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

Raw images:
All 16bit tiff images are for 12 weeks_section1
Format is baseX_cY_ORG
X indicates the hybridization round (1-4)
Y indicates the channel:
1: DAPI
2: FITC (T)
3: Cy3 (G)
4: TexR (C)
5: Cy5 (A)
6: AF750 (Anchor primer)

Raw
data in folders:

Lung csv files

Bacteria csv files

DAPI for plotting

HE folder

Scripts
folder:

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. Use raw images for 12weeks_section1 as input images (others are available on request) . After Tiling in "InsituSequencing.m", process tiled images in the cell profiler pipeline “Blob
identification”. Run decode and threshold in "InSituSequencing.m" to generate 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 DAPI images (10% of
original size) found in “DAPI for plotting” folder using Plotting global in "InSituSequencing.m". 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. For all images 1
pixel corresponds to 0.325 mm.

Identification
and plotting of transcripts in given proximity to bacteria:

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.

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.

Table 1-3 contain sequences of used specific primers, padlock probes and detection oligos.