Immune mapping of human tuberculosis and sarcoidosis lung granulomas
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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 “Immune mapping of human tuberculosis and sarcoidosis lung granulomas” by Carow et al, under revision in Frontiers of Immunology.
Raw data in folders
Lung csv files DAPI or HE files for plotting.
Matlab scripts Cellprofiler pipelines
Identification and plotting of transcripts
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.
InSituSequencing.m 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 are in “lung csv files” folder and can be plotted on high resolution H&E scans of in situ-sequenced lungs for lung section per time point are in the “HE folder” at 50% of original size, or alternatively on DAPI images. For all images 1 pixel corresponds to 0.325 μm.
The csv files uploaded correspond to different Tuberculosis (TB) or sarcoidosis (S) numbered patient samples. The analysis of TB1 sample which was repeated in independent consecutive sections named as TB1.1, TB1.2 and TB1.3.
The csv positional data could be produced from consolidated from images of different regions of the same sample. Thus the sample TB2 csv is produced by merging positional data from the images TB2.1, TB2.2, TB2.3 and TB.4. In the case of TB1.1 (the first of three consecutive sections), three regions were analysed (for example TB.1.1. 2 and TB1.1.3). All samples were sequenced using the SLig chemistry unless SHyb was indicated in the sample name. One of the S and one of the TB samples were not included due to low signal density.
Downstream analysis (Matlab Scripts folder)
DensityEstimation.m was used to display not absolute reads but a kernel density estimation of a certain gene in a 2log scale.
ROI_draw_onImage.m was applied to extract reads from annotated regions. Pictures of annotations can be found in the manuscript supplementary figure S1.
HexbinClustering.m performed an unsupervised clustering (kmeans) of spatial data with a given number of distinct clusters in a given radius.
