Single Cell Multiomic Approaches to Disentangle T Cell Heterogeneity
Paolo Abondio1, Carlo De Intinis2, João Lídio da Silva Gonçalves Vianez Júnior3
1Laboratory of Molecular Anthropology and Center for Genome Biology, Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy; Armenise-Harvard Immune Regulation Unit, Italian Institute for Genomic Medicine, Turin, Italy.
Immunology Letters
|May 16, 2022
Summary
Single-cell multi-omics reveals cell differences through gene expression patterns. This review details analytical methods, bioinformatic tools, and potential biases in single-cell data interpretation.
Area of Science:
- Biotechnology
- Bioinformatics
- Genomics
Background:
- Single-cell multi-omics is advancing rapidly due to technological improvements and computational tools.
- Distinguishing cell types based on gene expression patterns is a key application.
Purpose of the Study:
- To review methodological pipelines for single-cell analysis.
- To discuss advantages, limitations, and bioinformatic tools for crucial analytical steps.
- To cover T-cell receptor (TCR) reconstruction and compare single-cell sequencing technologies.
Main Methods:
- Review of existing literature on single-cell analysis pipelines.
- Discussion of bioinformatic tools for gene expression analysis and TCR reconstruction.
- Comparative analysis of different single-cell sequencing technologies.
Main Results:
- Detailed overview of common single-cell analysis workflows.
- Identification of advantages and limitations of various analytical steps and tools.
- Highlighting critical factors that can introduce biases and inaccuracies in data interpretation.
Conclusions:
- Single-cell multi-omics offers powerful insights into cellular heterogeneity.
- Careful consideration of analytical methods and bioinformatic tools is crucial for accurate data interpretation.
- Awareness of potential biases is essential for robust single-cell research.
