Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer's disease). We outline scRNA-seq techniques that are suitable for investigating cellula...

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Bibliographic Details
Published in:Cells (Basel, Switzerland) Switzerland), 2020-07, Vol.9 (8), p.1751
Main Authors: Gupta, Rishikesh Kumar, Kuznicki, Jacek
Format: Article
Language:English
Subjects:
Alzheimer's disease
Animals
Artificial intelligence
Biology
Biomarkers
Cancer therapies
Cardiovascular disease
Cardiovascular Diseases - genetics
Cardiovascular Diseases - pathology
Cell cycle
Cell differentiation
cell-to-cell heterogeneity
Computational Biology - methods
Gene expression
Gene Expression Profiling - methods
Genetic Heterogeneity
Genomes
Health aspects
Heart
Humans
Identification
Learning algorithms
Machine Learning
Medical importance
Methods
Neoplasms - genetics
Neoplasms - pathology
Neurodegenerative diseases
Neurosciences
Population
Precision medicine
Protocol
Review
Ribonucleic acid
RNA
RNA sequencing
RNA-Seq - methods
scRNA-seq
Single-Cell Analysis - methods
Stem cells
Transcriptome
Transcriptomics
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Summary:The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer's disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.
ISSN:2073-4409
2073-4409
DOI:10.3390/cells9081751