Spatial and temporal localization of immune transcripts defines hallmarks and diversity in the tuberculosis granuloma

This repository contains raw data and all scripts for the in situ sequencing processing pipeline (Mats Nilsson Lab, Stockholm University) and downstream analysis applied in the manuscript “<b>Spatial and temporal localization of immune transcripts defines hallmarks and diversity in the tuberculosis granuloma”.</b> <b>Raw data in folders:</b> Lung csv files Bacteria csv files DAPI for plotting HE folder <b>Scripts folder:</b> Matlab scripts Cellprofiler pipelines <b>Identification and plotting of transcripts:</b> For all matlab scripts: Download the “Matlab scripts” folder, add lib to MATLAB path. Except MATLAB, no additional Mathworks product is required. Tested on R2017b. <b>InSituSequencing.m</b> is the top-level script processing sequencing images to positional visualization of decoded transcripts. Input images are available on request, those were tiled and then processed in the cell profiler pipeline “Blob identification” generating csv files containing position and intensity of each identified signal.csv files for all lung scans (3 per time point) are in “lung csv files” folder and can be plotted on high resolution DAPI images (10% of original size) found in “DAPI for plotting” folder using InSituSequencing.m. High resolution H&amp;E scans of in situ-sequenced lungs for lung section per time point are in the “HE folder” at 50% of original size. For all images 1 pixel corresponds to 0.325 mm. <b>Identification and plotting of transcripts in given proximity to bacteria:</b> Use the cellprofiler pipeline “Bacteria identification” instead of “Blob identification” to identify signal in indicated distances from identified bacteria. The folder “bacteria csv files” contains identified signals in the indicated distances to identified bacteria. Input images are available on request. <b>Downstream analysis (Matlab Scripts folder)</b> <b>DensityEstimation.m</b> was used to display not absolute reads but a kernel density estimation of a certain gene in a 2log scale. <b>ROI_draw_onImage.m</b> was applied to extract reads from annotated regions. Pictures of annotations can be found in the manuscript supplementary figure S1. <b>HexbinClustering.m </b>performed an unsupervised clustering (kmeans) of spatial data with a given number of distinct clusters in a given radius. <br>

本仓库包含来自斯德哥尔摩大学马特斯·尼尔森实验室的**原位测序（in situ sequencing）**处理流程，以及用于论文《免疫转录本的时空定位定义结核肉芽肿的特征与多样性》中下游分析的全部原始数据与脚本。

**原始数据存放文件夹：** 肺组织CSV文件、细菌CSV文件、供绘图使用的DAPI图像文件夹、HE染色切片文件夹

**脚本文件夹：** 包含Matlab脚本与CellProfiler分析流程

**转录本的识别与可视化：**
所有Matlab脚本使用说明：请下载「Matlab脚本」文件夹，并将其中的lib目录添加至MATLAB路径。仅需MATLAB软件，无需额外MathWorks产品，已在R2017b版本中完成兼容性测试。

脚本`InSituSequencing.m`为顶层处理脚本，可将测序图像转换为解码后转录本的空间定位可视化结果。测序图像可按需获取：先对图像进行拼接，再通过CellProfiler的「Blob识别」分析流程处理，生成包含每个识别信号位置与强度的CSV文件。所有肺组织扫描的CSV文件（每个时间点含3份样本）存放于「lung csv files」文件夹，可通过`InSituSequencing.m`结合「DAPI for plotting」文件夹中的高分辨率DAPI图像（原始尺寸的10%）完成绘图。

每个时间点的原位测序肺组织切片的高分辨率HE染色扫描图像（原始尺寸的50%）存放于「HE folder」文件夹。所有图像的像素换算比例为：1像素对应0.325 mm。

**细菌邻近区域转录本的识别与可视化：**
请使用CellProfiler的「细菌识别」分析流程替代「Blob识别」，以识别与已识别细菌处于指定距离范围内的信号。「bacteria csv files」文件夹中存放了符合上述距离要求的识别信号对应的CSV文件。测序图像可按需获取。

**下游分析（Matlab脚本文件夹）：**
- 脚本`DensityEstimation.m`：用于以2对数刻度展示特定基因的核密度估计结果，而非原始测序读段的绝对计数。
- 脚本`ROI_draw_onImage.m`：用于从注释区域中提取测序读段。注释区域的可视化图像可参见论文补充图S1。
- 脚本`HexbinClustering.m`：用于对空间数据执行无监督聚类（kmeans），可指定聚类数量与聚类半径。