Published May 25, 2024 | Version 1.1
Journal article Open

Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy upon specific regional brain cells in mouse dementia model

  • 1. ROR icon Seoul National University
  • 2. ROR icon Seoul National University Hospital
  • 3. THERABEST Inc.

Contributors

Contact person:

  • 1. ROR icon Seoul National University
  • 2. ROR icon Seoul National University Hospital

Description

Brain hemispheres were prepared in frozen blocks using OCT compound (Sakura) and cryosectioned into 10 μm coronal and sagittal sections. According to the manufacturer’s protocols using Visium Spatial Tissue Optimization slides (10X Genomics), the permeabilization time was optimized to 12 minutes. The brain sections were methanol-fixed, hematoxylin and eosin (H&E)-stained and imaged on a TissueFAXS PLUS (TissueGenostics). The slides were merged into a picture of the whole brain using TissueFAXS imaging software. Then, the sections were permeabilized and processed to obtain cDNA Visium Spatial Gene Expression libraries according to the manufacturer’s protocol. To verify the size of PCR-enriched fragments, the template size distribution was checked using a high-sensitivity DNA assay (Agilent Technologies 2100 Bioanalyzer).

The libraries were sequenced using HiSeqXten (Illumina) with a read length of 28 bp for read 1 (Spatial Barcode and UMI), 10 bp index read (i7 index), 10 bp index read (i5 index), and 90 bp for read 2 (RNA read). Raw FASTQ data and H&E images were processed by the Space Ranger v1.1.0 (10X Genomics) pipeline for the gene expression analysis of the Visium Spatial Gene Expression library data. Illumina base call files from the Illumina sequencing instrument were converted to FASTQ format for each sample using the ‘mkfastq’ command. Visium spatial expression libraries were analyzed with the ‘count’ command. Image alignment to predefined spots was performed using the fiducial alignment grid of the tissue image to determine the orientation and position of the input image. Sequencing reads were aligned to the mm10 reference genome (mm10-2020-A) using STAR (v2.5.1b) aligner. Gene expression profiling in each spot was performed with the unique molecular identifier (UMI) and 10X barcode information.

'ST_dataset.tgz' is a GZIP file containing the output of the Space Ranger for Visium spatial transcriptomics datasets. 
The directory structure is as follows:

Abstract (English)

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer’s disease (AD), but the lack of methods to understand brain tissues make it difficult to examine therapeutics. Here, we investigated the changes of spatial transcriptomic signatures and brain cell type using the 10x Genomics Visium platform in immune modulated AD models by various treatments. To proceed with an analysis suitable for a single spot-based transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and anti-CD4 antibody, that ameliorate behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers to interpret real action of drug candidate by simultaneously investigating the dynamics of all transcripts for development of novel AD therapeutics.

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Additional details

Related works

Is published in
Journal article: 38796425 (PMID)

Funding

2017M3C7A1048079 2017M3C7A1048079
National Research Foundation of Korea
2020R1A2C2101069 2020R1A2C2101069
National Research Foundation of Korea
2017R1A5A1015626 2017R1A5A1015626
National Research Foundation of Korea

Software

Repository URL
https://github.com/bsungwoo/STquantool.git
Programming language
R