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Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives
Geyang Luo1,2, Qian Gao2, Shuye Zhang1
1Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Computational and Structural Biotechnology Journal
|October 27, 2020
Summary
Single-cell RNA sequencing (scRNA-seq) reveals cellular differences in key biological fields. This review explores scRNA-seq applications in infectious diseases, future challenges, and potential uses.
Area of Science:
- Biomedical research
- Life sciences
- Cellular heterogeneity studies
Background:
- Single-cell RNA sequencing (scRNA-seq) has become a pivotal technology.
- It significantly enhances understanding of cellular heterogeneity.
- Applications span immunology, oncology, and developmental biology.
Purpose of the Study:
- To review the evolution of scRNA-seq technologies.
- To highlight scRNA-seq applications in infectious diseases.
- To explore future directions, challenges, and potential of scRNA-seq.
Main Methods:
- Literature review of scRNA-seq technologies.
- Analysis of scRNA-seq applications in various research areas.
- Discussion of current advancements and future prospects.
Main Results:
- scRNA-seq provides deep insights into cellular diversity.
- The technology is increasingly applied to study infectious diseases.
- Significant progress has been made, with ongoing challenges and future potential.
Conclusions:
- scRNA-seq is a powerful tool for understanding cellular heterogeneity.
- Its application in infectious diseases is a growing area of research.
- Continued development will expand its utility in biomedical research.
Keywords:
3C, Chromosome Conformation CaptureACE2, Angiotensin-Converting Enzyme 2ARDS, acute respiratory distress syndromeATAC-seq, Assay for Transposase-Accessible Chromatin using sequencingBCR, B cell receptorCEL-seq, Cell Expression by Linear amplification and SequencingCLU, clusterinCOVID-19, corona virus disease 2019CRISPR, Clustered Regularly Interspaced Short Palindromic RepeatsCytoSeq, gene expression cytometryDENV, dengue virusFACS, fluorescence-activated cell sortingGNLY, granulysinGO analysis, Gene Ontology analysisHIV, Human Immunodeficiency VirusIAV, Influenza A virusIGHV/HD/HJ/HC, Immune globulin heavy V/D/J/C/ regionIGLV/LJ/LC, Immune globulin light V/J/C/ regionILC, Innate Lymphoid CellInfectious diseasesLIGER, Linked Inference of Genomics Experimental RelationshipsMAGIC, Markov Affinity-based Graph Imputation of CellsMARS-seq, Massively parallel single-cell RNA sequencingMATCHER, Manifold Alignment To CHaracterize Experimental RelationshipsMCMV, mouse cytomegalovirusMERFISH, Multiplexed, Error Robust Fluorescent In Situ HybridizationMLV, Moloney Murine Leukemia VirusMOFA, Multi-Omics Factor AnalysisMOI, multiplicity of infectionPBMCs, peripheral blood mononuclear cellsPLAC8, placenta-associated 8SARS-CoV-2, severe acute respiratory syndrome coronavirus 2SAVER, Single-cell Analysis Via Expression RecoverySPLit-seq, split pool ligation-based tranome sequencingSTARTRAC, Single T-cell Analysis by RNA sequencing and TCR TRACkingSTRT-seq, Single-cell Tagged Reverse Transcription sequencingSingle-cell RNA sequencingTCR, T cell receptorTSLP, thymic stromal lymphopoietinUMAP, Uniform Manifold Approximation and ProjectionUMI, Unique Molecular IdentifiermcSCRB-seq, molecular crowding single-cell RNA barcoding and sequencingpDCs, plasmacytoid dendritic cellsscRNA-seq, single cell RNA sequencing technologysci-RNA-seq, single-cell combinatorial indexing RNA sequencingseqFISH, sequential Fluorescent In Situ Hybridizationsmart-seq, switching mechanism at 5′ end of the RNA transcript sequencingt-SNE, t-Distributed stochastic neighbor embedding
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