Spatial Transcriptomics to define transcriptional patterns of zonation and structural components in the mouse liver

Reconstruction of heterogeneity through single cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we apply Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology.

This repository contains original data such as count matrices, spot coordinate files, Hematoxylin & Eosin stained images, vein-masks, etc. . Sample 1 to sample 3 refers to the individual ST experiments performed in this study. Sample 1 consists of 3 sections of the murine caudate lobe. Sample 2 consists of 5 sections of the caudate lobe, of which 2 were excluded for analysis due to different treatment during the experiment. Sample 3 consists of 4 sections of the right lobe, of which 2 were excluded for analysis due to different treatment during the experiment. These folders contain input data for the R markdown script, which can be found in this GitHub repository: https://github.com/almaan/ST-mLiver, doi: 10.5281/zenodo.55176001 . 

The Hepaquery folder contains all data necessary to reproduce the results of the study and instructions on how to install the python package and reproduce the data of this study can also be found in the same GitHub repository (https://github.com/almaan/ST-mLiver, doi: 10.5281/zenodo.55176001). 

The folder termed "Immunofluorescence" contains all original images from the orthogonal validations of vein types (Supplementary figures 21-22) in "Veins" and cell count estimations "Celltypes" by immunostaining. The Celltypes folder contains a folder with the orginal image of each section of the IFA and a subfolder "manual counting", with the randomly selected 100 µm openings on each of the corresponding images. The 100 µm openings are adapted from the grid of the ST experiments (Supplementary figures 1-2).