Multidimensional Single-Nuclei RNA-Seq Reconstruction of Adipose Tissue Reveals Mature Adipocyte Plasticity Underlying Thermogenic Response

Table of Contents

• What is this?
• Workflow
• How can I use this data, and where can I find it?
  • Downloading Data files
• Analysis and visualization programs
  • R and R’s integrated developmental environment RStudio
  • scRNAseq analysis pipeline Seurat
  • Pseudotemporal gene expression analysis using Monocle
  • Cell type classification using Metacell
  • Cellular component prediction
  • Finding the optimal number of clusters using SCCAF
• Setting up the environment
• Citation
• Acknowledgements

What is this?

This repository contains coding scripts utilized for the analysis performed in the “Multidimensional Single-Nuclei RNA-Seq Reconstruction of Adipose Tissue Reveals Mature Adipocyte Plasticity Underlying Thermogenic Response” publication (XXX). The purpose of providing the code here is to allow for transparency and robust data-analysis reproducibility. The methodology has already been described extensively in the manuscript. However, this analysis relies heavily on powerful snRNAseq analysis algorithms like Seurat (Butler et al., 2018: Nature Biotechnology; Stuart et al., 2018: Cell), SCCAF, Metacell (Baran et al., 2019: Genome Biology) and cellphonedb (Efremova et al., 2020: Nature; Vento-Tormo et al., 2018: Nature) (for a complete list of dependencies and code utilized see analysis & visualization programs).

Workflow

1. Dataset
2. Clustering
   1. Overclustering;
   2. Optimal number of clusters;
   3. Cell type identification;
   4. Markers expression.
3. Main analysis
   1. Differential Expression;
   2. Functional Enrichment;
   3. Transdifferentiation;
   4. Cellular component prediction.

How can I use this data, and where can I find it?

Downloading Data files

Public data files utilized in this analysis have been downloaded from Gene Expression Omnibus (GEO), gene expression data repository at the NIH. Data are part of the GSE133486 high-thoroughput sequencing repository and can be found here. The Cellranger output files were renamed to ‘matrix.mtx.gz’, ‘barcodes.tsv.gz’ and ‘features.tsv.gz’ to allow Seurat to read these files.

Analysis and visualization programs

R and R’s integrated developmental environment RStudio:

1. R v4.0.2 (x64 bit)
2. RStudio v1.3.1073 (x64 bit)
3. Tutorial for R
4. Tutorial for RStudio

scRNAseq analysis pipeline Seurat:

1. Source code for Seurat v3.2.2
2. Tutorials for Seurat

Pseudotemporal gene expression analysis using Monocle:

1. Source code for Monocle v2.16.0
2. Tutorial for Monocle

Cell type classification using Metacell:

1. Source code for metacell v0.3.41
2. Tutorial for metacell

Cellular component prediction:

1. cellphonedb
   1. Source code for cellphonedb v2.1.4
   2. Tutorial for cellphonedb
2. UniProtKB
3. Gene Ontology
4. SignalP v5.0
5. SecretomeP v2.0
6. TMHMM v2.0

Finding the optimal number of clusters using SCCAF:

1. Source code for SCCAF v0.09
2. Tutorial for SCCAF

Setting up the environment

1. Install R and Rstudio.
2. Once you have installed R and RStudio, run the setup script to install R packages dependencies.
3. Together with Seurat, a conda environment called r-reticulate will be installed. We will install the anndata, scanpy, igraph and louvain modules within this environment so that we can run the Python code inside R using the reticulate package previously installed. So, to check the installed environment full name just type the following commands in a new terminal:

conda env list

4. After checking the full name of the environment mentioned above, we will load it (replace the path below with the similar one shown on your terminal):

conda activate /Users/biagi/Library/r-miniconda/envs/r-reticulate

5. Then, we will install the modules with the following commands:

pip install anndata
pip install scanpy
pip install python-igraph
pip install louvain

6. Next step is to deactivate the r-reticulate environment typing:

conda deactivate /Users/biagi/Library/r-miniconda/envs/r-reticulate

7. Finally, we will install SCCAF and cellphonedb modules:

pip install sccaf
pip install cellphonedb

8. Any problem installing the softwares, please click in the following links: anndata, scanpy, igraph, louvain, SCCAF and cellphonedb.
9. If you run into problems, please open a new issue, you can do this by going to ‘issues’ and clicking on the ‘new issue’ icon. We will help you replicate our analysis! Do not fear single cell analysis!

Citation

xxxxx

Acknowledgements

This work was supported by grants from the NIH DK117161, DK117163 to SRF and P30-DK-046200 to Adipose Biology and Nutrient Metabolism Core of Boston Nutrition and Obesity Research Center, by Sao Paulo Research Foundation (FAPESP) Grants: 2018/20905-1 and 2013/08135-1562, the National Council for Scientific and Technological Development, CNPq (282 311319/2018-1 to MLBJr and scholarship #870415/1997-2 to SSC) and by the Coordination for the Improvement of Higher Education Personnel, CAPES (scholarship #88882.378695/2019-01 to CAOBJr)