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NKCE

Notebook and computational analysis workstream for NK cell engager multi-modal data analysis (scRNA-seq+CITE-seq). Here, we report on three main tools that have been applied to perform trajectory and pseudo-time inference, to estimate the RNA velocity and the “regulon”, which depict the cellular differentiation and molecular processes underlying NKCE effector function.

Trajectory and pseudo-time inference

We propose to use Monocle3 for trajectory and pseudo-time inference. Monocle3 is an analysis toolkit for single-cell RNA-Seq experiments. It uses an algorithm to learn the sequence of gene expression changes each cell must go through as part of a dynamic biological process. Once it has learned the overall "trajectory" of gene expression changes, Monocle3 can place each cell at its proper position in the trajectory.

Run the analysis

To run the analysis, install the libraries

devtools::install('monocle3')
BiocManager::install(c('org.Hs.eg.db', 'org.Mm.eg.db'))
devtools::install_github("cole-trapnell-lab/garnett", ref="monocle3")

and run the script monocle3.R on your data. You will need to update the file with the path to the scRNA-seq count matrix and, cell and genes metadata matrices.

RNA velocity

The mRNA velocity is estimated based on La Manno et al 2018 on spliced and un-spliced reads for a group of genes that can explain the differentiation of a cell type to another cell type such as CD56dim to CIML. Thus, this will allow to model the quantified reads and conduct trajectories analysis.

Basic information

The analysis was run by activating the AIDA-ODS anaconda image where the tools to run the analysis have been established. For a full list of packages please check Sanofi confluence page.

Analysis starts with exon and introns quantification on Magellan. Initialize an instance with r6i.4xlarge capabilities with an Ubuntu terminal and a SLURM cluster with 9 nodes.

sbatch NKCE/Tools/Intron_Exon_Quantification.sh

Loom files are then used for trajectory analysis and plot

Rscript NKCE/Script/RNA_Velocity.r sample.loom marker_genes.txt plot.pdf

Regulon activity inference

Regulon activity inference is performed using the Python implementation of SCENIC. The tool allows us to simultaneously reconstruct gene regulatory networks and identify stable cell states from single-cell RNA-seq data.

Run the analysis

To run the analysis you will need to install the pySCENIC package on your machine. We recommend installing it on a conda environment

conda create -y -n pyscenic python=3.10
conda activate pyscenic
pip install pyscenic

You will need to download motif rankings files and motif annotation file from https://resources.aertslab.org/cistarget/databases/. Update the pySCENIC.py file with your working directory information as well as the directory to your input data and the desired output folders, and run it on Python.