Package status

News:

• Beyondcell 2.2.2 has been added to bu_cnio conda channel.
• Beyondcell now features the immunotherapy geneset to compute BCS of immunotherapy-related signatures.

Introduction

Beyondcell is a methodology for the identification of drug vulnerabilities in single-cell RNA-seq (scRNA-seq) and Spatial Transcriptomics (ST) data. To this end, beyondcell focuses on the analysis of drug-related commonalities between cells/spots by classifying them into distinct Therapeutic Clusters (TCs).

Workflow overview

Beyondcell workflow. Given two inputs, the expression matrix and a collection of drug signatures, the methodology calculates a Beyondcell Score (BCS) for each drug-cell/spot pair. The BCS ranges from 0 to 1 and measures the susceptibility of each cell/spot to a given drug. The resulting BCS matrix can be used to determine the sample’s TCs. Furthermore, drugs are prioritized in a table and each drug score can be visualized in a UMAP. When using ST data, the TCs and individual scores can also be visualized on top of the tissue slice to dissect the therapeutic architecture of the sample.

Depending on the evaluated signatures, the BCS represents the cell/spot perturbation susceptibility (PSc) or the sensitivity to the drug effect (SSc). BCS can also be estimated from functional signatures to evaluate each cell/spot functional status.

Beyondcell's key applications

• Analyse the intratumoural heterogeneity (ITH) of your experiment
• Classify your cells/spots into TCs
• Prioritize cancer treatments
• If time points are available, identify the changes in drug tolerance
• Identify mechanisms of resistance

Installing beyondcell

The beyondcell package is implemented in R >= 4.0.0. We recommend running the installation via mamba:

# Create a conda environment.
conda create -n beyondcell 
# Activate the environment.
conda activate beyondcell
# Install beyondcell package and dependencies.
mamba install -c bu_cnio r-beyondcell

Results

We have validated beyondcell in a population of MCF7-AA cells exposed to 500nM of bortezomib and collected at different time points: t0 (before treatment), t12, t48, and t96 (72h treatment followed by drug wash and 24h of recovery) obtained from Ben-David U, et al., Nature, 2018. We integrated all four conditions using the Seurat pipeline (left). After calculating the BCS for each cell using PSc, a clustering analysis was applied. Beyondcell was able to cluster the cells based on their treatment time point, to separate untreated cells from treated cells (center), and to recapitulate the changes arising from the treatment with bortezomib (right).

How to run

For general instructions on running beyondcell, check out the analysis workflow and visualization tutorials. For more information about how beyondcell normalization works, please refer to this vignette. You can also find an example ST analysis here.

Authors

• Coral Fustero-Torre
• María José Jiménez-Santos
• Santiago García-Martín
• Carlos Carretero-Puche
• Luis García-Jimeno
• Tomás Di Domenico
• Gonzalo Gómez-López
• Fátima Al-Shahrour

Citation

Fustero-Torre, C., Jiménez-Santos, M.J., García-Martín, S. et al. Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data. Genome Med 13, 187 (2021). https://doi.org/10.1186/s13073-021-01001-x

Support

If you have any questions regarding the use of beyondcell, feel free to submit an issue.