README.Rmd

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title: spatialNAc
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## Overview
Welcome to the `spatialNAc` project! This project involves paired snRNA-seq and SRT (10x Visium) data as well as several interactive websites, all of which are publicly accessible for you to browse and download.

In this project we studied spatially resolved and single nucleus transcriptomics data from the human Nucleus Accumbens (NAc) from postmortem human brain samples. From 10 neurotypical controls we generated spatially-resolved transcriptomics data using using [10x Genomics **Visium**](https://www.10xgenomics.com/products/spatial-gene-expression) across the anterior, intermediate, and posterior NAc. We also generated single nucleus RNA-seq (**snRNA-seq**) data using [10x Genomics **Chromium**](https://www.10xgenomics.com/products/single-cell-gene-expression)

This project involves the GitHub repository [LieberInstitute/NAc](https://github.com/LieberInstitute/spatialNAc) 

If you tweet about this website, the data or the R package please use
the <code>\#spatialNAc</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



**Generation of paired single nucleus RNA-sequencing (snRNA-seq) and spatially-resolved transcriptomic data across NAc**. 
Tissue blocks containing the NAc were dissected from 10 neurotypical adult donors (6 male, 4 female). Paired single-nucleus RNA sequencing (snRNA-seq) and spatially-resolved transcriptomics (SRT) data were generated from adjacent tissue sections using 10x Genomics Chromium and Visium platforms. To capture the complete  NAc, tissue blocks were scored to align with the width of the Visium capture array and spatial profiling was performed across 2–5 capture arrays per donor (n = 38 total). snRNA-seq was performed on the same tissue blocks on PI sorted (PI+) and neuron-enriched (PI+ NeuN+) nuclei. Downstream analyses included (i) Identification of transcriptionally distinct cell clusters from snRNA-seq, (ii) Mapping spatial domains in SRT data, (iii) Integration of snRNA-seq and SRT data via cell-type deconvolution to resolve spatially-localized populations, (iv) Inference of disease relevant ligand-receptor (LR)–based cell–cell communication networks, and (v) Cross-species drug-response mapping by integrating transcriptional programs derived from rodent datasets with human SRT data.

## 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/spatialNAc/issues](https://github.com/LieberInstitute/spatialNAc/issues) and refrain from emailing us. Thank you again for your interest in our work!


## Citing our work


## Internal

* JHPCE locations:
  - `/dcs04/lieber/marmaypag/spatialNac_LIBD4125/spatial_NAc`