Sep 01, 2024

Public workspaceXenograft snRNA-seq Integration and Normalization

DOI
dx.doi.org/10.17504/protocols.io.81wgbzo4ngpk/v1
  • 1University of Sydney;
  • 2Florey Institute of Neuroscience and Mental Health
courtney.wright Wright
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DOI: dx.doi.org/10.17504/protocols.io.81wgbzo4ngpk/v1
Protocol Citation: Zac Chatterton, Chiara Pavan, Clare Parish 2024. Xenograft snRNA-seq Integration and Normalization. protocols.io https://dx.doi.org/10.17504/protocols.io.81wgbzo4ngpk/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: September 01, 2024
Last Modified: September 01, 2024
Protocol Integer ID: 106809
Keywords: ASAPCRN, snRNA-seq, Data integration, Normalization, Xenograft, Seurat
Funders Acknowledgements:
Michael J Fox Foundation
Grant ID: ASAP-000497
Abstract
This protocol describes the integration and normalization of single-nucleus RNA sequencing (snRNA-seq) data obtained from xenograft samples. The process involves loading data from multiple Cell Ranger outputs, creating Seurat objects, performing data integration, and normalizing the datasets. The protocol includes the visualization of results and statistical analyses to assess the quality and characteristics of the integrated datasets. The protocol includes the assignment of cell-type and differential gene expression analysis.
Materials
Equipment
  • High-performance computing system or workstation with R
  • Seurat v5
  • Cell Ranger output files (e.g., 10XGenomics)
  • R packages: patchwork, Matrix, tidyverse, plyr, ggpubr, HGNChelper, EnhancedVolcano, ggbreak
Before start
Setup and Data Loading:
Setup and Data Loading:
  • Set the working directory and specify the path for Cell Ranger outputs.

  • Load the necessary R libraries and configure global options.
Data Integration
Data Integration
  • Import multiple Cell Ranger outputs.

  • Create Seurat objects for each dataset.
Quality Control (QC) and Data Normalization
Quality Control (QC) and Data Normalization
  • Filter cells based on unique feature counts (e.g., remove cells with < 2000 unique counts or > 0.1% mitochondrial reads).

  • Integrate multiple datasets and normalize the data using Seurat’s standard workflow.
Selection of Human Cells
Selection of Human Cells
  • Differentiate between human and rodent cells using per-cell read alignments to the respective genomes.
Analysis and Visualization
Analysis and Visualization
  • Perform dimensionality reduction (e.g., PCA, UMAP) and clustering.

  • Visualize the integrated data using UMAP plots.
Cell-Type Definition
Cell-Type Definition
  • Classify human cell clusters using tools like CHETAH and scType.
Statistical Analysis
Statistical Analysis
  • Conduct differential expression analysis between human cell types of interest.