Related Experiment Video
Updated: Nov 8, 2025
Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors
Published on: August 16, 2024
Applications of single-cell and bulk RNA sequencing in onco-immunology
Maria Kuksin1, Daphné Morel2, Marine Aglave3
1ENS de Lyon, 15 Parvis René Descartes, 69007, Lyon, France; Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France.
Abstract:
The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such as differential expression analysis, dimensionality reduction, clustering and enrichment analysis. Additionally, we used public data sets to exemplify how deconvolution algorithms can identify and quantify multiple immune subpopulations from either bulk or single-cell RNA-seq. We give examples of machine and deep learning models used to predict patient outcomes and treatment effect from high-dimensional data. Finally, we balance the strengths and weaknesses of single-cell and bulk RNA-seq regarding their applications in the clinic.
Insights
RNA sequencing (RNA-seq) offers powerful insights into cancer immunotherapy by analyzing tumor immune contexture. This review covers single-cell and bulk RNA-seq methods, data analysis, and computational challenges for onco-immunology research.
Area of Science:
- Onco-immunology
- Computational Biology
- Genomics
Background:
- Precise characterization of tumor immune microenvironment is crucial for immunotherapy response.
- RNA sequencing (RNA-seq) is a key technology for understanding these complex interactions.
- Single-cell and bulk RNA-seq provide distinct but complementary data for onco-immunology.
Purpose of the Study:
- To review the principles and applications of single-cell and bulk RNA-seq in onco-immunology.
- To highlight data preprocessing, statistical analyses, and computational challenges.
- To showcase the utility of RNA-seq in identifying immune subpopulations and predicting treatment outcomes.
Main Methods:
- Overview of single-cell and bulk RNA sequencing principles.
- Description of standard data preprocessing and statistical analyses (e.g., differential expression, clustering).
- Application of deconvolution algorithms and machine/deep learning models using public datasets.
Main Results:
- RNA-seq enables detailed analysis of tumor immune contexture and immunotherapy response mechanisms.
- Deconvolution algorithms can identify and quantify immune cells from RNA-seq data.
- Machine and deep learning models show potential for predicting patient outcomes and treatment effects.
Conclusions:
- Both single-cell and bulk RNA-seq offer valuable tools for onco-immunology, each with specific strengths and weaknesses.
- Computational approaches are essential for extracting meaningful biological insights from high-dimensional RNA-seq data.
- RNA-seq-based analyses are increasingly important for clinical applications in cancer immunotherapy.
