spatialDLPFC

Overview

Welcome to the spatialDLPFC project! This project involves 3 data types as well as several interactive websites, all of which you are publicly accessible for you to browse and download.

In this project we studied spatially resolved and single nucleus transcriptomics data from the dorsolateral prefrontal cortex (DLPFC) from postmortem human brain samples. From 10 neurotypical controls we generated spatially-resolved transcriptomics data using using 10x Genomics Visium across the anterior, middle, and posterior DLPFC (n = 30). We also generated single nucleus RNA-seq (snRNA-seq) data using 10x Genomics Chromium from 19 of these tissue blocks. We further generated data from 4 adjacent tissue slices with 10x Genomics Visium Spatial Proteogenomics (SPG), that can be used to benchmark spot deconvolution algorithms. This work is being was performed by the Keri Martinowich, Leonardo Collado-Torres, and Kristen Maynard teams at the Lieber Institute for Brain Development as well as Stephanie Hicks’s group from JHBSPH’s Biostatistics Department.

This project involves the GitHub repositories LieberInstitute/spatialDLPFC and LieberInstitute/DLPFC_snRNAseq.

If you tweet about this website, the data or the R package please use the <code>#spatialDLPFC</code> hashtag. You can find previous tweets that way as shown <a href="https://twitter.com/search?q=%23spatialDLPFC&src=typed_query">here</a>.

Thank you for your interest in our work!

Study Design

Study design to generate paired single nucleus RNA-sequencing (snRNA-seq) and spatially-resolved transcriptomic data across DLPFC. (A) DLPFC tissue blocks were dissected across the rostral-caudal axis from 10 adult neurotypical control postmortem human brains, including anterior (Ant), middle (Mid), and posterior (Post) positions (n=3 blocks per donor, n=30 blocks total). The same tissue blocks were used for snRNA-seq (10x Genomics 3’ gene expression assay, n=1-2 blocks per donor, n=19 samples) and spatial transcriptomics (10x Genomics Visium spatial gene expression assay, n=3 blocks per donor, n=30 samples). (B) Paired snRNA-seq and Visium data were used to identify data-driven spatial domains (SpDs) and cell types, perform spot deconvolution, conduct cell-cell communication analyses, and spatially register companion PsychENCODE snRNA-seq DLPFC data. (C) t-distributed stochastic neighbor embedding (t-SNE) summarizing layer resolution cell types identified by snRNA-seq. (D) Tissue block orientation and morphology was confirmed by hematoxylin and eosin (H&E) staining and single molecule fluorescent in situ hybridization (smFISH) with RNAscope (SLC17A7 marking excitatory neurons in pink, MBP marking white matter (WM) in green, RELN marking layer (L)1 in yellow, and NR4A2 marking L6 in orange). Scale bar is 2mm. Spotplots depicting log transformed normalized expression (logcounts) of SNAP25, MBP, and PCP4 in the Visium data confirm the presence of gray matter, WM, and cortical layers, respectively. (E) Schematic of unsupervised SpD identification and registration using BayesSpace SpDs at k=7. Enrichment t-statistics computed on BayesSpace SpDs were correlated with manual histological layer annotations from (Maynard, Collado-Torres et al., 2021, Nat Neuro) to map SpDs to known histological layers. The heatmap of correlation values summarizes the relationship between BayesSpace SpDs and classic histological layers. Higher confidence annotations (⍴ > 0.25, merge ratio = 0.1) are marked with an “X”.

Introductory material

If you prefer to watch a video overview of this project, check the following journal club presentations of the main and supplementary results, respectively.

L. A. Huuki-Myers also wrote an overview blog post summarizing some of the key results from this spatialDLPFC project.

Interactive Websites

All of these interactive websites are powered by open source software, namely:

🔭 spatialLIBD
🔍 samui
👀 iSEE

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

Visium (n = 30)
- 🔭 spatialDLPFC_Visium_Sp09: spatialLIBD website showing the spatially-resolved Visium data (n = 30) with statistical results comparing the Sp09 domains.
- 🔭 spatialDLPFC_Visium_Sp16: similar but with the Sp16 domains.
- 🔭 spatialDLPFC_Visium_Sp09_position: similar to spatialDLPFC_Visium_Sp09 but with statistical results across the position (anterior, middle, posterior) adjusting for the Sp09 domains.
- 🔭 spatialDLPFC_Visium_Sp09_position_noWM: similar to spatialDLPFC_Visium_Sp09_position but after dropping the SP28D06, SP28D16, SP28D17, SP28D20 and SP28D28 spots which correspond to white matter (hence the noWM acronym).
- 👀 spatialDLPFC_Visium_Sp09_pseudobulk: iSEE website showing the pseudo-bulked Sp09 domains spatial data.
- 👀 spatialDLPFC_Visium_Sp16_pseudobulk: similar to spatialDLPFC_Visium_Sp09_pseudobulk but with the Sp16 domains data.
- 👀 spatialDLPFC_Visium_Sp28_pseudobulk: similar to spatialDLPFC_Visium_Sp09_pseudobulk but with the Sp28 domains data.
- 🔍 spatialDLPFC Visium on Samui: samui website that allows to zoom in at the spot or cell level.
snRNA-seq (n = 19)
- 👀 spatialDLPFC_snRNA-seq: iSEE website showing the n = 19 snRNA-seq samples at single nucleus resolution.
Visium SPG (n = 4)
- 🔭 spatialDLPFC_Visium_SPG: spatialLIBD website showing the spatially-resolved data Visium SPG (n = 4).
- 🔍 spatialDLPFC Visium SPG on Samui: samui website that allows to zoom in at the spot or cell level.

Local `spatialLIBD` apps

If you are interested in running the spatialLIBD applications locally, you can do so thanks to the spatialLIBD::run_app(), which you can also use with your own data as shown in our vignette for publicly available datasets provided by 10x Genomics.

## Run this web application locally with:
spatialLIBD::run_app()

## You will have more control about the length of the session and memory usage.
## See http://research.libd.org/spatialLIBD/reference/run_app.html#examples
## for the full R code to run https://libd.shinyapps.io/spatialDLPFC_Visium_Sp09
## locally. See also:
## * https://github.com/LieberInstitute/spatialDLPFC/tre

SpatialDLPFC

Install / Use

README

spatialDLPFC

Overview

Study Design

Introductory material

Interactive Websites

Local `spatialLIBD` apps

SpatialDLPFC

Install / Use

README

spatialDLPFC

Overview

Study Design

Introductory material

Interactive Websites

Local spatialLIBD apps

Local `spatialLIBD` apps