与分析步骤的详细说明，请参阅附带的README.md文件。