spatial_DG_lifespan

Spatial transcriptomics (Visium) in dentate gyrus of hippocampus over the lifespan

spatial_DG_lifespan

Overview

Welcome to the Spatial_DG_lifespan project! It is composed of:
 1. a shiny web application that we are hosting at https://libd.shinyapps.io/Lifespan_DG/ that can handle a limited set of concurrent users,
 2. and a research article with the scientific knowledge we drew from this dataset. The analysis code for our project is available here.

The web application allows you to browse the LIBD human lifespan dentate gyrus (DG) spatial transcriptomics data generated with the 10x Genomics Visium platform. Please check the manuscript or bioRxiv pre-print for more details about this research. If you tweet about this website, the data or the R package please use the #spatialLIBD hashtag. You can find previous tweets that way as shown here. Thank you!

Thank you for your interest in our work!

Study Design

As a quick overview, the data presented here is from hippocampus (HPC) that spans nine spatial domains plus white matter for a total of sixteen subjects. Each dissection of HPC was designed to center the granular cell layer to well represent the DG. Using this web application you can explore the spatial expression of known genes such as NCDN.

This web application was built such that we could annotate the spots to layers as you can see under the spot-level data tab. Both histologically and gene marker driven manual annotations as well as unsupervised spatial clusters with BayesSpace at k=10 are available. Once we annotated each spot to a layer, we compressed the information by a pseudo-bulking approach into layer-level data minus the layer representing choroid plexus to maximize variance between HPC spatial domains. We then analyzed the expression through a set of models whose results you can also explore through this web application. Finally, you can upload your own gene sets of interest as well as layer enrichment statistics and compare them with our LIBD human lifespan DG Visium dataset.

If you are interested in running this web application locally, you can do so thanks to the spatialLIBD R/Bioconductor package that powers this web application as shown below.

First download the processed spe objects and modeling results here:

Interactive Websites

We provide the following interactive websites, organized by software labeled with emojis:

• 🔭 spatial_DG_lifespan
  • https://libd.shinyapps.io/Lifespan_DG/: This web application was built such that we could annotate the spots to layers as you can see under the spot-level data tab. Both histologically and gene marker driven manual annotations as well as unsupervised spatial clusters with BayesSpace at k=10 are available. Once we annotated each spot to a layer, we compressed the information by a pseudo-bulking approach into layer-level data minus the layer representing choroid plexus to maximize variance between HPC spatial domains. We then analyzed the expression through a set of models whose results you can also explore through this web application.

Local spatialLIBD apps

If you are interested in running the spatialLIBD applications locally, you can do so thanks to the spatialLIBD::run_app(). First make sure to download the data from Zenodo. Then you can uncompress the files and store the spe object in your chosen directory. Then use readRDS(here::here()) to load the spe object in R. For example:

## Run this web application locally with:
## spe <- readRDS(here::here("processed-data", "spe.rds"))
## Deploy the website
###spatialLIBD::run_app(
###    spe,
###    title = "spatial_DG_lifespan, Visium",
###    spe_discrete_vars = c("BayesSpace", "ManualAnnotation"),
###    spe_continuous_vars = c("sum_umi", "sum_gene", "expr_chrM", "expr_chrM_ratio"),
###    default_cluster = "BayesSpace"
###)
## You will have more control about the length of the
## session and memory usage.

## You could also use this function to visualize your
## own data given some requirements described
## in detail in the package vignette documentation
## at http://research.libd.org/spatialLIBD/.

Contact

We value public questions, as they allow other users to learn from the answers. If you have any questions, please ask them at LieberInstitute/spatial_DG_lifespan/issues and refrain from emailing us. Thank you again for your interest in our work!

Citing our work

Pre-print

Anthony D. Ramnauth, Madhavi Tippani, Heena R. Divecha, Alexis R. Papariello, Ryan A. Miller, Elizabeth A. Pattie, Joel E. Kleinman, Kristen R. Maynard, Leonardo Collado-Torres, Thomas M. Hyde, Keri Martinowich, Stephanie C. Hicks, Stephanie C. Page, Spatially-resolved transcriptomics of human dentate gyrus across postnatal lifespan reveals heterogeneity in markers for proliferation, extracellular matrix, and neuroinflammation. bioRxiv 2023.11.20.567883; doi: https://doi.org/10.1101/2023.11.20.567883.

Here's the citation information on BibTeX format.

@article {Ramnauth2023.11.20.567883,
	author = {Anthony D. Ramnauth, Madhavi Tippani, Heena R. Divecha, Alexis R. Papariello, Ryan A. Miller, Elizabeth A. Pattie, Joel E. Kleinman, Kristen R. Maynard, Leonardo Collado-Torres, Thomas M. Hyde, Keri Martinowich, Stephanie C. Hicks, Stephanie C. Page},
	title = {Spatially-resolved transcriptomics of human dentate gyrus across postnatal lifespan reveals heterogeneity in markers for proliferation, extracellular matrix, and neuroinflammation},
	elocation-id = {2023.11.20.567883},
	year = {2023},
	doi = {10.1101/2023.11.20.567883},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/10.1101/2023.11.20.567883},
	eprint = {https://www.biorxiv.org/content/10.1101/2023.11.20.567883.full.pdf},
	journal = {bioRxiv}
}

Data Access

BioProject

FASTQ data is available at the NCBI BioProject page for this project.

Raw & Processed Data

You can access all raw data, that is the FASTQ files, and processed data through Zenodo. Processed data includes two spe objects and raw images for the haematoxylin and eosin-stained tissue: The basic spe object stores the raw counts for all the Visium experiments and metadata. The processed spe object is after quality control, batch correction, data integration, unsupervised spatial clustering, cell-type deconvolution, and also contains the metadata.

Internal

• JHPCE locations: /dcs04/lieber/marmaypag/lifespanDG_LIBD001/spatial_DG_lifespan/
• Slack channel: libd_lifespan_hpc_spatial.

Files:

• code: R, python, and shell scripts for running various analyses.
• plots: plots generated by R analysis scripts in .pdf or .png format
• processed-data
  • Images: images used for running SpaceRanger and other images
  • spaceranger: SpaceRanger output files
• raw-data
  • FASTQ: FASTQ files.
  • Images: raw images from the scanner in .tif format for each slide (around 8GB each). Each slide contains the image for four capture areas.

This GitHub repository is organized along the R/Bioconductor-powered Team Data Science group guidelines. It follows the LieberInstitute/template_project structure.

Other related files

• Reference transcriptome from 10x Genomics: /dcs04/lieber/lcolladotor/annotationFiles_LIBD001/10x/refdata-gex-GRCh38-2020-A/